NP – Nonlinear Processes in Geosciences

EGU22-1090 | Presentations | MAL20 | NP Division Outstanding ECS Award Lecture

The geometry of scales: chameleon attractors 

Tommaso Alberti

In 1963 Lorenz discovered what is usually known as “chaos”, that is the sensitive dependence of deterministic chaotic systems upon initial conditions. Since then, this concept has been strictly related to the notion of unpredictability pioneered by Lorenz. However, one of the most interesting and unknown facets of Lorenz ideas is that multiscale fluid flows could spontaneously lose their deterministic nature and become intrinsically random. This effect is radically different from chaos. Turbulent flows are the natural systems when Lorenz ideas can be touched by the hand. They can, indeed, be described via the Navier-Stokes equations, thus conforming to the class of deterministic dissipative systems, as well as, present rich dynamics originating from non-trivial energy fluxes in scale space, non-stationary forcings and geometrical constraints. This complexity appears via non-hyperbolic chaos, randomness, state-dependent persistence and predictability. All these features have prevented a full characterization of the underlying turbulent (stochastic) attractor, which will be the key object to unpin this complexity. 

Here we use a novel formalism to map unstable fixed points to singularities of turbulent flows and to trace the evolution of their structural characteristics when moving from small to large scales and vice versa, providing a full characterization of the attractor. We demonstrate that the properties of the dynamically invariant objects depend on the scale we are focusing on. Our results provide evidence that the large-scale properties of turbulent flows display universal statistical properties that are triggered by, but independent of specific physical properties at small scales. Given the changing nature of such attractors in time, space and scale spaces, we term them chameleon attractors.

How to cite: Alberti, T.: The geometry of scales: chameleon attractors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1090, https://doi.org/10.5194/egusphere-egu22-1090, 2022.

EGU22-10567 | Presentations | MAL20 | Lewis Fry Richardson Medal Lecture

Tipping phenomena and resilience of complex systems: Theory and applications to the earth system 

Ulrike Feudel

Many systems in nature are characterized by the coexistence of different stable states for a given set of environmental parameters and external forcing. Examples for such behavior can be found in different fields of science ranging from mechanical or chemical systems to ecosystem and climate dynamics. As a consequence of the coexistence of a multitude of stable states, the final state of the system depends strongly on the initial condition.  Perturbations, applied to those natural systems can lead to critical transitions from one stable state to another. Such critical transitions are called tipping phenomena in climate science, regime shifts in ecology. They can happen in various ways: (1) due to bifurcations, i.e. changes in the dynamics when external forcing or parameters are varied extremely slow, (2) due to fluctuations which are always inevitable in natural systems, (3) due to rate-induced transitions, i.e. when external forcing changes on characteristic time scales comparable to the intrinsic time scale of the considered dynamical system and (4) due to shocks or extreme events. We discuss these critical transitions and their characteristics and illustrate them with examples from climate science and ecosystem dynamics. Moreover, we discuss the concept of resilience, which has been originally introduced by C.S. Holling in ecology, and formulate it in terms of dynamical systems theory. This formulation offers mathematical and numerical tools to use it as a measure of the persistence of a function of a dynamical system.

How to cite: Feudel, U.: Tipping phenomena and resilience of complex systems: Theory and applications to the earth system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10567, https://doi.org/10.5194/egusphere-egu22-10567, 2022.

NP0 – L.F. Richardson's 1922 centennial

EGU22-1089 | Presentations | NP0.1

Tropical Background and Wave Spectra: Contribution of Wave–Wave Interactions in a Moderately Nonlinear Turbulent Flow 

Nathan Paldor, Chaim I. Garfinkel, and Ofer Shamir

Variability in the tropical atmosphere is concentrated at wavenumber–frequency combinations where linear theory indicates wave modes can freely propagate, but with substantial power in between. This study demonstrates that such a power spectrum can arise from small-scale convection triggering large-scale waves via wave–wave interactions in a moderately turbulent fluid. Two key pieces of evidence are provided for this interpretation of tropical dynamics using a nonlinear rotating shallow-water model: a parameter sweep experiment in which the amplitude of an external forcing is gradually ramped up, and also an external forcing in which only symmetric or only antisymmetric modes are forced. These experiments do not support a commonly accepted mechanism involving the forcing projecting directly onto the wave modes with a strong response, yet still simulate a power spectrum resembling that observed, though the linear projection mechanism could still complement the mechanism proposed here in observations. Interpreting the observed tropical power spectrum using turbulence offers a simple explanation as to why power should be concentrated at the theoretical wave modes, and also provides a solid footing for the common assumption that the background spectrum is red, even as it clarifies why there is no expectation for a turbulent cascade with a specific, theoretically derived slope such as −5/3. However, it does explain why the cascade should be toward lower wavenumbers, that is an inverse energy cascade, similar to the midlatitudes even as compressible wave modes are important for tropical dynamics.
It also explains why  in satellite observations and reanalysis data, the symmetric component is stronger than the anti-symmetric component, as any bias in the small-scale forcing from isotropy, whether symmetric or antisymmetric, leads to symmetric bias in the large-scale spectrum regardless of the source of variability responsible for the onset of the asymmetry.


Shamir, O., C. Schwartz, C.I. Garfinkel, and N. Paldor, The power distribution between symmetric and anti-symmetric components of the tropical wavenumber-frequency spectrum, JAS, https://doi.org/10.1175/JAS-D-20-0283.1 .
Garfinkel, C.I., O. Shamir, I. Fouxon, and N. Paldor, Tropical background and wave spectra: contribution of wave-wave interactions in a moderately nonlinear turbulent flow, JAS, https://doi.org/10.1175/JAS-D-20-0284.1.
Shamir, O., C.I. Garfinkel, O. Adam, and N. Paldor, A note on the power distribution between symmetric and anti-symmetric components of the tropical Brightness Temperature spectrum in the wavenumber-frequency plane , JAS,doi: 10.1175/JAS-D-21-0099.1.

How to cite: Paldor, N., Garfinkel, C. I., and Shamir, O.: Tropical Background and Wave Spectra: Contribution of Wave–Wave Interactions in a Moderately Nonlinear Turbulent Flow, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1089, https://doi.org/10.5194/egusphere-egu22-1089, 2022.

EGU22-2192 | Presentations | NP0.1

Nonlinear subcritical and supercritical thermal convection in a sphere 

Tobias Sternberg and Andrew Jackson
Fluids that are subject to temperature gradients (or internal heating) and a gravity force will begin convecting when the thermal forcing, conventionally measured by the nondimensional Rayleigh number Ra exceeds a critical value. The critical value RL for the transition from a static, purely conductive state to an advective state can be determined by linearising the equations of motion and formulating an associated characteristic value problem. We discuss two aspects of fluid behaviour away from this point:
(i) Highly supercritical behaviour, and the asymptotic behaviour of heat transport in the highly nonlinear regime. (ii) Subcritical behaviour for Ra<RL, which may be possible for finite amplitude fluid motions. We work in both full sphere and shell geometries, with various forms of heating and gravitational profiles. We report on both theoretical developments and direct numerical simulations using highly accurate fully spectral methods for solving the relevant equations of motion and of heat transfer.

How to cite: Sternberg, T. and Jackson, A.: Nonlinear subcritical and supercritical thermal convection in a sphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2192, https://doi.org/10.5194/egusphere-egu22-2192, 2022.

EGU22-2238 | Presentations | NP0.1

Direct evidence of an oceanic dual kinetic energy cascade and its seasonality from surface drifters 

Jin-Han Xie, Dhruv Balwada, Raffaele Marino, and Fabio Feraco

Ocean turbulence causes flows to split into smaller whirls or merge to make larger whirls, cascading energy to small or large scales respectively. Conventional ocean dynamics dictates that the kinetic energy in the ocean will cascade primarily to larger scales, via the inverse energy cascade, and has raised the question of how the kinetic energy in the ocean dissipates, which would necessarily require the transfer towards the molecular scales. However, so far no clear observational quantification of the energy cascade at the scales where these mechanisms are potentially active has been made. By using forcing-scale resolving third-order structure-function theory, which captures bidirectional energy fluxes and is applicable beyond inertial ranges, we analyse data from surface drifters, released in dense arrays in the Gulf of Mexico, to obtain the kinetic energy flux magnitude and directions along with the energy injection scales. We provide the first direct observational verification that the surface kinetic energy cascades to both small and large scales, with the forward cascade dominating at scales smaller than approximately 1-10km. Our results also show that there is a seasonality in these cascades, with winter months having a stronger injection of energy into the surface flows and a more energetic cascade to smaller scales. This work provides exciting new opportunities for further probing the energetics of ocean turbulence using non-gridded sparse observations, such as from drifters, gliders, or satellites.

How to cite: Xie, J.-H., Balwada, D., Marino, R., and Feraco, F.: Direct evidence of an oceanic dual kinetic energy cascade and its seasonality from surface drifters, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2238, https://doi.org/10.5194/egusphere-egu22-2238, 2022.

According to the classic energy cascade notion, large eddies as energy carrier are unstable to break up, through which energy is transferred from large scales till the smallest ones to dissipate the kinetic energy. A fundamental issue hereof is how to quantify the eddies of different sizes, else the energy cascade scenario remains illustrative. A possible remedy is the idea of dissipation element (DE) analysis, which is a topological approach based on extremal points. In this method, starting from each spatial point in a turbulent scalar field ϕ, a local minimum point and a local maximum point will inevitably be reached along the descending and ascending directions of the scalar gradient trajectory, respectively. The ensemble of spatial points whose gradient trajectories share the same pair of minimum and maximum points define a spatial region, called a DE. The entire filed can thus be partitioned into space-filling DEs. Typically, DE can be parameterized with l, the linear distance between the two extremal points, and ∆ϕ = ϕ_max – ϕ_min, the absolute value of the scalar quantity difference between the two extremal points. It needs to mention that dependence of the DE structure on the ϕ field is conformal with the physics that different variable fields are different structured, although related. In the past years, DE analysis has been implemented to understand the turbulence dynamics under different conditions. Since inside each DE, the monotonous change of the field variable (from ϕ_min  to ϕ_max  along the trajectory) depicts a laminar like structure in a local region, the space-filling DEs can be recognized as the smallest eddies.

In a more general sense, a newly defined multi-level DE structure has been developed. Introducing the size of the observation window S, extremal points are multi-level, based on which the DE structure can be extended to multi-level. At each S-level, the turbulent field can be decomposed into space-filling DEs, which makes it possible to understand to entire field from the properties of such individual units. In this sense, it is tentatively possible to define turbulent eddies of different scales as DEs at different S-levels. Conventional analyses based on “turbulent eddies” can be implemented using such idea. For instance, during energy cascade, eddy breakup corresponds to the splitting of DEs at higher levels (with larger S) to smaller ones at lower levels (with smaller S). Because of DE can be exactly defined, eddies can be quantified as well, but not just demonstrative. Such kind of multi-level DE structure is uniquely different from other existing approaches (e.g. vortex tube, PoD, Fourier analysis etc.) in the following senses. First, DEs at any S-level are quantitatively defined, rather than qualitatively visualized. Second, DEs at any S-level are space-filling.  The multi-level DE approach is generally applicable in turbulence analysis.

How to cite: Wang, L.: Quantification of “turbulent eddies” in energy cascade based on the multi-level dissipation element structure, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3335, https://doi.org/10.5194/egusphere-egu22-3335, 2022.

EGU22-3918 | Presentations | NP0.1

Turbulent intermittency as a consequence of stationarity of the energy balance 

Sébastien Aumaitre and Stéphan Fauve

In his seminal work on turbulence, Kolmogorov made use of the stationary hypothesis to determine the Power Density Spectra of velocity field in turbulent flows. However to our knowledge, the constraints that stationary processes impose on the fluctuations of power have never been used in the context of turbulence. Here we recall that the Power Density Spectra of the fluctuations of the injected power, the dissipated power and the energy flux have to converge to a common value at vanishing frequency. Hence, we show that the intermittent GOY-shell model fulfills these constraints on the power as well as on the energy fluxes. We argue that they can be related to intermittency. Indeed, we find that the constraints on the power fluctuations imply a relation between scaling exponents, which is consistent with the GOY-shell model and in agreement with the She-Leveque formula. It also fixes the intermittent parameter of the log-normal model at a realistic value. The relevance of these results for real turbulence is drawn in the concluding remarks.

How to cite: Aumaitre, S. and Fauve, S.: Turbulent intermittency as a consequence of stationarity of the energy balance, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3918, https://doi.org/10.5194/egusphere-egu22-3918, 2022.

EGU22-5934 | Presentations | NP0.1

Scalewise Universal Relaxation to Isotropy of Inhomogeneous Atmospheric Boundary Layer Turbulence 

Ivana Stiperski, Gabriel G. Katul, and Marc Calaf

The turbulent energy cascade is one of the most recognizable characteristics of turbulent flow. Still, representing this tendency of large-scale anisotropic eddies to redistribute their energy content with decreasing scale, a phenomenon referred to as return to isotropy, remains a recalcitrant problem in the physics of turbulence. Atmospheric turbulence is characterised by large scale separation between production and viscous destruction of turbulent kinetic energy making it suitable for exploring such scale-wise redistribution of energy among velocity components.  Moreover, real-world atmospheric turbulence offers an unprecedentedly diverse source of inhomogeneity and large-scale anisotropy (caused by shear, buoyancy, terrain-induced pressure perturbations, closeness to the wall) while maintaining a high Reynolds number state. It may thus be assumed that relaxation through the energy cascade may be dependent on the anisotropy source, thus adding to the ways that atmospheric turbulence differs from canonical turbulent boundary-layers.

Here, we examine the scalewise return to isotropy for an unprecedented dataset of atmospheric turbulence measurements covering flat to mountainous terrain, stratification spanning convective to very stable conditions, surface roughness ranging over several orders of magnitude, various distances from the surface, and Reynolds numbers that far exceed the limits of direct numerical simulations and laboratory experiments.  The results indicate that irrespective of the complexity of the dataset examined, the return-to-isotropy trajectories that start from specific initial anisotropy at large scales show surprising scalewise universality in their trajectories towards isotropy. This novel finding suggests that the effects of boundary conditions, once accounted for in the starting anisotropy of the trajectory in the cascade, cease to be important at much smaller scales. It can therefore be surmised that large-scale anisotropy encodes the relevant information provided by the boundary conditions, adding to the body of evidence that the information on anisotropy is a missing variable in understanding and modelling atmospheric turbulence [1-3].

 

[1]  Stiperski I, and M Calaf. Dependence of near-surface similarity scaling on the anisotropy of atmospheric turbulence. Quarterly Journal of the Royal Meteorological, 144, 641-657, 2017.

[2]  Stiperski I, M Calaf and MW Rotach. Scaling, anisotropy, and complexity in near-surface atmospheric turbulence. Journal of Geophysical Research: Atmospheres, 124, 1428-1448, 2019.

[3] Stiperski I, GG Katul, M Calaf. Universal return to isotropy of inhomogeneous atmospheric boundary layer turbulence. Physical Review Letters, 126 (19), 194501, 2021

How to cite: Stiperski, I., Katul, G. G., and Calaf, M.: Scalewise Universal Relaxation to Isotropy of Inhomogeneous Atmospheric Boundary Layer Turbulence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5934, https://doi.org/10.5194/egusphere-egu22-5934, 2022.

EGU22-7004 | Presentations | NP0.1

Turbulent Energy Cascade in the Gulf of Mexico 

Yinxiang Ma, Jianyu Hu, and Yongxiang Huang

Due  to the extreme complexity of the oceanic dynamics, e.g., stratification, air-sea interaction,  waves, current, tide, etc., the corresponding turbulent cascade remains unknown. The third-order longitudinal structure-function is often employed to diagnose  the cascade direction and intensity, which is written as  SLLL(r)=< Δ uL3(r)>, where Δ uL is the  velocity increment along the distance vector r, and r is the modulus of r. In the case of  three-dimension homogeneous and isotropic turbulence, SLLL(r) is scaled as -4/5εr in the inertial range, where ε is the energy dissipation rate per unit.  In this work, SLLL(r) is estimated for two experimental velocities that obtained in the Gulf of Mexico, namely Grand LAgrangian Deployment (GLAD) and the LAgrangian Submesoscale ExpeRiment (LASER). The experimental SLLL(r) for both experiments shows a transition from negative values to a positive one roughly at rT=10km, corresponding to a timescale  around τT=12-hour (e.g., τT=rT/urms with urms ≈0.24m/s.  Power-law is evident for the scale on the range 0.01≤ r≤1km as SLLL(r)∼ -r1.45±0.10, and for the scale on the range 30≤ r≤300km as SLLL(r)∼ r1.45±0.10. Note that a weak stratification with depth of 10∼15m has been reported for the GLAD experiment, indicating a quasi-2D flow topography. The scaling ranges are above this stratification depth. Hence, the famous Kraichnan's 2D turbulence theory or the geostrophic turbulence proposed by Charney are expected to be applicable. However, due to the complexity of real oceanic flows, hypotheses behind these theories cannot be verified either directly or indirectly. To simplify the situation, we still consider here the sign of  SLLL(r) as an indicator of the energy cascade. It thus suggests a possible forward energy cascade below the spatial scale rT, and an inverse one above the scale  spatial rT.  While, the scaling exponents 1.45 are deserved more studied in the future if more data is available.

 

Ref.

Charney, J. G. (1971). Geostrophic turbulence. J. Atmos. Sci., 28(6), 1087-1095.

Frisch, U., & Kolmogorov, A. N. (1995). Turbulence: the legacy of AN Kolmogorov. Cambridge University Press.

Alexakis, A., & Biferale, L. (2018). Cascades and transitions in turbulent flows. Phys. Rep., 767, 1-101.

Dong, S., Huang, Y., Yuan, X., & Lozano-Durán, A. (2020). The coherent structure of the kinetic energy transfer in shear turbulence. J. Fluid Mech., 892, A22.

Poje, A. C., Özgökmen, T. M., Bogucki, D. J., & Kirwan, A. D. (2017). Evidence of a forward energy cascade and Kolmogorov self-similarity in submesoscale ocean surface drifter observations. Phys. Fluids, 29(2), 020701.

How to cite: Ma, Y., Hu, J., and Huang, Y.: Turbulent Energy Cascade in the Gulf of Mexico, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7004, https://doi.org/10.5194/egusphere-egu22-7004, 2022.

EGU22-7115 | Presentations | NP0.1

Turbulent Cascade of  the Lithosphere Deformation in the Tibetan Plateau 

Tinghui Yan, Yinxiang Ma, Jianyu Hu, and Yongxiang Huang

Recently, multiscale statics is found to be relevant in description of the lithosphere deformation of the Tibetan Plateau (Jian et al, Phys. Rev. E, 2019). More precisely, a dual-power-law behavior is observed respectively on the spatial scale range of  50≤ r≤ 500km and 500≤ r ≤2000km, which coincidently agrees well with the one reported for the atmospheric movement (Nastrom et al., Nature, 1984). The corresponding high-order scaling exponents demonstrated a nonlinear shape, showing multifractality nature of the underlying dynamics. To diagnose further whether the lithosphere deformation is turbulent or not, the third-order longitudinal structure-function SLLL(r)=< ΔuL(r)3> is estimated, where r is the modulus of the distance vector  r, and  ΔuL is the velocity component that paralleling with r.  Due to the finite sample size, the experimental SLLL(r) is not reliable when r≤200km. The measured SLLL(r) is scaled as  -r4±0.2 on the spatial scale range of 500≤ r ≤ 2000km, indicating the existence of a turbulent cascade. Because of the complexity of the geodynamics, e.g., Coriolis force, mantle convection, India-Eurasia collision, to list a few, the exact force balance is remained unknown. Therefore, the full interpretation of the current observation is not feasible.

 

Ref.

A. Alexakis, &  L. Biferale (2018). Cascades and transitions in turbulent flows, Phys. Rep., 767, 1-101.

U. Frisch, (1995) Turbulence: The Legacy of A.N. Kolmogorov, Cambridge University Press

X. Jian, W. Zhang, Q. Deng & Y.X. Huang (2019) Turbulent lithosphere deformation in the Tibetan Plateau, Phys. Rev. E, 99:062122

G.D. Nastrom, K.S Gage & Jasperson (1984) Kinetic energy spectrum of large- and mesoscale atmospheric processes, Nature, 310:36

How to cite: Yan, T., Ma, Y., Hu, J., and Huang, Y.: Turbulent Cascade of  the Lithosphere Deformation in the Tibetan Plateau, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7115, https://doi.org/10.5194/egusphere-egu22-7115, 2022.

EGU22-7557 | Presentations | NP0.1

Upscale and forward transfer of kinetic energy: Impact on giant planetary jet and vortex formation 

Vincent Böning, Paula Wulff, Wieland Dietrich, Ulrich R. Christensen, and Johannes Wicht

In this study, we analyse the non-linear transfer of kinetic energy in simulations of convection in a 3D rotating shell. Our aim is to understand the role of upscale transfer of kinetic energy and a potential inverse cascade for the formation of zonal jets and large vortices on the giant planets Jupiter and Saturn. We find that the main driving of the jets is associated with upscale transfer directly from the convection scale to the jets. This transfer of energy is mediated by Reynolds stresses, i.e. statistical correlations of velocity components of the small-scale flow.  Intermediate scales are mostly not involved, therefore strictly speaking the jets are not powered by an inverse energy cascade. To a much smaller degree, energy is transferred upscale from the convective scale to large vortices. However, these vortices also receive energy from the jets, likely due to an instability of the jet flow.  Concerning transport in the forward direction, we find as expected that the 3D convective motions transfer energy to the even smaller dissipation scales in a forward cascade.

How to cite: Böning, V., Wulff, P., Dietrich, W., Christensen, U. R., and Wicht, J.: Upscale and forward transfer of kinetic energy: Impact on giant planetary jet and vortex formation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7557, https://doi.org/10.5194/egusphere-egu22-7557, 2022.

EGU22-8277 | Presentations | NP0.1

Scale-to-Scale Energy and Enstrophy Fluxes of Atmospheric Motions via CFOSAT 

Yang Gao, Francois G. Schmitt, Jianyu Hu, and Yongxiang Huang

Turbulence theory essentially describes energy and enstrophy flows crossing scales or a balance between input and output. A famous example is the Richardson-Kolmogorov forward energy cascade picture for three-dimensional homogeneous and isotropic turbulence. However, due to the complexity of turbulent systems, and the lack of an efficient method to describe the cascade quantitatively, the factual cascade features for most fluids are still unknown. In this work, an improved Filter-Space-Technique (FST) is proposed to extract the energy flux ΠE, and enstrophy flux ΠΩ between different scales for the ocean surface wind field which was remotely sensed by the China-France Oceanography Satellite (CFOSAT). With the improved FST method, ΠE and ΠΩ can be calculated for databases which contain gaps or with irregular boundary conditions. Moreover, the local information of the fluxes are preserved. A case study of the typhoon Maysak (2020) shows both inverse and forward cascades for the energy and enstrophy around the center of the typhoon, indicating a rich dynamical pattern. The global views of ΠE and ΠΩ for the wind field are studied for scales from 12.5 to 500 km. The results show that both ΠE and ΠΩ are hemispherically symmetric, with evident spatial and temporal variations for all the scales. More precisely, positive and negative ΠE  are found for the scales less and above 60 km, respectively. As for ΠΩ, the transition scale is around 150 km, forward and backward cascades are corresponding to the scales below and above this scale. In the physical space, stronger fluxes are occurring in midlatitudes than the ones in tropical regions, excepts for a narrow region around 10oN, where strong fluxes are observed. In the temporal space, the fluxes in winter are stronger than the ones in summer. Our study provides an improved approach to derive the local energy and enstrophy fluxes with complex field observed data. The results presented in this work contribute to the fundamental understanding of ocean surface atmospheric motions in their multiscale dynamics, and also provide a benchmark for atmospheric models.

 

Ref. 

Alexakis, A., & Biferale, L. (2018). Cascades and transitions in turbulent flows. Phys. Rep., 767, 1-101.

Dong, S., Huang, Y.X., Yuan, X., Lozano-Durán, A. (2020). The coherent structure of the kinetic energy transfer in shear turbulence. J. Fluid Mech., 892, A22.

Frisch, U., Kolmogorov, A. N. (1995). Turbulence: the legacy of AN Kolmogorov. Cambridge University Press.

Gao, Y. , Schmitt, F.G., Hu,  J.Y. &  Huang, Y.X. (2021) Scaling analysis of the China France Oceanography Satellite along-track wind and wave data. J. Geophys. Res. Oceans, 126:e2020JC017119

 

How to cite: Gao, Y., Schmitt, F. G., Hu, J., and Huang, Y.: Scale-to-Scale Energy and Enstrophy Fluxes of Atmospheric Motions via CFOSAT, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8277, https://doi.org/10.5194/egusphere-egu22-8277, 2022.

EGU22-8564 | Presentations | NP0.1

Global view of oceanic cascades from the Global Circulation Model 

Jingjing Song, Dan Zhang, Yan Peng, Yang Gao, and Yongxiang Huang

In his seminal work "Weather Prediction by Numerical Process" in 1922, Lewis Fry Richardson proposed the famous cascade picture qualitatively for a turbulent flow that energy transferring from large to small scale  structures, until the viscosity one where the kinetic energy is converted  into heat. This picture has been recognized further as the forward energy  cascade.  But, it cannot be applied directly to the real atmospheric  or oceanic motions. Whatever, the global circulation model is indeed established within this framework by considering more complex situations, e.g., earth rotation, stratification, tide, mesoscale eddies, to list a few. In  this work, an improved Filter-Space-Technique (FST) is applied to a reanalysis product provided by the CMEMS global ocean eddy-resolving (1/12o degree horizontal resolution).   The FST provides a global view of the  energy flux ΠE  that associated with the oceanic cascades for all resolved  scales, e.g., from mesoscale eddies to global circulations. For instance, at scale r=160 km (i.e., radius of the Gaussian filter kernel), a rich dynamic pattern is observed for an instantaneous flow filed. Both forward (ΠE>0, energy transferring from large scale to small scale structures) and inverse (ΠE<0, energy transferring from small scale to large scale structures) cascades are evident in the equator, western boundary current regions, Antarctic Circumpolar Current region, to name a few. While, the long-term averaged flux field show mainly a negative ΠE (inverse energy cascade) except for the equatorial region. Moreover, a high intensity negative flux is found for both the Loop Current and Kuroshio Current, indicating that the mesoscale eddies might be absorbed by the main flow.

 

Ref.

Charney, J. G. (1971). Geostrophic turbulence. J. Atmos. Sci., 28(6), 1087-1095.

Frisch, U.,  Kolmogorov, A. N. (1995). Turbulence: the legacy of AN Kolmogorov. Cambridge University Press.

Alexakis, A.,  Biferale, L. (2018). Cascades and transitions in turbulent flows. Phys. Rep., 767, 1-101.

Dong, S., Huang, Y.X., Yuan, X., & Lozano-Durán, A. (2020). The coherent structure of the kinetic energy transfer in shear turbulence. J. Fluid Mech., 892, A22.

How to cite: Song, J., Zhang, D., Peng, Y., Gao, Y., and Huang, Y.: Global view of oceanic cascades from the Global Circulation Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8564, https://doi.org/10.5194/egusphere-egu22-8564, 2022.

Big whirls have little whirls that feed on their velocity,

and little whirls have lesser whirls and so on to viscosity.

These famous words written in 1922 by Lewis Fry Richardson have become inspiration for intensively developing scientific field studying scales of climate variability and their interactions. In spite of ever growing interest in this research area, the description of this session states: ”We still lack an efficient methodology to diagnose the scale-to-scale energy or other physical quantities fluxes to characterize the cascade quantitatively, e.g., strength, direction, etc. ”  In this contribution we would like to remind the methodology able to identify causal relations and information transfer between dynamical processes on different time scales and even to quantify the effect of such causal influences. Moreover, in macroscopic systems the information transfer is tied to the transfer of mass and energy [1].

The detection of cross-scale causal interactions [2] starts with a wavelet (or other scale-wise) decomposition of a multi-scale signal into quasi-oscillatory modes of a limited bandwidth, described using their instantaneous phases and amplitudes. Then their statistical associations are tested in order to search interactions across time scales. An information-theoretic formulation of the generalized, nonlinear Granger causality [3] uncovers causal influence and information transfer from large-scale modes of climate variability, characterized by time scales from years to almost a decade, to regional temperature variability on short time scales.  In particular, a climate oscillation with the period around 7-8 years has been identified as a factor influencing variability of surface air temperature (SAT) on shorter time scales.  Its influence on the amplitude of the SAT annual cycle was estimated in the range 0.7-1.4 °C, while its strongest effect was observed in the interannual variability of the winter SAT anomaly means where it reaches 4-5 °C in central European stations and reanalysis data [4].  In the dynamics of El Niño-Southern Oscillation (ENSO), three principal time scales - the annual cycle (AC), the quasibiennial (QB) mode(s) and the low-frequency (LF) variability – and their causal network have been identified [5]. Recent results show how the phases of ENSO QB and LF oscillations influence amplitudes of precipitation variability in east Asia in the annual and QB scales.

Support from the Czech Science Foundation (GA19-16066S) and the Czech Academy of Sciences (Praemium Academiae) is gratefully acknowledged.

[1] J. Hlinka et al., Chaos 27(3), 035811 (2017)

[2] M. Palus, Phys. Rev. Lett. 112, 078702 (2014)

[3] M. Palus, M. Vejmelka, Phys. Rev. E 75, 056211  (2007)

[4] N. Jajcay, J. Hlinka, S. Kravtsov, A. A. Tsonis, M. Palus, Geophys. Res. Lett. 43(2), 902–909 (2016)

[5] N. Jajcay, S. Kravtsov, G. Sugihara, A. A. Tsonis, and M. Palus, npj Climate and Atmospheric Science 1, 33 (2018).  doi:10.1038/s41612-018-0043-7, https://www.nature.com/articles/s41612-018-0043-7

How to cite: Palus, M.: Big whirls talking to smaller whirls: detecting cross-scale information flow, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9028, https://doi.org/10.5194/egusphere-egu22-9028, 2022.

EGU22-9226 | Presentations | NP0.1

Study of Submesoscale Coherent Vortices (SCVs) in the Atlantic Ocean along different isopycnals 

Ashwita Chouksey, Xavier Carton, and Jonathan Gula

The ocean is densely populated with energetic coherent vortices of different sizes. Mesoscale and submesoscale vortices contribute to stirring of the ocean, transporting and redistributing water masses and tracers (active and passive), affecting ventilation pathways and thus impacting the large-scale circulation. Submesoscale Coherent Vortices (SCVs), i.e. vortices with radii between 1-30 km have been detected via satellite and in-situ measurements at surface or at depth (usually not more than ~2000 m depth). They are found to be of different shapes and sizes depending upon latitude and place of origin. Previous studies mostly describe the surface mesoscale and submesoscale eddies rather than the deep SCVs (> 2000 m). This study focuses on SCVs below the mixed layer along four different isopycnal surfaces: 26.60, 27.60, 27.80, and 27.86, which lie in the depth range of 10-500 m, 200-2000 m, 1200-3000 m, and 1800-4500 m, respectively. We aim to quantify their physical characteristics (radius, thickness, bias in polarity: cyclones versus anticyclones) in different parts of the Atlantic ocean, and analyze the dynamics involved in the generation and destruction of the SCVs throughout their life-cycle. We use the Coastal and Regional Ocean COmmunity model (CROCO) ocean model in a high resolution setup (3 km) of the Atlantic Ocean. The detection of SCVs are done every 12 hr using the Okubo-Weiss parameter along the isopycnal surfaces using the eddy-tracking algorithm by Mason et al., 2014. We consider only structures living for more than 21 days. The census of SCVs shows that there are in total more cyclonic than anticyclonic SCV detections. However cyclones are on average smaller and shorter lived, such that there is a dominance of anticyclones while considering long-lived and larger distance travelling SCVs. We concentrate on the strongest and longest lived SCVs among which meddies that we compare to previous in-situ observations. This study is the first step in the understanding of the formation, occurrences and structure of SCVs in the Atlantic Ocean, and their impact on the large-scale ocean circulation.

How to cite: Chouksey, A., Carton, X., and Gula, J.: Study of Submesoscale Coherent Vortices (SCVs) in the Atlantic Ocean along different isopycnals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9226, https://doi.org/10.5194/egusphere-egu22-9226, 2022.

In recent years a consensus has been reached regarding the direction of the energy cascade in the mesoscales in the Upper Tropospheric-Lower Stratospheric (UTLS) altitudes. Numerous measurements and model results confirm the existence of a predominantly forward spectral energy flux from low to high horizontal wavenumbers. However, the details to explain the observed -5/3 power law for Kinetic and Available Potential Energy (KE and APE) are still being debated.

In this study we performed simulations using the dry version of the Kühlungsborn Mechanistic general Circulation Model (KMCM) with high horizontal and vertical resolution for permanent January conditions. Horizontal diffusion schemes for horizontal momentum and sensible heat satisfy the Scale Invariance Criterion (SIC) using the Dynamic Smagorinsky Model (DSM). We investigated the simulated KE and APE spectra with regard to the scaling laws of Stratified Macro-Turbulence (SMT). Zonally and temporally averaged dissipation rates for KE & APE and SMT statistics correlate highly in subtropical mid-latitudes and the UTLS levels. Particularly the characteristic dimensionless numbers of Buoyancy Reynolds Number and turbulent-Rossby Number are pronounced in the regions, where the maximum of the forward spectral fluxes of nonlinear interactions are also found. During this process the spectral contribution of the negative buoyancy production term plays an important role by converting KE to APE. These findings are entirely in line with the spectral and statistical predictions of idealized Stratified Turbulence (ST) and confirms that the energy cascades that give rise to the simulated mesoscale shallowing are strongly nonlinear.

Furthermore level by level analyses of the horizontally averaged spectral tendencies and fluxes of both KE and APE reservoirs in this specific region revealed that there is a non-negligible spectral contribution by the energy deposition term of upward propagating Gravity Waves (GW). Further investigation indicate the dynamics of these resolved GWs look like a superposition of westward Inertia GWs that are subject to a Lindzen-type saturation condition. Their vertical propagation in UTLS heights is non-conservative above their generation level. These results associate directly for the first time ST and GW dynamics, which were thought to be distinct in character. Finally we present simulations with different diffusion schemes and show that the previously mentioned energy deposition contribution was only identified if both horizontal momentum and sensible heat diffusion schemes fulfill the SIC.

How to cite: Can, S.: Macro-Turbulent Energy Cascades in UpperTropospheric-Lower Stratospheric Mesoscales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9270, https://doi.org/10.5194/egusphere-egu22-9270, 2022.

EGU22-9329 | Presentations | NP0.1

Mesoscale Eddy Kinetic Energy budgets and transfers between vertical modes in the Agulhas Current 

Pauline Tedesco, Jonathan Gula, Pierrick Penven, and Claire Ménesguen

Western boundary currents are hotspots of the mesoscale oceanic variability and of energy transfers, channeled by topography, toward smaller scales and eventually down to dissipation. Here, we assess the main mesoscale eddies energy sinks in the Agulhas Current region, with an emphasize on the different paths of energy toward smaller scales, from a regional numerical simulation. 

We derive an eddy kinetic energy (EKE) budget in the framework of the vertical modes. This comprehensive method accounts for energy transfers between energy reservoirs and vertical modes, including transfers channeled by topography and by a turbulent vertical cascade. 

The variability is dominated by mesoscale eddies (barotropic and 1st baroclinic modes) in the path of intense mean currents. Eddy-topography interactions result in a major mesoscale eddy energy sink (50 % of the total EKE sink). They represent energy transfers both toward higher baroclinic modes (27 % of the total EKE sink) and mean currents (23 % of the total EKE sink). Energy transfers toward higher baroclinic modes take different forms in the Northern Agulhas Current, where it corresponds to non-linear transfers to smaller vertical eddies on the slope (5 % of the total EKE sink), and in the Southern Agulhas Current, where it is dominated by a (linear) generation of internal-gravity waves over topography (22 % of the total EKE sink). The vertical turbulent cascade is significant in offshore regions, away from topography and intense mean currents. In these regions the direction of the turbulent vertical cascade is inverse - energy transferred from higher baroclinic modes toward mesoscale eddies - and it can locally amounts for most of the mesoscale eddies energy gain (up to 68 % of the local EKE source).

However, the Agulhas Current region remains a net source of mesoscale eddy energy due to the strong generation of eddies, modulated by the topography, especially in the Southern Agulhas Current. In the complex Agulhas Current system, which includes an intense mean oceanic current and mesoscale eddies field as well as strong topographic constraint and stratification gradients, the local generation of mesoscale eddies dominates the net EKE budget. It is in contrast with the paradigm of mesoscale eddies decay upon western boundaries, suggested as being due to topographically-channeled interactions triggering a direct energy cascade.

How to cite: Tedesco, P., Gula, J., Penven, P., and Ménesguen, C.: Mesoscale Eddy Kinetic Energy budgets and transfers between vertical modes in the Agulhas Current, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9329, https://doi.org/10.5194/egusphere-egu22-9329, 2022.

EGU22-13450 | Presentations | NP0.1

Relative Dispersion with Finite Inertial Ranges 

Joe LaCasce and Thomas Meunier

The relative dispersion of pairs of particles was first considered in a seminal article by Richardson (1926). The dispersion subsequently was subsequently linked to turbulence, and pair separation statistics can advantageously be used to deduce energy wavenumber spectra. Thus one can, for example, employ surface drifters to identify turbulent regimes at scales well below those resolved by satellite altimetry. The identification relies on knowing how dispersion evolves with a specific energy spectrum. The analytical predictions commonly used apply to infinite inertial ranges, i.e. assuming the same dispersive behavior over all scales. With finite inertial ranges, the metrics are less conclusive, and often are not even consistent with each other.

We examine this using pair separation probability density functions (PDFs), obtained by integrating a Fokker-Planck equation with different diffusivity profiles. We consider time-based metrics, such as the relative dispersion, and separation-based metrics, such as the finite scale Lyapunov exponent (FSLE). As the latter cannot be calculated from a PDF, we introduce a new measure, the Cumulative Inverse Separation Time (CIST), which can. This behaves like the FSLE, but advantageously has analytical solutions in the inertial ranges. This allows establishing consistency between the time- and space-based metrics, something which has been lacking previously.

We focus on three dispersion regimes: non-local spreading (as in a 2D enstrophy inertial range), Richardson dispersion (as in the 3D and 2D energy inertial ranges) and diffusion (for uncorrelated pair motion). The time-based metrics are more successful with non-local dispersion, as the corresponding PDF applies from the initial time. Richardson dispersion is barely observed, because the self-similar PDF applies only asymptotically in time. In contrast, the separation-based CIST correctly captures the dependencies, even with a short (one decade) inertial range, and is superior to the traditional FSLE at large scales. Furthermore, the analytical solutions permit reconciling the CIST with the other measures, something which is generally not possible with the FSLE.

How to cite: LaCasce, J. and Meunier, T.: Relative Dispersion with Finite Inertial Ranges, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13450, https://doi.org/10.5194/egusphere-egu22-13450, 2022.

NP2 – Dynamical Systems Approaches to Problems in the Geosciences

EGU22-30 | Presentations | NP2.2

Downward counterfactual insights into weather extremes 

Gordon Woo

There are many regions where the duration of reliable scientific observations of key weather hazard variables, such as rainfall and wind speed, is of the order of just a few decades.  This length of dataset is often inadequate for the application of extreme value theory to rare events. Theoretical analysis of chaotic dynamical systems shows that extremes should be distributed according to the classical Pareto distribution, with explicit expressions for the scaling and shape parameter[1]. Discrepant results may be interpreted as indicating the need for a longer data time series.

Physicists acknowledge that history is just one realisation of what could have happened. One way of supplementing a brief duration observational dataset is to generate an ensemble of alternative realisations of history. Of special practical interest within this counterfactual ensemble are downward counterfactuals - where the outcome turned for the worse.  Extreme hazard events often cause surprise, which reflects an underlying degree of outcome cognitive bias. Downward counterfactual is a term originating in the cognitive psychological literature, which has been applied by Woo[2] to the search for extreme hazard events.  Most human counterfactual thoughts are upward, focusing on risk mitigation or prevention, rather than downward, focusing on potential rare Black Swan events. 

The insight gained from downward counterfactual analysis is illustrated with the example of rainfall and flooding in Cumbria, Northwest England.  Daily rainfall records at Honister Pass, Cumbria, from 1970 to 2004, were statistically analysed to estimate the return period for the rainfall of 301.4mm oberved on 20 November 2009.  This return period was estimated to be 396 years[3].  But six years later, on 5 December 2015, this was substantially exceeded by 341.4mm rainfall.

In 2009, there was only a moderate El Niňo.  Counterfactually, there might have been a strong El Niňo.  Indeed, in 2015 there was a very strong El Niňo. A downward counterfactual analysis of the heavy rainfall on 20 November 2009 would have included the possibility of a very strong El Niňo.  This is one of a number of exacerbating dynamical meteorological factors that might have elevated the rainfall.

Where the data duration is much shorter than the return period of extreme events, a downward counterfactual stochastic simulation of factors raising the hazard will provide important additional insight for geophysical hazard assessment.

 


[1] Lucarini V., Faranda D., Wouters J., Kuna T. (2014) Towards a general theory of extremes for observables of chaotic dynamical systems. J.Stat.Phys., 154, 723-750.

[2] Woo G. (2019) Downward counterfactual search for extreme events.  Front. Earth. Sci. doi:10.3389/feart.2019.00340.

[3] Stewart L., Morris D., Jones D., Spencer P. (2010) Extreme rainfall in Cumbria, November 2009 – an assessment of storm rarity. BHS Third Int. Symp., Newcastle.

How to cite: Woo, G.: Downward counterfactual insights into weather extremes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-30, https://doi.org/10.5194/egusphere-egu22-30, 2022.

EGU22-54 | Presentations | NP2.2

Quantification of model uncertainty in the projection of sub-daily maximum wet spell length under RCP 8.5 climate change scenario 

Archana Majhi, Chandrika Thulaseedharan Dhanya, and Sumedha Chakma

Global precipitation characteristics have been significantly altered due to the global warming. While, this is well-known, the sub-daily extreme precipitation events are more sensitive, as compared to the daily-scale. The future intensification of these sub-daily extremes worsen the risk of floods and droughts, thereby posing threat to the natural ecosystem and human society. The ability of general circulation models (GCMs) in simulating the sub-daily precipitation may be inferior, due to their coarser resolutions and complex parametrization schemes. In addition, the characteristics such as the intensity, frequency and duration of sub-daily precipitation may not be correctly simulated by the GCMs. Despite this fact, there are limited studies to investigate the credibility of sub-daily precipitation projections by GCMs, and the related uncertainty. Therefore, in order to investigate the reliability of GCMs in the projections of such extremes, we have used 20 Coupled Model Intercomparison Project phase 5 (CMIP5) models under RCP8.5 (Representative Concentration Pathway). The uncertainty is estimated in the projections of maximum wet spell length (WSL) i.e. maximum number of consecutive wet hours in four different meteorological seasons (DJF, MAM, JJA, and SON), for both near (2026-45) and far future (2081-99) time periods. The equatorial regions of Africa and South East Asia, showed higher model disagreement during every season. In contrast the equatorial regions of South America and South Asia showed significantly more disagreement during DJF and JJA season. Model uncertainty in each hemisphere is observed to be higher during their respective wet seasons. Though the model uncertainty in far future is varying when compared with that in near future, the uncertainty is not increasing globally. Also, the uncertainty is observed to have significantly decreased during MAM season in far future. The spatial contribution towards higher model uncertainty range, is less as compared to lower uncertainty range over the globe. While the magnitude of model uncertainty is varying with time, the latitudinal heterogeneity remains same in both the time period. 

Keywords: precipitation extremes, sub-daily, wet spell, GCM, projections, uncertainty, RCP 8.5

 

How to cite: Majhi, A., Dhanya, C. T., and Chakma, S.: Quantification of model uncertainty in the projection of sub-daily maximum wet spell length under RCP 8.5 climate change scenario, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-54, https://doi.org/10.5194/egusphere-egu22-54, 2022.

EGU22-258 | Presentations | NP2.2

The Regional Impact of Wet and Windy Extremes Over Europe, Following North American Cold Spells 

Richard Leeding, Gabriele Messori, and Jacopo Riboldi

Due to the compounding nature of co-occurring weather extremes, these events can be highly detrimental to economies, damaging to infrastructure and result in loss of life. Previous work has established a connection between cold spells over North America and extreme wet and windy weather over Europe. This work attempts to identify a statistical link between the regional impact of wet and windy extremes over Europe based on the regional impact of cold spells over North America. We identify cold spells for 41 overlapping regions over North America for full winter (DJF) seasons between 1979 and 2020 using ERA5 data, employing 4 methodologies for the computation of onset dates. The impact of extreme precipitation and wind events over 6 regions of western and central Europe is analysed. Consistent across all methodologies, cold spells over eastern and mid USA are followed by significant wind extremes over Iberia, whilst cold spells over eastern Canada are followed by significant wind extremes over northern Europe and the British Isles. The regional impact of precipitation extremes shows much greater variance, though we find significant Iberian and southern European precipitation for cold spells over eastern USA, consistent with that found for wind extremes. The majority of extreme precipitation and some significant wind extremes also precede the peak of the cold spell. We show also that the frequency of extreme precipitation and wind events over Iberia increases by 1.5 to more than 2 times the climatological frequency, following cold spells in most North American regions.

How to cite: Leeding, R., Messori, G., and Riboldi, J.: The Regional Impact of Wet and Windy Extremes Over Europe, Following North American Cold Spells, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-258, https://doi.org/10.5194/egusphere-egu22-258, 2022.

EGU22-470 | Presentations | NP2.2

Relating atmospheric persistence to heatwaves in Europe 

Emma Allwright and Gabriele Messori

Heatwaves cause widespread disruption to society and increased mortality across Europe. These events are often associated with persistent circulations, however, the maintenance mechanisms and characteristics of atmospheric persistence are comparatively poorly understood. We aim to help bridge the gap between qualitative meteorological arguments and mathematical theory relating to heatwaves by quantitatively identifying persistent atmospheric configurations. This will be achieved by calculating indicators associated with dynamical systems theory using ERA5 reanalysis data. We will then spatially compare these indicators with temperature anomalies to determine which regions of Europe are potentially sensitive to these quantities with regards to the occurrence of heatwaves, and if there are specific atmospheric configurations associated to these cases.

How to cite: Allwright, E. and Messori, G.: Relating atmospheric persistence to heatwaves in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-470, https://doi.org/10.5194/egusphere-egu22-470, 2022.

Unusual, long-lasting configurations of the North Atlantic jet stream affect the weather over Europe leading to persistent surface extremes. We study these persistent jet configurations in winter on intraseasonal and seasonal time scales using CMIP6 simulations, based on temporal averages of three jet indices: the jet latitude index, the jet speed index and the zonal jet index. We define these unusual configurations as long-lasting states, during which the jet stream is further south or further north, stronger or weaker, more split or more merged than usual. We estimate the probability of rare configurations, lasting at least 2 months, based on large deviation rate functions. The rate functions are asymmetric in case of the jet speed index, meaning that anomalously strong jet states are more persistent and more frequent than weak ones. Furthermore, we quantify the increased frequency of temperature and precipitation extremes over affected European regions. Here, we find a stronger link between jet events and precipitation extremes compared to temperature extremes. We observe the largest effects in case of precipitation extremes over the Mediterranean and Western Europe during anomalously strong jet configurations.

How to cite: Galfi, V. M. and Messori, G.: Persistent configurations of the North Atlantic jet stream from the perspective of large deviation theory, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-474, https://doi.org/10.5194/egusphere-egu22-474, 2022.

EGU22-1594 | Presentations | NP2.2

Past Evolution of Western Europe Large-scale Circulation and Link to Extreme Precipitation Trend in the Northern French Alps 

Antoine Blanc, Juliette Blanchet, and Jean-Dominique Creutin

Detecting trends in regional large-scale circulation (LSC) is an important challenge as LSC is a key driver of local weather conditions. In this work, we investigate the past evolution of Western Europe LSC based on the 500 hPa geopotential height fields from 20CRv2c (1851-2010), ERA20C (1900-2010) and ERA5 (1950-2010) reanalyses. We focus on the evolution of large-scale circulation characteristics using three atmospheric descriptors that are based on analogy, by comparing daily geopotential height fields to each other. They characterize the stationarity of geopotential shape and how well a geopotential shape is reproduced in the climatology. A non-analogy descriptor is also employed to account for the intensity of the centers of action. We then combine the four atmospheric descriptors with an existing weather pattern classification over the period 1950-2019 to study the recent changes in the main atmospheric influences driving precipitation in the Northern French Alps. Even though LSC characteristics and trends are consistent among the three reanalyses after 1950, we find major differences between 20CRv2c and ERA20C from 1900 to 1950 in accordance with previous studies. Notably, ERA20C produces flatter geopotential shapes in the beginning of the 20th century and shows a reinforcement of the meridional pressure gradient that is not observed in 20CRv2c. Over the period 1950-2019, we show that winter Atlantic circulations (zonal flows) tend to be shifted northward and they become more similar to known Atlantic circulations. Mediterranean circulations tend to become more stationary, more similar to known Mediterranean circulations and associated with stronger centers of action in autumn, while an opposite behaviour is observed in winter. Finally, we discuss the responsibility of these LSC changes for extreme precipitation in the Northern French Alps. We show these changes in LSC characteristics are linked to more circulations that are likely to generate extreme precipitation in autumn.

How to cite: Blanc, A., Blanchet, J., and Creutin, J.-D.: Past Evolution of Western Europe Large-scale Circulation and Link to Extreme Precipitation Trend in the Northern French Alps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1594, https://doi.org/10.5194/egusphere-egu22-1594, 2022.

EGU22-1832 | Presentations | NP2.2

Local drivers of marine heatwaves: A global analysis with an Earth system model 

Linus Vogt, Friedrich Burger, Stephen Griffies, and Thomas Frölicher

Marine heatwaves (MHWs) are periods of extreme warm ocean temperatures that can have devastating impacts on marine
organisms and socio-economic systems. Despite recent advances in understanding the underlying processes of individual events, a
global view of the local oceanic and atmospheric drivers of MHWs is currently missing. Here, we use daily-mean output of
temperature tendency terms from a comprehensive fully coupled Earth system model to quantify the main local processes leading
to the buildup and decay of MHWs in the surface ocean. Our analysis reveals that net ocean heat uptake associated with more
shortwave heat absorption and less latent heat loss is the primary driver of the buildup of MHWs in the subtropics and mid-to-high
latitudes. Reduced vertical mixing from the nonlocal portion of the KPP boundary layer scheme partially dampens the temperature
increase. In contrast, ocean heat uptake is reduced during the MHW build-up in the tropics, where reduced vertical local mixing
and diffusion cause the warming. In the subsequent decay phase, ocean heat loss to the atmosphere dominates the temperature
decrease globally. The processes leading to the buildup and decay of MHWs are similar for short and long MHWs. Different types of
MHWs with distinct driver combinations are identified within the large variability among events. Our analysis contributes to a
better understanding of MHW drivers and processes and may therefore help to improve the prediction of high-impact marine
heatwaves.

How to cite: Vogt, L., Burger, F., Griffies, S., and Frölicher, T.: Local drivers of marine heatwaves: A global analysis with an Earth system model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1832, https://doi.org/10.5194/egusphere-egu22-1832, 2022.

EGU22-1884 | Presentations | NP2.2

Meridional energy transport extremes and the general circulation of NH mid-latitudes: dominant weather regimes and preferred zonal wavenumbers 

Valerio Lembo, Federico Fabiano, Vera Melinda Galfi, Rune Graversen, Valerio Lucarini, and Gabriele Messori

The extratropical meridional energy transport in the atmosphere is fundamentally intermittent in nature, having extremes large enough to affect the net seasonal transport. Here, we investigate how these extreme transports are associated with the dynamics of the atmosphere at multiple scales, from planetary to synoptic. We use ERA5 reanalysis data to perform a wavenumber decomposition of meridional energy transport in the Northern Hemisphere mid-latitudes during winter and summer. We then relate extreme transport events to atmospheric circulation anomalies and dominant weather regimes, identified by clustering 500 hPa geopotential height fields. In general, planetary-scale waves determine the strength and meridional position of the synoptic-scale baroclinic activity with their phase and amplitude, but important differences emerge between seasons. During winter, large wavenumbers (= 2 − 3) are key drivers of the meridional energy transport extremes, and planetary and synoptic-scale transport extremes virtually never co-occur. In summer, extremes are associated with higher wavenumbers (= 4 − 6), identified as synoptic-scale motions. We link these waves and the transport extremes to recent results on exceptionally strong and persistent co-occurring summertime heat waves across the Northern Hemisphere mid-latitudes. We show that these events are typical, in terms of dominant regime patterns associated with extremely strong meridional energy transports.

Link to pre-print: https://wcd.copernicus.org/preprints/wcd-2021-85/

How to cite: Lembo, V., Fabiano, F., Galfi, V. M., Graversen, R., Lucarini, V., and Messori, G.: Meridional energy transport extremes and the general circulation of NH mid-latitudes: dominant weather regimes and preferred zonal wavenumbers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1884, https://doi.org/10.5194/egusphere-egu22-1884, 2022.

EGU22-2001 | Presentations | NP2.2

Text-mining of natural hazard impacts (TM-Impacts): an application to the 2021 flood in Germany 

Mariana Madruga de Brito, Jan Sodoge, Heidi Kreibich, and Christian Kuhlicke

Natural hazards cause a plethora of impacts on society, ranging from direct impacts such as loss of lives to cascading ones such as power outages and supply shortages. Despite the severe social and economic losses of extreme events, a comprehensive assessment of their impacts remains largely missing. Existing studies tend to focus on impacts that are relatively easy to measure (e.g. financial loss, number of deaths) and commonly break down impact assessments into specific sectors (e.g. forestry, agriculture). Thus, in the absence of multi-sector impact datasets, decision-makers have no baseline information for evaluating whether adaptation measures effectively reduce impacts. This can result in blind spots in adaptation.

In recent years, text data (e.g. newspapers, social media, and Wikipedia entries) have been used to elaborate impact datasets. However, the manual extraction of impact information by human experts is a time-consuming task. To develop comprehensive impact datasets, we propose using text-mining on text documents. We developed a tool termed TM-Impacts (text-mining of natural hazard impacts), which allows us to automatically extract information on impacts by applying natural language processing (NLP) and machine learning (ML) tools to text-corpora. TM-Impacts is built upon a previous prototype application (de Brito et al., 2020).

TM-Impacts consists of three complementary modules. The first focuses on using unsupervised topic modelling to identify the main topics covered in the text. These can include not only the disaster impacts but also information on response and recovery. The second module is based on the use of hand-crafted rules and pattern matching to extract information on specific impact types (e.g. traffic disruption, power outages). The final module builds upon the second one, and it uses the resulting labelled data to train supervised ML algorithms aiming to classify unlabeled text data into impact types.

We illustrate the application of TM-Impacts using the example of the 2021 flood in Germany. This event led to more than 180 fatalities and the disruption of critical infrastructure that continued for months after the event. We built a text corpus with more than 26,000 newspaper articles published in 200 different news outlets between July and November 2021. By using TM-Impacts, we were able to detect 20 different impact types, which were mapped at the NUTS 3 scale. We also identified temporal patterns. As expected, during the onset of the event, reporting on impacts tended to focus on deaths and missing people, whereas texts published in November focused on long term impacts such as the disruption of water supply.

In conclusion, we demonstrate that TM-Impacts allows scanning large amounts of text data to build multi-sector impact datasets with a great spatial and temporal stratification. We expect the use of text-mining to become widespread in assessing the impacts of natural hazards.

 

de Brito, M.M., Kuhlicke, C., Marx, A. (2020) Near-real-time drought impact assessment: A text mining approach on the 2018/19 drought in Germany. Environmental Research Letters. doi:org/10.1088/1748-9326/aba4ca

How to cite: Madruga de Brito, M., Sodoge, J., Kreibich, H., and Kuhlicke, C.: Text-mining of natural hazard impacts (TM-Impacts): an application to the 2021 flood in Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2001, https://doi.org/10.5194/egusphere-egu22-2001, 2022.

EGU22-2050 | Presentations | NP2.2 | Highlight

Intergenerational inequities in exposure to climate extremes 

Wim Thiery and the The kids aren't alright team

Under continued global warming, extreme events such as heatwaves will continue to rise in frequency, intensity, duration, and spatial extent over the next decades. Younger generations are therefore expected to face more such events across their lifetimes compared to older generations. This raises important questions about solidarity and fairness across generations that have fueled a surge of climate protests led by young people in recent years, and that underpin questions of intergenerational equity raised in recent climate litigation. However, the standard scientific paradigm is to assess climate change in discrete time windows or at discrete levels of warming, a “period” approach that inhibits quantification of how much more extreme events a particular generation will experience over its lifetime compared to another. By developing a “cohort” perspective to quantify changes in lifetime exposure to climate extremes and compare across generations, we estimate that children born in 2020 will experience a two to sevenfold increase in extreme events, particularly heatwaves, under current climate policy pledges. Our results highlight a severe threat to the safety of young generations and call for drastic emission reductions to safeguard their future.

 

Thiery, W., Lange, S., Rogelj, J., Schleussner, C.-F., Gudmundsson, L., Seneviratne, S.I., Frieler, K., Emanuel, K., Geiger, T., Bresch, D.N., Zhao, F., Willner, S.N., Büchner, M., Volkholz, J., Andrijevic, M., Bauer, N., Chang, J., Ciais, P., Dury, M., François, L., Grillakis, M., Gosling, S.N., Hanasaki, N., Hickler, T., Huber, V., Ito, A., Jägermeyr, J., Khabarov, N., Koutroulis, A., Liu, W., Lutz, W., Mengel, M., Müller, C., Ostberg, S., Reyer, C.P.O., Stacke, T., Wada, Y., 2021, Intergenerational inequities in exposure to climate extremes, Science, 374(6564), 158-160.

How to cite: Thiery, W. and the The kids aren't alright team: Intergenerational inequities in exposure to climate extremes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2050, https://doi.org/10.5194/egusphere-egu22-2050, 2022.

EGU22-3127 | Presentations | NP2.2

The influence of ENSO and Antarctic Oscillation on extreme precipitation over southeastern South America. 

Xinjia Hu, Damien Decremer, Laura Ferranti, Linus Magnusson, Daoyi Gong, Florian Pappenberger, and Holger Kantz

The Southeastern South American region (SESA) is one of the AR6 WGI reference regions which is used as an illustration of the interplay between climate variability drivers and regional response. Since most of the agricultural activities take place over this region, its climate variability has a strong impact on society. The region is sensitive to extreme precipitation and puts constraints on water resource management. In recent decades, positive rainfall trends have been detected especially during austral summer. Interactions between the El Nino Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO) also known as the Southern Annual mode, have been well documented indicating the crucial role of ENSO in modulating the AAO phase. In this paper, we explore the interplay between ENSO and AAO and their effect on extreme precipitation over the SESA region during austral spring and summer. Statistical approaches based on extreme value theory (EVT) are applied to daily precipitation amounts to model extreme precipitation, identifying the relative impact of ENSO and AAO. We obtained return values for different phases of ENSO and AAO. We also perform dynamical analysis for sea level pressure and wind field to relate large-scale atmospheric circulation patterns with extreme precipitation.

How to cite: Hu, X., Decremer, D., Ferranti, L., Magnusson, L., Gong, D., Pappenberger, F., and Kantz, H.: The influence of ENSO and Antarctic Oscillation on extreme precipitation over southeastern South America., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3127, https://doi.org/10.5194/egusphere-egu22-3127, 2022.

EGU22-3133 | Presentations | NP2.2

A framework for attributing explosive cyclones to climate change: the case study of Alex storm 2020 

Mireia Ginesta, Pascal Yiou, Gabriele Messori, and Davide Faranda

The Extreme Event Attribution field aims at evaluating the impact of global warming linked to anthropogenic emissions on extreme events. This work performs an attribution to climate change of the storm Alex, an explosive extratropical cyclone [1] that hit especially Southern France and Northern Italy at the beginning of October 2020. We apply the analogues method on sea-level pressure maps [2] to identify 30 cyclones that match the dynamical structure of Alex for two periods, the counterfactual and the factual world, namely 1950-1985 and 1985-2021, using 6-hourly ERA5 data. Results show that in the factual period the anticyclonic circulation over the North Atlantic and the cyclonic circulation over Northern Africa are deeper than in the counterfactual. Precipitation differences depict a significant increase over North Italy and the Alps. 2-meter air temperature differences consist of a positive non-uniform pattern, with a significant increase over the Alps and east of Newfoundland. We also have computed two indices in the frame of dynamical systems theory for each period: the persistence, which characterizes the average time that the sea-level pressure pattern remains stationary, and the local dimension, which gives a measure of the predictability of the storm [3]. We found that in the factual world there is a significant increase in the persistence and a modest decrease in the local dimension with respect to the counterfactual. Hence, storms like Alex are more persistent and more predictable in present-like conditions. Cyclone tracking shows that the backward trajectories of the analogues in the factual world are more meridional than in the counterfactual one, while the response for the forward trajectories is less clear. This suggests that under current conditions patterns like Alex are more wavy than in the past. Finally, using the metrics to identify explosive cyclones in [1] , we found the same number of analogues that are explosive cyclones in both periods, although in the counterfactual world they come from lower latitudes and the deepening rates are significantly larger.

References

[1]  Reale, M., M. L. Liberato, P. Lionello, J. G. Pinto, S. Salon, and S. Ulbrich, A global climatology of explosive cyclones using a multi-tracking approach, Tellus A: Dynamic Meteorology and Oceanography, 71 (1), 1611,340, 2019.

[2] Yiou, P., AnaWEGE: a weather generator based on analogues of atmospheric circulation, Geosci. Model Dev., 7, 531–543, 2014.

[3] Faranda, D., G. Messori, and P. Yiou, Dynamical proxies of North Atlantic predictability and extremes, Sci Rep, 7, 41,278, 2017.

Acknowledgments

This work is part of the EU International Training Network (ITN) European weather extremes: drivers, predictability and impacts (EDIPI). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N° 956396. 

How to cite: Ginesta, M., Yiou, P., Messori, G., and Faranda, D.: A framework for attributing explosive cyclones to climate change: the case study of Alex storm 2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3133, https://doi.org/10.5194/egusphere-egu22-3133, 2022.

EGU22-4021 | Presentations | NP2.2

Advances in rare event simulations using data-based estimation of committor functions 

Dario Lucente, Joran Rolland, Corentin Herbert, and Freddy Bouchet

Rare events, such as heat waves, floods, or hurricanes, play a crucial role in climate dynamics mainly due to the large impact they have. Predicting the occurrence of such events is thus a major challenge. 

In this talk, we introduce the relevant mathematical object for predicting a future event: the committor function is the probability that an event will occur, conditioned on the current state of the system. Computing this quantity from observations is an extremely difficult task since rare events have a very low probability of occurring and may not even have been observed in measurements made to date. Similarly, direct simulation of such events with comprehensive climate models comes at a prohibitive computational cost. Hence, rare event algorithms have been devised to simulate rare events efficiently, avoiding the computation of long periods of typical fluctuations.

The effectiveness of these algorithms strongly relies on the knowledge of a measure of how close the event of interest is to occur, called the “score function”. The main difficulty is that the optimal score function is the committor function which is exactly the quantity to be computed. Therefore, it is very natural to consider an iterative procedure where the data produced by the algorithm is used to improve the score function, which in turn improves the algorithm, and so on.

In this presentation, we propose a data-driven approach for computing the committor function, based on a Markov chain approximation of the dynamics of the system (the analogue method). We first illustrate this approach for a paradigmatic toy model of multistability for atmospheric dynamics with six variables (the Charney-Devore model). Secondly, we apply this methodology to data generated from a climate model, in order to study and predict the occurrence of extreme heat waves. In both cases, we show that it is possible to obtain fairly precise estimates of the committor function, even when few observations are available.

In the second part of the talk, we show the advantage of coupling the analogue Markov chain with a rare event algorithm. Indeed, the committor learned with the analogue Markov chain can be used as a score function performing better than user-defined score functions, as we show for the Charney-Devore model. 

This new approach is promising for studying rare events in complex dynamics: the rare events can be simulated with a minimal prior knowledge and the results are much more precise than those obtained with a user-designed score function.

How to cite: Lucente, D., Rolland, J., Herbert, C., and Bouchet, F.: Advances in rare event simulations using data-based estimation of committor functions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4021, https://doi.org/10.5194/egusphere-egu22-4021, 2022.

EGU22-5420 | Presentations | NP2.2

The relation between European heat waves and North Atlantic SSTs: a two-sided composite study 

Julian Krüger, Joakim Kjellsson, Robin Pilch Kedzierski, and Martin Claus
  • The occurrence of extreme weather events has increased during the two last decades.  European heat waves are responsible for social, economic and environmental damage and are projected to increase in magnitude, frequency and duration under global warming, heightening the  interest about the contribution of different drivers. 
  • By using the ERA5 Re-analysis product, we performed a two-sided composite analysis to investigate a potential relation between North Atlantic sea surface temperatures (SSTs) and the near-surface air temperature (T2m) over the European continent. Here, we show that in the presence of cold North Atlantic SSTs during summer, the distribution of European T2m shifts towards positive anomalies a few days later, increasing the likelihood for heat waves. During these events a predominant wave number three pattern in addition to regionally confined Rossby wave activity  contribute to a trough-ridge pattern in the North Atlantic-European sector. Specifically, five of 17 European heat waves within the period of 1979 to 2019 could be related to a cold North Atlantic SST event a few days in advance. In the upstream analysis we identify eleven of 17 European heat waves co-existent with cold North Atlantic SSTs. 
  • In order to confirm the crucial role of North Atlantic SSTs for European heat waves, we analysed output from a coupled climate model, HadGEM3, with three different horizontal resolutions. The high-resolution run revealed the closest resemblance to the ERA5 data, suggesting that mechanisms on the mesoscales (<50 km) play a role in the relationship between North Atlantic SSTs and European T2m. Results also highlight the importance of using a climate model with a high horizontal resolution for the purpose of studying the variability of European heat waves.
  • Based upon our results, conducted with ERA5 Re-analysis and HadGEM3 data, North Atlantic SSTs provide potential predictive skill of European heat waves.

How to cite: Krüger, J., Kjellsson, J., Pilch Kedzierski, R., and Claus, M.: The relation between European heat waves and North Atlantic SSTs: a two-sided composite study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5420, https://doi.org/10.5194/egusphere-egu22-5420, 2022.

EGU22-5511 | Presentations | NP2.2

Present and future synoptic circulation patterns associated with cold and snowy spells over Italy 

Flavio Pons, Miriam D’Errico, Pascal Yiou, Soulivanh Tao, Cesare Nardini, Frank Lunkeit, and Davide Faranda

Cold and snowy spells are compound extreme events with the potential of causing high socioeconomic impacts. Gaining insight on their dynamics in climate change scenarios could help anticipating the need for adaptation efforts. We focus on winter cold and snowy spells over Italy, reconstructing 32 major events in the past 60 years from documentary sources. Despite warmer winter temperatures,  very recent cold spells have been associated to abundant, and sometimes exceptional snowfall.
Our goal is to analyse the dynamical weather patterns associated to these events, and understand whether those patterns would be more or less recurrent in different emission scenarios using an intermediate complexity model (PlaSim). Our results, obtained by considering RCP2.6, RCP4.5 and RCP8.5 end-of-century CO2 concentrations, suggest that the likelihood of analogous synoptic configurations of these extreme cold spells would grow substantially under increased emissions.

This work was supported by the ANR-TERC grant BOREAS and by the Horizon 2020 research and innovation programme XAIDA (grant agreement No 101003469)

How to cite: Pons, F., D’Errico, M., Yiou, P., Tao, S., Nardini, C., Lunkeit, F., and Faranda, D.: Present and future synoptic circulation patterns associated with cold and snowy spells over Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5511, https://doi.org/10.5194/egusphere-egu22-5511, 2022.

EGU22-5734 | Presentations | NP2.2

Simulating extreme cold spells in France with empirical importance sampling 

Camille Cadiou and Pascal Yiou

Extreme winter cold spells in Europe have huge societal impacts. Being able to simulate worst case scenarios of such events for present and future climates is hence crucial for adaptation. Rare event algorithms have been applied to simulate extreme heatwaves. They have emphasized the role of the atmospheric circulation in such extremes. The goal of this study is to test such algorithms to extreme cold spells.
We focus on cold spells that occur in France since 1950. The analysis is based on the ERA5 reanalysis. We select cold events that have occurred for different time scales (10 days, 1 month, 3 months). We identify record shattering cold events for time scales of 1 and 3 months (in 1956 and 1963). We find that, although the frequency of extreme cold spells decreases with time, their intensity is stationary.
We applied a stochastic weather generator approach with importance sampling, to simulate the worst cold spells that could occur every year since 1950, with lengths of 1 month and 3 months. We hence simulated ensembles of worst winter cold spells that are consistent with observations. Those worst cases are slightly colder than the record shattering events, and do not yield the trend that is observed on the mean temperature. The atmospheric circulation that prevails during those events is analyzed and compared to the observed circulation during the record breaking events.

How to cite: Cadiou, C. and Yiou, P.: Simulating extreme cold spells in France with empirical importance sampling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5734, https://doi.org/10.5194/egusphere-egu22-5734, 2022.

EGU22-6141 | Presentations | NP2.2

Hot and Cold Marine Extreme Events in the Mediterranean over the last four decades 

Amelie Simon, Sandra Plecha, Ana Russo, Ana Teles-Machado, Markus Donat, and Ricardo Trigo

Marine heat waves (MHWs) and cold spells (MCSs) are anomalous ocean temperature events that occur in all oceans and seas with great ecological and economic impacts. The quantification of the relative importance of marine temperature extreme events is often done through the calculation of local metrics, the majority of them not considering explicitly the spatial extent of the events. Here, we propose a ranking methodology to evaluate the relative importance of marine temperature extreme events between 1982 and 2021 within the Mediterranean basin. We introduce a metric, generically termed activity, combining the number of events, duration, intensity and spatial extent of: i) summer MHWs and ii) winter MCSs. Results at the entire Mediterranean scale show that the former dominate in the last two decades while the latter are prevalent in the 1980s and 1990s. Summers with the highest MHW activity were 2018, 2003 and 2015 and winters with the strongest MCS activity took place in 1992, 1984 and 1983. The highest MHW activity occurred in the Gulf of Lion while the highest MCS activity took place preferably in the Aegean basin. According to our proposed definition, the three strongest MHWs almost double the duration, mean intensity, and activity of the three strongest MCSs. The long-term tendency of activity shows a rapid increase for summer MHWs and a linear decrease for winter MCSs in the Mediterranean over the last four decades.

 

We acknowledge the financing support from FCT – JPIOCEANS/0001/2019

How to cite: Simon, A., Plecha, S., Russo, A., Teles-Machado, A., Donat, M., and Trigo, R.: Hot and Cold Marine Extreme Events in the Mediterranean over the last four decades, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6141, https://doi.org/10.5194/egusphere-egu22-6141, 2022.

EGU22-6300 | Presentations | NP2.2

Preferred rossby waves and risks of synchronized heatwaves and harvest failures in observations and model projections 

Kai Kornhuber, Corey Lesk, Carl Schleussner, Jonas Jägermeyer, Peter Pfleiderer, and Radley Horton

Concurrent weather extremes due to a meandering Jetstream can reduce crop productivity across multiple agricultural regions. However, future changes in associated synoptic climate patterns and their agricultural impacts remain unquantified. Here we investigate the ability of coupled climate crop model simulations to reproduce observed regional production impacts and production co-variabilities across major breadbasket regions of the world. We find that although climate models accurately reproduce atmospheric patterns, they underestimate associated surface anomalies in climate models and yield covariability in crop model simulations. Model estimates of future multiple breadbasket failures are therefore likely conservative, despite a projected future intensification of wave pattern-related extremes identified regionally. Our results suggest that climate risk assessments need to account for these high-impact but deeply-uncertain hazards.

How to cite: Kornhuber, K., Lesk, C., Schleussner, C., Jägermeyer, J., Pfleiderer, P., and Horton, R.: Preferred rossby waves and risks of synchronized heatwaves and harvest failures in observations and model projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6300, https://doi.org/10.5194/egusphere-egu22-6300, 2022.

The occurrence of cold spells over North America leads, on average, to a zonalisation and intensification of the North Atlantic jet stream and results in an enhanced risk of extreme wind and precipitation events over Europe. Cold spells enhance low-level baroclinicity at the entrance of the North Atlantic storm track and enhance extratropical cyclogenesis next to the East coast of the United States. However, the mechanisms by which this impact propagates from the entrance to the exit of the storm track, where Europe is, remain unclear.

We investigate from a regime perspective the two-way relationship between the occurrence of cold spells over the eastern coast of North America and the North Atlantic storm track. We stratify the occurrence of cold spells over two different regime classifications of the state of the North Atlantic storm track: the first one based on more classical k-means clustering of 500hPa geopotential height, the other based on dynamical system theory. The regimes have been further characterized using diagnostics acquired from dynamical meteorology, as the E vector or the wave activity flux, and display very different patterns of Rossby wave propagation. The analysis will highlight whether the occurrence of cold spells is able to cause shifts in storm track regimes. On the other hand, if the state of the storm track remains unchanged, this would suggest that other factors rather than cold spells modulate the connection to European wind and temperature extremes.

 

How to cite: Riboldi, J.: A storm-track regime perspective on the connection between cold spells over North America and wet/windy extremes over Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6623, https://doi.org/10.5194/egusphere-egu22-6623, 2022.

EGU22-7381 | Presentations | NP2.2

Is the weather getting "weirder"? 

Aglae Jezequel and Davide Fararanda

Climate change has an influence on daily weather. It translates into a heightened public perception of any type of « weird » weather. For example, it has been shown that extreme weather events are seen as pointing towards the reality of climate change. These perceived attributions are not only related to heatwaves, but also to cold spells (Capstick and Pigeon (2014)), and floods (Taylor et al (2014)).

Extreme events however represent only a subset of the weather distribution experienced by the public. Another manifestation of « weird » weather is the succession of very different types of weather in a short period of time, e.g. two following days with a 10°C difference. While this is widely regarded as another manifestation of climate change by the general public, there are only a few studies exploring short timescale weather variability. For example, Cattiaux et al (2015) have found a projected increase in diurnal and interdiurnal variations of European summer temperatures in CMIP5 simulations.

Here, we use the ERA5 reanalyses (1950-2020) over Europe to study observed diurnal and interdiurnal (2, 3, 5 and 7 days) variations of temperature. We focus on extremes (below the 5th percentile and above the 95th percentile of the distribution of temperature differences) for all seasons and independently for each season and calculate trends. While the general result is that, contrarily to popular beliefs, the diurnal and interdiurnal variations have not increased in the observational periods, we show regional differences over Europe and discuss potential explanations for these differences. 

References:
Capstick, S.B., Pidgeon, N.F. Public perception of cold weather events as evidence for and against climate change. Climatic Change 122, 695–708 (2014). https://doi.org/10.1007/s10584-013-1003-1
Cattiaux, J., Douville, H., Schoetter, R., Parey, S. and Yiou, P. (2015), Projected increase in diurnal and interdiurnal variations of European summer temperatures. Geophys. Res. Lett., 42: 899– 907. doi: 10.1002/2014GL062531.
Taylor, A., de Bruin, W.B. and Dessai, S. (2014), Climate Change Beliefs and Perceptions of Weather-Related Changes in the United Kingdom. Risk Analysis, 34: 1995-2004. https://doi.org/10.1111/risa.12234

 

How to cite: Jezequel, A. and Fararanda, D.: Is the weather getting "weirder"?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7381, https://doi.org/10.5194/egusphere-egu22-7381, 2022.

EGU22-8626 | Presentations | NP2.2

Interrelation between the Indian and East Asian Summer Monsoon: A complex network-based approach 

Shraddha Gupta, Zhen Su, Niklas Boers, Jürgen Kurths, Norbert Marwan, and Florian Pappenberger

The Indian Summer Monsoon (ISM) and the East Asian Summer monsoon (EASM) are two integral components of the Asian Summer Monsoon system, largely influencing the agro-based economy of the densely populated southern and eastern parts of Asia. In our study, we use a complex network based approach to investigate the spatial coherence of extreme precipitation in the Asian Summer Monsoon region and gain a deep insight into the complex nature of the interaction between the ISM and the EASM. We identify two dominant modes of ISM-EASM interaction – (a) a southern mode connecting onset of the ISM over the Arabian Sea and southern India in June to the onset of Meiyu over south-eastern China, i.e., lower and middle reaches of the Yangtze river valley, and (b) a northern mode relating the occurrence and intensity of rainfall over the northern and central parts of India to that in northern China during July. Through determination of specific times of high synchronization of extreme precipitation, we distinctly identify the particular large-scale atmospheric circulation and moisture transport patterns associated with each mode. Thereafter, we investigate the role of the different components of the tropical intraseasonal oscillations, such as the Madden-Julian Oscillation and the boreal summer intraseasonal oscillation, in the intraseasonal variability of the relationship between the ISM and the EASM.

This work is funded by the CAFE project which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.

How to cite: Gupta, S., Su, Z., Boers, N., Kurths, J., Marwan, N., and Pappenberger, F.: Interrelation between the Indian and East Asian Summer Monsoon: A complex network-based approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8626, https://doi.org/10.5194/egusphere-egu22-8626, 2022.

EGU22-9156 | Presentations | NP2.2

Mechanisms and drivers of the 2021 Pacific Northwest heatwave 

Dominik L. Schumacher, Mathias Hauser, and Sonia I. Seneviratne

The Pacific Northwest is characterized by a temperate climate with mild to warm summers, yet in late June 2021, the region was ravaged by extreme heat and ensuing wildfires. With local daily maximum temperatures 20 °C above the long term mean, the occurrence of such a brute heatwave makes it imperative to understand the underlying physical processes. Using the Community Earth System Model, we simulate this exceptional event and disentangle its thermodynamic and dynamic drivers. A factorial experimental design based on the ExtremeX framework is employed, in which the mid and upper-tropospheric circulation and soil moisture are either prescribed using reanalysis (ERA5) data, or calculated interactively. With this setup, the lower troposphere can always respond to land and ocean surface fluxes. Our results indicate that, despite widespread drought conditions in the analysis region (including the metropolitan areas of Portland, Seattle and Vancouver) and surroundings, the dynamic contribution far exceeded the effect of anomalous soil moisture. We further disentangle the soil moisture contribution into initial and event-driven, and find that precipitation in the first half of June 2021 prevented even higher near-surface temperatures by weakening the initial effect. Overall, the analysis highlights the role of the anticyclone that governed the large-scale circulation, and whose intensity during summertime and within 45°N–60 °N surpasses any other event in recent decades. As such, this heatwave presents an opportunity to investigate whether our Earth System Model of choice is capable of generating similarly extreme heat at large spatial scales on its own, i.e. with fully interactive winds. While the mean intensity of hot anticyclonic summer events over land (45°N–60 °N) is underestimated with respect to our reference simulation with prescribed circulation, the model portrays stronger variability with an interactive atmosphere and hence generates heatwaves that rival and even surpass the large-scale temperature anomalies of the Pacific Northwest 2021 event. Our investigation also points to strong temperature anomalies aloft, which we track back in time with a Lagrangian trajectory model driven by ERA5 data. By doing so, we find evidence for intense latent heating of the air that would later be part of the anticyclone, and mixed into the unusually deep atmospheric boundary layer. We further demonstrate that in the absence of anthropogenic climate change, an otherwise identical heatwave would not have reached such extreme temperatures. Altogether, this study shows that for the right atmospheric configuration and fuelled by our changing climate, unprecedented heat may be unleashed even in regions traditionally considered devoid of excessive heatwaves.

How to cite: Schumacher, D. L., Hauser, M., and Seneviratne, S. I.: Mechanisms and drivers of the 2021 Pacific Northwest heatwave, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9156, https://doi.org/10.5194/egusphere-egu22-9156, 2022.

EGU22-9257 | Presentations | NP2.2

The response of intense Mediterranean cyclones to climate change 

M. Carmen Alvarez-Castro, Silvio Gualdi, Davide Faranda, Pedro Ribera, David Gallego, and Cristina Peña-Ortiz

Intense Mediterranean cyclones (IMC) are weather systems that have a high potential for destruction in the densely populated coastal areas around the Mediterranean sea and they cause high risk situations, such as flash floods and large-scale floods with significant impacts on human life and built environment. The aim of the study is to analyse and attribute future changes in IMC under different future forcings and to assess the effect of horizontal model resolution by comparing hydrostatic- versus convection-permitting models. Following a non-linear approach, we explore IMC events that are connected to anomalous atmospheric patterns. First, the analogs search is performed on ERA5 and historical simulations, so as to use the latter as a control run for future projections.  We then examine clusters and trends in the dates of analogs and study their predictability properties in the attractor space (e.g., local dimension and persistence). Then we explore how the trajectories of the precursors of the observed extreme event, emerging from the analog approach, may eventually lead to an IMC event in each available simulation. In this way, we can evaluate the probability of obtaining an observed event, given an initial condition. Finally, we evaluate the physical factors possibly connected to the change of probability of the event.

How to cite: Alvarez-Castro, M. C., Gualdi, S., Faranda, D., Ribera, P., Gallego, D., and Peña-Ortiz, C.: The response of intense Mediterranean cyclones to climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9257, https://doi.org/10.5194/egusphere-egu22-9257, 2022.

EGU22-9634 | Presentations | NP2.2 | Highlight

Climate Change on Extreme Winds Already Affects Wind Energy Availability in Europe 

Lia Rapella, Davide Faranda, and Marco Gaetani

Climate change is one of the most urgent challenges that humankind confronts nowadays. In order to mitigate its effects, the European Union aims to be climate-neutral, i.e. set the Greenhouse Gas (GHG) emissions to zero, by 2050. In this context, renewable energies (REs) play a key role: on the one hand their development and extensive usage can help to reduce the GHG emissions, on the other hand substantial local changes in atmospheric conditions could modify, for better or for worse, their efficiency. Extreme atmospheric events, in particular, can badly affect the efficiency of the RE infrastructures, preventing them from working or even damaging them. In this work, we focus on wind energy off shore, on the European panorama, with the purpose of estimate the behavior of extreme high winds, over the period 1950-2020, and their impact on wind energy availability. Indeed, the potential wind power production, according to the working regimes of a wind turbine, depends only on the wind speed and, over a certain wind speed threshold, called cut-off speed (25 m s-1), the turbine stops working. By using 6-hourly ERA5 reanalysis data-set and convection permitting simulations, covering the European domain and a period from 1950 to 2020 and from 2000 to 2009 respectively, we analysed the 100 m wind speed over the cut-off threshold and its relation with the geopotential height at 500 hPa, in order to investigate the large-scale weather regimes related to these extreme events. We focused especially on five regions, where high winds flow more frequently: United Kingdom, Denmark, Greece, and the areas off the south of France and north of Spain. By using the Mann-Kendall test, we analysed the trends in the occurrence of extreme events, and we detected significant increasing trends in large areas of the regions selected, particularly during the winter period (DJF). Finally, considering only the events over the 99th percentile, we found that they are often concurrently with storms, and, by means of the K-means clustering algorithm, we identified the different weather regimes at which they occur.

How to cite: Rapella, L., Faranda, D., and Gaetani, M.: Climate Change on Extreme Winds Already Affects Wind Energy Availability in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9634, https://doi.org/10.5194/egusphere-egu22-9634, 2022.

EGU22-10586 | Presentations | NP2.2

Recent changes in persistence over Europe and the World in reanalysis dataset 

Mehmet Sedat Gözlet, Joakim Kjellsson, Abhishek Savita, and Mojib Latif

The intensity and frequency of persistent heat waves and droughts have increased over the last few decades. While some of the changes may be attributed to natural variability, it is a known reality that climate change contributes to these tendencies. According to the Fifth Assessment Report of the IPCC, these anomalies are projected to be accelerated and impact humans, ecology, agricultural events, and natural systems.

Understanding the spatiotemporal structure of heat waves is crucial to deciding what environmental change will affect the above-mentioned impacts. In this study, the temporal autocorrelation of near-surface temperature and 850 hPa geopotential height from daily ERA-5 reanalysis data is examined. The focus is on the period from 1979 to 2019. To explore this 41-year long dataset, spatio-temporal trend analysis is also conducted along with autocorrelation. The trends are inspected under 3, 5, and 7-day lag autocorrelations.

In this context, the summer of 2003 shows a very high autocorrelation of geopotential height over central Europe in this analysis, which is consistent with a persistent heat wave that resulted in a death toll. Along with the yearly analyzed data, the trends are calculated both as a whole and divided into intervals. The trend analysis yields high results that cluster around Northern Africa, the Middle East, Middle China, and Middle Russia in the summer season. Furthermore, in the winter season, Siberia, Middle Africa, and the northern part of South America reflect high trends.

How to cite: Gözlet, M. S., Kjellsson, J., Savita, A., and Latif, M.: Recent changes in persistence over Europe and the World in reanalysis dataset, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10586, https://doi.org/10.5194/egusphere-egu22-10586, 2022.

EGU22-11389 | Presentations | NP2.2

Storylines of past and plausible future climates for recent extreme weather events with coupled climate models 

Antonio Sánchez Benítez, Thomas Jung, Marylou Athanase, Felix Pithan, and Helge Goessling

Under the ongoing climate change, extreme weather events are becoming more prolonged, intense, and frequent; and this trend is expected to continue in a future warmer climate. Several studies have found that the synoptic atmospheric circulation at the time of the event is the main contributing factor in most cases. Moreover, they are shaped by slower processes, including sea-surface temperature and soil moisture, in turn influenced by the history of preceding weather patterns, and by the background climate. The separation of influencing components is exploited by the storyline approach, where an atmosphere model is nudged toward the observed dynamics using different climate boundary conditions. Thus, the storyline approach focuses on the less uncertain thermodynamic influence of climate on extreme events, disregarding the somewhat controversial dynamical changes. This approach provides a very efficient way of making the impacts of climate change more tangible to experts and non-experts alike as events fresh in the people's memory are reproduced in different plausible climates with just moderate computational resources.

Spectral nudging experiments have been run with two coupled climate models, AWI-CM-1 and AWI-CM-3. In these simulations, the large-scale free-troposphere dynamics are constrained toward ERA5 data and the model is run for different boundary conditions. Here, the ocean and sea-ice state are consistently simulated, unlike previous studies which employed atmosphere-only models. Our setups reasonably reproduce daily to seasonal observed anomalies of relevant unconstrained parameters, including near-surface temperature, soil moisture or cloud cover. In particular, our configurations showed satisfactory skills in reproducing two different extreme events: the July 2019 European heat wave, and the July 2021 European extreme rainfall. Therefore, this methodology has been applied to study several extreme events in different climates. To do so, nudged simulations are branched off CMIP6 historical and scenario simulations of the same model. For the particular July 2021 extreme rainfall event, we have run five ensemble members for AWI-CM-1-1-MR for dynamical conditions from 1st January 2017 to 31st July 2021 in pre-industrial, present-day, +2K, and +4K climates. These simulations are complemented with similar experiments for AWI-CM-3. 

The most outstanding finding of these studies is a global warming amplification associated with some events, which exacerbates their exceptionality, especially in a high emission scenario.

How to cite: Sánchez Benítez, A., Jung, T., Athanase, M., Pithan, F., and Goessling, H.: Storylines of past and plausible future climates for recent extreme weather events with coupled climate models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11389, https://doi.org/10.5194/egusphere-egu22-11389, 2022.

EGU22-12152 | Presentations | NP2.2 | Highlight

Attribution of the fall 2021 extreme precipitation event over Italian region of Liguria 

Fabio Di Sante, Emanuela Pichelli, Erika Coppola, Robert Vautard, Paolo Scussolini, Jean-Michel Soubeyroux, and Brigitte Dubuisson

Climate change exhibits one of its strongest and shocking effects through extreme precipitation events. Extreme convective precipitation events are getting more intense and more frequent and their attribution to global warming is confirmed by recent studies in many regions of the world. During October the 4th and 5th a Nord-Atlantic trough entering the western Mediterranean favored the formation of deep convective systems feeded by the wet and warm prefrontal flow. One of them built up over the Ligurian Gulf on the 4th. Sustained by long-lasting interaction of large scale conditions and local forcings, the V-shape storm persisted over 24 hours locally accumulating more than 900 mm of rain. The event exceeded local and European precipitation records and caused landslides and flash-floods. In this study we try to objectively link the event to climate change through an extreme value theory analysis. This has been carried out through rain-gauge observations over Liguria, available continuously from 1960 for the fall season. The climate conditions of the event are compared to a pre-industrial period 1.2°C cooler than the present days. The Euro-CORDEX 12km resolution ensemble has been also used to confirm the event attribution to global warming. 

How to cite: Di Sante, F., Pichelli, E., Coppola, E., Vautard, R., Scussolini, P., Soubeyroux, J.-M., and Dubuisson, B.: Attribution of the fall 2021 extreme precipitation event over Italian region of Liguria, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12152, https://doi.org/10.5194/egusphere-egu22-12152, 2022.

EGU22-12461 | Presentations | NP2.2

S2S Extreme Weather Featurization: A Global Skill Assessment Study 

Zubeida Patel, Gciniwe Baloyi, Campbell Watson, Akram Zaytar, Bianca Zadrozny, Daniel Civitarese, Sibusisiwe Makhanya, and Etienne Vos

A more accurate characterization of S2S extremes may result in great positive societal impact. Featurized S2S forecasts in the form of risk or extreme indices will aid in disaster response (especially for drought and flood events), inform disease outbreaks and heatwave onset, persistence, and decay. In this study, we identify a set of ECMWF-derived extreme weather indices that have spatio-temporal windows of opportunity for better-than-climatology skill. We report on the correlation between ECMWF-derived indices and ground-truth values.  The selected indices can be calculated directly form probabilistic daily forecasts, or alternatively, by training specialized ML-models to process ensembles in a multi-task learning setup. Our goal is to find better approaches to communicate S2S climate risk by deploying a set of ECMWF-derived climate forecast products.

How to cite: Patel, Z., Baloyi, G., Watson, C., Zaytar, A., Zadrozny, B., Civitarese, D., Makhanya, S., and Vos, E.: S2S Extreme Weather Featurization: A Global Skill Assessment Study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12461, https://doi.org/10.5194/egusphere-egu22-12461, 2022.

EGU22-12484 | Presentations | NP2.2

Extreme Value Analysis of Madden-Julian Oscillation Events 

Mónica Minjares, Pascal Yiou, Isabel Serra, Marcelo Barreiro, and Álvaro Corral
The Madden-Julian Oscilation (MJO) is an eastward equatorially propagating mode with a strong influence on the precipitation in the tropics on sub-seasonal timescales. Although, several studies have widely analysed the MJO, its activation and evolution are not fully understood [1].
The purpose of this study is to analyse the statistical features of the most intense MJO events.
We perform the study using two different indices describing the MJO: The popular Wheeler and Hendon index (1979-2021), based on the first two principal components of a multivariate empirical orthogonal function analysis of a combination of outgoing longwave radiation (OLR) and 200 mb and 850 mb zonal winds, as well as the Oliver and Thompson index (1905-2015) based on surface pressures [2].
In this study an event takes place when the index amplitude exceeds a threshold for a certain number of days. With this, we define the observables of an event; these are, the maximum amplitude, duration and size, which is the sum of the amplitudes along the duration of an event.
We use extreme-value theory to fit the generalized Pareto distribution (GPD) to the different distributions of observables and we compare the results with the fitting of a simple power-law tail and other heavy-tailed distributions. We also compare the performance of several advanced extreme-value-statistics tools to find the threshold over which the GPD holds.
 
1.Kiladis, G. N., Dias, J., Straub, K. H., Wheeler, M. C., Tulich, S. N., Kikuchi, K., ... & Ventrice, M. J. (2014). A comparison of OLR and circulation-based indices for tracking the MJO. Monthly Weather Review, 142(5), 1697-1715.
2.Klotzbach, P. J., and E. C. J. Oliver (2015), Variations in global tropical cyclone activity and the Madden-Julian Oscillation since the midtwentieth century, Geophys. Res. Lett., 42, 4199–4207.

How to cite: Minjares, M., Yiou, P., Serra, I., Barreiro, M., and Corral, Á.: Extreme Value Analysis of Madden-Julian Oscillation Events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12484, https://doi.org/10.5194/egusphere-egu22-12484, 2022.

EGU22-12508 | Presentations | NP2.2

Complex interactions of extreme events in Southern Europe and Brazil: a compound event perspective 

Ana Russo, Renata Libonati, João L. Geirinhas, Alexandre M. Ramos, Patrícia S. Silva, Pedro M. Sousa, Carlos C. DaCamara, Diego G. Miralles, and Ricardo M. Trigo

Record-breaking natural hazards occur regularly throughout the world, leading to a variety of impacts [1]. According to the WMO, since 1970 there were more than 11000 reported disasters attributed to these hazards globally, with just over 2 million deaths and US$ 3.64 trillion in losses [2]. From 1970 to 2019, weather, climate and water hazards accounted for 50% of all disasters, 45% of all reported deaths and 74% of all reported economic losses [2]. Droughts and heatwaves are both included in the top 4 disasters in terms of human losses [2], with uneven impacts throughout the world and a high likelihood that anthropogenic climate forcing will increase economic inequality between countries [3].

Nowadays there is strong evidence that droughts and heatwaves are at times synergetic and that their combined occurrence is largely caused by land-atmosphere feedbacks [4]. In fact, increasing trends of Compound Dry and Hot (CDH) events have been observed in both South America [5,6] and Europe [7,8], some of them with aggravated impacts. Specifically, the severe 2020 Pantanal extreme fire season (Brazil) resulted from the interplay between extreme and persistent temperatures (maximum temperatures 6 ºC above-average) and long-term soil dryness conditions [6]. Similarly, in the Iberian Peninsula, CDH events were shown to have an influence on the dramatic 2017 fire season [9] and also on crop losses [8]. Moreover, future climate projections suggest that CDH conditions are expected to become more common in a warming climate [4]. Therefore, it is very important to address weather events in a compound manner, identifying synergies, driving mechanisms and dominant atmospheric modes controlling single and combined hazards.

[1] IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of WGI to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte  V. et al., (eds.)]. Cambridge University Press. 

[2] WHO, 2021. Weather-related disasters increase over past 50 years, causing more damage but fewer deaths, https://public.wmo.int/en/media/press-release/weather-related-disasters-increase-over-past-50-years-causing-more-damage-fewer

[3] Diffenbaugh N.S., Burke M. (2019) Global warming has increased global economic inequality, PNAS, 116, 20, 9808-9813

[4] Zscheischler J. et al. (2018). Future climate risk from compound events. Nat. Clim. Change, 8, 469–477.

[5] Geirinhas J.L. et al. (2021). Recent increasing frequency of compound summer drought and heatwaves in Southeast Brazil. Environ. Res.  Lett., 16(3).

[6] Libonati R. et al (2022) Assessing the role of compound drought and heatwave events on unprecedented 2020 wildfires in the Pantanal, Environ. Res. Lett. 17 015005.

[7] Geirinhas J.L. et al. (2020) Heat-related mortality at the beginning of the twenty-first century in Rio de Janeiro, Brazil. Int. J. Biometeorol., 64, 1319–1332

[8] Russo A. et al. (2019) The synergy between drought and extremely hot summers in the Mediterranean. Environ. Res. Lett., 14, 014011

[9] Ribeiro A.F.S. et al. (2020) Risk of crop failure due to compound dry and hot extremes estimated with nested copulas. Biogeosciences, 17, 4815–4830

[10] Turco M. et al. (2019) Climate drivers of the 2017 devastating fires in Portugal. Sci. Rep., 9, 1

 

This work was supported by Fundação para a Ciência e a Tecnologia (Portugal) under projects PTDC/CTA-CLI/28902/2017, JPIOCEANS/0001/2019 and FCT- UIDB/50019/2020 –IDL.

 

 

How to cite: Russo, A., Libonati, R., Geirinhas, J. L., Ramos, A. M., Silva, P. S., Sousa, P. M., DaCamara, C. C., Miralles, D. G., and Trigo, R. M.: Complex interactions of extreme events in Southern Europe and Brazil: a compound event perspective, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12508, https://doi.org/10.5194/egusphere-egu22-12508, 2022.

EGU22-230 | Presentations | NP2.4

Eddy saturation in a reduced two-level model of the atmosphere 

Melanie Kobras, Maarten H. P. Ambaum, and Valerio Lucarini

Eddy saturation describes the nonlinear mechanism in geophysical flows whereby, when average conditions are considered, direct forcing of the zonal flow increases the eddy kinetic energy, while the energy associated with the zonal flow does not increase. We present a minimal baroclinic model that exhibits complete eddy saturation. Starting from Phillips’ classical quasi-geostrophic two-level model on the beta channel of the mid-latitudes, we derive a reduced order model comprising of six ordinary differential equations including parameterised eddies. This model features two physically realisable steady state solutions, one a purely zonal flow and one where, additionally, finite eddy motions are present. As the baroclinic forcing in the form of diabatic heating is increased, the zonal solution loses stability and the eddy solution becomes attracting. After this bifurcation, the zonal components of the solution are independent of the baroclinic forcing, and the excess of heat in the low latitudes is efficiently transported northwards by finite eddies, in the spirit of baroclinic adjustment.

How to cite: Kobras, M., Ambaum, M. H. P., and Lucarini, V.: Eddy saturation in a reduced two-level model of the atmosphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-230, https://doi.org/10.5194/egusphere-egu22-230, 2022.

EGU22-269 | Presentations | NP2.4

Nonlinear Multiscale Modelling of Layering in Turbulent Stratified Fluids 

Paul Pruzina, David Hughes, and Samuel Pegler

One of the most fascinating, and surprising, aspects of stratified turbulence is the spontaneous formation of density staircases, consisting of layers with nearly constant density, separated by interfaces with large density gradients. Within a staircase, there are two key lengthscales: the layer depth, and the interface thickness. Density staircases appear in regions of the ocean where the overall stratification is stable, and can be induced experimentally by stirring a fluid with a stable salt gradient. Staircases also appear as a result of double diffusive convection, in both oceanic and astrophysical contexts. Turbulent transport through staircases is enhanced compared to non-layered regions, so understanding their dynamics is crucial for modelling salt and heat transport.

Progress has been made numerically and experimentally, but the fundamental aspects of the problem are not yet fully understood. One leading theory is the Phillips Effect: layering occurs due to the dependence of the turbulent density flux on the density gradient. If the flux is a decreasing function of the gradient for a finite range of gradients, then negative diffusion causes perturbations to grow into systems of layers and interfaces.

An important extension of the Phillips theory is by Balmforth, Llewellyn-Smith and Young [J. Fluid Mech., 335:329-358, 1998], who developed a k-ε style model of stirred stratified flow in terms of horizontally averaged energy and buoyancy fields. These fields obey turbulent diffusion equations, with fluxes depending on a mixing length. The parameterisation of this lengthscale is key to the model, as it must pick out both layer and interface scales. This phenomonological model parameterises terms based on dimensional arguments, and neglects diffusion for simplicity. This model produces clear density staircases, which undergo mergers where two interfaces combine to form one. Layers take up the interior of the domain, while edge regions on either side expand inwards at a rate of t1/2 , removing layers from the outside in. Eventually the edge regions fill the entire domain, so the long time behaviour of the layers cannot be seen.

We present a similar model for stirred stratified layering derived directly from the Boussinesq equations, including molecular and viscous diffusion, so the model can be tailored to specific conditions to make realistic predictions. We show that the layered  region can evolve indefinitely through mergers, by taking fixed-buoyancy boundary conditions to prevent the expansion of the edge regions. We investigate the effects of diffusion on layer formation and evolution, finding that it acts to stabilise the system, both by decreasing the range of buoyancy gradients that are susceptible to the layering instability, and by decreasing the growth rates of perturbations. The lengthscale of the instability also increases, with larger viscosities and diffusivities producing deeper layers with less sharp interfaces.

This model can be used as a more general framework for layering phenomena. Extending to equations for energy, temperature and salinity can model double diffusive layering. More general parameterisations for the fluxes allow it to be adapted to other settings, including potential vorticity staircases in atmospheres and E×B staircases in plasmas.

How to cite: Pruzina, P., Hughes, D., and Pegler, S.: Nonlinear Multiscale Modelling of Layering in Turbulent Stratified Fluids, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-269, https://doi.org/10.5194/egusphere-egu22-269, 2022.

EGU22-1171 | Presentations | NP2.4

Decomposing the Dynamics of the Lorenz 1963 model using Unstable Periodic Orbits: Averages, Transitions, and Quasi-Invariant Sets 

Chiara Cecilia Maiocchi, Valerio Lucarini, and Andrey Gritsun

Unstable periodic orbits (UPOs) are a valuable tool for studying chaotic dynamical systems, as they allow one to distill their dynamical structure. We consider here the Lorenz 1963 model with the classic parameters' value. We investigate how a chaotic orbit can be approximated using a complete set of UPOs up to symbolic dynamics' period 14. At each instant, we rank the UPOs according to their proximity to the position of the orbit in the phase space. We study this process from two different perspectives. First, we find that longer period UPOs overwhelmingly provide the best local approximation to the trajectory. Second, we construct a finite-state Markov chain by studying the scattering of the orbit between the neighbourhood of the various UPOs. Each UPO and its neighbourhood are taken as a possible state of the system. Through the analysis of the subdominant eigenvectors of the corresponding stochastic matrix we provide a different interpretation of the mixing processes occurring in the system by taking advantage of the concept of quasi-invariant sets.

How to cite: Maiocchi, C. C., Lucarini, V., and Gritsun, A.: Decomposing the Dynamics of the Lorenz 1963 model using Unstable Periodic Orbits: Averages, Transitions, and Quasi-Invariant Sets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1171, https://doi.org/10.5194/egusphere-egu22-1171, 2022.

On a synoptic time scale, the northern mid-latitudes weather is dominated by the influence of the eddy-driven jet stream and its variability. The usually zonal jet can become mostly meridional during so-called blocking events, increasing the persistence of cyclonic and anticyclonic structures and therefore triggering extremes of temperature or precipitations. During those events, the jet takes unusual latitudinal positions, either northerly or southerly of its mean position. Previous research proposed theoretically derived 1D models of the jet stream to represent the dynamics of such events. Here, we take a data-driven approach using ERA5 reanalysis data over the period 1979-2019 to investigate the variability of the eddy-driven jet latitudinal position and wind speed variability. We show that shifts of the jet latitudinal position occur on a daily time scale and are preceded by a strong decrease of the jet zonal wind speed 2-3 days prior to the shift. We also show that the dynamics of the jet zonal wind speed can be modelled by a non-linear oscillator with stochastic perturbations. We combine those two results to propose a simple 1D model capable of representing the statistics and dynamics of blocking events of the eddy-driven jet stream. The model is based on two stochastic coupled non-linear lattices representing the jet latitudinal position and zonal wind speed. Our model is able to reproduce temporal and spatial characteristics of the jet and we highlight a potential link between the propagation of solitary waves along the jet and the occurrence of blocking events.

How to cite: Noyelle, R., Faranda, D., and Yiou, P.: Modeling the Northern eddy-driven jet stream position and wind speed variability with stochastic coupled non-linear lattices, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1250, https://doi.org/10.5194/egusphere-egu22-1250, 2022.

We run a moist shallow water model with stochastic mesoscale forcing, to simulate the effects of mesoscale forcing on exciting large-scale flow structures. In previous work, we showed how the mesoscale forcing excites a classical -5/3 eddy kinetic energy upscale cascade to planetary scales where the linear tropical modes such as Rossby, Yanai, Intertial Gravity, and Kelvin waves form. In this work, we focus on the arising zonal mean flow.

We present results from ensembles of a few hundred simulations indicating multiple-equilibria in the tropical flow, once latent heat release passes a certain threshold in the first 1000 days. Runs up to one hundred thousand days confirm these results and show abrupt transitions in the dry and moist shallow-water turbulence lasting several thousand days. We will discuss the transient nature of the mean flow and suggest a possible new mechanism for the transition of the wind at the equator to super-rotation in a moist environment.

How to cite: Schröttle, J. and Harnik, N.: Spontaneous transitions between sub- and superrotation in dry and moist shallow-water turbulence on the sphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1307, https://doi.org/10.5194/egusphere-egu22-1307, 2022.

EGU22-1514 | Presentations | NP2.4

The Mid-Pleistocene Transition: A delayed response to an increasing positive feedback? 

Anne Willem Omta, John Shackleton, Mick Follows, and Peter Thomas

Glacial-interglacial cycles constitute large natural variations in Earth's climate. The Mid-Pleistocene Transition (MPT) marks a shift of the dominant periodicity of these climate cycles from ~40 to ~100 kyr. Ramping with frequency locking is a promising mechanism to explain the MPT, combining an increase in the internal period with lockings to an external forcing. We identify the strength of positive feedbacks as a key parameter to induce increases in the internal period and allow ramping with frequency locking. Using the calcifier-alkalinity model, we simulate changes in periodicity similar to the Mid-Pleistocene Transition through this mechanism. However, the periodicity shift occurs up to 10 Million years after the change in the feedback strength. This result puts into question the assumption that the cause for the MPT must have operated around the same time as the observed periodicity shift.

How to cite: Omta, A. W., Shackleton, J., Follows, M., and Thomas, P.: The Mid-Pleistocene Transition: A delayed response to an increasing positive feedback?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1514, https://doi.org/10.5194/egusphere-egu22-1514, 2022.

Heat waves result from large-scale stationary waves and have major impacts on the economy and mortality. However, the dynamical processes leading to and maintaining heat waves are still not well understood. Here we use a nonlinear stationary wave model (NSWM) to examine the role played by anomalous stationary waves and how they are forced during heat waves. We will discuss heat waves in Europe and Asia. We show that the NSWM can successfully reproduce the main features of the observed anomalous stationary waves in the upper troposphere. Our results indicate that the dynamics of heat waves are nonlinear, and transient momentum fluxes are the primary drivers of the observed anomalous stationary waves. We will also discuss the role of anomalous SSTs in influencing heat waves.

How to cite: Franzke, C. and Ma, Q.: The role of transient eddies and diabatic heating in the maintenance of heat waves: a nonlinear quasi-stationary wave perspective, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1571, https://doi.org/10.5194/egusphere-egu22-1571, 2022.

EGU22-1988 | Presentations | NP2.4

Modelling Abrupt Transitions in Past Ocean Circulation to Constrain Future Tipping Points 

Guido Vettoretti, Markus Jochum, and Peter Ditlevsen

Recent observationally based studies indicate that the Atlantic Meridional Overturning Circulation (AMOC) and the Greenland Ice Sheet (GIS) may be approaching critical thresholds or tipping points, although the timing is uncertain. The connection between both Greenland meltwater fluxes and anthropogenic greenhouse gas emissions and their impact on the future state of the AMOC is also uncertain. Here we investigate the role of ocean vertical mixing within the interior and surface boundary layer (the K-Profile Parameterization (KPP)) on past millennial scale climate variability in a coupled climate model. Previous studies have demonstrated a sensitivity of the period of millennial scale ice age oscillations to the KPP scheme. Here we show that small changes in the profiles of vertical mixing under ice age boundary conditions can drive the AMOC through a Hopf bifurcation and result in the appearance of millennial-scale AMOC oscillations. This has implications on whether changes in tidal energy dissipation in the coastal and deep ocean are important for modelling past climate variability. More importantly, the same changes in ocean vertical mixing can impact the stability and hysteresis behaviour of the modern AMOC under freshwater input to the North Atlantic as well as leading to abrupt transitions in AMOC strength under a doubling of carbon dioxide concentrations in the atmosphere. We show how understanding the sensitivity of the AMOC to ocean vertical mixing parameterizations used in coupled Earth System models may be important for constraining future climate tipping points.

How to cite: Vettoretti, G., Jochum, M., and Ditlevsen, P.: Modelling Abrupt Transitions in Past Ocean Circulation to Constrain Future Tipping Points, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1988, https://doi.org/10.5194/egusphere-egu22-1988, 2022.

The directional dependencies of different climate indices are explored using the Liang-Kleeman information flow in order to disentangle the influence of certain regions over the globe on the development of low-frequency variability of others. Seven key indices (the sea-surface temperature in El-Niño 3.4 region, the Atlantic Multidecadal Oscillation, the North Atlantic Oscillation, the North Pacific America pattern, the Arctic Oscillation, the Pacifid Decadal Oscillation, the Tropical North Atlantic index), together with three local time series located in Western Europe (Belgium), are selected. The analysis is performed on time scales from a month to 5 years by using a sliding window as filtering procedure.

A few key new results on the remote influence emerge: (i) The Arctic Oscillation plays a key role at short time (monthly) scales on the dynamics of the North Pacific and North Atlantic; (ii) the North Atlantic Oscillation is playing a global role at long time scales (several years); (iii) the Pacific Decadal Oscillation is indeed slaved to other influences; (iv) the local observables over Western Europe influence the variability on the ocean basins on long time scales. These results further illustrate the power of the Liang-Kleeman information flow in disentangling the dynamical dependencies.

How to cite: Vannitsem, S. and Liang, X. S.: Dynamical dependencies at monthly and interannual time scales in the Climate system: Study of the North Pacific and Atlantic regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1994, https://doi.org/10.5194/egusphere-egu22-1994, 2022.

The rise of the global sea-level due to the melting of the Greenland ice-sheet poses one of the biggest threats to human society in the 21st century (IPCC, 2021). The Greenland ice sheet has been hypothesized to exhibit multiple stable states with tipping point behavior when crossing specific thresholds of the global mean temperature (Robinson et al., 2012). In regards to the desultory efforts to reduce the global emissions it becomes more and more unlikely to reach the 1.5°C goal by the end of the century and a crossing of the tipping threshold for the Greenland ice sheet becomes inevitable. First early-warning signals of a possible transition have already been found (Boers&Rypdal, 2021). However, it is known that a short-term overshooting of a critical threshold is possible without prompting a change of the system state (Ritchie et al., 2021). Using a complex ice sheet model, we investigate the effects of different carbon-capture scenarios after crossing the tipping threshold for the Greenland ice sheet. We are able to sketch a stability diagram for a number of emission scenarios and show that temporarily overshooting the temperature threshold for Greenland might be quasi-irreversible for some of the emission scenarios.

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-
Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M.
Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)].
Cambridge University Press. In Press.

Robinson, A., Calov, R. & Ganopolski, A. Multistability and critical thresholds of the Greenland ice sheet. Nature Clim Change 2, 429–432 (2012).

Boers, N. & Rypdal, M. Critical slowing down suggests that the western Greenland Ice Sheet is close to a tipping point. PNAS 118, (2021).

Ritchie, P. D. L., Clarke, J. J., Cox, P. M. & Huntingford, C. Overshooting tipping point thresholds in a changing climate. Nature 592, 517–523 (2021).

How to cite: Bochow, N.: Overshooting the tipping point threshold for the Greenland ice-sheet using a complex ice-sheet model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2353, https://doi.org/10.5194/egusphere-egu22-2353, 2022.

EGU22-2396 | Presentations | NP2.4

Cascade of abrupt transitions in past climates 

Denis-Didier Rousseau, Valerio Lucarini, Witold Bagniewski, and Michael Ghil

The Earth’s climate has experienced numerous abrupt and critical transitions during its long history. Such transitions are evidenced in precise, high-resolution records at different timescales. This type of evidence suggests the possibility of identifying a hierarchy of past critical events, which would yield a more complex perspective on climatic history of the than the classical saddle-node two-dimension representation of tipping points. Such a context allows defining a tipping, or dynamical, landscape (Lucarini and Bódai, 2020), similar to the epigenetic landscape of Waddington (1957).

To illustrate a richer structure of critical transitions, we have analyzed 3 key high-resolution datasets covering the past 66 Ma and provided evidences of abrupt transitions detected with the augmented Kolmogorov-Smirnov test and a recurrence analysis (Bagniewski et al., 2021). These time series are the CENOGRID benthic d18O and d13C (Westerhold et al., 2020), the U1308 benthic d18O, d13C and the d18bulk carbonate (Hodell and Channell, 2016), and the NGRIP d18O (Rasmussen et al., 2014) records. The aim was to examine objectively the observed visual evidence of abrupt transitions and to identify among them the key thresholds indicating regime changes that differentiate among major clusters of variability. This identification is followed by establishing a hierarchy in the observed thresholds organized through a domino-like cascade of abrupt transitions that shaped the Earth’s climate system over the past 66 Ma.

This study is supported by the H2020-funded Tipping Points in the Earth System (TiPES) project.

References

Bagniewski, W., Ghil, M., and Rousseau, D. D.: Automatic detection of abrupt transitions in paleoclimate records, Chaos, 31, https://doi.org/10.1063/5.0062543, 2021.

Hodell, D. A. and Channell, J. E. T.: Mode transitions in Northern Hemisphere glaciation: co-evolution of millennial and orbital variability in Quaternary climate, Clim. Past, 12, 1805–1828, https://doi.org/10.5194/cp-12-1805-2016, 2016.

Lucarini, V. and Bódai, T.: Global stability properties of the climate: Melancholia states, invariant measures, and phase transitions, Nonlinearity, 33, R59–R92, https://doi.org/10.1088/1361-6544/ab86cc, 2020.

Rasmussen, S. O., Bigler, M., Blockley, S. P., et al.: A stratigraphic framework for abrupt climatic changes during the Last Glacial period based on three synchronized Greenland ice-core records: refining and extending the INTIMATE event stratigraphy, Quat. Sci. Rev., 106, 14–28, https://doi.org/10.1016/j.quascirev.2014.09.007, 2014.

Waddington, C. H.: The strategy of the genes., Allen & Unwin., London, 1957.

Westerhold, T., Marwan, N., Drury, A. J., et al.: An astronomically dated record of Earth’s climate and its predictability over the last 66 million years, Science, 369, 1383-+, https://doi.org/10.1126/science.aba6853, 2020.

How to cite: Rousseau, D.-D., Lucarini, V., Bagniewski, W., and Ghil, M.: Cascade of abrupt transitions in past climates, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2396, https://doi.org/10.5194/egusphere-egu22-2396, 2022.

EGU22-2689 | Presentations | NP2.4

Data-driven estimation of the committor function for an idealised AMOC model 

Valérian Jacques-Dumas, Henk Dijkstra, and René van Westen

The Atlantic Meridional Overturning Circulation (AMOC) transports warm, saline water towards the northern North Atlantic, contributing substantially to the meridional heat transport in the climate system. Measurements of the Atlantic freshwater divergence show that it may be in a bistable state and hence subject to collapsing under anthropogenic forcing. We aim at computing the probability of such a transition. We focus on timescales of the century and on temporary collapses of the AMOC. Using simulated data from an idealized stochastic AMOC model, where forcing and white noise are applied via a surface freshwater flux, we compute the transition probabilities versus noise and forcing amplitudes.

Such transitions are very rare and simulating long-enough trajectories in order to gather sufficient statistics is too expensive. Conversely, rare-events algorithms like TAMS (Trajectory-Adaptive Multilevel Sampling) encourage the transition without changing the statistics. In TAMS, N trajectories are simulated and evaluated with a score function; the poorest-performing trajectories are discarded, and the best ones are re-simulated.

The optimal score function is the committor function, defined as the probability that a trajectory reaches a zone A of the phase space before another zone B. Its exact computation is in general difficult and time-consuming. In this presentation, we compare data-driven methods to estimate the committor. Firstly, the Analogues Markov Chain method computes it from the transition matrix of a long re-simulated trajectory. The K-Nearest Neighbours method relies on an existing pool of states where the committor function is already known to estimate it everywhere. Finally, the Dynamical Modes Decomposition method is based on a Galerkin approximation of the Koopman operator. The latter is the most efficient one for the AMOC model when using adaptive dimensionality reduction of the phase space.

How to cite: Jacques-Dumas, V., Dijkstra, H., and van Westen, R.: Data-driven estimation of the committor function for an idealised AMOC model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2689, https://doi.org/10.5194/egusphere-egu22-2689, 2022.

EGU22-2784 | Presentations | NP2.4

Mechanisms behind climate oscillations in last glacial maximum simulations 

Yvan Romé, Ruza Ivanovic, and Lauren Gregoire

Millennial-scale variability has been extensively observed across the last glacial period records (115 to 12 thousand years ago) but reproducing it on general circulation models remains a challenge. In recent years, a growing number of climate models have reported simulations with oscillating behaviours comparable to typical abrupt climate changes, although often relying on unrealistic forcing fields and/or boundary conditions. This may become an issue when trying to review the mechanisms at stake because of glacial climates’ sensitivity to these parameters, notably ice sheets geometry and greenhouse gases concentration.

With the addition of snapshots of the early last deglaciation meltwater history over a last glacial maximum (~21 thousand years ago) equilibrium simulation, we obtained different regimes of climate variability, including oscillations that provides the perfect framework for studying abrupt climate changes dynamics in a glacial background. The oscillations consist of shifts between cold modes with a weak to almost collapsed Atlantic Meridional Ocean Circulation (AMOC) and warmer and stronger AMOC modes, with large reorganisation of the deep-water formation sites, surface ocean and atmospheric circulations. The phenomenon has a periodicity of roughly every 1500 years and can be linked to changes of about 10°C in Greenland. This new set of simulation suggests an intricate large-scale coupling between ice, ocean, and atmosphere in the North Atlantic when meltwater is discharged to the North Atlantic.

Most attempts at theorising millennial-scale variability have involved vast transfers of salt between the subtropical and subpolar gyres, often referred to as the salt oscillator mechanism, that in turn controlled the intensity of the north Atlantic current. We believe that the salt oscillator is in fact part of a larger harmonic motion spanning through all components of the climate system and that can enter into resonance under the specific boundary conditions and/or forcing. Illustrated by the mapping of the main salinity and heat fluxes on the oscillating simulations, we propose a new interpretation of the salt oscillator that includes the stochastic resonance phenomenon as well as the effect of meltwater forcing.

How to cite: Romé, Y., Ivanovic, R., and Gregoire, L.: Mechanisms behind climate oscillations in last glacial maximum simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2784, https://doi.org/10.5194/egusphere-egu22-2784, 2022.

EGU22-3973 | Presentations | NP2.4

A minimal SDE model of D-O events with multiplicative noise 

Kolja Kypke and Peter Ditlevsen

The abrupt transitions in the last glacial period between cold stadial and warmer interstadial climate states found in Greenlandic ice-core records, known as Dansgaard-Oeschger (D-O) events, are a rich topic of study not only due to their potential similarities in time scales and mechanisms to present and near-future climate transitions but also since their underlying physical mechanisms are not fully understood. The dynamics of the climate can be described by a Langevin equation dx = −∂U/∂x dt + η(t) where the potential U(x) has a bimodal distribution to represent the stable stadial and interstadial states and the stochastic process η(t) is usually realized as a Gaussian white noise process that causes jumps between these two states. From the steady-state of the Fokker-Planck equation associated with this Langevin equation, the potential U(x) can be determined from the probability distribution of the ice-core record time series. Thus this minimal model simulates time series with statistics similar to those of the original ice-core record. Novel to this study, we introduce a multiplicative noise term η(t, x) to represent the different statistical properties of the noise in the stadial and interstadial periods. The difference between the Itô and the Stratonovich integration of the Langevin equation with multiplicative noise results in slight differences in the attribution of the drift and diffusion terms for a transformed variable. This is illustrated by performing both.

How to cite: Kypke, K. and Ditlevsen, P.: A minimal SDE model of D-O events with multiplicative noise, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3973, https://doi.org/10.5194/egusphere-egu22-3973, 2022.

Several climate sub-systems are believed to be at risk of undergoing abrupt, irreversible changes as a tipping point (TP) in Greenhouse gas concentrations is reached. Since the current generation of climate models is likely not accurate enough to reliably predict TPs, a hope is to anticipate them from observations via early-warning signals (EWS). EWS have been designed to identify generic changes in variability that occur before a well-defined TP is crossed.

Such well-defined, singular TPs are believed to arise from a single dominant positive feedback that destabilizes the system. However, one may ask whether the large number of spatio-temporal scales in the climate system, and associated second-order feedbacks, could not lead to a variety of more subtle, but discontinuous reorganizations of the spatial climate pattern before the eventual catastrophic tipping. Such intermediate TPs could hinder predictability and mask EWS.

We performed simulations with a global ocean model that shows a TP of the Atlantic meridional overturning circulation (AMOC) due to freshening of the surface waters resulting from increased ice melt. Using a large ensemble of equilibrium simulations, we map out the stability landscape of the ocean circulation in high detail. While in a classical hysteresis experiment only one regime of bistability is found, by very slow increases in forcing we observe an abundance of discontinuous, qualitative changes in the AMOC variability. These are used to initialize smaller-scale hysteresis experiments that reveal a variety of multistable regimes with at least 4 coexisting alternative attractors.

We argue that due to chaotic dynamics, non-autonomous instabilities, and complex geometries of the basins of attraction, the realized path to tipping can be highly sensitive to initial conditions and the trajectory of the control parameter. Further, we discuss the degree to which the equilibrium dynamics are reflected in the transient dynamics for different rates of forcing. The results have implications regarding the expected abruptness of TPs, as well as their predictability and the design of EWS.

How to cite: Lohmann, J.: Abundant multistability and intermediate tipping points in a global ocean model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4470, https://doi.org/10.5194/egusphere-egu22-4470, 2022.

EGU22-5197 | Presentations | NP2.4

Investigating the 'Hothouse narrative' with dynamical systems 

Victor Couplet and Michel Crucifix

The 'hothouse narrative' states that tipping cascades could lead humanity to a binary choice between a 'governed Earth' and a 'hothouse' with no midway alternative. To investigate this scenario, we construct a toy model of interacting tipping elements and ask the following questions: Given a continuous family of emission scenarios, are there discontinuities in the family of responses, as suggested by the 'hothouse narrative'? How realistic is this given knowledge provided by climate simulations and paleo-climate evidence? The relatively low complexity of our model allows us to easily run it for several thousand years and a large range of emissions scenarios, helping us highlight the fundamental role of the different time scales involved in answering our questions. On the one hand, we find that the near-linear relationship predicted by GCMs between global temperature and GHG emissions for the next century can break up at millennial time scales due to cascades involving slower tipping elements such as the ice sheets. This translates as a discontinuity in the family of responses of our model. On the other hand, we find that different emissions scenarios respecting the same carbon budget could potentially lead to different tipping cascades and thus qualitatively different outcomes.

How to cite: Couplet, V. and Crucifix, M.: Investigating the 'Hothouse narrative' with dynamical systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5197, https://doi.org/10.5194/egusphere-egu22-5197, 2022.

EGU22-5268 | Presentations | NP2.4

Transition Probabilities of Wind-driven Ocean Flows 

René van Westen and Henk Dijkstra

The quasi-geostrophic wind-driven double-gyre ocean circulation in a midlatitude rectangular basin is a multi-stable system. Under time-independent forcing, the number of steady states is controlled by the Reynolds number. For a specific range of Reynolds numbers, at least two stable steady states exist. In the quasi-geostrophic model, sub-grid scale processes are usually heavily parameterised, either by deterministic or stochastic representation. In the stochastic case, noise-induced transitions between the steady states may occur.

A standard method to determine transition rates between different steady states is a Monte Carlo approach. One obtains sufficient independent realisations of the model and simply counts the number of transitions. However, this Monte Carlo approach is not well-suited for high-dimensional systems such as the quasi-geostrophic wind-driven ocean circulation. Moreover, when transition probabilities are rare, one needs long integration times or a large number of realisations.

Here we propose a new method to determine transition rates between steady states, by using Dynamically Orthogonal (DO) field theory. The stochastic dynamical system is decomposed using a Karhunen-Loéve expansion and separate problems arise for the ensemble mean state and the so-called time-dependent DO modes. Each DO mode has a specific probability density function, which represents the probability in that direction of phase space. In the case of two steady states, at least one of the DO modes has a bimodal distribution. We analyse transition probabilities using this specific DO mode, which is more efficient compared to the ordinary Monte Carlo approach. We will present the general method and show results for transition probabilities in the quasi-geostrophic wind-driven double-gyre ocean circulation.

How to cite: van Westen, R. and Dijkstra, H.: Transition Probabilities of Wind-driven Ocean Flows, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5268, https://doi.org/10.5194/egusphere-egu22-5268, 2022.

EGU22-5433 | Presentations | NP2.4

Tipping points in hydrology: observed regional regime shift and System Dynamics modeling 

Valentin Wendling, Christophe Peugeot, Manuela Grippa, Laurent Kergoat, Eric Mougin, Pierre Hiernaux, Nathalie Rouché, Geremy Panthou, Jean-Louis Rajot, Caroline Pierre, Olivier Mora, Angeles Garcia-Mayor, Abdramane Ba, Emmanuel Lawin, Ibrahim Bouzou-Moussa, Jerôme Demarty, Jordi Etchanchu, Basile Hector, Sylvie Galle, and Thierry Lebel and the TipHyc Project

River runoff and climate data existing from 1950 to present time in West Africa are analyzed over a climatic gradient from the Sahel (semi-arid) to the Gulf of Guinea (humid). The region experienced a severe drought in the 70s-90s, with strong impact on the vegetation, soils and populations. We show that the hydrological regime in the Sahel has shifted: the runoff increased significantly between pre- and post-drought periods and is still increasing. In the Guinean region, instead, no shift is observed.

This suggests that a tipping point could have been passed, triggered by climate and/or land use change. In order to explore this hypothesis, we developed a System Dynamics model representing feedbacks between soil, vegetation and flow connectivity of hillslopes, channels and aquifers. Model runs were initialized in 1950 with maps of land use/land cover, and fed with observed rainfall (climate external forcing).

The modeling results accurately represent the observed evolution of the hydrological regime on the watersheds monitored since the 50s (ranging from 1 to 50000 km²). The model revealed that alternative stable states can exist for the climatic conditions of the study period. From the model runs, we showed that the drought triggered the crossing of a tipping point (rainfall threshold), which explains the regime shift. We identified domains within the watersheds where tipping occurred at small scale, leading to larger scale shifts. This result supports that tipping points exist in semi-arid systems where ecohydrology plays a major role. This approach seems well suited to identify areas of high risk of irreversible hydrological regime shifts under different climate and land-use scenarios.

How to cite: Wendling, V., Peugeot, C., Grippa, M., Kergoat, L., Mougin, E., Hiernaux, P., Rouché, N., Panthou, G., Rajot, J.-L., Pierre, C., Mora, O., Garcia-Mayor, A., Ba, A., Lawin, E., Bouzou-Moussa, I., Demarty, J., Etchanchu, J., Hector, B., Galle, S., and Lebel, T. and the TipHyc Project: Tipping points in hydrology: observed regional regime shift and System Dynamics modeling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5433, https://doi.org/10.5194/egusphere-egu22-5433, 2022.

EGU22-5500 | Presentations | NP2.4

Conditions for detecting early warning of tipping. 

Peter Ditlevsen

The warning of tipping to an undesired state in a complex system, such as the climate, when a control parameter slowly approaching a critical value ($\lambda(t) \rightarrow \lambda_0$) relies on detecting early warning signals (EWS) in observations of the system. The primary EWS are increase in variance, due to loss of resilience, and increased autocorrelation due to critical slow down. They are statistical in nature, which implies that the reliability and statistical significance of the detection depends on the sample size in observations and the magnitude of the change away from the base value prior to the approach to the tipping point. Thus the possibility of providing useful early warning depends on the relative magnitude of several interdependent time scales in the problem. These are (a) the time before the critical value $\lambda_c$ is reached, (b) the (inverse) rate of approach to the bifurcation point (c) The size of the time window required to detect a significant change in the EWS and finally, (d) The escape time for noise-induced transition (prior to the bifurcation). Here we investigate under which conditions early warning of tipping can be provided. 

How to cite: Ditlevsen, P.: Conditions for detecting early warning of tipping., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5500, https://doi.org/10.5194/egusphere-egu22-5500, 2022.

EGU22-5725 | Presentations | NP2.4

Arctic summer sea-ice loss will accelerate in coming decades 

Anna Poltronieri, Nils Bochow, and Martin Rypdal

Every year, the area of the Arctic sea-ice decreases in the boreal spring and summer and reaches its yearly minimum in the early autumn. The continuous satellite-based time series shows that the September area has decreased from 4.5 x 106 km2 in 1979, to 2.8 x 106 km2 in 2020. The decline has been approximately linear in global mean surface temperature, with a rate of loss of 2.7 x 106 km2 per degree C of global warming.

In the CMIP6 ensemble, however, we find that the majority of the models that reach an Arctic sea-ice free state in the SSP585 runs show an accelerated loss of sea-ice for the last degree of warming compared to the second last degree of warming, which implies an increased sensitivity of the sea-ice to temperature changes. 

Both in the observational and CMIP6 data, we find that the decline in September sea-ice area is approximately proportional to the area north of which the zonal average temperature in spring and summer is lower than a critical threshold Tc. The Arctic amplification implies that the zonally averaged temperatures increase relative to the global temperatures, and with rates increasing with latitude. Linear extrapolation of the zonally averaged temperatures predicts that, with further warming, the September sea-ice area will depend non-linearly on global temperature, the sensitivity will increase and the September sea-ice area may become less that 1 x 106 km2 for global warming between 0.5 and 1.4oC above the current temperature. 

As a result of accelerated sea-ice loss, the average evolution of the sea-ice area among the CMIP6 models before the complete loss of the summer sea-ice shows an increase in the year-to-year fluctuations in minimum ice cover in the next decade. This implies exceptional accumulation of extreme events with very low or no sea-ice at all even before the final loss of the sea-ice. Likewise, an apparent short-term recovery of the sea-ice loss might be observable due to the increasing fluctuations. 

How to cite: Poltronieri, A., Bochow, N., and Rypdal, M.: Arctic summer sea-ice loss will accelerate in coming decades, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5725, https://doi.org/10.5194/egusphere-egu22-5725, 2022.

EGU22-5928 | Presentations | NP2.4

Commitment as Lost Opportunities 

Marina Martinez Montero, Michel Crucifix, Nicola Botta, and Nuria Brede

In the context of climate change, the word "commitment" was originally used to denote how much extra warming is to be expected eventually given a certain fixed concentration of CO2. The notion has evolved and now it is customary to encounter terms such as "constant emissions commitment", "sea level rise commitment" and "zero emissions commitment". All these notions refer to how much change with respect to the current climate state is expected at a given point in the future considering our current climate state and specified future anthropogenic emissions.

Here, we propose thinking about commitment as available options for future action that will allow future decision makers to avoid harmful futures. The definition requires the identification of unwanted outcomes e.g., too high temperature or too fast sea level rise and the specification of a range of possible future anthropogenic emission/intervention scenarios. Given an initial climate state, the measure of commitment is based on the diagnosis of which of those emission/intervention scenarios yield futures safe from the unwanted outcomes. This new definition of commitment explicitly captures the notion of legacy: It measures the range of options that the next generations have at their disposal to avoid harmful futures.

We illustrate the definition and methodology with a simple model featuring ice sheet tipping points and ocean carbonate chemical balance. After having introduced the model, we specify the considered future anthropogenic emission/intervention options available, along with the considered unwanted outcomes. We show how the safe options available for future generations would change in time if we were to follow some of the most standard emission scenarios used in the literature.

How to cite: Martinez Montero, M., Crucifix, M., Botta, N., and Brede, N.: Commitment as Lost Opportunities, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5928, https://doi.org/10.5194/egusphere-egu22-5928, 2022.

EGU22-5997 | Presentations | NP2.4

A fast-slow model for glacial cycles since the Mid-Pleistocene Transition 

Jade Ajagun-Brauns and Peter Ditlevsen

A new simple approach inspired by MacAyeal (1979) to explain the time-asymmetric ‘saw-toothed’ shape and 100,000-year quasi-period of glacial-interglacial cycles since the Middle Pleistocene Transition, is presented. Using a simple model with fast-slow dynamics, the global ice volume is taken to be a function of two independently varying parameters, the solar insolation and ‘alpha’, a secondary control parameter, the study of which is the focus this research. The steady state of the model is a partially folded surface in three-dimensional space where insolation, ‘alpha’, and global ice volume are orthogonal axes. The pleated surface allows for the gradual increase and sudden decrease in ice volume that is observed in the paleoclimate record. To derive a time series of global ice volume, the Euler integration method is used, producing a time series which replicates the ‘saw-toothed’ pattern of glacial cycles in the late Pleistocene. The second control parameter, ‘alpha’, is proposed to be related to internal dynamics of the climate system, such as ice sheet dynamics.

 

Reference

D. R.  MacAyeal, ‘A Catastrophe Model of the Paleoclimate Record’ , Journal of Glaciology , Volume 24 , Issue 90 , 1979 , pp. 245 – 257.

How to cite: Ajagun-Brauns, J. and Ditlevsen, P.: A fast-slow model for glacial cycles since the Mid-Pleistocene Transition, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5997, https://doi.org/10.5194/egusphere-egu22-5997, 2022.

EGU22-5999 | Presentations | NP2.4

AMOC Early-Warning Signals in CMIP6 

Lana Blaschke, Maya Ben-Yami, Niklas Boers, and Da Nian

The Atlantic Meridional Overturning Circulation (AMOC) is a vital part of the global climate that has been suggested to exhibit bi-stability. A collapse from its current strong state to the weak one would have significant consequences for the climate system. Early-warning signals (EWS) for such a transition have recently been found in observational fingerprints for the AMOC.

Some uncertainty in our understanding of the AMOC and its recent evolution is due to the varying quality of its representation in state-of-the-art models. In this work we examine the historical AMOC simulations in the 6th Coupled Model Intercomparison Project (CMIP6) by analyzing the AMOC strength in the models both directly and through the sea-surface temperature fingerprint. As well as examining the evolution of these AMOC time-series in the models, we calculate their associated EWS and use these to evaluate the models in terms of their representation of the AMOC.

How to cite: Blaschke, L., Ben-Yami, M., Boers, N., and Nian, D.: AMOC Early-Warning Signals in CMIP6, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5999, https://doi.org/10.5194/egusphere-egu22-5999, 2022.

The El Niño Southern Oscillation (ENSO) is the most important driver of interannual global climate variability and affects weather and climate in large parts of the world. Recently, we have developed a dynamical network approach for predicting the onset of El Niño events well before the spring predictability barrier. In the regarded climate network, the nodes are grid points in the Pacific, and the strengths of the links (teleconnections) between them are characterized by the cross-correlations of the atmospheric surface temperatures at the grid points. In the year before an El Niño event, the links between the eastern equatorial Pacific and the rest of the Pacific tend to strengthen such that the average link strength exceeds a certain threshold. This feature can be used to predict the onset of an El Niño with 73% probability and its absence with 90% probability. The p-value of the hindcasting and forecasting phase (1981-2021) for this performance based on random guessing with the climatological average is 4.6*10-5.

To assess whether this predictive feature is also present in coupled general circulation models, we apply our algorithm to historical and control runs of CMIP5 and CMIP6. We find that the predictive performance present in observational data is absent or very low in GCMs. The lack of this feature may explain the difficulties of GCMs to overcome the spring barrier.

How to cite: Ludescher, J., Bunde, A., and Schellnhuber, H. J.: El Niño forecasting by climate networks: comparison of the forecasting performance in observational data and in historical and controls runs of CMIP5 and CMIP6, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6329, https://doi.org/10.5194/egusphere-egu22-6329, 2022.

The potential impact of tipping points for climate dynamics is now widely recognized. Furthermore, paleoclimate records suggest that abrupt climate changes have indeed occurred in Earth’s past, potentially on timescales which do not exceed a decade. Several tipping elements, involving various components of the climate system, such as the ocean circulation, sea-ice, continental ice sheets, vegetation, and their couplings, have been suggested. Yet, it remains virtually unknown whether the large-scale atmospheric circulation, the component of the climate system with shortest response time, may undergo bifurcations that could trigger abrupt climate change.

    In this talk I will discuss the possibility of abrupt transitions of the large-scale circulation in the tropics. Specifically, I will consider potential reversals of the mean zonal winds, from the weak easterlies observed in current climate to a "superrotation" state with prevailing westerly winds. The superrotating state exhibits a strongly reduced Hadley circulation.
    I will discuss positive feedback mechanisms and their relevance for the Earth across a hierarchy of models of increasing complexity. A low-dimensional model based on Rossby wave resonance exhibits bistability, and provides a simple criterion for the region of parameter space where this regime exists. We then study the nature of the transition to superrotation in a dry dynamical core, forced in an idealized manner. The main result is that there exists a parameter regime where the dry primitive equations support two coexisting states, with and without an equatorial jet. We will discuss the role of parameters such as the meridional temperature gradient and the boundary layer friction on the existence of this bifurcation.

How to cite: Herbert, C.: Bistability and hysteresis of the large-scale tropical circulation in idealized GCM simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6519, https://doi.org/10.5194/egusphere-egu22-6519, 2022.

EGU22-7029 | Presentations | NP2.4

Global-scale Changes in Vegetation Resilience Mapped with Satellite Data 

Taylor Smith, Niklas Boers, and Dominik Traxl

It is theorized that the resilience of natural ecosystems – their ability to resist and recover from external perturbations – can be estimated from their natural variability. We test this hypothesis using a global set of recovery rates from large disturbances derived from satellite vegetation data, and find that the expected theoretical relationships between these empirical recovery rates and the lag-1 autocorrelation and variance indeed hold approximately. The spatial pattern of global vegetation resilience reveals a strong link to both precipitation availability and variability, implying that water plays a first-order role in controlling the resilience of global vegetation.

The resilience of vegetation is not, however, static – global changes in temperature, precipitation, and anthropogenic influence will all impact the ability of ecosystems to adapt to and recover from disturbances. We investigate the global spatial and temporal patterns of changes in resilience using the empirically confirmed metrics – lag-1 autocorrelation and variance – and find spatially heterogeneous long-term (1980s-) trends; recent trends (2000s-) in vegetation resilience are strongly negative across land-cover types and climate zones.

How to cite: Smith, T., Boers, N., and Traxl, D.: Global-scale Changes in Vegetation Resilience Mapped with Satellite Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7029, https://doi.org/10.5194/egusphere-egu22-7029, 2022.

EGU22-7496 | Presentations | NP2.4

Information flow in complex high-dimensional systems 

Mart Ratas and Peter Jan van Leeuwen
Knowledge on how information flows in complex Earth system models would be of great benefit for our understanding of the system Earth and its components. In principle the Kolmogorov or Fokker-Planck equation can be used to estimate the evolution of the probability density. However, this is not very practical since this equation can only be solved in very low dimensional systems. Because of that, mutual information and information flow have been used to infer information in complex systems. This usually involves integration over all state variables, which is generally numerically too expensive. Here we introduce an exact but much simpler way to find how information flows in numerical solutions that only involves integrations over the local state variables. It allows to infer both magnitude and direction of the information flow. The method is based on ensemble integrations of the system, but because the calculations are local the ensemble size can remain small, of  O(100). 
In this talk we will explain the methodology and demonstrate its use on the highly nonlinear Kumamoto-Sivashinsky model using a range of model sizes and exploring both 1-dimensional and multi-dimensional configurations. 

How to cite: Ratas, M. and van Leeuwen, P. J.: Information flow in complex high-dimensional systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7496, https://doi.org/10.5194/egusphere-egu22-7496, 2022.

EGU22-7531 | Presentations | NP2.4

Bifurcation diagram for vegetation patterns model: old ways for new insight 

Lilian Vanderveken and Michel Crucifix

Spatial organization is a well-known feature of vegetation in semi-arid regions. This phenomenon appears in various parts of the world where water is the limiting factor for plants growing. Those patterns can be reproduced by using reaction-diffusion equations. Rietkerk developed a vegetation patterns model where the joint effects of a local reaction and diffusion create heterogeneous solutions.

The existence of those solutions expands the range of precipitation conditions under which vegetation can prevail. The complete region in the bifurcation diagram where such stable patterns exist is called the Busse balloon.

To our knowledge, no full investigation of the Busse balloon in Rietkerk’s model is available. Here we address this gap and dissect this Busse balloon by analysing the patterned solution branches of the bifurcation diagram.

For a given domain length, those branches can be computed starting from the different zero modes at the edge of the Turing zone around the branch of homogeneous solutions. Then, we use a Newton-Raphson method to track each branch for precipitation changes. Two types of branches appear. What we call the main branches have a roughly constant wavenumber along the branch. What we call the “mixed state branches” originate at the transition between stability and instability along one main branch. The corresponding solutions appear as mixing the solutions of two main branches, which is why we call them that way. However, we show that the latter plays a minor role in the dynamics of the system.

The awareness of the various patterned branch sheds new light on the dynamics of wavenumber switching or R-tipping for patterned systems. More generally, this work gives new insights into the behaviour of patterned systems under changing environment.

How to cite: Vanderveken, L. and Crucifix, M.: Bifurcation diagram for vegetation patterns model: old ways for new insight, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7531, https://doi.org/10.5194/egusphere-egu22-7531, 2022.

Confirmation exists for the 1997 revolutionary date of 12.850 cal yr BP established for the Laacher See Eruption (LSE) and introduced to encourage US-research on the P/H-KISS impact with LSE as isochrone and impact volcanism proxy (Bujatti-Narbeshuber, 1997). Bayesian analysis by Wolbach et al. (2018) of 157 dated records of the YD-impact hypothesis of Firestone et al. (2007) confirms impact with 2.854 ± 0.056 ka BP. This now allows to introduce the much larger P/H-KISS paleoceanographic transition scenario relating also to Holocene up to the present global climate change. The Holocene era, because of the thermohaline damped flow scenario, is herein considered as permanent end of the ice age, suggested here as the climatic consequence of an ocean topography and threshold change. Decoded cave art navigation world maps with Pleistocene paleoceanography content from Altamira , La Pasiega and El Castillo document in each one of the three maps specific AMOC stable states for interstadial/ full stadial/ stadial paleoclimate. Each map-thermohaline stable state is differently relating to a geomorphological boundary condition that is the subaerial surface Topography of a large Mid Atlantic Plateau (MAP)-Island. It is modelled in the P/H-KISS scenario as primary Pleistocene thermohaline phase 0 geomorphological threshold. As physical boundary condition it is in interaction with the thermohaline gulfstream current (above /below/at threshold). This results in the 3 distinct AMOC equilibrium stages of interstadial/ full stadial /stadial, as Pleistocene criticality interconnected by their respective further transition thresholds. When the primary  geomorphological threshold is removed the result is the Holocene damped flow, a transition continuum of thermohaline phases 1, 2, 3. Geomorphological proof is first the MAP-Island, invariably shown on all three maps. Furthermore the MAP-Island is identified by its characteristic topography on decorated columns in Göbekli Tepe as a highly abstract island symbol with deeper political-territorial meanings. With paleo-astronomical precession dating on Pillar 43, the LSE 12.850 cal yr BP date was reproduced and the YD (P/H-KISS) impact series from comet fragments in the Taurid stream were decoded by M. Sweatman (2019).  The symbol sequence on Pillar 18, revealed here for the first time, is the (HI-T) = MAP-Island-Dual 90°-Transition-Tsunami Code of the two step Mid Atlantic Ridge MAR & MAP- Island isostatic submersion by the Taurid stream Koefels-comet oceanic-impact fragments: Paleoclimatology thus confirms and now extends the D. Paillard (1998) three equilibria ocean-box-climate-model with 3 thresholds for 3 transitions between the 3 thermohaline stable states of the ice age to the larger P/H-KISS transition scenario of paleo-climate change. It states that the above 3 AMOC states are exclusively based on the existence of the MAP-Island threshold. Isostatic MAR & MAP-Submergence brings their ice age ending collapse into the broad continuum of the Global warming Threshold Triad with thermohaline damped flow in a very long lasting Holocene interstadial.

 

*) Bujatti-Narbeshuber, M. - Pleistocene/Holocene (P/H) boundary oceanic Koefels-comet Impact Series Scenario (KISS) of 12.850 yr BP Global-warming Threshold Triad (GTT). -Climates: Past, Present and Future; Second European Palaeontological Congress Abstracts edited by D.K. Ferguson & H.A. Kollmann; Vienna, 1997.

 

How to cite: Dr. Bujatti-Narbeshuber, M.: Pleistocene/Holocene (P/H) boundary oceanic Koefels-comet Impact Series Scenario (KISS) of 12.850 yr BP Global-warming Threshold Triad (GTT)-Part II *), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8412, https://doi.org/10.5194/egusphere-egu22-8412, 2022.

EGU22-8745 | Presentations | NP2.4

Stochastic Modeling of Stratospheric Temperature 

Mari Eggen, Kristina Rognlien Dahl, Sven Peter Näsholm, and Steffen Mæland

This study suggests a stochastic model for time series of daily zonal (circumpolar) mean stratospheric temperature at a given pressure level. It can be seen as an extension of previous studies which have developed stochastic models for surface temperatures. The proposed model is a combination of a deterministic seasonality function and a Lévy-driven multidimensional Ornstein–Uhlenbeck process, which is a mean-reverting stochastic process. More specifically, the deseasonalized temperature model is an order 4 continuous-time autoregressive model, meaning that the stratospheric temperature is modeled to be directly dependent on the temperature over four preceding days, while the model’s longer-range memory stems from its recursive nature. This study is based on temperature data from the European Centre for Medium-Range Weather Forecasts ERA-Interim reanalysis model product. The residuals of the autoregressive model are well represented by normal inverse Gaussian-distributed random variables scaled with a time-dependent volatility function. A monthly variability in speed of mean reversion of stratospheric temperature is found, hence suggesting a generalization of the fourth-order continuous-time autoregressive model. A stochastic stratospheric temperature model, as proposed in this paper, can be used in geophysical analyses to improve the understanding of stratospheric dynamics. In particular, such characterizations of stratospheric temperature may be a step towards greater insight in modeling and prediction of large-scale middle atmospheric events, such as sudden stratospheric warming. Through stratosphere–troposphere coupling, the stratosphere is hence a source of extended tropospheric predictability at weekly to monthly timescales, which is of great importance in several societal and industry sectors.

How to cite: Eggen, M., Rognlien Dahl, K., Näsholm, S. P., and Mæland, S.: Stochastic Modeling of Stratospheric Temperature, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8745, https://doi.org/10.5194/egusphere-egu22-8745, 2022.

EGU22-8753 | Presentations | NP2.4

Is West-Antarctica’s Tipping Point a Fixed Value? 

Jan Swierczek-Jereczek, Marisa Montoya, Alexander Robinson, Jorge Alvarez-Solas, and Javier Blasco

Given large regions of ice grounded below sea level associated with a retrograde bedrock, the West Antarctic Ice Sheet (WAIS) is believed to be a tipping element whose tipping point could be reached within this century under high emission scenarios. As the WAIS represents the largest and most uncertain source of future sea-level rise, characterising its stability is crucial for defining safe emission pathways and protecting livelihoods in coastal regions. In the present work, we investigate its potential to undergo an abrupt change due to a fold bifurcation. To this end, we use a high-order ice sheet model with 16km spatial resolution. Rather than applying a fixed forcing rate as in previous studies, we apply a forcing scheme that adaptively increases the local temperature while keeping the system near equilibrium, which allows us to obtain a rigorous value for the bifurcation tipping point. More importantly, we show how this threshold can become relevant for much lower warming levels than expected - even within the bounds of relatively conservative emission scenarios. Subsequently, we explain the underlying mechanisms leading the marine ice-sheet instability to possibly arise earlier than suggested by the bifurcation study. We finally question whether the tipping point of the WAIS can be understood as a fixed temperature value and if not, by which information it should be extended to provide an early warning signal.

How to cite: Swierczek-Jereczek, J., Montoya, M., Robinson, A., Alvarez-Solas, J., and Blasco, J.: Is West-Antarctica’s Tipping Point a Fixed Value?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8753, https://doi.org/10.5194/egusphere-egu22-8753, 2022.

EGU22-9237 | Presentations | NP2.4

Using complex networks to predict abrupt changes in oscillatory systems 

Noemie Ehstand, Reik V. Donner, Cristóbal López, and Emilio Hernández-García

Functional networks are powerful tools to study statistical interdependency structures in extended systems. They have been used to get insights into the structure and dynamics of complex systems in various areas of science. In particular, several studies have suggested the use of precursors based on percolation transitions in correlation networks to forecast El Niño events.

Our aim is to provide a better understanding of the potential of such percolation precursors for the prediction of episodic events in generic systems presenting chaotic oscillations. To this end, we study the behavior of the precursors in a spatially extended stochastic Vallis model, an asymmetric Lorenz-63 type model for the El Niño-Southern Oscillation (ENSO). Our results demonstrate the ability of the largest connected component of the network to anticipate abrupt changes associated with the system's oscillatory dynamics.

This research was conducted as part of the CAFE Innovative Training Network (http://www.cafes2se-itn.eu/) which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844.

How to cite: Ehstand, N., Donner, R. V., López, C., and Hernández-García, E.: Using complex networks to predict abrupt changes in oscillatory systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9237, https://doi.org/10.5194/egusphere-egu22-9237, 2022.

EGU22-9322 | Presentations | NP2.4

The Antarctic and Greenland response to PlioMIP2 mPWP climatic fields 

Javier Blasco, Ilaria Tabone, Daniel Moreno-Parada, Jorge Alvarez-Solas, Alexander Robinson, and Marisa Montoya

Since the pre-industrial era, global sea level has been rising along with greenhouse gas emissions. Part of the contribution to this sea-level change is the mass lost from continental ice sheets, i.e. the Greenland (GrIS) and Antarctic (AIS) ice sheets, which are shrinking at an accelerated rate. However, how they will respond to future warming is highly uncertain due to our lack of knowledge and associated uncertainty in modelling several physical processes, as well as in warming projections. A way to gain insight into future projections is to study past warm periods that are, to some extent, comparable to the present day (PD) in terms of external forcing. The mid-Pliocene warm period (mPWP, 3.3-3.0 million years ago) offers an ideal benchmark, as it is the most recent period with CO2 levels comparable to PD (350-450 ppmv), showing global mean temperatures 2.5-4.0 degrees higher. Eustatic sea-level reconstructions from that period estimate a sea level 15-20 meters higher than PD, implying ice sheets were much smaller in size. The GrIS was starting to form and the AIS was most likely constrained to land-based regions. The Pliocene Model Intercomparison Project, Phase 2 (PlioMIP2) has brought together over 15 climate outputs from 11 General Circulation models from different institutions. These models have simulated mPWP conditions under 400 ppmv of CO2 concentration over a topography generated from an updated bedrock configuration for that time period. Here we use these model outputs to force offline a higher-order ice sheet model for the Antarctic and Greenland domain. Our aim is to investigate how polar continental ice sheets respond to these different climatic fields to pinpoint their most significant climatic and topographical discrepancies. In addition, several sources of structural dependence, from different dynamic states (i.e. basal friction laws) to different initial boundary conditions (starting from no ice-sheet to the PD configuration) are investigated in this modelling framework to create a comprehensive output database for statistical analysis.

How to cite: Blasco, J., Tabone, I., Moreno-Parada, D., Alvarez-Solas, J., Robinson, A., and Montoya, M.: The Antarctic and Greenland response to PlioMIP2 mPWP climatic fields, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9322, https://doi.org/10.5194/egusphere-egu22-9322, 2022.

EGU22-9340 | Presentations | NP2.4

Measuring Amazon rainforest resilience from remotely sensed data 

Da Nian, Lana Blaschke, Yayun Zheng, and Niklas Boers

The Amazon rainforest has a major contribution to the bio-geochemical functioning of the Earth system and has been projected to be at risk of large-scale, potentially irreversible, dieback to a savanna state. Measuring the resilience of the Amazon rainforest empirically is critical to helping us understand the magnitude and frequency of disturbances that the rainforest can still recover from. Different means to quantify resilience in practice have been proposed. Here we determine the Amazon rainforest resilience based on a space-for-time replacement, and then estimating the average residence time in the forest state. This 'global' notion of resilience is different from local measures based on variance or autocorrelation and thus provides complementary information. We study the dependence of the exit-time-base resilience on total rainfall and, in order to study the evolution of the Amazon rainforest, we also estimate changes in their resilience over the years.

How to cite: Nian, D., Blaschke, L., Zheng, Y., and Boers, N.: Measuring Amazon rainforest resilience from remotely sensed data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9340, https://doi.org/10.5194/egusphere-egu22-9340, 2022.

EGU22-9504 | Presentations | NP2.4

Synchronization of layer-counted archives using a statistical age-depth model 

Eirik Myrvoll-Nilsen, Keno Riechers, and Niklas Boers

Layer-counted paleoclimatic proxy records have non-negligible uncertainty arising from the dating process. Knowledge of this uncertainty is important for a rigorous propagation to further analyses; for example for identification and dating of abrupt transitions in climate or to provide a complete uncertainty quantification of early warning signals. This dating uncertainty can be quantified by assuming a probabilistic model for the age-depth relationship. We assume that the number of counted layers per unit of depth can be described using a Bayesian regression model with residuals following an autoregressive process. By synchronizing the chronologies with other archives one can constrain the uncertainties and correct potential biases in the dating process. This is done by matching the chronologies to tie-points obtained by analyzing different archives covering the same period in time. In practice, tie-points can be associated with a significant amount of uncertainty which also needs to be accounted for. We present a theoretically consistent approach which, under certain assumptions, allows for efficient sampling from synchronized age-depth models that match the tie-points under known uncertainty distributions. The model and associated methodology has been implemented into an R-package. 

How to cite: Myrvoll-Nilsen, E., Riechers, K., and Boers, N.: Synchronization of layer-counted archives using a statistical age-depth model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9504, https://doi.org/10.5194/egusphere-egu22-9504, 2022.

EGU22-10031 | Presentations | NP2.4

Early Warning Signals For Climate Tipping Points: Beyond White Noise 

Joseph Clarke, Chris Huntingford, Paul Ritchie, and Peter Cox

Tipping points in the Earth System could present challenges for society and ecosystems. The existence of tipping points also provides a major challenge for science, as the global warming thresholds at which they are triggered is highly uncertain. A theory of `Early Warning Signals' has been developed to 
warn of approaching tipping points. Although this theory uses generic features of a system approaching a Tipping Point, the conventional application of it relies on an implicit assumption that the system experiences white noise forcing. In the Earth system, this assumption is frequently invalid.
Here, we extend the theory of early warning signals to a system additively forced by an autocorrelated process. We do this by considering the spectral properties of both the system and also of the forcing.  We test our method on a simple dynamical system, before applying our method to a particular example from the Earth System: Amazon rainforest dieback. Using our new approach, we successfully forewarn of modelled rainforest collapse in a state-of-the-art CMIP6 Earth System Model.

How to cite: Clarke, J., Huntingford, C., Ritchie, P., and Cox, P.: Early Warning Signals For Climate Tipping Points: Beyond White Noise, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10031, https://doi.org/10.5194/egusphere-egu22-10031, 2022.

EGU22-10128 | Presentations | NP2.4

Nonautonomous dynamics and its applications to paleoclimate 

Michael Ghil

The dynamics of systems with time-dependent forcing or coefficients has become a matter of considerable interest in the last couple of decades in general and in the last dozen years or so in the climate sciences in particular (Ghil, 2019; Ghil & Lucarini, 2020; Ghil, 2021; Tel et al., 2021; and references therein). We shall provide a general introduction to the topic and illustrate it with several paleoclimate-related examples (Crucifix, 2012; Riechers et al., 2022; Rousseau et al., 2022). Perspectives for further applications of the concepts and methods of the theory of pullback and random attractors and of their tipping points to paleoclimate will also be provided.

References

  • Crucifix, M.: Oscillators and relaxation phenomena in Pleistocene climate theory, PTRSA, 370, 1140–1165, 2012.
  • Ghil, M., 2019: A century of nonlinearity in the geosciences, Earth & Space Science, 6, 1007–1042, doi: 1029/2019EA000599.
  • Ghil, M., 2020: Review article: Hilbert problems for the climate sciences in the 21st century – 20 years later, Nonlin. Processes Geophys., 27, 429–451, https://doi.org/10.5194/npg-27-429-2020.
  • Ghil, M., and V. Lucarini, 2020: The physics of climate variability and climate change, Mod. Phys., 92(3), 035002, doi: 10.1103/RevModPhys.92.035002.
  • Riechers, K., T. Mitsui, N. Boers, and M. Ghil, 2022: Orbital insolation variations, intrinsic climate variability, and Quaternary glaciations, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2021-136, in review.
  • Rousseau, D.-D., W. Bagnewski, and M. Ghil, 2021: Abrupt climate changes and the astronomical theory: are they related?, Clim. Past, accepted, doi: 10.5194/cp-2021-103 .
  • Tél, T., Bódai, T., Drótos, G., Haszpra, T., Herein, M., Kaszás, B. and Vincze, M., 2020. The theory of parallel climate realizations. Journal of Statistical Physics179(5), 1496–1530.

How to cite: Ghil, M.: Nonautonomous dynamics and its applications to paleoclimate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10128, https://doi.org/10.5194/egusphere-egu22-10128, 2022.

EGU22-10628 | Presentations | NP2.4

Loss of Earth System Resilience during Early Eocene Global Warming Events 

Shruti Setty, Margot Cramwinckel, Ingrid van de Leemput, Egbert H. van Nes, Lucas J. Lourens, Appy Sluijs, and Marten Scheffer

The Paleocene-Eocene Thermal Maximum (PETM; 56 Ma) and Eocene Thermal Maximum 2 and 3 (ETM2; 54.06 Ma and ETM3; 52.87 Ma) were three of a series of abrupt climate and carbon cycle perturbations, characterized by massive carbon input into the ocean-atmosphere system and strong global warming. These abrupt events, termed hyperthermals, potentially represent ‘tipping points’ at moments in time when the resilience of the system was low and reinforced by strong internal feedbacks, such as the catastrophic release of carbon from submarine methane hydrates. Alternatively, external mechanisms such as volcanism may have played a pronounced external role during the PETM. Here, we evaluate if the hyperthermals indeed resulted from reduced Earth System resilience and tipping point behaviour through the mathematical analyses of climate and carbon cycle indicators, namely, oxygen and stable carbon isotope ratios of deep ocean foraminifer calcite, across the late Paleocene and early Eocene. Our combined analysis using Dynamic Indicators of Resilience (DIORs) and Convergent Cross Mapping (CCM) reveals a loss of resilience and an increase in the causal interaction between the carbon cycle and climate towards the PETM, ETM2, and ETM3. A novel, windowed CCM approach indicates a tight coupling between carbon and climate across the early Eocene, further supporting dominant climate forcing on carbon cycle dynamics. This indicates that the internal rather than external mechanisms were responsible for the hyperthermals, suggesting a secondary role for endogenic processes such as volcanism. Furthermore, the CCM analysis in conjunction with the absence of major positive feedbacks such as the presence of polar ice caps during early Eocene could be employed to stipulate that these hyperthermal events may be caused by the increase in coupling between the carbon cycle and climate systems, eventually pushing both systems towards a tipping point through increasing positive feedbacks.

How to cite: Setty, S., Cramwinckel, M., Leemput, I. V. D., Nes, E. H. V., Lourens, L. J., Sluijs, A., and Scheffer, M.: Loss of Earth System Resilience during Early Eocene Global Warming Events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10628, https://doi.org/10.5194/egusphere-egu22-10628, 2022.

EGU22-11671 | Presentations | NP2.4

Abrupt climate events recorded in speleothems from the ante penultimate glacial 

Vanessa Skiba, Martin Trüssel, Birgit Plessen, Christoph Spötl, René Eichstädter, Andrea Schröder-Ritzrau, Tobias Braun, Takahito Mitsui, Norbert Frank, Niklas Boers, Norbert Marwan, and Jens Fohlmeister

Millennial-scale climate variability, especially abrupt stadial-interstadial transitions, are a prominent feature of the last glacial as recorded in Greenland ice core records (Dansgaard-Oeschger events). Event abruptness and presence of statistical early warning signals before these transitions indicate that they involve repeated crossing of a tipping point of the climate system. However, only little information is available for periods before the last glacial period as Greenland ice cores and many other high-resolution records do not extent beyond the last glacial cycle. Given the lack of understanding of the triggering mechanism responsible for glacial millennial-scale variability with palaeoclimate data from the last glacial, it is essential to investigate this phenomenon during earlier glacial periods.

Here, we present a new highly resolved, precisely U-Th-dated speleothem oxygen isotope record from the Northern European Alps, a region which has been previously shown to resemble the glacial millennial-scale climate variability obtained from Greenland ice core records very well. Our new data covers the time interval from the ante-penultimate glacial to the penultimate glacial (MIS8-MIS6) with a high degree of replication. For both glacial periods, we find phases of pronounced millennial-scale variability but also several, ~10 ka long phases with the climate system being exclusively in stadial conditions. We compare our data with conceptual model results and investigate the occurrence and absence of abrupt climate transitions of the last 300,000 a.

How to cite: Skiba, V., Trüssel, M., Plessen, B., Spötl, C., Eichstädter, R., Schröder-Ritzrau, A., Braun, T., Mitsui, T., Frank, N., Boers, N., Marwan, N., and Fohlmeister, J.: Abrupt climate events recorded in speleothems from the ante penultimate glacial, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11671, https://doi.org/10.5194/egusphere-egu22-11671, 2022.

EGU22-12053 | Presentations | NP2.4

Fitting and extrapolation of transient behaviour in the presence of tipping points 

Peter Ashwin, Robbin Bastiaansen, and Anna von der Heydt

One of the key problems in climate science is to understand the asymptotic behaviour of a climate model, such as Equilibrium Climate Sensitivity (ECS), from finite time computations of transients of a model that may be complex and realistic. Typically, this is done by fitting to some simpler model and then extrapolating to an asymptotic state. But how do transients behave in the presence of tipping points? More precisely, how much warning can one get of an approaching tipping point? In this work we highlight an illustrative example showing how a good fit of a transient to a simpler model does not necessarily guarantee a good extrapolation, and discuss some other implicit assumptions that may arise when estimating quantities such as ECS.

How to cite: Ashwin, P., Bastiaansen, R., and von der Heydt, A.: Fitting and extrapolation of transient behaviour in the presence of tipping points, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12053, https://doi.org/10.5194/egusphere-egu22-12053, 2022.

EGU22-12438 | Presentations | NP2.4

Updated assessment suggests >1.5°C global warming could trigger multiple climate tipping points 

David Armstrong McKay, Arie Staal, Jesse Abrams, Ricarda Winkelmann, Boris Sakschewski, Sina Loriani, Ingo Fetzer, Sarah Cornell, Johan Rockström, and Timothy Lenton

Climate tipping points occur when change in a part of the climate system becomes self-perpetuating beyond a forcing threshold, leading to abrupt and/or irreversible impacts. Synthesizing paleoclimate, observational, and model-based studies, we provide a revised shortlist of global ‘core’ tipping elements and regional ‘impact’ tipping elements and their temperature thresholds. Current global warming of ~1.1°C above pre-industrial already lies within the lower end of some tipping point uncertainty ranges. Several more tipping points may be triggered in the Paris Agreement range of 1.5-2°C global warming, with many more likely at the 2-3°C of warming expected on current policy trajectories. In further work we use these estimates to test the potential for and impact of tipping cascades in response to global warming scenarios using a stylised model. This strengthens the evidence base for urgent action to mitigate climate change and to develop improved tipping point risk assessment, early warning capability, and adaptation strategies.

Preprint: https://doi.org/10.1002/essoar.10509769.1

How to cite: Armstrong McKay, D., Staal, A., Abrams, J., Winkelmann, R., Sakschewski, B., Loriani, S., Fetzer, I., Cornell, S., Rockström, J., and Lenton, T.: Updated assessment suggests >1.5°C global warming could trigger multiple climate tipping points, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12438, https://doi.org/10.5194/egusphere-egu22-12438, 2022.

EGU22-12501 | Presentations | NP2.4

Paleoclimatic tipping points and abrupt transitions: An application of advanced time series analysis methods 

Witold Bagniewski, Michael Ghil, and Denis-Didier Rousseau

Paleoclimate proxy records contain abrupt transitions that may represent former instances of the climate system crossing a tipping point (TP). Properly identifying these TPs in the Earth’s past helps determine critical thresholds in present-day climate and better understand the climate system’s underlying bifurcation mechanisms.

Information contained in paleoclimate proxy records is often ambiguous because of the complexity of the system, which includes both deterministic and stochastic processes. Furthermore, paleoclimate time series differ in their time spans and periodicities, and often have high levels of noise and a nonuniform resolution. These combined sources of uncertainty highlight the need for using advanced statistical methods for robustly identifying and comparing TPs.

A recently developed method that uses an augmented Kolmogorov-Smirnov test has been shown to be highly effective for transition detection in different types of records. Here, we apply this method to a set of high-quality paleoproxy records exhibiting centennial-to millennial-scale variability that have been compiled in the PaleoJump database. We thereby detect previously unrecognized transitions in these records and identify potential TPs. Furthermore, we investigate regime changes with recurrence analysis and spectral analysis.

This study is supported by the H2020-funded Tipping Points in the Earth System (TiPES) project.

How to cite: Bagniewski, W., Ghil, M., and Rousseau, D.-D.: Paleoclimatic tipping points and abrupt transitions: An application of advanced time series analysis methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12501, https://doi.org/10.5194/egusphere-egu22-12501, 2022.

EGU22-12686 | Presentations | NP2.4

Early warning signals for topological tipping points 

Gisela Daniela Charó, Michael Ghil, and Denisse Sciamarella


The topology of the branched manifold associated with the Lorenz model’s random attractor (LORA) evolves in time. LORA’s time-evolving branched manifold robustly supports the point cloud associated with the system’s invariant measure at each instant in time. 

This manifold undergoes not only continuous deformations — with branches that bend, stretch or compress — but also discontinuous deformations, with branches that intersect at discrete times. These discontinuities in the system's invariant measure manifest themselves in the decrease or increase of the number of 1-holes, thus producing abrupt changes in the branched manifold’s topology.

Topological tipping points (TTPs) are defined as abrupt changes in the topology of a random attractor’s branched manifold. Branched Manifold Analysis through Homologies
(BraMAH) is a robust method that allows one to detect these fundamental changes. 
The existence of such TTPs is being confirmed by careful statistical analysis of LORA’s time-evolving branched manifold, following up on Charó et al. (Chaos, 2021, doi:10.1063/5.0059461). Research is being pursued on early warning signals for these TTPs, concentrating on local fluctuations in the system’s invariant measure.

How to cite: Charó, G. D., Ghil, M., and Sciamarella, D.: Early warning signals for topological tipping points, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12686, https://doi.org/10.5194/egusphere-egu22-12686, 2022.

EGU22-13023 | Presentations | NP2.4

Data-driven Reconstruction of Last Glacials' Climate Dynamics Suggests Monostable Greenland Temperatures and a Bistable Northern Hemisphere Atmosphere 

Keno Riechers, Leonardo Rydin, Forough Hassanibesheli, Dirk Witthaut, Pedro Lind, and Niklas Boers

Multiple proxy records from Greenland ice cores exhibit a series of concomitant abrupt climatic shifts during the last glacial. These so-called Dansgaard–Oeschger (DO) events comprise, among others, warming over Greenland, a sudden retreat of North Atlantic and Nordic Seas’ sea ice, and an atmospheric reorganisation of hemispheric extent. Typically DO events are followed by a phase of moderate cooling, before the climate abruptly transition back to its pre-event state. While the physics behind these dynamics are still subject to a vibrant debate, the idea that at least one of the involved climatic subsystems features bistability is widely accepted.

We assess the stability of Greenland temperatures and the Northern Hemisphere atmospheric circulation represented by δ¹⁸O and dust concentration records from the NGRIP ice core, respectively. We investigate the 27-59 ky b2k period of the combined record which covers 12 major DO events at high temporal resolution. Regarding the data as the realisation of a stochastic process we reconstruct the corresponding drift and diffusion by computing the Kramers–Moyal (KM) coefficients. In contrast to previous studies, we find the drift of the δ¹⁸O to be monostable, while analysis of the dust record yields a bistable drift. Furthermore, we find a non-vanishing 4th-order KM coefficient for the δ¹⁸O, which indicates that the δ¹⁸O time series cannot be considered a standard type Langevin process. In a second step, we treat the joint (δ¹⁸O , dust) time series as a two dimensional stochastic process and compute the corresponding coefficients of the two dimensional KM expansion. This reveals the position of the fixed point of δ¹⁸O to be controlled by the value of the dust. In turn, the drift of the dust undergoes an imperfect supercritical pitchfork bifurcation when transitioning from low to high δ¹⁸O values.

How to cite: Riechers, K., Rydin, L., Hassanibesheli, F., Witthaut, D., Lind, P., and Boers, N.: Data-driven Reconstruction of Last Glacials' Climate Dynamics Suggests Monostable Greenland Temperatures and a Bistable Northern Hemisphere Atmosphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13023, https://doi.org/10.5194/egusphere-egu22-13023, 2022.

Many generations of climate general circulation models (GCMs) have suggested that a radical reorganisation (tipping) of the Atlantic Meridional Overturning Circulation is unlikely in the 21st Century in response to the greenhouse gas emissions pathways considered by the Intergovernmental Panel on Climate Change (IPCC). Yet some studies suggest that GCMs as a class may represent an AMOC that is biased towards excessive stability. If this is the case then simply looking at AMOC response in the ensemble of current GCMs may give a misleading picture of the possible future pathways of the AMOC.

In this study we use a simple coupled climate model, including both the thermal and water cycle responses to greenhouse gas increase, to explore beyond the range of the current ensemble of ‘best estimate’ GCMs. What would the climate system need to look like in order for AMOC tipping to be a plausible outcome? We find that tipping behaviour would require key parameters controlling the response of the hydrological cycle to surface warming to be towards the edge of plausible ranges.

While AMOC tipping remains a ‘High Impact, Low Likelihood’ outcome, our results extend current knowledge by showing how AMOC tipping could occur in response to greenhouse gas forcing (as opposed to the common idealisation of ‘water hosing’ experiments). The results also show how monitoring key parameters of the climate system may over time allow the possibility of tipping to be more confidently assessed.

How to cite: Wood, R.: Climate storylines for AMOC tipping in response to increasing greenhouse gases, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13181, https://doi.org/10.5194/egusphere-egu22-13181, 2022.

EGU22-750 | Presentations | OS1.5

Adiabatic, Constrained, Stochastic Eddy Parameterisation 

Chris Wilson, Chris W. Hughes, Simon D. P. Williams, and Adam T. Blaker

Mesoscale eddy-permitting ocean models will be needed as a component of climate ensemble projections most likely for the next decade or more.   However, the kinetic energy and other measures of variability are typically an order of magnitude too weak at this nominal 0.25 degree lon-lat resolution.    This is predominantly due to excessive gridscale damping of momentum, needed for computational stability, which is believed to kill a large fraction of the energy source of the kinetic energy inverse cascade.   The KE inverse cascade is associated with the generation of intrinsic chaotic variability and ensemble spread, hence the estimation of potential predictability, but also with slower, larger-scale variability associated with climate.  The familiar Gent and McWilliams (1990) eddy parameterisation is problematic when applied to eddy-permitting models, where eddies are partially resolved, and it also tends to damp variability rather than energise it.   In response to this problem, several recent studies have focussed on the KE backscatter problem, which each attempt to increase the upscale transfer of KE, either deterministically or stochastically.

Stochastic parameterisation of sub-gridscale eddies has recently become a popular approach in ocean modelling, having been used in atmospheric modelling for many years, but there is still a diverse range of approaches for constraining either the underlying physics (how the forcing is applied) or the statistics (the spatiotemporal signature of the forcing).   This study explores some basic recipes for constructing the stochastic model from statistics of either observations or higher-resolution models.  The stochastic forcing, representing the sub-gridscale effects of eddies in our eddy-permitting simulations, is also applied adiabatically – to mimic the predominant behaviour of mesoscale eddies in the ocean interior and to preserve large-scale watermasses.   A theoretical challenge, which we explore, is to connect the applied, weakly imbalanced forcing, to a response in kinetic energy and upscale transfer.  This must also be applied without generating numerical instability.  

How to cite: Wilson, C., Hughes, C. W., Williams, S. D. P., and Blaker, A. T.: Adiabatic, Constrained, Stochastic Eddy Parameterisation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-750, https://doi.org/10.5194/egusphere-egu22-750, 2022.

EGU22-770 | Presentations | OS1.5

Diagnosing the thickness-weighted averaged eddy-mean flow interaction from an eddying North Atlantic ensemble 

Takaya Uchida, Quentin Jamet, William Dewar, Julien Le Sommer, Thierry Penduff, and Dhruv Balwada

The analysis of eddy-mean flow interaction provides key insights into the structures and dynamics of inhomogeneous and anisotropic flows such as atmospheric and oceanic jets. As the divergence of Eliassen-Palm (E-P) flux formally encapsulates the interaction, the community has had a long-standing interest in accurately diagnosing this term. Here, we revisit the E-P flux divergence with an emphasis on the Gulf Stream, using a 48-member, eddy-rich (1/12°) ensemble of the North Atlantic ocean partially coupled to identical atmospheric states amongst all members via an atmospheric boundary layer model. This dataset allows for an unique decomposition where we define the mean flow as the ensemble mean, and interpret it as the oceanic response to the atmospheric state. The eddies are subsequently defined as fluctuations about the ensemble mean. Our results highlight two points: i) the implementation of the Thickness-Weighted Averaged (TWA) framework for a realistic ocean simulation in diagnosing the E-P flux divergence, and ii) validity of the ergodic assumption where one treats the temporal mean equivalent to the ensemble mean, which is questionable for a temporally varying system such as the ocean and climate.

How to cite: Uchida, T., Jamet, Q., Dewar, W., Le Sommer, J., Penduff, T., and Balwada, D.: Diagnosing the thickness-weighted averaged eddy-mean flow interaction from an eddying North Atlantic ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-770, https://doi.org/10.5194/egusphere-egu22-770, 2022.

EGU22-1428 | Presentations | OS1.5

Non-local eddy-mean kinetic energy transfers in submesoscale-permitting ensemble simulations 

Quentin Jamet, Stephanie Leroux, William K. Dewar, Thierry Penduff, Julien Le Sommer, Jean-Marc Molines, and Jonathan Gula

Understanding processes associated with eddy-mean flow interactions helps our interpretation of the ocean energetic balance, and guides the development of parameterizations. Here, we focus on the non-local nature of Kinetic Energy (KE) transfers between mean (MKE) and turbulent (EKE) reservoirs. Following previous studies, we interpret these transfers as non-local when the energy extraction from the mean flow does not locally sustain energy production of the turbulent flow, or vice versa. The novelty of our approach is to use ensemble statistics, rather than time averaging or coarse-graining methods, to define the mean and the turbulent flow. Based on KE budget considerations, we first rationalize the eddy-mean separation in the ensemble framework, and discuss the interpretation of a mean flow (<u>) driven by the prescribed (surface and boundary) forcing and a turbulent flow (u') driven by non-linear dynamics sensitive to initial conditions. Our results, based on the analysis of 120-day long, 20-member ensemble simulations of the Western Mediterranean basin run at 1/60o, suggest that eddy-mean kinetic energy exchanges are largely non-local at small scales. Our main contribution is to recognize the prominent contribution of the cross energy term (<u>.u') to explain this non-locality, providing a strong constraint on the horizontal organization of eddy-mean flow KE exchanges since this term vanishes identically for perturbations (u') orthogonal to the mean flow ( Our results also highlight the prominent contribution of vertical turbulent fluxes for energy exchanges within the surface mixed layer. Analyzing the scale dependence of these non-local energy exchanges supports the local approximation usually made in the development of meso-scale, energy-aware parameterizations for non-eddying models, but points out to the necessity of accounting for these non-local effects in the meso-to-submeso scale range.

How to cite: Jamet, Q., Leroux, S., Dewar, W. K., Penduff, T., Le Sommer, J., Molines, J.-M., and Gula, J.: Non-local eddy-mean kinetic energy transfers in submesoscale-permitting ensemble simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1428, https://doi.org/10.5194/egusphere-egu22-1428, 2022.

EGU22-1973 | Presentations | OS1.5

The Characteristics and Significance of Hydrodynamical Internal Variability in Modelling Dynamics in Marginal Seas 

Lin Lin, Hans von Storch, Xueen Chen, and Shengquan Tang

Internal variability, unprovoked by external forcing, emerges in the hydrodynamics of the marginal seas. Ensemble ocean simulations are used to analyze the characteristics, scales, and intensities of such variability in the Bohai, Yellow Sea, and South China Sea. With the signal defined as the covariation in the ensemble, and the noises as the independent variations, a scale dependency of the Signal-to-Noise Ratio (S/N ratio) is found in the Bohai, Yellow Sea, and South China Sea. The external forcing and related signal are dominant for large scales, while most of the internal variability is generated for small scales. The intensities of internal variability of the Bohai and Yellow sea are about half of the intensities of South China Sea, likely because eddies are less energetic in the Bohai and Yellow Sea, which likely is the main source of noise in South China Sea.

In addition, we investigate the effect of tides on internal variability in the Bohai and Yellow Sea by three ensembles of numerical experiments with tidal forcing, with half tidal forcing, and without tidal forcing. When the tides are weakened or turned off, the S/N ratios are reduced in large and medium scales, more so in the Yellow Sea than in the Bohai. The increase in the S/N ratio is largest for large scales and for depth-averaged velocity. The reduction in tidal forcing results in an approximately 30% increase in S/N ratios in the Bohai at large scales. Thus, the absence of tidal forcing favours the emergence of unprovoked variability at large and medium scales but not at small scales. We suggest that the main mechanism for the increase of covarying variability when tides are active, is the additional mixing induced by the tides.

How to cite: Lin, L., von Storch, H., Chen, X., and Tang, S.: The Characteristics and Significance of Hydrodynamical Internal Variability in Modelling Dynamics in Marginal Seas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1973, https://doi.org/10.5194/egusphere-egu22-1973, 2022.

EGU22-2167 | Presentations | OS1.5

Intrinsic low-frequency variability of the Mediterranean Sea circulation studied using a multilayer ocean model 

Angelo Rubino, Stefano Pierini, Sara Rubinetti, and Davide Zanchettin

Intrinsic chaotic variability in the oceans is an active field of research in modern oceanography, with important implications concerning the understanding and predictability of the ocean system. The focus is mainly on open ocean basins while very little attention is devoted to enclosed or semi-enclosed seas. The intrinsic variability of the Mediterranean Sea, in particular, has not yet been investigated. Here, results obtained with an eddy-resolving nonlinear multilayer ocean model are presented shedding light on relevant aspects of the intrinsic low-frequency variability of the Mediterranean Sea circulation.

An ensemble of multi-centennial ocean runs is performed to allow for a significant statistical analysis. The statistically stationary state obtained after long simulations shows a robust meridional structure consistent with the observed Mediterranean mean state. Among the various features emerging in the decadal and multidecadal temporal ranges are abrupt shifts in the water mass stratification structure. Differences and similarities with observed patterns are finally discussed. 

How to cite: Rubino, A., Pierini, S., Rubinetti, S., and Zanchettin, D.: Intrinsic low-frequency variability of the Mediterranean Sea circulation studied using a multilayer ocean model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2167, https://doi.org/10.5194/egusphere-egu22-2167, 2022.

EGU22-2849 | Presentations | OS1.5

The Structure of North Atlantic Kinetic Energy Spectra 

William K. Dewar, Takaya Uchida, Quentin Jamet, and Andrew Poje

An ensemble of North Atlantic simulations is analyzed, providing estimates of kinetic energy spectra.  A wavelet transform technique is used permitting comparisons to be made between spectra at different locations in this highly inhomogeneous environment.  We find a strong tendency towards anisotropy in the spectra, with meridional spectra typically stronger than zonal spectra.  This holds even in the gyre interior where conditions might be expected to be homogeneous.  The spectra show reasonable ranges consistent with a downscale enstrophy cascade, but also a persistent tendency to exhibit steeper slopes at smaller scales.  The only location where the presence of an upscale cascade is supported is the Gulf Stream extension.  This is amongst first attempts to quantify and compare spectra and their differences in the inhomogeneous setting of the North Atlantic.

How to cite: Dewar, W. K., Uchida, T., Jamet, Q., and Poje, A.: The Structure of North Atlantic Kinetic Energy Spectra, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2849, https://doi.org/10.5194/egusphere-egu22-2849, 2022.

EGU22-3044 | Presentations | OS1.5

Linking contemporary parametric model uncertainties to projections of biogeochemical cycles 

Ulrike Löptien and Heiner Dietze

Anthropogenic emissions of greenhouse gases, such as CO2 and N2O, warm the earth. This in turn modulates the atmospheric greenhouse gas concentrations. The underlying feedback mechanisms are complex and can be counterintuitive. Earth system models have recently matured to standard tools tailored to assess and understand these feedback mechanisms. Along comes the need to determine poorly-known model parameters. This is especially problematic for the ocean biogeochemical component where respective observational data, such as nutrient concentrations and phytoplankton growth, are rather sparse in time and space. In the present study, we illustrate common problems when attempting to estimate such parameters based on typical model evaluation metrics. We find very different parameter sets which are, on the one hand, equally consistent with (synthetic) historical observations while, on the other hand, they propose strikingly differing projections into a warming climate. By the example of simulated oxygen concentrations we propose a step forward by applying variance-based sensitivity analyses to map the respective parameter uncertainties onto their local manifestations - for both contemporary climate and climate projections. The mapping relates local uncertainties of projections to the uncertainty of specific model parameters. In a nutshell, we present a practical approach to the general question of where the present-day model fidelity may be indicative for reliable projections.

 

How to cite: Löptien, U. and Dietze, H.: Linking contemporary parametric model uncertainties to projections of biogeochemical cycles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3044, https://doi.org/10.5194/egusphere-egu22-3044, 2022.

EGU22-3668 | Presentations | OS1.5

Uncertainty in ocean biogeochemical simulation: Application of ensemble data assimilation to a one-dimensional model 

Nabir Mamnun, Christoph Völker, Mihalis Vrekoussis, and Lars Nerger

Marine biogeochemical (BGC) models are important tools in the hands of scientists and policymakers when assessing the impacts of climate change. Therefore, including an ocean BGC component in Earth System Modeling efforts is essential for climate simulation and predictions. However, current BGC models, used to simulate and thus better understand the marine ecosystem processes, are associated with large undefined uncertainties. Similar to other geoscientific models, complex biological and chemical processes are converted to simplified schemes in BGCs, a methodology known as parameterization. However, these parameter values can be poorly constrained and also involve unknown uncertainties. In turn, the uncertainty in the parameter values translates into uncertainty in the model outputs. Therefore, a systematic approach to properly quantify the uncertainties of the parameters is needed. In this study, we apply an ensemble data assimilation method to quantify the uncertainty arising from the parameterization within BGC models. We apply an ensemble Kalman filter provided by the parallel data assimilation framework (PDAF) into a one-dimensional vertical configuration of the biogeochemical model Regulated Ecosystem Model 2 (REcoM2) at two BGC time-series stations: the Bermuda Atlantic Time-series Study (BATS) and the Dynamique des Flux Atmosphériques en Méditerranée (DYFAMED). Satellite chlorophyll-a concentration data and in situ net primary production data are assimilated to estimate ten selected biogeochemical parameters and the model state. We present convergence and interdependence features of the estimated parameters in relation to the major biological processes and discuss their uncertainties. The major improvements on the parameters involved changes in phytoplankton photosynthesis rate, chlorophyll degradation, and grazing. In general, the change in the estimates of these parameters results in improvements in the model prediction and reduced prediction uncertainty. 

How to cite: Mamnun, N., Völker, C., Vrekoussis, M., and Nerger, L.: Uncertainty in ocean biogeochemical simulation: Application of ensemble data assimilation to a one-dimensional model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3668, https://doi.org/10.5194/egusphere-egu22-3668, 2022.

EGU22-5287 | Presentations | OS1.5

Quasi-geostrophic coupled model under location uncertainty 

Long Li, Etienne Mémin, Bertrand Chapron, and Noé Lahaye

In this work, we aim to describe atmosphere-ocean coupling through a physically-based stochastic formulation. We adopt the framework of modelling under Location Uncertainty (LU) [Bauer2020a], which is based on a temporal-scale separation and a stochastic transport principle. One important characteristic of such random model is that it conserves the total energy of the resolved flow. This representation has been successfully tested for ocean-only models, such as the barotropic quasi-geostrophic (QG) model [Bauer2020b], the multi-layered QG model [Li2021], as well as the rotating shallow-water model [Brecht2021]. Here, we consider the ocean-atmosphere coupled QG model [Hogg2003]. The LU scheme has been tested for coarse-grid simulations, in which the spatial structure of ocean uncertainty is calibrated from eddy-resolving simulation data while the atmosphere component is parameterized from the ongoing simulation. In other words, the ocean dynamics has a data-driven stochastic component whereas the large-scale atmosphere dynamics is fully parameterized. Two major benefits of the resulting random model are provided on the coarse mesh: it enables us to reproduce the ocean eastward jet and its adjacent recirculation zones; it improves the prediction of intrinsic variability for both ocean and atmosphere components. These capabilities of the proposed stochastic coupled QG model are demonstrated through several statistical criteria and an energy transfers analysis.

References:

  • [Bauer2020a] W. Bauer, P. Chandramouli, B. Chapron, L. Li, and E. Mémin. Deciphering the role of small-scale inhomogeneity on geophysical flow structuration: a stochastic approach. Journal of Physical Oceanography, 50(4):983-1003, 2020.
  • [Bauer2020b] W. Bauer, P. Chandramouli, L. Li, and E. Mémin. Stochastic representation of mesoscale eddy effects in coarse-resolution barotropic models. Ocean Modelling, 151:101646, 2020.
  • [Li2021] Li, L., 2021. Stochastic modelling and numerical simulation of ocean dynamics. PhD Thesis. Université Rennes 1.
  • [Brecht2021] Rüdiger Brecht, Long Li, Werner Bauer and Etienne Mémin. Rotating Shallow Water Flow Under Location Uncertainty With a Structure-Preserving Discretization. Journal of Advances in Modeling Earth Systems, 13, 2021MS002492.
  • [Hogg2003] A.M. Hogg, W.K. Dewar, P.D. Killworth, J.R. Blundell. A quasi-geostrophic coupled model (Q-GCM). Monthly Weather Review, 131:2261-2278, 2003.

 

How to cite: Li, L., Mémin, E., Chapron, B., and Lahaye, N.: Quasi-geostrophic coupled model under location uncertainty, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5287, https://doi.org/10.5194/egusphere-egu22-5287, 2022.

EGU22-6041 | Presentations | OS1.5

Greenhouse gas forcing a necessary, but not sufficient, causation for the northeast Pacific marine heatwaves 

Armineh Barkhordarian, David M. Nielsen, and Johanna Baehr

Over the last decade, the northeast Pacific experienced strong marine heatwaves (MHWs) that produced devastating marine ecological impacts and received major societal concerns. Here, we assess the link between the well-mixed greenhouse gas (GHG) forcing and the occurrence probabilities of the duration and intensity of the North Pacific MHWs. We investigate whether GHG forcing was necessary for the North Pacific MHWs to occur and whether it is a sufficient cause for such events to continue to repeatedly occur in the 21st Century. To begin with, we apply attribution technique on the long-term SST time series, and detect a region of systematically and externally-forced SST increase -- the long-term warming pool -- co-located with the past notably Blob-like SST anomalies. We further show that the anthropogenic signal has recently emerged from the natural variability of SST over the warming pool, which we attribute primarily to increased GHG concentrations, with anthropogenic aerosols playing a secondary role.

After we demonstrate that the GHG forcing has a dominant influence on the base climate state in the region, we pursue an event attribution analysis for MHWs on a more localized region. Extreme event attribution analysis reveals that GHG forcing is a necessary, but not sufficient, causation for the multi-year persistent MHW events in the current climate, such as that happened in 2014/2015 and 2019/2020. However, the occurrence of the 2019/2020 (2014/2015) MHW was extremely unlikely in the absence of GHG forcing. Thus, as GHG emissions continue to firmly rise, it is very likely that GHG forcings will become a sufficient cause for events of the magnitude of the 2019/2020 record event.

 

 

How to cite: Barkhordarian, A., Nielsen, D. M., and Baehr, J.: Greenhouse gas forcing a necessary, but not sufficient, causation for the northeast Pacific marine heatwaves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6041, https://doi.org/10.5194/egusphere-egu22-6041, 2022.

EGU22-6754 | Presentations | OS1.5

Zonal jets in the eastern North Pacific in an ensemble of eddy-resolving ocean general circulation model runs 

Ryo Furue, Masami Nonaka, and Hideharu Sasaki

It has been known for some time that the ocean basins are populated by what is known as ‘‘zonal jets’’, ‘‘deep zonal jets’’, or ‘‘striations’’. Since the oceanic flow is, at least weakly, chaotic, it is not known whether the positions of the jets are ‘‘deterministic’’, that is, entirely determined by external parameters. A number of theories have been proposed to explain them, some of them predicting zonal jets at fixed latitudes and others implying that the positions of the jets are random. To investigate how deterministic the zonal jets are in the eastern North Pacific, a ten-member ensemble of long-term integrations of a semi-global, eddy-resolving ocean general circulation model is analyzed.

The positions of the equatorial jets, even their variability, seem to obey deterministic dynamics and some of the jets in the tropics (5°–15°N) migrate poleward coherently (similarly between ensemble members). The jets in the subtropics (15°–45°N) systematically migrate equatorward but their positions are less coherent; the jets in the subpolar region (45°N–) are random and without systematic migration. Jets near the coast of North and South America tend to have shorter meridional wavelengths than interior ones and those in the northern hemisphere are fairly coherent whereas those in the southern hemisphere seem more random. There are a few quasi-barotropic jets which are anchored to steep bottom topographic features and which also appear to trap shallower counter-flows on their poleward and equatorward flanks.

How to cite: Furue, R., Nonaka, M., and Sasaki, H.: Zonal jets in the eastern North Pacific in an ensemble of eddy-resolving ocean general circulation model runs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6754, https://doi.org/10.5194/egusphere-egu22-6754, 2022.

EGU22-7907 | Presentations | OS1.5

Stochastic data-driven model of mesoscale and submesoscale eddies in gyre circulation 

Francesco Tucciarone, Long Li, and Etienne Memin

Planetary flows and large scale circulation systems are characterised by an interaction between scales that range over several orders of magnitude, with contributions given by mesoscale and submesoscale dynamics. Resolving numerically  such interactions for realistic configuration is, however, far beyond reach. Any large-scale simulation must then rely on parameterizations of the effects of the small scales on  the large scales. In this work, a stochastic parameterization is proposed based on a decomposition of the flow in terms of a smooth-in-time large-scale contribution and a random fast-evolving uncorrelated small-scale part  accounting for  mesoscales and submesoscales unresolved eddies. This  approach, termed modelling under location uncertainty (LU) [4], relies on a stochastic version of Reynolds Transport Theorem to cast physically meaningful conservation principles in this scale-separated framework. Such a scheme has been successfully applied to several large-scale models of the  ocean dynamics [1, 2, 3, 5]. Here a LU version of the  hydrostatic primitive equations is  implemented within the  NEMO community code (https://www.nemo-ocean.eu) with a data-driven approach to establish the spatial correlation of the fast evolving scales. In comparison to a corresponding deterministic counterpart, this stochastic large-scale representation  is shown to improve, in terms of the eastward jet resolution and variabilities, the  flow prediction of an idealized wind forced double gyre circulation. The results are assessed through several statistical criterion as well as an energy transfer analysis [2,5].
[1] W. Bauer, P. Chandramouli, B. Chapron, L. Li, and E. Mémin. Deciphering the
role of small-scale inhomogeneity on geophysical flow structuration: a stochastic approach.
Journal of Physical Oceanography, 50(4):983-1003, 2020.
[2] W. Bauer, P. Chandramouli, L. Li, and E. Mémin. Stochastic representation of
mesoscale eddy effects in coarse-resolution barotropic models. Ocean Modelling, 151:101646,
2020.
[3] Rüdiger Brecht, Long Li, Werner Bauer and Etienne Mémin. Rotating Shallow
Water Flow Under Location Uncertainty With a Structure-Preserving Discretization. Journal of
Advances in Modeling Earth Systems, 13, 2021MS002492.
[4], E. Mémin Fluid flow dynamics under location uncertainty,(2014), Geophysical & Astrophysical Fluid Dynamics, 108, 2, 119–146.
[5] V. Resseguier, L. Li, G. Jouan, P. Dérian, E. Mémin, B. Chapron, (2021), New trends in ensemble forecast strategy: uncertainty quantification for coarse-grid computational fluid dynamics, Archives of Computational Methods in Engineering.

How to cite: Tucciarone, F., Li, L., and Memin, E.: Stochastic data-driven model of mesoscale and submesoscale eddies in gyre circulation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7907, https://doi.org/10.5194/egusphere-egu22-7907, 2022.

EGU22-8332 | Presentations | OS1.5

Dynamical Landscape and Noise-induced Transitions in a Box Model of the Atlantic Meridional Overturning Circulation 

Reyk Börner, Valerio Lucarini, and Larissa Serdukova

The multistability of the Atlantic Meridional Overturning Circulation (AMOC) challenges the predictability of long-term climate evolution. In light of an observed weakening in AMOC strength, it is crucial to study the probabilities of noise-induced transitions between the different competing flow regimes. From a dynamical systems perspective, the phase space of a multistable system can be characterised as a non-equilibrium potential landscape, with valleys corresponding to the different basins of attraction. Knowing the potential, one can infer the statistics and pathways of noise-induced transitions. Particularly, in the weak-noise limit, transition paths lead through special regions of the basin boundaries, called Melancholia states. Recent studies have applied these concepts to climate models of low and intermediate complexity. Here, we investigate the quasi-potential landscape of a three-box model of the AMOC, based on the popular model by Rooth. We analyse noise-induced transitions between the two stable circulation states and elucidate the role of the Melancholia state. Forcing the model with different noise laws, which represent fluctuations caused by different physical processes, we discuss how the properties of transitions change when considering non-Gaussian processes, specifically Lévy noise. Simulated transition rates are related to their theoretical values using the quasi-potential landscape. Our results yield a comprehensive picture of the dynamical properties of an inter-hemispheric three-box AMOC model under stochastic forcing. By relating the deterministic structure of this simple model to the statistics of critical transitions, we hope to build a basis for transferring this approach to more complex models of the AMOC.

How to cite: Börner, R., Lucarini, V., and Serdukova, L.: Dynamical Landscape and Noise-induced Transitions in a Box Model of the Atlantic Meridional Overturning Circulation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8332, https://doi.org/10.5194/egusphere-egu22-8332, 2022.

EGU22-741 | Presentations | CL4.7

Long-term evolution of eddying oceans in a warming world 

Nathan Beech, Thomas Jung, Tido Semmler, Thomas Rackow, Qiang Wang, and Sergey Danilov

Mesoscale ocean eddies impact atmosphere-ocean gas exchange, carbon sequestration, and nutrient transport. Studies have attempted to identify trends in eddy activity using satellite altimetry; however, it is difficult to distinguish between robust trends and natural variability within the short observational record. Using a novel climate model that exploits the variable-resolution capabilities of unstructured meshes in the ocean component to concentrate computational resources in eddy-rich regions, we assess global mesoscale eddies and their long-term response to climate change at an unprecedented scale. The modeled results challenge the significance of some trends identified by observational studies, as well as the effectiveness of linear trends in assessing eddy kinetic energy (EKE) change. Some anticipated changes to ocean circulation, such as a poleward shift of major ocean currents and eddy saturation in the Southern Ocean, are reinforced by the modeled EKE changes. Several novel insights regarding the evolution of EKE in a warming world are also proposed, such as a decrease of EKE along the Gulf Stream in unison with weakening Atlantic meridional overturning circulation (AMOC); increasing Agulhas leakage; and accelerating, non-linear increases of EKE in the basins of the Kuroshio Current, Brazil and Malvinas Currents, and the Antarctic Circumpolar Current (ACC).

How to cite: Beech, N., Jung, T., Semmler, T., Rackow, T., Wang, Q., and Danilov, S.: Long-term evolution of eddying oceans in a warming world, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-741, https://doi.org/10.5194/egusphere-egu22-741, 2022.

EGU22-3866 | Presentations | CL4.7

Recent Hadley circulation strengthening: a trend or multidecadal variability? 

Žiga Zaplotnik, Matic Pikovnik, and Lina Boljka

This study explores the possible drivers of the recent Hadley circulation strengthening in the modern reanalyses. Predominantly, two recent generations of reanalyses provided by the European Centre for Medium-Range Weather Forecasts are used: the fifth-generation atmospheric reanalysis (ERA5) and the interim reanalysis (ERA-Interim). Some results are also evaluated against other long-term reanalyses. To assess the origins of the Hadley cell (HC) strength variability we employ the Kuo-Eliassen (KE) equation. ERA5 shows that both HCs were strengthening prior to 2000s, but they have been weakening or remained steady afterwards. Most of the long-term variability in the strength of the HCs is explained by the meridional gradient of diabatic (latent) heating, which is related to precipitation gradients. However, the strengthening of both HCs in ERA5 is larger than the strengthening expected from the observed zonal-mean precipitation gradient (via Global Precipitation Climatology Project, GPCP). This suggests that the HC strength trends in the recent decades in ERA5 can be explained partly as an artifact of the misrepresentation of latent heating and partly through (physical) long-term variability. To show that the latter is true, we analyze ERA5 preliminary data for the 1950-1978 period, other long-term (e.g. 20th century) reanalyses, and sea surface temperature observational data. This reveals that the changes in the HC strength can be a consequence of the Atlantic multidecadal variability (AMV) and related diabatic and frictional processes, which in turn drive the global HC variability. This work has implications for further understanding of the long-term variability of the Hadley circulation.

How to cite: Zaplotnik, Ž., Pikovnik, M., and Boljka, L.: Recent Hadley circulation strengthening: a trend or multidecadal variability?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3866, https://doi.org/10.5194/egusphere-egu22-3866, 2022.

EGU22-5121 | Presentations | CL4.7

Changes in the global atmospheric energy transport separated by spatial scales in a warming world 

Patrick Stoll, Rune Grand Graversen, Tuomas Ilkka Henrikki Heiskanen, and Richard Bintanja

The global atmospheric circulation determines the local weather and climate. To better understand this circulation and how it may change in a warming world, we separate the atmospheric energy transport by the spatial scale, the quasi-stationary and transient nature, and the latent and dry-static component in the ERA-5 reanalysis and climate-model simulations with EC-Earth. Different to previous studies that distinguish the scale by wave-numbers, here the meso, synoptic and planetary scales are separated at wavelengths below 2000km, between 2-8000km, and above the latter, respectively. The scale (wavelength) of most transient energy transport is around 5000km for all latitudes and is associated with baroclinic, synoptic-scale cyclones. Transient, synoptic-scale waves are the largest contributor to the energy transport at all latitudes outside the tropics, where the meridional overturning circulation is dominant. Planetary-scale waves are both of quasi-stationary and transient character, strongest at latitudes with much orography, and responsible for most of the inter-annual variability of the energy transport. The energy transport associated with mesoscale waves is negligible.

In a warming world, the moisture transport increases everywhere and in all components, however strongest for planetary waves, making dry areas dryer and moist areas moister, and supporting large and long-lasting events that favour floods and droughts. The total energy transport increases at latitudes smaller than 60 degrees, with the main contribution from quasi-stationary, planetary-scale waves, indicating that weather patterns become more persistent. The changing energy transport can be associated both with changing zonal gradients in temperature and with an atmospheric circulation that becomes more effective in transporting energy.

How to cite: Stoll, P., Graversen, R. G., Heiskanen, T. I. H., and Bintanja, R.: Changes in the global atmospheric energy transport separated by spatial scales in a warming world, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5121, https://doi.org/10.5194/egusphere-egu22-5121, 2022.

I use thermodynamics and an Earth system approach to determine how much kinetic energy the atmosphere is physically capable of generating at large scales from the solar radiative forcing.  The work done to generate and maintain large-scale atmospheric motion can be seen as the consequence of an atmospheric heat engine, which is driven by the difference in solar radiative heating between the tropics and the poles.  The resulting motion transports heat, which depletes this differential solar heating and the associated, large-scale temperature difference, which drives this energy conversion in the first place.  This interaction between the thermodynamic driver (temperature difference) and the resulting dynamics (heat transport) is critical for determining the maximum power that can be generated.  It leads to a maximum in the global mean generation rate of kinetic energy of about 1.7 W m-2, which matches rates inferred from observations of about 2.1 - 2.5 W m-2 very well.  This represents less than 1% of the total absorbed solar radiation that is converted into kinetic energy. Although it would seem that the atmosphere is extremely inefficient in generating motion, thermodynamics shows that the atmosphere works as hard as it can to generate the energy contained in the winds.  I then show that this view of atmospheric dynamics is essentially the same as a maximised generation rate of Available Potential Energy (APE) for the Lorenz energy cycle, and that it is also consistent with the outcome of the proposed principle of Maximum Entropy Production (MEP) while representing a more physically interpretable approach.  This supports the notion that Earth system processes evolve to and operate near their thermodynamic limit, which permits the use of this constraint to do climate science analytically with less empirical input.

How to cite: Kleidon, A.: How much kinetic energy can the large-scale atmospheric circulation at best generate?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5417, https://doi.org/10.5194/egusphere-egu22-5417, 2022.

EGU22-5439 | Presentations | CL4.7

Water isotopic imprints of the Pliocene Pacific Walker Circulation 

Theodor Mayer, Ran Feng, and Tripti Bhattacharya

Ocean-atmosphere coupled models predict pronounced weakening of the Pacific Walker Circulation (PWC) with increasing CO2 concentration due to enhanced tropospheric stability and reduced convective mass overturning. However, current observational results are inconsistent and do not confirm a clear weakening signal. The detection of the signature of increasing CO2 is in part impeded by substantial internal variability and anthropogenic aerosol forcings. Here we explore the possibility of using a paleoclimatic analogue to understand the contemporary PWC sensitivity to CO2 changes. We focus on the interval from mid-Piacenzian (MP, 3.3 – 3.0 Ma) to early Pleistocene (~2.4 Ma). The MP had elevated CO2 concentrations (~400ppm) and geography, topology, and vegetation similar to today. Following the MP global CO2 and temperature decreased, leading to the intensification of the Northern hemisphere glaciation. We seek to identify potential proxy constraints on model simulated PWC sensitivity to CO2 forcing by focusing on changes in the hydroclimatology during this time interval. We developed several sets of isotope-tracking enabled CESM version 1.2 simulations, which utilize pre-Industrial and Pliocene boundary conditions, different CO2 levels, and water tagging of 11 oceanographic regions to track the life cycles of various water species (H216O, H218O and HD16O). Preliminary results show that Pliocene boundary conditions have little impact on the relationship between the CO2 forcing and the intensity of PWC. The precipitation δD contrast between the eastern and western tropical Pacific, scales well with the PWC strength, suggesting high potential for developing PWC strengths proxy with precipitation isotopic records from both sides of the tropical Pacific. Our ongoing work will further identify physical processes responsible for the simulated precipitation isotopic signals: i.e., whether they reflect changes in the moisture source, moisture transport, or moist convection at the destination. Additionally, prescribed-SST simulations will also be conducted to quantify the isotopic imprints of changing tropospheric instability from SST changes.

How to cite: Mayer, T., Feng, R., and Bhattacharya, T.: Water isotopic imprints of the Pliocene Pacific Walker Circulation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5439, https://doi.org/10.5194/egusphere-egu22-5439, 2022.

EGU22-5897 | Presentations | CL4.7

The variations of temperature extremes over the wintertime Tibetan Plateau from 1979 to 2018 

Yinglin Tian, Deyu Zhong, and Axel Kleidon

The Tibetan Plateau (TP), known as the “World Roof”, has significant influences on hydrological and atmospheric circulation at both regional and global scales. As a result, an adequate understanding of TP climate change is of great importance. In this study, the temporospatial variations of temperature extremes over the TP are investigated based on the station and gridded data provided by China Meteorological Administration (CMA) and the Mann-Kendall test. In addition, the typical large-scale circulations along with the temperature extremes are analyzed using the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis data. It is found that while the frequency of the temperature extremes is observed to have gone through significant variations from 1979 to 2018, the intensity of the temperature extremes has no significant change. On the one hand, the frequency of the warm days and nights is getting higher over the southeastern part and northwestern TP; on the other hand, most area of the eastern TP has witnessed a significant decreasing trend in the frequency of cold days and nights, together suggesting a warming TP. Moreover, the distribution of the long-term changes in the warm days and the cold nights resemble those of the multi-year tendencies of the maximum and minimum temperature. Furthermore, both warm days and nights occur with a significant anti-cyclone over the TP for continuous days, which might allow for more solar radiation arriving at the surface and also favors more adiabatic heating along with the sinking movement of the air parcels. Our results imply a possible linkage between the long-term climate change in the TP, the temperature extremes over the TP, and the large-scale circulations.

How to cite: Tian, Y., Zhong, D., and Kleidon, A.: The variations of temperature extremes over the wintertime Tibetan Plateau from 1979 to 2018, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5897, https://doi.org/10.5194/egusphere-egu22-5897, 2022.

EGU22-7235 | Presentations | CL4.7

Wave decomposition of energy transport using deep-learning 

Tuomas Ilkka Henrikki Heiskanen and Rune Graversen

Energy transport in the atmosphere is accomplished by systems of several length scales, from cyclones to Rossby waves. From recently developed Fourier and wavelet based methods it has been found that the planetary component of the latent heat transport affects the Arctic surface temperatures more than its dry-static counterpart and the synoptic scale component of the latent heat transport.  

However, both the Fourier and wavelet based methods require enormous amounts of data and are time consuming to process. The Fourier and wavelet decompositions are computed  from 6 hourly data, throughout the whole vertical column of the atmosphere. The data required are usually only available from reanalysis archives, or possibly from climate model experiments where a goal is to examine the decomposed energy transport. However, the vast CMIP5 and CMIP6 archives are out of reach for the exact computations of the Fourier and wavelet decompositions. Even if all the data were available in the CMIP archives, it would be a computationally, and storage-wise, intensive task to compute the Fourier and wavelet decompositions for a large selection of the CMIP experiments.

Here we suggest a deep-learning approach to approximate the decomposed energy transport from significantly less data than the original methods. The idea is to train a convolutional neural network (CNN) on ERA5 data, where we have already computed the Fourier decomposition of the energy transport. The CNN is trained on data at 850hPa in the atmosphere on a daily temporal resolution. The required data are only a small fraction of the data required to compute the exact Fourier decompositon of the energy transport. Once the CNN is trained, the model is tested on data from the EC-Earth climate model. For EC-Earth we have an ensemble of model runs where the energy transport is decomposed using the Fourier method, hence the CNN may be evaluated on the EC-Earth dataset.

The CNN based energy transport decomposition matches well with the classically computed energy transport from EC-Earth.The CNN captures the mean meridional transport well, and the projected changes from the 1950s to the 2090s in EC-Earth. Additionally the CNN model captures the day to day variability well, as regressions of temperature on the transport from the CNNcomputations and the classical Fourier decomposition are similar. Further we may investigate how the decomposed energy transport changes in a range of CMIP models and experiments

How to cite: Heiskanen, T. I. H. and Graversen, R.: Wave decomposition of energy transport using deep-learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7235, https://doi.org/10.5194/egusphere-egu22-7235, 2022.

EGU22-7317 | Presentations | CL4.7

Bias teleconnections: atmospheric variability associated with biases in remote regions 

Yuan-Bing Zhao, Frank Lunkeit, and Nedjeljka Žagar

Atmospheric spatial and temporal variability are closely related with the former being relatively well assessed compared to the latter. New opportunities for understanding the spatio-temporal variability spectrum are offered by coupled high-resolution climate models. However, the models still suffer from significant systematic errors (biases) calling for an approach that assesses circulation variability in relation to biases. Furthermore, biases in simulated variability are often of remote origin; for example, biases in the Atlantic sea-surface temperature in boreal winter may be responsible for changes in simulated variability over Asia.

We present a novel framework for the multivariate, multi-scale variability evaluation in relation to remote biases. Centennial simulations are carried out using a general circulation model PLASIM and a perfect-model framework. Biases in simulated circulation originate from regional errors in the surface forcing by prescribed sea surface temperature (SST). A reference simulation is forced with the monthly SST from ERA-20C reanalyses from January 1900 to December 2010. Sensitivity simulations are forced with the same SST with addition of regional perturbations that mimic the errors in the surface forcing of the atmosphere and lead to systematic errors in the simulated mean state and temporal variance. The erroneous SST is respectively located in tropical basins of Indian ocean, Western Pacific, Central Pacific, Eastern Pacific, and Atlantic, and in extra-tropical areas of North Pacific and North Atlantic.

The bias is the time-averaged difference between the reference and sensitivity simulations. Using the normal-mode function decomposition, the amplitude and phase of the bias can be related to deficiencies in spatial and temporal variance of the two main dynamical regimes: quasi-geostrophic regime and unbalanced circulation. The results show that biases are mainly established in the zonal-mean state and at planetary scales of balanced flow. In boreal winter, the biases at scales with zonal wavenumber k>0 are typically manifested in the barotropic Rossby wave train across the Northern Hemisphere. The structure of tropical biases is that of unbalanced flow, projecting predominantly on the Kelvin wave and the vertical baroclinic structure. The effects of biases on spatio-temporal variability are further investigated in spectral space.

How to cite: Zhao, Y.-B., Lunkeit, F., and Žagar, N.: Bias teleconnections: atmospheric variability associated with biases in remote regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7317, https://doi.org/10.5194/egusphere-egu22-7317, 2022.

EGU22-7957 | Presentations | CL4.7

The relationship between atmospheric heat transport and monsoonal precipitation variability 

MD Rabiul Awal, Andrew Turner, and David Ferreira

During the boreal summer monsoon, the temperature gradient between land and ocean in the Northern Hemisphere (NH) facilitates large transports of moist air masses towards the land regions, where their convergence causes precipitation. This is associated with an export of net energy (internal, potential, and latent energy) away from the land. On a global scale, there is a tight relationship between the location of the intertropical convergence zone (ITCZ) and the cross-equatorial atmospheric heat transport (AHT) on seasonal, interannual and climate time scales: a more northward cross-equatorial AHT is associated with a displacement of the ITCZ (as defined by precipitation) toward the equator. We further analyse the relationships between cross-equatorial AHT and common streamfunction-based measures of the ITCZ position and width found in the literature. However, it remains unclear whether links between energy transport and the monsoonal precipitation exist at the scale of monsoon regions.

To address this question, we combine data from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis ERA5 and Global Precipitation Climatology Project (GPCP-version 2.3) rainfall data. In the annual cycle, the cross-equatorial northward AHT transport peaks in July and the annual net northward cross-equatorial AHT is -0.34 PW (negative sign denotes southward). A regression analysis confirms that the global ITCZ shifts southward when the cross-equatorial AHT is anomalously large, although we demonstrate this mainly happens over the Pacific Ocean. Outside of the Pacific sector, the relationship between cross-equatorial AHT and JJA precipitation is complex. For the West African monsoon region, greater northward cross-equatorial AHT is related to weaker rainfall along the Gulf of Guinea coast, while there is stronger rainfall in the Atlantic Ocean ITCZ. In the Indian sector, anomalous northward AHT is associated with a weak monsoon, marked by strong decreases in precipitation on the Western coast of India and the southern flank of the Himalayas.

In future work, the CMIP6 multi-model dataset will be analysed to examine future projection of AHT and its impact on monsoonal precipitation. The characteristics of the ITCZ will be explored using the same datasets.

How to cite: Awal, M. R., Turner, A., and Ferreira, D.: The relationship between atmospheric heat transport and monsoonal precipitation variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7957, https://doi.org/10.5194/egusphere-egu22-7957, 2022.

Oceanic mesoscale eddies contain most of the kinetic energy (KE) in the ocean and therefore play an important role in determining the ocean’s response to future climate scenarios. Oceanic wind-forced internal waves (IWs) are energetic fast motions that contribute substantially to the vertical mixing of water, thereby affecting biogeochemical and climate processes. We study the effects of wind-forced IWs on the KE pathways in high-resolution numerical simulations of an idealized wind-driven channel flow. Using spectral fluxes, we demonstrate that solutions with wind-forced IWs are characterized by a forward KE cascade, whereas solutions without exhibit an inverse KE cascade. We further decompose the flow field into ‘eddy’ and ‘internal wave’ motions using a Helmholtz decomposition and temporal filtering. This allows us to identify three key processes responsible for the reversal in the KE cascade: IW scattering, direct extraction, and stimulated cascade. Each process is quantified and discussed in detail.

How to cite: shaham, M. and Barkan, R.: Eddy-Internal wave decomposition and kinetic energy transfers in high-resolution turbulent channel flow with near-inertial waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8284, https://doi.org/10.5194/egusphere-egu22-8284, 2022.

EGU22-9166 | Presentations | CL4.7

Diagnosing the effect of circulation trends on atmospheric temperature 

Rhidian Thomas, Tim Woollings, and Nick Dunstone

In studying recent climate, changes to atmospheric circulation are often understood as a response to temperature changes. This work instead quantifies the contribution to temperature trends from the atmospheric dynamics, by analysing trends in the ERA5 zonal-mean temperature budget over the satellite era. The results are consistent with several previously highlighted trends in the circulation. In the winter hemisphere, the region of subtropical descent and heating associated with the Hadley cell strengthens on its poleward side, and the deep diabatic heating in the ITCZ intensifies and shifts northward in the northern hemisphere (NH) winter. In keeping with other studies, we find a weakening of the transient eddy heating associated with the NH summer storm tracks. At high northern latitudes, the climatological eddy heating is weakened at low levels; this signal is strongest in NH winter, consistent with the reduced baroclinicity associated with arctic warming. Our work also points towards emerging trends in the transition seasons, SON and MAM, and underlines the importance of circulation changes in understanding trends in atmospheric temperature.

How to cite: Thomas, R., Woollings, T., and Dunstone, N.: Diagnosing the effect of circulation trends on atmospheric temperature, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9166, https://doi.org/10.5194/egusphere-egu22-9166, 2022.

EGU22-9501 | Presentations | CL4.7

Diagnosing differences in Bjerknes compensation in the IPSL-CM6A-LR model 

Yoania Povea Pérez, Eric Guilyardi, Brady Ferster, and Alexey Fedorov

Planetary heat transport can be separated into the oceanic and atmospheric components and plays a major role in shaping the climate. In a climate in equilibrium, the net heat flux at the top of the atmosphere is constant and the rate of change in ocean heat content is negligible. In such conditions, anomalies in the ocean heat transport are accompanied by changes in the atmosphere of the same magnitude but opposite sign [Bjerknes, 1964], known as Bjerknes compensation (BJC). BJC remains a hypothesis since it has not been found in observations due to the length of time series and large errors compared to the observed heat transports. Nevertheless, BJC has a great number of applications in climate sciences, especially in climate predictability. Here we study the BJC in the IPSL-CM6A-LR model and contrast its properties in piControl and abrupt-4xCO2 experiments. In order to address this goal, we characterize the different time scales dependence and explore BJC dynamics linked to the Atlantic Meridional Overturning Circulation (AMOC) changes and Intertropical Convergence Zone (ICTZ) shifts. We improve the BJC diagnostics by introducing the Turner Angle between ocean and atmospheric anomalies:  this allows both to quantify the BJC strength and to distinguish the contributions of ocean and atmosphere. In the IPSL-CM6A-LR model, we found two regions of stronger BJC corresponding to the mid-latitudes storm track region and the Marginal Ice Zone. The strong forcing in abrupt-4xCO2 leads to an AMOC reduction of 60% compared to the control experiment and dampening of the centennial signal of heat transport, however, the role of BJC in AMOC recovery in this experiment remains unclear. The ocean dominates BJC at decadal and centennial timescales both in natural and forced experiments. BJC is associated with the co-variability between AMOC strength and ITCZ location. Other forms of heat compensation are found in this model, such as a Bjerknes-like compensation between Atlantic and Indo-Pacific centennial ocean heat transport in the South Hemisphere.  

How to cite: Povea Pérez, Y., Guilyardi, E., Ferster, B., and Fedorov, A.: Diagnosing differences in Bjerknes compensation in the IPSL-CM6A-LR model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9501, https://doi.org/10.5194/egusphere-egu22-9501, 2022.

EGU22-10666 | Presentations | CL4.7

Monsoon Onset Response to Warming in Idealized GCM and CMIP6 Simulations 

Simona Bordoni and Katrina Hui

GCMs robustly project a delay in the timing of the global monsoon onset and tropical precipitation intensification with warming. However, a closer look at the response of different monsoon regions shows less consistency. To better understand how monsoons will respond to a warming climate, with a particular focus on the timing of monsoon onset, we use a hierarchy of climate models, starting from idealized aquaplanet simulations all the way to CMIP6 projections, to identify the robust and uncertain changes and investigate the underlying mechanisms. Our idealized work covers two sets of simulations: 1) aquaplanet runs with a uniform mixed layer depth (MLD) in a wide range of climates, from colder to warmer than the current climate, and 2) simulations with an idealized saturated zonally symmetric continent extending from 10oN to the North Pole in a similar range of colder to warmer climates. Monsoon onset is determined using a change point detection method on the cumulative moisture flux convergence (MFC) (or net precipitation), which robustly links monsoon onset to changes in the large-scale monsoonal circulation. The idealized uniform MLD aquaplanet simulations show a robust progressive delay of monsoon onset, consistent with results reported in the literature. Analyses of the atmospheric energy budget suggest this delay is due to the increased atmospheric latent heat capacity with warming. Interestingly, this delay is not evident in the simulations with the idealized saturated continent. Mechanisms are explored by analyzing changes in the energetics and dynamics of the tropical circulation and related monsoonal precipitation. CMIP6 projections in different monsoon regions are investigated to determine if mechanisms exposed in the idealized simulations can shed some light on the differing monsoon onset responses in more complex climate models.

How to cite: Bordoni, S. and Hui, K.: Monsoon Onset Response to Warming in Idealized GCM and CMIP6 Simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10666, https://doi.org/10.5194/egusphere-egu22-10666, 2022.

EGU22-10915 | Presentations | CL4.7

The impact of greenhouse gas and ozone forcing on the Southern Hemisphere climate system 

Houraa Daher and Ben Kirtman

Anthropogenic climate change in the Southern Hemisphere is driven by two forces, the greenhouse gas emissions and the stratospheric ozone levels. In the past, the combination of increasing greenhouse gas emissions and ozone depletion over Antarctica worked together leading to an increase in sea surface temperatures and a poleward shift of the storm tracks. With the ozone expected to recover by mid-century, however, the greenhouse gas and ozone forces will oppose each other and the changes observed previously will begin to weaken or reverse. The role the greenhouse gases and the ozone recovery play in the Southern Hemisphere climate system are examined using Community Climate System Model, version 4 (CCSM4) coupled ocean eddy-parameterized and eddy-resolving simulations. The greenhouse gas emissions and ozone levels are specified independently to represent the two extremes, peak greenhouse gas emissions and a recovered ozone. In the eddy-parameterized simulations, the ozone recovery signal is found to be stronger on average. In the case of the eddy-resolving simulations, however, the increase in greenhouse gases is stronger especially in eddy-rich regions like western boundary current regions and the Antarctic Circumpolar Current. The volume transport is also calculated for the Southern Hemisphere western boundary currents (Agulhas, Brazil, and East Australian Currents) and the two external forces are found to not play an important role in the mean transports, but the model resolution does. The eddy-parameterizing simulations yield a more accurate transport than the eddy-resolving simulations. The eddy-resolving simulations however, are able to resolve a more accurate eddy field in these highly active regions. The relationship between the sea surface temperatures in the western boundary currents and regional precipitation over nearby South Africa, South America, and Australia is then analyzed in greater detail.

How to cite: Daher, H. and Kirtman, B.: The impact of greenhouse gas and ozone forcing on the Southern Hemisphere climate system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10915, https://doi.org/10.5194/egusphere-egu22-10915, 2022.

EGU22-10935 | Presentations | CL4.7

Super recovery of the Hadley Cell edge to the CO2 removal 

Yeong-Ju Choi, Seo-Yeon Kim, Seok-Woo Son, Soon-il An, Sang-Wook Yeh, Jong-Seong Kug, Seung-Ki Min, and Jongsoo Shin

The poleward shift of the Hadley cell (HC) edge by global warming is widely documented. However, its reversibility to CO2 removal remains unknown. By conducting a climate model experiment where CO2 concentration is systematically increased and then decreased in time, this study shows that a poleward-shifted HC edge in warm climate returns equatorward as CO2 concentration decreases. It is also shown that the rate significantly differs between the two hemispheres. While the southern HC edge monotonically changes with CO2 concentration, the northern HC edge exhibits a super recovery, locating on the equatorward side of the present-climate HC edge when CO2 concentration returns to the present level. Such a super recovery is associated with the hysteresis of the North Atlantic sea surface temperature. Our findings suggest that the HC edge change may result in the super recovery of subtropical dryness in the northern hemisphere except California.

How to cite: Choi, Y.-J., Kim, S.-Y., Son, S.-W., An, S., Yeh, S.-W., Kug, J.-S., Min, S.-K., and Shin, J.: Super recovery of the Hadley Cell edge to the CO2 removal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10935, https://doi.org/10.5194/egusphere-egu22-10935, 2022.

EGU22-11288 | Presentations | CL4.7

Spectral analysis of the Southern Hemisphere atmospheric variability to assess the role of baroclinic instability in seasonal forecasts 

Laura Trentini, Sara Dal Gesso, Alessandro Dell'Aquila, and Marcello Petitta

Baroclinic instability in the mid-latitudes is a significant component of the climate system, as it is associated with the meridional transport of a large amount of energy and momentum. Hence, the ability of climate models to correctly predict the properties of atmospheric circulation in that latitudinal band is a very important requirement. This study aims to estimate the power content of the atmospheric phenomena typical of mid-latitudes, such as baroclinic perturbations, and to understand how seasonal forecasts can be practically used to assess energy transfer in the atmosphere. We compare the Southern Hemisphere mid-latitude winter variability of the long-range forecasting system SEAS5 with the ERA5 reanalysis. Both datasets are produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The analysis is carried out by computing the Hayashi spectra of the 500-hPa geopotential height field. Both the reanalysis and the seasonal forecast show a series of peaks in the spectral region of eastward-traveling waves, which corresponds to the high frequency-high wavenumber domain. We quantify the amount of energy released from the atmosphere by calculating the Baroclinic Amplitude Index. Results suggest that the seasonal forecasts correctly reflect the variability of the geopotential height power spectra in the Southern Hemisphere, with some minor discrepancies related to the sub-daily variability, which is not correctly discriminated. However, the energy associated with the baroclinic activity is well represented by the seasonal forecast in the Southern Hemisphere, where the orographic effect is negligible compared to the Northern Hemisphere. This work is carried out as part of the European FOCUS-Africa project, which develops innovative and sustainable climate services in the Southern African Development Community (SADC) region.

How to cite: Trentini, L., Dal Gesso, S., Dell'Aquila, A., and Petitta, M.: Spectral analysis of the Southern Hemisphere atmospheric variability to assess the role of baroclinic instability in seasonal forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11288, https://doi.org/10.5194/egusphere-egu22-11288, 2022.

EGU22-13178 | Presentations | CL4.7

Seasonality of the Mesoscale Inverse Cascade as Inferred from Global Scale-Dependent Eddy Energy Observations 

Jacob Steinberg, Sylvia Cole, Kyla Drushka, and Ryan Abernathey

Oceanic mesoscale motions including eddies, meanders, fronts, and filaments comprise a dominant fraction of oceanic kinetic energy and contribute to the redistribution of tracers in the ocean such as heat, salt, and nutrients. This reservoir of mesoscale energy is regulated by the conversion of potential energy and transfers of kinetic energy across spatial scales. Whether and under what circumstances mesoscale turbulence precipitates forward or inverse cascades, and the rates of these cascades, remain difficult to directly observe and quantify despite their impacts on physical and biological processes. Here we use global observations to investigate the seasonality of surface kinetic energy and upper ocean potential energy. We apply spatial filters to along-track satellite measurements of sea surface height to diagnose surface eddy kinetic energy across 60-300 km scales. A geographic and scale dependent seasonal cycle appears throughout much of the mid-latitudes, with eddy kinetic energy at scales less than 60 km peaking 1-4 months before that at 60-300 km scales. Spatial patterns in this lag align with geographic regions where the conversion of potential to kinetic energy are seasonally varying. In mid-latitudes, the conversion rate peaks 0-2 months prior to kinetic energy at scales less than 60 km. The consistent geographic patterns between the seasonality of potential energy conversion and kinetic energy across spatial scale provide observational evidence for the inverse cascade, and demonstrate that some component of it is seasonally modulated. Implications for mesoscale parameterizations and numerical modeling are discussed.

How to cite: Steinberg, J., Cole, S., Drushka, K., and Abernathey, R.: Seasonality of the Mesoscale Inverse Cascade as Inferred from Global Scale-Dependent Eddy Energy Observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13178, https://doi.org/10.5194/egusphere-egu22-13178, 2022.

EGU22-13547 | Presentations | CL4.7 | Highlight

Towards attributing change in tropical and subtropical precipitation 

Gabriele Hegerl, Andrew Ballinger, and Andrew Schurer

Precipitation changes are notoriously highly variable, and climate models misplace circulation features, making it difficult to evaluate if mechanisms of precipitation change are well reproduced in climate models. Several methods have been developed to detect externally forced precipitation change tracking circulation features rather than specific locations. For example, analysis of monthly ascending and descending regions in reanalysis show the increase of rainfall in ascending regions. Analysis of wet and dry regions in GPCP blended data shows that if the locations of wet and dry regions are tracked from month to month then trends over the past 3-4 decades can be attributed to a combination of human influences and the recovery from drying associated with the Mount Pinatubo eruption in wet regions. In response to volcanic eruptions, wet regions tend to dry and dry regions may get wetter, indicating a reduced moisture transport to the wettest regions of the tropics under strong volcanic forcing. However, this is also impacted by the hemispheric characteristics of the eruptions. 

How to cite: Hegerl, G., Ballinger, A., and Schurer, A.: Towards attributing change in tropical and subtropical precipitation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13547, https://doi.org/10.5194/egusphere-egu22-13547, 2022.

EGU22-2993 | Presentations | HS1.3.2 | Highlight

Spatio-temporal synchronization of heavy rainfall events triggered by atmospheric rivers in North America 

Frederik Wolf, Sara M. Vallejo-Bernal, Niklas Boers, Norbert Marwan, Dominik Traxl, and Jürgen Kurths

Atmospheric rivers (ARs) are filaments of extensive water vapor transport in the lower troposphere. They are important triggers of heavy rainfall events, contributing to more than 50% of the rainfall sums in some regions along the western coast of North America. ARs play a crucial role in the distribution of water, but can also cause natural and economical damage by facilitating heavy rainfall. Here, we investigate the large-scale spatio-temporal synchronization patterns of heavy rainfall triggered by ARs over the western coast and the continental regions of North America.

For our work, we employ daily ERA5 rainfall estimates at a spatial resolution of 0.25°x0.25° latitude and longitude which we threshold at the 95th percentile to obtain binary time series indicating the absence or presence of heavy rainfall. Subsequently, we separate periods with ARs and periods without ARs and investigate the differing spatial synchronization pattern of heavy rainfall. To establish that our results are not dependent on the chosen AR catalog, this is conducted in two different ways: first based on a recently published catalog by Gershunov et al. (2017) , and second based on a catalog constructed using the IPART algorithm (Xu et al, 2020). For both approaches, we subsequently utilize event synchronization and a complex network framework to reveal distinct spatial patterns of heavy rainfall events for periods with and without active ARs. Using composites of upper-level meridional wind, we attribute the formation of the rainfall synchronization patterns to well-known atmospheric circulation configurations, whose intensity scales with the strength of the ARs. Furthermore, we demonstrate that enhanced AR activity is going in hand with a suppressed seasonal shift of the characteristic meridional wind pattern. To verify and illustrate how small changes of the high-level meridional wind affect the distribution of heavy rainfall, we, additionally, perform a case study focusing on the boreal winter.

Our results indicate the strong sensitivity of the intensity, location, frequency, and pattern of synchronized heavy rainfall events related to ARs to small changes in the large-scale circulation.

How to cite: Wolf, F., Vallejo-Bernal, S. M., Boers, N., Marwan, N., Traxl, D., and Kurths, J.: Spatio-temporal synchronization of heavy rainfall events triggered by atmospheric rivers in North America, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2993, https://doi.org/10.5194/egusphere-egu22-2993, 2022.

EGU22-3694 | Presentations | HS1.3.2

Prediction of drain flow fraction at high spatial resolution by combining physically based models and machine learning 

Raphael Schneider, Hafsa Mahmood, Rasmus Rumph Frederiksen, Anker Lajer Højberg, and Simon Stisen

In Denmark, about half of the agricultural land is artificially drained. These drainage systems have a significant effect on the hydrological system. Knowledge about the spatio-temporal distribution of drain flow is crucial to understand aspects such as groundwater recharge, streamflow partitioning and nutrient transport. Still, quantification of drain flow at regional and large scale remains a major challenge: Data on the distribution of the installed subsurface drainage system are scarce, as are measurements of drain flow. Large-scale simulations of drain with physically-based hydrological models are challenged by scale, as drain flow is controlled by small-scale variations in groundwater depth often beyond the model resolution. Purely data-driven models can struggle representing the complex controls behind drain flow.

Here, we suggest a metamodel approach to obtain a more accurate estimate of generated drain flow at high spatial resolution of 10 m, combining physically-based with data-driven models. Our variable of interest is drain fraction, defined as the ratio between drain flow and recharge per grid cell, which is an indicator for flow partitioning between drain and recharge to deeper groundwater.

First, we setup distributed, integrated groundwater models at 10 m grid resolution for 28 Danish field-scale drain catchments with observations of drain flow timeseries. A joint calibration of these field-scale models against observed drain flow resulted in an average KGE of above 0.5. Subsequently, the simulated drain fractions from the field-scale models were used to train a decision tree machine learning algorithm. This metamodel uses various mappable covariates (topography and geology-related) available at high resolution for all of Denmark. The metamodel then is used to predict drain fractions, within its limits of applicability, across relevant areas of Denmark with significant drain flow outside of the field-scale models.

Eventually, the predicted drain fractions are intended to inform national, large-scale physically based hydrological models: An improved representation of drain can, for example, make those models more fit to improve national targeted nitrate regulation.

How to cite: Schneider, R., Mahmood, H., Frederiksen, R. R., Højberg, A. L., and Stisen, S.: Prediction of drain flow fraction at high spatial resolution by combining physically based models and machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3694, https://doi.org/10.5194/egusphere-egu22-3694, 2022.

EGU22-4076 | Presentations | HS1.3.2

Information theory approach for enhancing time series analysis and predictability of soil environments 

Luiza Cristina Corpaci, Sebastian Raubitzek, and Kevin Mallinger

Soil environments are naturally governed by a multitude of interdependent chemical, biological, and physical processes that define their macro-state. In the context of farming these features are further complemented and affected by anthropogenic activities (ploughing, fertilizing, use of pesticides, etc.) that systematically aim to change soil and plant environments to enhance yield, but often with unforeseen detrimental effects (biodiversity loss, erosion, etc.). Assessing strategies for sustainable environmental management is therefore a highly challenging task that is often accompanied by incomplete knowledge of systemic feedback mechanisms and a lack of continuous and reliable data. 

To address this issue, we investigate the use of complexity metrics from information theory to gain insights about underlying patterns of multivariate soil systems and their potential implications for time series analysis. Here we apply existing methods for the processing and analysis of similar systems, we verify current theories about the dynamics and mechanisms of ecological processes in time and study innate interactions between separate components. Thereby, we will use available agricultural datasets that display a wide range of soil properties and explore several notions of complexity approaches, such as entropy measures (e.g., Permutation entropy, transfer entropy, Shannon entropy) and the Hurst exponent. Characteristic features will be highlighted that can be used to enhance time series prediction accuracy and systemic soil functions understanding.

How to cite: Corpaci, L. C., Raubitzek, S., and Mallinger, K.: Information theory approach for enhancing time series analysis and predictability of soil environments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4076, https://doi.org/10.5194/egusphere-egu22-4076, 2022.

EGU22-4933 | Presentations | HS1.3.2

Integrating historical information, systematic data, and rainfall-runoff modelling to improve flood frequency estimates 

Miguel Angel Fernandez-Palomares and Luis Mediero

Abstract

Flood frequency curves are usually fitted to short time series of observations, leading to great uncertainties mainly for high return periods. However, reliable estimates are required for designing and assessing safety of hydraulic infrastructure, such as bridges and dams. Therefore, flood frequency analyses based on instrumental data collected at gauging stations can be improved by incorporating available information about historical floods before the beginning of the systematic period. This study presents how to identify and integrate all the information available, in order to improve flood frequency curve estimates. The Cuevas de Almanzora Dam located in southeast Spain is selected as case study.

The Cuevas de Almanzora Dam catchment has an area of 2122 km2 with a mean annual precipitation of 316 mm. However, daily precipitation can be higher, such as 600 mm for the 1973 flood event. Flood data are available at a gauging station located in the River Almanzora upstream of the dam, with a draining catchment of 1850 km2. The systematic period is 1963-2008 with information about 36 annual maximum floods. The largest flood in the 20th century was recorded at the gauging station in 1973. A two-dimensional (2D) hydrodynamic model of the River Almanzora was calibrated with such information.

Historical information about floods has been collated from local newspapers, books, chronicles, research papers, photographs, national archives of historical floods, and municipal archives. The three largest floods in the River Almanzora between 1830 and 1963 were identified, extending the systematic period to a total period of 191 years. Information about water depths and flood extensions at different cross sections of the River Almanzora were collected. The 2D hydrodynamic model was used to estimate the peak discharges in such historical flood events.

After the end of the systematic period, the hydrograph of the great 2012 flood event was estimated from the data recorded at the Cuevas de Almanzora reservoir. A rainfall-runoff model was calibrated in the catchment with 1-h precipitation data to estimate the flood hydrograph at the gauging station.

The five historical floods that exceed the perception threshold in the period 1830-2020 were integrated with the annual maximum floods extracted from the systematic data, using five techniques to incorporate historical information in the flood frequency curve. The Generalized Extreme Value (GEV) and the Two-Component Extreme Value (TCEV) distribution functions were considered. The best fit was selected considering the accuracy and the uncertainty of estimates by a stochastic procedure. Flood quantiles for the highest return periods triple the estimates obtained by using only the systematic data.

The methodology proposed can improve the reliability of flood quantile estimates, mainly in arid regions where the lack of information about the rare greatest flood, which can exceed several times the mean magnitude of floods in the systematic period, can lead to strong underestimates for the highest return periods that are needed to design and assess the safety of hydraulic infrastructure.

Acknowledgments: This research has been supported by the project SAFERDAMS (PID2019-107027RB-I00) funded by the Spanish Ministry of Science and Innovation.

How to cite: Fernandez-Palomares, M. A. and Mediero, L.: Integrating historical information, systematic data, and rainfall-runoff modelling to improve flood frequency estimates, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4933, https://doi.org/10.5194/egusphere-egu22-4933, 2022.

Beach cast is a material deposited on beaches after being washed up by storm (or tidal movement). The composition of beach cast usually includes seagrass or algae fragments, wracks of land plants and other materials of natural origin, (anthropogenic) marine litter, including plastic debris and microplastics. Beach casts monitoring is of current interest for beach management and maintenance of the sandy shores for recreational purposes, tracing marine litter transport and dispersion, evaluating environmental contamination by microplastiсs.

Large patches of marine debris appear on beaches after stormy weather. However, little is known about the sea state that precedes the formation of beach casts. From an observer's point of view, beach casts occur at random locations along the coast at unpredictable times. They may even be washed back to the sea at some time later. This work aims to disclose characteristic features of temporal variations of surface wave field parameters, which lead to beach cast formation.

Results of incidental surveys of the northern coast of the Sambia Peninsula, stretching from west to east in the southeastern part of the Baltic Sea, were analyzed. The presence of beach cast (at one or more locations) was observed during 234 days of 2011-2021. Some of the observations were performed during or shortly after the ending of the beaching process. Field information was collated with a freely available re-analysis database on surface waves (http://marine.copernicus.eu). Surface wave spectrum parameters were picked up from the database at the geographical point corresponding to the coastal zone's open-sea limit. Elements of Bayesian analysis were applied to overcome the lack of information on the very time of the beach casts formation and/or the unknown duration of the beaching process.

The analysis shows the values of significant wave height, peak period, and wave direction, which occurred before the beach cast appearance more often than follows from the overall time statistics ("climate"). A separate analysis of only recently formed beach casts made it possible to determine the evolution of wave spectrum parameters during the beaching process. Data suggests that most of the beach cast events on this coast are preceded by waves caused by cyclone passages from the northern direction.

Data analysis is carried out by I.I., with the support of the Russian Science Foundation, grant No 21-77-00027. Beach surveys are carried out by E.E. voluntarily and with partial support from IO RAS state assignment.

How to cite: Isachenko, I. and Esiukova, E.: Analysis of wind wave statistics preceding beach cast events on the southeastern shore of the Baltic Sea (Kaliningrad region): preliminary results, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5690, https://doi.org/10.5194/egusphere-egu22-5690, 2022.

EGU22-6761 | Presentations | HS1.3.2

Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data 

Shervan Gharari, Hoshin Gupta, Martyn Clark, Markus Hrachowitz, Fabrizio Fenicia, Patrick Matgen, and Hubert Savenije

Process-based hydrological models seek to represent the dominant hydrological processes in a catchment. However, due to unavoidable incompleteness of knowledge, the construction of “fidelius” process-based models depends largely on expert judgment. We present a systematic approach that treats models as hierarchical assemblages of hypotheses (conservation principles, system architecture, process parameterization equations, and parameter specification), which enables investigating how the hierarchy of model development decisions impacts model fidelity. Each model development step provides information that progressively changes our uncertainty (increases, decreases, or alters) regarding the input-state-output behavior of the system. Following the principle of maximum entropy, we introduce the concept of “minimally restrictive process parameterization equations—MR-PPEs,” which enables us to enhance the flexibility with which system processes can be represented, and to thereby investigate the important role that the system architectural hypothesis (discretization of the system into subsystem elements) plays in determining model behavior. We illustrate and explore these concepts with synthetic and real-data studies, using models constructed from simple generic buckets as building blocks, thereby paving the way for more-detailed investigations using sophisticated process-based hydrological models. We also discuss how proposed MR-PPEs can bridge the gap between current process-based modeling and machine learning. Finally, we suggest the need for model calibration to evolve from a search over “parameter spaces” to a search over “function spaces.”

How to cite: Gharari, S., Gupta, H., Clark, M., Hrachowitz, M., Fenicia, F., Matgen, P., and Savenije, H.: Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6761, https://doi.org/10.5194/egusphere-egu22-6761, 2022.

EGU22-8321 | Presentations | HS1.3.2 | Highlight

Partitioning of green-blue water fluxes around the world: ML model explainability and predictability 

Daniel Althoff and Georgia Destouni

The consequences of ever-increasing human interference with freshwater systems, e.g., through land-use and climate changes, are already felt in many regions of the world, e.g., by shifts in freshwater availability and partitioning between green (evapotranspiration) and blue (runoff) water fluxes around the world. In this study, we have developed a machine learning (ML) model for the possible prediction of green-blue water flux partitioning (WFP) under different climate, land-use, and other landscape and hydrological catchment conditions around the world. ML models have shown relatively high predictive performance compared to more traditional modelling methods for several tasks in geosciences. However, ML is also rightly criticized for providing theory-free “black-box” models that may fail in predictions under forthcoming non-stationary conditions. We here address the ML model interpretability gap using Shapley values, an explainable artificial intelligence technique. We also assess ML model predictability using a dissimilarity index (DI). For ML model training and testing, we use different parts of a total database compiled for 3482 hydrological catchments with available data for daily runoff over at least 25 years. The target variable of the ML model is the blue-water partitioning ratio between average runoff and average precipitation (and the complementary, water-balance determined green water partitioning ratio) for each catchment. The predictor variables are hydro-climatic, land-cover/use, and other catchment indices derived from precipitation and temperature time series, land cover maps, and topography data. As a basis for the ML modelling, we also investigate and quantify (through data averaging over moving sub-periods of different time lengths) a minimum temporal aggregation scale for water flux averaging (referred to as the flux equilibration time, Teq) required to reach a stable temporal average runoff (and evapotranspiration) fraction of precipitation in each catchment; for 99% of catchments, Teq is found to be ≤2 years, with longer Teq emerging for catchments estimated to have higher ratio Rgw/Ravg, i.e., higher groundwater flow contribution (Rgw) to total average runoff (Ravg). The cubist model used for the ML modelling yields a Kling-Gupta efficiency of 0.86, while the Shapley values analysis indicates mean annual precipitation and temperature as the most important variables in determining the WFP, followed by average slope in each catchment. A DI threshold is further used to label new data points as inside or outside the ML model area of applicability (AoA). Comparison between test data points outside and inside the AoA reveals which catchment characteristics are mostly responsible for ML model loss of predictability. Predictability is lower for catchments with: larger Teq and Rgw/Ravg; higher phase lag between peak precipitation and peak temperature over the year; lower forest and agricultural land fractions; and aridity index much higher or much lower than 1 (implying major water or energy limitation, respectively). Identifying such predictability limits is crucial for understanding, and facilitating user awareness of the applicability and forecasting ability of such data-driven ML modelling under different prevailing and changing future hydro-climatic, land-use, and groundwater conditions.

How to cite: Althoff, D. and Destouni, G.: Partitioning of green-blue water fluxes around the world: ML model explainability and predictability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8321, https://doi.org/10.5194/egusphere-egu22-8321, 2022.

EGU22-8372 | Presentations | HS1.3.2 | Highlight

Deciphering Hydroclimatic Complexity with Information Physics and Quantum Technologies 

Rui A. P. Perdigão and Julia Hall

Discerning the dynamics of complex systems in a mathematically rigorous and physically consistent manner is as fascinating as intimidating of a challenge, stirring deeply and intrinsically with the most fundamental Physics, while at the same time percolating through the deepest meanders of everyday life.

The socio-natural coevolution in hydroclimate dynamics is an example of that, exhibiting a striking articulation between governing principles and free will, in a stochastic-dynamic resonance that goes way beyond a reductionist dichotomy between deterministic and probabilistic approaches and between physical principles and information technologies.

Subjacent to the conceptual and operational interdisciplinarity of that challenge, lies the simple formal elegance of a “lingua franca” for communication with Nature. This emerges from the innermost mathematical core of Information Physics articulating the wealth of insights and flavours from frontier natural, social and technical sciences in a coherent, integrated manner.

Communicating thus with Nature, we equip ourselves by developing formal innovative methodologies and technologies to better appreciate and discern complexity in articulation with expert knowledge. Thereby opening new pathways to assess and predict elusive non-recurrent phenomena such as irreversible geophysical transformations and extreme hydro-meteorological events in a coevolutionary climate.

Our novel advances will be shared across the formal, structural and functional theory of the Information Physics of Coevolutionary Complex Systems, along with the analysis, modelling and decision support in crucial matters afflicting our environment and society, with special emphasis onto hydroclimatic problems.

In an optic of operational empowerment, some of our flagship initiatives will be addressed such as Earth System Dynamic Intelligence and Quantum Information Technologies in the Earth Sciences (QITES) on a synergy among our information physical and quantum technological developments.

The articulation between these flagships leverages our proprietary synergistic quantum gravitational and electrodynamic QITES constellation from deep undersea to outer space to take the pulse of our planet, ranging from high resolution 4D sensing and computation to unveiling early warning signs of critical transitions and extreme events.

How to cite: Perdigão, R. A. P. and Hall, J.: Deciphering Hydroclimatic Complexity with Information Physics and Quantum Technologies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8372, https://doi.org/10.5194/egusphere-egu22-8372, 2022.

EGU22-10119 | Presentations | HS1.3.2

Life cycles of glacial lakes in Norway: Insights from machine learning algorithms on Landsat series and Sentinel-2 

Ghazal Moghaddam, Liss Marie Andreassen, and Irina Rogozhina

The observed retreat of mountain glaciers on a global scale promotes the formation and growth of glacial lakes across newly exposed ice-free areas. In mainland Norway, this process drives the rise in glacial lake outburst floods (GLOFs), posing a considerable threat to people and infrastructure  downstream. Moreover, many glacial lakes are used as reservoirs for hydropower production and thus represent an important energy source, emphasizing the need for continuous monitoring of glacial lake life cycles.

Remote sensing is currently the most efficient technique for tracking changes in glacial lakes, understanding their responses to climate change and observing lakes prone to GLOFs. Recent advances in machine learning techniques have presented new opportunities to automatize glacial lake mapping over large areas. For the first time, this study presents a Norway-wide reconstruction of glacial lake changes through the last three decades using  machine learning algorithms and long-term satellite observations. It contrasts the performance of two classification methods - maximum likelihood  classification (MLC) and support vector machine (SVM) - to outline glacial lakes and study their evolution using the Landsat series and Sentinel-2 images.

This study zooms into the pros and cons of each classification method and satellite product through the prism of glacial lake processes occurring over  disparate temporal and spatial scales - from lake formation, growth and dissociation from the proximal glaciers to the aftermath of rapid GLOF events. Based on this analysis, I conclude that the recognition skills of supervised classification methods largely depend on the quality of satellite images and careful selection of training samples. Some of the factors that adversely affect the classification results are unfavourable weather conditions such as  cloud, snow and ice cover, image disturbances through atmospheric corrections and shadows on slopes that lead to misclassifications. Regardless of higher spatial and temporal resolution, Sentinel imagery has not revealed significant advantages over Landsat but has shown a potential for their  complementary use to continue glacial lake observations in the future. The performance of SVM is clearly superior to MLC, but it is difficult to use over  large spatial scales, at least in the form it is currently implemented in ENVI.

How to cite: Moghaddam, G., Andreassen, L. M., and Rogozhina, I.: Life cycles of glacial lakes in Norway: Insights from machine learning algorithms on Landsat series and Sentinel-2, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10119, https://doi.org/10.5194/egusphere-egu22-10119, 2022.

EGU22-10890 | Presentations | HS1.3.2

One Saddle Point and Two Types of Sensitivities Within the Lorenz 1963 and 1969 Models 

Bo-Wen Shen, Roger Pielke, Sr., and Xubin Zeng

The fact that both the Lorenz 1963 and 1969 models suggest finite predictability is well-known. However, it is less known that mechanisms (i.e., sensitivities) within both models that lead to finite predictability are different. Additionally, the mathematical and physical relationship between these two models has not been fully documented. New analyses along with literature review are performed here to provide insights on the similarities and differences for these two models. The models represent different physical systems, one for convection and the other for barotropic vorticity. From theperspective of mathematical complexities, the Lorenz 1963 (L63) model is limited-scale and nonlinear; and the Lorenz 1969 (L69) model is closure-based, physically multiscale, mathematically linear, and numerically ill-conditioned. The former possesses a sensitive dependence of solutions on initial conditions, known as the butterfly effect, and the latter contains numerical sensitivities due to an ill-conditioned matrix with a large condition number (i.e., a large variance of growth rates).

Here, we illustrate that the existence of a saddle point at the origin is a common feature that produces instability in both systems. Within the chaotic regime of the L63 nonlinear model, unstable growth is constrained by nonlinearity, as well as dissipation, yielding time varying growth rates along an orbit, and, thus, a dependence of (finite) predictability on initial conditions. Within the L69 linear model, multiple unstable modes at various growth rates appear, and the growth of a specific unstable mode (i.e., the most unstable mode during a finite time interval) is constrained by imposing a saturation assumption, thereby yielding a time varying system growth rate. Both models have been interchangeably applied for qualitatively revealing the nature of finite predictability in weather and climate. However, only single type solutions were examined (i.e., chaotic and linearly unstable solutions for the L63 and L69 models, respectively), and the L69 system is ill-conditioned and easily captures numerical instability. Thus, an estimate of the predictability limit using either of the above models, with or without additional assumptions (e.g., saturation), should be interpreted with caution and should not be generalized as an upper limit for predictability of the atmosphere.

How to cite: Shen, B.-W., Pielke, Sr., R., and Zeng, X.: One Saddle Point and Two Types of Sensitivities Within the Lorenz 1963 and 1969 Models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10890, https://doi.org/10.5194/egusphere-egu22-10890, 2022.

EGU22-11148 | Presentations | HS1.3.2 | Highlight

Disentangling direct and indirect soil moisture effects onecosystem carbon uptake with Causal Modeling 

Christian Reimers, Alexander Winkler, Vincent Humphrey, and Markus Reichstein

Soil moisture affects gross primary production through two pathways. First, directly through
drought stress and second, indirectly through temperature via evaporative cooling of the near-
surface atmospheric layer. Because it is not possible to disentangle these effects experimentally
at a biome level, Humphrey et al. (2021) has used Earth system model experiments in which soil
moisture is fixed to its seasonal cycle and evaluated the effects on gross primary production. In
contrast, we aim to use causal modeling to infer impacts directly from observation. To predict the
effects of soil moisture anomalies on gross primary production, we extend existing causal mod-
eling frameworks to cover situations where two variables influence one other. A major challenge
in applying causal modeling here lies in the bidirectional relationship between soil moisture and
temperature via evapotranspiration. On one hand, higher temperature leads to higher evapotran-
spiration and thus lower soil moisture. On the other hand, lower soil moisture leads to lower evap-
otranspiration and thus higher temperatures. Therefore, neither soil moisture nor temperature can
be adequately modeled as a function of the other. To address this challenge, we extend existing
causal modeling frameworks to account for these situations where the variables are not functions
of each other but are determined by equilibrium. We show that our method identifies the correct
links between variables in synthetic data. We further evaluate whether our new approach is con-
sistent with the results of Humphrey et al. (2021) based on idealized counterfactual experiments
using Earth system models. To this end, we use the control runs of the models to directly predict
the results of the idealized counterfactual experiment as proof-of-concept. Finally, we apply our
method to observations and determine the direct and indirect effect of soil moisture anomalies on
gross primary production.

References:
Vincent Humphrey, Alexis Berg, Philippe Ciais, Pierre Gentine, Martin Jung, Markus Reichstein,
Sonia I Seneviratne, and Christian Frankenberg. Soil moisture–atmosphere feedback dominates
land carbon uptake variability. Nature, 592(7852):65–69, 2021.

How to cite: Reimers, C., Winkler, A., Humphrey, V., and Reichstein, M.: Disentangling direct and indirect soil moisture effects onecosystem carbon uptake with Causal Modeling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11148, https://doi.org/10.5194/egusphere-egu22-11148, 2022.

EGU22-12346 | Presentations | HS1.3.2

Configuration entropy analysis of river water quality dynamics under fine time resolution and network topology 

Tianrui Pang, Jiping Jiang, Peng Wang, Yi Zheng, and Tong Zheng

The water environment is an important carrier of material processes, in which a large number of biochemical reactions and energy transmission processes occur. High-frequency water quality observation can help us understand the dynamics of solute transport in the water environment. The information-theoretic approaches to system dynamics are receiving more and more attention that it reveals the new laws and support board applications. Configuration entropy (H*) is one of the derivative indexes that originated from information entropy, which was first introduced in 1994 to describe the disorder in random morphologies. It can reflect the complexity of the system under different space or time resolutions. Researchers have analyzed the characteristics of configuration entropy in some of the environment scenarios, such as spatial arrangement of rainfall. In this paper, we analyzed the space structure of river basin water quality dynamic system under the network topology of rivers, together with the time structure of water quality dynamic system. We calculated the configuration entropy of six water quality parameter data from four monitoring stations at Potomac River in two dimensions of time and space with topological treatment of river water system map. We arranged the high-frequency water quality time series according to different time slices to form a two-dimensional pixel image for calculating configuration entropy and the variation under different time resolutions. Results show that with the increasing length of time slice (from 1 day to 9 days), except pH and turbidity, the configuration entropy curve of other parameters has only one peak (1 day, 1.5 days, 2 days) to the valley (2.5 days and later), which confirms a hypothesis that the configuration entropy will not have a valley when the length of time grid is significantly greater than the width. When the length of the time slice is more than 2.5 days, even if the length of the time slice is increased, the overall shape of configuration entropy curve does not change significantly, suggesting that the configuration entropy of specific water quality parameters did not show temporal heterogeneity in a long-time period observation. We also assumed that temporal fractal phenomena exist in some water quality parameters consistent with previous studies. More analysis is in progress.

How to cite: Pang, T., Jiang, J., Wang, P., Zheng, Y., and Zheng, T.: Configuration entropy analysis of river water quality dynamics under fine time resolution and network topology, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12346, https://doi.org/10.5194/egusphere-egu22-12346, 2022.

EGU22-12588 | Presentations | HS1.3.2

A complex network perspective on catchment water quality dynamics: characteristics, pattern, and drivers 

Qingzhi Wen, Jiping Jiang, and Bellie Sivakumar

Understanding the connections in the dynamics of water quality at different locations in a catchment is important for catchment studies and watershed management. Complex network science provides effective ways to uncover connections and patterns in catchment water quality dynamics. This study  investigates the spatial connections in each of five water quality indexes (Chloride, Dissolved oxygen, pH, Total nitrogen, and Total organic carbon) and flow rate in the Chesapeake Bay basin, USA.High-resolution data (five minutes) from 120 water quality monitoring stations are analyzed. 1) The clustering coefficient (CC) and degree distribution methods are employed to examine the connections and identify the type of the water quality networks. The results indicate that the networks of water quality parameters are  scale-free. The power-law (γ) values of for the networks of Chl, DO, flow rate, pH, TN, TOC are 0.74, 0.67, 0.37, 2.0, 0.57 and 1.2, respectively. 2) Monte Carlo simulation of degree distributions and clustering coefficients (CC) shown that all water quality parameters present a decrease in the CC along with the turn down of the threshold of correlation coefficient (R), but the R threshold for DO and flow rate was 0.9. Other water quality parameters showed a sharp decline in the range of correlation coefficient (R) of 0.3-0.6, show a gentle decrease, and then decrease sharply, with an inverse s-curve. 3) All the WQ parameters show stable patterns of CC versus R, for different sizes of networks, arrived by randomly reducing the number of nodes (i.e. stations) of the networks. This seems to indicate that the pattern is an internal systemically feature of the networks, regardless of the node selected for analysis. The variations of CC values for the different stations in the networks  with different R values also help identify the “heat area” of the whole catchment, which  has some nodes with stable large CC. For the different water quality parameters, the heat area is basically the same, except for pH and TN for which the area is much smaller. The present findings on the characteristics, patterns, and drivers of water quality dynamics in catchments have important implications for water quality studies, especially in large networks of monitoring stations.

How to cite: Wen, Q., Jiang, J., and Sivakumar, B.: A complex network perspective on catchment water quality dynamics: characteristics, pattern, and drivers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12588, https://doi.org/10.5194/egusphere-egu22-12588, 2022.

EGU22-13100 | Presentations | HS1.3.2 | Highlight

Machine Learning Methods with the Standardized VPD Drought Index to Identify and Assess Drought in the United States 

Brandi Gamelin, Vishwas Rao, Julie Bessac, and Mustafa Altinakar

Extreme drought has a strong socio-economic impact on the human environment, especially where surface and ground water supplies are significantly reduced due to reduced stream flow, reduced hydroelectric generation, and increased ground water pumping for agricultural and human consumption. This reduction will likely increase in the future as drought is expected to increase in the United States due to global warming and climate change. However, identifying drought is problematic due to the lack of standardized classification or reliable methods for drought prediction. Recently, machine learning techniques have been applied to drought indices to identify drought features and for risk assessment. For this work, we utilize unsupervised machine learning (ML) computational algorithms to identify drought characteristics with a new drought index based on vapor pressure deficit (VPD). The Standardized VPD Drought Index (SVDI) is used to cluster points with common features to characterize spatial and temporal drought characteristics. The SVDI is calculated with the NASA’s Land Surface Assimilation System (NLDAS) data from 1990-2010. Several ML cluster techniques (e.g. HMM, k_means, BIRCH, DBSCAN) are applied to the SVDI to identify known short and long term drought events. Optimized techniques will be applied to downscaled global climate models (e.g. CCSM4, GFDL-ESM2G, and HadGEM2-ES) based on the 8.5 Representative Concentration Pathway (RCP8.5). From the space-time clustering algorithm, we will extract the spatiotemporal information for each identified event as a means of determining the probability of each type of event under global warming in the future.

How to cite: Gamelin, B., Rao, V., Bessac, J., and Altinakar, M.: Machine Learning Methods with the Standardized VPD Drought Index to Identify and Assess Drought in the United States, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13100, https://doi.org/10.5194/egusphere-egu22-13100, 2022.

NP3 – Scales, Scaling and Nonlinear Variability

EGU22-1024 | Presentations | ITS3.5/NP3.1

Efficiency and synergy of simple protective measures against COVID-19: Masks, ventilation and more 

Ulrich Pöschl, Yafang Cheng, Frank Helleis, Thomas Klimach, and Hang Su

The public and scientific discourse on how to mitigate the COVID-19 pandemic is often focused on the impact of individual protective measures, in particular on vaccination. In view of changing virus variants and conditions, however, it seems not clear if vaccination or any other protective measure alone may suffice to contain the transmission of SARS-CoV-2. Accounting for both droplet and aerosol transmission, we investigated the effectiveness and synergies of vaccination and non-pharmaceutical interventions like masking, distancing & ventilation, testing & isolation, and contact reduction as a function of compliance in the population. For realistic conditions, we find that it would be difficult to contain highly contagious SARS-CoV-2 variants by any individual measure. Instead, we show how multiple synergetic measures have to be combined to reduce the effective reproduction number (Re) below unity for different basic reproduction numbers ranging from the SARS-CoV-2 ancestral strain up to measles-like values (R0 = 3 to 18).

Face masks are well-established and effective preventive measures against the transmission of respiratory viruses and diseases, but their effectiveness for mitigating SARS-CoV-2 transmission is still under debate. We show that variations in mask efficacy can be explained by different regimes of virus abundance (virus-limited vs. virus-rich) and are related to population-average infection probability and reproduction number. Under virus-limited conditions, both surgical and FFP2/N95 masks are effective at reducing the virus spread, and universal masking with correctly applied FFP2/N95 masks can reduce infection probabilities by factors up to 100 or more (source control and wearer protection).

Masks are particularly effective in combination with synergetic measures like ventilation and distancing, which can reduce the viral load in breathing air by factors up to 10 or more and help maintaining virus-limited conditions. Extensive experimental studies, measurement data, numerical calculations, and practical experience show that window ventilation supported by exhaust fans (i.e. mechanical extract ventilation) is a simple and highly effective measure to increase air quality in classrooms. This approach can be used against the aerosol transmission of SARS-CoV-2. Mechanical extract ventilation (MEV) is very well suited not only for combating the COVID19 pandemic but also for sustainably ventilating schools in an energy-saving, resource-efficient, and climate-friendly manner.  Distributed extract ducts or hoods can be flexibly reused, removed and stored, or combined with other devices (e.g. CO2 sensors), which is easy due to the modular approach and low-cost materials (www.ventilationmainz.de).

The scientific findings and approaches outlined above can be used to design, communicate, and implement efficient strategies for mitigating the COVID-19 pandemic.

References:

Cheng et al., Face masks effectively limit the probability of SARS-CoV-2 transmission, Science, 372, 1439, 2021, https://doi.org/10.1126/science.abg6296 

Klimach et al., The Max Planck Institute for Chemistry mechanical extract ventilation (MPIC-MEV) system against aerosol transmission of COVID-19, Zenodo, 2021, https://doi.org/10.5281/zenodo.5802048  

Su et al., Synergetic measures to contain highly transmissible variants of SARS-CoV-2, medRxiv, 2021, https://doi.org/10.1101/2021.11.24.21266824

 

How to cite: Pöschl, U., Cheng, Y., Helleis, F., Klimach, T., and Su, H.: Efficiency and synergy of simple protective measures against COVID-19: Masks, ventilation and more, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1024, https://doi.org/10.5194/egusphere-egu22-1024, 2022.

EGU22-1890 | Presentations | ITS3.5/NP3.1

Possible effect of the particulate matter (PM) pollution on the Covid-19 spread in southern Europe 

Jean-Baptiste Renard, Gilles Delaunay, Eric Poincelet, and Jérémy Surcin

The time evolution of the Covid-19 death cases exhibits several distinct episodes since the start of the pandemic early in 2020. We propose an analysis of several Southern Europe regions that highlights how the beginning of each episode correlates with a strong increase in the concentrations level of pollution particulate matter smaller than 2.5 µm (PM2.5). Following the original PM2.5 spike, the evolution of the Covid-19 spread depends on the (partial) lockdowns and vaccinate races, thus the highest level of confidence in correlation can only be achieved when considering the beginning of each episode. The analysis is conducted for the 2020-2022 period at different locations: the Lombardy region (Italy), where we consider the mass concentrations measurements obtained by air quality monitoring stations (µg.m-3), and the cities of Paris (France), Lisbon (Portugal) and Madrid (Spain) using in-situ measurements counting particles (cm-3) in the 0.5-2.5 µm size range obtained with hundreds of mobile aerosol counters. The particle counting methodology is more suitable to evaluate the possible correlation between PM pollution and Covid-19 spread because we can better estimate the concentration of the submicronic particles compared with a mass concentration measurement methodology which would result in skewed results due to larger particles. Very fine particles of lesser than one micron go deeper inside the body and can even cross the alveolar-capillary barrier, subsequently attacking most of the organs through the bloodstream, potentially triggering a pejorative systemic inflammatory reaction. The rapidly increasing number of deaths attributed to the covid-19 starts between 2 weeks and one month after PM events that often occur in winter, which is coherent with the virus incubation time and its lethal outcome. We suggest that the pollution by the submicronic particles alters the pulmonary alveoli status and thus significantly increase the lungs susceptibility to the virus.

How to cite: Renard, J.-B., Delaunay, G., Poincelet, E., and Surcin, J.: Possible effect of the particulate matter (PM) pollution on the Covid-19 spread in southern Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1890, https://doi.org/10.5194/egusphere-egu22-1890, 2022.

In the past two years, numerous advances have been made in the ability to predict the progress of COVID19 epidemics.  Basic forecasting of the health state of a population with respect to a given disease is based on the well-known family of SIR models (Susceptible Infected Recovered). The models used in epidemiology were based on deterministic behavior, so the epidemiological picture tomorrow depends exclusively on the numbers recorded today. The forecasting shortcomings of the deterministic SEIR models previously used in epidemiology were difficult to highlight before the advent of COVID19  because epidemiology was mostly not concerned with real-time forecasting.  From the first wave of COVID19 infections, the limitations of using deterministic models were immediately evident: to use them, one should know the exact status of the population and this knowledge was limited by the ability to process swabs. Futhermore, there is an intrinsic variability of the dynamics which depends on age, sex, characteristics of the virus, variants and vaccination status. 

Our main contribution was to define a SEIR model that assumes these parameters as constants could not be used for reliable predictions of COVID19 pandemis and that more realistic forecasts can be obtained by adding fluctuations in the model. The fluctuations in the dynamics of the virus induced by these factors do not just add variaiblity around the deterministic solution of the SIR models, the also introduce another timing of the pandemics which influence the epidemic peak. With our model we have found that even with a basic reprdocution number Rt less than 1 local epidemic peaks can occur that resume over a certain period of time. 

Introducing noise and uncertainty allows  to define a range of possible scenarios, instead of making a single prediction. This is what happens when we replace the deterministic approach, with a probabilistic approach. The probabilistic models used to predict the progress of the Covid-19 epidemic are conceptually very similar to those used by climatologists, to imagine future environmental scenarios based on the actions taken in the present.  As human beings we can intervene in both systems. Based on the choices we will make and the fluctuations of the systems, we can predict different responses. In the context of the emergency that we faced, the collaboration between different scientific fields was therefore fundamental, which, by comparing themselves, were able to provide more accurate answers. Furthermore, a close collaboration has arisen between epidemiologists and climatologists. A beautiful synergy that can give a great help to society in a difficult moment.

References

-Faranda, Castillo, Hulme, Jezequel, Lamb, Sato & Thompson (2020). Chaos: An Interdisciplinary Journal of Nonlinear Science30(5), 051107.

-Alberti & Faranda (2020).  Communications in Nonlinear Science and Numerical Simulation90, 105372.

-Faranda & Alberti (2020). Chaos: An Interdisciplinary Journal of Nonlinear Science30(11), 111101.

-Faranda, Alberti, Arutkin, Lembo, Lucarini. (2021).  Chaos: An Interdisciplinary Journal of Nonlinear Science31(4), 041105.

-Arutkin, Faranda, Alberti, & Vallée. (2021). Chaos: An Interdisciplinary Journal of Nonlinear Science31(10), 101107.

How to cite: Faranda, D.: How concepts and ideas from Statistical and Climate physics improve epidemiological modelling of the COVID 19 pandemics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2801, https://doi.org/10.5194/egusphere-egu22-2801, 2022.

EGU22-3690 | Presentations | ITS3.5/NP3.1

Improving the conservation of virus infectivity during airborne exposure experiments 

Ghislain Motos, Kalliopi Violaki, Aline Schaub, Shannon David, Tamar Kohn, and Athanasios Nenes

Recurrent epidemic outbreaks such as the seasonal flu and the ongoing COVID-19 are disastrous events to our societies both in terms of fatalities, social and educational structures, and financial losses. The difficulty to control COVID-19 spread in the last two years has brought evidence that basic mechanisms of transmission for such pathogens are still poorly understood.

             Three different routes of virus transmission are known: direct contact (e.g. through handshakes) and indirect contact through fomites; ballistic droplets produced by speaking, sneezing or coughing; and airborne transmission through aerosols which can also be produced by normal breathing. The latter route, which has long been ignored, even by the World Health Organization during the COVID-19 pandemics, now appears to play the predominant role in the spread of airborne diseases (e.g. Chen et al., 2020).

             Further scientific research thus needs to be conducted to better understand the mechanistic processes that lead to inactivate airborne viruses, as well as the environmental conditions which favour these processes. In addition to modelling and epidemiological studies, chamber experiments, where viruses are exposed to various types of humidity, temperature and/or UV dose, offer to simulate everyday life conditions for virus transmission. However, the current standard instrumental solutions for virus aerosolization to the chamber and sampling from it use high fluid forces and recirculation which can cause infectivity losses (Alsved et al., 2020) and also do not compare to the relevant production of airborne aerosol in the respiratory tract.

             In this study, we utilized two of the softest aerosolization and sampling techniques: the sparging liquid aerosol generator (SLAG, CH Technologies Inc., Westwood, NJ, USA), which forms aerosol from a liquid suspension by bubble bursting, thus mimicking natural aerosol formation in wet environments (e.g. the respiratory system but also lakes, sea, toilets, etc…); and the viable virus aerosol sampler (BioSpot-VIVAS, Aerosol Devices Inc., Fort Collins, CO, USA), which grows particle via water vapour condensation to gently collect them down to a few nanometres in size. We characterized these systems with particle sizers and biological analysers using non-pathogenic viruses such as bacteriophages suspended in surrogate lung fluid and artificial saliva. We compared the size distribution of produced aerosol from these suspensions against similar distributions generated with standard nebulizers, and assess the ability of these devices to produce aerosol that much more resembles that produced in human exhaled air. We also assess the conservation of viral infectivity with the VIVAS vs. conventional biosamplers.

 

Acknowledgment

 

We acknowledge the IVEA project in the framework of SINERGIA grant (Swiss National Science Foundation)

 

References

 

Alsved, M., Bourouiba, L., Duchaine, C., Löndahl, J., Marr, L. C., Parker, S. T., Prussin, A. J., and Thomas, R. J. (2020): Natural sources and experimental generation of bioaerosols: Challenges and perspectives, Aerosol Science and Technology, 54, 547–571.

Chen, W., Zhang, N., Wei, J., Yen, H.-L., and Li, Y. (2020): Short-range airborne route dominates exposure of respiratory infection during close contact, Building and Environment, 176, 106859.

How to cite: Motos, G., Violaki, K., Schaub, A., David, S., Kohn, T., and Nenes, A.: Improving the conservation of virus infectivity during airborne exposure experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3690, https://doi.org/10.5194/egusphere-egu22-3690, 2022.

EGU22-3936 | Presentations | ITS3.5/NP3.1

COVID-19 effects on measurements of the Earth Magnetic Field in the urbanized area of Brest 

Jean-Francois Oehler, Alexandre Leon, Sylvain Lucas, André Lusven, and Gildas Delachienne

COVID-19 effects on measurements of the Earth Magnetic Field in the urbanized area of Brest (Brittany, France)

Jean-François OEHLER1, Sylvain LUCAS1, Alexandre LEON1, André LUSVEN1, Gildas DELACHIENNE1

1Shom (Service Hydrographique et Océanographique de la Marine), Brest, France

 

Since September 2019, Shom’s Magnetic Station (SMS) has been deployed in the north neighbourhoods of the medium-sized city of Brest (Brittany, France, about 210,000 inhabitants). SMS continuously measures the intensity of the Earth Magnetic Field (EMF) with an absolute Overhauser sensor. The main goal of SMS is to derive local external variations of the EMF mainly due to solar activity. These variations consist of low and high parasitic frequencies in magnetic data and need to be corrected. Magnetic mobile stations or permanent observatories are usually installed in isolated areas, far from human activities and electromagnetic effects. It is clearly not the case for SMS, mainly for practical reasons of security, maintenance and data accessibility. However, despite its location in an urbanized area, SMS stays the far western reference station for processing marine magnetic data collected along the Atlantic and Channel coasts of France.

The corona pandemic has had unexpected consequences on the quality of measurements collected by SMS. For example, during the French first lockdown between March and May 2020, the noise level significantly decreased of about 50%. Average standard deviations computed on 1 Hz-time series over 1 min. periods fell from about 1.5 nT to 0.8 nT. This more stable behavior of SMS is clearly correlated with the drop of human activities and traffic in the city of Brest.

 

Keywords: Shom’s Magnetic Station (SMS), Earth Magnetic Field, COVID19.

 

How to cite: Oehler, J.-F., Leon, A., Lucas, S., Lusven, A., and Delachienne, G.: COVID-19 effects on measurements of the Earth Magnetic Field in the urbanized area of Brest, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3936, https://doi.org/10.5194/egusphere-egu22-3936, 2022.

Economic activities and the associated emissions have significantly declined during the 2019 novel coronavirus (COVID-19) pandemic, which has created a natural experiment to assess the impact of the emitted precursor control policy on ozone (O3) pollution. In this study, we utilized comprehensive satellite, ground-level observations, and source-oriented chemical transport modeling to investigate the O3 variations during the COVID-19 pandemic in China. Here, we found that the significant elevated O3 in the North China Plain (40%) and Yangtze River Delta (35%) were mainly attributed to the enhanced atmospheric oxidation capacity (AOC) in these regions, associated with the meteorology and emission reduction during lockdown. Besides, O3 formation regimes shifted from VOC-limited regimes to NOx-limited and transition regimes with the decline of NOx during lockdown. We suggest that future O3 control policies should comprehensively consider the effects of AOC on the O3 elevation and coordinated regulations of the O3 precursor emissions.

How to cite: Wang, P., Zhu, S., and Zhang, H.: Comprehensive Insights Into O3 Changes During the COVID-19 From O3 Formation Regime and Atmospheric Oxidation Capacity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4170, https://doi.org/10.5194/egusphere-egu22-4170, 2022.

EGU22-5126 | Presentations | ITS3.5/NP3.1

Nature-based Solutions in actions: improving landscape connectivity during the COVID-19 

Yangzi Qiu, Ioulia Tchiguirinskaia, and Daniel Schertzer

In the last few decades, Nature-based Solutions (NBS) has become widely considered a sustainable development strategy for the development of urban environments. Assessing the performances of NBS is significant for understanding their efficiency in addressing a large range of natural and societal challenges, such as climate change, ecosystem services and human health. With the rapid onset of the COVID-19 pandemic, the inner relationship between humans and nature becomes apparent. However, the current catchment management mainly focuses on reducing hydro-meteorological and/or climatological risks and improving urban climate resilience. This single-dimensional management seems insufficient when facing epidemics, and multi-dimensional management (e.g., reduce zoonosis) is necessary. With this respect, policymakers pay more attention to NBS. Hence, it is significant to increase the connectivity of the landscape to improve the ecosystem services and reduce the health risks from COVID-19 with the help of NBS.

This study takes the Guyancourt catchment as an example. The selected catchment is located in the Southwest suburb of Paris, with a total area of around 5.2 km2. The ArcGIS software is used to assess the patterns of structural landscape connectivity, and the heterogeneous spatial distribution of current green spaces over the catchment is quantified with the help of the scale-independent indicator of fractal dimension. To quantify opportunities to increase landscape connectivity over the catchment, a least-cost path approach to map potential NBS links urban green spaces through vacant parcels, alleys, and smaller green spaces. Finally, to prioritise these potential NBS in multiscale, a new scale-independent indicator within the Universal Multifractal framework is proposed in this study.

The results indicated that NBS can effectively improve the connectivity of the landscape and has the potential to reduce the physical and mental risks caused by COVID-19. Overall, this study proposed a scale-independent approach for enhancing the multiscale connectivity of the NBS network in urban areas and providing quantitative suggestions for on-site redevelopment.

How to cite: Qiu, Y., Tchiguirinskaia, I., and Schertzer, D.: Nature-based Solutions in actions: improving landscape connectivity during the COVID-19, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5126, https://doi.org/10.5194/egusphere-egu22-5126, 2022.

EGU22-5150 | Presentations | ITS3.5/NP3.1

The associations between environmental factors and COVID-19: early evidence from China 

Xia Meng, Ye Yao, Weibing Wang, and Haidong Kan

The Coronavirus (COVID-19) epidemic, which was first reported in December 2019 in Wuhan, China, has been becoming one of the most important public health issues worldwide. Previous studies have shown the importance of weather variables and air pollution in the transmission or prognosis of infectious diseases, including, but not limited to, influenza and severe acute respiratory syndrome (SARS). In the early stage of the COVID-19 epidemic, there was intense debate and inconsistent results on whether environmental factors were associated with the spread and prognosis of COVID-19. Therefore, our team conducted a series studies to explore the associations between atmospheric parameters (temperature, humidity, UV radiation, particulate matters and nitrogen dioxygen) and the COVID-19 (transmission ability and prognosis) at the early stage of the COVID-19 epidemic with data in early 2020 in China and worldwide. Our results showed that meteorological conditions (temperature, humidity and UV radiation) had no significant associations with cumulative incidence rate or R0 of COVID-19 based on data from 224 Chinese cities, or based on data of 202 locations of 8 countries before March 9, 2020, suggesting that the spread ability of COVID-19 among public population would not significantly change with increasing temperature or UV radiation or changes of humidity. Moreover, we found that particulate matter pollution significantly associated with case fatality rate (CFR) of COVID-19 in 49 Chinese cities based on data before April 12, 2020, indicating that air pollution might exacerbate negative prognosis of COVID-19. Our studies provided an environmental perspective for the prevention and treatment of COVID-19.

How to cite: Meng, X., Yao, Y., Wang, W., and Kan, H.: The associations between environmental factors and COVID-19: early evidence from China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5150, https://doi.org/10.5194/egusphere-egu22-5150, 2022.

EGU22-9213 | Presentations | ITS3.5/NP3.1

The Effects of COVID-19 Lockdown on Air Quality and Health in India and Finland 

Shubham Sharma, Behzad Heibati, Jagriti Suneja, and Sri Harsha Kota

The COVID-19 lockdowns worldwide provided a prospect to evaluate the impacts of restricted movements and emissions on air quality. In this study, we analyze the data obtained from the ground-based observation stations for six air pollutants (PM10, PM2.5, CO, NO2, O3 and SO2) and meteorological parameters from March 25th to May 31st in 22 cities representative of five regions of India and from March 16th to May 14th in 21 districts of Finland from 2017 to 2020. The NO2 concentrations dropped significantly during all phases apart from East India's exception during phase 1. O3 concentrations for all four phases in West India reduced significantly, with the highest during Phase 2 (~38%). The PM2.5 concentration nearly halved across India during all phases except South India, where a very marginal reduction (2%) was observed during Phase 4. SO2 (~31%) and CO (~41%) concentrations also reduced noticeably in South India and North India during all the phases. The air temperature rose by ~10% (average) during all the phases across India when compared to 2017-2019. In Finland, NO2 concentration reduced substantially in 2020. Apart from Phase 1, the concentrations of PM10 and PM2.5 reduced markedly in all the Phases across Finland. Also, O3 and SO2 concentrations stayed within the permissible limits in the study period for all four years but were highest in 2017 in Finland, while the sulfurous compounds (OSCs) levels increased during all the phases across Finland. The changes in the mobility patterns were also assessed and were observed to have reduced significantly during the lockdown. The benefits in the overall mortality due to the reduction in the concentrations of PM2.5 have also been estimated for India and Finland. Therefore, this research illustrates the effectiveness of lockdown and provides timely policy suggestions to the regulators to implement interventions to improve air quality.

How to cite: Sharma, S., Heibati, B., Suneja, J., and Kota, S. H.: The Effects of COVID-19 Lockdown on Air Quality and Health in India and Finland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9213, https://doi.org/10.5194/egusphere-egu22-9213, 2022.

EGU22-9812 | Presentations | ITS3.5/NP3.1

Changes in Global Urban Air Quality due to Large Scale Disruptions of Activity 

Will Drysdale, Charlotte Stapleton, and James Lee

Since 2020, countries around the world have implemented various interventions in response to a global public health crisis. The interventions included restrictions on mobility, promotion of working from home and the limiting of local and international travel. These, along with other behavioural changes from people in response to the crisis affected various sources of air pollution, not least the transport sector. Whilst the method through which these changes were implemented is not something to be repeated, understanding the effects of the changes will help direct policy for further improving air quality. 

 

We analysed NOx, O3 and PM2.5 data from many 100s of air quality monitoring sites in urban areas around the world, and examined 2020 in relation to the previous 5 years. The data were examined alongside mobility metrics to contextualise the magnitude of changes and were viewed through the lens of World Health Organisation guidelines as a metric to link air quality changes with human health. Interestingly, reductions in polluting activities did not lead to wholesale improvements in air quality by all metrics due to the more complex processes involved with tropospheric O3 production.

 

How to cite: Drysdale, W., Stapleton, C., and Lee, J.: Changes in Global Urban Air Quality due to Large Scale Disruptions of Activity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9812, https://doi.org/10.5194/egusphere-egu22-9812, 2022.

EGU22-11475 | Presentations | ITS3.5/NP3.1

Scaling Dynamics of Growth Phenomena: from Epidemics to the Resilience of Urban Systems 

Ioulia Tchiguirinskaia and Daniel Schertzer

Defining optimal COVID-19 mitigation strategies remains at the top of public health agendas around the world. It requires a better understanding and refined modeling of the intrinsic dynamics of the epidemic. The common root of most models of epidemics is a cascade paradigm that dates to their emergence with Bernoulli and d’Alembert, which predated Richardson’s famous quatrain on the cascade of atmospheric dynamics. However, unlike other cascade processes, the characteristic times of a cascade of contacts that spread infection and the corresponding rates are believed to be independent on the cascade level. This assumption prevents having cascades of scaling contamination.

In this presentation, we theoretically argue and empirically demonstrate that the intrinsic dynamics of the COVID-19 epidemic during the phases of growth and decline, is a cascade with a rather universal scaling, the statistics of which differ significantly from those of an exponential process. This result first confirms the possibility of having a higher prevalence of intrinsic dynamics, resulting in slower but potentially longer phases of growth and decline. It also shows that a fairly simple transformation connects the two phases. It thus explains the frequent deviations of epidemic models rather aligned with exponential growth and it makes it possible to distinguish an epidemic decline from a change of scaling in the observed growth rates. The resulting variability across spatiotemporal scales is a major feature that requires alternative approaches with practical consequences for data analysis and modelling. We illustrate some of these consequences using the now famous database from the Johns Hopkins University Center for Systems Science and Engineering.

Due to the significant increase over time of available data, we are no longer limited to deterministic calculus. The non-negligible fluctuations with respect to a power-law can be easily explained within the framework of stochastic multiplicative cascades. These processes are exponentials of a stochastic generators Γ(t), whose stochastic differentiation remains quite close to the deterministic one, basically adding a supplementary term σdt to the differential of the generator. When the generator Γ(t) is Gaussian, σ is the “quadratic variation”. Extensions to Lévy stable generators, which are strongly non-Gaussian, have also been considered. To study the stochastic nature of the cascade generator, as well as how it respects the above-mentioned symmetry between the phases of growth and decline, we use the universal multifractals. They provide the appropriate framework for joint scaling analysis of vector-valued time series and for introducing location and other dependencies. This corresponds to enlarging the domain, on which the process and its generator are defined, as well as their co-domain, on which they are valued. These clarifications should make it possible to improve epidemic models and their statistical analysis.

More fundamentally, this study points out to a new class of stochastic multiplicative cascade models of epidemics in space and time, therefore not limited to compartments. By their generality, these results pave the way for a renewed approach to epidemics, and more generally growth phenomena, towards more resilient development and management of our urban systems.

How to cite: Tchiguirinskaia, I. and Schertzer, D.: Scaling Dynamics of Growth Phenomena: from Epidemics to the Resilience of Urban Systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11475, https://doi.org/10.5194/egusphere-egu22-11475, 2022.

EGU22-11584 | Presentations | ITS3.5/NP3.1

Geophysicists facing Covid-19 

Daniel Schertzer, Vijay Dimri, and Klaus Fraedrich

There have been a series of sessions on the generic theme of “Covid-19 and Geosciences” on the occasion of AGU, AOGS and EGU conferences, since 2020 including during the first lockdown that required a very fast adaptation to unprecedented health measures. We think it is interesting and useful to have an overview of these sessions and try to capture what could be the lessons to learn.

To our knowledge, the very first such session was the Great e-Debate “Epidemics, Urban Systems and Geosciences” (https://hmco.enpc.fr/news-and-events/great-e-debate-epidemics-urban-systems-and-geosciences-invitations-and-replays/). It was virtually organised with the help of the UNESCO UniTwin CS-DC (Complex Systems Digital Campus) thanks to its expertise in organising e-conferences long before the pandemic and the first health measures. This would not have been possible without the strong personal involvement of its chair Paul Bourgine. It was held on Monday 4th May on the occasion of the 2020 EGU conference, which became virtual under the title “EGU2020: Sharing Geoscience Online” (4-8 May 2020). The Great e-Debate did not succeed in being granted as an official session of this conference, despite the fact that the technology used (Blue Button) by the Great e-Debate was much more advanced. Nevertheless, it was clearly an extension of the EGU session ITS2.10 / NP3.3: “Urban Geoscience Complexity: Transdisciplinarity for the Urban Transition”. 

Thanks to a later venue (7-11 December 2020) and the existence of a GeoHealth section of the AGU, the organisation of several regular sessions for the 2020 Fall Meeting was easier. For EGU 2021 (19-30 April 2021), a sub-part of the  inter- transdisciplinary sessions ITS1 “Geosciences and health during the Covid pandemic”, a Union Session US “Post-Covid Geosciences” and a Townhall meeting TM10 “Covid-19 and other epidemics: engagement of the geoscience communities” were organised. A brief of the special session SS02 “Covid-19 and Geoscience” of the (virtual) 18th Annual Meeting of AOGS (1-6 August 2021) is included in the proceedings of this conference (in press). 

We will review materials generated by these sessions that rather show a shift from a focus on the broad range of scientific responses to the pandemic, to which geoscientists could contribute with their specific expertise (from data collection to theoretical modelling), to an expression of concerns about the broad impacts on the geophysical communities that appear to be increasingly long-term and constitute a major transformation of community functioning (e.g., again data collection, knowledge transfer).

How to cite: Schertzer, D., Dimri, V., and Fraedrich, K.: Geophysicists facing Covid-19, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11584, https://doi.org/10.5194/egusphere-egu22-11584, 2022.

EGU22-11747 | Presentations | ITS3.5/NP3.1

To act or not to act. Predictability of intervention and non-intervention in health and environment 

Michalis Chiotinis, Panayiotis Dimitriadis, Theano Illiopoulou, Nikos Mamassis, and Demetris Koutsoyiannis

The COVID-19 pandemic has brought forth the question of the need for draconian interventions before concrete evidence for their need and efficacy is presented. Such interventions could be critical if necessary for avoiding threats, or a threat in themselves if harms caused by the intervention are significant.

The interdisciplinary nature of such issues as well as the unpredictability of various local responses considering their potential for global impact further complicate the question.

The study aims to review the available evidence and discuss the problem of weighting the predictability of interventions vis-à-vis their intended results against the limits of knowability regarding complex non-linear systems and thus the predictability in non-interventionist approaches.

How to cite: Chiotinis, M., Dimitriadis, P., Illiopoulou, T., Mamassis, N., and Koutsoyiannis, D.: To act or not to act. Predictability of intervention and non-intervention in health and environment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11747, https://doi.org/10.5194/egusphere-egu22-11747, 2022.

EGU22-12302 | Presentations | ITS3.5/NP3.1

COVID-19 waves: intrinsic and extrinsic spatio-temporal dynamics over Italy 

Tommaso Alberti and Davide Faranda

COVID-19 waves, mostly due to variants, still require timely efforts from governments based on real-time forecasts of the epidemics via dynamical and statistical models. Nevertheless, less attention has been paid in investigating and characterizing the intrinsic and extrinsic spatio-temporal dynamics of the epidemic spread. The large amount of data, both in terms of data points and observables, allows us to perform a detailed characteristic of the epidemic waves and their relation with different sources as testing capabilities, vaccination policies, and restriction measures.

By taking as a case-study the epidemic evolution of COVID-19 across Italian regions we perform the Hilbert-Huang Transform (HHT) analysis to investigate its spatio-temporal dynamics. We identified a similar number of temporal components within all Italian regions that can be linked to both intrisic and extrinsic source mechanisms as the efficiency of restriction measures, testing strategies and performances, and vaccination policies. We also identified mutual scale-dependent relations within different regions, thus suggesting an additional source mechanisms related to the delayed spread of the epidemics due to travels and movements of people. Our results are also extremely helpful for providing long term extrapolation of epidemics counts by taking into account both the intrinsically and the extrinsically non-linear nature of the underlying dynamics. 

How to cite: Alberti, T. and Faranda, D.: COVID-19 waves: intrinsic and extrinsic spatio-temporal dynamics over Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12302, https://doi.org/10.5194/egusphere-egu22-12302, 2022.

Black carbon (BC) not only warms the atmosphere but also affects human health. The nationwide lockdown due to the COVID-19 pandemic led to a major reduction in human activity during the past thirty years. Here, the concentration of BC in the urban, urban-industry, suburb, and rural areas of a megacity Hangzhou were monitored using a multi-wavelength Aethalometer to estimate the impact of the COVID-19 lockdown on BC emissions. The citywide BC decreased by 44% from 2.30 μg/m3 to 1.29 μg/m3 following the COVID-19 lockdown period. The source apportionment based on the Aethalometer model shows that vehicle emission reduction responded to BC decline in the urban area and biomass burning in rural areas around the megacity had a regional contribution of BC. We highlight that the emission controls of vehicles in urban areas and biomass burning in rural areas should be more efficient in reducing BC in the megacity Hangzhou.

How to cite: Li, W. and Xu, L.: Responses of concentration and sources of black carbon in a megacity during the COVID-19 pandemic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12907, https://doi.org/10.5194/egusphere-egu22-12907, 2022.

For many of us, the Covid-19 pandemic brought long-time scientific interest in epidemiology to the point of involvement. An important aspect of the evolution of acute respiratory epidemics is their seasonal character. Our toolkit for handling seasonal phenomena in the geosciences has increased in the last dozen years or so with the development and application of concepts and methods from the theory of nonautonomous and random dynamical systems (NDSs and RDSs). In this talk, I will briefly:

  • Introduce some elements of these two closely related theories.

  • Illustrate the two with an application to seasonal effects within a chaotic model of the El

    Niño–Southern Oscillation (ENSO).

  • Introduce to a geoscientific audience a simple epidemiological “box” model of the

    Susceptible–Exposed–Infectious–Recovered (SEIR) type.

  • Summarize NDS results for a chaotic SEIR model with seasonal effects.

  • Mention the utility of data assimilation (DA) tools in the parameter identification and

    prediction of an epidemic’s evolution

    References

    - Chekroun, M D, Ghil M, Neelin J D (2018) Pullback attractor crisis in a delay differential ENSO model, in Nonlinear Advances in Geosciences, A. Tsonis (Ed.), Springer, pp. 1–33, doi: 10.1007/978-3-319-58895-7

    - Crisan D, Ghil, M (2022) Asymptotic behavior of the forecast–assimilation process with unstable dynamics, Chaos, in preparation

    - Faranda D, Castillo I P, Hulme O, Jezequel A, Lamb J S, Sato Y, Thompson E L (2020) Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation<? Chaos, 30(5): 051107, doi: 10.1063/5.0009454

    - Ghil, M (2019) A century of nonlinearity in the geosciences. Earth & Space Science 6:1007–1042, doi:10.1029/2019EA000599

    - Kovács, T (2020) How can contemporary climate research help understand epidemic dynamics? Ensemble approach and snapshot attractors. J. Roy. Soc. Interface, 17(173):20200648, doi: 10.1098/rsif.2020.0648

How to cite: Ghil, M.: Time-dependent forcing in the geosciences and in epidemiology, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13522, https://doi.org/10.5194/egusphere-egu22-13522, 2022.

Standard epidemic models based on compartmental differential equations are investigated under continuous parameter change as external forcing. We show that seasonal modulation of the contact parameter superimposed upon a monotonic decay needs a different description from that of the standard chaotic dynamics. The concept of snapshot attractors and their natural distribution has been adopted from the field of the latest climate change research. This shows the importance of the finite-time chaotic effect and ensemble interpretation while investigating the spread of a disease. By defining statistical measures over the ensemble, we can interpret the internal variability of the
epidemic as the onset of complex dynamics—even for those values of contact parameters where originally regular behaviour is expected. We argue that anomalous outbreaks of the infectious class cannot die out until transient chaos is presented in the system. Nevertheless, this fact becomes apparent by using an ensemble approach rather than a single trajectory representation. These findings are applicable generally in explicitly time-dependent epidemic systems regardless of parameter values and time scales.

How to cite: Kovács, T.: How can contemporary climate research help understand epidemic dynamics? -- Ensemble approach and snapshot attractors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13534, https://doi.org/10.5194/egusphere-egu22-13534, 2022.

EGU22-1157 | Presentations | NP3.2

Evaluation of hydrological cycle intensification in response to temperature variability 

Shailendra Pratap and Yannis Markonis

As the climate warms, the hydrological cycle is expected to intensify. Also, in response to climate warming, hydrologic sensitivity is a major concern for the coming decades. Here, we aim to understand the relationship between hydroclimate and temperature variability during the past. The periods selected for investigation are the Mid-Miocene Climate Optimum (MMCO), the Eemian Interglacial (EI) Stage, the Last Glacial Maximum, the Heinrich and Dansgaard–Oeschger Events, the Bølling-Allerød, the Younger Dryas, the 8.2 ka event, the Medieval Climate Anomaly, and the Little Ice Age. In general, the proxy records suggest that the hydrological cycle is intensified under warmer climate conditions and weakened over colder periods. However, the spatial signals are not uniform worldwide. For instance, during the MMCO and EI, the global temperature was higher than the pre-industrial time; some regions were wetter, (northern Eurasia and Sahara Arabian desert), while others were more arid (Argentina, Bolivia, and Africa). Therefore, the hypothesis “a warmer climate is a wetter climate” could be considered as a simplified pattern of regional changes as a result of global warming. The reason is that the water cycle response is spatiotemporally not similar. Due to its wide distribution, hydroclimate variability is difficult to quantify on a regional, continental, and global scale. In this context, investigation of paleo-hydroclimatic changes, specifically during the warm periods, could provide relevant insights into the present and future climate.

How to cite: Pratap, S. and Markonis, Y.: Evaluation of hydrological cycle intensification in response to temperature variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1157, https://doi.org/10.5194/egusphere-egu22-1157, 2022.

EGU22-1326 | Presentations | NP3.2

Widespread changes in surface temperature persistence under climate change 

David WJ Thompson and Jingyuan Li

The effects of extreme temperature events on ecosystems and society depend critically on the persistence of the event. But to-date relatively little work has systematically explored the response of such persistence to climate change. In this talk, I will explore the evidence for changes in surface temperature persistence in output from a range of numerical simulations, including large-ensembles of climate change simulations run on Earth system models and simplified models with varying representations of radiative processes and large-scale dynamics. Together, the results indicate that climate change is expected to be accompanied by widespread changes in surface temperature persistence. The changes are generally most robust over ocean areas and arise due to a seemingly broad range of physical processes. The findings point to both the robustness of widespread changes in persistence under climate change, and the critical need to better understand, simulate and constrain such changes.

How to cite: Thompson, D. W. and Li, J.: Widespread changes in surface temperature persistence under climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1326, https://doi.org/10.5194/egusphere-egu22-1326, 2022.

EGU22-1530 | Presentations | NP3.2 | Highlight

Analyzing centennial variability in the Southern Ocean using data assimilation 

Hugues Goosse, Quentin Dalaiden, and Zhiqiang Lyu

The agreement between simulated and reconstructed multidecadal to centennial climate variability has improved over the past decades. However, significant disagreements still exist, especially at regional scale. In the Southern Ocean, both reconstructions and climate models display large variability at those timescales but models fail in reproducing some key elements such as the centennial variability in the strength of the westerly winds inferred from various types of proxy data. Data assimilation combines in an optimal way information from proxy data and climate models. It can help in identifying the cause of such model-data mismatch by improving the reconstructions as well as by testing the compatibility of those reconstructions with model physics or between different types of proxy data. Two examples will be discussed here. The first one focuses on the shift in the westerly winds between the 14th and 16th century, showing that it is clear in reconstructions based on classical statistical methods and on data assimilation but it is not simulated in models without data assimilation. In the second example, we will discuss the deep ocean convection and open ocean formation in the Southern Ocean that induce large multi-decadal to centennial variability in some global models while it is totally absent in many others. We will check how data assimilation can be used to test the validity of the simulations and to determine which model behavior is the most realistic. 

 

How to cite: Goosse, H., Dalaiden, Q., and Lyu, Z.: Analyzing centennial variability in the Southern Ocean using data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1530, https://doi.org/10.5194/egusphere-egu22-1530, 2022.

EGU22-1585 | Presentations | NP3.2

Trend analysis of Changing Temperature over the Time Period of 1979 to 2014 in Uttarakhand, Western Himalaya, India 

Sarita Palni, Deepanshu Parashar, and Arvind Pandey

Himalayan mountain region lying in the northern part of Indian sub-continent is among those zones which bears the most ecologically sensitive environments and is also a repository of biodiversity, fresh water storage and ecosystem services. Over the last three decades, land transformation related to exploitative land uses is among the main drivers of changing snow cover, vegetation cover and productivity in western Himalayas region. In a region where field-based research is challenging due to heterogenous relief and high altitude, quantifying the changes in temperature pattern using Remote Sensing Techniques can provide essential information regarding variating trends in different elements relating to temperature. This paper studies the trend analysis of changing temperature patterns using SWAT data (1979–2014) over Uttarakhand Himalayas and its association with altitudinal gradient. This paper investigates the trends in maximum (Tmax), minimum (Tmin) & mean (Tmean) temperatures in the annual, seasonal and monthly time-scales for 55 stations in the 5 regions of Uttarakhand’s Western Himalayan region which are categorized on the basis of elevation, from year 1979-2014. Statistical approaches are used to examine the effect of change in pattern of temperature upon the phenology of vegetation in the region under study, fresh water ecosystems, agricultural productivity, decreasing snow line & increasing tree line, change in duration of the seasons etc.

How to cite: Palni, S., Parashar, D., and Pandey, A.: Trend analysis of Changing Temperature over the Time Period of 1979 to 2014 in Uttarakhand, Western Himalaya, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1585, https://doi.org/10.5194/egusphere-egu22-1585, 2022.

EGU22-1792 | Presentations | NP3.2

Links between intermediate ocean circulation and cryosphere dynamics during Heinrich Stadials in the NE Atlantic: a foraminiferal perspective 

Pauline Depuydt, Meryem Mojtahid, Christine Barras, Fatima Bouhdayad, and Samuel Toucanne

Understanding the interaction between ocean circulation and ice sheet dynamics is fundamental to study the rapid Quaternary climate changes that punctuate major glacial-interglacial periods. Compared to the surface and deep compartments of the Atlantic Meridional Overturning Circulation (AMOC), intermediate water depths during key time periods, such as Heinrich Stadials (HSs), remain poorly documented, especially in the Northeast Atlantic.

In this study, we use benthic foraminiferal assemblage data from an upper slope sediment core from the Northern Bay of Biscay to reconstruct paleoenvironmental and paleohydrological changes at ~1000m water depth, from ~35 to 14 kyr cal BP. Our results show a strong response of benthic communities to hydrodynamic changes (related to AMOC) and to instabilities of the European Ice Sheet during the last three HSs. Benthic foraminifera provide species-specific responses to the induced physico-chemical changes, in coherence with the various geochemical and sedimentological proxies documented in the area. The three HSs are characterized by the low abundance of species indicative of high-energy environments (Cibicides lobatulus and Trifarina angulosa) and the simultaneous presence of Cibicidoides pachyderma (meso-oligotrophic species) and Globobulimina spp. (anoxia-tolerant species).   This species composition suggests a slowing of the intermediate circulation during the three HSs. Nevertheless, HS1 is very distinct from HS2 and HS3 by the high presence of high-organic flux indicator species (Cassidulina carinata and Bolivina spp.) during its early phase (Early HS1). This result confirms that EIS meltwaters were much less charged in organic material derived from the continent during HS2 and HS3 than during HS1 due to the scarcer vegetation cover and partially frozen soils. Finally, benthic foraminifera depict clearly the rapid "re-ventilation" during Mid-HS2, corresponding to a response to regional glacial instabilities.

How to cite: Depuydt, P., Mojtahid, M., Barras, C., Bouhdayad, F., and Toucanne, S.: Links between intermediate ocean circulation and cryosphere dynamics during Heinrich Stadials in the NE Atlantic: a foraminiferal perspective, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1792, https://doi.org/10.5194/egusphere-egu22-1792, 2022.

EGU22-3459 | Presentations | NP3.2 | Highlight

State-dependent effects of natural forcing on global and local climate variability 

Beatrice Ellerhoff, Moritz J. Kirschner, Elisa Ziegler, Max D. Holloway, Louise Sime, and Kira Rehfeld

Climate variability is the primary influence on climate extremes and affected by natural forcing from solar irradiance and volcanic eruptions. Global warming impacts climate variability, but there is contradictory and incomplete evidence on the spatio-temporal patterns. Strong volcanic eruptions have been suggested to reduce temperatures less in warmer climate states. However, the underlying question of state-dependent effect of natural forcing on local and global variability remains open. Moreover, there are uncertainties about the role of natural forcing in the mismatch between simulated and reconstructed local, long-term variability.  

Using a 12-member GCM ensemble with targeted boundary conditions, we present naturally-forced and equilibrium, millennium-length simulations for the Last Glacial Maximum (LGM) and the Pre-Industrial (PI). We quantify the local and global climate response to solar and volcanic forcing in the LGM and PI, and contrast variability from forced and control simulations on annual-to-multicentennial scales. We differentiate various contributions from the atmosphere, oceans, and particularly that of sea ice using a 2D energy balance model (EBM). Spectral analysis of simulated temperatures shows that global variability is predominately determined by natural forcing. Local mean spectra are more characteristic for the mean climate state and reveal a decrease in local variability with warming. The global and local response to natural forcing is robust against changes in the mean climate. Particularly, the spatial patterns of the surface climate's response to volcanic eruptions widely agree across states. Weak local differences resulted primarily from sea ice dynamics. The sea ice contribution is the strongest on interannual scales. It remains significant on decadal scales and longer, providing a key mechanism of long-term variability. We validate the simulated variability against observational and paleoclimate data. The variance obtained from proxies is increasingly larger on longer timescales compared to that from simulated time series. The inclusion of natural forcing reduces the model-data mismatch on decadal-to-multicentennial scales and, thus, provides a more accurate representation of climate variability. 

Consideration of natural forcing is therefore paramount for model-data comparison and future projections. The robust temperature response suggests that findings on the ability of models to simulate past variability should translate to future climates, and can thus help constrain variability. 

How to cite: Ellerhoff, B., Kirschner, M. J., Ziegler, E., Holloway, M. D., Sime, L., and Rehfeld, K.: State-dependent effects of natural forcing on global and local climate variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3459, https://doi.org/10.5194/egusphere-egu22-3459, 2022.

EGU22-3962 | Presentations | NP3.2

Beyond Hasselmann and Leith: The challenge of non-Markovian and fractional stochastic climate modelling 

Nicholas Wynn Watkins, Raphael Calel, Sandra Chapman, Aleksei Chechkin, Ian Ford, Rainer Klages, and David Stainforth

The stochastic energy balance models (SEBMs) pioneered by  Hasselmann and  Mitchell [1] have long been known to climate scientists to be important aids to gaining both qualitative insight and quantitative information about global mean temperatures.  SEBMs are now much more widely visible, after the award of last year’s Nobel Prize to Hasselmann, shared with Manabe and Parisi [1].

The earliest univariate SEBMs were, however, built around the simplest linear and Markovian stochastic process, and researchers have very intentionally exploited their equivalence to the Langevin equation of 1908. Although multivariate SEBMs have now been extensively studied [1,2] and provide one important route to incorporating non-Markovian memory effects into climate dynamics, my presentation will discuss the continuing value of univariate SEBMs, especially when coupled to other models. I  will also highlight how we and others (e.g. [4,5]) are going beyond the first SEBMs to incorporate more general models of temporal dependence, motivated by evidence of non-Markovian, and in particular long-ranged, memory in the climate system.  This effort has brought new and interesting challenges, both in mathematical methods and physical interpretation.

I will highlight our recent paper [3] on using a Hasselmann-type EBM to study the economic impacts of climate change and variability and our other ongoing work [6, and its updated version, 7] on  generalised (and in particular fractional) Hasselmann univariate SEBMs. I will compare our model [6,7] with Lovejoy and co-workers' FEBE [5], and discuss what the requirements are in order for such non-Markovian SEBMs to exhibit fluctuation-dissipation relations, which have been debated in the  SEBM field since the early work of Leith in the 1970s.

[1] Scientific background on the Nobel prize in physics 2021, Nobel Committee, Royal Swedish Academy of Sciences.

[2] Franzke and O’Kane, eds. Nonlinear and Stochastic Climate Dynamics, CUP, 2017.

[3] Calel et al, Nature Communications, 2020.

[4] Rypdal et al, Climate, 2018.

[5] Lovejoy et al, QJRMS, 2021.

[6] Watkins et al, On Generalized Langevin Dynamics and the Modelling of Global Mean Temperature, 2021, https://link.springer.com/chapter/10.1007%2F978-3-030-67318-5_29

[7] Watkins et al, arXiv: https://arxiv.org/abs/2007.06464v2.

How to cite: Watkins, N. W., Calel, R., Chapman, S., Chechkin, A., Ford, I., Klages, R., and Stainforth, D.: Beyond Hasselmann and Leith: The challenge of non-Markovian and fractional stochastic climate modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3962, https://doi.org/10.5194/egusphere-egu22-3962, 2022.

EGU22-4276 | Presentations | NP3.2

Northern Hemispheric extratropical cyclones during glacial times: impact of orbital forcing and ice sheet height 

Christoph C. Raible, Martina Messmer, Buzan Jonathan, and Russo Emmanuele

Extratropical cyclones are a major source of natural hazards in the mid latitudes as wind and precipitation extremes are associated to this weather phenomenon. Still the response of extratropical cyclones and their characteristics to strong external forcing changes is not yet fully understood. In particular, the impact of the orbital forcing as well as variations of the major ice sheets during glacial times on extratropical cyclones have not been investigated so far.  

Thus, the aim of this study is to fill this gap and to assess the impact of orbital forcing and northern hemispheric ice sheet height variations on extratropical cyclones and their characteristics during winter and summer. The main research tool is the Community Earth System Model CESM1.2. We performed a set of time slice sensitivity simulations under preindustrial (PI) conditions and for the following different glacial periods: Last Glacial Maximum (LGM), Marine Isotopic stage 4 (MIS4), MIS6, and MIS8. Additionally, we vary the northern hemispheric ice sheet height for all the different glacial periods by 33%, 66%, 100% and 125% of the ice sheet reconstructed for the LGM. For each of the simulations the extratropical cyclones are identified with a Lagrangian cyclone detection and tracking algorithm, which delivers a set of different cyclone characteristics, such as, cyclone frequency maps, cyclone area, central pressure, cyclone depth, precipitation associated to the extratropical cyclones as well as extremes in cyclone depth and extratropical cyclone-related precipitation. These cyclone characteristics are investigated for the winter and the summer season separately.

Preliminary results show that the extratropical cyclone tracks are shifted southwards on the Northern Hemisphere during the winter season. This shift has rather strong implication for the Mediterranean, with an increase of winter precipitation during glacial times over the western Mediterranean. The increase is modulated when changing the ice sheet height as extratropical cyclone tracks shift further south with increasing northern hemispheric ice sheet height. The orbital forcing shows a higher impact during summer, where mean precipitation is further reduced over Europe when comparing MIS4 and MIS8 with LGM. The changes in the cyclone tracks and related precipitation changes in the Mediterranean for the summer season need to be assessed. Additionally, the effect of the orbital forcing on changes in cyclone tracks and associated precipitation changes in the North Pacific must be evaluated for both seasons.

 

How to cite: Raible, C. C., Messmer, M., Jonathan, B., and Emmanuele, R.: Northern Hemispheric extratropical cyclones during glacial times: impact of orbital forcing and ice sheet height, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4276, https://doi.org/10.5194/egusphere-egu22-4276, 2022.

EGU22-4277 | Presentations | NP3.2

6000 years of winter climate variability revealed by a speleothem record from East-Central Europe 

Virgil Dragusin, Vasile Ersek, Dominik Fleitmann, Monica Ionita-Scholz, and Bogdan P. Onac

Holocene reconstructions of winter climate in East-Central Europe (ECE) are scarce, although several studies have brought more seasonal insights through the study of pollen in lake sediments, δ18O and deuterium excess from an ice cave deposit, as well as speleothem trace elements.

Here we present the δ18O record of stalagmite PU-2 from Urşilor Cave (W Romania) that could shed further light onto ECE Holocene hydroclimate variability for the past 6000 years. This previously published stalagmite benefits now from a more detailed age-depth model and an increased temporal resolution, to an average of 15 years across the whole record. More importantly, following recent monitoring studies, it was concluded that the δ18O signal in the cave drip water is representative of winter climate conditions.

In East-Central Europe there is a significant correlation between the winter temperature and the East Atlantic teleconnection pattern (EA), as this region witnesses higher than average temperatures during the positive phase of EA. The North Atlantic Oscillation teleconnection pattern (NAO) is known to modulate winter precipitation in the European realm, and many NAO reconstructions have sought to identify its variability in the past.

To investigate the drivers behind winter climate dynamics in the region surrounding the cave and across Europe, we compare our data with other speleothem winter temperature and rainfall records from Europe and the Levant. Further, we examine their variability on a complex time-evolving relationship with the coupled NAO/EA patterns.

How to cite: Dragusin, V., Ersek, V., Fleitmann, D., Ionita-Scholz, M., and Onac, B. P.: 6000 years of winter climate variability revealed by a speleothem record from East-Central Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4277, https://doi.org/10.5194/egusphere-egu22-4277, 2022.

EGU22-5789 | Presentations | NP3.2

Scaling invariance behaviour of thermal fluxes from an extensive green roof 

Leydy Alejandra Castellanos Diaz, Pierre-Antoine Versini, Olivier Bonin, and Ioulia Tchiguirinskaia

Green roofs are widely recognized as a Nature-Based Solution that regulates the air temperature within urban environments. Thanks to the shading effect and the evapotranspiration process (ET), the temperature decreases in the green roof surrounding area. Hence, the implementation of green roofs in urban environment for this purpose requires the quantification of ET-related processes at different scales. Nevertheless, because of complexity of the ET process, different methods of measurement have been used at different scales. However, no agreement about the way to assess ET rates over green roofs has been reached between the scientific community, as well as its behaviour at different urban scales is still unclear. Therefore, more investigations on ET measurements are required for better understand and analyse its spatial and temporal variability at different scales.

For this purpose, a Larger Aperture Scintillometer (LAS) MKI from Kipp&Zonen was installed over a wavy-green roof of 1 ha, the Blue Green Wave (BGW), located in the Ecole des Points ParisTech (France). The main objective of this set-up was to assess the refractive index-structure parameter (Cn2) fluctuations from which ET can be deduced by means of the Monin-Obukhov similarity theory and the surface energy balance. As LAS is mainly influenced by fluctuations of air temperature, a radiometer equipped with a temperature sensor was installed in addition over the BGW. Then, the scaling statistics of Cn2 and temperature were studied through their power spectral density and their structure function.

The results obtained from the power spectral density demonstrated the scaling invariance of Cnand temperature over certain ranges of scales. The spectral exponents are close to 5/3 for Cnand to 2 for the temperature. Regarding the scaling exponents of the structure functions, the multifractal feature of the structure parameter Cn2 and the temperature was confirmed. The scale-invariant properties of the empirical data were characterised using the Universal Multifractal framework.

How to cite: Castellanos Diaz, L. A., Versini, P.-A., Bonin, O., and Tchiguirinskaia, I.: Scaling invariance behaviour of thermal fluxes from an extensive green roof, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5789, https://doi.org/10.5194/egusphere-egu22-5789, 2022.

EGU22-6233 | Presentations | NP3.2 | Highlight

Disentangling the mechanisms of ENSO response to volcanic eruptions 

Francesco S.R. Pausata and the et al.

Large explosive volcanic eruptions can have major impacts on global climate, affecting both radiative balance and inducing interannual-to-decadal dynamical alterations of the atmospheric and oceanic circulation. Despite some discrepancies across studies regarding the response of ENSO to volcanism based on paleoclimate data, the majority of ENSO reconstructions display an El Niño–like warming in the year of eruption, while none display a significant La Niña–like response. Furthermore, there has been an emerging consensus from the numerous coupled General Circulation Model studies investigating the impact of tropical volcanism on ENSO, with the overwhelming majority displaying an El Niño–like warming occurring in the year following the eruption. However, the mechanisms that trigger ENSO anomalies following volcanic eruptions are still debated. The center of the argument is understanding how volcanism affects the trade winds along the equatorial Pacific.

We performed a series of sensitivity experiments using the Norwegian Earth System Model (NorESM1-M) designed to shed light on the processes that govern the ENSO response to volcanic eruptions as a function of the regional distribution of the aerosol forcing. Specifically, a uniform stratospheric volcanic aerosol loading was imposed over different parts of the tropics and extra-tropics to test the four main mechanisms invoked to explain the ENSO response to volcanic eruptions: 1) the ocean dynamical thermostat (ODT) mechanism; 2) the cooling of the Maritime Continent (MC) mechanism; 3) the cooling of tropical northern Africa (NAFR) mechanism; and 4) the Intertropical Convergence Zone shift mechanism. In this contribution, we will present results for NorESM1-M, illustrate their implications for understanding of forced ENSO dynamics and discuss how our approach can give benefit to multi-model assessments of ENSO response to volcanic forcing.

How to cite: Pausata, F. S. R. and the et al.: Disentangling the mechanisms of ENSO response to volcanic eruptions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6233, https://doi.org/10.5194/egusphere-egu22-6233, 2022.

EGU22-6807 | Presentations | NP3.2

Characterization of Typhoon Track Using Multifractal Analysis of Wind Fields 

Jisun Lee, Ioulia Tchiguirinskaia, Daniel Schertzer, and Dong-In Lee

In Korea, typhoons are becoming an essential issue as they cause huge damage and their occurrence frequency has increased since 2001. Many types of research and case studies related to the prediction of typhoon intensity and typhoon track are being conducted, especially with the help of numerical models. However, there is a lack of studies investigating the nonlinear behavior of typhoons, especially by using radar data, see however Lee et al. (2020, DOI: 10.1175/JAMC-D-18-0209.1). We perform such a detailed analysis of datasets of wind fields retrieved from radar in a multifractal framework. More precisely, we analyzed the difference of multifractality on each altitude depending on the different typhoon tracks and show that estimates of the multifractal parameters can be used to characterize the typhoon tracks. 

The radar dataset was collected depending on the category of the track of the typhoon. Track category 1: typhoon moving straight north from Jeju island to the Korean peninsula, and track category 2: typhoon making a curve northeastward as the typhoon passes Jeju island. Typhoon Khanun, Bolaven and Sanba (2012) are selected for track category 1. Tembin (2012) and Chaba (2016) are selected for track category 2. Then, the wind field of each typhoon case was calculated by using the dual-Doppler wind retrieval method and the analysis of each field was separately performed on its positive and negative parts. 


This large amount of space-time data was analyzed by calculating fractal dimension, the Trace Moments (TM, Schertzer and Lovejoy, 1987) and Double Trace Moment (DTM, Lavallée et al., 1992). The last two enable to quantify the mean fractality of the process with the help of its fractal co-dimension C1 and its multifractality index α, which measures how fast the intermittency evolves for higher singularities.


It was possible to estimate the category of the tracks of the typhoon by calculating the fractal dimension of wind velocity components U and V  (resp. East-West and South-North) before and after landfalling on Jeju island. Also, it was noted that the location of the typhoon center affects the decreasing trend of fractal dimension of positive V. Also, with the help of TM and DTM analysis, it was possible to verify the movement of the typhoon even with the same category of track moving north. The parameter  C1 quantifies the mean sparseness of the field but the dependence on 𝛼 of positive U showed the possibility of typhoon curving to the east. Also, the track category moving to the northeast, the dependence on 𝛼 of negative U makes the difference of degree of curvature of the track. Moreover, it was possible to identify the location of the typhoon track according to the UM parameters. If the curvature degree at the altitudes of 2-5 km is large, the typhoon center is located more on the east side of the island.

How to cite: Lee, J., Tchiguirinskaia, I., Schertzer, D., and Lee, D.-I.: Characterization of Typhoon Track Using Multifractal Analysis of Wind Fields, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6807, https://doi.org/10.5194/egusphere-egu22-6807, 2022.

Where full-scale minesite-drainage monitoring has been carried out at sufficiently high sampling frequencies and long durations, interesting and intriguing patterns have been seen in the time series.  Some observations include: flow rates and aqueous concentrations of minesite drainages are not simple or steady; they are not stochastic, but also not deterministic; they are not random or chaotic. They display periodicity in complex ways.

Based on spectral analyses of time series for minesite drainages as well as for non-mining-related rivers and catchments, the typical trend is decreasing spectral power of the peaks with decreasing wavelength.  The resulting slopes are commonly fractal, typically ranging between zero (random) to 2 (random walk).  The slope of 1 ("1-over-f") is the most complex and yet has been documented in many sciences and arts.  These fractal slopes are “ubiquitous” in some non-mining catchments.

Consistent with Earth-System Science, electrical fields in the Earth are inevitably linked to other processes like large and small physical movements, magnetic variations in the earth, weather systems, and cosmic radiation.  For example, the movement of natural water through a porous or fractured medium can create an electrical field that in turn affects the distribution of ions in that water.  Small changes in ground electrical potential, considered minor background electrical "noise" by some, can significantly affect aqueous chemistry.

This study asks the question, “Why?”  Why are fractal spectral slopes so common in drainage flows and chemistries whenever data have been sufficient to search for them?

A plausible answer begins with the fact that many minesite components are open systems in the surficial environment, well grounded to the earth which behaves like an electrical capacitor.  Thus, relatively large minesite components can act as first-order low-pass signal filters.  These filters cause the spectral powers of individual periodicities entering them to (1) decrease along a fractal slope of 2 at wavelengths shorter than the "cutoff wavelength" and (2) remain unfiltered at longer wavelengths.  When several mechanisms are simultaneously acting and overlapping as low-pass filters, fractal slopes including 1-over-f slopes can appear.  Based on this rationale, periodic processes grounded to the Earth can show fractal temporal slopes when sufficient data are collected.

How to cite: Morin, K.: A Plausible Explanation for Common Fractal Temporal-Spectral Slopes of Drainage Flows and Chemistries at Full-Scale Mining Operations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6840, https://doi.org/10.5194/egusphere-egu22-6840, 2022.

The systematic study of extreme geological events (e.g., plate collision and subduction, earthquakes, volcanoes, and mineralization) that occurred during the evolution of the earth is essential not only for understanding the "abrupt changes in the evolution of the earth", but also for an in-depth understanding of the co-evolution of material-life-environment of the livable earth. However, due to the temporal and spatial anomalies and complexity of extreme geological events, classical mathematical models cannot be effectively applied to quantitively describe such events. Comparative studies of many types of geological events indicate that such extreme geological events often depict "singular" characteristics (abnormal accumulation of matter or massive release of energy in a small space or time interval). On this basis, the author proposes a unified definition of extreme geological events, a new concept of "fractal density" and a "local singularity analysis” method for quantitative description and modeling of extreme geological events. Applications of these methods to several types of extreme geological events have demonstrated that the singularity theory and methods developed in the current research can be used as general approaches for the characterization, simulation, and prediction of geological events. The case studies to be introduced include anomalous heat flow over the mid-ocean ridges, and major flare up magmatism and marine sediment flux fluctuations over the past 3 Ga history of earth continental crust evolution.

How to cite: Cheng, Q.: Fractal density and local singularity analysis method for modeling extreme geological events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6850, https://doi.org/10.5194/egusphere-egu22-6850, 2022.

EGU22-6858 | Presentations | NP3.2

Combined multifractal analysis of wind power production and atmospheric fields using simultaneous measurement of high-resolution data 

Jerry Jose, Auguste Gires, Ernani Schnorenberger, Ioulia Tchiguirinskaia, and Daniel Schertzer

Atmospheric fields are known to exhibit extreme variability over wide range of temporal and spatial scales, which makes them complex to characterize. When it comes to wind power production, the power available at atmosphere and power extracted by turbines at multiple scales are affected by corresponding variations in coexisting fields. Understanding their variability and correlations helps in quantifying uncertainties in modeling as well as real data analysis. Here, we aim to characterize the variability and correlations across scales of wind power production, and atmospheric fields including 3D wind, rainfall and air density using simultaneous measurements in a wind farm relying on the framework of Universal Multifractal (UM) analysis. It is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over wide range of scales.

Towards this, high-resolution atmospheric data collected from a meteorological mast located in the wind farm of Pays d’Othe operated by Boralex (110 km south-east of Paris, France) is used. The data is being collected under the project RW-Turb (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency (ANR-19-CE05-0022). The campaign utilizes multiple 3D sonic anemometers (manufactured by Thies), mini meteorological stations (manufactured by Thies), and disdrometers (Parsivel2, manufactured by OTT) installed at turbine hub height along with turbines in the wind farm. The temporal resolution is 100 Hz for the 3D sonic anemometers, 1 Hz for the meteorological stations and 30 s for the disdrometers. Variability in power production is examined according to different meteorological conditions using the framework of UM and consequences of their correlations are discussed. In the process we also make short commentary on the actual sampling resolution at which fields should be considered for extracting useful statistical information about their variability.

How to cite: Jose, J., Gires, A., Schnorenberger, E., Tchiguirinskaia, I., and Schertzer, D.: Combined multifractal analysis of wind power production and atmospheric fields using simultaneous measurement of high-resolution data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6858, https://doi.org/10.5194/egusphere-egu22-6858, 2022.

EGU22-7783 | Presentations | NP3.2

Intermittency, stochastic Universal Multifractals and the deterministic Scaling Gyroscope Cascade model 

Xin li, Daniel schertzer, Yelva roustan, and Ioulia tchiguirinskaia

Intermittency is a fundamental feature of turbulence and more generally of geophysics, where its ubiquity is increasingly recognized. It corresponds to the concentration of the activity of a field, e.g. the vorticity of a flow, into very small fractions of the physical space. This induces strongly non-Gaussian fluctuations over a wide range of space-time scales. Multifractality corresponds to the fact that this concentration for increasing level of activity, in fact increasing singular behaviour, is supported by fractal sets of decreasing dimensions (and increasing codimensions). This is a general outcome of the (stochastic) multiplicative cascade models and of the universal multifractals, which statistics are defined with the help of two physically meaningful parameters:

  • the ‘mean codimension’ C1 ≥ 0 measures the mean concentration of the activity (C1 = 0 for a non-intermittent field);
  • the ‘multifractality index’ α ∈ (0, 2) measures how fast increases the concentration of the activity with the activity level (α=0 correspond to the monofractal case with a unique singularity / codimension C1, α= 2 corresponds to another exceptional case, the so-called ‘Log-normal’ model)

Multifractal analysis of various turbulence data, especially from lab experiments and atmospheric in-situ/remotely sensed data, have rather constantly yielded estimates of α ≈ 1.5 and C1 ≈ 0.25 , although error bars are difficult to assess. However, the relation between stochastic cascades and the deterministic Navier-Stokes equations have often been brought into question. We therefore analysed in more details the relation between stochastic multiplicative cascades, namely their universality case, and the deterministic Scaling Gyroscope Cascade (SGC, [1]), whose philosophy is rather different: it is based on a parsimonious discretisation of the Fourier transform of the Bernoulli’s form of the Navier-Stokes equations:

(∂/∂t -vΔ)u(x,t)=u(x,t)∧w(x,t)-grad(α), w(x,t)=curl(u(x,t)).


The discretization of the Bernoulli’s form is performed along a dyadic tree structure in a 2D cut: each eddy of velocity uimhas two interacting sub-eddies of velocities u2i−1m+1and u2im+1, where m indexes the cascade level of wave-number km = 2m, i ∈ [1, 2m] being the eddy location. This discretization preserves many symmetries, including the most important one: the non trivial ‘detailed energy conservation’, i.e. that nonlinearly transferred within the triad of a parent eddy and its two children.

We have performed numerous SGC simulations with a constant forcing at a low wave number, a number of cascade levels as high as N = 15 and a duration of 150 largest eddy turnover times. All these simulations display an extreme space-time intermittency. Their multifractal analysis confirms in a very robust manner the estimated α ≈ 1.5 , which is a very important result: it brings into question more than ever the relevance of the often used of the log-normal model, at least for hydrodynamic turbulence. We will present at the conference a similarly robust estimate of C1 after having clarified a recently noted, unexpected sensibility to simulation details.

Keywords: intermittency; the SGC model; multifractal 

Reference:

[1]Chigirinskaya Y, Schertzer D. Cascade of scaling gyroscopes: Lie structure, universal multifractals and self-organized criticality in turbulence[M]//Stochastic Models in Geosystems. Springer, New York, NY, 1997: 57-81.

 

How to cite: li, X., schertzer, D., roustan, Y., and tchiguirinskaia, I.: Intermittency, stochastic Universal Multifractals and the deterministic Scaling Gyroscope Cascade model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7783, https://doi.org/10.5194/egusphere-egu22-7783, 2022.

EGU22-8635 | Presentations | NP3.2

The role of the Aleutian Low in driving Pacific Decadal Variability 

William Dow, Amanda Maycock, Christine McKenna, Paloma Trascasa Castro, Manoj Joshi, Doug Smith, and Adam Blaker

Variability in the Aleutian Low is a known contributor to North Pacific sea surface temperature (SST) variability, but its role in forcing the basin-wide SST anomalies that characterise Pacific Decadal Variability (PDV) is unclear owing to the difficulty of disentangling coupled atmosphere-ocean processes. Here we perform a large ensemble experiment with an intermediate complexity GCM where the winter-time Aleutian Low is nudged to an anomalously strong state during successive winters. This ensemble is compared to a free-running simulation to isolate the impacts of the anomalous Aleutian Low. The nudged experiment produces a basin-scale SST response that closely resembles PDV in the free running simulation, confirming that the Aleutian Low can force PDV-like variability. Tropical Pacific sea surface temperatures (SSTs) are significantly warmer in response to the strong Aleutian Low, demonstrating that extratropical atmospheric forcing can impart a signature in tropical SSTs. The largest tropical Pacific warming is manifest in the season following nudging (boreal spring), though anomalies persist year-round. We use the Bjerknes Stability Index to attribute the drivers of the tropical Pacific SST response and find that the thermocline feedback is key, which itself is most dominant in summer. The results lend new understanding to the potential for extratropical atmospheric forcing of tropical ocean variability.

How to cite: Dow, W., Maycock, A., McKenna, C., Trascasa Castro, P., Joshi, M., Smith, D., and Blaker, A.: The role of the Aleutian Low in driving Pacific Decadal Variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8635, https://doi.org/10.5194/egusphere-egu22-8635, 2022.

EGU22-9120 | Presentations | NP3.2

Modelling Pore Size Distribution, Water Retention and Hydraulic Conductivity of Granular substrates using a Universal Multifractal-based approach for Nature-Based Solutions 

Arun Ramanathan S, Pierre-Antoine Versini, Daniel Schertzer, Ioulia Tchiguirinskaia, Remi Perrin, and Lionel Sindt

The hydrological behavior of granular substrates is of critical interest in Nature-Based Solutions (NBS) like green roofs. To simulate this behavior in a physically realistic manner it is indispensable to model the substrate’s Hydraulic Conductivity (HC) as it determines infiltration rate at various degrees of saturation. Since HC is directly dependent on water content retained by the substrate, it is necessary to physically model this Water Retention (WR) behavior too. Capillary water is stored or retained in pore spaces and this water content that can be retained by a substrate under different suction pressures is therefore dependent upon its Pore Size Distribution (PSD). Since pores in any granular media are spaces where grains are absent, their size distribution too is intrinsically related to the substrate’s Grain Size Distribution (GSD) which provides the probability of finding grains smaller than some diameter dgs. Although some earlier studies have attempted to model PSD, WR, and HC, they frequently use simplifying mono-fractal (fractal) approximations, whereas this study proposes a more generalized multifractal-based approach. Furthermore, while it is quite usual to incorporate pore tortuosity through some indirect parameter l in the HC model, a related ink-bottle effect which even though capable of affecting WR behavior is commonly ignored. Therefore this study suggests the use of a new parameter i in the WR model to physically represent this ink-bottle effect (a consequence of the substrate’s pore configuration or arrangement) which additionally takes into account the pore tortuosity without using l. The proposed models are validated using experimental measurements from 4 different commercially used green roof substrates.

Keywords: Multifractals, Non-linear geophysical systems, Cascade dynamics, Scaling, Hydrology, Green roof substrates.

How to cite: Ramanathan S, A., Versini, P.-A., Schertzer, D., Tchiguirinskaia, I., Perrin, R., and Sindt, L.: Modelling Pore Size Distribution, Water Retention and Hydraulic Conductivity of Granular substrates using a Universal Multifractal-based approach for Nature-Based Solutions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9120, https://doi.org/10.5194/egusphere-egu22-9120, 2022.

EGU22-9290 | Presentations | NP3.2

High resolution Sea Surface Temperature and seawater oxygen isotope records in IODP Site U1389 during MISs 2 to 4 

Javier Perez-Tarruella, Francisco J. Sierro, and Thibauld M. Béjard

IODP Site U1389 recovered a thick contouritic drift sequence deposited on the main branch of the Mediterranean Overflow Water in the gulf of Cadiz. That allows high resolution core recoveries for Quaternary period. In this study a high-resolution SST record was obtained by modern analogues method, using planktic foraminifer assemblages and Artificial Neural Networks. Seawater oxygen isotope composition was inferred by using the Globigerina bulloides δ18O record and the new SST data.

During MIS-3 the average amplitude of the SST change between Greenland stadials and interstadials is in the order of 2 to 4 °C. Foraminifer taxa that best reflects these minor changes is Globigeririnita glutinata. Heinrich stadial periods are represented by abrupt SST drops of about 8 °C compared to Interstadial values, and high abundance of polar and subpolar species Neogloboquadrina pachyderma sin and Turborotalita quinqueloba. During MIS-2 and MIS-4, SST is higher than expected for glacial maxima, with some subtropical species occurrence except in Heinrich events. Seawater δ18O also shows millennial variability, with higher values during Greenland Interstadials and the most pronounced drops or freshening in Heinrich stadial events. During glacial maxima stadials δ18O reaches its highest values, that reflects together with the high SST potential subtropical influence.

SST and seawater δ18O changes along the record precisely reflect the impact of the Greenland stadial-interstadial events and Heinrich events on sea surface conditions. Minor event Heinrich 2.2 (2b) has been identified by SST drop but not by water freshening. Otherwise, Greenland stadial 15, which corresponds to C-14 IRD event in North Atlantic shows Heinrich-like behavior according to both sea surface proxies.

How to cite: Perez-Tarruella, J., Sierro, F. J., and Béjard, T. M.: High resolution Sea Surface Temperature and seawater oxygen isotope records in IODP Site U1389 during MISs 2 to 4, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9290, https://doi.org/10.5194/egusphere-egu22-9290, 2022.

EGU22-10080 | Presentations | NP3.2

South American climate reconstruction during the mid-Holocene from an updated paleodata compilation 

Ilana Wainer, Iuri Gorenstein, Luciana F. Prado, Paula R. Bianchini, Michael L Griffiths, Francesco SR Pausata, and Elder Yokoyama
Investigating the extent and climate implications of the Northern Hemisphere Holocene warm peak that occurred during the mid-Holocene (MH, about 6000 years ago) is of vital interest to better understand and interpret the uncertainties associated with current global warming. Paleoclimate archives are a source of unique indirect measurements, used to characterize past climates. However, several paleo-archives in South America (SA) published before the 2000s have not yet had their derived radiometric ages calibrated, representing a large source of uncertainty in past climate reconstructions. Here we reconstruct eastern SA climate during the MH using 172 paleodata with fully calibrated age models. Our results show that for the MH the Amazon and Southern SA were drier and along the western South Atlantic conditions were saltier compared to  present day climate. Southern SA presents warmer than present MH and the region separating Northeast Brazil and Southern SA together with the easternmost part of Northeast Brazil shows divergent behavior, presenting dispersed higher than present rainfall rates.  

How to cite: Wainer, I., Gorenstein, I., F. Prado, L., R. Bianchini, P., L Griffiths, M., SR Pausata, F., and Yokoyama, E.: South American climate reconstruction during the mid-Holocene from an updated paleodata compilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10080, https://doi.org/10.5194/egusphere-egu22-10080, 2022.

EGU22-10537 | Presentations | NP3.2

How does the spatial scale of natural climate fluctuations vary across timescales? 

Torben Kunz and Thomas Laepple

What is the spatial scale of natural climate fluctuations, and how does it depend on timescale? To answer this question, we characterize the spatio-temporal correlation structure of global surface temperature fields by estimating frequency spectra of the effective spatial degrees of freedom (ESDOF), which can be interpreted as the effective number of independent spatial samples on the globe at each frequency. ESDOF spectra are estimated from the HadCRUT4 global gridded, monthly mean temperature anomaly dataset, based exclusively on instrumental measurements, covering the period 1850 to near-present. Because this dataset includes gaps (due to a lack of observations in certain months and regions on the globe), we employ a newly developed method that allows for bias-free spectral estimation from gappy data without interpolation across gaps. To correct for the anthropogenic warming trend, the data is detrended prior to the analysis, by subtracting the linear response to the anthropogenic global mean log(CO2-equivalent) forcing time series. The resulting ESDOF spectra reveal a reduction of the ESDOF value by a factor of 10, from about 130 (±15%) at sub-annual timescales to about 13 (±50%) at multi-decadal time scales. Uncertainties are estimated by applying the same analysis to a CMIP6 climate model ensemble, with HadCRUT4 data gaps imposed. To test for the possible impact of the data gaps, the ESDOF analysis is applied to global temperature fields with and without gaps, taken from both the climate model ensemble and from the NOAA 20th Century Reanalysis dataset. Results suggest slightly higher ESDOF values for complete fields, with the increase being negligible at sub-annual timescales and of the order of 15-20% at multi-decadal timescales. Overall, the results indicate that natural temperature variability at multi-decadal timescales is characterised by an ESDOF value between 10 and 20. Since it is unlikely, due to physical constraints, that the ESDOF value increases towards timescales longer than those resolved by the instrumental record, the above multi-decadal ESDOF estimate can be taken as an upper limit for centennial and longer timescales. This may have important implications in the context of paleo-climate reconstructions and their comparison with model simulations.

How to cite: Kunz, T. and Laepple, T.: How does the spatial scale of natural climate fluctuations vary across timescales?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10537, https://doi.org/10.5194/egusphere-egu22-10537, 2022.

EGU22-10736 | Presentations | NP3.2

Using proxy data to characterize the spatio-temporal structure of climate variablity 

Raphaël Hébert, Torben Kunz, and Thomas Laepple

The spatial scale of climate fluctuations, or effective spatial degrees of freedom (ESDOF), depends on the timescale and the forcing: while local scale variability between far away locations may be independent on short timescales, they may become coherent over sufficiently long timescales, or if they are driven by a common forcing. While ESDOF have been estimated from instrumental data over the historical period and climate model simulations, it remains difficult to perform such analysis on paleoclimate data given the time uncertainty and proxy-specific bias. We take advantage of a database of absolutely dated annual proxies comprising tree ring, corals and varved sediments in order to provide the first estimate of ESDOF for longer than multi-decadal timescales based on proxy-data.

How to cite: Hébert, R., Kunz, T., and Laepple, T.: Using proxy data to characterize the spatio-temporal structure of climate variablity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10736, https://doi.org/10.5194/egusphere-egu22-10736, 2022.

EGU22-10769 | Presentations | NP3.2 | Highlight

What triggered the Little Ice Age? 

Francois Lapointe and Raymond Bradley

The Little Ice Age (LIA) was one of the coldest periods of the postglacial period in the Northern Hemisphere. Although there is increasing evidence that this time interval was associated with weakening of the subpolar gyre (SPG), the sequence of events that led to its weakened state has yet to be explained. Here, we use a recently reconstructed record of Atlantic Multidecadal Variability (AMV) to provide insights into the sequence of events that led to the LIA. We show that the LIA was preceded by an intrusion of warm Atlantic water into the Nordic Seas in the late 1300s. The intrusion was a consequence of persistent atmospheric blocking over the North Atlantic, linked to unusually high solar forcing in times of lower volcanic activity. The warmer water led to the breakup of sea ice and calving of tidewater glaciers. Weakening of the blocking anomaly in the late 1300s allowed the large volume of ice that had accumulated to be exported into the North Atlantic, contributing to the weakening of the Atlantic Meridional Overturning Circulation (AMOC).

The modern spatial fingerprints involving fast AMOC changes are captured by many highly resolved records from around the Atlantic during the transition from the late 1300s to the early 1400s. Paleoclimatic evidence from the Tropics suggest a more northerly Intertropical Convergence Zone (ITCZ) in the late 1300s followed by a rapid southward shift of the ITCZ in the early 1400s, which is consistent with model simulations of the climatic response in the Tropics to a slowdown in AMOC. While this Atlantic intrusion into the Nordic Seas triggered the main phase of the LIA, the cooling condition was maintained by higher volcanic activity in the ensuing decades that was coincident with lower solar irradiance.

 

 

How to cite: Lapointe, F. and Bradley, R.: What triggered the Little Ice Age?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10769, https://doi.org/10.5194/egusphere-egu22-10769, 2022.

EGU22-10788 | Presentations | NP3.2

PM10 fluctuation modeling in the Caribbean area using the universal multifractals framework 

Thomas Plocoste, Rudy Calif, and Lovely Euphrasie-Clotilde

Every year, the Caribbean basin is strongly impacted by sand mists from African deserts. The health impact is significant for the population living there. This is the reason why a better understanding of the behavior of particulate matter that have an aerodynamic diameter less than 10 µm diameter (PM10) is crucial to predict their fluctuations. The aim of this study is to characterize the PM10 fluctuations in the fully developed turbulence framework. For that, this analysis is carried out using PM10 datasets sampled at daily basis during six years period for three Caribbean islands (Martinique–Guadeloupe-Puerto Rico). After a multifractal analysis, the results obtained show that the log-Lévy model is suitable, in comparison to the log normal model, to fit the scaling exponent function ζ(q) and the multifractal spectrum f(α). Under this basis, a PM10 fluctuations characterization for each island is proposed using the three universal multifractal parameters [1,2]. Hence, stochastic simulations can be envisaged to mimic the stochastic behavior of PM10 data.

References

[1] Schertzer, D., Lovejoy, S., 1987. Physical modeling and analysis of rain and clouds by anistropic multiplicative processes. J. Geophys. Res. 92 (D8), 9693-9714.

[2] Schertzer, D., Lovejoy, S., Schmitt, F., Chigirinskaya, Y., Marsan, D., 1997. Multifractal cascade dynamics and turbulent intermittency. Fractals 5 (3), 427-471.

How to cite: Plocoste, T., Calif, R., and Euphrasie-Clotilde, L.: PM10 fluctuation modeling in the Caribbean area using the universal multifractals framework, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10788, https://doi.org/10.5194/egusphere-egu22-10788, 2022.

EGU22-10981 | Presentations | NP3.2 | Highlight

The enigma of multidecadal to centennial global vs. local temperature variability in models and proxies 

Thomas Laepple, Oliver Bothe, Manuel Chevalier, Beatrice Ellerhoff, Raphaël Hébert, Annika Herbert, Belen Martrat, Eduardo Moreno Chamarro, Kira Rehfeld, Patrizia Schoch, Nils Weitzel, and Elisa Ziegler

Climate variability, resulting from natural radiative forcing and interactions within the climate system, is a major source of uncertainty for regional climate projections. Constraining the amplitude of these natural variations is fundamental to assess the range of plausible future scenarios. As the instrumental record is limited to the last two centuries, information about climate variations on multi-decadal to millennial timescales relies on the analysis of climate proxy records and climate model simulations. However, current results from systematic model-proxy comparisons of natural variability seem contradictory. Several studies suggest that simulated local temperature variability is consistently smaller than proxy-based reconstructions and conclude that climate models might have major deficiencies. Other studies find agreement in global temperature variability across timescales and argue that current models can faithfully simulate climate variability. 

Here, we review the evidence on the strength of natural temperature variability during recent millennia. We identify systematic biases in the reconstructions that may contribute to the model-proxy discrepancy but are likely not sufficient to reach consistency. Instead, we propose that the seemingly contradictory  findings on the (dis)agreement between proxies and simulations can be reconciled assuming that regional climate variations persist on longer time scales than currently simulated by climate models. The combined evidence argues for deficiencies in the simulation of internal variability but a faithful response of climate models to natural radiative forcing. We propose a strategy to test our hypothesis and discuss the implications for future climate projections.

How to cite: Laepple, T., Bothe, O., Chevalier, M., Ellerhoff, B., Hébert, R., Herbert, A., Martrat, B., Moreno Chamarro, E., Rehfeld, K., Schoch, P., Weitzel, N., and Ziegler, E.: The enigma of multidecadal to centennial global vs. local temperature variability in models and proxies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10981, https://doi.org/10.5194/egusphere-egu22-10981, 2022.

EGU22-12100 | Presentations | NP3.2 | Highlight

Climate change and North Atlantic Oscillation (NAO) index: Can entropy reveal a non-natural variability trend in NAO index? 

Antonia Chatzirodou and Angelos Livogiannis

The North Atlantic Oscillation (NAO) index describes the hemispheric meridional oscillation of atmospheric masses above near Iceland and the subtropical Atlantic regions. In specific, NAO index indicates the differences in atmospheric pressure patterns between the two regions. Strong positive NAO index values relate to significant pressure differences, exposing US East and Northern Europe to warmer weather conditions, and Southern Europe to colder weather conditions. Negative NAO index values indicate weaker pressure differences, exposing US East and Northern Europe to cold weather, and Southern Europe to warm weather. A significant portion of the Atlantic sector climate is associated with NAO index and its variability. Historically, NAO index values were in a positive trend between 1970s and 1980s. Highest positive values were reported in the early 1990s. By that time, it was suggested that NAO index positive trends contributed significantly to the global warming signal. Most recently, research outputs from climate model predictions suggest that NAO index values will be more at the positive phase as a result of strong global warming signal. In a warmer climate the overall number of storms is predicted to decrease but storms will be more intense. However, more research is needed to understand variability trends in NAO index and to what extent they might be attributed to climate change impacts. Hence referred in here as NAO natural and non-natural, or else, climate change related variability trends. This study investigated the NAO variability trends by use of Singular Spectrum Analysis (SSA) and SSA based Entropy index. SSA is a statistical mechanics tool used to study the non-linear behavioral characteristics in complex geophysical, meteorological and climatic systems, monitored by time series data. The main objective of this analysis is to reveal the evolution of the NAO index dynamical system and convey information about the changing dynamics of the system. By use of SSA Entropy based index, the chaotic behavior of NAO index is studied. An SSA entropy based chaotic descriptor might entail information of the non-natural variability trend for NAO index values. Also the same descriptor might prove capable of defining a historical milestone of when this NAO variability trend started changing in an unpredictable- non natural- way owed to climate change forcing factors. NAO Index data are extracted from Climatic Research Unit, University of East Anglia from 1979-2018. SSA Phase space reconstruction by method of delays has been applied first to characterize the statistical and chaotic behavior of NAO patterns, by calculating variability and inconsistency descriptors. Phase space reconstruction allows analyzing time series data within the dynamics systems theory context. Following that, reconstructed attractor from the NAO observed time series allowed to build an approximation of the unknown observed states. Results revealed a highly variable and inconsistent behavior in NAO patterns over time. SSA Entropy based index investigation is currently underway to further understand the nature of inconsistency revealed in NAO patterns. Further research is expected to establish wind and wave storm patterns connections with NAO index patterns, through transfer dynamics concepts.

How to cite: Chatzirodou, A. and Livogiannis, A.: Climate change and North Atlantic Oscillation (NAO) index: Can entropy reveal a non-natural variability trend in NAO index?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12100, https://doi.org/10.5194/egusphere-egu22-12100, 2022.

The static characterization and sedimentological/stratigraphic modelling of the Naturally Fractured and Vugular Deposits (YNFV) are carried out based on the multiscale and multi-physical/geological data (Big Geodata). To date, the reference analytical techniques used in the Oil Industry to integrate this information are uncertain. There are several reasons for this, the main one being the different nature and accuracy of the exploration data. Multifractal and p-Adic analyses of the architecture of the field of interest were carried out. It was documented that the trajectories' uncertainty and error in deviation depend on the scale of used information. From the 3D visualization of the YNF and its main structural elements (at scales from mega to micro), the corresponding maps of the heterogeneity and anisotropy of the effective porosity and permeability of the studied YNF were delivered. The main research goal is to develop accurate 2D and 3D maps of the productive horizon (or volume) of interest of the YNF Xikin, with a statistically- and structurally accurate forecast of the hydrocarbons distribution (made from the available seismic cube). The design of wells optimal trajectories and corresponding direction of the shots, based on the pattern of continuity/tortuosity of the corridors or networks of fractures. Muuk´ il Kaab (MIK) software, designed in conjunction with the Ku Maloob Zaap Field Assets and calibrated in several PEMEX fields used to construct the Effective Metric of Connected Fractures in the Xikin from the seismic records, analyze the geometry and topology of clusters detection of anomalous amplitudes/frequencies of seismic waves and to interpret it quantitatively from the point of view of their possible occupation by hydrocarbons and the geometry/topology of networks/fracture corridors. To reduce the bias of the final interpretation of the displayed data, at least ten techniques of nonlinear analysis, including multifractal and p-adic, were used. These techniques, applied to the original seismic records were visualized in the form of Textons (term that comes from Pattern Recognition area), which we will call: Macro- and MicroTexels , depending on the scale of observation and within which synthetic wells with optimal values of the variables selected as Direct Hydrocarbon Indicators (DIHO) were located.

The results of the analysis and visualization of the connected multiscale networks of fractures and according to the direct hydrocarbon indicators selected in this study for Xikin, the following  maps were constructed:
1. A probabilistic map of hydrocarbon concentration zones correlated with Xikin-specific sedimentological/stratigraphic features (with particular attention to the multiscale pattern of fracture);
2. 3D map of the optimal trajectories of the recommended wells, associated with the directional scheme of the shots in the preferential direction of each connected fracture pattern.

How to cite: Oleshko, K., Khrennikov, A., and de Jesús Correa López, M.: Multifractal and p-adic forecasting of distribution and continuity of faults, fracture corridors with a high probability of being associated with hydrocarbons, for the statistically-based design of trajectories of future production wells, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13127, https://doi.org/10.5194/egusphere-egu22-13127, 2022.

EGU22-575 | Presentations | GD6.1

Strain relaxation around stressed quartz inclusions in garnet 

Hugo van Schrojenstein Lantman, David Wallis, Mattia Bonazzi, Jay Thomas, Maartje Hamers, Martyn Drury, and Matteo Alvaro

The measurement of residual stresses in exhumed rocks yields valuable information about metamorphic temperature and pressure, deformation and rheology, and stress state. However, the state of elastic strain and stress at the surface of a sample does not necessarily correspond to the state well below the surface. When a sample under elastic strain is cut, polished, or otherwise prepared for analysis, a part of the constraining rock is removed, allowing for the partial relaxation of the elastic strain. To be able to work with residual elastic strain and stress with analytical methods that probe the upper few microns of a sample, the process of strain relaxation must be well understood.

For this work we used high-angular resolution EBSD to analyse stressed quartz inclusions in natural garnet from a range of settings, and in several samples grown in piston-cylinder experiments that were previously analysed with Raman spectroscopy for inclusion pressures. The experimental samples are not expected to have undergone plastic deformation in the garnet during cooling, as the majority of the pressure within the inclusion built up during decompression at room temperature. Additionally, the inclusion pressures in buried inclusions matches what is expected for the experimental conditions, suggesting no plastic yielding. Thus, in these samples we can isolate elastic strain from potential plastic deformation. One of the experimental samples was analysed with TEM to test this expectation.

Forescatter images reveal topographical effects resembling quartz and adjacent garnet “extruding” out of the sample. Furthermore, rotations of the quartz lattice and the garnet lattice immediately around the quartz inclusion are observed. The rotation axis of the misorientation generally lies in the plane of the sample surface. TEM analysis revealed a number of dislocations in experimental garnet where these were not expected. However, a significant degree of bending of a wedge of garnet between the original sample surface and a quartz inclusion is also observed.

The dislocations observed with TEM do not fit with the model of the experiments. Also, the formation of dislocations before sample preparation does not explain the dependence of the rotation axis on the surface orientation. A likely scenario for the deformation measured with EBSD is that the partial relaxation of elastic strains in stressed quartz inclusions in garnet as result of sample preparation induced local distortion of the inclusion and host. Additionally, the persistence of topographical features related to this relaxation despite several steps of polishing suggests that relaxation is not instantaneous but occurs over time.

How to cite: van Schrojenstein Lantman, H., Wallis, D., Bonazzi, M., Thomas, J., Hamers, M., Drury, M., and Alvaro, M.: Strain relaxation around stressed quartz inclusions in garnet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-575, https://doi.org/10.5194/egusphere-egu22-575, 2022.

EGU22-1247 | Presentations | GD6.1

Fast resetting of zircon in garnet inclusion pressures: implications for elastic geothermobarometry. 

Nicola Campomenosi, Boriana Mihailova, Ross John Angel, Marco Scambelluri, and Matteo Alvaro

The contrast in the thermoelastic properties between one inclusion and its surrounding host is commonly exploited to back-calculate the pressure (P) and temperature (T) conditions of inclusion entrapment. This is elastic thermobarometry and it is based on the elastic properties of minerals rather than chemical equilibrium. The effect of inclusion confinement is the inclusion residual pressure (P-inc), which can be determined via Raman spectroscopy. For a given host-inclusion system, a specific P-inc corresponds a P-T line along which the confinement effects between the two crystals disappear: the isomeke. By definition, this line potentially represents the P-T conditions of inclusion entrapment. Away from the isomeke, the inclusion exhibits over- or under-pressure with respect to the external pressure. The position and slope of the isomeke can be calculated using the equations of state of both the host and the inclusion [1].

In this contribution, we show how zircon-in-garnet isomekes can be partially investigated via in-situ Raman spectroscopy at high T and ambient P by comparing the evolution of the Raman peak position of the inclusion with respect to a free zircon crystal at the same temperature. Several zircon inclusions in pyrope-rich garnets from the Dora-Maira whiteschists (Western Alps) were heated up and brought from the over- to the under-pressure domain across their corresponding isomeke. At temperatures above the isomeke, we found that zircon inclusions in garnet can be reset on the timescale of laboratory experiments: after cooling down the P-inc was different from the original. We interpret this reset as the result of viscous relaxation at the host-inclusion boundary [2] and annealing of submicron dislocations of the garnet host at high temperature. Importantly, for similar heating rate and T range, viscous relaxation occurs more easily when the inclusions are in the under-pressure domain. This suggest that original confinement effects of zircon in a garnet host whose exhumation path mostly occurs within the inclusion under-pressure domain can be easily reset to record P-T conditions on the retrograde path, while those from a garnet host whose exhumation path mostly occurs within the inclusion over-pressure domain can be better preserved. Therefore, since the isomekes of zircon with garnet are steep in P-T, this system may be more reliable for high T and low P terranes for which the exhumation path passes directly or quickly into the over-pressure domain [3]. On the other hand, for UHP domains such as Dora-Maira resetting occurs [4] due to the exhumation path being steep and thus in the under-pressure domain until low pressures.   

[1] Angel et al. 2015 Journal of Metamorphic Geology33(8), 801-813. [2] Zhong et al. 2020 Solid Earth11(1), 223-240.  [3] Gilio et al. 2021 Journal of Metamorphic Geology 10.1111/jmg.12625 [4] Campomenosi et al. 2021 Contributions to Mineralogy and Petrology176(5), 1-17  

This work was supported by the Alexander von Humboldt foundation and the ERC-StG TRUE-DEPTHS grant (number 714936) to M. Alvaro

How to cite: Campomenosi, N., Mihailova, B., Angel, R. J., Scambelluri, M., and Alvaro, M.: Fast resetting of zircon in garnet inclusion pressures: implications for elastic geothermobarometry., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1247, https://doi.org/10.5194/egusphere-egu22-1247, 2022.

EGU22-2449 | Presentations | GD6.1

Hybridization of magmas by break down of partially molten granitic rock and its assimilation 

Pavlina Hasalová, Karel Schulmann, Anne-Sophie Tabaud, and Jitka Míková

During orogenic processes continental crust experiences significant partial melting. Repeated thermal pulses or fluctuation in fluid content can even cause multiple anatectic events that result in complex intrusion suits. The Vosges Mountains (NE France) reveal two chronologically and geochemically distinct tectono-magmatic events. An early major pulse of Mg‒K magmatism was followed ten millions years later by development of a magma-rich detachment zone and intrusion of Central Vosges Granite forming a felsic MASH zone. This MASH zone is characterized by the production of a large quantity of anatectic melts that interacted with the older Mg‒K granites and surrounding granulites and metasedimentary rocks. We aim to understand how such hybridization processes impact on the crustal rocks rheology, deformation as well as its geochemistry and geochronology. Three different granite varieties were distinguished: (i) the older Mg‒K granite end-member that is coarse-grained with a high proportion of feldspar phenocrysts, zircon U-Pb ages of 340 Ma and specific geochemical signature; (ii) Medium-grained type has a smaller amount of phenocrysts and shows advanced brecciation where fine-grained Pl+Kfs+Qtz form discontinuous corridors to an interconnected network surrounding fractured phenocrysts. Its geochemical signature suggests that this represents a mixing of Mg−K and Central Vosges granites, as confirmed by the presence of both inherited (340 Ma) and younger (330‒310 Ma) zircon domains; (iii) Isotropic medium-grained granite that shows geochemical signature typical for the Central Vosges Granite in which younger zircon domains (310‒320 Ma) dominate over inherited xenocrysts (340 Ma). These three granite varieties represent different stages of magma hybridization by the break up of the older Mg‒K granite by the younger Central Vosges Granite magmas. The interaction between new melt and previously crystallized granitoids results in variety of granite textures, fabrics, chemical compositions, isotopic signatures and deformational behavior. In summary, the resulting signature is result of interplay of melt transfer and interaction in the MASH zone.

How to cite: Hasalová, P., Schulmann, K., Tabaud, A.-S., and Míková, J.: Hybridization of magmas by break down of partially molten granitic rock and its assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2449, https://doi.org/10.5194/egusphere-egu22-2449, 2022.

EGU22-3549 | Presentations | GD6.1

Evolution of P-wave velocities during antigorite dehydration at pressures up to 2.5GPa 

Alexandre Schubnel, Arefeh Moarefvand, Julien Gasc, Damien Deldicque, and Loïc Labrousse

Antigorite dehydration is considered as one of the potential triggering mechanisms of intermediate depth earthquakes in subduction zones. Here, the evolution of p-wave velocities were measured during antigorite dehydration experiments at pressure and temperature conditions representative of the upper mantle (1 to 2.5 GPa) for the first time.

Experiments were realized on a natural antigorite serpentinite from Corsica (Gasc et al. 2011), using a 3rdgeneration Griggs-type apparatus equipped with p-wave velocity ultrasonic monitoring (Moarefvand et al. 2021).Velocities were measured maintaining constant hydrostatic pressure conditions at  1, 1.5, 2 and 2.5 GPa, and slowly heating the sample beyond dehydration temperatures. At each pressure conditions, two experiments were carried out at a maximum temperature of 650°C or 700°C respectively, in order to investigate reaction kinetics and equilibrium overstepping. Experiments were quenched once the dehydration was completed, in order to preserve the microstructure.

In all our experiments, P-wave velocity decreased dramatically at the onset of dehydration.  This important drop in elastic properties is related to the fracturing and porous space generated by water release. At 700°C temperature, observed velocity drops were faster, and more pronounced compared to experiments performed at 650°C, indicating that the dehydration reaction progress was faster and more important. The velocity drop also got smaller with increasing pressure, but remained noticeable, even at 2.5GPa, a pressure at which the reaction volume change is negative. This indicates that even in the absence of fluid overpressures, the reaction is accompanied by an important amount of microcracking/softening. Recovered samples were then analyzed using scanning electron microscopy (SEM) and Electron backscatter diffraction (EBSD). With these microstructural data, the final reaction progress/advancement was estimated and we show that in situ measurements of p-wave velocity represent a good proxy for reaction progress and kinetics.

Our study opens up the door to a vast domain, where mineral reactions kinetics could be monitored in situ outside the synchrotron environment, via a direct access to elastic properties. It also reveals our need to apply state of the art effective medium theory modeling of porous and cracked aggregates when computing elastic properties of hydrating/dehydrating mineral assemblages. Finally, the elastic softening observed upon dehydration, even above 2GPa, tends to confirm the dehydration stress transfer model (Ferrand et al. 2017) for intermediate depth earthquake triggering.

 

references:

- Ferrand, Thomas P., et al. "Dehydration-driven stress transfer triggers intermediate-depth earthquakes." Nature communications 8.1 (2017): 1-11.

- Gasc, Julien, et al. "Simultaneous acoustic emissions monitoring and synchrotron X-ray diffraction at high pressure and temperature: Calibration and application to serpentinite dehydration." Physics of the Earth and Planetary Interiors189.3-4 (2011): 121-133.

- Moarefvand, Arefeh, et al. "A new generation Griggs apparatus with active acoustic monitoring." Tectonophysics816 (2021): 229032.

How to cite: Schubnel, A., Moarefvand, A., Gasc, J., Deldicque, D., and Labrousse, L.: Evolution of P-wave velocities during antigorite dehydration at pressures up to 2.5GPa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3549, https://doi.org/10.5194/egusphere-egu22-3549, 2022.

EGU22-4325 | Presentations | GD6.1

Pervasive melt migration in hot continental crust – numerical models 

Petra Maierová, Pavlína Hasalová, Karel Schulmann, and Pavla Štípská

The common view of melt transport in the continental crust involves an initial stage of percolation along grain boundaries, melt segregation into leucosomes and dykes, coalescence of small melt conduits into larger ones and quick nearly vertical melt flow leading to formation of plutons. An entirely different style of melt migration was described in the Bohemian Massif, eastern European Variscan belt. There, a sequence of metaigneous migmatites was described where veins are lacking, leucosomes are rare and relics of melt are spread along grain boundaries. Textural, geochemical and compositional variations in these rocks show that they formed due to equilibration with melt coming from an external source, and that pervasive flow along grain boundaries was the dominant mechanism of melt transport.

The question arises, at what conditions this style of melt transport can operate and what consequences the different styles of melt transport have on the crustal-scale tectonics. We address this question by means of a 2D crustal-scale model of two-phase flow using the code ASPECT (aspect.geodynamics.org). The system of pores through which the melt flows is not resolved in our model and it is described only by its permeability. A low permeability describes material with pores along grain boundaries while a high permeability corresponds to a system of leucosomes, dykes or cracks

For different material properties and thermal conditions we obtain different styles of melt migration and characteristics of the modeled crust. The melt can form a diffuse zone in the lower–middle crust, km-scale waves of high melt fraction gathering into sub-vertical channels, or a horizontal zone with high melt fraction in the middle crust. The lower crust is depleted and the middle crust is enriched in incompatible elements, and composition of the middle crust typically shows km-scale variations. The compositional variations are obtained even in the models with low permeability that corresponds to the melt percolation along grain boundaries, in agreement with the characteristics of the Bohemian migmatites.

How to cite: Maierová, P., Hasalová, P., Schulmann, K., and Štípská, P.: Pervasive melt migration in hot continental crust – numerical models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4325, https://doi.org/10.5194/egusphere-egu22-4325, 2022.

EGU22-4494 | Presentations | GD6.1

Large-strain elastoplastic formulations for host-inclusion systems with applications to elasto-thermobarometry and geodynamic models 

Evangelos Moulas, Konstantin Zingerman, Anatoly Vershinin, Vladimir Levin, and Yuri Podladchikov

Elastic thermobarometry has been at the forefront of research during the last decade. Using state-of-the-art spectroscopic and diffraction methods it has been possible to assess the residual elastic strain of mineral inclusions in an in-situ manner (Mazzucchelli et al., 2021; Zhong et al., 2019). The interpretation of residual stress/strain and its extrapolation to geological conditions requires mechanical models, that are based on continuum mechanics, which provide the range of pressure-temperature (P-T) conditions where host and inclusion are under homogeneous stress. This set of conditions may correspond to the entrapment conditions if the system is purely elastic. In the case of viscous/plastic relaxation of the host-inclusion system, the inferred P-T conditions represent apparent-entrapment conditions that could lie anywhere between the conditions of the true entrapment and the conditions of viscous/plastic relaxation (Moulas et al., 2020; Zhong et al., 2020). Thus, the interpretation and validity of elastic barometry strongly relies on the purely elastic behavior of the host-inclusion system.

The commonly employed elastic solutions assume a linear-elastic behavior and deal only with small-strain approximations. However, large values of residual stresses/strains may indicate that the range of decompression for such host-inclusion systems requires the incorporation of material/geometric non-linearity. In this work, we provide new numerical and analytical solutions for the non-linear, elasto-plastic behavior of host-inclusion systems. Our analytical solutions are based on new published models that describe the Neo-Hookean behavior of materials and reduce to the Murnaghan equation of state when the deformation is purely volumetric (Levin et al., 2021). We find that for the range of residual pressures that is commonly employed in barometric applications (<1GPa) the incorporation of geometric non-linearity does not influence the results significantly. Nevertheless, the incorporation of plasticity and the combined non-linear elastic and plastic behavior may lead to results that render elasto-thermobarometry inapplicable for very large compression/decompression ranges. Our results can be useful for benchmarking: a) models relevant to elasto-thermobarometry and b) geodynamic models that require the treatment of large volumetric deformations during the exhumation from lithospheric/mantle depths.

References

Levin, V.A., Podladchikov, Y.Y., Zingerman, K.M., 2021. An exact solution to the Lame problem for a hollow sphere for new types of nonlinear elastic materials in the case of large deformations. European Journal of Mechanics - A/Solids 90, 104345. https://doi.org/10.1016/j.euromechsol.2021.104345

Mazzucchelli, M.L., Angel, R.J., Alvaro, M., 2021. EntraPT: An online platform for elastic geothermobarometry. American Mineralogist 106, 830–837. https://doi.org/10.2138/am-2021-7693CCBYNCND

Moulas, E., Kostopoulos, D., Podladchikov, Y., Chatzitheodoridis, E., Schenker, F.L., Zingerman, K.M., Pomonis, P., Tajčmanová, L., 2020. Calculating pressure with elastic geobarometry: A comparison of different elastic solutions with application to a calc-silicate gneiss from the Rhodope Metamorphic Province. Lithos 378–379, 105803. https://doi.org/10.1016/j.lithos.2020.105803

Zhong, X., Andersen, N.H., Dabrowski, M., Jamtveit, B., 2019. Zircon and quartz inclusions in garnet used for complementary Raman thermobarometry: application to the Holsnøy eclogite, Bergen Arcs, Western Norway. Contributions to Mineralogy and Petrology 174, 50. https://doi.org/10.1007/s00410-019-1584-4

Zhong, X., Moulas, E., Tajčmanová, L., 2020. Post-entrapment modification of residual inclusion pressure and its implications for Raman elastic thermobarometry. Solid Earth 11, 223–240. https://doi.org/10.5194/se-11-223-2020

How to cite: Moulas, E., Zingerman, K., Vershinin, A., Levin, V., and Podladchikov, Y.: Large-strain elastoplastic formulations for host-inclusion systems with applications to elasto-thermobarometry and geodynamic models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4494, https://doi.org/10.5194/egusphere-egu22-4494, 2022.

EGU22-6103 | Presentations | GD6.1 | Highlight

The role of mechanics in the modelling of common rock microstructures 

Lucie Tajcmanova, Yury Podladchikov, and Ivan Utkin

Understanding rocks at the microscale is essential to comprehending Earth's history and making reasonable predictions about how planetary processes may change in the future.  

Advanced models for complex rock microstructures, such as symplectites or a development of exsolution lamellae, have been developed (Kuhl & Schmid, 2007; Petrishcheva & Abart, 2009). Despite of this recent valuable progress in our understanding of these microstructures, the mechanisms controlling its evolution especially from slowly cooled rocks are still not complete.

Commonly, such models focus solely on the chemical process. Interestingly, mechanics, i.e. stress and pressure redistribution, may also play an important role on microstructure evolution. In this contribution, we investigate the coupled, chemo-mechanical, effect for representative rock microstructures. We provide a comparison between purely chemical vs. coupled chemo-mechanical systems and provide predictions on the evolution of the given microstructures in 3D.

References:

Kuhl, E., Schmid, D.W. (2007). Computational Modeling of Mineral Unmixing and Growth. Comput Mech 39, 439–451.

Petrishcheva, E., & Abart, R. (2009). Exsolution by Spinodal Decomposition I: Evolution Equation for Binary Mineral Solutions with Anisotropic Interfacial Energy. American Journal of Science, 309(6), 431-449.

 

How to cite: Tajcmanova, L., Podladchikov, Y., and Utkin, I.: The role of mechanics in the modelling of common rock microstructures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6103, https://doi.org/10.5194/egusphere-egu22-6103, 2022.

EGU22-7325 | Presentations | GD6.1

A multiscale model for coupled chemical reaction and deformation of porous rocks 

Viktoriya Yarushina and Yury Podladchikov

Coupled hydro-mechano-chemical (HMC) modeling is a topic of active ongoing research in various branches of Earth sciences and subsurface engineering. In engineering applications, HMC modeling is used to assess the feasibility of permanent CO2 storage in mafic and ultramafic rocks. The deformation and stresses building during the reaction is believed to induce fracturing, increase permeability and thus promote extensive reactions between CO2 and host rock. CCS in depleted reservoirs faces challenges related to possible CO2 leakage through old plugged and abandoned wells. When CO2 reaches the well, old cement compositions react with cement, compromising well integrity due to chemical degradation. In geology, coupled reactions and deformation are involved in melt extraction and migration, influencing the dynamics of volcanic systems and the evolution of subduction zones.

A large focus of previous studies was whether or not it is possible to achieve 100% of the reaction. Common reactive transport models predict that the reaction product will clog the pores, which will stop the fluid flow and thus further reactions. However, recent developments suggest that reaction progress depends on the assumed reaction kinetics and the constitutive models used in coupled models. Models that account for solid volume change as in mineral replacement reactions have a much higher potential for preserving porosity than the common dissolution-precipitation model, thus predicting the complete reaction. It is often assumed that reaction processes are transport-dominated, i.e., that all dissolved material is carried away by pore fluid. Then it precipitates on the available pore space leading to clogging and permeability reduction. However, recent observations suggest that while some reactions might be associated with dissolution and precipitation at the nano-scale, aqueous species transport is limited, and reaction products do not precipitate in the pores but rather stay attached to the primary mineral. Thus, the overall effect is the same as in mineral replacement reactions.

Using a combination of effective media theory and irreversible thermodynamics approaches, we propose a new model for reaction-driven mineral expansion, which preserves porosity and limits unrealistically high build-up of the force of crystallization by allowing inelastic failure processes at the pore scale. To fully account for the coupling between reaction, deformation, and fluid flow, we derive macroscopic poroviscoelastic stress-strain constitute laws that account for chemical alteration and viscoelastic deformation of porous rocks. These constitutive equations are further used with macroscopic conservation laws to illustrate the mutual impact of reactive transport and mechanical deformation on simple 1D examples of wellbore stability and fluid transport.

How to cite: Yarushina, V. and Podladchikov, Y.: A multiscale model for coupled chemical reaction and deformation of porous rocks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7325, https://doi.org/10.5194/egusphere-egu22-7325, 2022.

EGU22-8033 | Presentations | GD6.1

Modelling focused fluid flow: What matters? 

Lawrence Hongliang Wang, Viktoriya M. Yarushina, and Yury Podladchikov

Two-phase flow equations that couple solid deformation and fluid migration have opened new research trends in geodynamical simulations and modelling of subsurface engineering operations. The physical nonlinearity of fluid-rock systems and strong coupling between flow and deformation in such equations lead to interesting predictions such as the spontaneous formation of focused fluid flow in ductile/plastic rocks. However, numerical implementation of two-phase flow equations and their application to realistic geological environments with complex geometries and multiple stratigraphic layers is challenging. Here, we present an efficient pseudo-transient solver for two-phase flow equations. We first study the focused fluid flow under the viscous regime without considering the elasticity. The roles of material parameters, reservoir topology, geological heterogeneity, and porosity are investigated. We show that focused fluid channels are the natural outcome of the flow instability of the two-phase system with a low ratio (< 0.1) between shear viscosity and bulk viscosity. We also confirm the previous studies that  decompaction weakening is necessary to elongate the porosity profile. The permeability exponents play the dominant role in the speed of wave propagation. The numerical models study fluid leakage from high porosity reservoirs into less porous overlying rocks. Geological layers present in the overburden do not stop the propagation of the localized channels but rather modify their width, permeability, and growth speed. We further validate our conclusions by modelling the full two-phase system with viscoelastic rheology and elastic solid and fluid compressibility (Yarushina et al., 2015). The Deborah number (De), solid (Ks), and fluid (Kf) bulk moduli are thus introduced into the governing equations. We found that the elasticity makes a difference when the Deborah number approaches one by speeding up the channel propagation. At the same time, its effect is rather limited when Deborah's number is small (e.g., 0.1). The effects of compressibility of the solid and fluid, on the other hand, are not found significant within the reasonable ranges of the bulk moduli.

 

How to cite: Wang, L. H., Yarushina, V. M., and Podladchikov, Y.: Modelling focused fluid flow: What matters?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8033, https://doi.org/10.5194/egusphere-egu22-8033, 2022.

EGU22-8422 | Presentations | GD6.1 | Highlight

Chronometry of a nappe-scale thermal event inferred by thermobarometry and viscous relaxation of quartz inclusion pressure (Adula nappe, Alps) 

Xin Zhong, Marisa Germer, Alexandra Pohl, Vincent Könemann, Olga Brunsmann, Philip Groß, Jan Pleuger, and Timm John

The Adula nappe is located at the eastern flank of the Lepontine dome in the Swiss Alps. It consists mainly of orthogneiss and paragneiss with intercalated lenses of eclogite, amphibolite and metasediments. Previous petrological studies on the peak pressure and temperature (P-T) conditions yield somewhat inconsistent results, particularly the pressure in the southern part of the nappe, but in general exhibit an increasing trend in both P-T towards the south. In this work, we applied zirconium-in-rutile thermometer and quartz-in-garnet Raman elastic barometer to constrain the P-T conditions using samples covering most of the nappe with high spatial coverage within the 600 km2 area to obtain an internally consistent dataset. Based on the results of zirconium-in-rutile thermometer, the temperature gradually increases from the north at ca. 540 °C to the south at ca. 680 °C. Using the quartz-in-garnet elastic barometer, the calculated entrapment pressure increases from ca. 2.0 GPa to ca. 2.2 GPa from the north to the middle-south region of the Adula nappe, but rapidly falls to ca. 0.8-1.2 GPa towards the southern region, where the temperature exceeds ca. 650 °C. It is speculated that due to the temperature increase towards the south, viscous relaxation became activated that led to an apparent drop of the recorded residual quartz inclusion pressure. This suggests that by applying a pure elastic model to high temperature conditions, one may potentially underestimate of the formation pressure of garnets. Therefore, this study may provide information on the limit of the quartz-in-garnet (pure) elastic barometry technique. Moreover, it may offer a potential opportunity to constrain the duration of the near-isothermal decompression path if a viscoelastic model can be applied, which requires not only the equation of state of minerals but also the creep behavior of the inclusion-host system.

How to cite: Zhong, X., Germer, M., Pohl, A., Könemann, V., Brunsmann, O., Groß, P., Pleuger, J., and John, T.: Chronometry of a nappe-scale thermal event inferred by thermobarometry and viscous relaxation of quartz inclusion pressure (Adula nappe, Alps), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8422, https://doi.org/10.5194/egusphere-egu22-8422, 2022.

EGU22-8776 | Presentations | GD6.1

Local variations of metamorphic record from compositionally heterogeneous rocks: Inferences on exhumation processes of (U)HP-HT rocks (Cima di Gagnone, Adula-Cima Lunga unit) 

Stefania Corvò, Matteo Maino, Antonio Langone, Filippo Luca Schenker, Leonardo Casini, Sandra Piazolo, and Silvio Seno

The record of metamorphic conditions may be highly heterogeneous in spatially close rocks with different composition and rheology. The Cima di Gagnone area (Central Alps) represents an example of ultrahigh–pressure and high–temperature ultramafic lenses enveloped within amphibolite–facies metasediments. Structural investigations demonstrate that the rheologically strong ultramafics and eclogites and weak metapelites experienced a common Alpine deformation history in a single tectonic unit, excluding their coupling within a tectonic mélange (Maino et al., 2021). New structural, microstructural and petrological analyses and thermodynamic modelling results on the metasediments, confirming that all rocks generally experienced medium pressure and medium temperature conditions of 1.0–1.2 GPa and 640–700 °C, followed by a retrograde stage around 0.6–0.8 GPa and 600–675 °C. However, significantly higher P–T conditions of 1.3–3.0 GPa and 750–850 °C are locally developed close to the rheological boundary depicted by the micaschists-peridotite contact (Corvò et al., 2021; Piccoli et al., 2021). Rock and mineral chemistry changes during growth of new mineral phases indicate a local melt/fluid interaction (i.e., metasomatism) between metasediments and ultramafics during the high temperature deformation. The local occurrence of (U)HP and HT conditions is demonstrated by the absence of significant melting in the unit, although around the peridotite lenses, metapelites show hydrated assemblage at T>800 °C were stable at variable P stage. U-Pb zircon and monazite dating indicate that local HP and HT conditions were accomplished at the early stage of Alpine exhumation (~36 Ma), while the rocks fa form the rheological boundaries records only pre–Alpine ages. Our results documented that, even though weak metasediments share the same structural evolution with the strong UM, large differences in the local metamorphic conditions (ΔP up to 2 GPa; ΔT up to 160 °C) are recorded in relation to the distance from the UM lenses. Fluid–assisted metasomatism is further documented as being strongly localized at the interface between ultramafic lenses and the metapelitic host throughout all part of the metamorphic evolution, including the HP–HT stage. Therefore, in the Cima di Gagnone type–locality, the interplay between metapelites and ultramafic exerts a crucial first–order control to allow assemblage equilibrium during HT metamorphism and amphibolite–facies retrogression. These new findings exclude that the different metamorphic record may be attributed only to differential preservation during the retrograde path. Our new P–T–t–D paths highlight the crucial role of the rheological boundaries in modify the P-T metamorphic records without varying lithostatic pressure and thus depth conditions.

References:

Maino, M., Adamuszek, M., Schenker, F.L., Seno, S., Dabrowski, M., 2021. Sheath fold development around deformable inclusions: Integration of field-analysis (Cima Lunga unit, Central Alps) and 3D numerical models. J. Struct. Geol. 144, 104255.

Corvò, S., Maino, M., Langone, A., Schenker, F. L., Piazolo, S., Casini, L., & Seno, S., 2021. Local variations of metamorphic record from compositionally heterogeneous rocks (Cima di Gagnone, Central Alps): Inferences on exhumation processes of (U) HP–HT rocks. Lithos, 390, 106126.

Piccoli, F., Lanari, P., Hermann, J., & Pettke, T., 2021. Deep subduction, melting, and fast cooling of metapelites from the Cima Lunga Unit, Central Alps. Journal of metamorphic geology

How to cite: Corvò, S., Maino, M., Langone, A., Schenker, F. L., Casini, L., Piazolo, S., and Seno, S.: Local variations of metamorphic record from compositionally heterogeneous rocks: Inferences on exhumation processes of (U)HP-HT rocks (Cima di Gagnone, Adula-Cima Lunga unit), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8776, https://doi.org/10.5194/egusphere-egu22-8776, 2022.

EGU22-9093 | Presentations | GD6.1 | Highlight

Grain-scale equilibrium reactions guide fluid-driven eclogitization of dry crustal rocks 

Timm John, Sascha Zertani, Johannes C. Vrijmoed, Caroline Brachmann, and Oliver Plümper

When a fluid is introduced into dry rocks at high-pressure conditions, it acts as a catalyst and facilitates re-equilibration. This often promotes weakening and subsequent ductile deformation. Here, we present a detailed micro-structural and mineral chemical study of eclogitization of initially dry continental crustal rocks in the absence of ductile deformation. The studied sample features an incomplete (fluid-induced) transition from lower crustal granulite to eclogite, and the transition is fully preserved. None of the mineral phases show any signs of ductile deformation, indicating that the transformation was entirely static. Material transport during the reaction was limited to the availability of fluids. Detailed analysis of the local assemblages along the transect reveals that the reaction occurs in three distinct steps: The plagioclase-plagioclase grain boundaries were the first to re-equilibrate followed by clinopyroxene-plagioclase and garnet-plagioclase grain boundaries. Lastly, the grain boundaries that included only garnet and/or clinopyroxene are involved in the transformation. Thermodynamic modelling of local equilibria at dry conditions and with H2O in excess reveals that this stepwise transformation is caused by the varying reactivity of the local assemblages at the prevailing P-T conditions. Those reactions that result in the largest decrease of the Gibbs free energy from the dry case to the case with H2O in excess occur first. Once the reaction is facilitated, this effect is amplified because the density increase is largest at those grains boundaries that have reacted first, creating new fluid pathways through volume reduction. The calculated stable local mineral assemblages are consistent with those present in the sample indicating that element transport is limited, also supported by the observation that the fabric of the granulite is preserved in the eclogite. Our results demonstrate that reactive fluid flow is guided by the local energy budget along the grain boundaries, and that element transport during static re-equilibration is limited to the extent where it is thermodynamically advantageous.

How to cite: John, T., Zertani, S., Vrijmoed, J. C., Brachmann, C., and Plümper, O.: Grain-scale equilibrium reactions guide fluid-driven eclogitization of dry crustal rocks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9093, https://doi.org/10.5194/egusphere-egu22-9093, 2022.

EGU22-9608 | Presentations | GD6.1 | Highlight

Formation of olivine veins by dehydration during viscously deforming serpentinite: a numerical study 

Stefan Markus Schmalholz, Evangelos Moulas, Ludovic Räss, and Othmar Müntener

The dehydration of serpentinite during subduction and the associated formation of dehydration veins is an important process for the global water cycle and the dynamics of the subducting plate. Field observations suggest that olivine veins can form by dehydration during viscous shear deformation of serpentinite. However, this hypothesis of olivine vein formation, involving the coupling of rock deformation, dehydration reactions and fluid flow, has not been tested and quantified by hydro-mechanical-chemical (HMC) models. Here, we present a new two-dimensional HMC numerical model to test whether olivine veins can form by dehydration during viscous shearing of serpentinite. The applied numerical algorithm is based on the pseudo-transient finite difference method. We consider the simple reaction antigorite + brucite = forsterite + water. Volumetric deformation is viscoelastic and shear deformation is viscous with a shear viscosity that is an exponential function of porosity. In the initial model configuration, total and fluid pressures are homogeneous and in the antigorite stability field. Small, initial perturbations in porosity, and hence in viscosity, cause pressure perturbations during far-field simple shearing. During shearing, the fluid pressure can locally decrease and reach the thermodynamic pressure required for the dehydration reaction, so that dehydration is triggered locally. The simulations show that dehydration veins form during progressive shearing and grow in a direction parallel to the maximum principal stress. During the dehydration the porosity can increase locally from 2% (initial value) to more than 50% inside the dehydration vein. The numerical model allows quantifying the mechanisms and variables that control the evolution of porosity and fluid pressure. We show that the porosity evolution is controlled by three mechanisms: (1) volumetric deformation of the porous solid, (2) temporal variation of the solid density and (3) mass transfer during the dehydration reaction. We quantify the evolution of the fluid pressure that is controlled by five variables and processes: (1) the total pressure of the porous rock, (2) elastic effects of the total volumetric deformation, (3) the temporal variation of porosity, (4) the temporal variation of solid density and (5) mass transfer during the dehydration reaction. This model supports the observation-based hypothesis of the formation of olivine veins due to dehydration during viscous shearing of serpentinite. More generally, our HMC model provides quantitative insights into the evolution of porosity, and hence dynamic permeability, fluid pressure and mass transfer during dehydration reactions in deforming rock.

How to cite: Schmalholz, S. M., Moulas, E., Räss, L., and Müntener, O.: Formation of olivine veins by dehydration during viscously deforming serpentinite: a numerical study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9608, https://doi.org/10.5194/egusphere-egu22-9608, 2022.

EGU22-9773 | Presentations | GD6.1

The influence of non-hydrostatic stress on mineral equilibria: insights from Molecular Dynamics 

Mattia L. Mazzucchelli, Evangelos Moulas, Boris Kaus, and Thomas Speck

Mountain building, earthquake generation, and volcanic eruptions occur in Earth’s lithosphere and have direct impacts on society. Understanding the mechanism of geodynamic processes relies on the determination of the pressure-temperature history which is recorded by rocks that have been involved in geodynamic processes. In most cases, the interpretation of the conditions attained by rocks is based on the assumption that the stresses in the Earth are hydrostatic. However, non-hydrostatic stresses are observed in the lithosphere, and the significance of the magnitude of the differential stress on phase equilibria is still actively contested among researchers who hold completely incompatible views about the use of various thermodynamic potentials (e.g. [1-3]).

The problem of phase equilibria under non-hydrostatic stress has been explored in several rock-deformation experiments (on mm scale), in which recrystallization of minerals was observed under an applied non-hydrostatic stress [4-6]. However, during experiments, stress and pressure heterogeneities may develop in the sample (e.g. [6]). Therefore, the direct effect of the applied non-hydrostatic stress on the thermodynamics of the reactions cannot be separated from the effect caused by local pressure variations in the sample itself.

Here, we explore the effect of non-hydrostatic stress on the thermodynamics of mineral reactions by investigating a system at the molecular scale. With Molecular Dynamics (MD) we perform coexistence simulations in which two phases are brought in contact and equilibrated at given temperature, pressure, and stress conditions. As expected, the obtained stress component normal to the phase-phase interfaces is homogeneous across the system. Our data suggest that the direct effect of non-hydrostatic stress on the solid-liquid equilibria is rather minor for geological applications, consistent with theoretical predictions [7,8]. However, our analysis does not take into account the indirect effect of stress heterogeneities at the sample scale. Spatial variations of stress can reach GPa level and can therefore indirectly affect phase equilibria.

M.L. Mazzucchelli is supported by an Alexander von Humboldt research fellowship.

References

[1] Wheeler, J. Geology 42, 647–650 (2014);

[2] Hobbs, B. et al. Geology 43, e372 (2015);

[3] Tajčmanová, L. et al. Lithos 216–217, 338–351 (2015)

[4] Hirth, G. et al. J. Geophys. Res. 99, 11731–11747 (1994)

[5] Richter, B. et al. J. Geophys. Res. Solid Earth 121, 8015–8033 (2016)

[6] Cionoiu, S. et al. Sci. Rep. 9, 1–6 (2019)

[7] Sekerka, R. et al. Acta Mater., 52(6), 1663–1668 (2004)

[8] Frolov, T. et al. Phys. Rev. B Condens. Matter Mater. Phys. 82, 1–14 (2010)

How to cite: Mazzucchelli, M. L., Moulas, E., Kaus, B., and Speck, T.: The influence of non-hydrostatic stress on mineral equilibria: insights from Molecular Dynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9773, https://doi.org/10.5194/egusphere-egu22-9773, 2022.

EGU22-10147 | Presentations | GD6.1

H2O contents in nominally anhydrous minerals and its effect on the formation of eclogite-facies, hydrous shear zones (Holsnøy, Western Norway) 

Lisa Kaatz, Stefan M. Schmalholz, Julien Reynes, Jörg Hermann, and Timm John

High-grade dry granulites of Holsnøy (Western Norway) were subducted during the Caledonian orogeny and reached eclogite-facies conditions at ~2 GPa and 700° C. However, they stayed in a metastable state until brittle deformation enabled infiltration of an aqueous fluid, which triggered the kinetically delayed eclogitization. Field observations reveal an interconnected network of hydrated eclogite-facies shear zones surrounded by unaltered and pristine granulites. The formation of these features is highly controlled by deformation, fluid infiltration and fluid-rock interaction.

At first, the shear zone evolution was analyzed to better understand the relation between strain localization within the shear zones and the progressive widening of these shear zones from cm- to m-wide thickness. The results showed that widening overcomes the effect of stretching during progressive fluid-rock interaction and strain accumulation, if either a substantial amount of continuously infiltrating fluid and/or numerous repetitive fluid pulses enter the system.

Therefore, investigations have been carried on the H2O contents in nominally anhydrous minerals of the granulite and eclogite. The H2O contents were measured using Fourier transform infrared spectroscopy. Garnet (grt), clinopyroxenes (cpx) and plagioclase (plg) have been measured with a close look on spatial repartition of OH at the grain scale and at the shear zone scale. The aim is to decode the link between fluid infiltration, mineral reaction, and deformation. There are no significant compositional changes between granulite and eclogite, which means that the fluid mainly worked as a catalyst without mass transfer beside H2O. The analyses across a shear zone profile reveal three major observations: (i) average H2O contents of the grt cores increase from granulite towards the shear zone (from 10 to 50 µg/g), (ii) average H2O contents of the cpx increase, too (from 145 to 310 µg/g), (iii) the plg stores limited amounts of H2O until a phase separation leads into an symplectites consisting of albite-rich plg (anhydrous) and clinozoisite (hydrous). The H2O contents of the minerals are interpreted to be a result of two different diffusional mechanisms acting simultaneous at different spatial scales and rates. The H2O increase in grt and cpx cores without mineral reaction is a result of hydrogen diffusion (H+/H2), which is much faster and pervasive than the porous influx of an aqueous fluid (H2O), which, contemporaneously, caused the formation of hydrous phases.

The above findings are combined in a 1D numerical shear zone model to reproduce the measured mineral chemical data and the respective H2O-contents. The results shed light on the dynamic weakening processes caused by the influx of H+/H2 in combination with synkinematic mineral reactions.

How to cite: Kaatz, L., Schmalholz, S. M., Reynes, J., Hermann, J., and John, T.: H2O contents in nominally anhydrous minerals and its effect on the formation of eclogite-facies, hydrous shear zones (Holsnøy, Western Norway), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10147, https://doi.org/10.5194/egusphere-egu22-10147, 2022.

EGU22-10316 | Presentations | GD6.1 | Highlight

Thermolab: a thermodynamics laboratory for non-linear transport processes in open systems 

Johannes C. Vrijmoed and Yury Y. Podladchikov

We developed a numerical thermodynamics laboratory called “Thermolab” to study the effects of the thermodynamic behavior of non-ideal solution models on reactive transport processes in open systems. The equations of state of internally consistent thermodynamic datasets are implemented in MATLAB functions and form the basis for calculating Gibbs energy. A linear algebraic approach is used in Thermolab to compute Gibbs energy of mixing for multi-component phases to study the impact of the non-ideality of solution models on transport processes. The Gibbs energies are benchmarked with experimental data, phase diagrams and other thermodynamic software. Constrained Gibbs minimization is exemplified with MATLAB codes and iterative refinement of composition of mixtures may be used to increase precision and accuracy. All needed transport variables such as densities, phase compositions, and chemical potentials are obtained from Gibbs energy of the stable phases after the minimization in Thermolab. We demonstrate the use of precomputed local equilibrium data obtained with Thermolab in reactive transport models. In reactive fluid flow the shape and the velocity of the reaction front vary depending on the non-linearity of the partitioning of a component in fluid and solid. We argue that non-ideality of solution models has to be taken into account and further explored in reactive transport models. Thermolab Gibbs energies can be used in Cahn-Hilliard models for non-linear diffusion and phase growth. This presents a transient process towards equilibrium and avoids computational problems arising during precomputing of equilibrium data.

How to cite: Vrijmoed, J. C. and Podladchikov, Y. Y.: Thermolab: a thermodynamics laboratory for non-linear transport processes in open systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10316, https://doi.org/10.5194/egusphere-egu22-10316, 2022.

EGU22-10318 | Presentations | GD6.1 | Highlight

Eclogitization of the Allalin gabbro under heterogeneous stress conditions 

Cindy Luisier, Philippe Yamato, Horst R. Marschall, Evangelos Moulas, and Thibault Duretz

Eclogitization reactions in mafic rocks involve large volume changes, porosity evolution and fluid transfer. They impact many important geological processes such as the localization of deformation and fluid channeling at intermediate depth in subduction zone. The study of exhumed eclogitic bodies in orogens shows that eclogitization of the oceanic crust is heterogeneous from both a structural and metamorphic point of view. For example, in the European Alps, the Allalin metagabbro shows high strain areas, consisting of hydrous metagabbros, fully equilibrated under eclogite-facies conditions during the Alpine orogeny. Conversely, large volumes of low strain, fluid-undersaturated gabbros remained largely unaffected by the high-pressure (HP) metamorphism, locally preserving igneous textures and even, occasionally, relics of their magmatic mineralogy. The intensity of deformation as well as the degree of eclogitization in the metagabbro have been shown to be directly related to the extent of pre-Alpine hydration during high-temperature hydrothermal alteration [1]. However, the influence of this degree of hydration on (1) reaction kinetics and/or (2) enhancing rheological contrasts leading to heterogeneous deformation patterns and metamorphic conditions is still debated.

In order to address this issue, we propose a multidisciplinary study involving petrographic and microtextural observations combined with 2D thermo-mechanical numerical models allowing to discuss the role of pre-Alpine hydrothermal alteration on the development of HP metamorphic assemblages.

We present petrographic and textural data from three different types of rocks from the Allalin metagabbros: i) undeformed and mostly untransformed metagabbros, with relics of igneous augite and plagioclase, ii) coronites, with olivine pseudomorphs showing different levels of hydration, rimmed by a garnet corona, and iii) eclogitized metagabbros, with olivine and plagioclase sites fully replaced by high-pressure assemblages.

The role of protolith hydration on the observed range in metamorphic facies is then tested by using 2D thermo-mechanical models that allow to simulate the deformation of a strong and dry rock with several randomly oriented weak and hydrous zones. Our results show that the shearing of heterogeneous rock can lead to the formation of localized ductile shear zone within a matrix that remains relatively undeformed but where plastic deformation can occur. The associated P field is also highly heterogeneous, with P ranging from 1 to 3 GPa. The deformation patterns and P modelled may suggest that locally hydrated portions of the gabbro acted as rheological perturbations sufficiently efficient in producing the structural and metamorphic record now observed in the field.

 

 

[1] Barnicoat, A. C. & Cartwright, I. (1997) Journal of Metamorphic Geology 15, 93–104

How to cite: Luisier, C., Yamato, P., Marschall, H. R., Moulas, E., and Duretz, T.: Eclogitization of the Allalin gabbro under heterogeneous stress conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10318, https://doi.org/10.5194/egusphere-egu22-10318, 2022.

EGU22-10383 | Presentations | GD6.1

Geodynamic constraints on ophiolite emplacement 

Iskander Ibragimov and Evangelos Moulas

Ophiolite complexes are commonly found outcropping along ancient suture zones in continental regions. Many geological studies suggest that, during subduction initiation, a small remnant of the oceanic crust can be thrusted upon continenal regions. This thrusting occurs during a process that is generally termed as “ophiolite obduction”. Despite the relatively small volume of the ophiolite rocks, their occurence provides important geologic/geodynamic constraints for the processes of subduction initiation. 
Following the seminal work of Cloos (1993), oceanic lithosphere that is older than 10 Myrs is dense enough, and as a result, facilitates oceanic subduction in a spontaneous manner. This suggestion is based on the fact that buoyancy is one of the most important forces relevant to large-scale geodynamics. However, old oceanic lithosphere is also expected to be cold and, as a consequence, mechanically strong. The increased strength of the oceanic lithosphere hinders subduction initiation and makes ophiolite obduction difficult.
In this work we perform systematic numerical simulations to investigate the effects of initial geometry and convergence velocity on subduction initiation and ophiolite obduction. We use LaMEM to calculate 2D thermo-mechanical models that include the effects of visco-elasto-plastic rheology. In addition, we have incorporated a thermodynamically-consistent density structure for the crust and mantle. In this way, buoyancy forces are calculated in a consistent manner based on the pressure and temperature fields of the thermo-mechanical models. Our results show that when the oceanic lithosphere is older than 10Myr, subduction is very difficult and does not initiate in a spontaneous manner. Our systematic simulations provide insights for the range of conditions and parameters of oceanic subduction and ophiolite emplacement.

References
Cloos, M. (1993) Lithospheric Buoyancy and Collisional Orogenesis: Subduction of Oceanic Plateaus, Continental Margins, Island Arcs, Spreading Ridges, and Seamounts. Geological Society of America Bulletin, 105, 715-737.
https://doi.org/10.1130/0016-7606(1993)105<0715:LBACOS>2.3.CO;2

How to cite: Ibragimov, I. and Moulas, E.: Geodynamic constraints on ophiolite emplacement, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10383, https://doi.org/10.5194/egusphere-egu22-10383, 2022.

EGU22-10445 | Presentations | GD6.1

Reactive Melt Transport Using Porosity Waves Across the Thermal Boundary Layer. 

Marko Repac, Annelore Bessat, Stefan Schmalholz, Yury Podladchikov, Kurt Panter, and Sebastien Pilet

The lithosphere and the asthenosphere are characterized by different heat transport mechanisms, conductive for the lithosphere, convective for the asthenosphere. The zone associated with the transition between these two distinct mechanisms is known as the "Thermal Boundary Layer" (TBL). How the melt is transported across this zone is an important question regarding intraplate magmatism and for the nature of the seismic Low-Velocity Zone. Numerous studies and models suggest that primary magmas from intraplate volcanos are the product of low degree partial melting in the asthenosphere, while the differentiation process takes place in the crust or shallow lithospheric mantle. The question is how low degree melt ascends through the TBL and the lithospheric mantle. The thermal structure of the lithosphere is characterized by a high geothermal gradient, which questions the ability of melt to cross the lithospheric mantle without cooling and crystallizing. Since the base of the lithosphere is ductile, the possible modes of magma transport are porous flow or porosity waves. For these reasons, we would like to understand how melt is transported and what are the implications on the evolution of primitive melt, going from the convective part of the geotherm to the conductive part of the geotherm and further across the lithosphere.

We present the results of a thermo-hydro-mechanical-chemical (THMC) model1 for reactive melt transport using the finite difference method. This model considers melt migration by porosity waves and a chemical system of forsterite-fayalite-silica. Variables, such as solid and melt densities or MgO and SiO2 mass concentrations, are functions of pressure, temperature, and total silica mass fraction (CtSiO2). These variables are pre-computed with Gibbs energy minimization and their variations with evolving P, T, and CtSiO2 are implemented in the THMC model. We consider P and T conditions relevant across the TBL. With input parameters characteristic for alkaline melt and conditions at the base of the lithosphere, we obtain velocities between 1 to 150 m yr-1,which is a velocity similar to melt rising at mid-ocean ridges2. This implies the inability of primary melts to cross the lithosphere. However, melt addition to the base of the lithosphere is important to understand mantle metasomatism, and could, to some extent, contribute to physical properties of the Lithosphere-Asthenosphere Boundary and Mid Lithosphere Discontinuity observed with geophysical methods. We suggest that the appearance of alkaline magmas at the surface requires multiple stage processes as melts rising in the lithosphere progressively modify the geotherm allowing new melts to propagate to the surface. Our earlier modeling results1 demonstrated that a single porosity wave has a minor impact on chemical evolution. In this study, we search for a mechanism responsible for stabilizing porosity wave motion to some lateral location forcing consecutive waves to follow the same ascent path. The passage of a large number of quickly rising porosity waves over a long time through the same path would accumulate large melt to rock ratios and cause significant chemical evolution.

 

  • Bessat et at., 2022, G3, in press
  • Connolly et al. 2009, Nature 462, 209-212.

How to cite: Repac, M., Bessat, A., Schmalholz, S., Podladchikov, Y., Panter, K., and Pilet, S.: Reactive Melt Transport Using Porosity Waves Across the Thermal Boundary Layer., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10445, https://doi.org/10.5194/egusphere-egu22-10445, 2022.

EGU22-11149 | Presentations | GD6.1

Early reaction of plagioclase : an underrated alteration step during burial of the continental crust 

Loic Labrousse, Marie Baisset, and Alexandre Schubnel

Mutual links between metamorphic reactions and rheological properties of rocks under pressure, temperature and deviatoric stress are a major source of discrepancy of thermo-mechanical models when it comes to predict strain localization for instance. The interactions between metamorphism and strain are also considered as a possible cause for unexpected mechanical instabilities, e.g. mechanical failure, in lithological units buried deep in convergent plate boundaries.

The partially transformed granulite facies anorthosites on the Holsnøy Island, Bergen Arcs, Norwegian Caledonides, constitute one of the few archetypical exposure of crustal rocks deforming and reacting at the same time in the eclogite facies conditions. In these rocks, eclogite-facies paragenesis develops with devitrification patterns in « brittle » pseudotachylyte, and in their damage walls, along a pervasive network of « ductile » shear zones, as well as « statically » along digitations following the preserved granulite facies foliation, with no apparent relation to strain.

The present study, that follows recent advances in the understanding of relationships between crystallization of pyroxene and local scale pressure field, or modeling of the interaction between the eclogitization reactions sequence and strain localization, focuses on the first steps of incipient plagioclase destabilization along eclogite facies « fingers ». 

Granulite facies plagioclase, close to 40 % anorthite in composition, is subject to reactions both in the NASH and CASH subsystems, with contrasted stoechiometries and kinetics. Petrological observations evidence that the lowermost pressure reaction in the CASH system (an + H2O = zo + ky + qz), occurs unbalanced, with high kinetics and reaction volume change and therefore initiates strain within plagioclase grains, that react by twinning and subgrains individualization. This early stage of intra-grain transformation induces an effective grain size reduction, and favors fluid percolation, therefore promoting the eclogitization progression. The reaction occurring inside of plagioclase grains also affects their grain boundaries where kyanite and transient reactions products, such as potential melts, accumulate also altering the overall aggregate properties. 

We claim that this early, fast and pervasive reaction is a significative, yet underrated, step of mechanical alteration of the burying continental rocks.

How to cite: Labrousse, L., Baisset, M., and Schubnel, A.: Early reaction of plagioclase : an underrated alteration step during burial of the continental crust, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11149, https://doi.org/10.5194/egusphere-egu22-11149, 2022.

EGU22-11487 | Presentations | GD6.1 | Highlight

Deformation-facilitated melting of plagioclase 

Sarah Incel, Marie Baisset, Loic Labrousse, and Alexandre Schubnel

Geological processes involving deformation and/or reactions are highly influenced by the rock grain size, especially if diffusion-controlled processes take place such as long-range metamorphic reactions and diffusion creep. Although many processes, inducing grain-size reduction, are documented and understood at relatively high stresses and low temperatures (e.g., cataclasis) as well as at lower stress and higher temperature conditions (e.g., bulging, subgrain rotation), deformation twinning, a plastic deformation mechanism active in various minerals at lower temperatures, has been neglected as cause for grain-size reduction so far. We conducted experiments on natural plagioclase-bearing aggregates at 2.5 to 3 GPa confining pressure and temperatures of 720 to 950 °C using two different deformation apparatus, a DDIA and a Griggs press, as well as a piston-cylinder apparatus. Regardless of the apparatus type, we observe the breakdown of plagioclase into an eclogite-facies paragenesis, which is associated with partial melting in the high pressure, high temperature domain of the eclogite facies. In contrast to the sample that experienced hydrostatic conditions in the piston-cylinder press, the deformed samples reveal melt patches inside of several plagioclase grains. These patches coincide with the occurrence of deformation twins in plagioclase that formed due to differential stress. The ability of plagioclase to form deformation twins and their exploitation for melt initiation significantly lowers the effective grain size of plagioclase-rich rocks and thus impacts their reactivity and deformation behavior.

How to cite: Incel, S., Baisset, M., Labrousse, L., and Schubnel, A.: Deformation-facilitated melting of plagioclase, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11487, https://doi.org/10.5194/egusphere-egu22-11487, 2022.

EGU22-11490 | Presentations | GD6.1

Creep and acoustic emission in Shales from the Barents Sea 

Alina Sabitova, Sergey Stanchits, Viktoriya Yarushina, Georgy Peshkov, Lyudmila Khakimova, and Vladimir Stukachev

Nowadays, environmental awareness has become one of the key directions of humankind development. There are a lot of projects aimed at preserving the environment: ensuring the environmental safety of geothermal energy facilities; study of global geodynamics and its influence on the composition, state, and evolution of the biosphere; geoecological substantiation of safe placement, storage, and disposal of toxic, radioactive and other wastes, etc. An essential role is assigned to the storage of increasing volumes of carbon dioxide gas. This problem requires complex approaches and solutions. Given that both CO2 and radioactive storage are long-term projects, it is necessary to investigate the creep process to monitor the state of the underground environment and assess the risks of leakage. A viscous deformation of the formation accompanies the prolonged loading. Viscosity is an essential parameter in coupling fluid flow and deformation processes occurring on Earth [Sabitova et al., 2021]. At the same time, focused fluid flow is a common phenomenon in sedimentary basins worldwide. Flow structures often penetrate the sandy reservoir rocks and clay-rich caprocks [Peshkov et al., 2021]. The impacts of the viscoelastic deformation of clay-rich materials need to be evaluated from an experimental and modeling perspective to understand better the mechanisms forming such structures. Here, we present multistage triaxial laboratory creep experiments with acoustic emission analysis conducted on samples from the Barents Sea. We performed lithological and geochemical characterization of each sample as a petroleum system element. Bulk and shear viscosities used in numerical models are calculated for all samples. The experimental curves are explained using the theoretical model for porous rock viscoelastoplastic (de)compaction [Yarushina et al., 2020].

References:

Sabitova, A., Yarushina, V. M., Stanchits, S., Stukachev, V., Khakimova, L., & Myasnikov, A. (2021). Experimental compaction and dilation of porous rocks during triaxial creep and stress relaxation. Rock Mechanics and Rock Engineering, 54(11), 5781-5805.

Peshkov, G. A., Khakimova, L. A., Grishko, E. V., Wangen, M., & Yarushina, V. M. (2021). Coupled Basin and Hydro-Mechanical Modeling of Gas Chimney Formation: The SW Barents Sea. Energies, 14(19), 6345.

Yarushina, V. M., Podladchikov, Y. Y., & Wang, L. H. (2020). Model for (de) compaction and porosity waves in porous rocks under shear stresses. Journal of Geophysical Research: Solid Earth, 125(8), e2020JB019683.

How to cite: Sabitova, A., Stanchits, S., Yarushina, V., Peshkov, G., Khakimova, L., and Stukachev, V.: Creep and acoustic emission in Shales from the Barents Sea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11490, https://doi.org/10.5194/egusphere-egu22-11490, 2022.

EGU22-11811 | Presentations | GD6.1

The simplest visco- or elasto-plastic rheology allowing to spontaneous earthquake nucleation 

Yury Alkhimenkov, Ivan Utkin, Lyudmila Khakimova, Celso Alvizuri, and Yury Podladchikov

Understanding the physical processes governing earthquake nucleation has been a hot topic since the last decade. A lot of research has been done trying to explain the physics of seismic triggering events. However, the exact physics behind seismic events nucleation is still poorly understood. The outcome of our recent research is the new theory of earthquake nucleation (Alkhimenkov et. al., 2021). The simplest visco-plastic or elasto-plastic rheology allows us to model spontaneous earthquake nucleation. We consider pure shear boundary conditions and slowly increase stress in the model reflecting the stress increase e.g., due to tectonic forces in real rocks. Once the stress field reaches the yield surface, the strain localization occurs, resulting in slowly developing fractal shear bands. As time evolves, shear bands grow spontaneously, and stress drops take place in the medium. Such stress drops are caused by the instantaneous development of new shear bands, their intersections, and intersections with the boundaries of the numerical domain. A stress drop corresponds to a particular new strain localization pattern. The new strain localizations act as seismic sources and trigger seismic wave propagation (Minakov and Yarushina, 2021). We suggest that the (seismic) radiation pattern of the focal mechanism might be similar to a particular moment tensor source, typical for realistic earthquakes (Alvizuri et al., 2018). This new modeling approach is based on conservation laws without any experimentally derived constitutive relations.

References

Alkhimenkov Y., Utkin I., Khakimova L., Alvizuri C., Quintal Q., Podladchikov Y. Spontaneous earthquake nucleation in elasto-plastic media. 19th Swiss Geoscience Meeting 2021, Geneva, Switzerland.

Minakov, A. and Yarushina, V., 2021. Elastoplastic source model for microseismicity and acoustic emission. Geophysical Journal International, 227(1), pp.33-53.

Alvizuri, C., Silwal, V., Krischer, L. and Tape, C., 2018. Estimation of full moment tensors, including uncertainties, for nuclear explosions, volcanic events, and earthquakes. Journal of Geophysical Research: Solid Earth, 123(6), pp.5099-5119.

How to cite: Alkhimenkov, Y., Utkin, I., Khakimova, L., Alvizuri, C., and Podladchikov, Y.: The simplest visco- or elasto-plastic rheology allowing to spontaneous earthquake nucleation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11811, https://doi.org/10.5194/egusphere-egu22-11811, 2022.

EGU22-11836 | Presentations | GD6.1

Thermo-chemo-mechanical coupling in Maxwell-Stefan multi-component diffusion 

Lyudmila Khakimova, Evangelos Moulas, Ivan Utkin, and Yury Podladchikov

Classical Fickian linear diffusion of inert or trace-like elements is restricted to ideal solution models of components with equal molar mass. Simultaneous diffusion of multiple concentrations is well-treated by the classical Maxwell-Stefan model. Quantitative predictions of concentrations evolution in real mixtures require careful replacement of concentration gradients by gradients of chemical potentials. Coupling of multi component diffusion to mechanics result in pressure gradients that contribute to Gibbs-Duhem relationship. We aim at developing of thermodynamically admissible multicomponent thermo-chemo-mechanical (TMC) model with ensured non-negative entropy production. We also ensure correct equilibrium limit with zero gradients of chemical potentials of individual components and satisfaction of classical Gibbs-Duhem and Maxwell relationships under pressure gradients. Following recent Tajčmanová et al. (2021) we consider both molar and mass formulations. We present optimal pseudo-transient numerical scheme for multi-diffusional fluxes coupled to visco-elastic bulk deformation.

Tajčmanová, L., Podladchikov, Y., Moulas, E. and L. Khakimova. The choice of a thermodynamic formulation dramatically affects modelled chemical zoning in minerals. Sci Rep 11, 18740 (2021).

How to cite: Khakimova, L., Moulas, E., Utkin, I., and Podladchikov, Y.: Thermo-chemo-mechanical coupling in Maxwell-Stefan multi-component diffusion, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11836, https://doi.org/10.5194/egusphere-egu22-11836, 2022.

EGU22-12215 | Presentations | GD6.1

Channelizing of melt flow by reactive porosity waves and its impact on chemical differentiation 

Andrey Frendak, Yury Alkhimenkov, Lyudmila Khakimova, Ivan Utkin, Yury Podladchikov, and Stefan Schmalholz

Many geodynamic processes are coupled. For example, in the partially molten mantle, the solid and molten mantle phases interact chemically during porous melt flow. For such two-phase reactive melt migration, solid and melt densities are functions of temperature, pressure, and chemical composition. Numerical models of such coupled physical-chemical systems require special treatment of the various couplings and concise numerical implementation. We elaborate a 2-D thermo-hydro-mechanical-chemical (THMC) numerical model for melt migration by porosity waves coupled to chemical reactions (Bessat et. al., 2021). We consider a simple ternary chemical system of forsterite-fayalite-silica to model melt migration within partially molten peridotite around the lithosphere-asthenosphere boundary. Our THMC model can simulate porosity waves of different shapes depending on the ratio of shear to bulk viscosity and the ratio of decompaction to compaction bulk viscosity. For an initial circular (blob-like) porosity perturbation, having a 2-D Gaussian shape, the geometry of the propagating reactive porosity wave remains blob-like if all viscosities are similar. If the decompaction bulk viscosity is smaller than the compaction bulk viscosity, so-called decompaction weakening, then the propagating porosity wave evolves into a channelized form. Our simulations quantify the variation from a blob-like to a channel-like porosity wave as a function of the viscosity ratios. We describe the 2-D THMC numerical algorithm which is based on the pseudo-transient finite difference method. Furthermore, we quantify the impact of channelization on the chemical differentiation during melt flow. Particularly, we quantify the evolution of the total silica concentration during melt migration as a function of the degree of channelization.

References

Bessat, A., Pilet, S., Podladchikov, Y. Y., & Schmalholz, S. M. (2022). Melt migration and chemical differentiation by reactive porosity waves. Geochemistry, Geophysics, Geosystems. In press.  

How to cite: Frendak, A., Alkhimenkov, Y., Khakimova, L., Utkin, I., Podladchikov, Y., and Schmalholz, S.: Channelizing of melt flow by reactive porosity waves and its impact on chemical differentiation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12215, https://doi.org/10.5194/egusphere-egu22-12215, 2022.

EGU22-12337 | Presentations | GD6.1

Experimental and numerical investigation of acoustic emission and its moment tensors in sandstones during failure based on the elastoplastic approach 

Elena Grishko, Viktoriya Yarushina, Maria Bobrova, Sergei Stanchits, Alexander Minakov, and Vladimir Stukachev

Microseismicity and acoustic emission (AE) studies are a part of earthquake science. Compared to ordinary earthquakes, microseismic events are characterized by higher frequencies, lower magnitudes, shorter duration, and more complex source mechanisms. The researchers associate the induced seismicity with different processes: borehole breakouts, tunnel excavations, hydraulic fracturing, wastewater injection, and stimulation of geothermal reservoirs.

Acoustic emission represents elastic waves generated spontaneously due to the formation of microfractures when the rock is undergoing a sufficiently high load. AE can be used to obtain continuous data at various stages of the deformation process: from distributed plastic failure to localized macroscopic failure. The spatial distribution of AE events indicates the location of fractures, and the source mechanism provides information about the failure mode: a tensile fracture, a shear fracture, or a combination of both.

This work shows the results of an experimental study of borehole breakouts in sandstones. We measured AE during the deformation experiments and applied the moment tensor analysis to microseismic waveforms. We used a continuum mechanics model of Minakov and Yarushina [2021] to relate the laboratory AE data to the deformation processes. The comparison of the failure patterns and corresponding seismic responses obtained in laboratory and simulations, allows to classify the deformation regimes in real rocks based on seismic observables.

EG, MB, SS, and VS gratefully acknowledge support from the Ministry of Science and Higher Education of the Russian Federation under agreement No. 075-15-2020-119 within the framework of the development program for a world-class Research Center.

 

References:

  • Minakov, A., Yarushina, V., Elastoplastic source model for microseismicity and acoustic emission, Geophysical Journal International, Volume 227, Issue 1, October 2021, Pages 33–53, https://doi.org/10.1093/gji/ggab207

How to cite: Grishko, E., Yarushina, V., Bobrova, M., Stanchits, S., Minakov, A., and Stukachev, V.: Experimental and numerical investigation of acoustic emission and its moment tensors in sandstones during failure based on the elastoplastic approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12337, https://doi.org/10.5194/egusphere-egu22-12337, 2022.

The simplest kinetic normal growth model assumes linear dependence of the transformation rate (or the velocity of the phase boundary) on overstepping of equilibrium conditions (or the degree of metastability).   Under pressure gradients within the phases, the equilibrium state requires zero spatial gradient of difference of the chemical potentials of the two chemical components. This can be achieved by diffusional redistribution of the fraction of two components. At the phase boundary, equilibrium requires the equality of both chemical potentials. Accordingly, at the phase boundary, the linear kinetic model may assume the first component exchange between the phases to be proportional to the chemical potential difference of this component and the phase boundary velocity to be proportional to the chemical potential difference of the second complementary component. The phenomenological proportionality constants are needed to quantify the "mobility" of the phase boundary and intensity mass exchange between phases. These phenomenological material parameters can either be taken from an experiment or derived from a Cahn-Hilliard-type model. Cahn-Hilliard-type model resolving the fine structure of advancing phase boundary  ‘can derive, rather than postulate, a kinetic relation governing the mobility of the phase boundary and check the validity of the "normal growth" approximation’ (Truskinovsky, 1994).

Truskinovsky, L. About the “normal growth” approximation in the dynamical theory of phase transitions. Continuum Mech. Thermodyn 6, 185–208 (1994). https://doi.org/10.1007/BF01135253

How to cite: Podladchikov, Y. and Utkin, I.: Normal growth versus Cahn-Hilliard models for kinetics of the first-order phase transformations in binary mixtures under pressure gradients, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12437, https://doi.org/10.5194/egusphere-egu22-12437, 2022.

EGU22-12496 | Presentations | GD6.1 | Highlight

Numerical modelling of lithospheric deformations with frictional plasticity 

Thibault Duretz, René de Borst, Ludovic Räss, Phillippe Yamato, Tim Hageman, and Laetitia Le Pourhiet
Strain localisation is a key process that allows for the emergence of tectonic plates and controls their long-term deformation. Upper crustal levels are relatively cold and their rheology is thus governed by frictional plasticity. In order to predict the formation of tectonic plates and quantify the deformation of the Earth's upper shell, geodynamic modelling simulation tools must reliably account for deformation in the frictional plastic realm. 
Nevertheless, the simulation of frictional plastic strain localisation poses severe issues. Commonly employed implementations (visco-plastic and visco-elasto-plastic) often fail to accurately satisfy force balance and suffer from a lack of convergence upon mesh refinement. These problems are intimately linked to the fact that commonly employed models do not encompass any characteristic spatial or temporal scales of localisation. Various regularisation techniques can thus be used as a remedy. Here we investigate three popular regularisation techniques, namely viscoplasticity, gradient plasticity and the use of a Cosserat medium, and discuss their potential application for geodynamic modelling.  

How to cite: Duretz, T., de Borst, R., Räss, L., Yamato, P., Hageman, T., and Le Pourhiet, L.: Numerical modelling of lithospheric deformations with frictional plasticity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12496, https://doi.org/10.5194/egusphere-egu22-12496, 2022.

EGU22-13185 | Presentations | GD6.1 | Highlight

Brittle failure at high-pressure conditions: the key role of reaction-induced volume changes 

Philippe Yamato, Thibault Duretz, Marie Baïsset, and Cindy Luisier

Metamorphic reactions can lead to drastic changes in rocks mechanical properties. Indeed, during such transformations, the nucleation of new phases with different strength, grain size and/or density compared to the primary phases is enhanced, and transient processes due to the ongoing reaction are then activated.

Eclogitization of lower crustal rocks during continental subduction constitutes an emblematic transformation illustrating these processes. In such tectonic context, it has been shown that eclogitization seems to be closely associated with the occurrence of seismogenic events. However, the mechanisms that trigger brittle failure in such high pressure environments remain highly debated. Indeed, whether the change in density or the change in rheology can lead to embrittlement is still enigmatic.

By using 2D compressible mechanical numerical models we studied the impact of the strong negative volume change of the eclogitization reaction on the rocks rheological behaviour. We show that eclogitization-induced density change occurring out of equilibrium can, by itself, generates sufficient shear stress to fail the rocks at high-pressure conditions.

Rupture initiation at depth in continental subduction zones could therefore be explained by volume changes, even without considering the modifications of the rheological properties induced by the transformation. Our results also indicate that the negative volume change associated with brittle failure can enhance the propagation of the eclogitization process by a runaway mechanism as long as the reaction is not limited by the lack of reactants.

 

How to cite: Yamato, P., Duretz, T., Baïsset, M., and Luisier, C.: Brittle failure at high-pressure conditions: the key role of reaction-induced volume changes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13185, https://doi.org/10.5194/egusphere-egu22-13185, 2022.

In the recent decade, numerical modelling approaches based on combination of staggered finite differences with marker in cell techniques became increasingly popular in geodynamics due to their simplicity, flexibility and computational efficiency. Here, I present new version of popular 3D thermomechanical code i3ilvis, which has been fundamentally revised to include the following methodological advances (Gerya, 2019 and references therein):

  • Full thermomechanical coupling (through global Picard iteration) including compressible time-dependent mass conservation equation and adiabatic and shear heating effects in the energy conservation equation.
  • Regularized visco-elasto-viscoplastic rheological model with/without dilation. (Duretz et al., 2019) based on global thermomechanical Picard iteration.
  • Accurate continuity-based velocity interpolation for marker advection applicable for both compressible and incompressible flows.
  • Free surface stabilization against “drunken sailor” instability.
  • Accurate 3D rotation of elastic stresses on markers.
  • Dislocation-diffusion creep rheology with grainsize evolution(Bercovici and Ricard, 2012) including newton iteration for dislocation creep to compute effective viscosity for markers.

The new code is OpenMP parallel and has already been successfully tested for cases of realistic 3D geodynamic modeling including tectono-magmatic model of continental breakup to oceanic spreading transition and spontaneous subduction initiation scenario associated with slab bending and normal faulting.

 

Bercovici, D., Ricard, Y. (2012) Mechanisms for the generation of plate tectonics by two- phase grain-damage and pinning. Phys. Earth. Planet. Inter. 202-203, 27–55.

Duretz, T., de Borst, R., Le Pourhiet, L. (2019) Finite thickness of shear bands in frictional viscoplasticity and implications for lithosphere dynamics. Geochemistry, Geophysics, Geosystems, 20, 5598–5616.

Gerya T.V. (2019) Introduction to Numerical Geodynamic Modelling. Second Edition. Cambridge University Press, 472 pp.

 

How to cite: Gerya, T.: New i3elvis: Robust visco-elasto-plastic geodynamic modelling code based on staggered finite differences and marker in cell, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13215, https://doi.org/10.5194/egusphere-egu22-13215, 2022.

Atmospheric water management or cloud seeding technologies might be effectively applied to assess the impacts from changing climate on water security and renewable energy use. During said assessments it might be possible to exploit their observations to mitigate the negative impacts from climate change by enhancing the water supply as part of a water security plan, and/or by effectively removing low-level supercooled cloud decks/fogs to facilitate renewable energy use providing added sunshine during typically overcast day-time periods. Cloud seeding technologies are used to positively affect the natural hydrologic cycle, while respecting and avoiding damage to public health, safety and the environment.  This talk summarizes atmospheric water management technologies and their use, how these technologies might be applied as part of a strategy to ensure water security and how their application might provide a potential opportunity for recouping lost energy potential.

How to cite: DeFelice, T.: The role atmospheric water management technologies might play in Nature-based solutions (NbS), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1941, https://doi.org/10.5194/egusphere-egu22-1941, 2022.

EGU22-2263 | Presentations | GI6.3

EasyGeoModels: a New Tool to Investigate Seismic and Volcanic Deformations Retrieved through Geodetic Data. Software Implementation and Examples on the Campi Flegrei Caldera and the 2016 Amatrice Earthquake 

Giuseppe Solaro, Sabatino Buonanno, Raffaele Castaldo, Claudio De Luca, Adele Fusco, Mariarosaria Manzo, Susi Pepe, Pietro Tizzani, Emanuela Valerio, Giovanni Zeni, Simone Atzori, and Riccardo Lanari

The increasingly widespread use of space geodesy has resulted in numerous, high-quality surface deformation data sets. DInSAR, for instance, is a well-established satellite technique for investigating tectonically active and volcanic areas characterized by a wide spatial extent of the inherent deformation. These geodetic data can provide important constraints on the involved fault geometry and on its slip distribution as well as on the type and position of an active magmatic source. For this reason, over last years, many researchers have developed robust and semiautomatic methods for inverting suitable models to infer the source type and geometry characteristics from the retrieved surface deformations.

In this work we will present a new software we have implemented, named easyGeoModels, that can be used by geophysicists but also by less skilled users who are interested in sources modeling to determine ground deformation in both seismo-tectonic and volcanic contexts. This software is characterized by some innovative aspects compared to existing similar tools, such as (i) the presence of an easy-to-use graphic interface that allows the user, even if not particularly expert, to manage the data to be inverted, the input parameters of one or more sources, the choice of the deformation source (s), effective and simple way; (ii) the possibility of selecting the GPS data to be inverted, simply by selecting the area of interest: in this case the software will automatically consider for the inversion only the GPS stations present in the selected area and will download the relative data from the Nevada Geodetic Laboratory site; (iii) the generation of output files in Geotiff, KMZ and Shapefile format, which allow a faster and more immediate visualization through GIS tools or Google Earth.

Finally, as applications, we will show some preliminary results obtained through the easyGeoModels software on areas characterized by huge deformation both in a volcanic context, such as that of the Campi Flegrei caldera, and a seismo-tectonic one, as for the case of the Amatrice earthquake (central Italy) which occurred on 24 August 2016.

How to cite: Solaro, G., Buonanno, S., Castaldo, R., De Luca, C., Fusco, A., Manzo, M., Pepe, S., Tizzani, P., Valerio, E., Zeni, G., Atzori, S., and Lanari, R.: EasyGeoModels: a New Tool to Investigate Seismic and Volcanic Deformations Retrieved through Geodetic Data. Software Implementation and Examples on the Campi Flegrei Caldera and the 2016 Amatrice Earthquake, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2263, https://doi.org/10.5194/egusphere-egu22-2263, 2022.

EGU22-4876 | Presentations | GI6.3 | Highlight

Geodetic imaging of the magma ascent process during the 2021 Cumbre Vieja (La Palma, Canary Islands) eruption 

Monika Przeor, José Barrancos, Raffaele Castaldo, Luca D’Auria, Antonio Pepe, Susi Pepe, Takeshi Sagiya, Giuseppe Solaro, and Pietro Tizzani

On the 11th of September of 2021, a seismic sequence began on La Palma (Canary Islands), followed by a rapid and significant ground deformation reaching more than 10 cm in the vertical component of the permanent GNSS station ARID (Aridane) operated by the Instituto Volcanológico de Canarias (INVOLCAN). The pre-eruptive episode lasted only nine days and was characterized by an intense deformation in the western part of the island and intense seismicity with the upward migration of hypocenters. After the onset of the eruption, which occurred on the 19th of September of 2021, the deformation increased a few cm more, reaching a maximum on the 22nd of September and subsequently showing a nearly steady deflation trend in the following months.

We obtained a Sentinel-1 DInSAR dataset along both ascending and descending orbits, starting from the 27th of February of 2021 and the 13th of January of 2021, respectively. We selected the study area at the radial distance of 13 km from the eruption point (Latitude: 28.612; Longitude: -17.866) to realize an inverse model of the geometry of the causative sources of the observed ground deformation. While the ascending orbit that passed on the 18th of September indicated mainly a dike intrusion in the shallow depth, the descending orbit from the 20th of September seemed to indicate a deformation caused by at least two sources: the pre-eruptive intrusion and the nearly-vertical eruptive dike. The deeper source spatially coincides with the location of most of the pre-eruptive volcano-tectonic hypocenters.

Finally, based on the preliminary inverse model of the DInSAR dataset, we applied the geodetic imaging of D’Auria et al., (2015) to retrieve the time-varying spatial distribution of volumetric ground deformation sources. The final results show the kinematics of the upward dike propagation and magma ascent.

 

References

D’Auria, L., Pepe, S., Castaldo, R., Giudicepietro, F., Macedonio, G., Ricciolino, P., ... & Zinno, I. (2015). Magma injection beneath the urban area of Naples: a new mechanism for the 2012–2013 volcanic unrest at Campi Flegrei caldera. Scientific reports, 5(1), 1-11.

How to cite: Przeor, M., Barrancos, J., Castaldo, R., D’Auria, L., Pepe, A., Pepe, S., Sagiya, T., Solaro, G., and Tizzani, P.: Geodetic imaging of the magma ascent process during the 2021 Cumbre Vieja (La Palma, Canary Islands) eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4876, https://doi.org/10.5194/egusphere-egu22-4876, 2022.

EGU22-5431 | Presentations | GI6.3

Modeling Potential Impacts of Gas Exploitation on the Israeli Marine Ecosystem Using Ecopath with Ecosim 

Ella Lahav, Peleg Astrahan, Eyal Ofir, Gideon Gal, and Revital Bookman

Exploration, production, extraction and transport of fossil fuels in the marine environment are accompanied by an inherent risk to the surrounding ecosystems as a result of the on-going operations or due to technical faults, accidents or geo-hazards. Limited work has been conducted on potential impacts on the Mediterranean marine ecosystem due to the lack of information on organism responses to hydrocarbon pollution. In this study, we used the Ecopath with Ecosim (EwE) modeling software which is designed for policy evaluation and provides assessments of impacts of various stressors on an ecosystem. An existing EwE based Ecospace food-web model of the Israeli Exclusive Economic Zone (EEZ) was enhanced to include local organism response curves to various levels of contaminants, such as crude oil, in the water and on the sea floor sediments. The goal of this study is to evaluate and quantify the possible ecological impacts of pollution events that might occur due to fossil fuel exploitation related activities. Multiple spatial static and dynamic scenarios, describing various pollution quantities and a range of habitats and locations were constructed. Using the enhanced Ecospace models for assessing the potential impacts of gas exploitation on organism biomass, the spatial and temporal distribution and food-web functioning was tested and evaluated. The results of this study will show a quantitative assessment of the expected ecological impacts that could assist decision makers in developing management and conservation strategies.

How to cite: Lahav, E., Astrahan, P., Ofir, E., Gal, G., and Bookman, R.: Modeling Potential Impacts of Gas Exploitation on the Israeli Marine Ecosystem Using Ecopath with Ecosim, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5431, https://doi.org/10.5194/egusphere-egu22-5431, 2022.

EGU22-5618 | Presentations | GI6.3

Slope stability monitoring system via three-dimensional simulations of rockfalls in Ischia island, Southern Italy 

Ada De Matteo, Massimiliano Alvioli, Antonello Bonfante, Maurizio Buonanno, Raffaele Castaldo, and Pietro Tizzani

Volcanoes are dynamically active systems in continuous evolution. This behaviour is emphasized by many different processes, e.g., fumarolic activity, earthquakes, volcanic slope instabilities and volcanic climax eruptions. Volcanic edifices experience slope instability as consequence of different solicitations such as i) eruption mechanism and depositional process, ii) tectonic stresses, iii) extreme weather conditions; all these events induce the mobilization of unstable fractured volcanic flanks.

Several methods exist to gather information about slope stability and to map trajectories followed by individual falling rocks in individual slopes. These methods involve direct field observation, laser scanning, terrestrial or aerial photogrammetry. Such information is useful to infer the likely location of future rockfalls, and represent a valuable input for the application of three-dimensional models for rockfall trajectories.

The Ischia island is volcano-tectonic horst that is a part of the Phlegrean Volcanic District, Southern Italy. It covers an area of about 46 km2 and it has experienced a remarkable ground uplift events due to a resurgence phenomenon. Slope instability is correlated both with earthquakes events and with volcanism phenomena. Specifically, evidences suggest that rockfalls occurred as an effect of the gravitational instability on the major scarps generated by the rapid resurgence, eased by the widespread rock fracturing.

We present results of an analysis relevant to the most probable individual masses trajectories of rockfall affecting the slopes of Ischia island. We first identified the prospective rockfall sources through an expert-mapping of source area in sample locations and statistical analysis on the whole island. Probabilistic sources are the main input of the three-dimensional rockfalls simulation software STONE.

The software assumes point-like masses falling under the sole action of gravity and the constraints of topography, and it calculates trajectories dominated by ballistic dynamics during falling, bouncing and rolling on the ground. Analysis of high-definition critical sector pictures, achieved by using UAV (Unmanned Aerial Vehicle) platform, will allow a detailed localization of source areas and an additional more robust simulations.

The procedure can be viewed as a multiscale analysis and allows besting allocating computational efforts and economic resources, focusing on a more detailed analysis on the slopes identified as the most risky ones during the first, large-scale analysis of the whole area.

How to cite: De Matteo, A., Alvioli, M., Bonfante, A., Buonanno, M., Castaldo, R., and Tizzani, P.: Slope stability monitoring system via three-dimensional simulations of rockfalls in Ischia island, Southern Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5618, https://doi.org/10.5194/egusphere-egu22-5618, 2022.

EGU22-6226 | Presentations | GI6.3

The framework for improving air quality monitoring over Indian cities 

Arindam Roy, Athanasios Nenes, and Satoshi Takahama

Indian air quality monitoring guideline is directly adopted from World Health Organization (1977) guidelines without place-based modification. According to Indian air quality guidelines (2003), the location of monitoring sites should be determined from air quality modeling and previous air quality information. If such information is not available, the use of emission densities, wind data, land-use patterns and population information is recommended for prioritizing areas for air quality monitoring. The mixed land-use distribution over Indian cities and randomly distributed sources pose serious challenges, as Indian cities (unlike in other parts of the world) are characterized by a lack of distinct residential, commercial, and industrial regions, so the concept of “homogeneous emissions” (which have guided site monitoring decisions) simply does not apply. In addition, the decision-making data emission and population information, are either not available or outdated for Indian cities. Unlike the cities in Global North, the Indian urban-scape has distinguished features in terms of land use, source and population distribution which has not been addressed in air quality guidelines.

We have developed an implementable place-based framework to address the above problem of establishing effective new air quality stations in India and other regions with complex land-use patterns. Four Indian million-plus cities were selected for the present study; Lucknow, Pune, Nashik and Kanpur. We broadly classified air quality monitoring objectives into three; monitoring population exposure, measurements for compliance with the national standards and characterization of sources. Each monitoring station over four cities was evaluated and metadata has been created for each station to identify its monitoring objective for each of the stations. We find that present air quality monitoring networks are highly inadequate in characterizing average population exposure throughout each city, as current stations are predominantly located at the site of pedestrian exposure, and are not representative of the city-wide exposure.

Possible new sites for monitoring were identified using night-time light data, satellite-derived PM2.5, existing emission inventories, land-use patterns and other ancillary open-sourced data. Over Lucknow, Pune and Nashik, setting up stations at highly populated areas is recommended to fulfill the knowledge gaps on the average population exposure. Over Kanpur, it was recommended to incorporate stations to measure short-term pollution exposure in traffic and industrial sites. Rapidly developing peri-urban regions were identified using night-time light data and recommendations were provided for setting up monitoring stations in these regions.

How to cite: Roy, A., Nenes, A., and Takahama, S.: The framework for improving air quality monitoring over Indian cities, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6226, https://doi.org/10.5194/egusphere-egu22-6226, 2022.

EGU22-6374 | Presentations | GI6.3

Geochemical monitoring of the Tenerife North-East and North West Rift Zones by means of diffuse degassing surveys 

Lía Pitti Pimienta, Fátima Rodríguez, María Asensio-Ramos, Gladys Melián, Daniel Di Nardo, Alba Martín-Lorenzo, Mar Alonso, Rubén García-Hernández, Víctor Ortega, David Martínez Van Dorth, María Cordero, Tai Albertos, Pedro A. Hernández, and Nemesio M. Pérez

Tenerife (2,034 km2), the largest island of the Canarian archipelago, is characterized by three volcanic rifts NW-SE, NE-SW and N-S oriented, with a central volcanic structure in the middle, Las Cañadas Caldera, hosting Teide-Pico Viejo volcanic complex. The North-West Rift-Zone (NWRZ) is one of the youngest and most active volcanic systems of the island, where three historical eruptions (Boca Cangrejo in 16th Century, Arenas Negras in 1706 and Chinyero in 1909) have occurred, whereas the North-East Rift-Zone (NERZ) is more complex than the others due to the existence of Pedro Gil stratovolcano that broke the main NE-SW structure 0.8 Ma ago. The most recent eruptive activity along the NERZ took place during 1704 and 1705 across 13 km of fissural eruption in Siete Fuentes (Arafo-Fasnia). To monitor potential volcanic activity through a multidisciplinary approach, diffuse degassing studies have been carried out since 2000 at the NWRZ (72 km2) and since 2001 at the NERZ (210 km2) in a yearly basis. Long-term variations in the diffuse CO2 output in the NWRZ have shown a temporal correlation with the onsets of seismic activity at Tenerife, supporting unrest of the volcanic system, as is also suggested by anomalous seismic activity recorded in the studied area during April, 2004 and October, 2016 (Hernández et al., 2017). In-situ measurements of CO2 efflux from the surface environment were performed according to the accumulation chamber method using a portable non-dispersive infrared (NDIR) sensor. Soil CO2 efflux values for the 2021 survey ranged between non-detectable values and 104 g·m-2·d-1, with an average value of 8 g·m-2·d-1 for NWRZ. For NERZ, soil CO2 efflux values ranged between non-detectable values and 79 g·m2·d-1, with an average value of 7 g·m-2·d-1. The probability plot technique applied to the data allowed to distinguish different geochemical populations. Background population represented 49.2% and 74.0% of the total data for NWRZ and NERZ, respectively, with a mean value (1.7 - 2.0 g·m-2·d-1) similar to the background values calculated for other volcanic systems in the Canary Islands with similar soils, vegetation and climate (Hernández et al. 2017). Peak population represented 0.9 and 0.7% for NWRZ and NERZ, respectively and with a mean value of 45 and 57 g·m-2·d-1. Soil CO2 efflux contour maps were constructed to identify spatial-temporal anomalies and to quantify the total CO2 emission using the sequential Gaussian simulation (sGs) interpolation method. Diffuse emission rate of 506 ± 22 t·d-1 for NWRZ and 1,509 ± 58 t·d-1 NERZ were obtained. The normalized CO2 emission value by area was estimated in 7.03 t·d-1·km-1 for NWRZ and in 7.2 t·d-1·km-1 for NERZ. The monitorization of the diffuse CO2 emission contributes to detect early warning signals of volcanic unrest, especially in areas where visible degassing is non-existent as in the Tenerife NWRZ and NERZ.

Hernández et al. (2017). Bull Volcanol, 79:30, DOI 10.1007/s00445-017-1109-9.

How to cite: Pitti Pimienta, L., Rodríguez, F., Asensio-Ramos, M., Melián, G., Di Nardo, D., Martín-Lorenzo, A., Alonso, M., García-Hernández, R., Ortega, V., Martínez Van Dorth, D., Cordero, M., Albertos, T., Hernández, P. A., and Pérez, N. M.: Geochemical monitoring of the Tenerife North-East and North West Rift Zones by means of diffuse degassing surveys, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6374, https://doi.org/10.5194/egusphere-egu22-6374, 2022.

Two moderate earthquakes with magnitude ML5.0 happened on 11th of November 2020 near the Mavrovo lake in northwestern Macedonia. The lake is an artificial lake with a dam built between 1947 and filled by 1953. Its maximum length is 10km, width is 5km and the depth is 50m. Given its water volume, it is possible that geological factors causing earthquakes could also affect the hydrobiological characteristics of the flow system surrounding the lake.

A list of 180 earthquakes registered by the local stations with magnitudes equal or greater than ML1.7 was analysed in terms of temporal and spatial distribution around the lake. No specific clustering of events was noticed in the foreshock period from July 2020. In the aftershock period, the most numerous events lasted about a month after the main events. However, there was another period of increased seismicity during March 2021, followed by gradual decrease onwards. The distribution of epicentres was mainly along the terrain of Radika river and a few smaller tributaries to the lake system.

A comparative analysis was done with the dataset collected by the program run at the department of Biology at the Faculty of Natural Sciences, University UKIM in Skopje. Environmental investigations in Europe have shown stress reactions of hydrobionts in respect to water temperature and heavy metal pollution, for example the influence of radioactive radiation. Earthquake-induced seismic changes most often affect the chemical-physical properties of water quality and temperature stratification, i.e., mixing of water masses. In our research, we analyse for the first time the relationship between the seismological activities in the Jul 2020-Nov 2021 period in details and a possible impact to environment thru the population of macrozoobenthos from Mavrovo Lake.

How to cite: Sinadinovski, C. and Smiljkov, S.: Numerical analysis of Seismic and Hydrobiological data around lake Mavrovo in the period Jul.2020-Nov.2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6452, https://doi.org/10.5194/egusphere-egu22-6452, 2022.

EGU22-6468 | Presentations | GI6.3

Measuring greenhouse gas fluxes – what methods do we have versus what methods do we need? 

David Bastviken, Julie Wilk, Nguyen Thanh Duc, Magnus Gålfalk, Martin Karlson, Tina Neset, Tomasz Opach, Alex Enrich Prast, and Ingrid Sundgren

Appropriate methods to measure greenhouse gas (GHG) fluxes are critical for our ability to detect fluxes, understand regulation, make adequate priorities for climate change mitigation efforts, and verify that these efforts are effective. Ideally, we need reliable, accessible, and affordable measurements at relevant scales. We surveyed present GHG flux measurement methods, identified from an analysis of >11000 scientific publications and a questionnaire to sector professionals and analysed method pros and cons versus needs for novel methodology. While existing methods are well-suited for addressing certain questions, this presentation presents fundamental limitations relative to GHG flux measurement needs for verifiable and transparent action to mitigate many types of emissions. Cost and non-academic accessibility are key aspects, along with fundamental measurement performance. These method limitations contribute to the difficulties in verifying GHG mitigation efforts for transparency and accountability under the Paris agreement. Resolving this mismatch between method capacity and societal needs is urgently needed for effective climate mitigation. This type of methodological mismatch is common but seems to get high priority in other knowledge domains. The obvious need to prioritize development of accurate diagnosis methods for effective treatments in healthcare is one example. This presentation provides guidance regarding the need to prioritize the development of novel GHG flux measurement methods.

How to cite: Bastviken, D., Wilk, J., Duc, N. T., Gålfalk, M., Karlson, M., Neset, T., Opach, T., Enrich Prast, A., and Sundgren, I.: Measuring greenhouse gas fluxes – what methods do we have versus what methods do we need?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6468, https://doi.org/10.5194/egusphere-egu22-6468, 2022.

EGU22-8458 | Presentations | GI6.3

Temporal evolution of dissolved gases in groundwater of Tenerife Island 

Cecilia Amonte, Nemesio M. Pérez, Gladys V. Melián, María Asensio-Ramos, Eleazar Padrón, Pedro A. Hernández, and Ana Meire Feijoo

The oceanic active volcanic island of Tenerife (2,034 km2) is the largest of the Canarian archipelago. There are more than 1,000 galleries (horizontal drillings) in the island, which are used for groundwater exploitation and allow reaching the aquifer at different depths and elevations. This work presents the first extensive study on the temporal variation of dissolved gases in groundwaters from Fuente del Valle and San Fernando galleries (Tenerife, Spain) since April 2016 to June 2020. This investigation is focused on the chemical and isotopic content of several dissolved gas species (CO2, He, O2, N2 and CH4) present in the groundwaters and its relationship with the seismic activity registered in the island. The results show CO2 as the major dissolved gas specie in the groundwater from both galleries presenting a mean value of 260 cm3STP·L-1 and 69 cm3STP·L-1 for Fuente del Valle and San Fernando, respectively. The average δ13C-CO2 data (-3.9‰ for Fuente del Valle and -6.4‰ for San Fernando) suggest a clear endogenous origin as result of interaction of them with deep-origin fluid. A bubbling gas sample from Fuente del Valle gallery was analysed, obtaining a CO2 rich gas (87 Vol.%) with a considerable He enrichment (7.3 ppm). The isotopic data of both components in the bubbling gas support the results obtained in the dissolved gases, showing an endogenous component that could be affected by the different activity of the hydrothermal system. During the study period, an important seismic swarm occurred on October 2, 2016, followed by an increase of the seismic activity in and around Tenerife. After this event, important geochemical variations were registered in the dissolved gas species, such as dissolved CO2 and He content and the CO2/O2, He/CO2, He/N2 and CH4/CO2 ratios. These findings suggest an injection of fluids into the hydrothermal system during October 2016, a fact that evidences the connection between the groundwaters and the hydrothermal system. The present work demonstrates the importance of dissolved gases studies in groundwater for volcanic surveillance.

How to cite: Amonte, C., Pérez, N. M., Melián, G. V., Asensio-Ramos, M., Padrón, E., Hernández, P. A., and Meire Feijoo, A.: Temporal evolution of dissolved gases in groundwater of Tenerife Island, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8458, https://doi.org/10.5194/egusphere-egu22-8458, 2022.

Land surface temperature (LST) is a manifestation of the surface thermal environment (LSTE) and an important driver of physical processes of surface land energy balance at local to global scales. Tenerife is one of the most heterogeneous islands among the Canaries from a climatological and bio-geographical point of view. We study the surface thermal conditions of the volcanic island with remote sensing techniques. In particular, we consider a time series of Landsat 8 (L8) level 2A images for the period 2013 to 2019 to estimate LST from surface reflectance (SR) and brightness Temperature (BT) images. A total of 26 L8 dates were selected based on cloud cover information from metadata (land cloud cover < 10%) to estimate pixel-level LST with an algorithm based on Radiative Transfer Equations (RTE). The algorithm relies on the Normalized Difference Vegetation Index (NDVI) for estimating emissivity pixel by pixel. We apply the Independent Component Analysis (ICA) that revealed to be a powerful tool for data mining and, in particular, to separate multivariate LST dataset into a finite number of components, which have the maximum relative statistical independence. The ICA allowed separating the land surface temperature time series of Tenerife into 11 components that can be associated with geographic and bioclimatic zones of the island. The first ten components are related to physical factors, the 11th component, on the contrary, presented a more complex pattern resulting possibly from its small amplitude and the combination of various factors into a single component. The signal components recognized with the ICA technique, especially in areas of active volcanism, could be the basis for the space-time monitoring of the endogenous component of the LST due to surface hydrothermal and/or geothermal activity. Results are encouraging, although the 16-day revisit frequency of Landsat reduces the frequency of observation that could be increased by applying techniques of data fusion of medium and coarse spatial resolution images. The use of such systems for automatic processing and analysis of thermal images may in the future be a fundamental tool for the surveillance of the background activity of active and dormant volcanoes worldwide.

How to cite: Stroppiana, D., Przeor, M., D’Auria, L., and Tizzani, P.: Analysis of thermal regimes at Tenerife(Canary Islands) with Independent Component Analysis applied to time series of Remotely Sensed Land Surface Temperatures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8580, https://doi.org/10.5194/egusphere-egu22-8580, 2022.

EGU22-9376 | Presentations | GI6.3

An IoT based approach to ultra high resolution air quality mapping thorigh field calibrated monitoring devices 

Saverio De Vito, Grazia Fattoruso, and Domenico Toscano

Recent advances in IoT and chemical sensors calibration technologies have led to the proposal of Hierarchical air quality monitoring networks. They are indeed complex systems relying on sensing nodes which differs from size, cost, accuracy, technology, maintenance needs while having the potential to empower smart cities and communiities with increased knowledge  on the highly spatiotemporal variance Air Quality phenomenon (see [1]). The AirHeritage project, funded by Urban Innovative Action program have developed and implemented a hierarchical monitoring system which allows for offering real time assessments and model based forecasting services including 7 fixed low cost sensors station, one (mobile and temporary located) regulatory grade analyzer and a citizen science based ultra high resolution AQ mapping tool based on field calibrated mobile analyzers. This work will analyze the preliminary results of the project by focusing on the machine learning driven sensors calibration methodology and citizen science based air quality mapping campaigns. Thirty chemical and particulate matter multisensory devices have been deployed in Portici, a 4Km2 city located 7 km south of Naples which is  affected by significant car traffic. The devices have been  entrusted to local citizens association for implementing 1 preliminary validation campaign (see [2]) and 3 opportunistic 2-months duration monitoring campaigns. Each 6 months, the devices undergoes a minimum 3 weeks colocation period with a regulatory grade analyzer allowing for training and validation dataset building. Multilinear regression sw components are trained to reach ppb level accuracy (MAE <10ug/m^3 for NO2 and O3, <15ug/M^3 for PM2.5 and PM10, <300ug/M^3 for CO) and encoded in a companion smartphone APP which allows the users for real time assessment of personal exposure. In particular, a novel AQI strongly based on European Air Quality Index ([3]) have been developed for AQ real time data communication. Data have been collected using a custom IoT device management platform entrusted with inception, storage and data-viz roles. Finally data have been used to build UHR (UHR) AQ maps, using spatial binning approach (25mx25m) and median computation for each bin receiving more than 30 measurements during the campaign. The resulting maps have hown the possibility to allow for pinpointing city AQ hotpots which will allows fact-based remediation policies in cities lacking objective technologies to locally assess concentration exposure.  

 

[1] Nuria Castell et Al., Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?, Environment International, Volume 99, 2017, Pages 293-302 ISSN 0160-4120, https://doi.org/10.1016/j.envint.2016.12.007.

[2] De Vito, S, et al., Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation. Sensors 202121, 5219. https://doi.org/10.3390/s21155219

[3] https://airindex.eea.europa.eu/Map/AQI/Viewer/

How to cite: De Vito, S., Fattoruso, G., and Toscano, D.: An IoT based approach to ultra high resolution air quality mapping thorigh field calibrated monitoring devices, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9376, https://doi.org/10.5194/egusphere-egu22-9376, 2022.

EGU22-10290 | Presentations | GI6.3

Soil gas Rn monitoring at Cumbre Vieja prior and during the 2021 eruption, La Palma, Canary Islands 

Daniel Di Nardo, Eleazar Padrón, Claudia Rodríguez-Pérez, Germán D. Padilla, José Barrancos, Pedro A. Hernández, María Asensio-Ramos, and Nemesio M. Pérez

Cumbre Vieja volcano (La Palma, Canary Islands, Spain) suffered a volcanic eruption that started on September 19 and finished on December 13, 2021. The eruption is considered the longest volcanic event since data are available on the island: it finished after 85 days and 8 hours of duration and 1,219 hectares of lava flows. La Palma Island is the fifth in extension (706 km2) and the second in elevation (2,423 m a.s.l.) of the Canarian archipelago. Cumbre Vieja volcano, where the volcanic activity has taken place exclusively in the last 123 ka, forms the sand outhern part of the island. In 2017, two remarkable seismic swarms interrupted a seismic silence of 46 years in Cumbre Vieja volcano with earthquakes located beneath Cumbre Vieja volcano at depths ranging between 14 and 28 km with a maximum magnitude of 2.7. Five additional seismic swarms were registered in 2020 and four in 2021. The eruption started ~1 week after the start of the last seismic swarm.

As part of the INVOLCAN volcano monitoring program of Cumbre Vieja, soil gas radon (222Rn) and thoron (220Rn) is being monitored at five sites in Cumbre Vieja using SARAD RTM2010-2 RTM 1688-2 portable radon monitors. 222Rn and 220Rn are two radioactive isotopes of radon with a half-life of 3.8 days and 54.4 seconds, respectively. Both isotopes can diffuse easily trough the soil and can be detected at very low concentrations, but their migration in large scales, ten to hundreds of meters, is supported by advection (pressure changes) and is related to the existence of a carrier gas source (geothermal fluids or fluids linked to magmatic and metamorphic phenomena), and to the existence of preferential routes for degassing (deep faults). Previous results on the monitoring of soil Rn in the Canary Islands with volcano monitoring purposes are promising (Padilla et al, 2013).     

The most remarkable result of the Rn monitoring network of Cumbre Vieja was observed in LPA01 station, located at the north-east of Cumbre Vieja. Since mid-March 2021, soil 222Rn activity experienced a sustained until reaching maximum values of ~1.0E+4 222Rn Bq/m3 days before the eruption onset. During the eruptive period, soil 222Rn activity showed a gradual decreasing trend. The increase of magmatic-gas pressure due to magma movement towards the surface and the transport of anomalous 222Rn originated from hydrofracturing of rock, from direct magma degassing or from both, is the most plausible explanation for the increases in radon activity before the eruption onset observed at LPA01. As soil gas radon activity increased prior to the eruption onset, this monitoring technique can be efficiently used as an initial warning sign of the pressurization of magma beneath La Palma Island.

Padilla, G. D., et al. (2013), Geochem. Geophys. Geosyst., 14, 432–447, doi:10.1029/2012GC004375.

 

How to cite: Di Nardo, D., Padrón, E., Rodríguez-Pérez, C., Padilla, G. D., Barrancos, J., Hernández, P. A., Asensio-Ramos, M., and Pérez, N. M.: Soil gas Rn monitoring at Cumbre Vieja prior and during the 2021 eruption, La Palma, Canary Islands, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10290, https://doi.org/10.5194/egusphere-egu22-10290, 2022.

EGU22-10603 | Presentations | GI6.3 | Highlight

The "Campania Trasparente" multiscale and multimedia monitoring project: an unprecedented experience in Italy. 

Stefano Albanese, Annamaria Lima, Annalise Guarino, Chengkai Qu, Domenico Cicchella, Mauro Esposito, Pellegrino Cerino, Antonio Pizzolante, and Benedetto De Vivo

In 2015, the "Campania Trasparente" project (http://www.campaniatrasparente.it), a monitoring plan focused on assessing the environmental conditions of the territory of the Campania region, started thanks to the financial support of the regional government. The project's general management was in charge of the Experimental Zooprophylactic Institute of Southern Italy (IZSM).
In the project framework, the collection and analysis of many environmental and biological samples (including soil and air and human blood specimen) were completed. The primary aim of the whole project was to explore the existence of a link between the presence of some illnesses in the local population and the status of the environment and generate a reliable database to assess local foodstuff healthiness.
Six research units were active in the framework of the project. As for soil and air, the Environmental Geochemistry Working Group (EGWG) at the Department of Earth, Environment and Resources Sciences, University of Naples Federico II, was in charge of most of the research activities. Specifically, the EGWG completed the elaboration of the data on potentially toxic metals/metalloids (PTMs) and organic contaminants (PAHs, OCPs, Dioxins) in the regional soils and air.
The monitoring of air contaminants lasted more than one year, and it was completed employing passive air samplers (PAS) and deposimeters spread across the whole region.
Three volumes were published, including statistical elaborations and geochemical maps of all the contaminants analysed to provide both the regional government and local scientific and professional community with a reliable tool to approach local environmental problems starting from a sound base of knowledge.
Geochemical distribution patterns of potentially toxic elements (PTEs), for example, were used to establish local geochemical background/baseline intervals for those metals (naturally enriched in regional soils) found to systematically overcome the national environmental guidelines (set by the Legislative Decree 152/2006).
Data from the air, analysed in terms of concentration and time variation, were, instead, fundamental to discriminate the areas of the regional territory characterised by heavy contamination associated with the emission of organic compounds from anthropic sources.

The integration of all the data generated within the "Campania Trasparente" framework, including the data proceeding from the Susceptible Population Exposure Study (SPES), focusing on human biomonitoring (based on blood), allowed the development of a regional-wide conceptual model to be used as a base to generate highly specialised risk assessments for regional population and local communities affected by specific environmental problems.

How to cite: Albanese, S., Lima, A., Guarino, A., Qu, C., Cicchella, D., Esposito, M., Cerino, P., Pizzolante, A., and De Vivo, B.: The "Campania Trasparente" multiscale and multimedia monitoring project: an unprecedented experience in Italy., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10603, https://doi.org/10.5194/egusphere-egu22-10603, 2022.

EGU22-10659 | Presentations | GI6.3

Long-term variations of diffuse CO2, He and H2 at the summit crater of Teide volcano, Tenerife, Canary Islands during 1999-2021 

Germán D. Padilla, Fátima Rodríguez, María Asensio-Ramos, Gladys V. Melián, Mar Alonso, Alba Martín-Lorenzo, Beverley C. Coldwell, Claudia Rodríguez, Jose M. Santana de León, Eleazar Padrón, José Barrancos, Luca D'Auria, Pedro A. Hernández, and Nemesio M. Pérez

Tenerife Island (2,034 km2) is the largest island of the Canarian archipelago. Its structure is controlled by a volcano-tectonic rift-system with NW, NE and NS directions, with the Teide-Pico Viejo volcanic system located in the intersection. Teide is 3,718 m.a.s.l. high and its last eruption occurred in 1798 through an adventive cone of Teide-Pico Viejo volcanic complex. Although Teide volcano shows a weak fumarolic system, volcanic gas emissions observed in the summit cone consist mostly of diffuse CO2 degassing.

 

In this study we investigate the Teide-Pico Viejo volcanic system evolution using a comprehensive diffuse degassing geochemical dataset 216 geochemical surveys have been performed during the period 1999-2021 at the summit crater of Teide Volcano covering an area of 6,972 m2. Diffuse CO2 emission was estimated in 38 sampling sites, homogeneously distributed inside the crater, by means of a portable non dispersive infrared (NDIR) CO2 fluxmeter using the accumulation chamber method. Additionally, soil gases were sampled at 40 cm depth using a metallic probe with a 60 cc hypodermic syringe and stored in 10 cc glass vials and send to the laboratory to analyse the He and H2 content by means of quadrupole mass spectrometry and micro-gas chromatography, respectively. To estimate the He and H2 emission rates at each sampling point, the diffusive component was estimated following the Fick’s law and the convective emission component model was estimated following the Darcy’s law. In all cases, spatial distribution maps were constructed averaging the results of 100 simulations following the sequential Gaussian simulation (sGs) algorithm, in order to estimate CO2, He and H2 emission rates.

 

During 22 years of the studied period, CO2 emissions ranged from 2.0 to 345.9 t/d, He emissions between 0.013 and 4.5 kg/d and H2 between 1.3 and 64.4 kg/d. On October 2, 2016, a seismic swarm of long-period events was recorded on Tenerife followed by an increase of the seismic activity in and around the island (D’Auria et al., 2019; Padrón et al., 2021). Several geochemical parameters showed significant changes during ∼June–August of 2016 and 1–2 months before the occurrence of the October 2, 2016, long-period seismic swarm (Padrón et al., 2021). Diffuse degassing studies as useful to conclude that the origin of the 2 October 2016 seismic swarm an input of magmatic fluids triggered by an injection of fresh magma and convective mixing. Thenceforth, relatively high values have been obtained in the three soil gases species studied at the crater of Teide, with the maximum emission rates values registered during 2021. This increase reflects a process of pressurization of the volcanic-hydrothermal system. This increment in CO2, He and H2 emissions indicate changes in the activity of the system and can be useful to understand the behaviour of the volcanic system and to forecast future volcanic activity. Monitoring the diffuse degassing rates has demonstrated to be an essential tool for the prediction of future seismic–volcanic unrest, and has become important to reduce volcanic risk in Tenerife.

D'Auria, L., et al. (2019). J. Geophys. Res.124,8739-8752

Padrón, E., et al., (2021). J. Geophys. Res.126,e2020JB020318

How to cite: Padilla, G. D., Rodríguez, F., Asensio-Ramos, M., Melián, G. V., Alonso, M., Martín-Lorenzo, A., Coldwell, B. C., Rodríguez, C., Santana de León, J. M., Padrón, E., Barrancos, J., D'Auria, L., Hernández, P. A., and Pérez, N. M.: Long-term variations of diffuse CO2, He and H2 at the summit crater of Teide volcano, Tenerife, Canary Islands during 1999-2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10659, https://doi.org/10.5194/egusphere-egu22-10659, 2022.

EGU22-11493 | Presentations | GI6.3

Analysis and Modelling of 2009-2013 Unrest Episodes at Campi Flegrei Caldera 

Raffaele Castaldo, Giuseppe Solaro, and Pietro Tizzani

Geodetic modelling is a valuable tool to infer volume and geometry of volcanic source system; it represents a key procedure for detecting and characterizing unrest and eruption episodes. In this study, we analyse the 2009–2013 uplift phenomenon at Campi Flegrei (CF) caldera in terms of spatial and temporal variations of the stress/strain field due to the effect of the retrieved inflating source. We start by performing a 3D stationary finite element (FE) modelling of geodetic datasets to retrieve the geometry and location of the deformation source. The geometry of FE domain takes into account both the topography and the bathymetry of the whole caldera. For what concern the definition of domain elastic parameters, we take into account the Vp/Vs distribution from seismic tomography. We optimize our model parameters by exploiting two different geodetic datasets: the GPS data and DInSAR measurements. The modelling results suggest that the best-fit source is a three-axis oblate spheroid ~3 km deep, similar to a sill-like body. Furthermore, in order to verify the reliability of the geometry model results, we calculate the Total Horizontal Derivative (THD) of the vertical velocity component and compare it with those performed with the DInSAR measurements. Subsequently, starting from the same FE modelling domain, we explore a 3D time-dependent FE model, comparing the spatial and temporal distribution of the shear stress and volumetric strain with the seismic swarms beneath the caldera. Finally, We found that low values of shear stress are observed corresponding with the shallow hydrothermal system where low-magnitude earthquakes occur, whereas high values of shear stress are found at depths of about 3 km, where high-magnitude earthquakes nucleate.

How to cite: Castaldo, R., Solaro, G., and Tizzani, P.: Analysis and Modelling of 2009-2013 Unrest Episodes at Campi Flegrei Caldera, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11493, https://doi.org/10.5194/egusphere-egu22-11493, 2022.

EGU22-11874 | Presentations | GI6.3

Time evolution of Land Surface Temperature (LST) in active volcanic areas detected via integration of satellite and ground-based measurements: the Campi Flegrei caldera (Southern Italy) case study. 

Andrea Barone, Daniela Stroppiana, Raffaele Castaldo, Stefano Caliro, Giovanni Chiodini, Luca D'Auria, Gianluca Gola, Ferdinando Parisi, Susi Pepe, Giuseppe Solaro, and Pietro Tizzani

Thermal features of environmental systems are increasingly investigated after the development of remote sensing technologies; the increasing availability of Earth Observation (EO) missions allows the retrieval of the Land Surface Temperature (LST) parameter, which is widely used for a large variety of applications (Galve et al., 2018). In volcanic environment, the LST is an indicator of the spatial distribution of thermal anomalies at the ground surface, supporting designed tools for monitoring purposes (Caputo et al., 2019); therefore, LST can be used to understand endogenous processes and to model thermal sources.

In this framework, we present the results of activities carried out in the FLUIDs PRIN project, which aims at the characterization and modeling of fluids migration at different scales (https://www.prinfluids.it/). We propose a multi-scale analysis of thermal data at Campi Flegrei caldera (CFc); this area is well known for hosting thermal processes related to both magmatic and hydrothermal systems (Chiodini et al., 2015; Castaldo et al., 2021). Accordingly, data collected at different scales are suitable to search out local thermal trends with respect to regional ones. In particular, in this work we compare LST estimated from Landsat satellite images covering the entire volcanic area and ground measurements nearby the Solfatara crater.

Firstly, we exploit Landsat data to derive time series of LST by applying an algorithm based on Radiative Transfer Equations (RTE) (Qin et al., 2001; Jimenez-Munoz et al., 2014). The algorithm exploits both thermal infrared (TIR) and visible/near infrared (VIS/NIR) bands of different Landsat missions in the period 2000-2021; we used time series imagery from Landsat 5 (L5), Landsat 7 (L7) and Landsat 8 (L8) satellite missions to retrieve the thermal patterns of the CFc area with spatial resolutions of 30 m for VIS/NIR bands and 60 m to 120 m for TIR bands. Theoretical frequency of acquisition of the Landsat missions is 16 days that is reduced over the study area by cloud cover: Landsat images with high cloud cover were in fact discarded from the time series.

In particular, we process both the daily acquisitions as well nighttime data to provide thermal features at the ground surface in the absence of solar radiation. To emphasize the thermal anomalies of endogenous phenomena, the retrieved LST time-series are corrected following these steps: (i) removal of spatial and temporal outliers; (ii) correction for adiabatic gradient of the air with the altitude; (iii) detection and removal of the seasonal component.

Regarding to the ground-based acquisitions, we consider the data collected by the Osservatorio Vesuviano, National Institute of Geophysics and Volcanology (OV- INGV, Italy, Naples); the dataset consists of 151 thermal measurements distributed within the 2004-2021 time-interval and acquired inside the Solfatara and Pisciarelli areas at a depth of 0.01 m below the ground surface. Similarly, we process this dataset following corrections (i) and (iii).

Finally, we compare the temporal evolution of thermal patterns retrieved by the satellite and ground-based measurements, highlighting the supporting information provided by LST and its integration with data at ground.

How to cite: Barone, A., Stroppiana, D., Castaldo, R., Caliro, S., Chiodini, G., D'Auria, L., Gola, G., Parisi, F., Pepe, S., Solaro, G., and Tizzani, P.: Time evolution of Land Surface Temperature (LST) in active volcanic areas detected via integration of satellite and ground-based measurements: the Campi Flegrei caldera (Southern Italy) case study., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11874, https://doi.org/10.5194/egusphere-egu22-11874, 2022.

EGU22-11990 | Presentations | GI6.3

Integrating geophysical, geochemical, petrological and geological data for the thermal and rheological characterization of unconventional geothermal fields: the case study of Long Valley Caldera 

Gianluca Gola, Andrea Barone, Raffaele Castaldo, Giovanni Chiodini, Luca D'Auria, Rubén García-Hernández, Susi Pepe, Giuseppe Solaro, and Pietro Tizzani

We propose a novel multidisciplinary approach to image the thermo-rheological stratification beneath active volcanic areas, such as Long Valley Caldera (LVC), which hosts a magmatic-hydrothermal system. Geothermal facilities near the Casa Diablo locality supply 40 MWe from three binary power plants, exploiting about 850 kg s−1 of 160–180 °C water that circulates within the volcanic sediments 200 to 350 meters deep. We performed a thermal fluid dynamic model via optimization procedure of the thermal conditions of the crust. We characterize the topology of the hot magmatic bodies and the hot fluid circulation (the permeable fault-zones), using both a novel imaging of the a and b parameters of the Gutenberg-Richter law and an innovative procedure analysis of P-wave tomographic models. The optimization procedure provides the permeability of a reservoir (5.0 × 10−14 m2) and of the fault-zone (5.0 · 10−14 – 1.0 × 10−13 m2), as well as the temperature of the magma body (750–800°C). The imaging of the rheological properties of the crust indicates that the brittle/ductile transition occurs about 5 km b.s.l. depth, beneath the resurgent dome. There are again deeper brittle conditions about 15 km b.s.l., agreeing with the previous observations. The comparison between the conductive and the conductive-convective heat transfer models highlights that the deeper fluid circulation efficiently cools the volumes above the magmatic body, transferring the heat to the shallow geothermal system. This process has a significant impact on the rheological properties of the upper crust as the migration of the B/D transition. Our findings show an active magmatic system (6–10 km deep) and confirm that LVC is a long-life silicic caldera system. Furthermore, the occurrence of deep-seated, super-hot geothermal resources 4.5 – 5.0 km deep, possibly in supercritical conditions, cannot be ruled out.

How to cite: Gola, G., Barone, A., Castaldo, R., Chiodini, G., D'Auria, L., García-Hernández, R., Pepe, S., Solaro, G., and Tizzani, P.: Integrating geophysical, geochemical, petrological and geological data for the thermal and rheological characterization of unconventional geothermal fields: the case study of Long Valley Caldera, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11990, https://doi.org/10.5194/egusphere-egu22-11990, 2022.

EGU22-12331 | Presentations | GI6.3 | Highlight

The evaluation of soil organic carbon through VIS-NIR spectroscopy to support the soil health monitoring 

Haitham Ezzy, Anna Brook, Claudio Ciavatta, Francesca Ventura, Marco Vignudelli, and Antonello Bonfante

Increasing the organic matter content of the soil has been presented in the:”4per1000″ proposal as a significant climate mitigation measure able to support the achievement of Sustainable Development Goal 13 - Climate Action of United Nations.

At the same time, the report of the Mission Board for Soil health and Food, "Caring for soil is caring for life," indicates that one of the targets that must be reached by 2030 is the conservation and increase of soil organic carbon stock.  De facto, the panel clearly indicates the soil organic carbon as one of the indicators that can be used to monitor soil health, and at the same time, if the current soil use is sustainable or not.

Thus it is to be expected that the monitoring of SOC will become requested to check and monitor the sustainability of agricultural practices realized in the agricultural areas. For all the above reasons, the development of a reliable and fast indirect methods to evaluate the SOC is necessary to support different stakeholders (government, municipality, farmer) to monitor SOC at different spatial scales (national, regional, local).

Over the past two decades, data mining approaches in spatial modeling of soil organic carbon using machine learning techniques and artificial neural network (ANN) to investigate the amount of carbon in the soil using remote sensing data has been widely considered. Accordingly, this study aims to design an accurate and robust neural network model to estimate the soil organic carbon using the data-based field-portable spectrometer and laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The measurements will be on two sets of the same soil samples, the first by the standard protocol of requested laboratories for soil scanning, The second set of the soil samples without any cultivation to simulate the soil condition in the sampling field emphasizes the predictive capabilities to achieve fast, cheap and accurate soil status. Carbon soil parameter will determine using, multivariate regression method used for prediction with Least absolute shrinkage and selection operator regression (Lasso) in interval way (high, medium, and low). The results will increase accuracy, precision, and cost-effectiveness over traditional ex-situ methods.

The contribution has been realized within the international EIT Food project MOSOM (Mapping of Soil Organic Matter; https://www.eitfood.eu/projects/mosom)

How to cite: Ezzy, H., Brook, A., Ciavatta, C., Ventura, F., Vignudelli, M., and Bonfante, A.: The evaluation of soil organic carbon through VIS-NIR spectroscopy to support the soil health monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12331, https://doi.org/10.5194/egusphere-egu22-12331, 2022.

EGU22-12364 | Presentations | GI6.3

Stromboli Volcano observations through the Airborne X-band Interferometric SAR (AXIS) system 

Paolo Berardino, Antonio Natale, Carmen Esposito, Gianfranco Palmese, Riccardo Lanari, and Stefano Perna

Synthetic Aperture Radar (SAR) represents nowadays a well-established tool for day and night and all-weather microwave Earth Oobservation (EO) [1]. In last decades, a number of procedures EO techniques based on SAR data have been indeed devised developed for investigating several natural and anthropic phenomena the monitoring of affecting our planet. Among these, SAR Interferometry (InSAR) and Differential SAR Interferometry (DInSAR) undoubtedly represent a powerful techniques to characterize the deformation processes associated to several natural phenomena, such as eEarthquakes, landslides, subsidences andor volcanic unrest events [2] - [4].

In particular, such techniques can benefit of the operational flexibility offered by airborne SAR systems, which allow us to frequently monitor fast-evolving phenomena, timely reach the region of interest in case of emergency, and observe the same scene under arbitrary flight tracks.

In this work, we present the results relevant to multiple radar surveys carried out over the Stromboli Island, in Italy, through the Italian Airborne X-band Interferometric SAR (AXIS) system. The latter is based on the Frequency Modulated Continuous Wave (FMCW) technology, and is equipped with a three-antenna single-pass interferometric layout [5].

The considered dataset has been collected during three different acquisition campaigns, carried out from July 2019 to June 2021, and consists of radar data acquired along four flight directions (SW-NE, NW-SE, NE-SW, SE-NW), as to describe flight circuits around the island and to illuminate the Stromboli volcano under different points of view.

References

[1] Moreira, P. Prats-Iraola, M. Younis, G. Krieger, I. Hajnsek, K. P. Papathanassiou, “A tutorial on Synthetic Aperture Radar”, IEEE Geoscience and Remote Sensing Magazine, pp. 6-43, March 2013.

[2] Bamler, R., Hartl, P., 1998. Synthetic Aperture Radar Interferometry. Inverse problems, 14(4), R1.

[3] P. Berardino, G. Fornaro, R. Lanari and E. Sansosti, “A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms”, IEEE Trans. Geosci. Remote Sens., vol. 40, no. 11, pp. 2375-2383, Nov. 2002.

[4] R. Lanari, M. Bonano, F. Casu, C. De Luca, M. Manunta, M. Manzo, G. Onorato, I. Zinno, “Automatic Generation of Sentinel-1 Continental Scale DInSAR Deformation Time Series through an Extended P-SBAS Processing Pipeline in a Cloud Computing Environment”, Remote Sensing, 2020, 12, 2961.

[5] C. Esposito, A. Natale, G. Palmese, P. Berardino, R. Lanari, S. Perna, “On the Capabilities of the Italian Airborne FMCW AXIS InSAR System”, Remote Sens. 2020, 12, 539.

 

How to cite: Berardino, P., Natale, A., Esposito, C., Palmese, G., Lanari, R., and Perna, S.: Stromboli Volcano observations through the Airborne X-band Interferometric SAR (AXIS) system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12364, https://doi.org/10.5194/egusphere-egu22-12364, 2022.

EGU22-12927 | Presentations | GI6.3 | Highlight

FRA.SI.project - AN INTEGRATED MULTI-SCALE METHODOLOGIES FOR THE ZONATION OF LANDSLIDE-INDUCED HAZARD IN ITALY 

Pietro Tizzani, Paola Reichenbach, Federica Fiorucci, Massimiliano Alvioli, Massimiliano Moscatelli, and Antonello Bonfante and the Fra.Si. Team

Fra. Si. a national research project supported by the Ministry of the Environment and Land and Sea Protection, develops a coherent set of multiscale methodologies for the assessment and zoning of earthquake-induced landslide hazards. To achieve the goal, the project operates at different geographical, temporal, and organizational scales, and in different geological, geomorphological, and seismic-tectonic contexts. Given the complexity, variability, and extent of earthquake-induced landslides in Italy, operating at multiple scales allows you to (a) maximize the use of available data and information; (b) propose methodologies and experiment with models that operate at different scales and in different contexts, exploiting their peculiarities at the most congenial scales and coherently exporting the results at different scales; and (c) obtain results at scales of interest for different users.

The project defines a univocal and coherent methodological framework for the assessment and zoning of earthquake-induced landslide hazard, integrating existing information and data on earthquake-induced landslide in Italy, available to proponents, available in technical literature and from "open" sources - in favor of the cost-effectiveness of the proposal. The integration exploits a coherent set of modeling tools, expert (heuristic) and numerical (statistical and probabilistic, physically-based, FEM, optimization models). The methodology considers the problem at multiple scales, including: (a) three geographic scales - the national synoptic scale, the regional mesoscale and the local scale; (b) two time scales - the pre-event scale typical of territorial planning and the deferred time of civil protection, and the post-event scale, characteristic of real civil protection time; and (c) different organizational and management scales - from spatial planning and soil defense, including post-seismic reconstruction, to civil protection rapid response. Furthermore, the methodology considers the characteristics of the seismic-induced landslide and the associated hazard in the main geological, geomorphological and seismic-tectonic contexts in Italy.

The project develops methodologies and products for different users and/or users. The former concern methodologies for (i) the synoptic zoning of the hazard caused by earthquake-induced landslides in Italy; (ii) the zoning and quantification of the danger from earthquake-induced landslides on a regional scale; (iii) the quantification of the danger of single deep landslides in the seismic phase; and for (iv) the identification and geological-technical modeling of deep co-seismic landslides starting from advanced DInSAR analyzes from post-seismic satellites.

How to cite: Tizzani, P., Reichenbach, P., Fiorucci, F., Alvioli, M., Moscatelli, M., and Bonfante, A. and the Fra.Si. Team: FRA.SI.project - AN INTEGRATED MULTI-SCALE METHODOLOGIES FOR THE ZONATION OF LANDSLIDE-INDUCED HAZARD IN ITALY, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12927, https://doi.org/10.5194/egusphere-egu22-12927, 2022.

EGU22-614 | Presentations | CL5.1.5

Evaluating statistical downscaling for daily maximum and minimum temperatures in Argentina 

Rocio Balmaceda-Huarte and Maria Laura Bettolli

Empirical statistical downscaling (ESD) under the perfect prognosis approach was carried out to simulate daily maximum (Tx) and minimum temperatures (Tn) in the different climatic regions of Argentina. In this regard, three ESD techniques: analogs (AN), generalized linear models (GLM) and neural networks (NN) were evaluated considering multiple predictor sets with a variety of configurations driven by three different reanalysis. ESD models were cross-validated with folds of non-consecutive years (1979-2014) and then evaluated in a warmer set of years ( 2015-2018). The focus of the assessment of the ESD models was put on some marginal and temporal aspects of Tx and Tn. Depending on the aspect analyzed, AN ,GLM or NN models were more/less skillful but no method fulfilled all the features of both predicand variables. In this sense, the predictor set and model configuration were key factors. The different predictor structures (point-wise, spatial-wise and combinations of them) introduced the main differences for each ESD method, regardless of the predictand variable, region and reanalysis choice. In addition, the differences observed in ESD models due to the reanalysis choice were notably lower than the ones obtained due to changes in the statistical family and model structure. In the case of predictor variables, no improvements were observed in Tx and Tn simulations when a more complex predictor set was considered. In the case of Tn, models’ skills considerably increased when humidity information was included in the predictor set.  Our results showed that downscaling models were able to capture the general characteristics of Tx and Tn in all regions, with better performance in the latter variable. Overall, promising results were obtained in the evaluation of the ESD models in Argentina which encourage us to continue exploring their potential in different applications. 

How to cite: Balmaceda-Huarte, R. and Bettolli, M. L.: Evaluating statistical downscaling for daily maximum and minimum temperatures in Argentina, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-614, https://doi.org/10.5194/egusphere-egu22-614, 2022.

EGU22-737 | Presentations | CL5.1.5 | Highlight

MIdAS---MultI-scale bias AdjuStment 

Peter Berg, Thomas Bosshard, Wei Yang, and Klaus Zimmermann

Bias adjustment is the practice of statistically transforming climate model data in order to reduce systematic deviations from a reference data set, typically some sort of observations. There are numerous proposed methodologies to perform the adjustments -- ranging from simple scaling approaches to advanced multi-variate distribution based mapping. In practice, the actual bias adjustment method is a small step in the application, and most of the processing handles reading, writing and linking different data sets. These practical processing steps become especially heavy with increasing model domain size and resolution in both time and space. Here, we present a new implementation platform for bias adjustment, which we call MIdAS (MultI-scale bias AdjuStment). MIdAS is a modern code implementation that supports features such as: modern Python libraries that are suitable for large computing clusters, state-of-the-art bias adjustment methods based on quantile mapping, "day-of-year" based adjustments to avoid artificial discontinuities, and also introduces cascade adjustment in time and space. The MIdAS platform has been set up such that it will support development of methods aimed at higher resolution climate model data, explicitly targeting cases where there is a scale mismatch between data sets. In this presentaton, we describe the MIdAS assumptions and features, and present results from the main evaluation of the method for different regions around the world. We also present the most recent development of MIdAS towards different parameters.

How to cite: Berg, P., Bosshard, T., Yang, W., and Zimmermann, K.: MIdAS---MultI-scale bias AdjuStment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-737, https://doi.org/10.5194/egusphere-egu22-737, 2022.

EGU22-882 | Presentations | CL5.1.5

How soil, water, and crop change along with farm sizes: insights from a new high-resolution farm-size specific and crop-specific map covering 56 countries 

Han Su, Barbara Willaarts, Diana Luna Gonzalez, Maarten S. Krol, and Rick J. Hogeboom

Over 608 million farms exist around the world, occupying 36.9% of global land and 72% of annual freshwater withdrawals. These farms are highly diverse and heterogeneous. More than 80% of them are smaller than 2 hectares and they only utilize around 20% of farmlands but support millions of livelihoods in the rural area. Many datasets are available describing the global crops, soil conditions, and water availability. A few of them are farm-size specific. There is a lack of a global overview on how the soil-water-agriculture system is different across farm sizes.
This study aims to explore how soil, water, and crop change along with farm sizes. Specifically, we used the current best available databases on cropland extent, farm size structure, crop-specific harvested area, and field size distribution to develop a gridded, farm-size specific, and crop-specific harvested area map for 56 countries, representing half global cropland, using a downscaling algorithm. The resulting maps were validated by empirical data and compared to previous similar studies. We then overlapped the farm-size specific, and crop-specific map with global soil and water scarcity maps to explore differences between small and large farms on planted crops, soil nutrient availability, and level of water scarcity.
Our results show, in comparison to larger farms, smaller farms plant more pulses, roots and tubers, vegetables, and fewer oilcrops, sugar crops, and fodder crops. The majority of small farms do not have severe limitations on soil nutrient availability, but they do face severe water scarcity. Large farms, on the other hand, do not confront severe water scarcity but do face severe limitations on soil nutrient availability. Small farms may also be less capable of adapting to water scarcity through irrigation. However, there is still spatial variation in our results. Our results can help to further differentiate the sustainable soil, water, and agriculture management for small and large farms. 

How to cite: Su, H., Willaarts, B., Luna Gonzalez, D., S. Krol, M., and J. Hogeboom, R.: How soil, water, and crop change along with farm sizes: insights from a new high-resolution farm-size specific and crop-specific map covering 56 countries, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-882, https://doi.org/10.5194/egusphere-egu22-882, 2022.

EGU22-1043 | Presentations | CL5.1.5 | Highlight

The added value of high-resolution EURO-CORDEX simulations to describe daily wind speed over Europe 

María Ofelia Molina, Joao Careto, Claudia Gutiérrez, Enrique Sánchez, and Pedro Soares


In the context of the CORDEX project, an ensemble of regional climate simulations of high resolution on a 0.11º grid has been generated for Europe with the objective of improving the representation of regional to local-scale atmospheric phenomena. However, such simulations are computationally expensive and do not always reveal added value.

In this study, a recently proposed metric (the distribution added value, DAV) is used to determine the added value of the available EURO-CORDEX high-resolution simulations at 0.11º for daily mean wind speed compared to their coarser-gridded 0.44º counterparts and their driving global simulations, from hindcast and historical experiments. The analysis is performed using observations data as a reference. Furthermore, the use of a normalized metric allows for a spatial comparison among different regions and time periods.

In general, results show that RCMs add value to their forcing model or reanalysis, but the nature and magnitude of the improvement on the representation of wind speed vary depending on the model, the region or the season. We found that most RCMs at 0.11º outperform models at 0.44º resolution in terms of their quality to represent measured wind speed PDF. However, the benefits of downscaling are not as clear in the upper tail of the wind speed.
At regional scale, higher DAVs are obtained at 0.11º than 0.44º resolution for all subdomains studied, particularly over the Mediterranean, the Iberian Peninsula and the Alps. With altitude, the 0.11º models represent better the locations below 50 m and above 350 m, while the 0.44º models under-perform with increasing altitude. Overall, DAVs are higher at 0.11º than at 0.44º resolution, probably due to a better performance of local-scale feedbacks at high resolution.

How to cite: Molina, M. O., Careto, J., Gutiérrez, C., Sánchez, E., and Soares, P.: The added value of high-resolution EURO-CORDEX simulations to describe daily wind speed over Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1043, https://doi.org/10.5194/egusphere-egu22-1043, 2022.

EGU22-2720 | Presentations | CL5.1.5

Beyond the usual suspects P&T: deriving multivariate high-resolution transient forcings for land surface models 

Jean-Philippe Vidal, Pere Quintana Seguí, and Santiago Beguería

Climate projections downscaled with statistical methods often focus on precipitation (P) and temperature (T) variables, which is not sufficient to run offline land surface models (LSMs) and derive hydrological projections based on both water and energy budgets.

This work extends a proposition by Clemins et al. (2019) to build on a multivariate high-resolution reanalysis dataset to infer projected ancillary variables from P & T projections based on analogue resampling. The refined method is a multisite and multivariate resampling method based on analogy of spatially distributed P & T daily anomalies. Anomalies are derived with respect to a baseline monthly climatology. The method thus makes use of spatial and multivariate consistency available in the archive reanalysis to supplement projections for additional variables and for a possibly extended spatial domain. The baseline climatology is considered as linearly transient for temperature variables to deal with anomalies not experienced during the archive period, and large-scale additional transient changes in T are passed on ancillary variables based on present-day anomaly relationships.

The new proposed method is exemplified for the Greater Pyrenean Region (GPR) defined as all basins draining the Pyrenees mountain range and extending over France, Spain, and Andorra. The archive reanalysis used here is the 2.5 km gridded SAFRAN-PIRAGUA surface reanalysis for the Pyrenees over 1965-2005 derived from existing SAFRAN reanalyses over France (Vidal et al., 2010) and Spain (Quintana-Seguí et al., 2017).

The projections considered here are 6 CMIP5 GCMs run under both RCP4.5 and RCP8.5 previously downscaled and including only P, TN, and TX variables and not available north of the Pyrenees (Amblar Francés et al., 2020). Applying the resampling method over the GPR led to 2.5 km gridded projections of daily time series of all variables required by LSMs. Results show – on top of an increasing temperature and a decreasing precipitation over the 21st century – a decrease in wind speed, relative humidity, and infrared radiation, and an increase in visible radiation and evapotranspiration. These projections come with a large inter-GCM dispersion and more pronounced changes under RCP8.5

This work was funded by the Interreg V-A POCTEFA 2014-2020 through the EFA210/16 PIRAGUA project.

 

Amblar-Francés, M. P., Ramos-Calzado, P., Sanchis-Lladó, J., Hernanz-Lázaro, A., Peral-García, M. C., Navascués, B., Dominguez-Alonso, M., Pastor-Saavedra, M. A. & Rodríguez-Camino, E. (2020) High resolution climate change projections for the Pyrenees region. Advances in Science and Research, 17, 191-208

Clemins, P. J., Bucini, G., Winter, J. M., Beckage, B., Towler, E., Betts, A., Cummings, R. & Chang Queiroz, H. (2019) An analog approach for weather estimation using climate projections and reanalysis data. Journal of Applied Meteorology and Climatology, 58, 1763-1777

Quintana-Seguí, P., Turco, M., Herrera, S. & Miguez-Macho, G. Validation of a new SAFRAN-based gridded precipitation product for Spain and comparisons to Spain02 and ERA-Interim (2017) Hydrology and Earth System Sciences, 21, 2187-2201

Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M. & Soubeyroux, J.-M. A 50-year high-resolution atmospheric reanalysis over France with the Safran system (2010) International Journal of Climatology, 30, 1627-1644

How to cite: Vidal, J.-P., Quintana Seguí, P., and Beguería, S.: Beyond the usual suspects P&T: deriving multivariate high-resolution transient forcings for land surface models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2720, https://doi.org/10.5194/egusphere-egu22-2720, 2022.

The Hellmann rainfall recorders have been one of the primary instruments of the rainfall intensity measurement, mainly in the countries of the central part of Europe, during the 20th century. These water level measurement-based rainfall recorders ensure the continuity of measurement by periodically emptying siphoned measurement cylinder. During the emptying, the measurement is paused, resulting in under-measurement. The duration and number of emptying can be known, and the correction of under-measurement can be adjusted if the registration ribbon is available. In the case of historical data, the registration ribbon is often unavailable, and only extracted values of the most intensive periods of rainfalls can be gained from databases. A procedure for correcting such kinds of data is shown, and also the influence of the correction of Hellmann-Fuess recorder’s data on the IDF curves.

The procedure is based on the method published in 2002 by Luyckx and Berlamont, which was compiled based on the physical process of the siphoning, and it assumed the existence of the original records (ribbons), with the exact time of siphoning. The extracted data tables nor time nor number of siphoning was registered, so the method of Luyckx and Berlamont for the data correction cannot be used. The proposed procedure’s principal is the estimation of the number and length of pauses in the extracted measurement interval. These estimated data make passible to fix the rainfall quantity and intensity for the given interval. The measurement cylinder of the device is generally not empty at the beginning of the most intensive period of rainfall. The water level is assumed as a uniformly distributed probability variable what can be estimated with its expected value. The raw data comprise underestimation in all cases, so the raw data represent only the possible minima of the plausible intensity values. The fixed data result in the expected value of the plausible intensities, which are sometimes higher and sometimes lower than the actual intensity values, with the same probability; so, if there are a high number of fixed data, the positive and negative deviations diminish the errors statistically. The use of the correction formula is presented with the parameters of a Hellmann-Fuess rainfall writer. The correction range has been the highest over ten years of average recurrence, and its measure was 10%. For 30 minutes and longer sampling intervals, the magnitude of the correction is not relevant. After that, the correction is based on statistical estimation, the exact value of the rainfall intensity cannot be retrieved, but the underestimation can be decreased significantly. The fixed data modify the IDF curves as well, and in this way, the effect of climate change can be investigated more appropriately.

How to cite: Racz, T.: Correction of siphoning error in processed historical rainfall intensity data, a case study of data measured by Hellmann-Fuess type rainfall recorder, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3709, https://doi.org/10.5194/egusphere-egu22-3709, 2022.

EGU22-3769 | Presentations | CL5.1.5

Daily 1 km gridded temperature and precipitation in Bhutan 

Fabian Lehner and Herbert Formayer

Gridded climatological data derived for Bhutan from internationally available data sets either come at very low spatial resolution, do not provide daily data or contain few in-situ measurements. We present a newly developed daily high-resolution (1 km) gridded data set for Bhutan covering the years 1996 to 2019 for precipitation and for maximum and minimum temperature (BhutanClim) and compare it with state-of-the-art global observation data sets. As input, we used quality controlled and homogenized data from up to 67 weather stations from the National Center for Hydrology and Meteorology of Bhutan (NCHM).

The spatial interpolation method of temperature is based on methods that have already been successfully implemented in Austria, Switzerland and Germany. It allows non-linear lapse rates and considers geographical obstacles in the interpolation. The new climatology benefits from the use of local measurements and shows plausible small scale spatial patterns. Compared to other available state-of-the-art data sets BhutanClim there are new features especially in the precipitation field: Some valleys in central Bhutan border a dry climate classification according to Köppen-Geiger.

How to cite: Lehner, F. and Formayer, H.: Daily 1 km gridded temperature and precipitation in Bhutan, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3769, https://doi.org/10.5194/egusphere-egu22-3769, 2022.

EGU22-4305 | Presentations | CL5.1.5

Providing useful local climate information through statistical bias correction 

Muralidhar Adakudlu, Elena Xoplaki, Heiko Paeth, Chibuike Ibebuchi, and Daniel Schoenbein

Daily precipitation and temperature simulated by regional climate models carry large systematic biases owing to multiple factors including inadequate model resolution and limitations in the parameterization of important processes. Reduction of these biases is a crucial process in rendering the model information more reliable for climate change and hydrological assessments. We present an evaluative study of bias correction of daily precipitation and temperature from an ensemble of regional climate models from the EUR-11 CORDEX domain (CLMCOM-CCLM4, GERICS-REMO15, SMHI-RCA4, DMI-HIRHAM5, and CanRCM4 driven by MPI-ESM). This is an important milestone within a larger framework of the RegiKlim consortium towards generating high-resolution bias corrected and statistically downscaled fields for providing useful climate information in specific areas in Germany. A quantile delta mapping (QDM) approach is applied to adjust the biases in the distribution characteristics of precipitation and temperature. The delta factor, derived from the ratio of the projected value of a given quantile to that of the present value, is applied to the standard transfer function so that the modelled climate change signal can be preserved. High-resolution (0.1°) gridded dataset from the German Weather Service, DWD-HYRAS, is used as the reference for bias correcting the variables. The impact of the bias adjustment on important parameters such as the number and frequency of wet/dry and cold/hot spells are quantified. The response of the quantile mapping method to the seasonal variations in the dominant driving processes is further investigated. 

How to cite: Adakudlu, M., Xoplaki, E., Paeth, H., Ibebuchi, C., and Schoenbein, D.: Providing useful local climate information through statistical bias correction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4305, https://doi.org/10.5194/egusphere-egu22-4305, 2022.

EGU22-4717 | Presentations | CL5.1.5

Assessing the Suitability of A Posteriori Random Forests for Downscaling Climate Change Projections 

Mikel N. Legasa and Rodrigo Manzanas

Statistical downscaling (SD) methods are extensively used to provide high-resolution climate information based on the coarse outputs from Global Climate Models (GCM). In the context of climate change and under the perfect prognosis approach, these methods learn the relationships that link several large-scale predictor variables coming from a reanalysis (e.g. humidity) with the local variables of interest (e.g. precipitation) over a reference historical period. Subsequently, the so-learnt relationships are applied to GCM predictors to obtain downscaled projections for a future period.

In a recent paper, Legasa et al. (2021) introduced a posteriori random forests (AP-RFs), a modification of classical random forests which make use of all the data in the leaves to estimate any probability distribution. Following the experimental framework proposed in Experiment 1 of VALUE (http://www.value-cost.eu, Gutiérrez et al. 2018), the study showed that AP-RFs obtained reliable stochastic time-series over several locations in Europe using reanalysis predictors. As compared to more classical techniques like generalized linear models (GLMs), this study concluded that AP-RFs are a competitive SD method in terms of different forecast aspects, with one of their key advantages being the ability to automatically perform predictor/feature selection. This avoids the task of manually selecting the most adequate large-scale variables and geographical domain of interest, something which, at present, relies on human expertise and constitutes a substantial source of uncertainty for downscaling climate change projections.

Nevertheless, an assessment of the suitability of AP-RFs for producing local climate change projections from GCM predictors is still lacking. This work aims to fill this gap by providing a fair comparison of AP-RFs with GLMs and state-of-the-art convolutional neural networks (CNNs), which were recently shown to provide satisfactory results for this task (Baño-Medina et al. 2021). We build on VALUE’s Experiment 2a and train the different methods considered using ERA-Interim “perfect” predictors. Afterwards, the EC-Earth model is used to generate downscaled projections for 86 locations distributed across Europe under a strong emission scenario, the RCP8.5. 

Our preliminary results suggest that AP-RFs generate plausible downscaled future projections of precipitation. In particular, differently to traditional GLMs, which are very sensitive to the predictor set considered and may produce implausible climate change projections (Manzanas et al. 2020), this technique yields delta changes consistent with those obtained from both the raw EC-EARTH outputs and the CNNs.

References
Baño-Medina, J., Manzanas, R. & Gutiérrez, J.M. On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections. Clim Dyn 57, 2941–2951 (2021). doi: https://doi.org/10.1007/s00382-021-05847-0

Gutiérrez, J.M., Maraun, D., Widmann, M. et al. An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment. Int. J. Climatol. 2019; 39: 3750– 3785. doi:  https://doi.org/10.1002/joc.5462

Legasa M.N., Manzanas R., Calviño, A. et al. A Posteriori Random Forests for Stochastic Downscaling of Precipitation by Reliably Predicting Probability Distributions. Submitted to Water Resources Research.

Manzanas, R., Fiwa, L., Vanya, C. et al. Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi. Climatic Change 162, 1437-1453 (2020). doi:  https://doi.org/10.1007/s10584-020-02867-3

How to cite: Legasa, M. N. and Manzanas, R.: Assessing the Suitability of A Posteriori Random Forests for Downscaling Climate Change Projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4717, https://doi.org/10.5194/egusphere-egu22-4717, 2022.

EGU22-6150 | Presentations | CL5.1.5 | Highlight

Downscaling UK rainfall using machine-learning emulation of a convection-permitting model 

Henry Addison, Peter Watson, Laurence Aitchison, and Elizabeth Kendon

Climate change is causing the intensification of rainfall extremes in the UK [1]. Physics-based numerical simulations for creating precipitation projections are computationally expensive and must be run many times to quantify the natural variability of precipitation. Local-scale projections such as those from the Met Office's 2.2km convection-permitting model are possible [2] but the computational expense of these simulations requires trade-offs in the duration, domain size, ensemble size and emission scenarios for which to produce projections [1]. 

Here, we apply state-of-the-art machine learning methods to predict precipitation from the 2.2km model given large-scale predictors that are represented in GCMs. By conditioning on outputs from a physical model, rainfall can be downscaled in both past and future climates. We test the extent these methods can reproduce the complex spatial and temporal structure of rainfall, with which past statistical approaches struggle. We are interested in the methods’ ability to capture the distribution of extreme rainfall and to reproduce extreme events. Our methods are neural-network-based and explore generative approaches for representing the stochastic component of high-resolution precipitation. Compared to physical models, these approaches are computationally much cheaper and have a simple interface allowing them to be used to downscale other large GCM datasets. 

References 

[1] Kendon, E. J. et al. (2021). Update to the UKCP Local (2.2km) projections. Science report, Met Office Hadley Centre, Exeter, UK. [Online]. Available: https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/ukcp18_local_update_report_2021.pdf. 

[2] Met Office Hadley Centre. (2019). UKCP18 Local Projections at 2.2km Resolution for 1980-2080, Centre for Environmental Data Analysis. [Online]. Available: https://catalogue.ceda.ac.uk/uuid/d5822183143c4011a2bb304ee7c0baf7.

How to cite: Addison, H., Watson, P., Aitchison, L., and Kendon, E.: Downscaling UK rainfall using machine-learning emulation of a convection-permitting model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6150, https://doi.org/10.5194/egusphere-egu22-6150, 2022.

As the most severe drought over the Northeastern United States (NEUS) in the past century, the 1960s drought had pronounced socioeconomic impacts. Although a persistent wet period followed, the conditions driving the 1960s extreme drought could return in the future, along with its challenges to water management. To project the potential consequences of such a future drought, pseudo-global warming (PGW) simulations using the Weather Research and Forecasting Model are performed to simulate the dynamical conditions of the historical 1960s drought, but with modified thermodynamic conditions under the shared socioeconomic pathway SSP585 scenario in the early (2021-2027), middle (2041-2047) and late (2091-2097) 21st century. Our analysis focuses on essential hydroclimatic variables including temperature, precipitation, evapotranspiration, soil moisture, snowpack and surface runoff. In contrast to the historical 1960s drought, similar dynamical conditions will generally produce more precipitation, increased soil moisture and evapotranspiration, and reduced snowpack. However, we also find that although wet months do become much wetter, dry months also may become drier, meaning that wetting trends that are significant in wet months can be essentially negligible for extremely dry months. For these months, the trend towards wetter conditions provides little relief from drying. These conditions may even aggravate water shortages due to an increasingly rapid transition from wet to dry conditions. Other challenges emerge for residents and stakeholders in this region, including more extreme hot days, record-low snow pack, frozen ground degradation and subsequent decreases in surface runoff.

Although the PGW approach pursued in this study is analogous to other recent studies, there is also a pressing need to ascertain confidence in projections using the PGW method.  Most PGW studies only modify the temperature forcing since it is the most significant for driving impacts on climate, but other meteorological forcings may also impact regional climate trends. For example, the large geopotential height increments at higher atmosphere levels tend to increase the stability and weaken the precipitation events associated with typhoons.  PGW studies usually only consider the changes at the regional mean scale but ignore spatially-dependent contributions from climate change. Therefore, in order to investigate the sensitivity of PGW-based projections, additional simulations were conducted under the RCP8.5 emission scenario but with different forcing modification methods.  We thus answer three questions: Are PGW simulations sensitive to the spatial scale of climate perturbations? Besides temperature, which climatological variables are crucial to PGW simulations? And finally, how should researchers design and conduct their PGW simulations?

How to cite: Xue, Z. and Ullrich, P.: PGW projections of the returned 1960s U.S. Northeast drought and sensitivity examinations of PGW methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6745, https://doi.org/10.5194/egusphere-egu22-6745, 2022.

EGU22-7049 | Presentations | CL5.1.5 | Highlight

Dynamical Downscaling and Data Assimilation: Insights from the Case Study of the "Year Without a Summer" 1816 

Lucas Pfister, Peter Stucki, Andrey Martynov, and Stefan Brönnimann

One year after the eruption of the Tambora volcano, the “Year Without a Summer” of 1816 was characterised by extraordinarily cold periods in (Central) Europe, and it was associated with severe crop failures, food shortages, famine and socio-economic disruptions.

The summer of 1816, has been analysed based on a number of early meteorological measurements, as well as on ample documentary information. A statistical reconstruction of spatial fields with daily resolution has been conducted for Switzerland. However, this dataset encompasses only a limited set of variables. In turn, dynamical downscaling methods allow to reconstruct past weather on a higher temporal and spatial resolution. In our work, we simulate a particularly cold episode in June 1816 by downscaling data from the Twentieth Century Reanalysis version 3 (20CRv3). The simulation uses the Weather Research and Forecasting (WRF) model with three nested domains for the greater Alpine region and provides hourly output with a 3-km resolution. In addition, we include recently digitised station series of temperature and pressure for a Three-Dimensional Variational (3DVAR) data assimilation in the innermost domain. Results are then validated against independent station observations.

First results suggest that dynamical downscaling and data assimilation may become a promising approach to obtain physically consistent information on past weather on a local and subdaily scale. This may hold even for extreme events in an era with a scarce network of instrumental weather observations compared to today, although erroneous results may occur. A successful application of dynamical downscaling and data assimilation for the early 19th century might open the door for a regional atmospheric reanalysis product that covers the last two centuries.

How to cite: Pfister, L., Stucki, P., Martynov, A., and Brönnimann, S.: Dynamical Downscaling and Data Assimilation: Insights from the Case Study of the "Year Without a Summer" 1816, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7049, https://doi.org/10.5194/egusphere-egu22-7049, 2022.

EGU22-7306 | Presentations | CL5.1.5 | Highlight

A hybrid statistical-dynamical method to downscale global climate models over Europe 

Julien Boé and Alexandre Mass

To characterize the impacts of climate change, robust high-resolution climate change information is generally needed. The resolution of global climate models is currently too coarse to provide directly such information. A specific spatial downscaling step is therefore generally needed, either (1) dynamical downscaling with regional climate models or (2) statistical downscaling.

In this study, we present a new hybrid statistical-dynamical downscaling approach, intended to combine the respective strengths of statistical and dynamical downscaling, while overcoming their respective limitations. This hybrid method aims to emulate regional climate models and is based on a constructed analogues method.

Contrary to dynamical downscaling, the computational cost of the method is low, allowing to downscale a large number of global climate projections and therefore to correctly assess the climate uncertainties in impact studies. Contrary to statistical downscaling, the method does not rely on the assumption that the downscaling relationship established in the present climate with observations remains valid in the future climate perturbed by anthropogenic forcings. Therefore, the hybrid approach should be as robust as regional climate models in projecting future climate change.

In this presentation, the hybrid statistical-dynamical downscaling method is first presented. Elements of evaluation, in a perfect model framework based on an ensemble of regional climate models over Europe, are then shown and discussed to demonstrate the interest of the method and its applicability to study future climate changes over western Europe. Finally, results of the application of the method to downscale global climate projections over western Europe are shown, and important implications of the results are discussed.   

How to cite: Boé, J. and Mass, A.: A hybrid statistical-dynamical method to downscale global climate models over Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7306, https://doi.org/10.5194/egusphere-egu22-7306, 2022.

EGU22-7485 | Presentations | CL5.1.5 | Highlight

The Spatial Weather Generator SPAGETTA: Hard Times of its Adolescence 

Martin Dubrovsky, Ondrej Lhotka, Jiri Miksovsky, Petr Stepanek, and Jan Meitner

Stochastic weather generators (WGs) are tools for producing weather series, mimicking statistical properties of their real-world counterparts. They are often used in climate change impact experiments as a source of the data representing the present and/or future climates (alternative to RCMs and GCMs). Development of our SPAGETTA generator started in 2016 (Dubrovsky et al 2020; https://doi.org/10.1007/s00704-019-03027-z). The presentation will focus on (A) Basic details. (B) Functionalities of the generator. (C) Results obtained with the generator by now. (D) Most critical problems, which were met (and not yet satisfactorily solved) while making the generator fully operational.

A. SPAGETTA is a multivariate multisite parametric generator, which is based on autoregressive modeling (following the D. Wilks’ papers). It is designed mainly (but not solely) for use in agricultural and hydrological modeling. It may produce time series of up to 8 variables for as many as (approx.) 200 stations or grid points. Typically, it produces time series of temperature, precipitation, solar radiation, humidity and wind speed. It usually runs with a daily time step.

B. The main functionalities include: (1) It may produce arbitrarily long time series representing the climate defined by the data used for calibrating the generator (might be observational data or, for example, RCM outputs). (2) Having modified the WG parameters by the climate change scenario (typically derived from GCM or RCM simulations), SPAGETTA may produce weather series representing the future climate. In this case, one may study sensitivity of selected climatic indices to changes in various statistics (e.g. means and standard deviations of weather variables, and characteristics of temporal and spatial structure of the time series). (3) SPAGETTA may be interpolated so that it can produce weather series for sites with no observational data. (4) It can be linked with the circulation generator so that WG may better represent larger-scale (both in space and time) weather variability.

C. The results obtained with the generator by now include: (a) Validation of the generator in terms of WG parameters, various climatic indices, and outputs of hydrological model fed by the synthetic series produced by SPAGETTA. (b) Impacts of the forthcoming climate change on various climatic characteristics (RCM-based climate change scenarios were used here). Focus was put on spatial temperature-precipitation compound characteristics. (c) Validation of the interpolated generator. (d) Validation of the generator driven by the larger scale circulation generator.

D. Problems to be solved: (i) Under some circumstances (especially when a large number of the stations is used, or while interpolating the generator), matrices of the AR model imply unstable AR process which diverges to unrealistic values of weather variables. (ii) The generator underestimates the low frequency variability. Development of the larger scale circulation generator, which would eliminate this drawback, is still under development.

Only examples of the previous points will be shown in the presentation.

How to cite: Dubrovsky, M., Lhotka, O., Miksovsky, J., Stepanek, P., and Meitner, J.: The Spatial Weather Generator SPAGETTA: Hard Times of its Adolescence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7485, https://doi.org/10.5194/egusphere-egu22-7485, 2022.

EGU22-8086 | Presentations | CL5.1.5 | Highlight

Evaluating the adequacy-for-purpose of downscaling methods and products 

Wendy Parker

How should downscaling methods, and the products of downscaling, be evaluated? An adequacy-for-purpose approach attempts to determine whether a method or product can be used successfully for specific purposes of interest. Purposes can take various forms: predicting variable X in region R with a specified level of accuracy, guiding a particular policy decision, etc. Depending on the purpose, different tests or checks will be performed and different levels of performance, on different metrics, will be deemed acceptable. A product that is grossly inaccurate in some respects may nevertheless be entirely adequate for the purpose at hand. Likewise, higher-resolution products are not necessarily more adequate (or fit); it depends on whether they provide the information required for the purpose of interest and in a way that can be interpreted and employed by users.

How to cite: Parker, W.: Evaluating the adequacy-for-purpose of downscaling methods and products, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8086, https://doi.org/10.5194/egusphere-egu22-8086, 2022.

EGU22-9658 | Presentations | CL5.1.5

A machine learning approach for refining ECHAM-HAMMOZ-derived PM2.5 

Tuuli Miinalainen, Harri Kokkola, Antti Lipponen, Kari E. J. Lehtinen, and Thomas Kühn

In this study, we present our findings for correcting global model-derived surface fine particulate mass (PM2.5) concentrations with a machine learning approach. We simulated the PM2.5 concentrations with an aerosol-climate model ECHAM-HAMMOZ, and trained a machine learning model to downscale the PM2.5 concentrations modelled for an Indian mega city, New Delhi. This way, we are able to utilize a global atmospheric model for analyzing aerosol emission mitigation effects on both Earth's energy budget and local air quality.

Similarly as with many other global-scale models, ECHAM-HAMMOZ underestimates surface PM2.5 at several urban locations. One apparent explanation for this is the coarse grid resolution of global climate models, which results in averaged aerosol concentrations over a much larger area than what urban cities typically cover. Therefore, due to averaging over a large grid box, the very high peak concentrations from urban areas can be evened out. Furthermore, the input fields describing aerosol emissions might lack information of some local emission sources, which can as well affect the simulated surface air pollution levels.

We used the random forest (RF) regression algorithm in order to downscale ECHAM-HAMMOZ-derived surface PM2.5 concentrations towards measured PM2.5 values from the New Delhi capital region in India. In addition, we applied the trained RF model to additional simulations where we had future anthropogenic aerosol emissions according to a business as usual scenario and a mitigation scenario. This allowed us to evaluate the effects of aerosol emission reductions on both global radiative balance, and on local air quality in New Delhi.

The obtained results indicate that surface PM2.5 concentrations from RF prediction correlate with the measured PM2.5 concentrations much better than the original ECHAM-HAMMOZ particulate concentrations for New Delhi region. However, with the current setup and input variables, the PM2.5 concentrations produced by the RF model seems to be lacking some of the short-term variations and very low and high values.

All in all, the downscaling method used in this project shows very promising potential, but requires further adjustment with the selection of input variables and the RF hyperparameters.

How to cite: Miinalainen, T., Kokkola, H., Lipponen, A., Lehtinen, K. E. J., and Kühn, T.: A machine learning approach for refining ECHAM-HAMMOZ-derived PM2.5, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9658, https://doi.org/10.5194/egusphere-egu22-9658, 2022.

Regional climate models (RCMs) are widely used to dynamically downscale the general circulation models (GCMs). Downscaled products can provide a clearer understanding of atmospheric processes compared to the parent models. However, several uncertainties are associated with downscaling, such as structural differences in climate models and biases in GCMs and RCMs. Post-processing methods such as univariate bias correction have been widely used to reduce the bias in the individual variable. However, these methods are applied to variables independently without considering the inter-variable dependence. In compound events such as heat stress, multiple drivers, surface air temperature (SAT), and relative humidity (RH) play crucial roles. Therefore, a multi-variable bias adjustment is necessary to retain the interdependence between the drivers for reliable information on heat stress. The present study focuses on a Multi-variable Bias Adjustment (MBA) method adapted from a topographical adjustment of SAT and RH and its impact on added values in a multi-model ensemble. We investigated added values and biases before and after adjusting the variables. There are gains and losses throughout the process of bias adjustment. Some added values show pseudo nature over some regions after the bias adjustment. Overall, the bias adjustment shows improvement in reducing bias over low-altitude urban areas, encouraging its application to assess heat stress.

How to cite: Kelkar, S. and Dairaku, K.: Investigation of added values in multi-model and multi-variable bias adjustments for heat stress assessment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10974, https://doi.org/10.5194/egusphere-egu22-10974, 2022.

High-quality snow is critical for the Winter Olympic Games. Snow quality is very sensitive to the changes of meteorological elements, especially temperature and humidity. Forecasts on snow quality can provide information for snow maintenance and require high-resolution meteorological forecasts. However, the complex terrain of the mountainous areas where the winter sports are often carried out could result in complex local meteorological fields, which makes it difficult to forecast. Zhangjiakou Competition Zone is one of the three competition zones for the Beijing 2022 Winter Olympics and includes two districts: Genting snow park and Guyangshu Ski Resort. Taking Genting snow park as an example, there is a difference of about 350m in altitude in Genting snow park which covers an area of about 2×2km2, and there is an average difference of about 3℃ in hourly temperature and about 10% in hourly relative humidity at noon.

Short-term forecasts in the past Winter Olympic Games were usually based on mesoscale NWP models with a horizontal resolution of up to 1×1km. Due to the limitation of boundary layer parameterization schemes, some small-scale air processes affected by local topography cannot be caught in mesoscale models. Some MOS methods can correct the systematic bias of the models but are unable to deal with the non-systematic errors caused by these small-scale processes.

The purposes of this study were to develop statistical downscaling methods for the temperature and humidity forecasts, which are required in the snow-quality risk classification for the Zhangjiakou Competition Zone of the Beijing 2022 Winter Olympics.  

Hourly data during 2018-2021 from 20 meteorological stations and ERA5-Land reanalysis in the study area were used for the calibration and validation of models. A decaying average method which is similar to the Kalman Filter method was applied to develop the downscaling models. To evaluate the efficiency of the models on snow-quality risk forecasts. A classification model of snow-quality risk developed by the Climate Centre of Hebei Province was applied. Snow-quality risk classification model was developed based on the four years’ meteorological and snow-quality observations in the study area, in which the risk of snow quality was classified into 4 levels: zero-risk, low-risk, medium-risk and high-risk based on the input temperature and humidity. The downscaled prediction fell into 3 cases: (1) the predicted risk level equal to the observed risk level (Accuracy); (2) the predicted risk level lower than the observed risk level (Miss); (3) the predicted risk level higher than the observed risk level (False-Alarm).

The results showed that: (1) the downscaling models can decrease the RMSE of the ERA5 by ~13% for the temperature and by ~14% for the dewpoint temperature; (2) the accuracy of the snow-quality risk classification increased from 72% to 76% on average comparing the inputs of ERA5 and the downscaled temperature and humidity. For the stations with high elevation, the ratio of False-Alarm decreased by ~13%. Further research will focus on improving the statistical model by calibrating the model for different locations and different circulation patterns.

How to cite: Yue, T., Yin, S., and Wang, H.: Statistical downscaling of temperature and humidity for snow-quality risk forecasts for Beijing 2022 Winter Olympics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11128, https://doi.org/10.5194/egusphere-egu22-11128, 2022.

EGU22-11296 | Presentations | CL5.1.5

Near-past and future trends of European extreme heat and heat waves from WRF downscaling experiments 

Zhuyun Ye, Ulas Im, Jesper Christensen, Camilla Geels, Marit Sandstad, Carley Iles, and Clemens Schwingshackl

In frame of the H2020-EXHAUSTION project, we present estimated heat indicators from WRF (Weather Research and Forecasting model) downscaling simulations for the periods of 1980-2014 and 2015-2049 at 20 km horizontal resolution over the European domain. WRF simulations are forced by the CESM2 global model simulations, using three shared socio-economic pathways (SSP) future scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6): SSP1-2.6, SSP2-4.5 and SSP3-7.0, addressing different levels of mitigation and adaptation. For the period of 1980-2014, another WRF simulation forced by ERA5 is used as comparison in model validation. These near-past simulations have been rigorously evaluated with observations and reanalysis data including European Climate Assessment & Dataset (ECA&D), E-OBS, and ERA5-land for the surface air temperatures. The dynamical downscaling showed clear added value on spatial distribution related to the important coastal or orographic aspects widely present over Europe. Two heat wave indicators, the Warm Spell Duration Index (WSDI) and the Heat Wave magnitude Index daily (HWMId), and four extreme heat indicators, annual maximum temperature (TX­x), NOAA heat index (HIx), wet-bulb globe temperature (WBGTx), and universal thermal climate index (UTCIx), are used to study the heat extremes trends in Europe. During the past 35 years, TXx has been estimated to increase 2.5 °C in WRF_CESM2 and 1.4 °C in WRF_ERA5; the increasing trend is estimated to remain or slow down slightly in the next 35 years with estimated smaller increase of 1.5-2.5 °C in three scenarios. The trends of other extreme heat indicators showed very similar trends with TXx. However, future heat wave duration and magnitude present a contrasted pattern. Heat waves have been estimated to increase 11.2 days of duration, and 2.1 of magnitude during 1980-2014, very similar to the observed increase of 9.1-11 days and 1.8-2.1. Whereas in 2015-2049, heat waves duration and magnitude are estimated to increase 12.3-13 days and 2.5-4.6, respectively. These heat wave changes are also not uniform from a spatial point of view. Heat wave duration and magnitude in Southern Europe are both estimated to increase significantly faster than other zones, with rates at 1.4-2.9 times of which for the whole of Europe. Heat wave indicators in future scenarios also showed much larger interannual variations compared with the past, whereas there are no distinct differences among three mitigation scenarios for all heat indicators. In summary, these results suggested that even though the future increase of air temperatures and heat extreme indicators showed a slowing down sign compared with the near-past, whereas the severity of heat waves are estimated to increase even faster than the past under different levels of mitigation. Southern Europe is expected to be the region that needs the most attention in terms of severe future heat waves.

How to cite: Ye, Z., Im, U., Christensen, J., Geels, C., Sandstad, M., Iles, C., and Schwingshackl, C.: Near-past and future trends of European extreme heat and heat waves from WRF downscaling experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11296, https://doi.org/10.5194/egusphere-egu22-11296, 2022.

EGU22-11855 | Presentations | CL5.1.5

Testing deep learning methods for downscaling climate change projections: The DeepESD multi-model dataset 

Jorge Baño-Medina, Rodrigo Manzanas, Ezequiel Cimadevilla, Jesús Fernández, Antonio S. Cofiño, and Jose Manuel Gutiérrez

Deep Learning (DL) has recently emerged as a powerful approach to downscale climate variables from low-resolution GCM fields, showing promising capabilities to reproduce the local scale in present conditions [1]. There have also been some prospects assessing the potential of DL techniques to downscale climate change projections, in particular convolutional neural networks (CNNs) [2]. However, it is still an open question whether they are able to properly generalize to climate change conditions which have been never seen before and produce plausible results. 

Following the “perfect-prognosis” approach, we use in this study the CNNs assessed in [2] to downscale precipitation and temperature for the historical (1975-2005) and RCP8.5 (2006-2100) scenarios of  an ensemble of eight Global Climate Models (GCMs) over Europe. The resulting future projections, which are gathered in a new dataset called DeepESD, are compared with 1) those derived from benchmark statistical models (linear and generalized linear models), and 2) a set of state-of-the-art regional climate models (RCM) which are considered the “ground-truth”. Overall, CNNs lead to climate change signals that are in good agreement with those obtained from RCMs (especially for precipitation), which indicates their potential ability to generalize to future climates. Nevertheless, for some GCMs we find  that there are considerable regional differences between the “raw” and the downscaled climate change signals, an important aspect which was unnoticed in a previous work that focused exclusively on one single GCM [2]. This highlights the importance of considering  muti-model ensembles of downscaled projections (such as the one presented here) to conduct a comprehensive analysis of the suitability of DL techniques for climate change applications. Indeed, understanding the nature of the mentioned differences is necessary and future work towards this aim would imply carefully analyzing some of the assumptions made in“perfect-prognosis” downscaling (e.g., stationarity of the predictor-predictand link, adaptation of the statistical function to the climate model space). Therefore, following the FAIR (Findability, Accessibility, Interoperability and Reuse) principles we have made publicly available DeepESD through the Earth System Grid Federation (ESGF), which will allow the scientific community to continue exploring the benefits and shortcomings of DL techniques for statistical downscaling of climate change projections. 

References:

[1] Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and intercomparison of deep learning neural models for statistical downscaling, Geoscientific Model Development, 13, 2109–2124, 2020.

[2] Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections, Climate Dynamics, pp. 1–11, 2021

 

Acknowledgements

The authors would like to acknowledge projects ATLAS (PID2019-111481RB-I00) and CORDyS (PID2020-116595RB-I00), funded by MCIN/AEI (doi:10.13039/501100011033). We also acknowledge support from Universidad de Cantabria and Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabria via the “instrumentación y ciencia de datos para sondear la naturaleza del universo” project for funding this work. A.S.C and E.C. acknowledge project IS-ENES3 funded by the EU H2020 (#824084).



How to cite: Baño-Medina, J., Manzanas, R., Cimadevilla, E., Fernández, J., Cofiño, A. S., and Gutiérrez, J. M.: Testing deep learning methods for downscaling climate change projections: The DeepESD multi-model dataset, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11855, https://doi.org/10.5194/egusphere-egu22-11855, 2022.

EGU22-12499 | Presentations | CL5.1.5

Downscaling GCM data using a bias-correction method for the Eastern Mediterranean. 

Ezgi Akyuz, Burcak Kaynak, and Alper Unal

Climate change and air pollution are two phenomena that can no longer be considered separately. Changes in climate also alter the effects of air pollution. For example, emissions of ammonia (NH3) and non-methane organic compounds (NMVOC), which are precursors of ozone (O3) and secondary particles (PM), are drastically sensitive to temperature and humidity changes. Moreover, the impacts of O3 and secondary PMs on the climate were previously investigated. The first step for air quality modeling studies is the modeling of meteorological fields. In this study, important meteorological parameters in terms of air pollution were obtained from global climate models for a historical and future periods (SSP585). Selected parameters will be corrected and downscaled to a high resolution for Eastern Mediterranean. Then, the bias-corrected and downscaled meteorology outputs will be used in other studies related to air quality.

Countries in the Mediterranean Region are being affected significantly by the changing climate due to their location. Previously conducted studies evaluated the meteorological parameters of global climate models with low resolutions. Within the scope of this study, future estimates will be downscaled to a selected domain in Eastern Mediterranean with a spatial resolution of 4×4 km2 However, recent studies have argued that a bias-correction method should be implemented to the selected meteorological parameters prior to downscaling. In previous studies, CMIP simulation outputs were evaluated for Turkey with or without downscaling. There are also studies that biases between observation/reanalysis and GCM model data are calculated. However, according to our knowledge, evaluation of downscaled climate change scenarios in the Mediterranean Region using a bias-correction method has not been conducted yet. Here, a bias correction methodology (Xu et al. (2021)) was used, and an ensemble was generated by choosing appropriate global climate models which are compatible with reanalysis data for the selected region.

Native global climate model simulation results and non-linear long-term global climate model simulation trends were evaluated as the preliminary investigation. The temperature means of the global climate models (GCMs) and ERA5 reanalysis data were compared globally and for the EMEP domain. Initial findings showed underestimation or overestimation for the same GCM depending on the selected study domain. This result highlights the importance of the selection of the model for the study domain for weather generation and the models to be chosen for the ensemble. After calculating the long-term non-linear trend, the standard deviations were calculated for the interannual variability for the GCM and ERA5. For the historical period (1979-2014), annual temperature means of BCC-CSM2-MR, CMCC-CM2-SR5, EC-EARTH3, EARTH3-Veg, FIO-ESM-2-0, and KIOST-ESM showed similarity between ERA5 (r2 > 0.70). Summer and fall months show mostly higher correlations compared to other seasons. 22 model ensemble global domain (EMEP Domain) temperature mean, minimum and maximum values were found as 7.62 (6.09), 7.18 (5.28), and 8.12oC (6.89oC), respectively. The values for reanalysis data are 7.95 (6.92), 7.57 (5.94), and 8.27 oC (7.87oC).

How to cite: Akyuz, E., Kaynak, B., and Unal, A.: Downscaling GCM data using a bias-correction method for the Eastern Mediterranean., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12499, https://doi.org/10.5194/egusphere-egu22-12499, 2022.

EGU22-526 | Presentations | HS7.2

Throughfall variability at the hillslope scale: the role of topography and tree characteristics 

Matteo Verdone, Marco Borga, Andrea Dani, Federico Preti, Paolo Trucchi, Giulia Zuecco, Ilja van Meerveld, Christian Massari, and Daniele Penna

Understanding the role of forest on rainfall interception is fundamental for a correct analysis and modelling of runoff generation and catchment hydrological response. Despite many studies were carried out at the stand and hillslope scale, very little is known about the role of hillslope topography and the associated tree population characteristics on throughfall spatio-temporal variability. Therefore, this work aimed at better understanding the dominant factors on throughfall variability and on the temporal persistence of throughfall spatial patterns along a transect on a steep hillslope characterized by trees with different size and density.

The experimental activities were carried out in the upper part of the densely-forested Re della Pietra catchment, Tuscany Apennines, Central Italy. The hillslope is roughly 110 m long and 60 m wide, has a mean slope of 30°, and is dominantly covered by beech trees and by sparce individuals of oak trees. A grid of 126 throughfall collectors was installed in July 2020 and divided in three sub-plots: two plots of 144 m2 with 2-m spaced 49 collectors at the bottom and the top of the hillslope, and a transect of 28 1-m spaced collectors from the bottom to the top of the hillslope. A survey was conducted to measure the diameter and basal area of the stand. Throughfall was manually measured from the collectors approximately monthly from June 2020 to November 2021, and compared with gross precipitation measured by a rain gauge placed outside the vegetation cover. Moreover, five automatic gauges connected to 1.5 m-long gutter to increase the collection area were installed in November 2021 along the hillslope to measure throughfall at high temporal resolution.

Preliminary results from 25 manual measurements over the experimental grid highlighted a large temporal variability of interception (mean: 17%, standard deviation: ±31%), reflecting the variable seasonal precipitation pattern of Mediterranean areas and the phenological stage of trees (leaves/no leaves). Overall, the spatial variability in throughfall increased with increasing gross precipitation. Particularly, the bottom plot, characterized by lower tree density and larger tree size compared to the top plot, showed a lower spatial variability with respect to the top plot, while the longitudinal transect exhibited an intermediate variability. Analogously, the temporal stability analysis revealed that the most temporally-stable and representative measurement points laid on the transect that, overall, captured the different tree characteristics along the hillslope.

Future work will make use of the high-resolution measurements of the five gauges to assess their representativeness compared to the manual grid and to test and validate an interception model at the hillslope scale to be possibly upscaled to the entire catchment.

How to cite: Verdone, M., Borga, M., Dani, A., Preti, F., Trucchi, P., Zuecco, G., van Meerveld, I., Massari, C., and Penna, D.: Throughfall variability at the hillslope scale: the role of topography and tree characteristics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-526, https://doi.org/10.5194/egusphere-egu22-526, 2022.

EGU22-961 | Presentations | HS7.2

Evaluation of the Performance of Multi-Source Satellite Products in Simulating Precipitation over the Tensift Basin in Morocco 

wiam salih, Abdelghani chehbouni, and Terence Epule Epule

The Tensift basin in Morocco is prominent for its ecological and hydrological diversity. This diversity is marked by rivers flowing into areas such as Ourika. In addition to agriculture, the basin is a hub of variable land use systems. It is important to have a better understanding of the relationship between simulated and observed precipitation measurements in this region to be able to better understand the role of precipitation in the variability of the climate and water resources in the basin. This study aims at evaluating the performance of multi-source satellite products against weather stations precipitation in the basin. In this work, the satellite product based data were first culled for seven satellite products namely PERSIANN, PERSIANN CDR, TRMM3B42, ARC2, RFE2, CHIRPS, and ERA5 (simulated precipitation) from, CHRS iRain, RainSphere, NASA, EUMETSAT, NOAA, FEWS NET, ECMWF respectively. Precipitation observations data from six weather stations, located at Tachedert (2343 m), Imskerbour (1404 m), Asni (1170 m), Grawa (550 m), Agdal (489 m), and Agafay (487 m) at different altitudes, latitudes and temporal scales (1D, 1M, 1Y), over the period 13/05/2007 and 31/09/2019, at Tensift basin were used. The data were compared and analyzed through inferential statistics such as Nash-Sutcliffe Efficiency Coefficient, Bias, Root Mean Square Error (RMSE), Root Mean Square Deviation (RMSD), the standard deviation, the Correlation Coefficient (R) and the Coefficient of Determination (R²) and visualized through taylor diagrams and scatter plots to have a visual idea of the closeness between the seven satellite products and the observed precipitation data. A second analysis was carried out on the monthly precipitation resulting from the six weather stations based on standardized precipitation index (SPI) in order to  determine the onset, duration, and magnitude of the meteorological drought. The results show that PERSIANN CDR performs best and is more reliable with regrad to its ability to estimate precipitation rates over a wide spatial and temporal scale over the basin. The precipitation of Persiann CDR  has significant rates for the different statistics (Bias: -0.05 (Daily asni), RMSE: 2.86 (Daily Agdal), R: 0.83, R²:0.687 (Monthly Agdal)). However, most of the time, this product records low or negative Nash values (-6.06 (Annual Grawa)), due to the insufficient weather station data in the study area (Tensift). It  was observed that TRMM overestimates precipitation during heavy precipitation and underestimates during low precipitation. This makes it important for the latter observations to be viewed with caution due to the quality of annual comparison results and underscores the need to develop more efficient precipitation comparison approaches. Also, the performance of the satellite products is better at low altitudes and during wet years. Finally, it was concluded from the SPI that Tensift Region has experienced 13 drought periods over the study period, with the longest event of 12 months was from Marsh 2015 to February 2016 and  the most intense event with the highest drought severity (19.6) and the lowest SPI value (-2.66) was in 2019.

How to cite: salih, W., chehbouni, A., and Epule, T. E.: Evaluation of the Performance of Multi-Source Satellite Products in Simulating Precipitation over the Tensift Basin in Morocco, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-961, https://doi.org/10.5194/egusphere-egu22-961, 2022.

Data availability and accuracy is predominantly an issue for building hydrological applications, particularly in data-scare regions, like Africa. This is further one of the challenges that hinders understanding the climate variability and its subsequent extreme flood and drought events. Forcing data from different sources, e.g., satellite sensors, in-situ observations, or reanalysis products, are required to derive hydrological models. Reanalysis products have recently become an alternative tool of meteorological data given their long record at various temporal and spatial scales. The overarching goal of this study is to evaluate the primary forcing data for hydrological models; precipitation, as produced by six different reanalysis data (JRA55, 20CRv3, ERA5, ERA-20C, MERRA, NCEP/NCAR). We here focused our evaluation on the major river basins in Africa during a 15-year period spanning from 2001 to 2015. The five major river basins include the Nile River, Congo River, Zambezi River, Orange River, and Niger River basins. Our evaluation method is summarized as follows: Firstly, precipitation data is compared with the gridded gauged data, e.g., CHIRPS for precipitation. Secondly, statistical indices, including categorical and continuous statistical metrics, will be used to assess the accuracy of reanalysis products over each of the major basins. Finally, we present the intercomparison of reanalysis products for extreme events including floods and droughts. The results from our evaluation will pinpoint the skill of reanalysis products and thus benefit the future development of hydrological modeling over the river basins in Africa.

How to cite: Abdelmoneim, H. and Eldardiry, H.: Intercomparison of reanalysis products during extreme flood and drought events: evaluation over the major river basins of Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-970, https://doi.org/10.5194/egusphere-egu22-970, 2022.

EGU22-2494 | Presentations | HS7.2

Storm movement effects on the flash flood response of the Kan catchment 

Shahin Khosh Bin Ghomash, Daniel Bachmann, Daniel Caviedes-Voullième, and Christoph Hinz

Rainfall is a complex, spatial and temporally variated process and one of the core inputs for hydrological and hydrodynamic modelling. Most rainfalls are known to be moving storms with varying directions and velocities. Storm movement is known to be an important influence on runoff generation, both affecting peak discharge and the shape of hydrographs. Therefore, exploring the extent rainfall dynamics affect runoff generation and consequently flooded areas, can be an asset in effective flood risk management.

In this work, we study how storm movement (e.g. characterized by velocity and direction) can affect surface flow generation, water levels and flooded areas within a catchment. Moreover, the influence of rainfall temporal variability in correlation with storm movement is taken into account. This is achieved by means of numerical-based, spatially explicit surface flow simulations using the tool ProMaIDes (2021), a free software for risk-based evaluation of flood risk mitigation measures. The storm events are generated using a microcanonical random cascade model and further on trajected across the catchment area.

The study area is the Kan river catchment located in the province of Tehran (Iran) with a total area of 836 km², which has experienced multiple flooding events in recent years. Due to its semi-arid climate, steep topography with narrow valleys, this area has high potential for flash flood occurrence as a result of high intensity precipitation.

The results of this study show a range of possible magnitudes of influence of rainfall movement on the catchment´s runoff response. The resulting flood maps highlight the importance of rainfall velocity and most importantly the direction of the movement in the estimation of flood events as well as their likelihood in catchment area. Moreover, its shown that the magnitude of influence of storm velocity and direction on discharge  strongly depends on the location within the river network which it is measured.

ProMaIDes (2021): Protection Measures against Inundation Decision support. https://promaides.h2.de

How to cite: Khosh Bin Ghomash, S., Bachmann, D., Caviedes-Voullième, D., and Hinz, C.: Storm movement effects on the flash flood response of the Kan catchment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2494, https://doi.org/10.5194/egusphere-egu22-2494, 2022.

EGU22-2840 | Presentations | HS7.2

Dual-polarisation X-band radar estimates of precipitation assessed using a distributed hydrological model for mountainous catchments in Scotland 

John R. Wallbank, Steven J. Cole, Robert J. Moore, David Dufton, Ryan R. Neely III, and Lindsay Bennett

Observing, in a quantitative and robust way, the dynamic space-time pattern of precipitation in mountainous terrain presents a major challenge of great practical importance. The difficulties of this task are further exacerbated in mid to high latitudes where the typical melting layer for precipitation (i.e. the 0°C isotherm) is often close to the surface during winter months. One way to address this challenge is by improving observations made using networks of weather radars. Quantitative Precipitation Estimates (QPEs) derived from these instruments have many applications, for example as input to a hydrological model to simulate river flow for flood forecasting purposes. 


Here, a set of QPEs - obtained from an observation campaign using the National Centre for Atmospheric Science’s mobile X-band dual-polarisation Doppler weather radar (NXPol) in a mountainous area of Northern Scotland - are assessed with reference to observed river flows. Each form of QPE is used as an input to Grid-to-Grid (G2G), a distributed hydrological model used for flood forecasting across Great Britain, and the simulated river flows compared to observations. The location of the radar was specially chosen to infill an area of reduced coverage in the existing C-band radar network for the British Isles.

Assessments of radar QPE often only examine a final precipitation “best estimate” product and typically with reference to raingauges at specific locations. Here, we exploit the processing capabilities of NXPol and the hydrological modelling framework to investigate the benefits of ten separate processing methods that increase with complexity and make differing use of dual-polarisation variables. The role of the radar beam elevation and distance from the radar is investigated, and NXPol QPEs are compared to that provided by the radar network. Additionally, a preliminary investigation is carried out into the role of the drop-size distribution on the relationship between radar-reflectivity and rain-rate using disdrometer data.

The hydrological assessment reported on here has the benefit of integrating the precipitation over space and time which serves to complement and extend a previous meteorological assessment using raingauge data alone. The assessment proves to be insensitive to issues affecting both raingauges (e.g. representativity, wind-induced under-catch) and local artefacts in the space-time radar-rainfall field. It facilitates a direct assessment of whether potential benefits in the new QPEs are carried forward to an end-use such as flood forecasting, providing fresh insights for the development of new dual-polarisation radar QPE methods.

How to cite: Wallbank, J. R., Cole, S. J., Moore, R. J., Dufton, D., Neely III, R. R., and Bennett, L.: Dual-polarisation X-band radar estimates of precipitation assessed using a distributed hydrological model for mountainous catchments in Scotland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2840, https://doi.org/10.5194/egusphere-egu22-2840, 2022.

EGU22-2887 | Presentations | HS7.2

Intercomparison between 2DVD and MRR Datasets 

Christopher K. Blouin, Carson A. Barber, and Michael L. Larson

Simultaneous measurements of the rain drop size distribution were made by a 2-dimensional video disdrometer (2DVD, Joanneum Research, Graz, Austria) and a MicroRain Radar-Pro (MRR-Pro, Metek, Elmshorn, Germany) deployed near Charleston, South Carolina, USA and horizontally separated by approximately 20 meters. The 2DVD data was post-processed to correct for spurious drop detection and incorrect assignment of effective sensor area, and the MRR-Pro spectral data was corrected to incorporate a height-dependent estimate of the ambient vertical wind. Surface 2DVD drop measurements were utilized to reconstruct an approximation of the drop size distribution aloft at different heights and times to compare to the inferred MRR-Pro drop spectrum and bulk rain parameters. Despite fundamentally different measurement principles and different sets of assumptions associated with the reconstruction of drop size distributions aloft, the agreement between the 2DVD and MRR-Pro data showed promise. The two data sets are further investigated in order to reveal possible features of boundary layer rain vertical variability, estimates of drop-drop collision rates, and near-surface rain microphysical phenomena.

How to cite: Blouin, C. K., Barber, C. A., and Larson, M. L.: Intercomparison between 2DVD and MRR Datasets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2887, https://doi.org/10.5194/egusphere-egu22-2887, 2022.

EGU22-3256 | Presentations | HS7.2

Dual-frequency radar retrievals of snowfall using Random Forest 

Tiantian Yu, Chandra V.Chandrasekar, Hui Xiao, Ling Yang, and Li Luo

The microphysical parameters of snowfall directly impact the hydrological and atmospheric models. Dual-frequency radar retrievals of particle size distribution (PSD) parameters are developed and evaluated over complex terrain during the International Collaborative Experiment held during the Pyeongchang 2018 Olympics and Paralympic winter games (ICE-POP 2018). The observations used to develop retrievals were included the NASA Dual-frequency Dualpolarized Doppler Radar (D3R) and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometer. Conventional look-up table method (LUT) and random forest method are applied to the disdrometer data to develop retrievals for volume-weighted mean diameter Dm, the shape factor mu, snowfall rate S, and ice water content IWC. Evaluations are performed between D3R radar and disdrometer observations using these two methods. The results show that the random forest method performs better in retrieving microphysical parameters because the mean errors of the retrievals relative to disdrometer observations are small compared with the LUT method.

How to cite: Yu, T., V.Chandrasekar, C., Xiao, H., Yang, L., and Luo, L.: Dual-frequency radar retrievals of snowfall using Random Forest, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3256, https://doi.org/10.5194/egusphere-egu22-3256, 2022.

EGU22-4319 | Presentations | HS7.2

Testing nonlinearity and nonstationarity of the connection between Palmer drought indices and Danube discharge in the lower basin 

Ileana Mares, Constantin Mares, Venera Dobrica, and Crisan Demetrescu

The aim of the study is to reduce the uncertainty of the influence of Palmer-type drought indices in estimating seasonal discharge in the lower Danube basin. For this, four indices were considered: Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Weighted PDSI (WPLM) and Palmer Z-index (ZIND). These indices were quantified by PC1 of EOF decomposition, obtained from 15 stations located along the Danube basin.

The influences of these indices on the Danube discharge were tested, both simultaneously and with certain lags, by linear and nonlinear methods applying the elements of information theory. Nonstationarity was tested by wavelet analysis. The results differ depending on the season and the Palmer index.

The linear connections are generally obtained for synchronous links, and the nonlinear and nonstationary ones for the predictors considered with certain lags (in advance) compared to the discharge predictand. This result is useful for estimating the discharge, as Palmer indices can be estimated from the simulated data by the General Circulation Models or Regional Climate Models.

 

How to cite: Mares, I., Mares, C., Dobrica, V., and Demetrescu, C.: Testing nonlinearity and nonstationarity of the connection between Palmer drought indices and Danube discharge in the lower basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4319, https://doi.org/10.5194/egusphere-egu22-4319, 2022.

EGU22-4756 | Presentations | HS7.2

Impact of Additional Assimilation of Dual-Polarimetric Parameters: Analysis and Forecasts in a Real Case 

Bing-Xue Zhuang, Kao-Shen Chung, and Chih-Chien Tsai

The purpose of this study is to investigate the impact of assimilating dual-polarimetric parameters, i.e. differential reflectivity (ZDR) and specific differential phase (KDP), in addition to reflectivity (ZH) and radial wind (Vr) in a severe weather system. A squall line case forced by the synoptic southwesterly wind is selected to conduct the assimilation experiments. Besides, different microphysics parameterization schemes, including GCE, MOR, WSM6 and WDM6, are examined in the experiments. The results of the analysis field show that assimilating additional ZDR with single moment schemes (GCE and WSM6) can capture better mean raindrop size, yet it deteriorates the intensity of simulated ZH and KDP. Differ from GCE and WSM6, assimilating additional ZDR with double moment schemes (MOR and WDM6) would not lead to significant deterioration in the simulated ZH and KDP since the prognostic hydrometeor variables in double moment schemes include both mixing ratio and total number concentration. There will be more flexibility in adjusting microphysical states with two independent prognostic hydrometeor variables. The results of the short-term quantitative precipitation forecast (QPF) show that assimilating additional dual-polarimetric parameters with either single or double moment schemes increases the maximum of accumulated rainfall and the probability of heavy rainfall. In conclusion, double moment schemes can make better use of the extra information from dual-polarimetric parameters; furthermore, assimilating additional dual-polarimetric parameters, even with single moment schemes, can improve the performance of QPF, especially heavy rainfall events.

How to cite: Zhuang, B.-X., Chung, K.-S., and Tsai, C.-C.: Impact of Additional Assimilation of Dual-Polarimetric Parameters: Analysis and Forecasts in a Real Case, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4756, https://doi.org/10.5194/egusphere-egu22-4756, 2022.

EGU22-5361 | Presentations | HS7.2

Mesoscale precipitation nowcasting from weather radar data using space-time-separable graph convolutional networks 

Daniele Trappolini, Luca Scofano, Alessio Sampieri, Francesco Messina, Fabio Galasso, Saverio Di Fabio, and Frank Silvio Marzano

Forecasting weather systems are capable to model atmospheric phenomena at various space-time scales. At very short space-time nowcasting techniques are still relying on measured data processing from ground-based microwave radars and satellite-based geostationary spectrometers. In this respect, precipitation field nowcasting from a few minutes up to a few hours is one of the most challenging goals to provide rapid and accurate updated features for civil prevention and protection decision-makers (e.g., from emergency services, marine services, sport, and cultural events, air traffic control, emergency management, agricultural sector and moreover flood early-warning system). Deep learning precipitation nowcasting models, based on weather radar network reflectivity measurements, have recently exceeded the overall performance of traditional extrapolation models, becoming one of the hottest topics in this field. This work proposes a novel network architecture to increase the performance of deep learning mesoscale precipitation prediction. Since precipitation nowcasting can be viewed as a video prediction problem, we present an architecture based on Graph Convolutional Neural Network (GCNN) for video frame prediction. Our solution exploits, as a cornerstone, the topology of Space-Time-Separable Graph-Convolutional- Network (STS-GCN), originally used for posing forecasting. We have applied our model on the TAASRAD19 radar data set with the aim of comparing our performance with other models, namely the Stacked Generalization (SG) Trajectory Gated Recurrent Unit (TrajGRU) and S-PROG Spectral Lagrangian extrapolation program (S-PROG).

The proposed model, named STSU-GCN (Space-Time-Separable Unet3d Graph Convolutional Network), has a structure composed of an encoder, decoder, and forecaster. The role of the encoder and decoder are accomplished by a Unet3d a structure borrowed with the specific purpose of modifying the spatial component, but not the temporal component. In the bottleneck of this Unet3D network, we use a graph-based forecaster. The performance of the STSU-GCN has been quantified using conventional metrics, such as the Critical Success Index (CSI), widely used in the meteorological community for the nowcasting task. Using TAASRAD19 radar data set and literature data, these CSI metrics have been applied to 4 different classes of rain rate, that is 5, 10, 20, 30 mm/h. Our STSU-GCN model has overperformed both TrajGRU and S-PROG in the classes 10 mm/h and 20 mm/h obtaining a CSI respectively of 0.148 and 0.097. On the other hand, STSU-GCN is underperforming in class 5mm per hour getting a CSI respectively of 0.099. Our STSU-GCN model is aligned with the results of the S-PROG benchmark, for the class 30 mm/h confirming a model skillful for classes with a high rain rate. In this work, we will also illustrate the results of the proposed STSU-GCN algorithm using case studies in the area of interest of the Italian Central Apennines during the summer of 2021. Statistical performances, potential developments, and critical issues of the STSU-GCN algorithm will be also discussed.

How to cite: Trappolini, D., Scofano, L., Sampieri, A., Messina, F., Galasso, F., Di Fabio, S., and Marzano, F. S.: Mesoscale precipitation nowcasting from weather radar data using space-time-separable graph convolutional networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5361, https://doi.org/10.5194/egusphere-egu22-5361, 2022.

Vertically pointing radars (VPRs) provide detailed observations of precipitating cloud systems as they pass directly over the radar site. Two VPRs operating side-by-side and at different millimeter wavelengths (mm-wave) will observe the same raindrops but will have different return signals due to wavelength dependent raindrop backscattering and attenuation characteristics. These differences enable the retrieval of raindrop size distributions and vertical air motions. Yet, as the rain rate increases, the attenuation increases. Eventually, at some combination of path length [km] and rain specific attenuation [dB/km], the attenuation [dB] will extinguish high frequency VPR return signals; limiting high frequency VPRs to studying rain processes close to the ground. 

In order to estimate how far VPRs can measure into rain shafts, this study simulated constant rain rate precipitation columns and then estimated the path length needed to produced enough attenuation to drop the VPR signal-to-noise ratio below the VPR’s detection limit. This study used surface disdrometer observations and publically available T-Matrix scattering code to produce realistic VPR measurements at frequencies from 3 to 200 GHz.

These simulations found that in order to observe raindrops above a 3.5 km rain shaft, the constant rain rate needed to be less than 138, 67, 26, 14, and 4 mm/h for VPRs operating in the X-, Ku-, K-, Ka-, and W-bands, respectively (i.e., 9, 13.6, 24, 35.6, and 94 GHz). Additionally, due solely to atmospheric gas attenuation, the G-band (200 GHz) VPR return signal will be completely extinguished by 3.5 km. Preventing a G-band VPR from detecting raindrops above 3.5 km.

How to cite: Williams, C.: How far into a rain shaft can mm-wave vertically pointing radars detect raindrops?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6386, https://doi.org/10.5194/egusphere-egu22-6386, 2022.

EGU22-6420 | Presentations | HS7.2

An Examination of Alternate Partitioning Methods for Disdrometer Data 

Brianna G. Brunson and Michael L. Larsen

Historically, disdrometer data records have been divided into disjoint, equal-time intervals (often of 1- or 5-minute durations). Previous research of drop-resolving disdrometer data taken by the two-dimensional video disdrometer (Joanneum Research, Graz, Austria) has noted evidence of statistical structures on sub-minute timescales, which could lead to underestimations of rainfall variability when 1- or 5-minute partitionings are used. Here, we introduce and explore alternatives to the standard fixed-duration partitioning of disdrometer data. We compare the distributions of standard bulk rain measurements (rainfall rate and mass weighted mean diameter) under each partitioning method to demonstrate the utility of these alternative partitioning methods.

How to cite: Brunson, B. G. and Larsen, M. L.: An Examination of Alternate Partitioning Methods for Disdrometer Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6420, https://doi.org/10.5194/egusphere-egu22-6420, 2022.

EGU22-6968 | Presentations | HS7.2

Improvement of rainfall estimates using opportunistic sensors - the example of the flood in Rhineland-Palatinate in July 2021 

Micha Eisele, András Bárdossy, Christian Chwala, Norbert Demuth, Abbas El Hachem, Maximilian Graf, Harald Kunstmann, and Jochen Seidel

Abstract

Precipitation is highly variable in space and time. Ground-based precipitation gauging networks such as those from national weather services are often not able to capture this variability. Weather radars have the potential to capture the spatio-temporal characteristics of rainfall fields but they also suffer from specific errors such as attenuation. The increasing number and availability of opportunistic sensors (OS), such as commercial microwave links (CML) and personal weather stations (PWS), provides new opportunities to improve rainfall estimates based on ground observations.

We have developed a geostatistical interpolation method that allows a combination of different opportunistic sensors and their specific features and geometric properties, e.g., point and line information. In addition, the uncertainty of the different data sets can be considered [1].

The flood event in the western provinces of Germany in July 2021 showed that both, the precipitation interpolations based on rain gauge data from the German National Weather Service and radar-based precipitation products, underestimated precipitation. We show that the additional information of OS data can improve precipitation estimates in terms of areal precipitation amounts and spatial distribution.  

 

References
[1] Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H. and Bárdossy, A.: Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales, https://doi.org/10.1016/j.ejrh.2021.100883

How to cite: Eisele, M., Bárdossy, A., Chwala, C., Demuth, N., El Hachem, A., Graf, M., Kunstmann, H., and Seidel, J.: Improvement of rainfall estimates using opportunistic sensors - the example of the flood in Rhineland-Palatinate in July 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6968, https://doi.org/10.5194/egusphere-egu22-6968, 2022.

EGU22-7339 | Presentations | HS7.2

A precision raindrop generator to calibrate non-catching rain gauges 

Enrico Chinchella, Mattia Stagnaro, Arianna Cauteruccio, and Luca G. Lanza

The need for high-resolution and low maintenance weather stations is the major factor behind the increasing adoption of Non-Catching Gauges (NCGs) by national weather services and research institutions. Data from such instruments are used for several applications and in numerous research fields, where instrumental biases can have a strong impact. For NCGs, rigorous testing and calibration are more challenging than for catching gauges. Hydrometeor characteristics like particle size, shape, fall velocity and density must be carefully reproduced to provide the reference precipitation, instead of the equivalent water flow used for the calibration of catching gauges. Instrument calibration is usually declared by the manufacturers, using internal procedures developed for the specific technology employed. No standard calibration methodology exists, that encompass all or at least most of the available NCGs (Lanza et al. 2021). The EURAMET project 18NRM03 ‘INCIPIT’ on the ‘Calibration and accuracy of non-catching instruments to measure liquid/solid atmospheric precipitation’, was initiated in 2019 to address such issues.

A calibration device was developed to achieve individual drop generation on demand and in-flight measurement of the released drops. Water drops in the range from 0.5 to 6 mm in diameter are generated to mimic natural raindrops. A high-precision syringe pump is used to form the drop of the desired volume at the tip of a calibrated nozzle. A high-voltage power supply is used to apply a large potential difference between the nozzle and a metallic ring, and the resulting electric field triggers the release of the drop. A precision motorized gantry moves the generator across the horizontal plane, to cover different releasing positions within the instrument sensing area. By either varying the release height or accelerating the drop using compressed air, different fractions of the terminal velocity can be achieved, depending on the drop size. A second gantry, just above the gauge under test, aligns the plane of focus of a high-resolution camera with the fall trajectory of the drop. Three images of the same drop are captured in a single picture, using speedlights triggered at fixed time intervals. Photogrammetric techniques and a photodiode to measure the time between flashes provide the shape, size, speed, and acceleration of the drop. This characterizes each released drop before it reaches the instrument sensing area and, by comparison with the gauge measurement, the instrumental bias is obtained. Laboratory tests are presented to assess the performance of the calibration device.

This work is funded as part of the activities of the EURAMET project 18NRM03 “INCIPIT Calibration and Accuracy of Non-Catching Instruments to measure liquid/solid atmospheric precipitation”. The project INCIPIT has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.

References:

Lanza L.G. and co-authors, 2021: Calibration of non-catching precipitation measurement instruments: a review. J. Meteorological Applications, 28.3(2021):e2002.

How to cite: Chinchella, E., Stagnaro, M., Cauteruccio, A., and Lanza, L. G.: A precision raindrop generator to calibrate non-catching rain gauges, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7339, https://doi.org/10.5194/egusphere-egu22-7339, 2022.

EGU22-7482 | Presentations | HS7.2

Comparison of rainfall retrieval from collocated commercial microwave links with adjusted radar reference 

Anna Špačková, Martin Fencl, and Vojtěch Bareš

One of the pivotal variables in the hydrological system processes is precipitation. In this context, many hydrological applications require a reliably captured structure and temporal development of rainfalls. Therefore, the crucial challenge is to monitor rainfall in high spatial and temporal resolution. The opportunistic sensors for rainfall measurements have a great potential since they can complete standard observation networks with high number of alternative measuring sensors. Nowadays, one of the most prominent opportunistic source of rainfall information are telecommunication networks composed of commercial microwave links (CMLs). CMLs can supply dense path-averaged rainfall information derived from power-law relationship of the microwave signal attenuation and the rainfall intensity.

However, the actual implementation and employment requires a careful consideration of the errors and uncertainties of the measurements. In this study, the influence of different state of the rainfall is excluded using the set of pairs of collocated independent CMLs with paths in the immediate vicinity. Therefore, each pair of collocated CMLs can be assumed as identically influenced by the same rainfall conditions, while their characteristics (e.g., lengths, frequencies, polarizations) vary. The dataset consists of 33 rainfall periods within the years 2014 – 2016 monitored by 13 groups of collocated CMLs.

High correlation (around 0.95) was found for collocated CMLs. Compared to conventional rainfall sensors, for example, Peleg et al. (2013) demonstrated a correlation of 0.92 for collocated tipping bucket rain gauges. The CMLs are also compared with the adjusted weather radar rainfall information which is used as a reference. The dispersion of the data within five intensity ranges was used to set the boundaries (5 % and 95 % quantile). Subsequently, the fit of the CML measurements into the boundaries was examined. CMLs with 0.2 dB/mm/h sensitivity had the highest fit ratio, almost 80 %. Contrastingly, sensors with sensitivity 1.5 dB/mm/h just exceeded the fit ratio of 60 %. Observed differences describe the uncertainties which are not directly driven by the propagation of the signal. The uncertainties of CML need to be further studied to maximize the knowledge-based use of the favourable spatial and temporal resolution of this opportunistic sensing network.

References
Peleg, N., Ben-Asher, M., and Morin, E. (2013) Radar subpixel-scale rainfall variability and uncertainty: lessons learned from observations of a dense rain-gauge network, Hydrol. Earth Syst. Sci., 17, 2195–2208, https://doi.org/10.5194/hess-17-2195-2013.


This study is supported by the project SpraiLINK (20-14151J) of the Czech Science Foundation and by the grant of Czech Technical University in Prague no. SGS21/052/OHK1/1T/11.

How to cite: Špačková, A., Fencl, M., and Bareš, V.: Comparison of rainfall retrieval from collocated commercial microwave links with adjusted radar reference, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7482, https://doi.org/10.5194/egusphere-egu22-7482, 2022.

Adjustments of the wind-induced bias of conventional catching type rain gauges derive from collection efficiency (CE) curves that can be obtained either from field experiments or from numerical simulation (Lanza and Cauteruccio, 2021). The use of numerical simulation allows to overcome the limitations of the experimental installations and monitoring campaigns (e.g., the many influencing variables involved and the variability of the rainfall process) to cover a wide range of wind speed and rainfall intensity (RI) conditions. Also, the accuracy of the measurements taken as a reference is still an issue in field experiments.

A Lagrangian particle tracking (LPT) model, suitably validated in the wind tunnel (see Cauteruccio et al., 2021), is applied to the results of computational fluid dynamic (CFD) simulations of the airflow field surrounding a rain gauge to derive a simple formulation of the collection efficiency curves as a function of wind speed (Cauteruccio and Lanza, 2020). A new parameterization is proposed to highlight the influence of rainfall intensity, based on the typical form of the drop size distribution (DSD) of rainfall events (data from the Italian territory). The methodology is applied to a cylindrical gauge, which has the typical outer shape of most tipping-bucket rain gauges, as a representative specimen of operational measurement instruments.

Using rainfall intensity as a controlling factor for the collection efficiency has solid physical bases in the relationship between RI and the DSD (Colli et al., 2020), and the role of RI can only be quantified using numerical simulations of both the airflow field (using CFD) and the particle motion (via the LPT).

A simple formulation of the adjustment curves is obtained, which can be easily applied in an operational context, since wind velocity is the only ancillary variable required to perform the adjustment. Wind is often measured by operational weather stations together with the precipitation intensity, so the correction adds no relevant burden to the cost of meteo-hydrological networks.

References

Cauteruccio, A. and L.G. Lanza (2020). Parameterization of the collection efficiency of a cylindrical catching-type rain gauge based on rainfall intensity. Water, 12(12), 3431. https://doi.org/10.3390/w12123431.

Cauteruccio, A., Brambilla, E., Stagnaro, M., Lanza, L.G. and D. Rocchi (2021). Wind tunnel validation of a particle tracking model to evaluate the wind-induced bias of precipitation measurements. Water Resour. Res., 57(7), e2020WR028766. https://doi.org/10.1029/2020WR028766.

Colli, M., Stagnaro, M., Lanza, L.G., R. Rasmussen and J.M. Thériault (2020). Adjustments for Wind-Induced Undercatch in Snowfall Measurements Based on Precipitation Intensity, J. Hydrometerol., 21, 1039-1050, https://doi.org/10.1175/JHM-D-19-0222.1.

Lanza, L.G and A. Cauteruccio (2021). Accuracy assessment and intercomparison of precipitation measurement instruments. Chapter 1, p. 3 – 35. In: Michaelides, S. (ed.), Precipitation Science. Elsevier, Amsterdam, Netherlands. ISBN: 978-0-12-822973-6, pp. 833. https://doi.org/10.1016/B978-0-12-822973-6.00007-X.

How to cite: Lanza, L. G. and Cauteruccio, A.: Influence of the drop size distribution on the collection efficiency of catching gauges as a function of rainfall intensity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7871, https://doi.org/10.5194/egusphere-egu22-7871, 2022.

This research addresses a strong need to precisely improve the statewide seasonal precipitation intensity duration frequency (IDF) estimates at ungauged locations. In order to obtain IDFs at unvisited sites, IDFs observation locations are interpolated. Therefore, different deterministic and geostatistical approaches include Inverse Distance Weighted (IDW), Ordinary kriging (OK), Regression Kriging (RK), Co-Kriging (CoK), Kriging with External Drift (KED), and Functional Kriging (FK) have been taken into account for comparison. Apart from visual assessment, a cross-validation approach is used to compare these methods to judge their prediction accuracy.

Annual or intra-annual IDF calculations across the state is not well correlated with other variables except elevation, thus directionally smoothed altitude is only considered as a covariate that offered a significant reduction in bias.  All results indicate that IDW interpolation is incapable of improving the regional point IDF approximations provided by kriging algorithms except in the case of annual IDF predictions at shorter scales where its performance is more or less similar to OK.  Whereas summer IDF observations are well predicted by KED that also exhibits good behavior for longer duration extremes of all seasons. Moreover, the shorter duration winter IDF guesstimates are best achieved with CoK. From now, it can be noticed that the accuracy of the interpolator changes according to the hydrological seasons and storm durations.

Overall, this study ensures to design of a well-planned map in advance for the entire state of Baden Wurttemberg on the basis of accurate forecasting of seasonal IDF estimates of precipitation extremes at unsampled sites. Hence, this crucial step will surely help us to tackle the natural disasters due to climate change before time.

How to cite: Amin, B. and Bárdossy, A.: Evaluation of various regionalization techniques for the seasonal precipitation IDF estimates of Baden Württemberg, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8106, https://doi.org/10.5194/egusphere-egu22-8106, 2022.

EGU22-8119 | Presentations | HS7.2

Using radar-derived precipitation data for hydrological modelling in selected study sides in Norway 

Jessica Sienel, Lennart Schönfelder, and Jochen Seidel

Gathering accurate precipitation data is an important task for setting up hydrological models. In Norway, the gauge network density is higher in the southern parts and decreases in the north. Furthermore, the amount of high evaluated precipitation gauges is rather scarce. Radar data is available but lacks an accurate reflectivity-precipitation relation and errors in precipitation estimation are caused for example by beam blockage.

For modelling purposes, this study aims to evaluate whether the application of radar derived data gives any benefit, especially when modelling in a higher temporal resolution. The results of this study can give decision support for modellers having difficulties choosing the precipitation product. For that cause, spatial interpolated precipitation products were evaluated and compared in terms of performance in hydrological models. The Meteorological Institute Norway publishes gridded hourly datasets covering the Norwegian mainland: seNorge2, where gauge data is interpolated using an optimal interpolation, and the numerical weather prediction product (NWP), a combination of gauge data, radar data and a numerical weather model. Five different catchments were simulated in the numerical precipitation-runoff model HYPE with both datasets for comparison. The catchments vary in area, hydrological regime and availability of nearby gauges. The simulation was done in an hourly time step in order to compare precipitation variability on a small time scale.

In this study, a calibration method was developed that generates comparable and stable performance results in terms of the Kling–Gupta efficiency (KGE) for each catchment and dataset. The resulting discharges and water balances of the catchments were analysed and compared. Additionally, selected precipitation events, where the precipitation products were not able to describe atmospheric processes appropriately, were analysed. The datasets were further compared by spatially accumulating annual precipitation sums over the catchments, by using a private weather station to evaluate the fit of the data and by comparing the runoff and precipitation volume of the basins.

Preliminary results show the significant differences in water volume and spatial distribution of precipitation between these products. Furthermore, when comparing a private gauge with the precipitation products at an ungauged area, daily precipitation data tends to be more accurate than hourly data.

How to cite: Sienel, J., Schönfelder, L., and Seidel, J.: Using radar-derived precipitation data for hydrological modelling in selected study sides in Norway, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8119, https://doi.org/10.5194/egusphere-egu22-8119, 2022.

EGU22-9096 | Presentations | HS7.2

The Fresnel Platform for increasing the Greater Paris resilience to spatio-temporal variability of local rainfall 

Guillaume Drouen, Daniel Schertzer, Auguste Gires, and Ioulia Tchiguirinskaia
Urban areas are at stake under the threat of climate change. To overcome this challenge it is necessary to deepen our understanding of heavier and particularly local rainfall to avoid flooding and build resilient cites that can become sustainable. The main difficulty is that geophysics and urban dynamics are strongly nonlinear with associated extreme variability over a wide range of space-time scales.

To better connect theoretical and experimental research on these topics, an advanced urban hydro-meteorological observatory with associated SaaS (Software as a Service) developments, the Fresnel platform of the Co-Innovation Lab of the École des Ponts ParisTech, has been purposely set-up. The mission of the Fresnel platform is to facilitate synergies between research and innovation in the pursuit of upstream research and the development of innovative downstream applications. With profiled access for specialized services, it provides the concerned communities with the necessary high resolution measurements in real time and in replay form, that easily yield Big Data.

The Fresnel platform unites several components. One of them, the RadX SaaS platform, provides online tools to study rainfall data over the greater Paris area (i.e., about 50 km radius and more). It provides an easy access to various products based on precipitation measurements performed by the ENPC polarimetric X-band radar at the pixel scale of 125 m. It broadcasts these measurements in free access and in real-time (https://radx.enpc.fr) together with a point measured environmental parameters provided by another component of Fresnel, namely the exTreme and multi-scAle RAiNdrop parIS observatory (Taranis) observatory, containing several, a 3D sonic anemometer and a meteorological station.

The RadX platform was developed in participatory co-creation, and in scientific collaboration with the world industrial leader in water management. As the need for data accessibility, fast and reliable infrastructure were major challenges, the platform was constructed as a cloud-based solution. The components that make up this platform are designed to be configurable for specific case studies using an adjustable visual interface. Depending on a case study, specific components can be integrated to meet particular needs using maps, other visual tools and forecasting systems, eventually from third parties.

Developments are still in progress, with a constant loop of requests and feedback from the scientific and professional world.

How to cite: Drouen, G., Schertzer, D., Gires, A., and Tchiguirinskaia, I.: The Fresnel Platform for increasing the Greater Paris resilience to spatio-temporal variability of local rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9096, https://doi.org/10.5194/egusphere-egu22-9096, 2022.

EGU22-9515 | Presentations | HS7.2

Effect of diverse microwave link characteristics on rainfall retrieval errors 

Martin Fencl, Anna Spackova, and Vojtech Bares

Commercial microwave links (CMLs), point-to-point radio connections forming the backbone of cellular networks, can be used as opportunistic rainfall sensors and provide rain rate at high temporal resolution. The CML rainfall retrieval methods have been mostly developed for devices operating between 13 – 40 GHz where attenuation-rainfall relation is relatively insensitive to drop size distribution. New deployments have, however, an extensive share of E-band CMLs operating at 71 – 81 GHz frequency where drop size distribution (DSD) represents a major source of errors (Fencl et al., 2020). This study investigates for the first time the joint use of 13-40 GHz and 71-86 GHz CMLs with focus on evaluating different sources of errors.

Rainfall retrieved from 250 CMLs located in the city of Prague and its vicinity are compared to the quantitative precipitation estimates from C-band weather radar adjusted to the local network of 23 municipal rain gauges. Diverse path-lengths and frequencies of CMLs enable us to distinguish between different sources of errors. Shorter CMLs operated at lower frequencies are dominantly disturbed by errors related to antenna wetting whereas E-band CMLs are significantly more affected by DSD variability and non-uniform distribution of rain rates along the CML path. Moreover, longer E-band CMLs suffer from outages during heavy rainfalls. In general, E-band CMLs are more sensitive to low rain rates and thus suitable for retrieving light rainfalls whereas CMLs operating at lower frequencies are more accurate during heavy rainfalls.

Diverse characteristics of CMLs typically occurring in real-world cellular networks pose a challenge as each CML is affected by the instrumental errors in a different manner. On the other hand, the diversity in CML characteristics can be also exploited to quantify and possibly reduce these errors, especially in cities, where CML networks are usually dense and thus often provide collocated (redundant) rain rate measurements.

References:

Fencl, M., Dohnal, M., Valtr, P., Grabner, M., and Bareš, V.: Atmospheric observations with E-band microwave links – challenges and opportunities, 13, 6559–6578, https://doi.org/10.5194/amt-13-6559-2020, 2020.

Acknowledgements: This study was conducted within SpraiLINK project (20-14151J) and supported by Czech Science Foundation.

How to cite: Fencl, M., Spackova, A., and Bares, V.: Effect of diverse microwave link characteristics on rainfall retrieval errors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9515, https://doi.org/10.5194/egusphere-egu22-9515, 2022.

EGU22-9843 | Presentations | HS7.2

Measuring rainfall with microwave links: the influence of temporal sampling strategies 

Luuk van der Valk, Miriam Coenders-Gerrits, Rolf Hut, Hidde Leijnse, Aart Overeem, Bas Walraven, and Remko Uijlenhoet

Single-frequency microwave links can be used to monitor path-averaged precipitation by determining the rain-induced attenuation along the link path, as for example is done with commercial microwave links (CMLs) from cellular telecommunication networks. However, using these networks to estimate precipitation, the temporal resolution of these estimates is bound to the temporal sampling strategy employed by the network operator, which solely uses the information on the link signal to assure the functioning of the network. Moreover, not all operators store the same variables describing the link signal. Most commonly, a temporal resolution of 15 minutes with a recording of the minimum and maximum values during this interval is applied. For research purposes, often higher temporal resolutions in combination with averaged values are preferred. Yet, it is uncertain how these sampling strategies affect the computed amount and intensity of rainfall. To address this uncertainty, we investigate the influence of various temporal sampling strategies regarding the link signal on the estimated amounts and intensities of rainfall events from a single microwave link. For the analysis, we resample microwave link data to multiple intervals and variables characterizing the measured signal. The original data consist of three collocated microwave links sampled at 20 Hz, all operational for more than a year, and covering a 2.2 km path over the city Wageningen in the Netherlands. Additionally, the resulting rainfall estimates for the intervals and variables are compared to measurements of five disdrometers deployed along the link path. Overall, the results of this study can help to quantify the uncertainties associated with rainfall estimates from microwave links.

How to cite: van der Valk, L., Coenders-Gerrits, M., Hut, R., Leijnse, H., Overeem, A., Walraven, B., and Uijlenhoet, R.: Measuring rainfall with microwave links: the influence of temporal sampling strategies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9843, https://doi.org/10.5194/egusphere-egu22-9843, 2022.

EGU22-9945 | Presentations | HS7.2

Toward a low-cost disdrometer: Measuring drop size with a cantilever piezo film 

Chi-Ling Wei and Li-Pen Wang

Raindrop size distribution (DSD) is the key factor to derive reliable rainfall estimates. It is highly related to a number of integral rainfall parameters, including rain intensity (R), rain water content (W) and radar echo (Z). Disdrometers are the sensors commonly used to measure DSD based upon microwave or laser technologies; for example, JWD (Joss-Waldvogel Disdrometer), Parsivel and 2DVD (Two-Dimensional Video Disdrometer). These sensors may have their own strengths and weakness, but they are all relatively expensive. This hinders the possibility to have a high-density network for observing DSD at large scales. In this work, the ultimate goal is to develop a lightweight and low-cost disdrometer with descent accuracy.

We started with establishing a model that can well simulate the signal response of a single drop falling on a cantilever piezo film. A series of experiments were conducted to test the reaction of drops at different sizes (i.e. diameters ranging from 2 - 4 mm) and as drops fall onto various locations of the film. We then modelled the collision by assuming the piezo film to be a damped cantilever beam and drop force to be a step force. The drop force can be derived based upon the measurement of the deflection of beam end, which can be further used to calibrate the damp ratio. Preliminary results suggest that the signal response of a single drop hits can be well simulated based upon the proposed model under current experimental setting. We then developed an algorithm to optimize the simulation of signal responses with four four variables; these include drop’s weight, film thickness, film damping ratio and drop force. The result shows that the simulated drop force constitutes a strong linear relationship with the real drop’s weight.

We are now experimenting on the capacity of the developed model to work with a more complex yet realistic setting. For this purpose, we have created a more realistic rainfall condition by employing a micro pump. This pump can help control the size and timing of drops, so we can generate continuous single drops of consistent quality. In addition, we utilise a simple 1-D laser device to simultaneously measure the size of drops by analyzing the fluctuation in the laser signal. This would enable better understanding the actual size distribution of drops.  We expect that the outcome of the experiments  will provide useful insights on developing low-cost disdrometers with a cantilever piezo film.

How to cite: Wei, C.-L. and Wang, L.-P.: Toward a low-cost disdrometer: Measuring drop size with a cantilever piezo film, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9945, https://doi.org/10.5194/egusphere-egu22-9945, 2022.

EGU22-11125 | Presentations | HS7.2

Missing extremes in CML rainfall estimates due to total loss of signal 

Christian Chwala, Julius Polz, Maximilian Graf, and Harald Kunstmann

Attenuation data from commercial microwave links (CMLs) has proven to provide useful rainfall information. With their high density in urban areas, CMLs offer a great potential to estimate and study rainfall variability on small scales. Since the transmission power of CML hardware is limited, heavy rainfall can, however, lead to a complete loss of signal at the receiving end. As a consequence, very high rain rates can be missing in CML-derived rainfall information. The rain rate for which a specific CML experiences complete loss of signal depends on its length and frequency as well as on its dynamic range which is defined by transmit power, receiver noise level and antenna gain.

We analyze the occurrence and effect of such complete losses of signal, which we term “blackouts”, using two different datasets. First, a CML dataset with one minute temporal resolution consisting of 4000 CMLs in Germany is used to investigate the blackouts in real CML attenuation data over a period of three years. Second, the gauge-adjusted radar climatology RADKLIM-YW from the German Meteorological Service is used to derive synthetic rain induced attenuation data for each CMLs path with 5-minute temporal resolution for a period of 20 years.

For the real CML observations we introduce and apply a new algorithm to detect rain induced blackout gaps. This allows us to quantify the number and length of the blackout gaps stemming from heavy rainfall. Using the path-averaged RADKLIM-YW data as reference, we then quantify the rain rates and rainfall amount that is missed due to the CML blackout gaps. We find that longer CMLs are more likely to be affected by blackout gaps. This effect occurs even though the CMLs in our dataset are configured so that longer CMLs have a larger dynamic range to account for the increasing attenuation with increasing length. Using the dynamic range of each CML, we derive the long-term statistics of potential blackout occurrence from the synthetic attenuation data based on RADKLIM-YW. We find a pattern similar to the one in the real CML attenuation data, albeit with a smaller fraction of time steps affected by blackouts for all CMLs.

Our results provide a reliable basis for researchers to judge the capability of their CML dataset to capture rainfall extremes. Furthermore, it can serve as an improved basis for planning the layout and configuration and thus the dynamic range of individual CMLs.

How to cite: Chwala, C., Polz, J., Graf, M., and Kunstmann, H.: Missing extremes in CML rainfall estimates due to total loss of signal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11125, https://doi.org/10.5194/egusphere-egu22-11125, 2022.

EGU22-12993 | Presentations | HS7.2

Using weather radar to classify wet and dry periods for Commercial Microwave Links 

Erlend Øydvin, Vegard Nilsen, Nils-Otto Kitterød, Mareile Astrid Wolff, and Christoffer Artturi Elo

Using Commercial Microwave Links (CMLs) for measuring precipitation have gained more and more attention the past 10 years as it seems like a promising supplement to weather radar and rain gauge observations. It works by relating rainfall to signal attenuation along the CMLs path. As the signal level also can change due to other meteorological conditions such as air temperature and water vapor content, this opportunistic sensing method requires sophisticated data processing in order to relate signal attenuation to rain rate. One of the processing steps involves detecting wet and dry periods. 

For this presentation, we classified wet and dry periods using a weather radar and a rain gauge in Ås, Norway. We use data like equivalent reflectivity and phase shift between horizontal and vertical polarization and compare it to ground truth measurements. The resulting wet dry classifications are then compared with a single CML link in the same area.

How to cite: Øydvin, E., Nilsen, V., Kitterød, N.-O., Wolff, M. A., and Artturi Elo, C.: Using weather radar to classify wet and dry periods for Commercial Microwave Links, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12993, https://doi.org/10.5194/egusphere-egu22-12993, 2022.

EGU22-323 | Presentations | HS7.1

Study of Downscaling Techniques and Standings of Bias Corrected Global Climate Models for Brahmani Basin at Odisha, India 

Minduri Uma Maheswar Rao, Kanhu Charan Patra, and Akhtar Jahan

Climate change is emerging as one of the most pressing issues facing our environment since it will have severe consequences for both natural and human systems. The ability to estimate future climate is required to investigate the influence of climate change on a river basin. The most reliable instruments for simulating climate change are Global Climate Models (GCMs), also known as General Circulation Models. The performance of a precipitation simulation for the Brahmani river basin spanning 94 locations (with a grid resolution of 0.25° X 0.25°) is evaluated in the present study. The observed and model historical temperature datasets cover the period from 2000-2019. Twelve Coupled Model Intercomparison Project – Phase 6 (CMIP6) GCMs (ACCESS- CM2, CESM2, CIESM, FGOALS- g3, HadGEM3, GFDL- ESM4, INM- CM5-0, MIROC- ES2L, NESM3, UKESM1, MPI- ESM1, NorESM2) are used for the climate variable (Pr) using five indicators of performance. Indicators used are Average Absolute Relative Deviation (AARD), Skill Score (SS), Absolute Normalized Mean Bias Deviation (ANMBD), Correlation Coefficient (CC), Normalized Root Mean Square Deviation (NRMSD). GCMs are downscaled to finer spatial resolution before ranking them. The statistical downscaling technique is applied to eliminate the systematic biases in GCM simulations. Weights are determined using the Entropy technique for each performance metric. Cooperative Game Theory (CGT), Compromise programming (CP), Weighted Average Technique, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE-2) methods are utilized to rank the GCMs for the study area. GDM is an approach utilized to integrate the ranking techniques of GCMs to get a collective single rank. The results obtained for precipitation suggest that MIROC-ES2L, HadGEM3, GFDL-ESM4, UKESM1, FGOALS-g3 are the top five models that are preferred for the prediction of precipitation in the Brahmani River Basin.

How to cite: Rao, M. U. M., Patra, K. C., and Jahan, A.: Study of Downscaling Techniques and Standings of Bias Corrected Global Climate Models for Brahmani Basin at Odisha, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-323, https://doi.org/10.5194/egusphere-egu22-323, 2022.

EGU22-1996 | Presentations | HS7.1

Reduced rainfall in future heavy precipitation events tied to decreased rain area and takes place despite increased rain rate 

Moshe Armon, Francesco Marra, Chaim Garfinkel, Dorita Rostkier-Edelstein, Ori Adam, Uri Dayan, Yehouda Enzel, and Efrat Morin

Heavy precipitation events (HPEs) can lead to deadly and costly natural disasters and, especially in regions where rainfall variability is high, such as the eastern Mediterranean, they are critical to the hydrological budget. Reliable projections of future HPEs are needed, but global climate models are too coarse to explicitly represent rainfall processes during HPEs. In this study we used pseudo global warming high-resolution (1 km2) weather research and forecasting (WRF) model simulations to provide rainfall patterns projections based on simulations of 41 pairs of historic and “future” (end of 21st century) HPEs under global warming conditions (RCP8.5 scenario). Changes in rainfall patterns were analyzed through different properties: storm mean conditional rain rate, storm duration, and rain area. A major decrease in rainfall accumulation occurs in future HPEs (−30% averaged across events). This decrease results from a substantial reduction of the storms rain area (−40%) and duration (−9%), and occurs despite an increase in the mean conditional rain intensity (+15%). The consistency of results across events, driven by varying synoptic conditions, suggests that these changes have low sensitivity to the specific synoptic evolution during the events. Future HPEs in the eastern Mediterranean will therefore likely be drier and more spatiotemporally concentrated, with substantial implications on hydrological outcomes of storms. (For hydrological results see: abstract #EGU22-4777)

  • Armon, M., Marra, F., Enzel, Y., Rostkier‐Edelstein, D., Garfinkel, C. I., Adam, O., et al. (2022). Reduced Rainfall in Future Heavy Precipitation Events Related to Contracted Rain Area Despite Increased Rain Rate. Earth’s Future, 10(1), 1–19. https://doi.org/10.1029/2021ef002397

How to cite: Armon, M., Marra, F., Garfinkel, C., Rostkier-Edelstein, D., Adam, O., Dayan, U., Enzel, Y., and Morin, E.: Reduced rainfall in future heavy precipitation events tied to decreased rain area and takes place despite increased rain rate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1996, https://doi.org/10.5194/egusphere-egu22-1996, 2022.

Limited-area convection-permitting climate models (CPMs) with horizontal grid-spacing less than 4km and not relying on deep convection parameterisations (CPs) are being used more and more frequently. CPMs represent small-scale features such as deep convection more realistically than coarser regional climate models (RCMs) with deep CPs. Because of computational costs CPMs tend to use smaller horizontal domains than RCMs. As all limited-area models (LAMs), CPMs suffer issues with lateral boundary conditions (LBCs) and nesting. We investigated these issues using idealised Big-Brother (BB) experiments with the LAM COSMO-CLM. Grid-spacing of the reference BB simulation was 2.4 km. Deep convection was triggered by idealised hills with driving data from simulations with different spatial resolutions, with/without deep CP, and with different nesting frequencies and LBC formulations. All our nested idealised 2.4-km Little-Brother (LB) experiments performed worse than a coarser CPM simulation (4.9km) which used a four times larger computational domain and yet spent only half the computational cost. A boundary zone of >100 grid-points of the LBs could not be interpreted meteorologically because of spin-up of convection and boundary inconsistencies. Hosts with grid-spacing in the so-called grey zone of convection (ca. 4 - 20km) were not advantageous to the LB performance. The LB's performance was insensitive to the applied LBC formulation and updating (if smaller or equal 3-hourly). Therefore, our idealised experiments suggested to opt for a larger domain instead of a higher resolution even if coarser than usual (~5km) as a compromise between the harmful boundary problems, computational cost and improved representation of processes by CPMs.

How to cite: Ahrens, B. and Leps, N.: On the Challenge of Convection Permitting Precipitation Simulations: Results from Idealised Experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2531, https://doi.org/10.5194/egusphere-egu22-2531, 2022.

EGU22-3074 | Presentations | HS7.1 | Highlight

A global scale assessment of the intensification of rainfall extremes 

Athanasios Paschalis, Yiannis Moustakis, and Yuting Chen

Intensification of precipitation extremes under a changing climate is expected to severely impact societies due to increased flooding, and its impacts on infrastructure, agriculture, and ecosystems. Extensive research in the last decades has identified multiple facets of precipitation changes, from super Clausius – Clapeyron scaling of precipitation extremes with temperature increase, to the change of the intensity and spatial extent of mesoscale convective systems.

In this study we attempt to compile state of the art data and simulations to understand the multiple facets of the changes in precipitation extremes across the world. To do that we combined data from thousands of weather stations globally, reanalysis datasets, and general circulation and convection permitting model simulations. Our results show that:

  • Hourly precipitation extremes scale with temperature at a rate of ~7%/K globally, albeit very large spatial heterogeneities were found, linked to topography, large-scale weather dynamics and local features of atmospheric convection
  • Precipitation extremes change beyond this thermodynamic basis, with increases in the heaviness of the tails of precipitation distribution at fine scales
  • The spatial extent of convective systems is expected to increase
  • Precipitation extremes with shorter spell duration that are distributed more uniformly throughout the year are expected

How to cite: Paschalis, A., Moustakis, Y., and Chen, Y.: A global scale assessment of the intensification of rainfall extremes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3074, https://doi.org/10.5194/egusphere-egu22-3074, 2022.

EGU22-3117 | Presentations | HS7.1 | Highlight

Identifying a regional model for extreme rainfall in current climates – quo vadis? 

Ida Bülow Gregersen, Karsten Arnbjerg-Nielsen, Hjalte Jomo Danielsen Sørup, and Henrik Madsen

Establishing a regional model for intensity-duration-frequency (IDF) curves remain a vital task for design of urban infrastructures such as sewer systems and storm water detention ponds. However, identifying a suitable model remains tricky as subjective decisions and assumptions, that easily can be challenged, is needed. The talk will focus on recognizing and overcoming these shortcomings to develop a framework that is trusted by the users, i.e., the engineering professionals.

Since 1999 a regional model for IDF-curves has been developed and employed in Denmark. The model consists of a Partial Duration Series (PDS) framework using covariates to explain the regional variation supplemented with a regression across different durations. The first model was based on 41 series with a total of 650 station-years. Currently a fourth model based on a total of 132 series with almost 3000 station-years is being developed. The underlying data for all models come from a network of tipping bucket gauges initiated in 1979.

While the PDS modelling framework to describe extreme rainfall data has been applied and validated every time, the model setup has changed during each of the three updates. The second model, released in 2006, focussed on describing a significant increase in the design intensities and identifying a new regionalization, reducing the number of regions in the country from three to two. The third model, released in 2014, further increased the design intensities substantially, but more importantly, a cycle of precipitation extremes in Denmark with a frequency of around 35 years was acknowledged, and new co-variates were identified, enabling a description of Denmark as one region with variations that could be explained by two spatially continuous covariates.

Presently a new model is being developed. Most parts of the model are unchanged. However, inclusion of many recent relatively short series (10-20 years) both increase the sampling uncertainty and bias the model towards the very peak of the cyclic variation of the precipitation extremes, whereby the mean intensities will increase, as well as the overall uncertainty of the model. Hence the short series have been excluded.  As a result hereof, the engineering community expresses a concern that such an update will not, in general, increase design intensities in a current climate that is regarded as non-stationary with increasing extreme rainfall. For the scientists it could be an indication that the model may have reached a mature state, where the changes are small and random over a 5-year horizon. For the practitioners there is a concern that this may lead to infrastructure design that over time proves inadequate and fails to meet the service levels set to protect the citizens and important assets.  

As indicated above having much data at hand for a regional model does not hinder large structural uncertainties. What are reasonable assumptions and how can they be communicated to the users? When looking across Europe the structural differences in the model setups are even larger, not only reflecting variations in climate, but also choices made by different groups of scientists.

How to cite: Gregersen, I. B., Arnbjerg-Nielsen, K., Danielsen Sørup, H. J., and Madsen, H.: Identifying a regional model for extreme rainfall in current climates – quo vadis?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3117, https://doi.org/10.5194/egusphere-egu22-3117, 2022.

EGU22-4004 | Presentations | HS7.1

Regridding and interpolation of climate data for impacts modelling – some cautionary notes 

Richard Chandler, Clair Barnes, Chris Brierley, and Raquel Alegre

Users of climate data must often confront the problem that information is not available at the precise spatial locations of interest; or the related problem that multiple sources of information provide data at different collections of locations. An example of the first situation is the use of weather station data to calibrate a hydrological or land surface model requiring inputs on a regular grid; an example of the second is the use of information from an ensemble of climate models to sample structural uncertainty, but where each model produces output on its own grid. Dealing with this spatial misalignment is a common first step in any analysis, and is usually done by some form of interpolation. In this poster, we use standard approaches to convert regional climate model (RCM) outputs from the EuroCORDEX ensemble to the common grid used in the UK national Climate Projections (UKCP). We find that although these standard approaches perform acceptably in some situations, in others they can induce surprisingly large biases and inconsistencies in the statistical properties of the resulting fields – particularly those relating to variability and extremes. For example, although the resolutions of the UKCP grid and the EuroCORDEX RCMs are all similar, it is not hard to find locations where the maximum daily precipitation within a month is systematically underestimated by 5-10% in the regridded data; and where the maximum daily precipitation over a 20-year period is systematically underestimated by 25%. These effects could have major implications for impacts studies carried out using interpolated or regridded data, if they are not recognised and dealt with appropriately. We offer some suggestions, varying in ease of implementation, for dealing with the problem.

How to cite: Chandler, R., Barnes, C., Brierley, C., and Alegre, R.: Regridding and interpolation of climate data for impacts modelling – some cautionary notes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4004, https://doi.org/10.5194/egusphere-egu22-4004, 2022.

EGU22-4405 | Presentations | HS7.1

Evaluation of precipitation reanalysis products in space and time for ungauged sites in Slovenia 

Hannes Müller-Thomy, Patrick Nistahl, Nejc Bezak, and Marcos Alexopoulos

Precipitation reanalysis products (PRP) are a promising data source for ungauged regions. Since observed time series are often i) too short, ii) their temporal resolution is not sufficient or iii) the network density is too low, they cannot be used as e.g. input for rainfall-runoff (r-r) modelling and derived flood frequency analysis. Reanalysis products as global simulation of the atmosphere over the last decades solve the aforementioned issues.

From the latest PRP three are most promising due to their spatial and temporal resolution for r-r modelling of small to mesoscale catchments: ERA5-Land (raster with approx. 9 km width), REA6 (6 km) and CFSv2 (22 km). These three PRP are able to cope with the dynamics of the r-r process due to their hourly resolution. The PRP are evaluated for Slovenia (Europe) with both, precipitation characteristics in space and time, and runoff characteristics. For areal precipitation, continuous and event-based characteristics are evaluated as well as precipitation extreme values. Simple correction methods for identified biases are suggested and applied. It can be seen that although the PRP clearly differ from each other, there is no clear ‘favourite’ to use as input for the r-r modelling.

To conclude about the suitability of the PRP for r-r modelling, continuous simulations are carried out with GR4H for 20 catchments in Slovenia (55 km²-480 km²). Models are re-calibrated for each PRP input based on KGE. Simulation results of calibration and validation period are evaluated by runoff extreme values, KGE, flow duration curve and intra-annual cycle. Interestingly, first results show that the deviations of some rainfall characteristics do not necessarily transfer to deviations in runoff characteristics, which can be explained by the high nonlinearity of the r-r process. PRP lead to better, at least similar results for runoff characteristics for catchments without rain gauges in their centre.

How to cite: Müller-Thomy, H., Nistahl, P., Bezak, N., and Alexopoulos, M.: Evaluation of precipitation reanalysis products in space and time for ungauged sites in Slovenia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4405, https://doi.org/10.5194/egusphere-egu22-4405, 2022.

EGU22-4453 | Presentations | HS7.1

Complexity of rainfall dynamics in India in the context of climate change 

Bhadran Deepthi and Bellie Sivakumar

Global climate change has become one of the major environmental issues today. Climate change impacts rainfall (and other hydroclimatic processes) in many ways, including its temporal and spatial variability. Hence, understanding the impact of climate change on rainfall is important to devise and undertake more effective and efficient adaptation and management strategies. The present study attempts to determine the temporal dynamic complexity of monthly historical and future rainfall in India at a spatial resolution of 1º × 1º. The historical and future rainfall data are simulated from 27 General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The historical rainfall consists of the rainfall data simulated by GCMs for the period 1961–2014, and the rainfall simulated by the GCMs under shared socio-economic pathway scenarios (SSPs) constitutes the future rainfall. Four scenarios (SSP126, SSP245, SSP370, and SSP585) and two different timeframes (near future (2015–2060) and far future (2061–2099)) are considered to determine how the rainfall and its dynamic complexity vary across the scenarios and timescales. The false nearest neighbor (FNN) algorithm is employed to determine the dimensionality and, hence, the complexity of the rainfall dynamics. The algorithm involves two major steps: (i) reconstruction of the single-variable rainfall time series in a multi-dimensional phase space; and (ii) identification of “false” neighbors in the reconstructed phase space and estimation of the dimension of the rainfall time series. The results suggest that the FNN dimensions of both the historical rainfall and future rainfall simulated by the 27 GCMs across India under all scenarios range from 3 to 20, indicating low to high-level complexity of the rainfall dynamics. However, only less than 1% of the study area shows high-level complexity in historical and future rainfall dynamics. Moreover, around 20 GCMs exhibit low to medium-level complexity of rainfall dynamics in 80% of the study area, with the dimensionality in the range from 3 to 10. Therefore, considering both the historical rainfall and future rainfall under all the four scenarios and the two timeframes considered in this study, the number of GCMs simulating rainfall that exhibits dimensionality in the range 11 to 20 are few. This may be an indication that the complexity of rainfall dynamics in India in the future will be low-to-medium dimensional.

How to cite: Deepthi, B. and Sivakumar, B.: Complexity of rainfall dynamics in India in the context of climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4453, https://doi.org/10.5194/egusphere-egu22-4453, 2022.

EGU22-5071 | Presentations | HS7.1

Impact of GPM Precipitation Error Characteristics on Hydrological Applications 

Ankita Pradhan and Indu Jayaluxmi

Precipitation-measuring satellites constitute a constellation of microwave and infrared sensors in geosynchronous earth orbit. The limited sampling of passive microwave constellations continues to be a problem, affecting applications such as hydrological modeling. Recent constellations have contributed in the construction of the next generation of earth and space science missions by allowing measurement settings to be customized to meet changing scientific understanding. Our study focuses on examining the Global Precipitation Measurement (GPM) constellation mission. The aim of the study is to examine the impact of different uncertainties carried by the GPM constellation on hydrological applications. Firstly we investigated the evaluation and comparison of spatial sampling error for the Global Precipitation Measurement (GPM) mission orbital data products. The region over India with high seasonal rainfall appears to have lower sampling uncertainty, and vice versa, with some exceptions due to differences in precipitation variability and space-time correlation length.  Second, we investigated how intermittency produced by low temporal sampling propagates through a hydrological model and contributes to stream flow uncertainty. We also examined the effect of grid resolution and how it relates to Clausius-Clapeyron scaling. This paper proposes and discusses techniques for quantifying the influence of grid resolution as a function of spatial–temporal characteristics of heavy precipitation based on these findings. Thirdly, we have quantified the influence of two different algorithms i.e top down and bottom up approach utilizing precipitation products that includes the Global Precipitation Measurement mission's (GPM) integrated Multi-satellite Retrievals (IMERG) late run, the SM2RAIN-Climate Change Initiative (SM2RAIN-CCI), and the SM2RAIN-Advanced SCATerometer (SM2RAIN-ASCAT) on hydrological simulations. The results from our study indicate that precipitation forcing at 6-hourly integration outperforms the stream flow simulations as compared to 3-hourly and 12-hourly forcing integration times. IMERG based precipitation also contains significant bias which is propagated into hydrological models when used as precipitation forcing.

How to cite: Pradhan, A. and Jayaluxmi, I.: Impact of GPM Precipitation Error Characteristics on Hydrological Applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5071, https://doi.org/10.5194/egusphere-egu22-5071, 2022.

We present an analysis of uncertainty in model-based Probable Maximum Precipitation (PMP) estimates. The focus of the study is on “model-based” PMP derived from WRF (Weather Research and Forecasting) model reconstructions of severe historical storms and amplified by the addition of moisture in the boundary conditions (so-called Relative Humidity Maximization technique). Model-based PMP offers numerous advantages over the currently-used approach that is described in NOAA Hydrometeorological Reports. By scaling moisture and producing the resulting precipitation according to model formulation, the model-based approach circumvents the need for linearly scaling precipitation. Despite the significant improvement this represents, model-based PMP retains some degree of uncertainty that precludes its use in operational settings until the uncertainty is rigorously evaluated. This paper presents an ensemble of PMP simulations that samples recognized sources of uncertainty: (1) initial/boundary condition error, (2) choice of physics parametrizations and (3) model error due to unresolved subgrid processes. To our knowledge, this is the first uncertainty analysis conducted for model-based PMP. We applied this ensemble approach to the Feather River watershed (Oroville dam) in California. We first carried out in-depth evaluation of model reconstructions and found that the performance of some storm reconstructions that underlie the PMP estimate is not ideal, though the lack of uncertainty information about observations currently prevents us from identifying “well-reconstructed” storms or performing bias correction. That being said, our ensemble indicates that the 72-hour maximized precipitation totals used for PMP estimation do not differ greatly (110% at most) from the single-value estimate when model uncertainty is considered. We emphasize that model-based PMP estimates should always be presented as a range of values that reflects the uncertainties that exist, but concerns about model uncertainty should not hinder the development of model-based PMP.

How to cite: Tarouilly, E., Cannon, F., and Lettenmaier, D.: Improving confidence in model-based Probable Maximum Precipitation : Assessing sources of model uncertainty in storm reconstruction and maximization, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6217, https://doi.org/10.5194/egusphere-egu22-6217, 2022.

EGU22-6342 | Presentations | HS7.1 | Highlight

Influence of morphology on the spatial variability of rainstorms over Italy 

Paola Mazzoglio, Ilaria Butera, Massimiliano Alvioli, and Pierluigi Claps

The investigation of the influence of terrain morphology on rainfall extremes has never been conducted over the entire Italy, where some studies have been carried out over limited areas. We then present the first systematic investigation of the role of elevation and other morphological attributes on rainfall extremes over Italy, that is made possible by using the Improved Italian – Rainfall Extreme Dataset (I2-RED). I2-RED is a database of short duration (1 to 24 hours) annual maximum rainfall depths collected from 1916 until 2019 by more than 5200 rain gauges.

The analyses involved the relations between morphology and the mean annual rainfall extremes (index rainfall) using univariate and multivariate regressions. These relations, built countrywide, demonstrated that the elevation alone can explain only a part of the spatial variance. The inclusion of regression covariates as longitude, latitude, distance from the coastline, indexes of obstructions and the mean annual rainfall depth demonstrated to be significant in relations built at the national scale.

However, high local bias with notable spatial correlation derives from the national-scale analysis. This led us to focus on smaller areas. We started dividing Italy into 4 main regions: the Alps, the Apennines, and the two main islands (i.e. Sicily and Sardinia). A dedicated multiple linear regression analysis was conducted over each of these areas. Evident improvements were obtained through this approach; nevertheless, clusters of high residuals persisted, especially in orographically-complex areas. A different approach was then undertaken, based on a preemptive subdivision of Italy in morphologically similar regions, to both reduce the clustering of errors and better define the role of elevation. Using four morphological classifications of Italy from the literature, we applied simple regression models to the rain gauges available inside each region. Among all, the classification that embeds hydrological information turned out to produce the best results in terms of local bias, MAE and RMSE, outperforming the multivariate relations obtained at the national scale. This approach proved to better reproduce the effects of geography and morphology on the spatial variability of rainfall extremes.

Our analysis confirmed a general increase of 24-hour rainfall depths with elevation, as already pointed out by studies conducted over smaller areas. For 1-hour rainfall depths, in flat or in pre-hill zones a modest increase with elevation is visible, while over the Alps and in most of the Apennines a reverse orographic effect (i.e., a reduction of rainfall depth with increasing elevation) is clearly detected, confirming previous outcomes in those areas.

How to cite: Mazzoglio, P., Butera, I., Alvioli, M., and Claps, P.: Influence of morphology on the spatial variability of rainstorms over Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6342, https://doi.org/10.5194/egusphere-egu22-6342, 2022.

EGU22-6677 | Presentations | HS7.1

Can Radar Quantitative Precipitation Estimates Reproduce Extreme Precipitation Statistics in Central Arizona? 

Nehal Ansh Srivastava and Giuseppe Mascaro

In this study, we assess the ability of 4-km, 1-h Quantitative Precipitation Estimates (QPEs) from the Stage IV analysis of the NEXRAD radar network to reproduce the statistics of extreme precipitation (P) in central Arizona, USA. As reference, we use 19 years of records from a dense network of 257 rain gages. For each radar pixel and gage record, we fit the generalized extreme value (GEV) distribution to the series of annual maximum P at durations, τ, from 1 to 24 hours. We found that the GEV scale and shape parameters estimated from the radar QPEs are slightly negatively biased when compared to estimates from gage records at τ = 1 h; this bias tends to 0 for τ ≥ 6 h. As a result, the radar GEV quantiles for return period, TR, from 2 to 50 years exhibit negative bias at τ = 1 h (median between -23% and -12% for different TR’s), but the bias is gradually reduced as τ increases (average of +4% for τ = 24 h). The relative root-mean-square-error (RRMSE) ranges from 17% to 44% across all τ’s and TR’s and these values are similar to those computed between gages and operational design storms from NOAA Atlas 14. Lastly, we found that radar QPEs reproduce fairly well observed scaling relationships between the GEV location and scale parameters and P duration, τ. Results of our work provide confidence in the utility of Stage IV QPEs to characterize the spatiotemporal statistical properties of extreme P and, in turn, to improve the generation of design storm values.

How to cite: Srivastava, N. A. and Mascaro, G.: Can Radar Quantitative Precipitation Estimates Reproduce Extreme Precipitation Statistics in Central Arizona?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6677, https://doi.org/10.5194/egusphere-egu22-6677, 2022.

EGU22-8792 | Presentations | HS7.1

Space-time simulation of storms and beyond! 

Simon Michael Papalexiou, Francesco Serinaldi, and Emilio Porcu

Simulating storms, or hydro-environmental fluxes in general, in space and time is challenging and crucial to inform environmental risk analysis and decision making under variability and uncertainty. Here, we advance space-time modelling by enabling simulation of random fields (RF) described by general velocity fields and anisotropy. This advances the skills of the Complete Stochastic Modeling Solution (CoSMoS) framework in space and time and enables RF's simulations that reproduce desired: (a) non-Gaussian marginal distribution, (b) spatiotemporal correlation structure (STCS), (c) velocity fields with locally varying speed and direction that describe advection, and (d) locally varying anisotropy. We demonstrate applications of CoSMoS by simulating storms at fine spatiotemporal scales that move across an area, spiraling fields such weather cyclones, air masses converging to (or diverging from) a point and more. The methods are implemented in the CoSMoS R package freely available in CRAN.

Reference: Papalexiou, S. M., Serinaldi, F., & Porcu, E. (2021). Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond. Water Resources Research, 57(8), e2020WR029466. https://doi.org/10.1029/2020WR029466

How to cite: Papalexiou, S. M., Serinaldi, F., and Porcu, E.: Space-time simulation of storms and beyond!, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8792, https://doi.org/10.5194/egusphere-egu22-8792, 2022.

EGU22-10253 | Presentations | HS7.1

Spatial and temporal variability of rainfall on different time scales 

András Bárdossy

Rainfall is highly variable in space and time. The knowledge of precipitation variability is very important for design or for uncertainty assessment of models. In this contribution two different aspects of variability are investigated – the treatment of zero observations for spatial interpolation and the problem of high order dependence. The finer the temporal resolution of precipitation observations the more zeros have to be considered. Should one include all zeros for the description of the spatial variability (for example variograms)? Examples corresponding to different time aggregations are show that zeros need a specific treatment. High order dependence is investigated using time series observed at multiple sites. Results are compared to a meta-Gaussian approach. A large high-resolution dataset from South-West Germany is used to demonstrate the problems and the different approaches.

How to cite: Bárdossy, A.: Spatial and temporal variability of rainfall on different time scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10253, https://doi.org/10.5194/egusphere-egu22-10253, 2022.

EGU22-10355 | Presentations | HS7.1

Combining commercial microwave link and rain gauge observations to estimate countrywide precipitation: a stochastic reconstruction and pattern analysis approach 

Nico Blettner, Christian Chwala, Barbara Haese, Sebastian Hörning, and Harald Kunstmann

Precipitation is characterized by large spatial variability. For hydrological applications it is crucial to estimate precipitation such that spatial correlation lengths and precipitation patterns are represented accurately.

We derive countrywide precipitation estimates using approx. 4000 commercial microwave links (CMLs) obtained from Ericsson and approx. 1000 rain gauges operated by the German Weather Service. CML and gauge observations are regarded as non-linear and linear constraints on the spatial estimate, respectively.

We apply the Random-Mixing-Whittaker-Shannon method in a Python based environment (RMWSPy) to reconstruct ensembles of precipitation fields. With RMWSPy, linear combinations of unconditional random spatial fields are conditioned to the observational data. This involves the exact local representation of rain gauge observations as well as the consideration of the path-averaged precipitation along the CMLs. Additionally, the method ensures that resulting estimates are similar to the data with respect to spatial correlations and marginal distributions. The stochastic process allows for variability at unobserved locations and thereby the calculation of ensembles.

We evaluate the spatial pattern of our results by performance characteristics such as ensemble Structure-, Amplitude-, and Location-error (eSAL). This approach considers precipitation objects as connected areas that exceed a certain precipitation value, and involves the analysis of the objects’ shapes and locations. Thereby, it is possible to quantify aspects of precipitation patterns in a way that is not possible with standard performance metrics which are based on pixel-by-pixel comparisons.

We find that our precipitation estimates are in good agreement with the gauge-adjusted weather radar product RADOLAN-RW of the German Weather Service which we use as a reference. In particular, we see advantages in reproducing the pattern of precipitation objects, in terms of smaller structure- and location-errors, when comparing our ensemble-based Random-Mixing approach to an Ordinary Kriging interpolation.

How to cite: Blettner, N., Chwala, C., Haese, B., Hörning, S., and Kunstmann, H.: Combining commercial microwave link and rain gauge observations to estimate countrywide precipitation: a stochastic reconstruction and pattern analysis approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10355, https://doi.org/10.5194/egusphere-egu22-10355, 2022.

EGU22-10437 | Presentations | HS7.1

Uncertainty Quantification of Precipitation Measurement with Weather Radar 

Angelica Caseri and Carlos Frederico Angelis

Extreme rainfall events can cause flash floods and are responsible for socioeconomic damage worldwide. In Campinas, southeastern Brazil, countless events take place throughout the year. In order to monitor and predict these events, with the support of Fapesp's SOS-Chuva project, a mobile rainfall radar was installed in the region. With the purpose to identify the accuracy of this data, the radar data were compared with rain gauge data. Through this study, it is noted that, at some points, the difference between the rain gauges measurements and the radar data is significant, which may hinder the calibration and performance of a rainfall-runoff hydrological model. To improve the rainfall measurement considering both data source, this study proposes to combine both information and generate rainfall probabilistic maps, derived from geostatistical methods, thus making possible to quantify the uncertainty of these data.

How to cite: Caseri, A. and Angelis, C. F.: Uncertainty Quantification of Precipitation Measurement with Weather Radar, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10437, https://doi.org/10.5194/egusphere-egu22-10437, 2022.

EGU22-10931 | Presentations | HS7.1

Assessing future extreme rainfall trends through multifractal scaling arguments: A CONUS-wide analysis based on NA-CORDEX model outputs 

Stergios Emmanouil, Andreas Langousis, Efthymios I. Nikolopoulos, and Emmanouil N. Anagnostou

The quantification of future flood risk, as well as the assessment of impacts attributed to the evolution of extreme rainfall events under rapidly changing climatic conditions, require multi-year information at adequately high spatiotemporal scales. The spatial and temporal evolution of regional extreme rainfall patterns, however, is quite challenging to describe due to natural climate variability and local topography. Hence, the use of conventional climate model outputs to evaluate the frequency of extreme events may not be conclusive due to significant epistemic uncertainties.  To date, there is limited knowledge on how extreme precipitation patterns will evolve under the influence of climate change, at spatiotemporal resolutions suitable for hydrological modeling, and considering the non-stationarity of rainfall as a process. In this study, we evaluate future trends related to extreme rainfall using hourly estimates acquired through the North American (NA) CORDEX Program (see Mearns et al., 2017), spanning from 1979 to 2100, over a 25-km CONUS-wide grid. In view of the practical importance of high spatial and temporal resolutions in hydrological modeling, we first simultaneously bias-correct and statistically downscale the NA-CORDEX model outputs, by using the two-component theoretical distribution framework described in Emmanouil et al. (2021), as well as the Stage IV weather radar-based gridded precipitation data (4-km spatial resolution) as a high-resolution reference. To investigate the validity of the yielded rainfall intensity quantiles, we use as benchmark the hourly rainfall measurements offered by NOAA’s rain gauge network (National Centers for Environmental Information, 2017). Finally, to evaluate the effects of climate change on the spatial and temporal evolution of rare precipitation events while taking into consideration the nonstationary nature of rainfall, we apply a robust (Emmanouil et al., 2020) parametric approach founded on multifractal scaling arguments (Langousis et al., 2009) to sequential 10-year segments of the data, where conditions can be fairly assumed stationary. In view of revealing future infrastructure vulnerabilities over a wide range of characteristic temporal scales and exceedance probability levels, our analysis is founded on Intensity-Duration-Frequency (IDF) curves, which are derived using the previously acquired CORDEX-based, gridded (4-km), hourly precipitation estimates, and cover the entire CONUS for a period of 120 years.

References

Emmanouil, S., Langousis, A., Nikolopoulos, E. I., & Anagnostou, E. N. (2020). Quantitative assessment of annual maxima, peaks-over-threshold and multifractal parametric approaches in estimating intensity-duration-frequency curves from short rainfall records. Journal of Hydrology, 589, 125151. https://doi.org/10.1016/j.jhydrol.2020.125151

Emmanouil, S., Langousis, A., Nikolopoulos, E. I., & Anagnostou, E. N. (2021). An ERA-5 Derived CONUS-Wide High-Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework. Water Resources Research, 57(6), 1–17. https://doi.org/10.1029/2020WR029548

Langousis, A., Veneziano, D., Furcolo, P., & Lepore, C. (2009). Multifractal rainfall extremes: Theoretical analysis and practical estimation. Chaos, Solitons and Fractals, 39(3), 1182–1194. https://doi.org/10.1016/j.chaos.2007.06.004

Mearns, L. O., McGinnis, S., Korytina, D., Arritt, R., Biner, S., Bukovsky, M., et al. (2017). The NA-CORDEX dataset, version 1.0. NCAR Climate Data Gateway. Boulder (CO): The North American CORDEX Program, 10.

National Centers for Environmental Information. (2017). Cooperative Observers Program Hourly Precipitation Dataset (C-HPD), Version 2.0 Beta. NOAA National Centers for Environmental Information, [accessed July 17, 2020].

How to cite: Emmanouil, S., Langousis, A., Nikolopoulos, E. I., and Anagnostou, E. N.: Assessing future extreme rainfall trends through multifractal scaling arguments: A CONUS-wide analysis based on NA-CORDEX model outputs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10931, https://doi.org/10.5194/egusphere-egu22-10931, 2022.

EGU22-11055 | Presentations | HS7.1

Intensity-dependence of interarrival times and run lengths in multifractal rainfall 

Alin-Andrei Carsteanu, Andreas Langousis, and Roberto Deidda

Mass scaling of atmospheric precipitation has been successfully characterized by multifractal frameworks in the literature dedicated to this subject. However, the dependence of the statistics of interarrival times and run lengths on the employed detection threshold, as theoretically predicted by multiplicative cascade models with different degrees of multifractality, is yet another aspect of interest when such models are being used for the purpose of rainfall modelling. It must be noted that interarrival times and run lengths are complementary variables, by representing uninterrupted time intervals above and below the detection threshold, respectively. The present communication deals with the intricacies of parametrizing and validating those aspects of multifractal rainfall models.

How to cite: Carsteanu, A.-A., Langousis, A., and Deidda, R.: Intensity-dependence of interarrival times and run lengths in multifractal rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11055, https://doi.org/10.5194/egusphere-egu22-11055, 2022.

EGU22-11126 | Presentations | HS7.1

Accounting for anisotropy in the simulation of rainfall fields with blunt extension of discrete Universal Multifractal cascades 

Auguste Gires, Ioulia Tchiguirinskaia, and Daniel Schertzer

Universal Multifractals have been widely used to characterize and simulate geophysical fields extremely variable over a wide range of scales such as rainfall. Despite strong limitations, notably its non-stationnarity, discrete cascades are often used to simulate such fields. Recently, blunt cascades have been introduced in 1D, 2D, and space-time to cope with this issue while remaining in the simple framework of discrete cascades. It basically consists in geometrically interpolating over moving windows the multiplicative increments at each cascade steps.

 

While being a well-known feature of rainfall fields, anisotropy is not yet addressed with blunt extensions of discrete Universal Multifractal cascades. In this paper, we suggest to extend this framework to account for anisotropy. It basically consists in using different sizes according to the direction for the moving window over which the interpolation is carried out. In a first step Multifractal expected behaviour is theoretically established. Then it is numerically confirmed with the help of ensembles of stochastic simulations. Finally, the features of simulated fields are compared with actual rainfall data ones. Data collected with help of a dual polarisation X-band radar operated by HM&Co-ENPC is used (radx.enpc.fr/).

 

Authors acknowledge the RW-Turb project (supported by the French National Research Agency - ANR-19-CE05-0022), for partial financial support.

How to cite: Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Accounting for anisotropy in the simulation of rainfall fields with blunt extension of discrete Universal Multifractal cascades, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11126, https://doi.org/10.5194/egusphere-egu22-11126, 2022.

EGU22-11273 | Presentations | HS7.1

A new perspective on projected precipitation changes in Tanzania 

Stephanie Gleixner, Jascha Lehmann, and Christoph Gornott

Informed decision-making on adaptation strategies for future climate change need reliable climate information. In particular, vulnerable economies like Tanzania, which is strongly reliant on rain-fed agriculture, struggle with the lack of agreement on precipitation changes between the climate models. In order to find robustness in these projections, we compare precipitation simulations from the CORDEX Africa Ensemble under three emission scenarios (RCP 2.6, RCP 4.5, RCP 8.5) within different precipitation categories defined by the Standardized Precipitation Index (SPI). We find that despite the disagreement on the sign of the total precipitation trend, there is strong agreement among on a decrease in normal conditions and an increase in both extreme wet and extreme dry conditions throughout the 21st century. The differences between the projections in terms of total precipitation are related to shifts of (near) normal conditions to wetter conditions in the case of ‘wetter’ projections and to drier conditions for ’drier’ projections. These results indicate an overall broadening of the rainfall distribution especially toward extremely wet conditions.

How to cite: Gleixner, S., Lehmann, J., and Gornott, C.: A new perspective on projected precipitation changes in Tanzania, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11273, https://doi.org/10.5194/egusphere-egu22-11273, 2022.

EGU22-11629 | Presentations | HS7.1

Clarifying the importance of serial correlation and field significance in detection of trends in extreme rainfall 

Stefano Farris, Roberto Deidda, Francesco Viola, and Giuseppe Mascaro

Rainfall extremes are expected to intensify in a warmer environment according to theoretical arguments and climate model projections. Inferential analysis involving statistical trend testing procedures are frequently used to validate this scenario by investigating whether significant changes in precipitation measurements can be detected. Recent studies have shown that statistical trend tests applied to hydrological data might be misinterpreted if (1) the analyzed time series exhibit autocorrelation, and (2) field significance is not considered when tests are applied multiple times. In this study, these aspects have been investigated using time series of frequencies (or counts) of rainfall extremes derived from long-term (100 years) daily rainfall records of 1087 gauges of the Global Historical Climate Network (GHCN) database. Monte Carlo experiments are carried out by generating random synthetic count time series with the Poisson first-order Integer-valued AutoRegressive model (Poisson-INAR(1)) characterized by different sample size, level of autocorrelation, and trend magnitude. The main results are as follows. (1) Empirical autocorrelations are highly consistent with those exhibited by uncorrelated and non-stationary count time series, while empirical trends cannot be explained as the exclusive effect of autocorrelation; moreover, accounting for the impact of serial correlation has a limited impact on tests’ performance. (2) Accounting for field significance prevents wrong interpretations of results of multiple tests by limiting type-I errors, but it may reduce test power; a careful use of local test outcomes could help identify regions with potentially significant changes where clusters of multiple trends with coherent signs are detected. (3) Statistical trend tests based on linear and Poisson regressions are more powerful than nonparametric tests (e.g., Mann-Kendall) when applied to count time series. Finally, using these methodological insights, spatial patterns of statistically significant increasing (decreasing) trends emerge in central and eastern North America, northern Europe, part of northern Asia, and central regions of Australia (southwestern North America, part of southern Europe, and southwestern and southeastern regions of Australia).

How to cite: Farris, S., Deidda, R., Viola, F., and Mascaro, G.: Clarifying the importance of serial correlation and field significance in detection of trends in extreme rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11629, https://doi.org/10.5194/egusphere-egu22-11629, 2022.

Evaluation of winter mean precipitation over North India in CMIP6 models

Nischal Sharma1, Raju Attada1*, A. R. Dandi2, R. K. Kunchala3, Anant Parekh2, J. S. Chowdary2

1Department of Earth and Environmental Sciences - Indian Institute of Science Education and Research Mohali, Punjab – 140306

2 Indian Institute of Tropical Meteorology, Pune, India

3Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, India

*E-mail of corresponding author: rajuattada@iisermohali.ac.in

 

Abstract

North India receives a significant proportion of annual precipitation during winter (December to February) through mid-latitudinal cyclonic perturbations (Western Disturbances) embedded in subtropical westerly jet stream. This region accounts for a paucity of available in-situ observations owing to complex topography which underpins the necessity of other non-conventional tools for precipitation estimation. Global Climate Models are an effective tool to investigate global monsoon systems and are being extensively used to better understand spatio-temporal characteristics of precipitation. In the present study, north Indian winter precipitation (NIWP) and its variability has been characterized in 30 CMIP6 historical simulations (1979-2014) and compared with IMD gridded data observations. Normalized biases in different models relative to observations have been used to categorize models as wet (11), dry (8) and normal (11) models and further composite analysis has been conducted for these model categories. Our findings suggest that all the models show highest precipitation orientation along the western Himalayan belt, with the normal model category showcasing quite similar results to observations. Wet models show highest variability, errors and positive bias over the region while dry models exhibit least variability and negative bias. Majority of the models show an overall good correlation with observations. The representation of winter mean dynamical and circulation patterns has been carried out using composite analysis of three model categories relative to observations. The composite analysis reveals an intensified jet in both wet and dry model categories, with a southward shift of the jet position in wet models.  Detailed results will be discussed.

Keywords: Global climate models, CMIP6, winter precipitation

How to cite: Sharma, N.: Evaluation of winter mean precipitation over North India in CMIP6 models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12030, https://doi.org/10.5194/egusphere-egu22-12030, 2022.

EGU22-1155 | Presentations | ST1.9

Energy transfer, discontinuities and heating in the inner solar wind measured with a weak and local formulation of the Politano-Pouquet law 

Vincent David, Sébastien Galtier, Fouad Sahraoui, and Lina Hadid

The solar wind is a highly turbulent plasma for which the mean rate of energy transfer ε has been measured for a long time using the Politano-Pouquet (PP98) exact law. However, this law assumes statistical homogeneity that can be violated by the presence of discontinuities. Here, we introduce a new method based on the inertial dissipation DI whose analytical form is derived from incompressible magnetohydrodynamics (MHD); it can be considered as a weak and local (in space) formulation of the PP98 law whose expression is recovered after integration is space. We used DI to estimate the local energy transfer rate from the THEMIS-B and Parker Solar Probe (PSP) data taken in the solar wind at different heliospheric distances. Our study reveals that discontinuities near the Sun lead to a strong energy transfer that affects a wide range of scales σ. We also observe that switchbacks seem to be characterized by a singular behavior with an energy transfer varying as σ−3/4, which slightly differs from classical discontinuities characterized by a σ−1 scaling. A comparison between the measurements of ε and DI shows that in general the latter is significantly larger than the former.

https://arxiv.org/pdf/2201.02377.pdf

How to cite: David, V., Galtier, S., Sahraoui, F., and Hadid, L.: Energy transfer, discontinuities and heating in the inner solar wind measured with a weak and local formulation of the Politano-Pouquet law, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1155, https://doi.org/10.5194/egusphere-egu22-1155, 2022.

EGU22-1298 | Presentations | ST1.9

Can instabilities work in a turbulent plasma and if so, what conditions are needed for instabilities to act? 

Simon Opie, Daniel Verscharen, Chris Chen, and Christopher Owen

The solar wind is a continuous outflow of plasma from the Sun, which expands into the space between the planets in our solar system and forms the heliosphere. The solar wind is inherently turbulent and characterised by kinetic micro-instabilities on a range of scales.  Large-scale compressions (ubiquitous in solar-wind turbulence) create conditions for proton, alpha-particle and electron micro-instabilities, which transfer energy to small-scale fluctuations. These instabilities are driven by various sources of free energy (e.g. particle beams, differential flows, heat fluxes, temperature anisotropies) and make a significant contribution to the fluctuation spectrum at kinetic scales, where energy dissipation occurs. This presentation investigates the occurrence and the behaviour of kinetic instabilities in turbulent space plasmas with particular emphasis on the conditions necessary for instabilities to act.

We consider instabilities driven by proton temperature anisotropy in the turbulent solar wind by using statistical methods to analyse the Solar Orbiter data and characterise the turbulence at the relevant scales and amplitude. We compare theoretical calculations with the high-resolution data available from the Solar Orbiter MAG and SWA instruments. From this analysis we infer conditions that are necessary for instabilities to act in a turbulent plasma and demonstrate how these conditions relate to the assumptions that underpin theoretical analyses at kinetic scales. We will also introduce the next steps in this research, including the modelling and quantification of energy transfer processes at kinetic scales with particular reference to scaling law behaviours in the turbulent solar wind.   

 

How to cite: Opie, S., Verscharen, D., Chen, C., and Owen, C.: Can instabilities work in a turbulent plasma and if so, what conditions are needed for instabilities to act?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1298, https://doi.org/10.5194/egusphere-egu22-1298, 2022.

EGU22-1369 | Presentations | ST1.9

An examination of the magnetic fluctuations in long-lasting radial IMF events 

Gilbert Pi, Alexander Pitna, Zdenek Nemecek, and Jana Safrankova

This study investigates long-lasting radial interplanetary magnetic field (IMF) intervals in which IMF points along the solar wind flow direction for several hours. We use 419 such events identified in Wind observations during 1995-2019, and we focus on the behavior of magnetic field fluctuations. Using the power spectral density (PSD) calculated over 1-hour radial IMF intervals and PSDs in adjacent regions prior to and after the radial IMF interval, we address: (i) the power of IMF fluctuations, (ii) median slopes of PSDs in both inertial and kinetic ranges, (iii) the proton temperature and its anisotropy, and (vi) the occurrence rate of wavy structures and their polarization. Comparison of PSDs in radial IMF intervals with those in prior and after them revealed that the fluctuation magnitude is low in the radial IMF intervals in both MHD and kinetic ranges and the spectral power increases with the cone angle in the MHD range. It may be related to the observation limitations because the dominant 2D component of the magnetic fluctuation is hard to observe if the sampling direction is aligned with the mean magnetic field. Moreover, the proton temperature is more isotropic, and the occurrence rate of wave structures is higher for radial IMF events. The waves have no preferred polarization in the frequency range from 0.1 to 1 Hz. It suggests that the radial IMF structure leads to a different development of turbulence than the typical Parker-spiral orientation.

How to cite: Pi, G., Pitna, A., Nemecek, Z., and Safrankova, J.: An examination of the magnetic fluctuations in long-lasting radial IMF events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1369, https://doi.org/10.5194/egusphere-egu22-1369, 2022.

EGU22-3967 | Presentations | ST1.9

Electric Field Turbulence in the Solar Wind from MHD down to Electron Scales: Artemis Observations 

Chadi Salem, John Bonnell, Jordan Huang, Christopher Chaston, Luca Franci, Kristopher Klein, and Daniel Verscharen

Recent observational and theoretical work on solar wind turbulence and dissipation suggests that kinetic-scale fluctuations are both heating and isotropizing the solar wind during transit to 1 AU.  The nature of these fluctuations and associated heating processes are poorly understood. Whatever the dissipative process that links the fields and particles - Landau damping, cyclotron damping, stochastic heating, or energization through coherent structures - heating and acceleration of ions and electrons occurs because of electric field fluctuations. The dissipation due to the fluctuations depends intimately upon the temporal and spatial variations of those fluctuations in the plasma frame.  In order to derive that distribution in the plasma frame, one must also use magnetic field and density fluctuations, in addition to electric field fluctuations, as measured in the spacecraft frame (s/c) to help constrain the type of fluctuation and dissipation mechanisms that are at play.

We present here an analysis of electromagnetic fluctuations in the solar wind from MHD scales down to electron scales based on data from the Artemis spacecraft at 1 AU. We focus on a few time intervals of pristine solar wind, covering a reasonable range of solar wind properties (temperature ratios and anisotropies; plasma beta; and solar wind speed). We analyze magnetic, electric field, and density fluctuations from the 0.01 Hz (well in the inertial range) up to 1 kHz. We compute parameters such as the electric to magnetic field ratio, the magnetic compressibility, magnetic helicity, compressibility and other relevant quantities in order to diagnose the nature of the fluctuations at those scales between the ion and electron cyclotron frequencies, extracting information on the dominant modes composing the fluctuations. We also use the linear Vlasov-Maxwell solver, PLUME, to determine the various relevant modes of the plasma with parameters from the observed solar wind intervals. We discuss the results and the relevant modes as well as the major differences between our results in the solar wind and results in the magnetosheath.

How to cite: Salem, C., Bonnell, J., Huang, J., Chaston, C., Franci, L., Klein, K., and Verscharen, D.: Electric Field Turbulence in the Solar Wind from MHD down to Electron Scales: Artemis Observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3967, https://doi.org/10.5194/egusphere-egu22-3967, 2022.

In recent years, the Kolmogorov's statistical formalism of exact law that describes incompressible hydrodynamic turbulence, has been extended to compressible magnetized fluid described by isothermal or polytropic closure. Such exact laws permit an evaluation of the energy cascade rate, assumed within this formalism to be equivalent to the dissipation rate. Its estimation in the solar wind can help to better understand particle heating in such collisionless media. But previous exact laws are insufficient in a system led by pressure anisotropy. We propose a general exact law of Hall-MHD turbulence based on models with a pressure tensor that allows us to study various known equations of state as particular limits, derive a new one corresponding to the CGL (i.e., gyrotropic pressure tensor), and correlate the cascade rate to instable plasma conditions. In the incompressible MHD limit we provide a generalization of the Politano & Pouquet law to pressure-anisotropic plasmas.

How to cite: Simon, P. and Sahraoui, F.: A link between turbulent cascade and gyrotropic pressure instabilities in compressible and magnetized fluids., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4524, https://doi.org/10.5194/egusphere-egu22-4524, 2022.

EGU22-5115 | Presentations | ST1.9

Quantification of the cross-helicity cascade with Karman-Howarth-Monin and Spectral transfer equations 

Victor Montagud-Camps, Petr Hellinger, Andrea Verdini, Emanuele Papini, Luca Franci, Lorenzo Matteini, and Simone Landi

Spectral transfer equations allow to  quantify the value of the energy flux of a turbulent flow across concentric shells in Fourier space. Karman-Howarth-Monin equations serve as a complement to the Spectral Transfer analysis, since they  quantify  as well the energy transfer rate of turbulence across scales via third-order structure functions, but also provide information on the directionality of the flux. We have extended the use of these methods to study the cascade of cross-helicity and compare it to the energy cascade  in 3D compressible MHD simulations. Our results show that the cross-helicity cascade reaches stationarity after the energy cascade, thus indicating a slower turbulence development for this invariant. Once fully developed, the cross-helicity cascade matches the main features of the energy one.

How to cite: Montagud-Camps, V., Hellinger, P., Verdini, A., Papini, E., Franci, L., Matteini, L., and Landi, S.: Quantification of the cross-helicity cascade with Karman-Howarth-Monin and Spectral transfer equations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5115, https://doi.org/10.5194/egusphere-egu22-5115, 2022.

EGU22-6199 | Presentations | ST1.9

Magnetic field fluctuations in CME-driven sheath regions 

Emilia Kilpua, Simon Good, Matti Ala-Lahti, Adnane Osmane, Dominique Fontaine, Sanchita Pal, Juska Räsänen, Stuart Bale, Lingling Zhao, Lina Hadid, Miho Janvier, and Emiliya Yordanova

The sheath regions driven by coronal mass ejections (CMEs) are large-scale heliospheric structures where magnetic field fluctuations are observed over various temporal scales. Their internal structure and nature of embedded  fluctuations are currently poorly understood. We report here the key characteristics of  magnetic field fluctuations in CME-driven sheaths, including their spectral index, intermittency, amplitude and compressibility. The results highlight the gradual formation of sheaths over several days as they propagate through interplanetary and the presence of intermittent coherent structures such as strong current sheets. The Jensen-Shannon permutation entropy and complexity analysis suggest that sheath fluctuations are stochastic, but have lower entropy and higher complexity than the preceding wind.  We also show the analysis results during the slow sheath at ~0.5 AU detected by Parker Solar Probe, highlighting that slow CMEs can have prominent sheaths with distinct fluctuation properties. 

How to cite: Kilpua, E., Good, S., Ala-Lahti, M., Osmane, A., Fontaine, D., Pal, S., Räsänen, J., Bale, S., Zhao, L., Hadid, L., Janvier, M., and Yordanova, E.: Magnetic field fluctuations in CME-driven sheath regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6199, https://doi.org/10.5194/egusphere-egu22-6199, 2022.

EGU22-6596 | Presentations | ST1.9

Analysis of Turbulence Energy Transfer at an Interplanetary Shock Observed by MMS 

Nawin Ngampoopun, David Ruffolo, Riddhi Bandyopadhyay, and William Matthaeus

Turbulence near an interplanetary shock is of practical interest because turbulent magnetic fluctuations are key to the diffusive shock acceleration and transport of energetic particles, which can lead to significant space weather effects.  In this work, we examine burst-mode observations by the Magnetospheric Multiscale Mission (MMS) for an interplanetary shock passage at a distance of 25 Re­ on 8 January 2018.  The instrumental resolution offers an opportunity to examine the energy transfer rate of solar wind turbulence in both the upstream and downstream regions. We implement a Hampel filtering-based technique to mitigate the instrumental noise in plasma moment data. We use a Kolmogorov-Yaglom Law for the third-order structure function and a von Kármán-decay law to calculate the energy dissipation rates at the inertial scale and energy-containing scale, respectively. The results show that the region downstream of the shock has stronger and better developed turbulence and a higher energy transfer rate than the upstream region. N.N. has been supported by STFC studentship and UCL Doctoral School. This research has also been supported by grant RTA6280002 from Thailand Science Research and Innovation, by the MMS Theory and Modeling team grant 80NSSC19K0565, and the NASA LWS program grant 80NSSC20K0377 under NMC subcontract 655-001.

How to cite: Ngampoopun, N., Ruffolo, D., Bandyopadhyay, R., and Matthaeus, W.: Analysis of Turbulence Energy Transfer at an Interplanetary Shock Observed by MMS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6596, https://doi.org/10.5194/egusphere-egu22-6596, 2022.

EGU22-7142 | Presentations | ST1.9

Characterization of space-time structures in 3D simulations of plasma turbulence with Fast Iterative Filtering. 

Emanuele Papini, Antonio Cicone, Mirko Piersanti, Luca Franci, Andrea Verdini, Victor Montagud-Camps, Petr Hellinger, and Simone Landi

We present results from a multiscale spatiotemporal analysis of 3D Hall-MHD and hybrid kinetic numerical simulations of decaying plasma turbulence. By combining Fourier analysis and Fast Iterative Filtering, we compute the 3D k-ω power spectrum of the magnetic and velocity fluctuations at the time when turbulence has fully developed. We find that the magnetic fluctuations around and just below the ion characteristic scales mainly consist of strongly anisotropic perturbations, with temporal frequencies smaller than the ion-cyclotron frequency and with wave vectors almost perpendicular to the ambient magnetic field. Further analysis reveals that such perturbations cannot be described in terms of wave-like fluctuations, but rather consist of localized structures that are organized in a filamentary network of current sheets, which continuously form and disrupt as a consequence of magnetic reconnection, spontaneously induced by the interaction of turbulent structures. We discuss similarities and differences with respect to previous findings from 2D simulations, and we put our results in the context of spacecraft observations in the solar wind.

How to cite: Papini, E., Cicone, A., Piersanti, M., Franci, L., Verdini, A., Montagud-Camps, V., Hellinger, P., and Landi, S.: Characterization of space-time structures in 3D simulations of plasma turbulence with Fast Iterative Filtering., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7142, https://doi.org/10.5194/egusphere-egu22-7142, 2022.

EGU22-7265 | Presentations | ST1.9

What is the role of oblique whistler waves in shaping of the solar wind electron function between 0.17 and 1 AU ? 

Lucas Colomban, Matthieu Kretzschmar, Vladimir Krasnoselskikh, Milan Maksimovic, Daniel Graham, Yuri Khotyainsev, Laura Berĉiĉ, Matthieu Berthomier, and Clara Froment

In the solar wind, whistler waves are thought to play an important role on the evolution of the electron velocity distribution function as a function of distance. In particular, oblique whistler waves may diffuse the Strahl electrons into the halo population. Using AC magnetic and electric field measured by the SCM (search coil magnetometer) and electric antenna of Solar Orbiter and Parker Solar Probe, we search for the presence of whistler waves at heliocentric distance between 0.17 and 1 AU. Spectral matrices computation and minimum variance analysis on continuous waveforms make it possible to identify whistler wave modes and to determine their direction of propagation with respect to the ambiant magnetic field (angle and direction : sunward or anti-sunward) . A statistical study of the inclination of these waves and of their parameters is presented and allows us to make assumptions about their roles. Single events are also presented in details

How to cite: Colomban, L., Kretzschmar, M., Krasnoselskikh, V., Maksimovic, M., Graham, D., Khotyainsev, Y., Berĉiĉ, L., Berthomier, M., and Froment, C.: What is the role of oblique whistler waves in shaping of the solar wind electron function between 0.17 and 1 AU ?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7265, https://doi.org/10.5194/egusphere-egu22-7265, 2022.

EGU22-8357 | Presentations | ST1.9

Ion-scale transition of plasma turbulence: Pressure-strain effect 

Petr Hellinger, Victor Montagud-Camps, Luca Franci, Lorenzo Matteini, Emanuele Papini, Andrea Verdini, and Simone Landi

We investigate properties of solar-wind like plasma turbulence using direct numerical simulations. We analyze the transition from large (magnetohydrodynamic) scales to ion ones using two-dimensional hybrid (fluid electrons, kinetic ions) simulations of decaying turbulence. To quantify turbulence properties we apply spectral transfer and Karman-Howarth-Monin equations for extended compressible Hall MHD to the simulated results. The simulation results indicate that the transition from MHD to ion scales (the so called ion break) results from a combination of an onset of Hall physics and of an effective dissipation owing to the pressure-strain energy-exchange channel and resistivity. We discuss the simulation results in the context of the solar wind.

How to cite: Hellinger, P., Montagud-Camps, V., Franci, L., Matteini, L., Papini, E., Verdini, A., and Landi, S.: Ion-scale transition of plasma turbulence: Pressure-strain effect, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8357, https://doi.org/10.5194/egusphere-egu22-8357, 2022.

EGU22-8501 | Presentations | ST1.9

Analysis of Magnetohydrodynamic Perturbations in the Radial-field Solar Wind from Parker Solar Probe Observations 

Siqi Zhao, Huirong Yan, Terry Liu, Mingzhe Liu, and Mijie Shi

We report analysis of sub-Alfvénic magnetohydrodynamic (MHD) perturbations in the low-ß radial-field solar wind employing the Parker Solar Probe spacecraft data from 31 October to 12 November 2018. We calculate wave vectors using the singular value decomposition method and separate MHD perturbations into three eigenmodes (Alfvén, fast, and slow modes) to explore the properties of sub-Alfvénic perturbations and the role of compressible perturbations in solar wind heating. The MHD perturbations show a high degree of Alfvénicity in the radial-field solar wind, with the energy fraction of Alfvén modes dominating (~45%-83%) over those of fast modes (~16%-43%) and slow modes (~1%-19%). We present a detailed analysis of a representative event on 10 November 2018. Observations show that fast modes dominate magnetic compressibility, whereas slow modes dominate density compressibility. The energy damping rate of compressible modes is comparable to the heating rate, suggesting the collisionless damping of compressible modes could be significant for solar wind heating. These results are valuable for further studies of the imbalanced turbulence near the Sun and possible heating effects of compressible modes at MHD scales in low-ß plasma.

How to cite: Zhao, S., Yan, H., Liu, T., Liu, M., and Shi, M.: Analysis of Magnetohydrodynamic Perturbations in the Radial-field Solar Wind from Parker Solar Probe Observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8501, https://doi.org/10.5194/egusphere-egu22-8501, 2022.

EGU22-9796 | Presentations | ST1.9

PSP observations of the solar wind coherent structures from MHD to sub-ion scales at 0.17 AU 

Alexander Vinogradov, Olga Alexandrova, Milan Maksimovich, Anton Artemyev, Andre Mangeney, Alexei Vasiliev, Karine Issautier, Michel Moncuquet, and Anatoly Petrukovich

First perihelion Parker Solar Probe magnetic field measurements (MAG and SCM merged data) allow to resolve the fluctuations on a wide range of scales: from MHD to ion plasma scales and smaller. We trace the cascade of the fluctuations and investigate the structures formed. Using the total energy of magnetic fluctuations in time and scales, we show that coherent structures cover all the observed scales. The filling factor of the structures is a few percents. We analyze the magnetic fluctuations at different frequency ranges. We observe the coexistence of events at MHD, ion and sub-ion scales in the form of sharp discontinuities and/or vortex-like events. The approach of selecting structures by total energy alone is not complete, as it can miss structures with change in magnetic field modulus. For completeness, we perform the same analysis on longitudinal magnetic fluctuations.

How to cite: Vinogradov, A., Alexandrova, O., Maksimovich, M., Artemyev, A., Mangeney, A., Vasiliev, A., Issautier, K., Moncuquet, M., and Petrukovich, A.: PSP observations of the solar wind coherent structures from MHD to sub-ion scales at 0.17 AU, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9796, https://doi.org/10.5194/egusphere-egu22-9796, 2022.

EGU22-10131 | Presentations | ST1.9

Turbulence anisotropy observed by Parker Solar Probe 

Lingling Zhao, Gary Zank, Laxman Adhikari, Masaru Nakanotani, Daniele Telloni, Qiang Hu, and Jiansen He

Parker Solar Probe provides a unique opportunity to study anisotropic turbulence in the inner heliosphere. We summarize our recent investigations of solar wind turbulence observed by Parker Solar Probe during its first seven orbits ranging from 0.1 to 0.6 AU. First, we analyzed turbulence anisotropy based on the 2D + slab model and determined the power ratio between the 2D and slab components. We find that the fraction of the 2D component increases with radial distance. Second, we developed a method to identify small-scale magnetic flux ropes and Alfvenic structures based on the reduced magnetic helicity. Alfvenic structures are prevalent in both slow and fast solar wind in PSP's measurements, while the small flux ropes are quasi-2D structures and are relatively abundant near the heliospheric current sheet and slow solar wind. Finally, we analyzed intervals with solar wind velocity strictly parallel to the mean magnetic field. We find a Kolmogorov-like power spectrum with a power-law index of -5/3. Wave activities in both MHD and kinetic scales are also analyzed in these field-aligned intervals. Fast magnetosonic waves and ion-scale waves are identified.

How to cite: Zhao, L., Zank, G., Adhikari, L., Nakanotani, M., Telloni, D., Hu, Q., and He, J.: Turbulence anisotropy observed by Parker Solar Probe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10131, https://doi.org/10.5194/egusphere-egu22-10131, 2022.

EGU22-10948 | Presentations | ST1.9

On the scaling features of magnetic field fluctuations at sub-protonic scales 

Giuseppe Consolini, Simone Benella, Tommaso Alberti, and Mirko Stumpo

Fluctuations of magnetic field in space plasmas at sub-protonic scales have been supposed to be the result of a turbulence process involving different wave modes (EMHD, KAW, …). However, the observed spectral and scaling features seem to be non-universal. Furthermore, there is a wide evidence for the occurrence of a global scale invariance. Now, the complex nature of the fluctuations at these scales could be due to the interweaving of fluid and kinetic processes that might alter the usual scenario expected for the occurrence of strong turbulence. Here, using high-resolution data from the Parker’ Solar Probe mission we attempt an analysis of the scaling features of magnetic field fluctuations at sub-protonic scales using different approaches: i) the structure function analysis, ii) the singularity spectrum analysis and the rank-ordered multifractal analysis. The aim of these multiple approaches is to unveil the inherent complexity of fluctuation field at sub-protonic scale and to understand the controversial issues related to the occurrence of intermittency at these scales.

We acknowledge financial support by Italian MIUR-PRIN grant 2017APKP7T on Circumterrestrial Environment: Impact of Sun-Earth Interaction.

How to cite: Consolini, G., Benella, S., Alberti, T., and Stumpo, M.: On the scaling features of magnetic field fluctuations at sub-protonic scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10948, https://doi.org/10.5194/egusphere-egu22-10948, 2022.

EGU22-11274 | Presentations | ST1.9

Cross helicity of interplanetary coronal mass ejections 

Simon Good, Lauri Hatakka, Matti Ala-Lahti, Juska Soljento, Adnane Osmane, and Emilia Kilpua

Like the solar wind in general, interplanetary coronal mass ejections (ICMEs) display magnetic field and velocity fluctuations across a wide range of scales. These fluctuations may be interpreted as Alfvénic wave packets propagating parallel or anti-parallel to the local magnetic field direction, with cross helicity, σc, quantifying the difference in power between the counter-propagating fluxes. We have determined σc at inertial range frequencies in a large sample of ICME flux ropes and sheaths observed by the Wind spacecraft at 1 au. The mean σc value was low for both the flux ropes and sheaths, with the balance tipped towards the positive, anti-sunward direction. The low values indicate that Alfvénic fluxes are more balanced in ICMEs than in the solar wind at 1 au, where σc tends to be larger and anti-sunward fluctuations show a greater predominance. Superposed epoch profiles show σc falling sharply in the upstream sheath and being typically close to balance inside the flux rope near the leading edge. More imbalanced, solar wind-like σc values are found towards the trailing edge and further from the rope axis. The presence or absence of an upstream shock also has a significant effect on σc. Coronal and interplanetary origins of low σc in ICMEs are discussed.

How to cite: Good, S., Hatakka, L., Ala-Lahti, M., Soljento, J., Osmane, A., and Kilpua, E.: Cross helicity of interplanetary coronal mass ejections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11274, https://doi.org/10.5194/egusphere-egu22-11274, 2022.

EGU22-11566 | Presentations | ST1.9

Inverse transfer of magnetic helicity in isothermal supersonic turbulence 

Jean-Mathieu Teissier and Wolf-Christian Müller
The inverse transfer of magnetic helicity is studied through direct numerical simulations of the isothermal magnetohydrodynamics equations. Turbulent systems driven at large scales by either a solenoidal or a compressive forcing are considered, exhibiting root mean square Mach numbers ranging from 0.1 to about 10. The Fourier spectra of magnetic helicity present scaling exponents which become flatter with increasing compressibility. Considering the Alfvén velocity in place of the magnetic field leads however to more invariant spectra. A shell-to-shell transfer analysis reveals the presence of a subdominant direct transfer in the global picture of the inverse transfer, and that the inverse transport entails both local and non-local aspects. These three features (direct transfer, local inverse transfer, non-local inverse transfer) can be clearly associated with velocity fluctuations in distinct intervals of scale.
The results have been gained through a high-order finite-volume solver. Some practical aspects, benefits and challenges linked to the use of high-order numerics will also be discussed.

How to cite: Teissier, J.-M. and Müller, W.-C.: Inverse transfer of magnetic helicity in isothermal supersonic turbulence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11566, https://doi.org/10.5194/egusphere-egu22-11566, 2022.

In the solar wind, the differential flow between the alpha particles and the protons is an important source of free energy for driving A/IC waves and FM/W waves unstable. Large-scale slow-mode waves can modulate the differential flow, leading to non-negligible locally time-dependent changes in the drift velocity.

We investigate the behaviour of the maximum differential flow with multi-fluid wave theory in the parameter range 0<Uα/VA,p<1.5 and 0.1<βp<10 assuming quasi-perpendicular propagation of the slow mode wave, where Uα is the background alpha particle beam speed, VA,p is the proton Alfvén velocity, and βp is the ratio of the thermal proton energy to the magnetic field energy. We derive an analytical expression for the fluctuation in differential flow, the result of which we confirm through numerical evaluation of the multi-fluid wave equation. The thresholds in terms of Uα/VA,p for the instability of the A/IC and FM/W instabilities in the presence of slow mode waves decrease with increasing slow-mode amplitude and decreasing βp.

We statistically investigate the differential flow between alpha particles and protons based on spacecraft measurements with Solar Orbiter for intervals with clearly identified slow-mode waves as an observational test of our theoretical predictions. We find that slow mode fluctuations play an important role in the driving of A/IC and FM/W instabilities which are important for the energy transfer in the solar wind.

How to cite: Zhu, X., Verscharen, D., He, J., and Owen, C. J.: Slow-Mode-Driven Alfvén/Ion-Cyclotron (A/IC) and Fast-Magnetosonic/Whistler (FM/W) Instabilities in the Presence of an Alpha-Particle Beam in the Solar Wind, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11941, https://doi.org/10.5194/egusphere-egu22-11941, 2022.

EGU22-12969 | Presentations | ST1.9

Local Emission of Whistler Waves by Landau Resonance As a Signature of a Converging Magnetic Hole 

Wence Jiang, Daniel Verscharen, Hui Li, Chi Wang, and Kristopher Klein

Magnetic holes are plasma structures that trap a large number of particles in a magnetic field that is weaker than its surroundings. The unprecedented high time-resolution in-situ observations by NASA's Magnetospheric Multi-Scale (MMS) mission enable us to study the particle dynamics in the Earth's magnetosheath plasma in great detail. For the first time, we reveal the local generation of whistler waves by the Landau-resonant instability of electron beams as a response to the large-scale evolution of a magnetic hole. As the magnetic hole converges, we find a pair of counter-streaming electron beams are formed near the hole's center as a consequence of the combined action of betatron cooling and Fermi acceleration. The beams trigger the generation of slightly oblique whistler waves near the hole center, which is supported by a remarkable agreement between observations and our ALPS model predictions. Our findings show that kinetic effects and wave-particle interactions are fundamental to the dynamics and the evolution of magnetic holes as an important type of coherent structures in collisionless plasmas.

How to cite: Jiang, W., Verscharen, D., Li, H., Wang, C., and Klein, K.: Local Emission of Whistler Waves by Landau Resonance As a Signature of a Converging Magnetic Hole, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12969, https://doi.org/10.5194/egusphere-egu22-12969, 2022.

EGU22-12990 | Presentations | ST1.9

HelioSwarm: The Nature of Turbulence in Space Plasma 

Kristopher Klein and Harlan Spence and the The HelioSwarm Science Team

Quantifying the nature of turbulent fluctuations and the associated cascade of energy requires simultaneous measurements at multiple points spanning several characteristic length scales. Here, we present the HelioSwarm mission concept, which has been designed to reveal the three-dimensional, dynamic mechanisms controlling the physics of plasma turbulence. The HelioSwarm Observatory measures the plasma and magnetic fields with a novel configuration of spacecraft in the solar wind, magnetosheath, and magnetosphere. These simultaneous multi-point, multi-scale measurements span MHD, transition, and ion-scales, allowing us to address two overarching science goals: 1) Reveal the 3D spatial structure and dynamics of turbulence in a weakly collisional plasma and 2) Ascertain the mutual impact of turbulence near boundaries and large-scale structures. Addressing these goals is achieved using a first-ever "swarm" of nine spacecraft, consisting of a "hub" spacecraft and eight "node" spacecraft. The nine spacecraft co-orbit in a lunar resonant Earth orbit, with a 2-week period and an apogee/perigee of ~60/11 Earth radii. Flight dynamics design and on-board propulsion produce ideal inter-spacecraft separations ranging from fluid scales (1000's of km) to sub-ion kinetic scales (10's of km) in the necessary geometries to enable the application of a variety of established analysis techniques that distinguish between proposed models of turbulence. Each node possesses an identical instrument suite that consists of a Faraday cup, a fluxgate magnetometer, and a search coil magnetometer. The hub has the same instrument suite as the nodes, plus an ion electrostatic analyzer. With these measurements, the HelioSwarm Observatory promises an unprecedented view into the nature of space plasma turbulence.

How to cite: Klein, K. and Spence, H. and the The HelioSwarm Science Team: HelioSwarm: The Nature of Turbulence in Space Plasma, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12990, https://doi.org/10.5194/egusphere-egu22-12990, 2022.

NP4 – Time Series and Big Data Methods

EGU22-91 | Presentations | NP4.1

The role of teleconnections in complex climate network 

Ruby Saha

A complex network provides a robust framework to statistically investigate the topology of local and long-range connections, i.e., teleconnections in climate dynamics. The Climate network is constructed from meteorological data set using the linear Pearson correlation coefficient to measure similarity between two regions. Long-range teleconnections connect remote geographical sites and are crucial for climate networks. In this study, we discuss that during El Ni\~no Southern Oscillation onset, the teleconnections pattern changes according to the episode's strength. The long-range teleconnections are significant and responsible for the episodes' extremum ONI attained gradually after onset. We quantify the betweenness centrality measurement and note that the teleconnection distribution pattern and the betweenness measurements fit well.

How to cite: Saha, R.: The role of teleconnections in complex climate network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-91, https://doi.org/10.5194/egusphere-egu22-91, 2022.

EGU22-1831 | Presentations | NP4.1

Quantifying space-weather events using dynamical network analysis of Pc waves with global ground based magnetometers. 

Shahbaz Chaudhry, Sandra Chapman, Jesper Gjerloev, Ciaran Beggan, and Alan Thompson

Geomagnetic storms can impact technological systems, on the ground and in space, including damage to satellites and power blackouts. Their impact on ground systems such as power grids depends upon the spatio-temporal extent and time-evolution of the ground magnetic perturbation driven by the storm.

Pc waves are Alfven wave resonances of closed magnetospheric field lines and are ubiquitous in the inner magnetosphere. They have been extensively studied, in particular since  Pc wave power tracks the onset and evolution of geomagnetic storms.  We study the spatial and temporal evolution of Pc waves with a network analysis of the 100+ ground-based magnetometer stations collated by the SuperMAG collaboration with a single time-base and calibration. 

Network-based analysis of 1 min cadence SuperMAG magnetometer data has been applied to the dynamics of substorm current systems (Dods et al. JGR 2015, Orr et al. GRL 2019) and the magnetospheric response to IMF turnings (Dods et al. JGR 2017). It has the potential to capture the full spatio-temporal response with a few time-dependent network parameters. Now, with the availability of 1 sec data across the entire SuperMAG network we are able for the first time to apply network analysis globally to resolve both the spatial and temporal correlation patterns of the ground signature of Pc wave activity as a geomagnetic storm evolves. We focus on Pc2 (5-10s period) and Pc3 (10-45s period) wave bands. We obtain the time-varying global Pc wave dynamical network over individual space weather events.

To construct the networks we sample each magnetometer time series with a moving window in the time domain (20 times Pc period range) and then band-pass filter each magnetometer station time-series to obtain Pc2 and Pc3 waveforms. We then compute the cross correlation (TLXC) between all stations for each Pc band. Modelling is used to determine a threshold of significant TLXC above which a pair of stations are connected in the network. The TLXC as a function of lag is tested against a criterion for sinusoidal waveforms and then used to calculate the phase difference. The connections with a TLXC peak at non zero lag form a directed network which characterizes propagation or information flow. The connections at TLXC lag peak close to zero form am undirected network which characterizes a response which is globally instantaneously coherent.

We apply this network analysis to isolated geomagnetic storms. We find that the network connectivity does not simply track Pc wave power, it therefore contains additional information. Geographically short range connections are prevalent at all times, the storm onset marks a transition to a network which has both enhancement of geographically short-range connections, and the growth of geographically long range, global scale, connections extending spatially over a region exceeding 9h MLT. These global scale connections, indicating globally coherent Pc wave response are prevalent throughout the storm with considerable (within a few time windows) variation. The stations are not uniformly distributed spatially. Therefore, we distinguish between long range connections to avoid introducing spatial correlation. 

How to cite: Chaudhry, S., Chapman, S., Gjerloev, J., Beggan, C., and Thompson, A.: Quantifying space-weather events using dynamical network analysis of Pc waves with global ground based magnetometers., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1831, https://doi.org/10.5194/egusphere-egu22-1831, 2022.

EGU22-2014 | Presentations | NP4.1

OBS noise reduction using music information retrieval algorithms 

Zahra Zali, Theresa Rein, Frank Krüger, Matthias Ohrnberger, and Frank Scherbaum

Since the ocean covers 71% of the Earth’s surface, records from ocean bottom seismometers (OBS) are essential for investigating the whole Earth’s structure. However, data from ocean bottom recordings are commonly difficult to analyze due to the high noise level especially on the horizontal components. In addition, signals of seismological interest such as earthquake recordings at teleseismic distances, are masked by the oceanic noises. Therefore, noise reduction of OBS data is an important task required for the analysis of OBS records. Different approaches have been suggested in previous studies to remove noise from vertical components successfully, however, noise reduction on records of horizontal components remained problematic. Here we introduce a method, which is based on harmonic-percussive separation (HPS) algorithms used in Zali et al., (2021) that is able to separate long-lasting narrowband signals from broadband transients in the OBS records. In the context of OBS noise reduction using HPS algorithms, percussive components correspond to earthquake signals and harmonic components correspond to noise signals. OBS noises with narrowband horizontal structures in the short time Fourier transform (STFT) are readily distinguishable from transient, short-duration seismic events with vertical exhibitions in the STFT spectrogram. Through HPS algorithms we try to separate horizontal structures from vertical structures in the STFT spectrograms. Using this method we can reduce OBS noises from both vertical and horizontal components, retrieve clearer broadband earthquake waveforms and increase the earthquake signal to noise ratio. The applicability of the method is checked through tests on synthetic and real data.

How to cite: Zali, Z., Rein, T., Krüger, F., Ohrnberger, M., and Scherbaum, F.: OBS noise reduction using music information retrieval algorithms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2014, https://doi.org/10.5194/egusphere-egu22-2014, 2022.

EGU22-2097 | Presentations | NP4.1 | Highlight

Medium- to long-term forecast of sea surface temperature using EEMD-STEOF-LSTM hybrid model 

Rixu Hao, Yuxin Zhao, Xiong Deng, Di Zhou, Dequan Yang, and Xin Jiang

Sea surface temperature (SST) is a vitally important variable of the global ocean, which can profoundly affect the climate and marine ecosystems. The field of forecasting oceanic variables has traditionally relied on numerical models, which effectively consider the discretization of the dynamical and physical oceanic equations. However, numerical models suffer from many limitations such as short timeliness, complex physical processes, and excessive calculation. Furthermore, existing machine learning has been proved to be able to capture spatial and temporal information independently without these limitations, but the previous research on multi-scale feature extraction and evolutionary forecast under spatiotemporal integration is still inadequate. To fill this gap, a multi-scale spatiotemporal forecast model is developed combining ensemble empirical mode decomposition (EEMD) and spatiotemporal empirical orthogonal function (STEOF) with long short-term memory (LSTM), which is referred to as EEMD-STEOF-LSTM. Specifically, the EEMD is applied for adaptive multi-scale analysis; the STEOF is adopted to decompose the spatiotemporal processes of different scales into terms of a sum of products of spatiotemporal basis functions along with corresponding coefficients, which captures the evolution of spatial and temporal processes simultaneously; and the LSTM is employed to achieve medium- to long-term forecast of STEOF-derived spatiotemporal coefficients. A case study of the daily average of SST in the South China Sea shows that the proposed hybrid EEMD-STEOF-LSTM model consistently outperforms the optimal climatic normal (OCN), STEOF, and STEOF-LSTM, which can accurately forecast the characteristics of oceanic eddies. Statistical analysis of the case study demonstrates that this model has great potential for practical applications in medium- to long-term forecast of oceanic variables.

How to cite: Hao, R., Zhao, Y., Deng, X., Zhou, D., Yang, D., and Jiang, X.: Medium- to long-term forecast of sea surface temperature using EEMD-STEOF-LSTM hybrid model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2097, https://doi.org/10.5194/egusphere-egu22-2097, 2022.

In this presentation, we introduce the IMFogram method ( pronounced like "infogram" ), which is a new, fast, local, and reliable time-frequency representation (TFR) method for nonstationary signals. This technique is based on the Intrinsic Mode Functions (IMFs) decomposition produced by a decomposition method, like the Empirical Mode Decomposition-based techniques, Iterative Filtering-based algorithms, or any equivalent method developed so far. We present the mathematical properties of the IMFogram, and show the proof that this method is a generalization of the Spectrogram. We conclude the presentation with some applications, as well as a comparison of its performance with other existing TFR techniques.

How to cite: Cicone, A.: The IMFogram: a new time-frequency representation algorithm for nonstationary signals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2560, https://doi.org/10.5194/egusphere-egu22-2560, 2022.

EGU22-2922 | Presentations | NP4.1

Constraining the uncertainty in CO2 seasonal cycle metrics by residual bootstrapping. 

Theertha Kariyathan, Wouter Peters, Julia Marshall, Ana Bastos, and Markus Reichstein

The analysis of long, high-quality time series of atmospheric greenhouse gas measurements helps to quantify their seasonal to interannual variations and impact on global climate. These discrete measurement records contain, however, gaps and at times noisy data, influenced by local fluxes or synoptic scale events, hence appropriate filtering and curve-fitting techniques are often used to smooth and gap-fill the atmospheric time series. Previous studies have shown that there is an inherent uncertainty associated with curve-fitting processes which introduces biases based on the choice of mathematical method used for data processing and can lead to scientific misinterpretation of the signal. Further the uncertainties in curve-fitting can be propagated onto the metrics estimated from the fitted curve that could significantly influence the quantification of the metrics and their interpretations. In this context we present a novel-methodology for constraining the uncertainty arising from fitting a smooth curve to the CO2 dry air mole fraction time-series, and propagate this uncertainty onto commonly used metrics to study the seasonal cycle of CO2. We generate an ensemble of fifitted curves from the data using residual bootstrap sampling with loess-fitted residuals, that is representative of the inherent uncertainty in applying the curve-fitting method to the discrete data. The spread of the selected CO2 seasonal cycle metrics across bootstrap time-series provides an estimate of the inherent uncertainty in curve fitting to the discrete data. Further we show that the approach can be extended to other curve-fitting methods by generating multiple bootstrap samples by resampling residuals obtained from processing the data using the widely used CCGCRV filtering method by the atmospheric greenhouse gas measurement community.

How to cite: Kariyathan, T., Peters, W., Marshall, J., Bastos, A., and Reichstein, M.: Constraining the uncertainty in CO2 seasonal cycle metrics by residual bootstrapping., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2922, https://doi.org/10.5194/egusphere-egu22-2922, 2022.

EGU22-4795 | Presentations | NP4.1

Robust Causal Inference for Irregularly Sampled Time Series: Applications in Climate and Paleoclimate Data Analysis 

Aditi Kathpalia, Pouya Manshour, and Milan Paluš

To predict and determine the major drivers of climate has become even more important now as climate change poses a big challenge to humankind and our planet earth. Different studies employ either correlation, causality methods or modelling approaches to study the interaction between climate and climate forcing variables (anthropogenic or natural). This includes the study of interaction between global surface temperatures and CO2; rainfall in different locations and El Niño–Southern Oscillation (ENSO) phenomena. The results produced by different studies have been found to be different and debatable, presenting an ambiguous situation. In this work, we develop and apply a novel robust causality estimation technique for time-series data (to estimate causal influence between given observables), that can help to resolve the ambiguity. The discrepancy in existing results arises due to challenges with the acquired data and limitations of the causal inference/ modelling approaches. Our novel approach combines the use of a recently proposed causality method, Compression-Complexity Causality (CCC) [1], and Ordinal/ Permutation pattern-based coding [2]. CCC estimates have been shown to be robust for bivariate systems with low temporal resolution, missing samples, long-term memory and finite length data [1]. The use of ordinal patterns helps to extend bivariate CCC to the multivariate case by capturing the multidimensional dynamics of the given variables’ systems in the symbolic temporal sequence of a single variable. This methodology is tested on dynamical systems data which are short in length and have been corrupted with missing samples or subsampled to different levels. The superior performance of ‘Permutation CCC’ on such data relative to other causality estimation methods, strengthens our trust in the method. We apply the method to study the interaction between CO2-temperature recordings on three different time scales, CH4-temperature on the paleoclimate scale, ENSO-South Asian monsoon on monthly and yearly time scales, North Atlantic Oscillation-surface temperature on daily and monthly time scales. These datasets are either short in length, have been sampled irregularly, have missing samples or have a combination of the above factors. Our results are interesting, which validate some existing studies while contradicting others. In addition, the development of the novel permutation-CCC approach opens the possibility of its application for making useful inferences on other challenging climate datasets.


This study is supported by the Czech Science Foundation, Project No.~GA19-16066S and by the Czech Academy of Sciences, Praemium Academiae awarded to M. Paluš.


References:
[1] Kathpalia, A., & Nagaraj, N. (2019). Data-based intervention approach for Complexity-Causality measure. PeerJ Computer Science, 5, e196.
[2] Bandt, C., & Pompe, B. (2002). Permutation entropy: a natural complexity measure for time series. Physical review letters, 88(17), 174102.

How to cite: Kathpalia, A., Manshour, P., and Paluš, M.: Robust Causal Inference for Irregularly Sampled Time Series: Applications in Climate and Paleoclimate Data Analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4795, https://doi.org/10.5194/egusphere-egu22-4795, 2022.

Rainfall time series prediction is crucial for geoscientific system monitoring, but it is challenging and complex due to the extreme variability of rainfall. In order to improve prediction accuracy, a hybrid deep learning model (VMD-RNN) was proposed. In this study, variational mode decomposition (VMD) is first applied to decompose the original rainfall time series into several sub-sequences according to the frequency domain. Following that, different recurrent neural network (RNN) models are utilized to predict individual sub-sequences and the final prediction is reconstructed by summing the prediction results of sub-sequences. These RNN models are long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM) and bidirectional GRU (BiGRU), which are optimal for sequence prediction. The root mean square error (RMSE) of the predicted performance is then used to select the ideal RNN model for each sub-sequences. In addition to RMSE, the framework of universal multifractal (UM) is also introduced to evaluate prediction performances, which enables to characterize the extreme variability of predicted rainfall time series. The study employed two rainfall datasets from 2001 to 2020 in Paris, with daily and hourly resolutions. The results show that, when compared to directly predicting the original time series, the proposed hybrid VMD-RNN model improves prediction of high or extreme values for the daily dataset, but does not significantly enhance the prediction of zero or low values. Additionally, the VMD-RNN model also outperforms existing deep learning models without decomposition on the hourly dataset when evaluated with the help of RMSE, while universal multifractal analyses point out limitations. 

How to cite: Zhou, H., Schertzer, D., and Tchiguirinskaia, I.: Combining variational mode decomposition and recurrent neural network to predict rainfall time series and evaluating prediction performance by universal multifractals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6014, https://doi.org/10.5194/egusphere-egu22-6014, 2022.

EGU22-6281 | Presentations | NP4.1

Application of information theoretical measures for improved machine learning modelling of the outer radiation belt 

Constantinos Papadimitriou, Georgios Balasis, Ioannis A. Daglis, and Simon Wing

In the past ten years Artificial Neural Networks (ANN) and other machine learning methods have been used in a wide range of models and predictive systems, to capture and even predict the onset and evolution of various types of phenomena. These applications typically require large datasets, composed of many variables and parameters, the number of which can often make the analysis cumbersome and prohibitively time consuming, especially when the interplay of all these parameters is taken into consideration. Thankfully, Information-Theoretical measures can be used to not only reduce the dimensionality of the input space of such a system, but also improve its efficiency. In this work, we present such a case, where differential electron fluxes from the Magnetic Electron Ion Spectrometer (MagEIS) on board the Van Allen Probes satellites are modelled by a simple ANN, using solar wind parameters and geomagnetic activity indices as inputs, and illustrate how the proper use of Information Theory measures can improve the efficiency of the model by minimizing the number of input parameters and shifting them with respect to time, to their proper time-lagged versions.

How to cite: Papadimitriou, C., Balasis, G., Daglis, I. A., and Wing, S.: Application of information theoretical measures for improved machine learning modelling of the outer radiation belt, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6281, https://doi.org/10.5194/egusphere-egu22-6281, 2022.

EGU22-7256 | Presentations | NP4.1

Identifying patterns of teleconnections, a curvature-based network analysis 

Jakob Schlör, Felix M. Strnad, Christian Fröhlich, and Bedartha Goswami

Representing spatio-temporal climate variables as complex networks allows uncovering nontrivial structure in the data. Although various tools for detecting communities in climate networks have been used to group nodes (spatial locations) with similar climatic conditions, we are often interested in identifying important links between communities. Of particular interest are methods to detect teleconnections, i.e. links over large spatial distances mitigated by atmospheric processes.

We propose to use a recently developed network measure based on Ricci-curvature to visualize teleconnections in climate networks. Ricci-curvature allows to distinguish between- and within-community links in networks. Applied to networks constructed from surface temperature anomalies we show that Ricci-curvature separates spatial scales. We use Ricci-curvature to study differences in global teleconnection patterns of different types of El Niño events, namely the Eastern Pacific (EP) and Central Pacific (CP) types. Our method reveals a global picture of teleconnection patterns, showing confinement of teleconnections to the tropics under EP conditions but showing teleconnections to the tropics, Northern and Southern Hemisphere under CP conditions. The obtained teleconnections corroborate previously reported impacts of EP and CP.
Our results suggest that Ricci-curvature is a promising visual-analytics-tool to study the topology of climate systems with potential applications across observational and model data.

How to cite: Schlör, J., Strnad, F. M., Fröhlich, C., and Goswami, B.: Identifying patterns of teleconnections, a curvature-based network analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7256, https://doi.org/10.5194/egusphere-egu22-7256, 2022.

EGU22-8399 | Presentations | NP4.1

Using neural networks to detect coastal hydrodynamic phenomena in high-resolution tide gauge data 

Felix Soltau, Sebastian Niehüser, and Jürgen Jensen

Tide gauges are exposed to various kinds of influences that are able to affect water level measurements significantly and lead to time series containing different phenomena and artefacts. These influences can be natural or anthropogenic, while both lead to actual changes of the water level. Opposed to that, technical malfunction of measuring devices as another kind of influence causes non-physical water level data. Both actual and non-physical data need to be detected and classified consistently, and possibly corrected to enable the supply of adequate water level information. However, there is no automatically working detection algorithm yet. Only obvious or frequent technical malfunctions like gaps can be detected automatically but have to be corrected manually by trained staff. Consequently, there is no consistently defined data pre-processing before, for example, statistical analyses are performed or water level information for navigation is passed on.

In the research project DePArT*, we focus on detecting natural phenomena like standing waves, meteotsunamis, or inland flood events as well as anthropogenic artefacts like operating storm surge barriers and sluices in water level time series containing data every minute. Therefore, we train artificial neural networks (ANNs) using water level sequences of phenomena and artefacts as well as redundant data to recognize them in other data sets. We use convolutional neural networks (CNNs) as they already have been successfully conducted in, for example, object detection or speech and language processing (Gu et al., 2018). However, CNNs need to be trained with high numbers of sample sequences. Hence, as a next step the idea is to synthesize rarely observed phenomena and artefacts to gain enough training data. The trained CNNs can then be used to detect unnoticed phenomena and artefacts in past and recent time series. Depending on sequence characteristics and the results of synthesizing, we will possibly be able to detect certain events as they occur and therefore provide pre-checked water level information in real time.

In a later stage of this study, we will implement the developed algorithms in an operational test mode while cooperating closely with the officials to benefit from the mutual feedback. In this way, the study contributes to a future consistent pre-processing and helps to increase the quality of water level data. Moreover, the results are able to reduce uncertainties from the measuring process and improve further calculations based on these data.

* DePArT (Detektion von küstenhydrologischen Phänomenen und Artefakten in minütlichen Tidepegeldaten; engl. Detection of coastal hydrological phenomena and artefacts in minute-by-minute tide gauge data) is a research project, funded by the German Federal Ministry of Education and Research (BMBF) through the project management of Projektträger Jülich PTJ under the grant number 03KIS133.

Gu, Wang, Kuen, Ma, Shahroudy, Shuai, Liu, Wang, Wang, Cai, Chen (2018): Recent advances in convolutional neural networks. In: Pattern Recognition, Vol. 77, Pages 354–377. https://doi.org/10.1016/j.patcog.2017.10.013

How to cite: Soltau, F., Niehüser, S., and Jensen, J.: Using neural networks to detect coastal hydrodynamic phenomena in high-resolution tide gauge data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8399, https://doi.org/10.5194/egusphere-egu22-8399, 2022.

EGU22-8899 | Presentations | NP4.1

Body wave extraction by using sparsity-promoting time-frequency filtering 

Bahare Imanibadrbani, Hamzeh Mohammadigheymasi, Ahmad Sadidkhouy, Rui Fernandes, Ali Gholami, and Martin Schimmel

Different phases of seismic waves generated by earthquakes carry considerable information about the subsurface structures as they propagate within the earth. Depending on the scope and objective of an investigation, various types of seismic phases are studied. Studying surface waves image shallow and large-scale subsurface features, while body waves provide high-resolution images at higher depths, which is otherwise impossible to be resolved by surface waves. The most challenging aspect of studying body waves is extracting low-amplitude P and S phases predominantly masked by high amplitude and low attenuation surface waves overlapping in time and frequency. Although body waves generally contain higher frequencies than surface waves, the overlapping frequency spectrum of body and surface waves limits the application of elementary signal processing methods such as conventional filtering. Advanced signal processing tools are required to work around this problem. Recently the Sparsity-Promoting Time-Frequency Filtering (SP-TFF) method was developed as a signal processing tool for discriminating between different phases of seismic waves based on their high-resolution polarization information in the Time-Frequency (TF)-domain (Mohammadigheymasi et al., 2022). The SP-TFF extracts different phases of seismic waves by incorporating this information and utilizing a combination of amplitude, directivity, and rectilinearity filters. This study implements SP-TFF by properly defining a filter combination set for specific extraction of body waves masked by high-amplitude surface waves. Synthetic and real data examinations for the source mechanism of the  Mw=7.5 earthquake that occurred in November 2021 in Northern Peru and recorded by 58 stations of the United States National Seismic Network (USNSN) is conducted. The results show the remarkable performance of SP-TFF extracting P and SV phases on the vertical and radial components and SH phase on the transverse component masked by high amplitude Rayleigh and Love waves, respectively. A range of S/N levels is tested, indicating the algorithm’s robustness at different noise levels. This research contributes to the FCT-funded SHAZAM (Ref. PTDC/CTA-GEO/31475/2017) and IDL (Ref. FCT/UIDB/50019/2020) projects. It also uses computational resources provided by C4G (Collaboratory for Geosciences) (Ref. PINFRA/22151/2016).

REFERENCE
Mohammadigheymasi, H., P. Crocker, M. Fathi, E. Almeida, G. Silveira, A. Gholami, and M. Schimmel, 2022, Sparsity-promoting approach to polarization analysis of seismic signals in the time-frequency domain: IEEE Transactions on Geoscience and Remote Sensing, 1–1.

How to cite: Imanibadrbani, B., Mohammadigheymasi, H., Sadidkhouy, A., Fernandes, R., Gholami, A., and Schimmel, M.: Body wave extraction by using sparsity-promoting time-frequency filtering, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8899, https://doi.org/10.5194/egusphere-egu22-8899, 2022.

EGU22-9626 | Presentations | NP4.1

A Recurrence Flow based Approach to Attractor Reconstruction 

Tobias Braun, K. Hauke Kraemer, and Norbert Marwan

In the study of nonlinear observational time series, reconstructing the system’s state space represents the basis for many widely-used analyses. From the perspective of dynamical system’s theory, Taken’s theorem states that under benign conditions, the reconstructed state space preserves the most fundamental properties of the real, unknown system’s attractor. Through many applications, time delay embedding (TDE) has established itself as the most popular approach for state space reconstruction1. However, standard TDE cannot account for multiscale properties of the system and many of the more sophisticated approaches either require heuristic choice for a high number of parameters, fail when the signals are corrupted by noise or obstruct analysis due to their very high complexity.

We present a novel semi-automated, recurrence based method for the problem of attractor reconstruction. The proposed method is based on recurrence plots (RPs), a computationally simple yet effective 2D-representation of a univariate time series. In a recent study, the quantification of RPs has been extended by transferring the well-known box-counting algorithm to recurrence analysis2. We build on this novel formalism by introducing another box-counting measure that was originally put forward by B. Mandelbrot, namely succolarity3. Succolarity quantifies how well a fluid can permeate a binary texture4. We employ this measure by flooding a RP with a (fictional) fluid along its diagonals and computing succolarity as a measure of diagonal flow through the RP. Since a non-optimal choice of embedding parameters impedes the formation of diagonal lines in the RP and generally results in spurious patterns that block the fluid, the attractor reconstruction problem can be formulated as a maximization of diagonal recurrence flow.

The proposed state space reconstruction algorithm allows for non-uniform embedding delays to account for multiscale dynamics. It is conceptually and computationally simple and (nearly) parameter-free. Even in presence of moderate to high noise intensity, reliable results are obtained. We compare the method’s performance to existing techniques and showcase its effectiveness in applications to paradigmatic examples and nonlinear geoscientific time series.

 

References:

1 Packard, N. H., Crutchfield, J. P., Farmer, J. D., & Shaw, R. S. (1980). Geometry from a time series. Physical review letters, 45(9), 712.

2 Braun, T., Unni, V. R., Sujith, R. I., Kurths, J., & Marwan, N. (2021). Detection of dynamical regime transitions with lacunarity as a multiscale recurrence quantification measure. Nonlinear Dynamics, 1-19.

3 Mandelbrot, B. B. (1982). The fractal geometry of nature (Vol. 1). New York: WH freeman.

4 de Melo, R. H., & Conci, A. (2013). How succolarity could be used as another fractal measure in image analysis. Telecommunication Systems, 52(3), 1643-1655.

How to cite: Braun, T., Kraemer, K. H., and Marwan, N.: A Recurrence Flow based Approach to Attractor Reconstruction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9626, https://doi.org/10.5194/egusphere-egu22-9626, 2022.

EGU22-11064 | Presentations | NP4.1

The Objective Deformation Component of a Velocity Field 

Bálint Kaszás, Tiemo Pedergnana, and George Haller

According to a fundamental axiom of continuum mechanics, material response should be objective, i.e., indifferent to the observer. In the context of geophysical fluid dynamics, fluid-transporting vortices must satisfy this axiom and hence different observers should come to the same conclusion about the location and size of these vortices. As a consequence, only objectively defined extraction methods can provide reliable results for material vortices.

As velocity fields are inherently non-objective, they render most Eulerian flow-feature detection non-objective. To resolve this issue,  we discuss a general decomposition of a velocity field into an objective deformation component and a rigid-body component. We obtain this decomposition as a solution of a physically motivated extremum problem for the closest rigid-body velocity of a general velocity field.

This extremum problem turns out to have a unique,  physically interpretable,  closed-form solution. Subtracting this solution from the velocity field then gives an objective deformation velocity field that is also physically observable. As a consequence, all common Eulerian feature detection schemes, as well as the momentum, energy, vorticity, enstrophy, and helicity of the flow, become objective when computed from the deformation velocity component. We illustrate the use of this deformation velocity field on several velocity data sets.

How to cite: Kaszás, B., Pedergnana, T., and Haller, G.: The Objective Deformation Component of a Velocity Field, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11064, https://doi.org/10.5194/egusphere-egu22-11064, 2022.

EGU22-11118 | Presentations | NP4.1

Explainable community detection of extreme rainfall events using the tangles algorithmic framework 

Merle Kammer, Felix Strnad, and Bedartha Goswami

Climate networks have helped to uncover complex structures in climatic observables from large time series data sets. For instance, climate networks were used to reduce rainfall data to relevant patterns that can be linked to geophysical processes. However, the identification of regions that show similar behavior with respect to the timing and spatial distribution of extreme rainfall events (EREs) remains challenging. 
To address this, we apply a recently developed algorithmic framework based on tangles [1] to discover community structures in the spatial distribution of EREs and to obtain inherently interpretable communities as an output. First, we construct a climate network using time-delayed event synchronization and create a collection of cuts (bipartitions) from the EREs data. By using these cuts, the tangles algorithmic framework allows us to both exploit the climate network structure and incorporate prior knowledge from the data. Applying tangles enables us to create a hierarchical tree representation of communities including the likelihood that spatial locations belong to a community. Each tree layer can be associated to an underlying cut, thus making the division of different communities transparent. 
Applied to global precipitation data, we show that tangles is a promising tool to quantify community structures and to reveal underlying geophysical processes leading to these structures.

 

[1] S. Klepper, C. Elbracht, D. Fioravanti,  J. Kneip, L. Rendsburg, M. Teegen, and U. von Luxburg. Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees. CoRR, abs/2006.14444v2, 2021. URL https://arxiv.org/abs/2006.14444v2.

How to cite: Kammer, M., Strnad, F., and Goswami, B.: Explainable community detection of extreme rainfall events using the tangles algorithmic framework, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11118, https://doi.org/10.5194/egusphere-egu22-11118, 2022.

EGU22-11667 | Presentations | NP4.1

Spurious Behaviour in Networks from Spatio-temporal Data 

Moritz Haas, Bedartha Goswami, and Ulrike von Luxburg

Network-based analyses of dynamical systems have become increasingly popular in climate science. Instead of focussing on the chaotic systems aspect, we come from a statistical perspective and highlight the often ignored fact that the calculated correlation values are only empirical estimates. We find that already the uncertainty stemming from the estimation procedure has major impact on network characteristics. Using isotropic random fields on the sphere, we observe spurious behaviour in commonly constructed networks from finite samples. When the data has locally coherent correlation structure, even spurious link-bundle teleconnections have to be expected. We reevaluate the outcome and robustness of existing studies based on their design choices and null hypotheses.

How to cite: Haas, M., Goswami, B., and von Luxburg, U.: Spurious Behaviour in Networks from Spatio-temporal Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11667, https://doi.org/10.5194/egusphere-egu22-11667, 2022.

EGU22-12351 | Presentations | NP4.1

VAE4OBS: Denoising ocean bottom seismograms using variational autoencoders 

Maria Tsekhmistrenko, Ana Ferreira, Kasra Hosseini, and Thomas Kitching

Data from ocean-bottom seismometers (OBS) are inherently more challenging than their land counterpart because of their noisy environment. Primary and secondary microseismic noises corrupt the recorded time series. Additionally, anthropogenic (e.g., ships) and animal noise (e.g., Whales) contribute to a complex noise that can make it challenging to use traditional filtering methods (e.g., broadband or Gabor filters) to clean and extract information from these seismograms. 

OBS deployments are laborious, expensive, and time-consuming. The data of these deployments are crucial in investigating and covering the "blind spots" where there is a lack of station coverage. It, therefore, becomes vital to remove the noise and retrieve earthquake signals recorded on these seismograms.

We propose analysing and processing such unique and challenging data with Machine Learning (ML), particularly Deep Learning (DL) techniques, where conventional methods fail. We present a variational autoencoder (VAE) architecture to denoise seismic waveforms with the aim to extract more information than previously possible. We argue that, compared to other fields, seismology is well-posed to use ML and DL techniques thanks to massive datasets recorded by seismograms. 

In the first step, we use synthetic seismograms (generated with Instaseis) and white noise to train a deep neural network. We vary the signal-to-noise ratio during training. Such synthetic datasets have two advantages. First, we know the signal and noise (as we have injected the noise ourselves). Second, we can generate large training and validation datasets, one of the prerequisites for high-quality DL models.

Next, we increased the complexity of input data by adding real noise sampled from land and OBS to the synthetic seismograms. Finally, we apply the trained model to real OBS data recorded during the RHUM-RUM experiment.

We present the workflow, the neural network architecture, our training strategy, and the usefulness of our trained models compared to traditional methods.

How to cite: Tsekhmistrenko, M., Ferreira, A., Hosseini, K., and Kitching, T.: VAE4OBS: Denoising ocean bottom seismograms using variational autoencoders, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12351, https://doi.org/10.5194/egusphere-egu22-12351, 2022.

EGU22-13053 | Presentations | NP4.1

Causal Diagnostics for Observations - Experiments with the L63 system 

Nachiketa Chakraborty and Javier Amezcua

Study of cause and effect relationships – causality - is central to identifying mechanisms that cause the phenomena we observe. And in non-linear, dynamical systems, we wish to understand these mechanisms unfolding over time. In areas within physical sciences like geosciences, astrophysics, etc. there are numerous competing causes that drive the system in complicated ways that are hard to disentangle. Hence, it is important to demonstrate how causal attribution works with relatively simpler systems where we have a physical intuition. Furthermore, in earth and atmospheric sciences or meteorology, we have a plethora of observations that are used in both understanding the underlying science beneath the phenomena as well as forecasting. However in order to do this, optimally combining the models (theoretical/numerical) with the observations through data assimilation is a challenging, computationally intensive task. Therefore, understanding the impact of observations and the required cadence is very useful. Here, we present experiments in causal inference and attribution with the Lorenz 63 system – a system studied for a long time. We first test the causal relations between the variables characterising the model. And then we simulate observations using perturbed versions of the model to test the impact of the cadence of observations of each combination of the 3 variables.

How to cite: Chakraborty, N. and Amezcua, J.: Causal Diagnostics for Observations - Experiments with the L63 system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13053, https://doi.org/10.5194/egusphere-egu22-13053, 2022.

An accurate understanding of dynamical similarities and dissimilarities in geomagnetic variability between quiet and disturbed periods has the potential to vastly improve Space Weather diagnosis. During the last years, several approaches rooted in dynamical system theory have demonstrated their great potentials for characterizing the instantaneous level of complexity in geomagnetic activity and solar wind variations, and for revealing indications of intermittent large-scale coupling and generalized synchronization phenomena in the Earth’s electromagnetic environment. In this work, we focus on two complementary approaches based on the concept of recurrences in phase space, both of which quantify subtle geometric properties of the phase space trajectory instead of taking an explicit temporal variability perspective. We first quantify the local (instantaneous) and global fractal dimensions and associated local stability properties of a suite of low (SYM-H, ASY-H) and high latitude (AE, AL, AU) geomagnetic indices and discuss similarities and dissimilarities of the obtained patterns for one year of observations during a solar activity maximum. Subsequently, we proceed with studying bivariate extensions of both approaches, and demonstrate their capability of tracing different levels of interdependency between low and high latitude geomagnetic variability during periods of magnetospheric quiescence and along with perturbations associated with geomagnetic storms and magnetospheric substorms, respectively. Ultimately, we investigate the effect of time scale on the level of dynamical organization of fluctuations by studying iterative reconstructions of the index values based on intrinsic mode functions obtained from univariate and multivariate versions of empirical mode decomposition. Our results open new perspectives on the nonlinear dynamics and (likely intermittent) mutual entanglement of different parts of the geospace electromagnetic environment, including the equatorial and westward auroral electrojets, in dependence of the overall state of the geospace system affected by temporary variations of the solar wind forcing. In addition, they contribute to a better understanding of the potentials and limitations of two contemporary approaches of nonlinear time series analysis in the field of space physics.

How to cite: Donner, R., Alberti, T., and Faranda, D.: Instantaneous fractal dimensions and stability properties of geomagnetic indices based on recurrence networks and extreme value theory, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13342, https://doi.org/10.5194/egusphere-egu22-13342, 2022.

EGU22-1346 | Presentations | ESSI1.2

Enhance pluvial flood risk assessment using spatio-temporal machine learning models 

Andrea Critto, Marco Zanetti, Elena Allegri, Anna Sperotto, and Silvia Torresan

Extreme weather events (e.g., heavy rainfall) are natural hazards that pose increasing threats to many sectors and across sub-regions worldwide (IPCC, 2014), exposing people and assets to damaging effects. In order to predict pluvial flood risks under different spatio-temporal conditions, three generalized Machine Learning models were developed and applied to the Metropolitan City of Venice: Logistic Regression, Neural Networks and Random Forest. The models considered 60 historical pluvial flood events, occurred in the timeframe 1995-2020. The historical events helped to identify and prioritize sub-areas that are more likely to be affected by pluvial flood risk due to heavy precipitation. In addition, while developing the model, 13 triggering factors have been selected and assessed: aspect, curvature, distance to river, distance to road, distance to sea, elevation, land use, NDVI, permeability, precipitation, slope, soil and texture. A forward features selection method was applied to understand which features better face spatio-temporal overfitting in pluvial flood prediction based on AUC score. Results of the analysis showed that the most accurate models were obtained with the Logistic Regression approach, which was used to provide pluvial flood risk maps for each of the 60 major historical events occurred in the case study area. The model showed high accuracy and most of the occured events in the Metropolitan City of Venice have been properly predicted, demostrating that Machine Learning could substantially improve and speed up disaster risk assessment and mapping helping in overcoming most common bottlenecks of physically-based simulations such as the computational complexity and the need of large datasets of high-resolution information.

How to cite: Critto, A., Zanetti, M., Allegri, E., Sperotto, A., and Torresan, S.: Enhance pluvial flood risk assessment using spatio-temporal machine learning models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1346, https://doi.org/10.5194/egusphere-egu22-1346, 2022.

EGU22-3131 | Presentations | ESSI1.2

Language model for Earth science for semantic search 

Rahul Ramachandran, Muthukumaran Muthukumaran Ramasubramanian, Prasanna Koirala, Iksha Gurung, and Manil Maskey

Recent advances in technology have transformed the Natural Language Technology (NLT) landscape, specifically, the use of transformers to build language models such as BERT and GPT3. Furthermore, it has been shown that the quality and the domain-specificity of input corpus to language models can improve downstream application results. However, Earth science research has minimal efforts focused on building and using a domain-specific language model. 

We utilize a transfer learning solution that uses an existing language model trained for general science (SciBERT) and fine-tune it using abstracts and full text extracted from various Earth science journals to create BERT-E (BERT for Earth Science). The training process utilized the input of 270k+ Earth science articles with almost 6 million paragraphs. We used Masked Language Modeling (MLM) to train the transformer model. MLM works by masking random words in the paragraph and optimizing the model for predicting the right masked word. BERT-E was evaluated by performing a downstream keyword classification task, and the performance was compared against classification results using the original SciBERT Language Model. The SciBERT-based model attained an accuracy of 89.99, whereas the BERT-E-based model attained an accuracy of 92.18, showing an improvement in overall performance.

We investigate employing language models to provide new semantic search capabilities for unstructured text such as papers. This search capability requires utilizing a knowledge graph generated from Earth science corpora with a language model and convolutions to surface latent and related sentences for a natural language query. The sentences in the papers are modeled in the graph as nodes, and these nodes are connected through entities. The language model is used to give sentences a numeric representation. Graph convolutions are then applied to sentence embeddings to obtain a vector representation of the sentence along with combined representation of the  surrounding graph structure. This approach utilizes both the power of adjacency inherently encoded in graph structures and latent knowledge captured in the language model. Our initial proof of concept prototype used SIMCSE training algorithm (and the tinyBERT architecture) as the embedding model. This framework has demonstrated an improved ability to surface relevant, latent information based on the input query. We plan to show new results using the domain-specific BERT-E model.

How to cite: Ramachandran, R., Muthukumaran Ramasubramanian, M., Koirala, P., Gurung, I., and Maskey, M.: Language model for Earth science for semantic search, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3131, https://doi.org/10.5194/egusphere-egu22-3131, 2022.

EGU22-3855 | Presentations | ESSI1.2

CGC: an open-source Python module for geospatial data clustering 

Ou Ku, Francesco Nattino, Meiert Grootes, Emma Izquierdo-Verdiguier, Serkan Girgin, and Raul Zurita-Milla

With the growing ubiquity of large multi-dimensional geodata cubes, clustering techniques have become essential to extracting patterns and creating insights from data cubes. Aiming to meet this increasing need, we present Clustering Geodata Cubes (CGC): an open-source Python package designed for partitional clustering of geospatial data. CGC provides efficient clustering methods to identify groups of similar data. In contrast to traditional techniques, which act on a single dimension, CGC is able to perform both co-clustering (clustering across two dimensions e.g., spatial and temporal) and tri-clustering (clustering across three dimensions e.g., spatial, temporal, and thematic), as well as of subsequently refining the identified clusters. CGC also entails scalable approaches that suit both small and big datasets. It can be efficiently deployed on a range of computational infrastructures, from single machines to computing clusters. As a case study, we present an analysis of spring onset indicator datasets at continental scale.

How to cite: Ku, O., Nattino, F., Grootes, M., Izquierdo-Verdiguier, E., Girgin, S., and Zurita-Milla, R.: CGC: an open-source Python module for geospatial data clustering, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3855, https://doi.org/10.5194/egusphere-egu22-3855, 2022.

EGU22-3940 | Presentations | ESSI1.2

The Analysis of the Aftershock Sequence of the Recent Mainshock in Arkalochori, Crete Island Greece 

Alexandra Moshou, Antonios Konstantaras, and Panagiotis Argyrakis

Forecasting the evolution of natural hazards is a critical problem in natural sciences. Earthquake forecasting is one such example and is a difficult task due to the complexity of the occurrence of earthquakes. Until today, earthquake prediction is based on the time before the occurrence of the main earthquake and is based mainly on empirical methods and specifically on the seismic history of a given area. Τhe analysis and processing of its seismicity play a critical role in modern statistical seismology. In this work, a first attempt is made to study and draw safe conclusions regarding the prediction for the seismic sequence, specifically using appropriate statistical methods like Bayesian predictive, taking into account the uncertainties of the model parameters. The above theory was applied in the recent seismic sequence in the area of ​​Arkalochori in Crete Island, Greece (2021, Mw 6.0). Τhe rich seismic sequence that took place immediately after the main 5.6R earthquake with a total of events for the next three months, approximately 4,000 events of magnitude ML > 1 allowed calculating the probability of having the most significant expected earthquake during a given time as well as calculating the probability that the most significant aftershock is expected to be above a certain magnitude after a major earthquake.

References:

  • Ganas, A., Fassoulas, C., Moshou, A., Bozionelos, G., Papathanassiou, G., Tsimi, C., & Valkaniotis, S. (2017). Geological and seismological evidence for NW-SE crustal extension at the southern margin of Heraklion basin, Crete. Bulletin of the Geological Society of Greece, 51, 52-75. doi: https://doi.org/10.12681/bgsg.15004
  • Konstantaras, A.J. (2016). Expert knowledge-based algorithm for the dynamic discrimination of interactive natural clusters. Earth Science Informatics. 9 (1), 95-100.
  • Konstantaras, A. (2020). Deep learning and parallel processing spatio-temporal clustering unveil new Ionian distinct seismic zone. Informatics. 7 (4), 39.
  • Moshou, A., Papadimitriou, E., Drakatos, G., Evangelidis, C., Karakostas, V., Vallianatos, F., & Makropoulos, K. (2014, May). Focal Mechanisms at the convergent plate boundary in Southern Aegean, Greece. In EGU General Assembly Conference Abstracts (p. 12185)
  • Moshou, A., Argyrakis, P., Konstantaras, A., Daverona, A.C. & Sagias, N.C. (2021). Characteristics of Recent Aftershocks Sequences (2014, 2015, 2018) Derived from New Seismological and Geodetic Data on the Ionian Islands, Greece. 6 (2), 8.
  • C.B., Nolet. G., 1997. P and S velocity structure of the Hellenic area obtained by robust nonlinear inversion of travel times. J. Geophys. Res. 102 (8). 349–367

How to cite: Moshou, A., Konstantaras, A., and Argyrakis, P.: The Analysis of the Aftershock Sequence of the Recent Mainshock in Arkalochori, Crete Island Greece, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3940, https://doi.org/10.5194/egusphere-egu22-3940, 2022.

EGU22-5487 | Presentations | ESSI1.2

3D Mapping of Active Underground Faults Enabled by Heterogeneous Parallel Processing Spatio-Temporal Proximity and Clustering Algorithms 

Alexandra Moshou, Antonios Konstantaras, Nikitas Menounos, and Panagiotis Argyrakis

Underground faults cast energy storage elements of the accumulated strain energy in border areas of active tectonic plates. Particularly in the southern front of the Hellenic seismic arc, a steady yearly flow in the accumulation of strain energy is being due to the constant rate of motion at which the African plate sub-sinks beneath the Eurasian plate. Partial release of the stored energy from a particular underground fold manifests in the form of an earthquake once reaching the surface of the Earth’s crust. The information obtained for each recorded earthquake includes among others the surface location and the estimated hypocentre depth. Considering that hundreds of thousands earthquakes have been recorded in that particular area, the accumulated hypocentre depths provide a most valuable source of information regarding the in-depth extent of the seismically active parts of the underground faults. This research work applies expert knowledge spatio-temporal clustering in previously reported distinct seismic cluster zones, aiming to associate each individual main earthquake along with its recoded foreshocks and aftershocks to a single underground fault in existing two-dimensional mappings. This process is being enabled by heterogeneous parallel processing algorithms encompassing both proximity and agglomerative density-based clustering algorithms upon main seismic events only to mapped. Once a main earthquake is being associated to a particular known underground fault, then the underground fault’s point with maximum proximity to the earthquake’s hypocentre appends its location parameters, additionally incorporating the dimension of depth to the initial planar dimensions of latitude and longitude. The ranges of depth variations provide a notable indication of the in-depth extent of the seismically active part(s) of underground faults enabling their 3D model mapping.

Indexing terms: spatio-temporal proximity and clustering algorithms, heterogeneous parallel processing, Cuda, 3D underground faults’ mapping

References

Axaridou A., I. Chrysakis, C. Georgis, M. Theodoridou, M. Doerr, A. Konstantaras, and E. Maravelakis. 3D-SYSTEK: Recording and exploiting the production workflow of 3D-models in cultural heritage. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 51-56, 2014.

Konstantaras A. Deep learning and parallel processing spatio-temporal clustering unveil new Ionian distinct seismic zone. Informatics. 7 (4), 39, 2020.

Konstantaras A.J. Expert knowledge-based algorithm for the dynamic discrimination of interactive natural clusters. Earth Science Informatics. 9 (1), 95-100, 2016.

Konstantaras A.J., E. Katsifarakis, E. Maravelakis, E. Skounakis, E. Kokkinos and E. Karapidakis. Intelligent spatial-clustering of seismicity in the vicinity of the Hellenic Seismic Arc. Earth Science Research 1 (2), 1-10, 2012.

Konstantaras A., F. Valianatos, M.R. Varley, J.P. Makris. Soft-Computing modelling of seismicity in the southern Hellenic Arc. IEEE Geoscience and Remote Sensing Letters, 5 (3), 323-327, 2008.

Konstantaras A., M.R. Varley, F. Valianatos, G. Collins and P. Holifield. Recognition of electric earthquake precursors using neuro-fuzzy methods: methodology and simulation results. Proc. IASTED Int. Conf. Signal Processing, Pattern Recognition and Applications (SPPRA 2002), Crete, Greece, 303-308, 2002.

Maravelakis E., A. Konstantaras, K. Kabassi, I. Chrysakis, C. Georgis and A. Axaridou. 3DSYSTEK web-based point cloud viewer. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, 262-266, 2014.

How to cite: Moshou, A., Konstantaras, A., Menounos, N., and Argyrakis, P.: 3D Mapping of Active Underground Faults Enabled by Heterogeneous Parallel Processing Spatio-Temporal Proximity and Clustering Algorithms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5487, https://doi.org/10.5194/egusphere-egu22-5487, 2022.

As the interpretability and explainability of artificial intelligence decisions has been gaining attention, novel approaches are needed to develop diagnostic tools that account for the unique challenges of geospatial and environmental data, including spatial dependence and high dimensionality, which are addressed in this contribution. Building upon the geostatistical tradition of distance-based measures, spatial prediction error profiles (SPEPs) and spatial variable importance proles (SVIPs) are introduced as novel model-agnostic assessment and interpretation tools that explore the behavior of models at different prediction horizons. Moreover, to address the challenges of interpreting the joint effects of strongly correlated or high-dimensional features, often found in environmental modeling and remote sensing, a model-agnostic approach is developed that distills aggregated relationships from complex models. The utility of these techniques is demonstrated in two case studies representing a regionalization task in an environmental-science context, and a classification task from multitemporal remote sensing of land use. In these case studies, SPEPs and SVIPs successfully highlight differences and surprising similarities of geostatistical methods, linear models, random forest, and hybrid algorithms. With 64 correlated features in the remote-sensing case study, the transformation-based interpretation approach successfully summarizes high-dimensional relationships in a small number of diagrams.

The novel diagnostic tools enrich the toolkit of geospatial data science, and may improve machine-learning model interpretation, selection, and design.

How to cite: Brenning, A.: Novel approaches to model assessment and interpretation in geospatial machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6955, https://doi.org/10.5194/egusphere-egu22-6955, 2022.

EGU22-7529 | Presentations | ESSI1.2

Global maps from local data: Towards globally applicable spatial prediction models 

Marvin Ludwig, Álvaro Moreno Martínez, Norbert Hölzel, Edzer Pebesma, and Hanna Meyer

Global-scale maps are an important tool to provide ecologically relevant environmental variables to researchers and decision makers. Usually, these maps are created by training a machine learning algorithm on field-sampled reference data and the application of the resulting model to associated information from satellite imagery or globally available environmental predictors. However, field samples are often sparse and clustered in geographic space, representing only parts of the global environment. Machine learning models are therefore prone to overfit to the specific environments they are trained on - especially when a large set of predictor variables is utilized. Consequently, model validations have to include an analysis of the models transferability to regions where no training samples are available e.g. by computing the Area of Applicability (AOA, Meyer and Pebesma 2021) of the prediction models.

Here we reproduce three recently published global environmental maps (soil nematode abundances, potential tree cover and specific leaf area) and assess their AOA. We then present a workflow to increase the AOA (i.e. transferability) of the machine learning models. The workflow utilizes spatial variable selection in order to train generalized models which include only predictors that are most suitable for predictions in regions without training samples. We compared the results to the three original studies in terms of prediction performance and AOA. Results indicate that reducing predictors to those relevant for spatial prediction, leads to a significant increase of model transferability without significant decrease of the prediction quality in areas with high sampling density.

Meyer, H. & Pebesma, E. Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution 2041–210X.13650 (2021) doi:10.1111/2041-210X.13650.

How to cite: Ludwig, M., Moreno Martínez, Á., Hölzel, N., Pebesma, E., and Meyer, H.: Global maps from local data: Towards globally applicable spatial prediction models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7529, https://doi.org/10.5194/egusphere-egu22-7529, 2022.

EGU22-8323 | Presentations | ESSI1.2

Multi-attribute geolocation inference from tweets 

Umair Qazi, Ferda Ofli, and Muhammad Imran

Geotagged social media messages, especially from Twitter, can have a substantial impact on decision-making processes during natural hazards and disasters. For example, such geolocation information can be used to enhance natural hazard detection systems where real-time geolocated tweets can help identify the critical human-centric hotspots of an emergency where urgent help is required.

Our work can extract geolocation information from tweets by making use of five meta-data attributes provided by Twitter. Three of these are free-form text, namely tweet text, user profile description, and user location. The other two attributes are GPS coordinates and place tags.

Tweet text may or may not have relevant information to extract geolocation. In the cases where location information is available within tweet text, we follow toponym extraction from the text using Named Entity Recognition and Classification (NERC). The extracted toponyms are then used to obtain geolocation information using Nominatim (which is open-source geocoding software that powers OpenStreetMap) at various levels such as country, state, county, city.

Similar process is followed for user profile description where only location toponyms identified by NERC are stored and then geocoded using Nominatim at various levels.

User location field, which is also a free form text, can have mentions of multiple locations such as USA, UK. To extract location from this field a heuristic algorithm is adopted based on a ranking mechanism that allows it to be resolved to a single point of location which can be then mapped at various levels such as country, state, county, city.

GPS coordinates provide the exact longitude and latitude of the device's location. We perform reverse geocoding to obtain additional location details, e.g., street, city, or country the GPS coordinates belong to. For this purpose, we use Nominatim’s reverse API endpoint to extract city, county, state, and country information.

Place tag provides a bounding box or an exact longitude and latitude or name information of location-tagged by the user. The place field data contains several location attributes. We extract location information from different location attributes within the place using different algorithms. Nominatim’s search API endpoint to extract city, county, state, and country names from the Nominatim response if available.

Our geo-inference pipeline is designed to be used as a plug-in component. The system spans an elasticsearch cluster with six nodes for efficient and fast querying and insertion of records. It has already been tested on geolocating more than two billion covid-related tweets. The system is able to handle high insertion and query load. We have implemented smart caching mechanisms to avoid repetitive Nominatim calls since it is an expensive operation. The caches are available both for free-form text (Nominatim’s search API) and exact latitude and longitude (Nominatim’s reverse API). These caches help reduce the load on Nominatim and give quick access to the most commonly queried terms.

With this effort, we hope to provide the necessary means for researchers and practitioners who intend to explore social media data for geo-applications.

How to cite: Qazi, U., Ofli, F., and Imran, M.: Multi-attribute geolocation inference from tweets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8323, https://doi.org/10.5194/egusphere-egu22-8323, 2022.

EGU22-8648 | Presentations | ESSI1.2

A graph-based fractality index to characterize complexity of urban form using deep graph convolutional neural networks 

Lei Ma, Stefan Seipel, S. Anders Brandt, and Ding Ma

Inspection of the complexity of urban morphology facilitates understanding of human behaviors in urban space, leading to better conditions for the sustainable design of future cities. Fractal indicators, such as fractal dimension, ht-index, and cumulative rate of growth (CRG) index, have been proposed as measures of such complexity. However, these major fractal indicators are statistical rather than spatial, which leads to failure of characterizing the spatial complexity of urban morphology, such as building footprints. To overcome this problem, in this paper a graph-based fractality index (GFI), based on a hybrid of fractal theories and deep learning techniques, is proposed. To quantify the spatial complexity, several fractal variants were synthesized to train a deep graph convolutional neural network. Building footprints of London were used to test the method and the results show that the proposed framework performs better than traditional indices. Moreover, the possibility of bridging fractal theories and deep learning techniques on complexity issues opens up new possibilities of data-driven GIScience.

How to cite: Ma, L., Seipel, S., Brandt, S. A., and Ma, D.: A graph-based fractality index to characterize complexity of urban form using deep graph convolutional neural networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8648, https://doi.org/10.5194/egusphere-egu22-8648, 2022.

EGU22-8891 | Presentations | ESSI1.2

Infilling Spatial Precipitation Recordings with a Memory-Assisted CNN 

Johannes Meuer, Laurens Bouwer, Étienne Plésiat, Roman Lehmann, Markus Hoffmann, Thomas Ludwig, Wolfgang Karl, and Christopher Kadow

Missing climate data is a widespread problem in climate science and leads to uncertainty of prediction models that rely on these data resources. So far, existing approaches for infilling missing precipitation data are mostly numerical or statistical techniques that require considerable computational resources and are not suitable for large regions with missing data. Most recently, there have been several approaches to infill missing climate data with machine learning methods such as convolutional neural networks or generative adversarial networks. They have proven to perform well on infilling missing temperature or satellite data. However, these techniques consider only spatial variability in the data whereas precipitation data is much more variable in both space and time. Rainfall extremes with high amplitudes play an important role. We propose a convolutional inpainting network that additionally considers a memory module. One approach investigates the temporal variability in the missing data regions using a long-short term memory. An attention-based module has also been added to the technology to consider further atmospheric variables provided by reanalysis data. The model was trained and evaluated on the RADOLAN data set  which is based on radar precipitation recordings and weather station measurements. With the method we are able to complete gaps in this high quality, highly resolved spatial precipitation data set over Germany. In conclusion, we compare our approach to statistical techniques for infilling precipitation data as well as other state-of-the-art machine learning techniques. This well-combined technology of computer and atmospheric research components will be presented as a dedicated climate service component and data set.

How to cite: Meuer, J., Bouwer, L., Plésiat, É., Lehmann, R., Hoffmann, M., Ludwig, T., Karl, W., and Kadow, C.: Infilling Spatial Precipitation Recordings with a Memory-Assisted CNN, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8891, https://doi.org/10.5194/egusphere-egu22-8891, 2022.

The real world does not live on a regular grid. The observations with the best spatiotemporal resolution are generally irregularly distributed over space and time, even though as data they are generally stored in arrays in files. Storing the diverse data types of Earth science, including grid, swath, and point based spatiotemporal distributions, in separate files leads to computer-native array layouts on disk or working memory having little or no connection with the spatiotemporal layout of the observations themselves. For integrative analysis, data must be co-aligned both spatiotemporally and in computer memory, a process called data harmonization. For data harmonization to be scalable in both diversity and volume, data movement must be minimized. The SpatioTemporal Adaptive Resolution Encoding (STARE) is a hierarchical, recursively subdivided indexing scheme for harmonizing diverse data at scale. 

STARE indices are integers embedded with spatiotemporal attributes key to efficient spatiotemporal analysis. As a more computationally efficient alternative to conventional floating-point spatiotemporal references, STARE indices apply uniformly to all spatiotemporal data regardless of their geometric layouts. Through this unified reference, STARE harmonizes diverse data in their native states to enable integrative analysis without requiring homogenization of the data by interpolating them to a common grid first.

The current implementation of STARE supports solid angle indexing, i.e. longitude-latitude, and time. To fully support Earth science applications, STARE must be extended to indexing the radial dimension for a full 4D spatiotemporal indexing. As STARE’s scalability is based on having a universal encoding scheme mapping spatiotemporal volumes to integers, the variety of existing approaches to encoding the radial dimension arising in Earth science raises complex design issues for applying STARE’s principles. For example, the radial dimension can be usefully expressed via length (altitude) or pressure coordinates. Both length and pressure raise the question as to what reference surface should be used. As STARE’s goal is to harmonize different kinds of data, we must determine whether it is better to have separate radial scale encodings for length and pressure, or should we have a single radial encoding, for which we provide tools for translating between various (radial) coordinate systems. The questions become more complex when we consider the wide range of Earth science data and applications, including, for example, model simulation output, lidar point clouds, spacecraft swath data, aircraft in-situ measurements, vertical or oblique parameter retrievals, and earthquake-induced movement detection. 

In this work, we will review STARE’s unifying principle and the unique nature of the radial dimension. We will discuss the challenges of enabling scalable Earth science data harmonization in both diversity and volume, particularly in the context of detection, cataloging, and statistical study of fully 4D hierarchical phenomena events such as extratropical cyclones. With the twin challenges of exascale computing and increasing model simulation resolutions opening new views into physical processes, scalable methods for bringing best-resolution observations and simulations together, like STARE, are becoming increasingly important.

How to cite: Rilee, M. and Kuo, K.-S.: Design Considerations for the 3rd Spatial Dimension of the Spatiotemporal Adaptive Resolution Encoding (STARE), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10799, https://doi.org/10.5194/egusphere-egu22-10799, 2022.

EGU22-10823 | Presentations | ESSI1.2

Scalable Feature Extraction and Tracking (SCAFET): A general framework for feature extraction from large climate datasets 

Arjun Nellikkattil, June-Yi Lee, and Axel Timmermann

The study describes a generalized framework to extract and track features from large climate datasets. Unlike other feature extraction algorithms, Scalable Feature Extraction and Tracking (SCAFET) is independent of any physical thresholds making it more suitable for comparing features from different datasets. Features of interest are extracted by segmenting the data on the basis of a scale-independent bounded variable called shape index (Si). Si gives a quantitative measurement of the local shape of the field with respect to its surroundings. To illustrate the capabilities of the method, we have employed it in the extraction of different types of features. Cyclones and atmospheric rivers are extracted from the ERA5 reanalysis dataset to show how the algorithm extracts points as well as surfaces from climate datasets. Extraction of sea surface temperature fronts depicts how SCAFET handles unstructured grids. Lastly, the 3D structures of jetstreams is extracted to demonstrate that the algorithm can extract 3D features too. The detection algorithm is implemented as a jupyter notebook[https://colab.research.google.com/drive/1D0rWNQZrIfLEmeUYshzqyqiR7QNS0Hm-?usp=sharing] accessible to anyone to test out the algorithm.

How to cite: Nellikkattil, A., Lee, J.-Y., and Timmermann, A.: Scalable Feature Extraction and Tracking (SCAFET): A general framework for feature extraction from large climate datasets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10823, https://doi.org/10.5194/egusphere-egu22-10823, 2022.

With the far-reaching impact of Artificial Intelligence (AI) becoming more acknowledgeable across various dimensions and industries, the Geomatics scientific community has reasonably turned to automated (in some cases, autonomous) solutions while looking to efficiently extract and communicate patterns in high-dimensional geographic data. This, in turn, has led to a range of AI platforms providing grounds for cutting-edge technologies such as data mining, image processing and predictive/prescriptive modelling. Meanwhile, coastal management bodies around the world, are striving to harness the power of AI and Machine Learning (ML) applications to act upon the wealth of coastal information, emanating from disparate data sources (e.g., geodesy, hydrography, bathymetry, mapping, remote sensing, and photogrammetry). The cross-disciplinarity of stakeholder engagement calls for thorough risk assessment and coastal defence strategies (e.g., erosion/flooding control), consistent with the emerging need for participatory and integrated policy analyses. This paper addresses the issue of seeking techno-centric solutions in human-understandable language, for holistic knowledge engineering (from acquisition to dissemination) in a spatiotemporal context; namely, the benefits of setting up a unified Visual Analytics (VA) system, which allows for real-time monitoring and Online Analytical Processing (OLAP) operations on-demand, via role-based access. Working from an all-encompassing data model could form seamlessly collaborative workspaces that support multiple programming languages (packaging ML libraries designed to interoperate) and enable heterogeneous user communities to visualize Big Data at different granularities, as well as perform task-specific queries with little, or no, programming skill. The proposed solution is an integrated coastal management dashboard, built natively for the cloud (aka leveraging batch and stream processing), to dynamically host live Key Performance Indicators (KPIs) whilst ensuring wide adoption and sustainable operation. The results reflect the value of effectively collecting and consolidating coastal (meta-)data into open repositories, to jointly produce actionable insight in an efficient manner.

How to cite: Anthis, Z.: Reading Between the (Shore)Lines: Real-Time Analytical Processing to Monitor Coastal Erosion, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13102, https://doi.org/10.5194/egusphere-egu22-13102, 2022.

EGU22-766 | Presentations | CL5.3.1

A new perspective on permafrost boundaries in France during the Last Glacial Maximum 

Kim Helen Stadelmaier, Patrick Ludwig, Pascal Bertran, Pierre Antoine, Xiaoxu Shi, Gerrit Lohmann, and Joaquim G. Pinto

During the Last Glacial Maximum (LGM), a very cold and dry period around 26.5–19 kyr BP, permafrost was widespread across Europe. In this work, we explore the possible benefit of using regional climate model data to improve the permafrost representation in France, decipher how the atmospheric circulation affects the permafrost boundaries in the models, and test the role of ground thermal contraction cracking in wedge development during the LGM. With these aims, criteria for possible thermal contraction cracking of the ground are applied to climate model data for the first time. Our results show that the permafrost extent and ground cracking regions deviate from proxy evidence when the simulated large-scale circulation in both global and regional climate models favours prevailing westerly winds. A colder and, with regard to proxy data, more realistic version of the LGM climate is achieved given more frequent easterly winds conditions. Given the appropriate forcing, an added value of the regional climate model simulation can be achieved in representing permafrost and ground thermal contraction cracking. Furthermore, the model data provide evidence that thermal contraction cracking occurred in Europe during the LGM in a wide latitudinal band south of the probable permafrost border, in agreement with field data analysis. This enables the reconsideration of the role of sand-wedge casts to identify past permafrost regions.

How to cite: Stadelmaier, K. H., Ludwig, P., Bertran, P., Antoine, P., Shi, X., Lohmann, G., and Pinto, J. G.: A new perspective on permafrost boundaries in France during the Last Glacial Maximum, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-766, https://doi.org/10.5194/egusphere-egu22-766, 2022.

EGU22-784 | Presentations | CL5.3.1

Converging constraints on the glacial Atlantic overturning circulation from multiple proxies 

Frerk Pöppelmeier, Aurich Jeltsch-Thömmes, Fortunat Joos, Jeemijn Scheen, Jörg Lippold, and Thomas Stocker

The Atlantic overturning circulation plays a critical role in inter-hemispheric transport of heat, carbon, and nutrients, and its potential collapse under anthropogenic forcing is thought to be a major tipping point in the climate system. As such, painstaking efforts have been dedicated to a better understanding of the Atlantic circulation’s past variability and mean-state under different boundary conditions. Yet, despite decades of research many uncertainties remain regarding the state of the ocean circulation over the past 20,000 years, during which Earth’s climate was propelled out of the last ice age. Here, we employed the Bern3D intermediate complexity model, which is equipped with all major water mass tracers (Δ14C, δ13C, δ18O, εNd, Pa/Th, nutrients, and temperature), to search for converging constraints on the often conflicting interpretations of paleo-reconstructions from individual proxies focusing on the Last Glacial Maximum (LGM). By varying formation rates of northern- and southern-sourced waters we explore a wide range of circulation states and test their ability to reproduce the spatial patterns of newly compiled proxy data of the LGM. Generally, we find that late-Holocene to LGM anomalies give more consistent pictures of proxy distributions than absolute values, since systematic biases, that plague some of the proxies, cancel out. This has the additional advantage that also systematic model biases are minimized. Considering this, we find that the previously opposing neodymium and stable carbon isotope-based interpretations of the glacial water mass structure can be reconciled when non-conservative effects are appropriately taken into account. Furthermore, combining the information from all proxies indicates some shoaling of glacial northern-sourced water, yet not to the same extent as previous studies suggested.

How to cite: Pöppelmeier, F., Jeltsch-Thömmes, A., Joos, F., Scheen, J., Lippold, J., and Stocker, T.: Converging constraints on the glacial Atlantic overturning circulation from multiple proxies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-784, https://doi.org/10.5194/egusphere-egu22-784, 2022.

EGU22-839 | Presentations | CL5.3.1

Dansgaard-Oeschger events in climate models: A PMIP baseline MIS3 protocol 

Irene Malmierca Vallet, Louise C. Sime, and Paul J. Valdes

Frequent well documented Dansgaard-Oeschger (D-O) events occurred throughout the MIS3 period. This study lays the ground-work for a MIS3 D-O protocol for CMIP-class models. We consider the over-arching question: Are our models too stable? In the course of laying out groundwork we review: necessary D-O definitions; current progress on simulating D-O events in IPCC-class models (processes and published examples); and consider evidence of boundary conditions under which D-O events occur. Greenhouse gases and ice-sheet configurations are found to be crucial and the effect of orbital parameters is found to be small on the important features of MIS3 simulations. Oscillatory D-O type behaviour is found to be more likely, although not guaranteed, when run with low-intermediate MIS3 CO2 values, and reduced ice-sheets compared to the LGM. Thus, we propose performing a MIS3 baseline experiment centered at 38 ky (40 to 35 ky) period, which (1) shows a regular sequence of D-O events, and (2) yields the ideal intermediate ice-sheet configuration and central-to-cold GHG values. We suggest a protocol for a single baseline MIS3 PMIP protocol, alongside a preconditioned (kicked Heinrich) meltwater variant. These protocols aim to help unify the work of multiple model groups when investigating these cold-period instabilities. The protocol covers insolation-, freshwater-, GHG-, and NH ice sheet-related forcing. This addresses the currently gap in PMIP guidance for the simulation of a MIS3 state conducive to D-O oscillations under a common framework

How to cite: Malmierca Vallet, I., Sime, L. C., and Valdes, P. J.: Dansgaard-Oeschger events in climate models: A PMIP baseline MIS3 protocol, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-839, https://doi.org/10.5194/egusphere-egu22-839, 2022.

EGU22-913 | Presentations | CL5.3.1

Disentangling the contribution of moisture source change to isotopic proxy signatures: Deuterium tracing with WRF-Hydro-iso-tag and application to Southern African Holocene sediment archives 

Joel Arnault, Kyle Niezgoda, Gerlinde Jung, Annette Hahn, Matthias Zabel, Enno Schefuss, and Harald Kunstmann

It is well accepted that global circulation models equipped with stable water isotopologues help to better understand the relationships between atmospheric circulation changes and isotope records in paleoclimate archives. Still, isotope-enabled models do not allow to precisely understand the processes affecting precipitation isotopic compositions, such as changes in precipitation amounts or moisture sources. Furthermore, the relevance of this model-oriented approach relies on the realism of modeled isotope results, that would support the interpretation of the records in terms of modeled climate changes. In order to alleviate these limitations, the newly developed WRF-Hydro-iso-tag, that is the version of the isotope-enabled regional coupled model WRF-Hydro-iso enhanced with an isotope tracing procedure, is presented. Physics-based WRF-Hydro-iso-tag ensembles are used to regionally downscale the isotope-enabled Community Earth System Model for Southern Africa, for two 10-year slices of mid-Holocene and pre-industrial times. The isotope tracing procedure is tailored in order to assess the origin of the hydrogen-isotope deuterium contained in Southern African precipitation, between two moisture sources that are the Atlantic and Indian Oceans. In comparison to the global model, WRF-Hydro-iso-tag simulates lower precipitation amounts with more regional details, and mid-Holocene-to-pre-industrial changes in precipitation isotopic compositions that better match plant-wax deuterium records from two marine sediment cores off the Orange and Limpopo River basins. Linear relationships between mid-Holocene-to-pre-industrial changes in temperature, precipitation amount, moisture source and precipitation deuterium compositions are derived from the ensembles results.

How to cite: Arnault, J., Niezgoda, K., Jung, G., Hahn, A., Zabel, M., Schefuss, E., and Kunstmann, H.: Disentangling the contribution of moisture source change to isotopic proxy signatures: Deuterium tracing with WRF-Hydro-iso-tag and application to Southern African Holocene sediment archives, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-913, https://doi.org/10.5194/egusphere-egu22-913, 2022.

EGU22-1224 | Presentations | CL5.3.1

Glacial Ocean Carbon and Oxygen Cycles: Biological Pump or Disequilibrium? 

Andreas Schmittner, Samar Khatiwala, and Ellen Cliff

Increased ocean carbon storage and reductions in deep ocean oxygen content during the cold phases of the Pleistocene ice age cycles have been mostly attributed to a stronger biological pump. However, recent studies have emphasized that changes in air-sea disequilibrium played a major role. Here we diagnose a data-constrained model of the ocean during the Last Glacial Maximum to decompose carbon and oxygen cycling into its different components. Individual drivers such as temperature, sea ice, circulation and iron fertilization have been quantified for each component. We show that due to differences in air-sea gas exchange between carbon and oxygen, the components respond differently, which complicates/invalidates interpretations of oxygen changes in terms of carbon. We find changes in disequilibrium dominate both carbon and oxygen changes, whereas the biological pump was not more efficient in terms of global changes for both elements. However, whereas for carbon both the physical and the biological disequilibrium play important roles, for oxygen the biological disequilibrium is dominant, while the physical disequilibrium is negligible. Moreover, whereas for carbon temperature (amplified by physical disequilibrium) and iron fertilization (amplified by biological disequilibrium) are the dominant drivers, oxygen disequilibrium changes are driven mostly by sea ice, with iron fertilization playing a secondary role.

How to cite: Schmittner, A., Khatiwala, S., and Cliff, E.: Glacial Ocean Carbon and Oxygen Cycles: Biological Pump or Disequilibrium?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1224, https://doi.org/10.5194/egusphere-egu22-1224, 2022.

EGU22-1468 | Presentations | CL5.3.1

The African monsoon during the early Eocene from the DeepMIP simulations 

Charles J. R. Williams and the The African monsoon DeepMIP team

Here we present a study of African climate (with a focus on precipitation) during the early Eocene (~55-50 million years ago, Ma), as simulated by an ensemble of state-of-the-art climate models under the auspices of the Deep-time Model Intercomparison Project (DeepMIP).  The early Eocene is of particular interest, because with CO2 levels ranging between 1200-2500 ppmv (and a resulting temperature increase of ~5°C in the tropics and up to ~20°C at high latitudes) it provides a partial analogue for a possible future climate state by the end of the 21st century (and beyond) under extreme emissions scenarios.  This study is novel because it investigates the relatively little-studied subject of African hydroclimate during the early Eocene, a period from which there are very few proxy constraints, requiring more reliance on model simulations.

 

A comparison between the DeepMIP pre-industrial simulations and modern observations suggest that model biases aremodel- and geographically dependent.  However, the model ensemble mean reduces these biases and is showing the best agreement with observations.  A comparison between the DeepMIP Eocene simulations and the pre-industrial suggests that, when all individual models are considered separately, there is no obvious wetting or drying trend as the CO2 increases.  However, concerning the ensemble mean, the results suggest that changes to the land sea mask (relative to the modern) in the models may be responsible for the simulated increases in precipitation to the north of Eocene Africa, whereas it is likely that changes in vegetation (again relative to the modern geographical locations) in the models are responsible for the simulated region of drying over equatorial Eocene Africa.  When CO2 is increased in the simulations, at the lower levels of increased CO2, precipitation over the equatorial Atlantic and West Africa appears to be increasing in response.  At the higher levels of CO2, precipitation over West Africa is even more enhanced relative to the lower levels.  These precipitation increases are associated with enhanced surface air temperature, a strongly positive P-E balance and cloud cover increases.  At the lower levels of increased CO2, anticyclonic low-level circulation increases with CO2, drawing in more moisture from the equatorial Atlantic and causing a relative drying further north.  At higher levels of CO2, the increased anticyclonic low-level circulation is replaced by increased south-westerly flow.

 

Lastly, a model-data (using newly-compiled Nearest Living Relative reconstructions) comparison suggests that whether the Eocene simulations (regardless of CO2 experiment) over- or underestimate African precipitation is highly geographically dependent, with some of the CO2 experiments at some of the locations lying within the uncertainty range of the reconstructions.  Concerning the ensemble mean, the results suggest a marginally better fit with the reconstructions at lower levels of CO2.

How to cite: Williams, C. J. R. and the The African monsoon DeepMIP team: The African monsoon during the early Eocene from the DeepMIP simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1468, https://doi.org/10.5194/egusphere-egu22-1468, 2022.

EGU22-2496 | Presentations | CL5.3.1

Quantifying uncertainties in global monthly mean sea surface temperature and sea ice at the Last Glacial Maximum 

Ruza Ivanovic, Lauren Gregoire, Lachlan Astfalk, Danny Williamson, Niall Gandy, Andrea Burke, and Dani Schmidt

Studying the Last Glacial Maximum (LGM), 21000 years ago, provides insights into climate sensitivity to greenhouse gases and critical interactions within the earth system (e.g. atmosphere, ocean, cryosphere) operating in a climate different from today. Much effort has been put into reconstructing the Sea Surface Temperatures (SST) at the LGM using a range of palaeoclimate records, statistical techniques and models. Large disagreements exist amongst reconstructions and between models and data. Disentangling the causes of these differences is challenging. How much of these differences are due to the choice of data used, their interpretation, the statistical method or climate models used? The polar regions are particularly difficult to reconstruct, yet are key for assessing polar amplification and key processes driving cryospheric changes. Combining the information gained from sea ice and SST proxies has the potential to improve reconstructions in those regions.  

Here, we provide a new probabilistic joint reconstruction of global SST and sea ice concentration (SIC) that incorporates information from the ensemble of PMIP3 and PMIP4 models (Kageyama et al., 2021) and existing compilations of SST and sea ice. Our reconstruction was specifically designed to provide ensembles of plausible monthly mean fields that can be used to drive atmosphere models to investigate uncertainty in LGM climate and their potential effects/interactions on e.g. vegetation, ice and atmospheric circulation.  

We present our statistical approach (Astfalk et al., 2021) in simplified terms for non-specialists, and discuss how different interpretations of the palaeo-records can be included in our statistical framework. Our results are compared to other recent reconstructions such as Tierney et al. (2020) and Paul et al. (2021). To interpret these differences, we test the effect of the choices of input proxy data and models on the reconstructed monthly mean SSTs and SIC.  

References: 

  • Astfalck, L., Williamson, D., Gandy, N., Gregoire, L. & Ivanovic, R. Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction. arXiv:2111.12283 [stat] (2021).
  • Kageyama, M. et al. The PMIP4 Last Glacial Maximum experiments: preliminary results and comparison with the PMIP3 simulations. Climate of the Past 17, 1065–1089 (2021).
  • Paul, A., Mulitza, S., Stein, R. & Werner, M. A global climatology of the ocean surface during the Last Glacial Maximum mapped on a regular grid (GLOMAP). Climate of the Past 17, 805–824 (2021).
  • Tierney, J. E. et al. Glacial cooling and climate sensitivity revisited. Nature 584, 569–573 (2020).

How to cite: Ivanovic, R., Gregoire, L., Astfalk, L., Williamson, D., Gandy, N., Burke, A., and Schmidt, D.: Quantifying uncertainties in global monthly mean sea surface temperature and sea ice at the Last Glacial Maximum, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2496, https://doi.org/10.5194/egusphere-egu22-2496, 2022.

During the Phanerozoic (the last ~0.5 billion years), the Earth has experienced massive changes in climate, spanning the extensive glaciations of the Permo-Carboniferous (~300 million years ago), to the mid-Cretaceous super-greenhouse (~100 million years ago). Recently, several studies have used geological data to reconstruct global mean temperatures through this period, as a way of characterising the zeroth-order response of the Earth system to its primary forcings.  However, there has been little modelling work that has focussed on these long timescales, due to uncertainties in the associated boundary conditions (e,g., CO2 and paleogeography) and to the computational expense of carrying simulations spanning these long timescales.  Recently, paleogeographic (Scotese and Wright, 2018) and CO2 reconstructions (Foster et al, 2017) have emerged, and model and computational developments mean that we can now run large ensembles of relatively complex model simulations.  In particular, here we present an ensemble of 109 simulations through the Phanerozoic, with a tuned version of HadCM3L that performs comparably with CMIP5 models for the modern, and is also able to produce meridional temperature gradients in warm climates such as the Eocene in good agreement with proxy data.  We show that the model produces global mean temperatures in good agreement with proxy records.  We partition the response to changes in the different boundary conditions (CO2, paleogeography, ice extent, and insolation), and, through energy balance analysis, to surface albedo versus cloud versus water vapour changes.  We also illustrate the ocean and atmospheric circulation changes, with a focus on the role of the changing geography (e.g. the role of a coherent circumglobal ocean in the early Phanerozoic). 

How to cite: Lunt, D. and Valdes, P.: Modelling 500,000,000 years of climate change with a GCM – the role of CO2, paleogeography, insolation, and ice extent during the Phanerozoic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3239, https://doi.org/10.5194/egusphere-egu22-3239, 2022.

EGU22-3684 | Presentations | CL5.3.1

PTBox, a toolbox to facilitate palaeoclimate model-data analyses 

Jean-Philippe Baudouin, Oliver Bothe, Manuel Chevalier, Beatrice Ellerhoff, Moritz Adam, Patrizia Schoch, Nils Weitzel, and Kira Rehfeld

Recent progress in modelling the Earth system has made it possible to produce transient climate simulations longer than 10.000 years with comprehensive ESMs. These simulations improve our understanding of slow climatic feedbacks, climate state transitions, and abrupt climate changes. However, assessing the quality and reliability of such paleoclimate simulations is particularly challenging due to the inherent characteristic differences between model data and the climate reconstructions used to validate them.

Here, we present a collection of software packages for inter-model and model-data comparisons called Palaeo ToolBox (PTBox). Its first intent is to evaluate transient simulations of the PalMod project (deglaciation, glacial inception, MIS3) using several proxy data syntheses. Various variables are evaluated (including temperature, precipitation, oxygen isotopes, vegetation, carbon storages and fluxes), across a range of timescales (from decadal to multi-millenial). PTBox provides integrated model-data workflows, from data pre-processing to visualisations, organised into a series of (mostly R) packages. So far, PTBox includes 1) tools for pre-processing simulations and proxy data, 2) ensemble and pseudo-proxy methods to bridge the gap between simulations and proxies and to quantify uncertainties, 3) spectral methods to analyse timescale-dependent climate variability, and 4) newly developed metrics for spatio-temporal model-data comparisons.

Finally, PTBox is accompanied by a website (http://palmodapp.cloud.dkrz.de/) with examples on how to use PTBox and interactive visualisations of the datasets produced in the PalMod project.

How to cite: Baudouin, J.-P., Bothe, O., Chevalier, M., Ellerhoff, B., Adam, M., Schoch, P., Weitzel, N., and Rehfeld, K.: PTBox, a toolbox to facilitate palaeoclimate model-data analyses, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3684, https://doi.org/10.5194/egusphere-egu22-3684, 2022.

EGU22-3771 | Presentations | CL5.3.1

Drivers of LGM AMOC change from PMIP2 to PMIP4 

Marlene Klockmann and Sam Sherriff-Tadano

Understanding the response of the Atlantic Meridional Overturning Circulation (AMOC) to different climate conditions is a crucial part of understanding the climate system. Proxy-based reconstructions suggested that the AMOC during the Last Glacial Maximum (LGM) was likely shallower than today. Generations of climate models from PMIP2 to PMIP4 have shown large inter-model differences and often struggled to simulate a shallower AMOC. In the present study, we revisit hypotheses that have emerged over time and test them consistently across the PMIP ensembles from phase 2 to 4. We start by repeating the analyses by Weber et al (2007), who showed that there was a relationship between the glacial AMOC change and the density difference between the Southern Ocean and the subpolar North Atlantic in many PMIP2 models. Additional analysis will include hydrographic changes (e.g., stratification, water mass properties), the role of global and local LGM cooling as well as biases in the models. In our model evaluation, we will also consider recent reconstructions based on multi-proxy evaluations which indicate that the response of the glacial AMOC geometry and strength may have been less unambiguous than previously thought.

How to cite: Klockmann, M. and Sherriff-Tadano, S.: Drivers of LGM AMOC change from PMIP2 to PMIP4, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3771, https://doi.org/10.5194/egusphere-egu22-3771, 2022.

EGU22-4376 | Presentations | CL5.3.1

Validation of a CDF-t bias correction method using palaeo-data for the Mid-Holocene and the Last Glacial Maximum 

Anhelina Zapolska, Mathieu Vrac, Aurélien Quiquet, Frank Arthur, Hans Renssen, Louis François, and Didier M. Roche

The main objective of this study is to develop and test a method of bias correction for paleoclimate model simulations using the “Cumulative Distribution Functions – transform” (CDF-t) method. The CDF-t is a quantile-mapping based method, extended to account for climate change signal. Here we apply the CDF-t to climate model outputs for the Mid-Holocene and the Last Glacial Maximum, simulated by the climate model of intermediate complexity iLOVECLIM at 5.625° resolution. Additionally, we test the proposed methodology on iLOVECLIM model outputs dynamically downscaled on a  0.25° resolution.

The results are validated through inverse and forward modelling approaches. The inverse approach implies comparing the obtained results with proxy-based reconstructed climatic variables. Here we use temperature and precipitation reconstructions, obtained with inverse modelling methods from pollen data. In this study, both gridded and point-based multi-proxy reconstruction datasets were used for the analysis.

The forward approach includes a further step of vegetation modelling, using the climatologies derived from bias-corrected outputs of the iLOVECLIM model in CARAIB (CARbon Assimilation In the Biosphere) global dynamic vegetation model. The modelled biomes are evaluated in comparison with pollen-based biome reconstructions BIOME6000.

The findings of this study indicate that the use of the proposed methodology results in significant improvements in climate and vegetation modelling and suggest that the CDF-t method is an valuable approach to reduce biases in paleoclimate modelling.

How to cite: Zapolska, A., Vrac, M., Quiquet, A., Arthur, F., Renssen, H., François, L., and Roche, D. M.: Validation of a CDF-t bias correction method using palaeo-data for the Mid-Holocene and the Last Glacial Maximum, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4376, https://doi.org/10.5194/egusphere-egu22-4376, 2022.

The use of paleoclimates to constrain the equilibrium climate sensitivity (ECS) has seen a growing interest. In particular, the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period have been used in emergent constraint approaches using simulations from the Paleoclimate Modelling Intercomparison Project (PMIP). Despite lower uncertainties regarding geological proxy data for the LGM in comparison with the Pliocene, the robustness of the emergent constraint between LGM temperature and ECS is weaker at both global and regional scales. Here, we investigate the climate of the LGM in models through different PMIP generations, and how various factors contribute to the spread of the model ensemble. Certain factors have large impact on an emergent constraint, such as state-dependency in climate feedbacks or model-dependency on ice sheet forcing. Other factors, such as models being out of energetic balance and sea-surface temperature not responding below -1.8°C in polar regions have a restricted influence. We quantify some of the contributions and show they mostly have extratropical origins, which contribute to a weak global constraint, and remotely impact tropical temperatures. Statistically, PMIP model generations do not differ substantially, unlike what has been previously suggested. Furthermore, we find that the lack of high or low ECS models in the ensembles critically limits the strength and reliability of the emergent constraints.

How to cite: Renoult, M., Sagoo, N., Zhu, J., and Mauritsen, T.: Causes of the weak relationship between modeled Last Glacial Maximum cooling and climate sensitivity, with consequences for emergent constraints, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4582, https://doi.org/10.5194/egusphere-egu22-4582, 2022.

EGU22-5069 | Presentations | CL5.3.1

Characterising simulated changes of jet streams since the Last Glacial Maximum 

Patrizia Schoch, Jean-Philippe Baudouin, Nils Weitzel, Marie Kapsch, Thomas Kleinen, and Kira Rehfeld

Jet streams control hydroclimate variability in the mid-latitudes with important impacts on water availability and human societies. According to future projections, global warming will change jet stream characteristics, including its mean position. Variability of these characteristics on hourly-to-daily timescales is key to understanding the mid-latitudes circulation. Therefore, most analysis methods of present-day jet streams are designed for 6-hourly data. By modelling the climate since the Last Glacial Maximum, we can investigate the long-term drivers of jet stream characteristics. However, for transient simulations of the last deglaciation, 3d wind fields are only archived with a monthly resolution due to storage limitations. Hence, jet variability at shorter timescales cannot be identified, and established methods can’t be used.

Here, we study to what extent changes of jet stream characteristics can be inferred from monthly wind fields. Therefore, we compare latitudinal jet stream positions, strength, tilt and their variability from daily and monthly wind fields in reanalysis data and for LGM and PI simulations. We test three different methods to construct jet stream typologies and metrics. This comparison identifies to which extend these jet stream characteristics can be robustly studied from monthly wind fields. In addition, our analysis assesses the added value of archived daily data for future research. Once the limitations of monthly wind output are known, jet stream characteristics in transient simulations of the last deglaciation can be analysed. This analysis provides new insights on jet stream changes on decadal-to-orbital timescales and identifies the factors controlling these changes.

How to cite: Schoch, P., Baudouin, J.-P., Weitzel, N., Kapsch, M., Kleinen, T., and Rehfeld, K.: Characterising simulated changes of jet streams since the Last Glacial Maximum, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5069, https://doi.org/10.5194/egusphere-egu22-5069, 2022.

EGU22-5710 | Presentations | CL5.3.1

Changes in Arctic Meridional Overturning (ArMOC) under past abrupt warming 

Anais Bretones, Kerim Hestnes Nisancioglu, and Chuncheng Guo
According to the recent generation of global climate models, a weakening of the Atlantic Meridional Overturning Circulation (AMOC) is unequivocal in the context of global warming. However, a recent study (Bretones et al, 2021) showed that the weakening of the AMOC at the reference latitude of 26N is decorrelated from the overturning trend north of the Greenland-Scotland Ridge.
From a paleo perspective, AMOC oscillations are believed to be one of the main drivers of the Dansgaard–Oeschger events, an alternation of cold and warm periods during the last glacial period in Greenland and with global signatures. During a warming phase, the AMOC is believed to be in a strong mode compared to the cold phase, thereby with increased amount of northward heat transport, and hence increased air temperature.
 In this study, we investigate the presence and evolution of the Arctic Meridional Overturning Circulation(ArMOC) during the abrupt warming transition from Heinrich event 4 (H4) to the Greenland interstadial 8 (GI8) in the NorESM climate model (Guo et al, 2019). The simulation is based on a validated GI8 simulation and freshwater hosing experiments to simulate H4 conditions. In the model, the transition of H4 to GI8 presents a warming of around 10°C within 30 years in Greenland, which is similar with what was observed in ice cores.

How to cite: Bretones, A., Nisancioglu, K. H., and Guo, C.: Changes in Arctic Meridional Overturning (ArMOC) under past abrupt warming, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5710, https://doi.org/10.5194/egusphere-egu22-5710, 2022.

EGU22-5749 | Presentations | CL5.3.1

Evaluating atmospheric simulations of the Last Glacial Maximum using oxygen isotopes in ice cores and speleothems 

André Paul, Thejna Tharammal, Alexandre Cauquoin, and Martin Werner

Our goal is to investigate the structural uncertainty in the isotope-enabled atmospheric general circulation models iCAM5 and ECHAM6-wiso. In order to reduce all other sources of uncertainties, in particular, those that stem from different boundary conditions, we forced the two models by the same sets of pre-industrial (PI) and Last Glacial Maximum (LGM) surface boundary conditions; the latter were taken from GLOMAP  (Paul et al., 2021), which in turn were based on the MARGO project (MARGO Project Members, 2009) and recent estimates of LGM sea-ice extent. We compared our model results to reconstructions from ice cores (cf. Risi et al., 2010) and speleothems (cf. Comas-Bru et al., 2020). This comparison showed to what degree realizations of the atmospheric state of the LGM obtained from different models, due to different model set-ups and parameterizations, are in agreement with the proxy data. For example, the precipitation during the LGM was generally less depleted in the ECHAM6-wiso as compared to iCAM5, and as it turned out, the iCAM5 simulation produced only a rather weak LGM anomaly during summer (June-July-August, JJA) over the South Asian monsoon region.

How to cite: Paul, A., Tharammal, T., Cauquoin, A., and Werner, M.: Evaluating atmospheric simulations of the Last Glacial Maximum using oxygen isotopes in ice cores and speleothems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5749, https://doi.org/10.5194/egusphere-egu22-5749, 2022.

EGU22-6562 | Presentations | CL5.3.1

Towards spatio-temporal comparison of transient simulations and temperature reconstructions for the last deglaciation 

Nils Weitzel, Heather Andres, Jean-Philippe Baudouin, Oliver Bothe, Andrew M. Dolman, Lukas Jonkers, Marie Kapsch, Thomas Kleinen, Uwe Mikolajewicz, André Paul, and Kira Rehfeld

An increasing number of Earth System Models has been used to simulate the climatic transition from the Last Glacial Maximum to the Holocene. This creates a demand for benchmarking against environmental proxy records, which have been synthesized for the same time period. Comparing these two data sources in space and time over a period with changing background conditions requires new methods. We employ proxy system modeling for probabilistic quantification of the deviation between temperature reconstructions and transient simulations. Regional and global scores quantify the mismatch in the pattern and magnitude of orbital- as well as millennial-scale temperature variations.

In pseudo-proxy experiments, we test the ability of our algorithm to accurately rank an ensemble of simulations according to their deviation from a prescribed temperature history, dependent upon the amount of added non-climatic noise. To this purpose, noisy pseudo-proxies are constructed by perturbing a reference simulation. We show that the algorithm detects the main features separating the ensemble members. When scores are aggregated spatially, the algorithm ranks simulations robustly and accurately in the presence of uncertainties. In contrast, erroneous rankings occur more often if only a single location is assessed.

Having established the effectiveness of the algorithm in idealized experiments, we apply our method to quantify the deviation between data from the PalMod project: an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. No simulation performs consistently well across different regions and components of the temperature evolution which we attribute to the larger spatial heterogeneity in reconstructions. Our work provides a basis for a standardized model-data comparison workflow, which can be extended subsequently with additional proxy data, new simulations, and improved representations of uncertainties.

How to cite: Weitzel, N., Andres, H., Baudouin, J.-P., Bothe, O., Dolman, A. M., Jonkers, L., Kapsch, M., Kleinen, T., Mikolajewicz, U., Paul, A., and Rehfeld, K.: Towards spatio-temporal comparison of transient simulations and temperature reconstructions for the last deglaciation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6562, https://doi.org/10.5194/egusphere-egu22-6562, 2022.

EGU22-6979 | Presentations | CL5.3.1 | Highlight

The deglacial forest conundrum 

Anne Dallmeyer, Thomas Kleinen, Martin Claussen, Nils Weitzel, Xianyong Cao, and Ulrike Herzschuh

The forest expansion in the Northern Hemispheric extra-tropics during the deglaciation, i.e. the last some 22,000 years, starts earlier and occurs much faster in our model simulation using the MPI-ESM 1.2 than in the recently published synthesis of biome reconstructions by Cao et al. (2019). As a result, the simulated Northern Hemisphere maximum in forest cover is reached at 11ka in the model, whereas the forest distribution peaks substantially later (at 7ka in the spatial mean) in the reconstructions. The model-data mismatch is largest in Asia, particularly in Siberia and the East Asian monsoon margin. The simulated temperature trend is in line with pollen-independent temperature reconstructions for Asia. Since the simulated vegetation adapt to the simulated climate within decades, the temporal model-data mismatch with respect to the forest cover may indicate that pollen records are not in equilibrium with climate on multi-millennial timescales.

Our study has some far-reaching consequences. Pollen-based vegetation and climate reconstructions are commonly used to evaluate Earth System Models against past climate states, but to what extent the reconstructed vegetation is in equilibrium with the climate at the reconstructed time slice is still a matter of discussion. Our results raise the question on which time-scales pollen-based reconstructions are reliable. Although, it is so far not possible to identify the causes of the mismatch between our simulations and the reconstruction, we suggest critical re-assessment of pollen-based climate reconstructions. Last, but not least, our results may also point to a much slower response of forest biomes to current and future ongoing climate changes than Earth System Models currently predict.

 

References:

Cao, X., Tian, F., Dallmeyer, A. and Herzschuh, U.: Northern Hemisphere biome changes (>30°N) since 40 cal ka BP and their driving factors inferred from model-data comparisons, Quat. Sci. Rev., 220, 291–309, doi:10.1016/j.quascirev.2019.07.034, 2019.

How to cite: Dallmeyer, A., Kleinen, T., Claussen, M., Weitzel, N., Cao, X., and Herzschuh, U.: The deglacial forest conundrum, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6979, https://doi.org/10.5194/egusphere-egu22-6979, 2022.

EGU22-7379 | Presentations | CL5.3.1

New estimation of critical orbital forcing – CO2 relationship for triggering of glacial inception 

Stefanie Talento, Matteo Willeit, Reinhard Calov, Dennis Höning, and Andrey Ganopolski

Glacial inception represents a bifurcation transition between interglacial and glacial states and is governed by the non-linear dynamics of the climate-cryosphere system. It has been previously proposed that to trigger glacial inception, the orbital forcing defined as the maximum of summer insolation at 65oN and determined by Earth’s orbital parameters must be lower than a critical level. This critical level depends on the atmospheric CO2 concentration. While paleoclimatic data do not constrain the critical dependence, its accurate estimation is of fundamental importance for predicting future glaciations and the effect that anthropogenic CO2 emissions might have on them. 

In this study we use the new Earth system model of intermediate complexity CLIMBER-X (which includes modules for atmosphere, ocean, land surface, sea ice and the new version of the 3-D polythermal ice sheet model SICOPOLIS) to estimate the critical orbital forcing - CO2 relationship for triggering glacial inception. We perform a series of experiments in which different combinations of orbital forcing and atmospheric CO2 concentration are maintained constant in time. Each model simulation is run for 1 million years using an acceleration technique with asynchronous coupling between the climate and ice sheet model components. SICOPOLIS is applied only to the Northern Hemisphere with a 40 km horizontal resolution.

We analyse for which combinations of orbital forcing and CO2 glacial inception occurs and trace the critical relationship between them, separating conditions under which glacial inception is possible from those where glacial inception is not materialised. We study how adequate it is to use the maximum summer insolation at 65°N as a single metric for orbital forcing, as well as consider the differential effect each one of Earth’s orbital parameters might have. In addition, we investigate the spatial and temporal patterns of ice cover during glacial inception under different orbital forcings.

How to cite: Talento, S., Willeit, M., Calov, R., Höning, D., and Ganopolski, A.: New estimation of critical orbital forcing – CO2 relationship for triggering of glacial inception, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7379, https://doi.org/10.5194/egusphere-egu22-7379, 2022.

EGU22-8364 | Presentations | CL5.3.1

Modelled equilibrium LGM seawater temperatures inconsistent with plankton biodiversity 

Lukas Jonkers, Thomas Laepple, Marina Rillo, Andrew Dolman, Gerrit Lohman, André Paul, Alan Mix, and Michal Kucera

The Last Glacial Maximum (23,000 – 19,000 years ago; LGM) is the most recent time when Earth’s climate was fundamentally different from today. The LGM hence remains a prime target to evaluate climate models outside current boundary conditions. Evaluation of paleoclimate simulations is usually done using proxy-based reconstructions. However, such reconstructions are indirect and associated with marked uncertainty, which often renders model-data comparison equivocal. Here we take a different approach and use macro-ecological patterns preserved in fossil marine zooplankton to evaluate simulations of LGM near-surface ocean temperature.

 

We utilise the distance-decay pattern in planktonic foraminifera to evaluate modelled temperature gradients. Distance decay emerges because of differences in habitat preferences among species that cause the compositional similarity between assemblages to decrease the further apart they are from each other in environmental space. Distance decay is a fundamental concept in ecology and is observed in many different taxa and ecosystems, including planktonic foraminifera that show a monotonous decrease in similarity with increasing difference in temperature. Because the ecological niches of planktonic foraminifera are unlikely to have changed since the LGM, the distance-decay relationship based on simulated LGM temperatures and LGM assemblages should in principle be identical to the modern distance decay pattern. Thus we can use fossil planktonic foraminifera species assemblages to evaluate climate model simulations based on ecological principles.

 

Our analysis is based on an extended new LGM planktonic foraminifera database (2,085 assemblages from 647 unique sites) and a suite of 10 simulations from state-of-the-art climate models (PMIP3 and 4). We find markedly different planktonic foraminifera distributions during the LGM, primarily due to the equatorward expansion of polar assemblages at the expense of transitional assemblages. The distance-decay pattern that emerges when the LGM assemblages are combined with simulated ocean temperatures is different from the modern pattern. All simulations suggest large thermal gradients between regions where the planktonic foraminifera indicate no, or only weak, gradients. This pattern arises from the pronounced shift to polar species assemblages in the North Atlantic where the simulations predict only moderate cooling. In general, the models predict spatially rather uniform cooling, whereas the microfossil evidence suggests more pronounced regional differences in the temperature change. The difference between reconstructions and the simulations reaches up to 10 K in the North Atlantic.

 

Importantly, simulations with a reduced AMOC and hence lower North Atlantic near sea surface temperatures, yield a distance-decay pattern that is much more similar to the modern pattern. The planktonic foraminifera assemblages thus question the view of the LGM ocean as an equilibrium response to external forcing.

How to cite: Jonkers, L., Laepple, T., Rillo, M., Dolman, A., Lohman, G., Paul, A., Mix, A., and Kucera, M.: Modelled equilibrium LGM seawater temperatures inconsistent with plankton biodiversity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8364, https://doi.org/10.5194/egusphere-egu22-8364, 2022.

EGU22-8423 | Presentations | CL5.3.1

Implementation of Climate Forcings (volcanic, orbital, solar, LUC, GHG) for Paleoclimate Simulations (500BCE-2000CE) in the COSMO-CLM 

Eva Hartmann, Mingyue Zhang, Elena Xoplaki, Sebastian Wagner, and Muralidhar Adakudlu

The climate of the last 2500 years is documented in natural (speleothems, tree rings, sediments and pollen) and human-historical archives. Proxy records and subsequent climate reconstructions can be subject to a considerable amount of uncertainty, as the proxies can only capture a fraction of the entire variability. Climate model simulations can contribute to the interpretation of variations observed in the paleoclimate data and better understanding of dynamics, mechanisms and procedures. The state-of-the-art simulations following the CMIP6-protocol are highly resolved in time but still present a rather coarse horizontal resolution (200 km or more) to adequately address regional paleoclimate questions/hypotheses. Dynamical downscaling can close the gap between the regional archives and the coarsely resolved Earth System Models (ESMs). Using regional climate models to downscale ESM output requires a consistent implementation of the climate forcings in the regional model used also for the driving ESM. State-of-the-art and CMIP6 compliant reconstructions of volcanic (stratospheric aerosol optical depth), orbital (eccentricity, obliquity, precession), solar (irradiance), land-use and greenhouse-gas changes used for the MPI-ESM are therefore implemented in the regional climate model COSMO-CLM (CCLM, COSMO 5.0 clm16). The functionality of each implemented forcing is tested separately and in combination for the period (1255-1265) that covers the Samalas volcanic eruption of 1257. The orbital forcing is found to have the largest impact in general and the volcanic forcing has a strong but short-lasting effect after the eruption. The other climate forcings only show very small impact in the chosen period. At the moment, a transient CCLM simulation with all forcings implemented with a horizontal resolution of 50 km is running for the last 2500 years in the Eastern Mediterranean, the Middle East and the Nile River basin.

How to cite: Hartmann, E., Zhang, M., Xoplaki, E., Wagner, S., and Adakudlu, M.: Implementation of Climate Forcings (volcanic, orbital, solar, LUC, GHG) for Paleoclimate Simulations (500BCE-2000CE) in the COSMO-CLM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8423, https://doi.org/10.5194/egusphere-egu22-8423, 2022.

EGU22-8788 | Presentations | CL5.3.1

Temperature and precipitation distribution changes in response to global warming – results from transient simulations of the Last Deglaciation from a hierarchy of climate models 

Elisa Ziegler, Christian Wirths, Heather Andres, Lauren Gregoire, Ruza Ivanovic, Marie-Luise Kapsch, Steffen Kutterolf, Uwe Mikolajewicz, Julie Christin Schindlbeck-Belo, Matthew Toohey, Paul J. Valdes, Nils Weitzel, and Kira Rehfeld

Projections of anthropogenic climate change suggest possible surface temperature increases similar to those during past major shifts of the mean climate like the Last Deglaciation. Such shifts do not only affect the mean but rather the full probability distributions of climatic variables such as temperature and precipitation. Changes to their distributions can thus be expected for the future as well and need to be constrained.  

To this end, we examine transient simulations of the Last Deglaciation from a hierarchy of climate models, ranging from an energy balance model to state-of-the-art Earth System Models. Besides the mean, we use the higher moments of variability – variance, skewness, and kurtosis – to characterize changes of the distribution. The analysis covers annual to millennial timescales and examines how patterns vary with timescale and region in response to warming. Furthermore, we evaluate how the changes of the distributions affect the occurrence of extremes.  

To analyze the influence of forcings on the distributions, we compare the patterns of the fully-forced simulations to those in sensitivity experiments that isolate the effects of individual forcings. In particular, the effect of volcanism is examined across the hierarchy, as well as changes in ice cover, freshwater input, CO2, and orbit. While large-scale global patterns can be found, there are significant regional differences as well as differences between simulations, relating for example to differences in the modelling of ice cover changes and freshwater input. Finally, we investigate whether climate model projections show the same trends with respect to the change in moments as those found in the deglacial simulations and thus whether the patterns found might hold for future climate. 

How to cite: Ziegler, E., Wirths, C., Andres, H., Gregoire, L., Ivanovic, R., Kapsch, M.-L., Kutterolf, S., Mikolajewicz, U., Schindlbeck-Belo, J. C., Toohey, M., Valdes, P. J., Weitzel, N., and Rehfeld, K.: Temperature and precipitation distribution changes in response to global warming – results from transient simulations of the Last Deglaciation from a hierarchy of climate models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8788, https://doi.org/10.5194/egusphere-egu22-8788, 2022.

EGU22-8892 | Presentations | CL5.3.1

A Next Generation Ocean Carbon Isotope Model for Climate Studies 

Rolf Sonnerup and Mariona Claret

The 13C/12C of dissolved inorganic carbon (δ13C DIC ) carries valuable information on ocean
biological C-cycling, air-sea CO2 exchange, and circulation. Paleo-reconstructions of oceanic 13C
from sediment cores provide key insights into past as changes in these three drivers. As a step
toward full inclusion of 13C in the next generation of Earth system models, we implemented 13C-
cycling in a 1° lateral resolution ocean-ice-biogeochemistry Geophysical Fluid Dynamics
Laboratory (GFDL) model driven by Common Ocean Reference Experiment perpetual year
forcing. The model improved the mean of modern δ13C DIC over coarser resolution GFDL-model
implementations, capturing the Southern Ocean decline in surface δ13C DIC that propagates to the
deep sea via deep water formation. The model is used here to quantify controls on modern and
anthropogenic δ13C DIC as well as to test their sensitivity to wind speed/gas exchange
parameterizations.
We found that reducing the coefficient for air-sea gas exchange following OMIP-CMIP6
protocols reduces deep sea modern δ13C DIC by 0.2 permil and improves the depth-integrated
anthropogenic δ13C DIC relative to previous gas exchange parameterizations. This is because the
δ13C DIC of the endmembers ventilating the deep sea and intermediate waters are highly sensitive to
the wind speed dependence of the air-sea CO2 gas exchange. Additionally, meridional gradients
of surface modern δ13C DIC are better resolved with OMIP-CMIP6. While this model was initially
constructed to study the anthropogenic 13C response, it has promising applications toward longer
time scales. For example, BLING 13 C includes controls on the biological C-pump thought to be
important in the glacial ocean: light and iron limitation, and controls on 13C of organic matter
formation, and thus on ocean δ13C DIC and its vertical gradient, that depend on pCO2 .

How to cite: Sonnerup, R. and Claret, M.: A Next Generation Ocean Carbon Isotope Model for Climate Studies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8892, https://doi.org/10.5194/egusphere-egu22-8892, 2022.

EGU22-9768 | Presentations | CL5.3.1

Reconstructing the surface temperature fields of the Last Glacial Maximum using climate models and data. 

James Annan, Julia Hargreaves, and Thorsten Mauritsen

We present a new reconstruction of global climatological temperature fields for the Last Glacial Maximum, which improves on our previous work in several important ways.

The method combines globally complete modelled temperature fields, with sparse proxy-based estimates of local temperature anomalies. We use a localised Ensemble Kalman Smoother, which ensures spatially coherent fields that both respect the physical principles embodied in the models, and are also tied closely to observational estimates.

We use the full set of PMIP2/3/4 model simulations, but perform some filtering of the simulations to remove duplicates and closely related models. We also de-bias the ensemble and show via sensitivity tests that this can be an essential step in the process, although it has little effect in this particular application. Specifically, any bias in the prior ensemble leads to a significant bias (which may take roughly 70-80% of its initial magnitude) in the posterior estimate. Thus we recommend that this step is taken in similar reconstructions unless the researcher is confident that the bias in the prior ensemble is low.

We combine the prior ensemble with a wide range of proxy-based SST and SAT estimates of local temperature to ensure the best possible global coverage. Our reconstruction has a global mean surface air temperature anomaly of -4.5 +- 0.9C relative to the pre-industrial climate, and thus is slightly cooler than the estimate of Annan and Hargreaves (2013), but rather less cold than the estimate of Tierney et al (2020). We show that much of the reason for this latter discrepancy is due to the choice of prior.

How to cite: Annan, J., Hargreaves, J., and Mauritsen, T.: Reconstructing the surface temperature fields of the Last Glacial Maximum using climate models and data., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9768, https://doi.org/10.5194/egusphere-egu22-9768, 2022.

EGU22-9897 | Presentations | CL5.3.1

The relationship between the global mean deep-sea and surface temperature during the Early Eocene 

Barbara Goudsmit, Angelique Lansu, Anna S. von der Heydt, Yurui Zhang, and Martin Ziegler

Under continued high anthropogenic CO2 emissions, the atmospheric CO2 concentration around 2100 will be like that of the Early Eocene Climate Optimum (EECO, 56–48 Ma) hothouse period. Hence, reconstructions of the EECO climate give insight into the workings of the climate system under the possible future CO2 conditions. Our current understanding of global mean surface temperature (GMST) during the Cenozoic era relies on paleo-proxy estimates of deep-sea temperature (DST) combined with assumed relationships between global mean DST (GMDST), global mean sea-surface temperature (GMSST), and GMST. The validity of these assumptions is essential in our understanding of past and future climate states under hothouse conditions.
We analyse the relationship between these global temperature indicators for the end-of-simulation global mean temperature values in 25 different millennia-long model simulations of the EECO climate under varying CO2 levels, performed as part of the Deep-Time Model Intercomparison Project (DeepMIP). The model simulations show limited spatial variability in DST, indicating that local DST estimates can be regarded representative of GMDST. Linear regression analysis indicates that GMDST and GMST respond stronger to changes in atmospheric CO2 than GMSST by factors 1.18 and 1.17, respectively. Consequently, the responses of GMDST and GMST to atmospheric CO2 changes are similar in magnitude. This model-based analysis indicates that changes in GMDST can be used to estimate changes in GMST during the EECO, validating the assumed relationships. To test the robustness of these results, other Cenozoic climate states besides EECO should be analysed similarly.

How to cite: Goudsmit, B., Lansu, A., von der Heydt, A. S., Zhang, Y., and Ziegler, M.: The relationship between the global mean deep-sea and surface temperature during the Early Eocene, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9897, https://doi.org/10.5194/egusphere-egu22-9897, 2022.

EGU22-10449 | Presentations | CL5.3.1

Modelling the regional paleoclimate of southern Africa: Sub-orbital-scale changes and sensitivity to coastline shifts 

Ozan Mert Göktürk, Stefan Pieter Sobolowski, Margit Hildegard Simon, Zhongshi Zhang, and Eystein Jansen

Paleoclimatic changes in South Africa, especially around the southern Cape region, are of intense interdisciplinary interest; as this is an important area in the context of human evolution, hosting a number of prominent archaeological sites such as Klipdrift Shelter and Blombos Cave (both located near today’s shoreline). Questions surrounding how large-scale and local variability (and change) influenced the local human populations are abundant. Here we present results from downscaling simulations performed for southern Africa, with a high resolution (12 km) regional climate model (WRF), forced by a global earth system model (NorESM). We focus on two time-slices, 82 and 70 ka BP, when orbital parameters and global sea level were markedly different from each other. Changes from 82 to 70 ka BP are generally in line with orbital forcing; indicating, for example, a widespread and significant (> 40%) increase in summer precipitation over inland southern Africa (south of 15°S) due to higher insolation at 70 ka BP compared to 82 ka BP. In contrast, the western and southern Cape coasts became drier at 70 ka BP, owing in part to a 40 m lower sea level, as the coastline shifted and the paleo-Agulhas plain got exposed. The effect of the coastline shift on temperatures in the southern Cape region is evident from the significant (up to 6°C) increases (decreases) in maximum (minimum) temperatures, which are strong enough to overwhelm changes arising from orbital forcing. These inferences are further supported with a separate set of coastline-sensitivity simulations at 70 ka BP, which indicate not only drying, but also larger diurnal and interseasonal temperature ranges when the coastline extends southwards, and once-coastal areas become more continental. For instance, at the archaeological site of Blombos Cave, temperature extremes (1st and 99th percentiles) of the modelled marine climate become 25 to 50-fold more probable to occur as the coastline shift leads to a continental climate. Our results indicate that regional to local-scale processes, which tend to not be represented in most coarse resolution global models, have a strong influence on the paleoclimate of southern Africa, highlighting both the coastal-inland contrasts and the importance of changes in coastline position. 

How to cite: Göktürk, O. M., Sobolowski, S. P., Simon, M. H., Zhang, Z., and Jansen, E.: Modelling the regional paleoclimate of southern Africa: Sub-orbital-scale changes and sensitivity to coastline shifts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10449, https://doi.org/10.5194/egusphere-egu22-10449, 2022.

EGU22-10696 | Presentations | CL5.3.1

The Kuroshio Current at the Last Glacial Maximum and implications for coral palaeobiogeography 

Noam Vogt-Vincent and Satoshi Mitarai

The Kuroshio Current is the western boundary current of the North Pacific Subtropical Gyre and flows through the East China Sea, entering through a relatively narrow, 800m-deep sill (the Yonaguni Depression). The warm surface waters associated with the Kuroshio support habitable conditions in the East China Sea for some of the world’s most northerly warm-water coral reefs. However, it has been suggested that sea-level fall at the LGM, with a possible further contribution from tectonics, obstructed the glacial Yonaguni Depression and diverted the Kuroshio to the east of the Ryukyu Arc.

Using a set of 2km-resolution dynamically downscaled ocean simulations with LGM boundary conditions from four PMIP3 contributions, we present regional state estimates for the glacial East China Sea which are both physically consistent, and compatible with sea-surface temperature proxy compilations. We find that, whilst the Kuroshio Current transport in the East China Sea is slightly reduced at the LGM, its path is relatively unchanged, with limited sensitivity to glacioeustatic sea-level change, glacial-interglacial changes in climate, and tectonic shoaling of the Yonaguni Depression. Simulations with the best model-proxy agreement predict only minor changes in the zone of habitability for warm-water coral reefs in the glacial East China Sea. Strong surface currents associated with the glacial Kuroshio may have maintained or even improved long-distance coral larval dispersal along the Ryukyu Arc, suggesting that conditions may have enabled coral reefs in this region to remain widespread throughout the last glacial. These findings are supported by seismic evidence for glacial coral reefs in the northern East China Sea. Further field studies are needed to investigate whether this is genuinely the case, and to provide additional constraints on how the coral reef front responds to long-term environmental change.

How to cite: Vogt-Vincent, N. and Mitarai, S.: The Kuroshio Current at the Last Glacial Maximum and implications for coral palaeobiogeography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10696, https://doi.org/10.5194/egusphere-egu22-10696, 2022.

EGU22-10715 | Presentations | CL5.3.1

Understanding climate, precipitation and δ18O linkages over Eastern Asia 

Nitesh Sinha, Axel Timmermann, Jasper A. Wessenburg, and Sun-Seon Lee

The interpretation of East Asian monsoon speleothem δ18O records is heavily debated in the paleoclimate community. Besides developing new speleothem proxies, the use of isotope-enabled climate simulations is one of the key tools to enhance our understanding of speleothem δ18O records. Here we present results from novel climate simulations performed with the fully coupled isotope-enabled Community Earth System Model (iCESM1.2), which simulates global variations in water isotopes in the atmosphere, land, ocean, and sea ice. The model closely captures the major observed features of the isotopic compositions in precipitation over East Asia for the present-day conditions. To better understand the physical mechanisms causing interannual to orbital timescale variations in δ18O in East Asian speleothems, we ran a series of experiments with iCESM. We perturbed solar, orbital, bathymetry, ice-sheet, and greenhouse gas radiative forcings. The simulations supporting of observations/reconstructed records (GNIP/SISAL) from East Asia, help understand the controls on the isotope composition of East Asian monsoon rainfall and how speleothem δ18O records may be interpreted in terms of climate. The study provides new insights into the mechanisms of East Asian monsoon changes on different timescales.

How to cite: Sinha, N., Timmermann, A., Wessenburg, J. A., and Lee, S.-S.: Understanding climate, precipitation and δ18O linkages over Eastern Asia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10715, https://doi.org/10.5194/egusphere-egu22-10715, 2022.

EGU22-11090 | Presentations | CL5.3.1 | Highlight

Tracing the snowball bifurcation of aquaplanets through time reveals a fundamental shift in critical-state dynamics 

Georg Feulner and Mona Bukenberger

The instability with respect to global glaciation is a fundamental property of the climate system caused by the positive ice-albedo feedback. The atmospheric concentration of carbon dioxide (CO2) at which this Snowball bifurcation occurs changes through Earth’s history because of the slowly increasing solar luminosity. Quantifying this critical CO2concentration is not only interesting from a climate dynamics perspective, but also an important prerequisite for understanding past "snowball Earth" episodes and the conditions for habitability on Earth and other planets. Earlier studies are limited to investigations with very simple climate models for Earth’s entire history or studies of individual time slices carried out with a variety of more complex models and for different boundary conditions, making comparisons difficult. Here we use a coupled climate model of intermediate complexity to trace the Snowball bifurcation of an aquaplanet through Earth’s history in one consistent model framework. We find that the critical CO2concentration decreases more or less logarithmically with increasing solar luminosity until about 1 billion years ago, but drops faster in more recent times. Furthermore, there is a fundamental shift in the dynamics of the critical state about 1.8 billion years ago, driven by the interplay of wind-driven sea-ice dynamics and the surface energy balance.

How to cite: Feulner, G. and Bukenberger, M.: Tracing the snowball bifurcation of aquaplanets through time reveals a fundamental shift in critical-state dynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11090, https://doi.org/10.5194/egusphere-egu22-11090, 2022.

EGU22-11116 | Presentations | CL5.3.1

Sensitivity of glacial states to orbits and ice sheet heights in CESM1.2 

Jonathan Buzan, Emmanuele Russo, Woonmi Kim, and Christoph Raible

Changes between icehouse and greenhouse states are known to be the result from non-linear climate responses. However, the magnitudes of these responses are not well constrained. Recent work shows that climate models, specifically the Community Earth System Model version 1 (CESM1), have improved substantially in their capacity to quantify the Last Glacial Maximum (LGM) state. Given that CESM1 can reproduce the LGM well, we consider the combined impacts of estimated ice sheet heights, Quaternary orbits, and greenhouse gas changes for a range of Quaternary climate states. To that end, we conducted two sets of experiments: first, a series of sensitivity experiments on the Preindustrial climate and second, experiments on Quaternary glacial states.

In the first set of the experiments, we show how CESM1 quantifies the impacts of ice height, orbit, and greenhouse gas changes by considering each component incrementally. Then we demonstrate that they combine through non-linear impacts. The analysis is based on seven sensitivity experiments: 1) Late Holocene orbit, 2) Representative Concentration Pathway 8.5 (RCP85) greenhouse gases, 3) LGM orbit, 4) LGM greenhouse gasses, and 5) Greenland icesheet height changes, 6) LGM orbit with Greenland icesheet height changes, and 7) LGM orbit with LGM greenhouse gases and Greenland icesheet height changes. We show that adding individually these component changes do not linearly combine to match the simulations with combined changes.

These non-linear effects guide the second set of experiments, because non-linear systems are predictable due to state dependent outcomes. We use of 4 glacial ice sheet height differences and 4 glacial maximum orbital states (LGM, and Marine Isotopic Stage 4,6, and 8), for a total of 16 sensitivity experiments. These orbits are known glacial maximal states, and the 4 ice sheet heights are within the range of estimated ice volumes. We analyze these simulations in two ways, 1) the explicit effect of changes in orbit while holding the ice sheet constant, and 2) the explicit effect of changes in ice sheet height, while holding the orbit constant.

Our results show that ice sheet heights dominate the changes in climate system, regardless of orbit. But, there are subtle regional effects that orbit has that are not explained by ice sheet height changes. For example, higher ice sheets induce a global temperature increase, but regionally within Europe, there are non-linear changes in warming or cooling that are unexplained by the ice sheets. As the ice sheet height is lowered, the changes in Europe do not linearly change, and are dependent on the orbit configuration.

These results show that there are specific pathways for climate that occur due to the combination of icesheet height and orbit, and theoretically imply a constraint on the real climate state. In a linear system, these 16 states would represent the variability of the Quaternary, but as this is a non-linear system only 1 state is physical for a given orbit. As proxy data spatial and temporal resolution improves for the Quaternary, combined with these modeled climates, we expect substantial constraints on the available realistic climate states.

How to cite: Buzan, J., Russo, E., Kim, W., and Raible, C.: Sensitivity of glacial states to orbits and ice sheet heights in CESM1.2, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11116, https://doi.org/10.5194/egusphere-egu22-11116, 2022.

EGU22-11955 | Presentations | CL5.3.1 | Highlight

Last Glacial Maximum atmospheric lapse rates: a model-data study on the American Cordillera case 

Masa Kageyama, Pierre-Henri Blard, Stella Bourdin, Julien Charreau, Lukas Kluft, Guillaume Leduc, and Etienne Legrain

The amplitude of the Last Glacial Maximum (LGM) cooling compared to pre-industrial has long been a topic of debate, which partly arises from the fact that this cooling is spatially heterogeneous. Paleotemperature reconstructions shows that this cooling is larger on land than over the oceans, a feature which is well captured by Global Climate Models. However the amplitude of the LGM cooling at high altitudes is still not well constrained, with available data showing an important disparity from a region to another (Blard et al., 2007; Tripati et al., 2014). Here we present a new compilation of glacier-based temperature reconstructions at high elevation (> 2500 m) for the LGM, which are compared to synchronous changes of sea surface temperatures (Pacific Ocean), along the American Cordillera, from 40°S to 40°N. This new reconstruction confirms that lapse rates were steeper during the LGM in the tropics and shows that this feature relates to a drier atmosphere. To further analyse this observation, we first use the IPSL global climate model PMIP4 results (Kageyama et al., 2021), which, in agreement with the reconstructions, yields a steeper tropical lapse rate in its LGM simulation, compared with the pre-industrial one. Next, we disentangle the impacts of the lower atmospheric CO2 concentration and of lower humidity using a single column radiative-convective equilibrium model (Kluft et al., 2019), and show the strong impact of changes in humidity in the tropical lapse rate steepening at the LGM.

References

Blard, P.-H., Lavé, J., Wagnon, P. and Bourlès, D : Persistence of full glacial conditions in the central Pacific until 15,000 years ago, Nature, 449, 591–594, https://doi.org/10.1038/nature06142, 2007.

Tripati, A. K., Sahany, S., Pittman, D., Eagle, R. A., Neelin, J. D., Mitchell, J. L. and Beaucoufort, L.: Modern and glacial tropical snowlines controlled by sea surface temperature and atmospheric mixing, Nature Geoscience, 7, 205–209, https://doi.org/10.1038/ngeo2082, 2014.

Kageyama, M., Harrison, S. P., Kapsch, M.-L., Lofverstrom, M., Lora, J. M., Mikolajewicz, U., Sherriff-Tadano, S., Vadsaria, T., Abe-Ouchi, A., Bouttes, N., Chandan, D., Gregoire, L. J., Ivanovic, R. F., Izumi, K., LeGrande, A. N., Lhardy, F., Lohmann, G., Morozova, P. A., Ohgaito, R., Paul, A., Peltier, W. R., Poulsen, C. J., Quiquet, A., Roche, D. M., Shi, X., Tierney, J. E., Valdes, P. J., Volodin, E., and Zhu, J.: The PMIP4 Last Glacial Maximum experiments: preliminary results and comparison with the PMIP3 simulations, Clim. Past, 17, 1065–1089, https://doi.org/10.5194/cp-17-1065-2021, 2021.

Kluft, L., Dacie, S., Buehler, S. A., Schmidt, H., & Stevens, B. (2019). Re-Examining the First Climate Models: Climate Sensitivity of a Modern Radiative–Convective Equilibrium Model, Journal of Climate, 32(23), 8111-8125

How to cite: Kageyama, M., Blard, P.-H., Bourdin, S., Charreau, J., Kluft, L., Leduc, G., and Legrain, E.: Last Glacial Maximum atmospheric lapse rates: a model-data study on the American Cordillera case, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11955, https://doi.org/10.5194/egusphere-egu22-11955, 2022.

EGU22-12620 | Presentations | CL5.3.1

Climate analogs as input for ice sheet models during the glacial 

Tobias Zolles and Andreas Born

Simulations of continental ice sheets require climate forcing over time periods that are infeasible to run with comprehensive climate models. The alternative to use climate models of reduced complexity often yields data of insufficient quality for a good simulation of the ice sheet surface mass balance. Here we reconstruct the climate of the last glacial climate based on 22 marine proxy records and two Greenland ice cores for the Atlantic region. The reconstruction is based on multiple climate simulations, which serve as potential analogs.

The analog search is based on air and sea surface temperatures.  To mitigate regional biases due to the availability of reconstructions, and to filter non-essential modes of variability, the search is carried out in the reduced space of the first few principal components. For every hundred years of proxy data the best ten climate analogs are identified and their weighted sum serves as the reconstruction. The obtained climate fields provide a full set of atmospheric variables to be used as input for our surface mass balance model.

We assess the quality and uncertainty of our reconstruction by using different objectives for the analog search as well as accounting for the different spatial and temporal distributions of the proxies. In addition, the method is evaluated in comparison to reconstructions based on the glacial index. 

The performance of the method decreases during the deep glacial period with the used model pool. In addition, the climate model data does not sufficiently explain the variability observed in the marine proxy data.

How to cite: Zolles, T. and Born, A.: Climate analogs as input for ice sheet models during the glacial, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12620, https://doi.org/10.5194/egusphere-egu22-12620, 2022.

EGU22-20 | Presentations | ITS2.6/AS5.1

PRECISIONPOP: a multi-scale monitoring system for poplar plantations integrating field, aerial and satellite remote sensing 

Francesco Chianucci, Francesca Giannetti, Clara Tattoni, Nicola Puletti, Achille Giorcelli, Carlo Bisaglia, Elio Romano, Massimo Brambilla, Piermario Chiarabaglio, Massimo Gennaro, Giovanni d'Amico, Saverio Francini, Walter Mattioli, Domenico Coaloa, Piermaria Corona, and Gherardo Chirici

Poplar (Populus spp.) plantations are globally widespread in the Northern Hemisphere, and provide a wide range of benefits and products, including timber, carbon sequestration and phytoremediation. Because of poplar specific features (fast growth, short rotation) the information needs require frequent updates, which exceed the traditional scope of National Forest Inventories, implying the need for ad-hoc monitoring solutions.

Here we presented a regional-level multi-scale monitoring system developed for poplar plantations, which is based on the integration of different remotely-sensed informations at different spatial scales, developed in Lombardy (Northern Italy) region. The system is based on three levels of information: 1) At plot scale, terrestrial laser scanning (TLS) was used to develop non-destructive tree stem volume allometries in calibration sites; the produced allometries were then used to estimate plot-level stand parameters from field inventory; additional canopy structure attributes were derived using field digital cover photography. 2) At farm level, unmanned aerial vehicles (UAVs) equipped with multispectral sensors were used to upscale results obtained from field data. 3) Finally, both field and unmanned aerial estimates were used to calibrate a regional-scale supervised continuous monitoring system based on multispectral Sentinel-2 imagery, which was implemented and updated in a Google Earth Engine platform.

The combined use of multi-scale information allowed an effective management and monitoring of poplar plantations. From a top-down perspective, the continuous satellite monitoring system allowed the detection of early warning poplar stress, which are suitable for variable rate irrigation and fertilizing scheduling. From a bottom-up perspective, the spatially explicit nature of TLS measurements allows better integration with remotely sensed data, enabling a multiscale assessment of poplar plantation structure with different levels of detail, enhancing conventional tree inventories, and supporting effective management strategies. Finally, use of UAV is key in poplar plantations as their spatial resolution is suited for calibrating metrics from coarser remotely-sensed products, reducing or avoiding the need of ground measurements, with a significant reduction of time and costs.

How to cite: Chianucci, F., Giannetti, F., Tattoni, C., Puletti, N., Giorcelli, A., Bisaglia, C., Romano, E., Brambilla, M., Chiarabaglio, P., Gennaro, M., d'Amico, G., Francini, S., Mattioli, W., Coaloa, D., Corona, P., and Chirici, G.: PRECISIONPOP: a multi-scale monitoring system for poplar plantations integrating field, aerial and satellite remote sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-20, https://doi.org/10.5194/egusphere-egu22-20, 2022.

EGU22-124 | Presentations | ITS2.6/AS5.1

Unsupervised machine learning driven Prospectivity analysis of REEs in NE India 

Malcolm Aranha and Alok Porwal

Traditional mineral prospectivity modelling for mineral exploration and targeting relies heavily on manual data filtering and processing to extract desirable geologic features based on expert knowledge. It involves the integration of geological predictor maps that are manually derived by time-consuming and labour-intensive pre-processing of primary geoscientific data to serve as spatial proxies of mineralisation processes. Moreover, the selection of these spatial proxies is guided by conceptual genetic modelling of the targeted deposit type, which may be biased by the subjective preference of an expert geologist. This study applies Self-Organising Maps (SOM), a neural network-based unsupervised machine learning clustering algorithm, to gridded geophysical and topographical datasets in order to identify and delineate regional-scale exploration targets for carbonatite-alkaline-complex-related REE deposits in northeast India. The study did not utilise interpreted and processed or manually generated data, such as surface or bed-rock geological maps, fault traces, etc., and relies on the algorithm to identify crucial features and delineate prospective areas. The obtained results were then compared with those obtained from a previous supervised knowledge-driven prospectivity analysis. The results were found to be comparable. Therefore, unsupervised machine learning algorithms are reliable tools to automate the manual process of mineral prospectivity modelling and are robust, time-saving alternatives to knowledge-driven or supervised data-driven prospectivity modelling. These methods would be instrumental in unexplored terrains for which there is little or no geological knowledge available. 

How to cite: Aranha, M. and Porwal, A.: Unsupervised machine learning driven Prospectivity analysis of REEs in NE India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-124, https://doi.org/10.5194/egusphere-egu22-124, 2022.

EGU22-654 | Presentations | ITS2.6/AS5.1

On the derivation of data-driven models for partially observed systems 

Said Ouala, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, and Ronan Fablet

When considering the modeling of dynamical systems, the increasing interest in machine learning, artificial intelligence and more generally, data-driven representations, as well as the increasing availability of data, motivated the exploration and definition of new identification techniques. These new data-driven representations aim at solving modern questions regarding the modeling, the prediction and ultimately, the understanding of complex systems such as the ocean, the atmosphere and the climate. 

In this work, we focus on one question regarding the ability to define a (deterministic) dynamical model from a sequence of observations. We focus on sea surface observations and show that these observations typically relate to some, but not all, components of the underlying state space, making the derivation of a deterministic model in the observation space impossible. In this context, we formulate the identification problem as the definition, from data, of an embedding of the observations, parameterized by a differential equation. When compared to state-of-the-art techniques based on delay embedding and linear decomposition of the underlying operators, the proposed approach benefits from all the advances in machine learning and dynamical systems theory in order to define, constrain and tune the reconstructed sate space and the approximate differential equation. Furthermore, the proposed embedding methodology naturally extends to cases in which a dynamical prior (derived for example using physical principals) is known, leading to relevant physics informed data-driven models. 

How to cite: Ouala, S., Chapron, B., Collard, F., Gaultier, L., and Fablet, R.: On the derivation of data-driven models for partially observed systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-654, https://doi.org/10.5194/egusphere-egu22-654, 2022.

EGU22-1255 | Presentations | ITS2.6/AS5.1

A Deep Learning approach to de-bias Air Quality forecasts, using heterogeneous Open Data sources as reference 

Antonio Pérez, Mario Santa Cruz, Johannes Flemming, and Miha Razinger

The degradation of air quality is a challenge that policy-makers face all over the world. According to the World Health Organisation, air pollution causes an estimate of 7 million premature deaths every year. In this context, air quality forecasts are crucial tools for decision- and policy-makers, to achieve data-informed decisions.

Global forecasts, such as the Copernicus Atmosphere monitoring service model (CAMS), usually exhibit biases: systematic deviations from observations. Adjusting these biases is typically the first step towards obtaining actionable air quality forecasts. It is especially relevant in health-related decisions, when the metrics of interest depend on specific thresholds.

AQ (Air quality) - Bias correction was a project funded by the ECMWF Summer of Weather Code (ESOWC) 2021 whose aim is to improve CAMS model forecasts for air quality variables (NO2, O3, PM2.5), using as a reference the in-situ observations provided by OpenAQ. The adjustment, based on machine learning methods, was performed over a set of specific interesting locations provided by the ECMWF, for the period June 2019 to March 2021.

The machine learning approach uses three different deep learning based models, and an extra neural network that gathers the output of the three previous models. From the three DL-based models, two of them are independent and follow the same structure built upon the InceptionTime module: they use both meteorological and air quality variables, to exploit the temporal variability and to extract the most meaningful features of the past [t-24h, t-23h, … t-1h] and future [t, t+1h, …, t+23h] CAMS predictions. The third model uses the station static attributes (longitude, latitude and elevation), and a multilayer perceptron interacts with the station attributes. The extracted features from these three models are fed into another multilayer perceptron, to predict the upcoming errors with hourly resolution [t, t+1h, …, t+23h]. As a final step, 5 different initializations are considered, assembling them with equal weights to have a more stable regressor.

Previous to the modelisation, CAMS forecasts of air quality variables were actually biassed independently from the location of interest and the variable (on average: biasNO2 = -22.76, biasO3 = 44.30, biasPM2.5 = 12.70). In addition, the skill of the model, measured by the Pearson correlation, did not reach 0.5 for any of the variables—with remarkable low values for NO2 and O3 (on average: pearsonNO2 = 0.10, pearsonO3 = 0.14).

AQ-BiasCorrection modelisation properly corrects these biases. Overall, the number of stations that improve the biases both in train and test sets are: 52 out of 61 (85%) for NO2, 62 out of 67 (92%) for O3, and 80 out of 102 (78%) for PM2.5. Furthermore, the bias improves with declines of -1.1%, -9.7% and -13.9% for NO2, O3 and PM2.5 respectively. In addition, there is an increase in the model skill measured through the Pearson correlation, reaching values in the range of 100-400% for the overall improvement of the variable skill.

How to cite: Pérez, A., Santa Cruz, M., Flemming, J., and Razinger, M.: A Deep Learning approach to de-bias Air Quality forecasts, using heterogeneous Open Data sources as reference, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1255, https://doi.org/10.5194/egusphere-egu22-1255, 2022.

EGU22-1992 | Presentations | ITS2.6/AS5.1

Approximating downward short-wave radiation flux using all-sky optical imagery using machine learning trained on DASIO dataset. 

Vasilisa Koshkina, Mikhail Krinitskiy, Nikita Anikin, Mikhail Borisov, Natalia Stepanova, and Alexander Osadchiev

Solar radiation is the main source of energy on Earth. Cloud cover is the main physical factor limiting the downward short-wave radiation flux. In modern models of climate and weather forecasts, physical models describing the passage of radiation through clouds may be used. This is a computationally extremely expensive option for estimating downward radiation fluxes. Instead, one may use parameterizations which are simplified schemes for approximating environmental variables. The purpose of this work is to improve the accuracy of the existing parametrizations of downward shortwave radiation fluxes. We solve the problem using various machine learning (ML) models for approximating downward shortwave radiation flux using all-sky optical imagery. We assume that an all-sky photo contains complete information about the downward shortwave radiation. We examine several types of ML models that we trained on dataset of all-sky imagery accompanied by short-wave radiation flux measurements. The Dataset of All-Sky Imagery over the Ocean (DASIO) is collected in Indian, Atlantic and Arctic oceans during several oceanic expeditions from 2014 till 2021. The quality of the best classic ML model is better compared to existing parameterizations known from literature. We will show the results of our study regarding classic ML models as well as the results of an end-to-end ML approach involving convolutional neural networks. Our results allow us to assume one may acquire downward shortwave radiation fluxes directly from all-sky imagery. We will also cover some downsides and limitations of the presented approach.

How to cite: Koshkina, V., Krinitskiy, M., Anikin, N., Borisov, M., Stepanova, N., and Osadchiev, A.: Approximating downward short-wave radiation flux using all-sky optical imagery using machine learning trained on DASIO dataset., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1992, https://doi.org/10.5194/egusphere-egu22-1992, 2022.

EGU22-2058 | Presentations | ITS2.6/AS5.1

Deep learning for ensemble forecasting 

Rüdiger Brecht and Alexander Bihlo
Ensemble prediction systems are an invaluable tool for weather prediction. Practically, ensemble predictions are obtained by running several perturbed numerical simulations. However, these systems are associated with a high computational cost and often involve statistical post-processing steps to improve their qualities.
Here we propose to use a deep-learning-based algorithm to learn the statistical properties of a given ensemble prediction system, such that this system will not be needed to simulate future ensemble forecasts. This way, the high computational costs of the ensemble prediction system can be avoided while still obtaining the statistical properties from a single deterministic forecast. We show preliminary results where we demonstrate the ensemble prediction properties for a shallow water unstable jet simulation on the sphere. 

How to cite: Brecht, R. and Bihlo, A.: Deep learning for ensemble forecasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2058, https://doi.org/10.5194/egusphere-egu22-2058, 2022.

Numerical weather prediction (NWP) models are currently popularly used for operational weather forecast in meteorological centers. The NWP models describe the flow of fluids by employing a set of governing equations, physical parameterization schemes and initial and boundary conditions. Thus, it often face bias of prediction due to insufficient data assimilation, assumptions or approximations of dynamical and physical processes. To make gridded forecast of rainfall with high confidence, in this study, we present a data-driven deep learning model for correction of rainfall from NWP model, which mainly includes a confidence network and a combinatorial network. Meanwhile, a focal loss is introduced to deal with the characteristics of longtail-distribution of rainfall. It is expected to alleviate the impact of the large span of rainfall magnitude by transferring the regression problem into several binary classification problems. The deep learning model is used to correct the gridded forecasts of rainfall from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model (ECMWF-IFS) with a forecast lead time of 24 h to 240 h in Eastern China. First, the rainfall forecast correction problem is treated as an image-to-image translation problem in deep learning under the neural networks. Second, the ECMWF-IFS forecasts and rainfall observations in recent years are used as training, validation, and testing datasets. Finally, the correction performance of the new machine learning model is evaluated and compared to several classical machine learning algorithms. By performing a set of experiments for rainfall forecast error correction, it is found that the new model can effectively forecast rainfall over East China region during the flood season of the year 2020. Experiments also demonstrate that the proposed approach generally performs better in bias correction of rainfall prediction than most of the classical machine learning approaches .

How to cite: Ma, L.: A Deep Learning Bias Correction Approach for Rainfall Numerical Prediction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2095, https://doi.org/10.5194/egusphere-egu22-2095, 2022.

EGU22-2893 | Presentations | ITS2.6/AS5.1 | Highlight

Bias Correction of Operational Storm Surge Forecasts Using Neural Networks 

Paulina Tedesco, Jean Rabault, Martin Lilleeng Sætra, Nils Melsom Kristensen, Ole Johan Aarnes, Øyvind Breivik, and Cecilie Mauritzen

Storm surges can give rise to extreme floods in coastal areas. The Norwegian Meteorological Institute (MET Norway) produces 120-hour regional operational storm surge forecasts along the coast of Norway based on the Regional Ocean Modeling System (ROMS). Despite advances in the development of models and computational capability, forecast errors remain large enough to impact response measures and issued alerts, in particular, during the strongest storm events. Reducing these errors will positively impact the efficiency of the warning systems while minimizing efforts and resources spent on mitigation.

Here, we investigate how forecasts can be improved with residual learning, i.e., training data-driven models to predict, and correct, the error in the ROMS output. For this purpose, sea surface height data from stations around Norway were collected and compared with the ROMS output.

We develop two different residual learning frameworks that can be applied on top of the ROMS output. In the first one, we perform binning of the model error, conditionalized by pressure, wind, and waves. Clear error patterns are visible when the error conditioned by the wind is plotted in a polar plot for each station. These error maps can be stored as correction lookup tables to be applied on the ROMS output. However, since wind, pressure, and waves are correlated, we cannot simultaneously correct the error associated with each variable using this method. To overcome this limitation, we develop a second method, which resorts to Neural Networks (NNs) to perform nonlinear modeling of the error pattern obtained at each station. 

The residual NN method strongly outperforms the error map method, and is a promising direction for correcting storm surge models operationally. Indeed, i) this method is applied on top of the existing model and requires no changes to it, ii) all predictors used for NN inference are available operationally, iii) prediction by the NN is very fast, typically a few seconds per station, and iv) the NN correction can be provided to a human expert who gets to inspect it, compare it with the ROMS output, and see how much correction is brought by the NN. Using this NN residual error correction method, the RMS error in the Oslofjord is reduced by typically 7% for lead times of 24 hours, 17% for 48 hours, and 35% for 96 hours.

How to cite: Tedesco, P., Rabault, J., Sætra, M. L., Kristensen, N. M., Aarnes, O. J., Breivik, Ø., and Mauritzen, C.: Bias Correction of Operational Storm Surge Forecasts Using Neural Networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2893, https://doi.org/10.5194/egusphere-egu22-2893, 2022.

EGU22-3977 | Presentations | ITS2.6/AS5.1 | Highlight

Learning quasi-geostrophic turbulence parametrizations from a posteriori metrics 

Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, and Redouane Lguensat

Machine learning techniques are now ubiquitous in the geophysical science community. They have been applied in particular to the prediction of subgrid-scale parametrizations using data that describes small scale dynamics from large scale states. However, these models are then used to predict temporal trajectories, which is not covered by this instantaneous mapping. Following the model trajectory during training can be done using an end-to-end approach, where temporal integration is performed using a neural network. As a consequence, the approach is shown to optimize a posteriori metrics, whereas the classical instantaneous training is limited to a priori ones. When applied on a specific energy backscatter problem, found in quasi-geostrophic turbulent flows, the strategy demonstrates long-term stability and high fidelity statistical performance, without any increase in computational complexity during rollout. These improvements may question the future development of realistic subgrid-scale parametrizations in favor of differentiable solvers, required by the a posteriori strategy.

How to cite: Frezat, H., Le Sommer, J., Fablet, R., Balarac, G., and Lguensat, R.: Learning quasi-geostrophic turbulence parametrizations from a posteriori metrics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3977, https://doi.org/10.5194/egusphere-egu22-3977, 2022.

EGU22-4062 | Presentations | ITS2.6/AS5.1

Climatological Ocean Surface Wave Projections using Deep Learning 

Peter Mlakar, Davide Bonaldo, Antonio Ricchi, Sandro Carniel, and Matjaž Ličer

We present a numerically cheap machine-learning model which accurately emulates the performances of the surface wave model Simulating WAves Near Shore (SWAN) in the Adriatic basin (north-east Mediterranean Sea).

A ResNet50 inspired deep network architecture with customized spatio-temporal attention layers was used, the network being trained on a 1970-1997 dataset of time-dependent features based on wind fields retrieved from the COSMO-CLM regional climate model (The authors acknowledge Dr. Edoardo Bucchignani (Meteorology Laboratory, Centro Italiano Ricerche Aerospaziali -CIRA-, Capua, Italy), for providing the COSMO-CLM wind fields). SWAN surface wave model outputs for the period of 1970-1997 are used as labels. The period 1998-2000 is used to cross-validate that the network very accurately reproduces SWAN surface wave features (i.e. significant wave height, mean wave period, mean wave direction) at several locations in the Adriatic basin. 

After successful cross validation, a series of projections of ocean surface wave properties based on climate model projections for the end of 21st century (under RCP 8.5 scenario) are performed, and shifts in the emulated wave field properties are discussed.

How to cite: Mlakar, P., Bonaldo, D., Ricchi, A., Carniel, S., and Ličer, M.: Climatological Ocean Surface Wave Projections using Deep Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4062, https://doi.org/10.5194/egusphere-egu22-4062, 2022.

EGU22-4493 | Presentations | ITS2.6/AS5.1 | Highlight

Semi-automatic tuning procedure for a GCM targeting continental surfaces: a first experiment using in situ observations 

Maëlle Coulon--Decorzens, Frédérique Cheruy, and Frédéric Hourdin

The tuning or calibration of General Circulation Models (GCMs) is an essential stage for their proper behavior. The need to have the best climate projections in the regions where we live drives the need to tune the models in particular towards the land surface, bearing in mind that the interactions between the atmosphere and the land surface remain a key source of uncertainty in regional-scale climate projections [1].

For a long time, this tuning has been done by hand, based on scientific expertise and has not been sufficiently documented [2]. Recent tuning tools offer the possibility to accelerate climate model development, providing a real tuning formalism as well as a new way to understand climate models. High Tune explorer is one of these statistic tuning tool, involving machine learning and based on uncertainty quantification. It aims to reduce the range of free parameters that allow realistic model behaviour [3]. A new automatic tuning experiment was developed with this tool for the atmospheric component of the IPSL GCM model, LMDZ. It was first tuned at the process level, using several single column test cases compared to large eddies simulations; and then at the global level by targeting radiative metrics at the top of the atmosphere [4].

We propose to add a new step to this semi-automatic tuning procedure targeting atmosphere and land-surface interactions. The first aspect of the proposition is to compare coupled atmosphere-continent simulations (here running LMDZ-ORCHIDEE) with in situ observations from the SIRTA observatory located southwest of Paris. In situ observations provide hourly joint colocated data with a strong potential for the understanding of the processes at stake and their representation in the model. These data are also subject to much lower uncertainties than the satellite inversions with respect to the surface observations. In order to fully benefit from the site observations, the model winds are nudged toward reanalysis. This forces the simulations to follow the effective meteorological sequence, thus allowing the comparison between simulations and observations at the process time scale. The removal of the errors arising from the representation of large-scale dynamics makes the tuning focus on the representation of physical processes «at a given meteorological situation». Finally, the model grid is zoomed in on the SIRTA observatory in order to reduce the computational cost of the simulations while preserving a fine mesh around this observatory.

We show the results of this new tuning step, which succeeds in reducing the domain of acceptable free parameters as well as the dispersion of the simulations. This method, which is less computationally costly than global tuning, is therefore a good way to precondition the latter. It allows the joint tuning of atmospheric and land surface models, traditionally tuned separately [5], and has the advantage of remaining close to the processes and thus improving their understanding.

References:

[1] Cheruy et al., 2014, https://doi.org/10.1002/2014GL061145

[2] Hourdin et al., 2017, https://doi.org/10.1175/BAMS-D-15-00135.1

[3] Couvreux et al., 2021, https://doi.org/10.1029/2020MS002217

[4] Hourdin et al., 2021, https://doi.org/10.1029/2020MS002225

[5] Cheruy et al., 2020, https://doi.org/10.1029/2019MS002005

How to cite: Coulon--Decorzens, M., Cheruy, F., and Hourdin, F.: Semi-automatic tuning procedure for a GCM targeting continental surfaces: a first experiment using in situ observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4493, https://doi.org/10.5194/egusphere-egu22-4493, 2022.

EGU22-4923 | Presentations | ITS2.6/AS5.1

Constrained Generative Adversarial Networks for Improving Earth System Model Precipitation 

Philipp Hess, Markus Drüke, Stefan Petri, Felix Strnad, and Niklas Boers

The simulation of precipitation in numerical Earth system models (ESMs) involves various processes on a wide range of scales, requiring high temporal and spatial resolution for realistic simulations. This can lead to biases in computationally efficient ESMs that have a coarse resolution and limited model complexity. Traditionally, these biases are corrected by relating the distributions of historical simulations with observations [1]. While these methods successfully improve the modelled statistics, unrealistic spatial features that require a larger spatial context are not addressed.

Here we apply generative adversarial networks (GANs) [2] to transform precipitation of the CM2Mc-LPJmL ESM [3] into a bias-corrected and more realistic output. Feature attribution shows that the GAN has correctly learned to identify spatial regions with the largest bias during training. Our method presents a general bias correction framework that can be extended to a wider range of ESM variables to create highly realistic but computationally inexpensive simulations of future climates. We also discuss the generalizability of our approach to projections from CMIP6, given that the GAN is only trained on historical data.

[1] A.J. Cannon et al. "Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?." Journal of Climate 28.17 (2015): 6938-6959.

[2] I. Goodfellow et al. "Generative adversarial nets." Advances in neural information processing systems 27 (2014).

[3] M. Drüke et al. "CM2Mc-LPJmL v1.0: Biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model." Geoscientific Model Development 14.6 (2021): 4117--4141.

How to cite: Hess, P., Drüke, M., Petri, S., Strnad, F., and Boers, N.: Constrained Generative Adversarial Networks for Improving Earth System Model Precipitation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4923, https://doi.org/10.5194/egusphere-egu22-4923, 2022.

EGU22-5219 | Presentations | ITS2.6/AS5.1 | Highlight

Neural Partial Differential Equations for Atmospheric Dynamics 

Maximilian Gelbrecht and Niklas Boers

When predicting complex systems such as parts of the Earth system, one typically relies on differential equations which can often be incomplete, missing unknown influences or higher order effects. Using the universal differential equations framework, we can augment the equations with artificial neural networks that can compensate these deficiencies. We show that this can be used to predict the dynamics of high-dimensional spatiotemporally chaotic partial differential equations, such as the ones describing atmospheric dynamics. In a first step towards a hybrid atmospheric model, we investigate the Marshall Molteni Quasigeostrophic Model in the form of a Neural Partial Differential Equation. We use it in synthetic examples where parts of the governing equations are replaced with artificial neural networks (ANNs) and demonstrate how the ANNs can recover those terms.

How to cite: Gelbrecht, M. and Boers, N.: Neural Partial Differential Equations for Atmospheric Dynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5219, https://doi.org/10.5194/egusphere-egu22-5219, 2022.

EGU22-5631 | Presentations | ITS2.6/AS5.1

Autonomous Assessment of Source Area Distributions for Sections in Lagrangian Particle Release Experiments 

Carola Trahms, Patricia Handmann, Willi Rath, Matthias Renz, and Martin Visbeck

Lagrangian experiments for particle tracing in atmosphere or ocean models and their analysis are a cornerstone of earth-system studies. They cover diverse study objectives such as the identification of pathways or source regions. Data for Lagrangian studies are generated by releasing virtual particles in one or in multiple locations of interest and simulating their advective-diffusive behavior backwards or forwards in time. Identifying main pathways connecting two regions of interest is often done by counting the trajectories that reach both regions. Here, the exact source and target region must be defined manually by a researcher. Manually defining the importance and exact location of these regions introduces a highly subjective perspective into the analysis. Additionally, to investigate all major target regions, all of them must be defined manually and the data must be analyzed accordingly. This human element slows down and complicates large scale analyses with many different sections and possible source areas.

We propose to significantly reduce the manual aspect by automatizing this process. To this end, we combine methods from different areas of machine learning and pattern mining into a sequence of steps. First, unsupervised methods, i.e., clustering, identify possible source areas on a randomized subset of the data. In a successive second step, supervised learning, i.e., classification, labels the positions along the trajectories according to their most probable source area using the previously automatically identified clusters as labels. The results of this approach can then be compared quantitatively to the results of analyses with manual definition of source areas and border-hitting-based labeling of the trajectories. Preliminary findings suggest that this approach could indeed help greatly to objectify and fasten the analysis process for Lagrangian Particle Release Experiments.

How to cite: Trahms, C., Handmann, P., Rath, W., Renz, M., and Visbeck, M.: Autonomous Assessment of Source Area Distributions for Sections in Lagrangian Particle Release Experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5631, https://doi.org/10.5194/egusphere-egu22-5631, 2022.

EGU22-5632 | Presentations | ITS2.6/AS5.1

Data-Driven Sentinel-2 Based Deep Feature Extraction to Improve Insect Species Distribution Models 

Joe Phillips, Ce Zhang, Bryan Williams, and Susan Jarvis

Despite being a vital part of ecosystems, insects are dying out at unprecedented rates across the globe. To help address this in the UK, UK Centre for Ecology & Hydrology (UKCEH) are creating a tool to utilise insect species distribution models (SDMs) for better facilitating future conservation efforts via volunteer-led insect tracking procedures. Based on these SDM models, we explored the inclusion of additional covariate information via 10-20m2 bands of temporally-aggregated Sentinel-2 data taken over the North of England in 2017 to improve the predictive performance. Here, we matched the 10-20m2 resolution of the satellite data to the coarse 1002 insect observation data via four methodologies of increasing complexity. First, we considered standard pixel-based approaches, performing aggregation by taking both the mean and standard deviation over the 10m2 pixels. Second, we explored object-based approaches to address the modifiable areal unit problem by applying the SNIC superpixels algorithm over the extent, with the mean and standard deviation of the pixels taken within each segment. The resulting dataset was then re-projected to a resolution of 100m2 by taking the modal values of the 10m2 pixels, which were provided with the aggregated values of their parent segment. Third, we took the UKCEH-created 2017 Land Cover Map (LCM) dataset and sampled 42,000, random 100m2 areas, evenly distributed about their modal land cover classes. We trained the U-Net Deep Learning model using the Sentinel-2 satellite images and LCM classes, by which data-driven features were extracted from the network over each 100m2 extent. Finally, as with the second approach, we used the superpixels segments instead as the units of analysis, sampling 21,000 segments, and taking the smallest bounding box around each of them. An attention-based U-Net was then adopted to mask each of the segments from their background and extract deep features. In a similar fashion to the second approach, we then re-projected the resulting dataset to a resolution of 100m2, taking the modal segment values accordingly. Using cross-validated AUCs over various species of moths and butterflies, we found that the object-based deep learning approach achieved the best accuracy when used with the SDMs. As such, we conclude that the novel approach of spatially aggregating satellite data via object-based, deep feature extraction has the potential to benefit similar, model-based aggregation needs and catalyse a step-change in ecological and environmental applications in the future.

How to cite: Phillips, J., Zhang, C., Williams, B., and Jarvis, S.: Data-Driven Sentinel-2 Based Deep Feature Extraction to Improve Insect Species Distribution Models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5632, https://doi.org/10.5194/egusphere-egu22-5632, 2022.

EGU22-5681 | Presentations | ITS2.6/AS5.1

AtmoDist as a new pathway towards quantifying and understanding atmospheric predictability 

Sebastian Hoffmann, Yi Deng, and Christian Lessig

The predictability of the atmosphere is a classical problem that has received much attention from both a theoretical and practical point of view. In this work, we propose to use a purely data-driven method based on a neural network to revisit the problem. The analysis is built upon the recently introduced AtmoDist network that has been trained on high-resolution reanalysis data to provide a probabilistic estimate of the temporal difference between given atmospheric fields, represented by vorticity and divergence. We define the skill of the network for this task as a new measure of atmospheric predictability, hypothesizing that the prediction of the temporal differences by the network will be more susceptible to errors when the atmospheric state is intrinsically less predictable. Preliminary results show that for short timescales (3-48 hours) one sees enhanced predictability in warm season compared to cool season over northern midlatitudes, and lower predictability over ocean compared to land. These findings support the hypothesis that across short timescales, AtmoDist relies on the recurrences of mesoscale convection with coherent spatiotemporal structures to connect spatial evolutions to temporal differences. For example, the prevalence of mesoscale convective systems (MCSs) over the central US in boreal warm season can explain the increase of mesoscale predictability there and oceanic zones marked by greater predictability corresponds well to regions of elevated convective activity such as the Pacific ITCZ. Given the dependence of atmospheric predictability on geographic location, season, and most importantly, timescales, we further apply the method to synoptic scales (2-10 days), where excitation and propagation of large-scale disturbances such as Rossby wave packets are expected to provide the connection between temporal and spatial differences. The design of the AtmoDist network is thereby adapted to the prediction range, for example, the size of the local patches that serve as input to AtmoDist is chosen based on the spatiotemporal atmospheric scales that provide the expected time and space connections.

By providing to the community a powerful, purely data-driven technique for quantifying, evaluating, and interpreting predictability, our work lays the foundation for efficiently detecting the existence of sub-seasonal to seasonal (S2S) predictability and, by further analyzing the mechanism of AtmoDist, understanding the physical origins, which bears major scientific and socioeconomic significances.

How to cite: Hoffmann, S., Deng, Y., and Lessig, C.: AtmoDist as a new pathway towards quantifying and understanding atmospheric predictability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5681, https://doi.org/10.5194/egusphere-egu22-5681, 2022.

EGU22-5746 | Presentations | ITS2.6/AS5.1

Model Output Statistics (MOS) and Machine Learning applied to CAMS O3 forecasts: trade-offs between continuous and categorical skill scores 

Hervé Petetin, Dene Bowdalo, Pierre-Antoine Bretonnière, Marc Guevara, Oriol Jorba, Jan Mateu armengol, Margarida Samso Cabre, Kim Serradell, Albert Soret, and Carlos Pérez García-Pando

Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with Model Output Statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate to what extent AQ forecasts can be improved using a variety of MOS methods, including persistence (PERS), moving average (MA), quantile mapping (QM), Kalman Filter (KF), analogs (AN), and gradient boosting machine (GBM). We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a very comprehensive set of continuous and categorical metrics at various time scales (hourly to daily), along different lead times (1 to 4 days), and using different meteorological input data (forecast vs reanalyzed).

Our results show that O3 forecasts can be substantially improved using such MOS corrections and that this improvement goes much beyond the correction of the systematic bias. Although it typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. When considering MOS methods relying on meteorological information and comparing the results obtained with IFS forecasts and ERA5 reanalysis, the relative deterioration brought by the use of IFS is minor, which paves the way for their use in operational MOS applications. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with lowest errors and highest correlations. However, they are not necessarily the best in predicting the highest O3 episodes, for which simpler MOS methods can give better results. Although the complex impact of MOS methods on the distribution and variability of raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.

Petetin, H., Bowdalo, D., Bretonnière, P.-A., Guevara, M., Jorba, O., Armengol, J. M., Samso Cabre, M., Serradell, K., Soret, A., and Pérez Garcia-Pando, C.: Model Output Statistics (MOS) applied to CAMS O3 forecasts: trade-offs between continuous and categorical skill scores, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-864, in review, 2021.

How to cite: Petetin, H., Bowdalo, D., Bretonnière, P.-A., Guevara, M., Jorba, O., Mateu armengol, J., Samso Cabre, M., Serradell, K., Soret, A., and Pérez García-Pando, C.: Model Output Statistics (MOS) and Machine Learning applied to CAMS O3 forecasts: trade-offs between continuous and categorical skill scores, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5746, https://doi.org/10.5194/egusphere-egu22-5746, 2022.

With the goal of developing a data-driven parameterization of unresolved gravity waves (GW) momentum transport for use in general circulation models (GCMs), we investigate neural network architectures that emulate the Alexander-Dunkerton 1999 (AD99) scheme, an existing physics-based GW parameterization. We analyze the distribution of errors as functions of shear-related metrics in an effort to diagnose the disparity between online and offline performance of the trained emulators, and develop a sampling algorithm to treat biases on the tails of the distribution without adversely impacting mean performance. 

It has been shown in previous efforts [1] that stellar offline performance does not necessarily guarantee adequate online performance, or even stability. Error analysis reveals that the majority of the samples are learned quickly, while some stubborn samples remain poorly represented. We find that the more error-prone samples are those with wind profiles that have large shears– this is consistent with physical intuition as gravity waves encounter a wider range of critical levels when experiencing large shear;  therefore parameterizing gravity waves for these samples is a more difficult, complex task. To remedy this, we develop a sampling strategy that performs a parameterized histogram equalization, a concept borrowed from 1D optimal transport. 

The sampling algorithm uses a linear mapping from the original histogram to a more uniform histogram parameterized by $t \in [0,1]$, where $t=0$ recovers the original distribution and $t=1$ enforces a completely uniform distribution. A given value $t$ assigns each bin a new probability which we then use to sample from each bin. If the new probability is smaller than the original, then we invoke sampling without replacement, but limited to a reduced number consistent with the new probability. If the new probability is larger than the original, then we repeat all the samples in the bin up to some predetermined maximum repeat value (a threshold to avoid extreme oversampling at the tails). We optimize this sampling algorithm with respect to $t$, the maximum repeat value, and the number and distribution (uniform or not) of the histogram bins. The ideal combination of those parameters yields errors that are closer to a constant function of the shear metrics while maintaining high accuracy over the whole dataset. Although we study the performance of this algorithm in the context of training a gravity wave parameterization emulator, this strategy can be used for learning datasets with long tail distributions where the rare samples are associated with low accuracy. Instances of this type of datasets are prevalent in earth system dynamics: launching of gravity waves, and extreme events like hurricanes, heat waves are just a few examples. 

[1] Espinosa, Z. I., A. Sheshadri, G. R. Cain, E. P. Gerber, and K. J. DallaSanta, 2021: A Deep Learning Parameterization of Gravity Wave Drag Coupled to an Atmospheric Global Climate Model,Geophys. Res. Lett., in review. [https://edwinpgerber.github.io/files/espinosa_etal-GRL-revised.pdf]

How to cite: Yang, L. and Gerber, E.: Sampling strategies for data-driven parameterization of gravity wave momentum transport, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5766, https://doi.org/10.5194/egusphere-egu22-5766, 2022.

EGU22-5980 | Presentations | ITS2.6/AS5.1 | Highlight

Probabilistic forecasting of heat waves with deep learning 

George Miloshevich, Valerian Jacques-Dumas, Pierre Borgnat, Patrice Abry, and Freddy Bouchet
Extreme events such as storms, floods, cold spells and heat waves are expected to have an increasing societal impact with climate change. However the study of rare events is complicated due to computational costs of highly complex models and lack of observations. However, with the help of machine learning synthetic models for forecasting can be constructed and cheaper resampling techniques can be developed. Consequently, this may also clarify more regional impacts of climate change. .

In this work, we perform detailed analysis of how deep neural networks (DNNs) can be used in intermediate-range forecasting of prolonged heat waves of duration of several weeks over synoptic spatial scales. In particular, we train a convolutional neural network (CNN) on the 7200 years of a simulation of a climate model. As such, we are interested in probabilistic prediction (committor function in transition theory). Thus we discuss the proper forecasting scores such as Brier skill score, which is popular in weather prediction, and cross-entropy skill, which is based on information-theoretic considerations. They allow us to measure the success of various architectures and investigate more efficient pipelines to extract the predictions from physical observables such as geopotential, temperature and soil moisture. A priori, the committor is hard to visualize as it is a high dimensional function of its inputs, the grid points of the climate model for a given field. Fortunately, we can construct composite maps conditioned to its values which reveal that the CNN is likely relying on the global teleconnection patterns of geopotential. On the other hand, soil moisture signal is more localized with predictive capability over much longer times in future (at least a month). The latter fact relates to the soil-atmosphere interactions. One expects the performance of DNNs to greatly improve with more data. We provide quantitative assessment of this fact. In addition, we offer more details on how the undersampling of negative events affects the knowledge of the committor function. We show that transfer learning helps ensure that the committor is a smooth function along the trajectory. This will be an important quality when such a committor will be applied in rare event algorithms for importance sampling. 
 
While DNNs are universal function approximators the issue of extrapolation can be somewhat problematic. In addressing this question we train a CNN on a dataset generated from a simulation without a diurnal cycle, where the feedbacks between soil moisture and heat waves appear to be significantly stronger. Nevertheless, when the CNN with the given weights is validated on a dataset generated from a simulation with a daily cycle the predictions seem to generalize relatively well, despite a small reduction in skill. This generality validates the approach. 
 

How to cite: Miloshevich, G., Jacques-Dumas, V., Borgnat, P., Abry, P., and Bouchet, F.: Probabilistic forecasting of heat waves with deep learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5980, https://doi.org/10.5194/egusphere-egu22-5980, 2022.

EGU22-6479 | Presentations | ITS2.6/AS5.1

Parameter inference and uncertainty quantification for an intermediate complexity climate model 

Benedict Roeder, Jakob Schloer, and Bedartha Goswami

Well-adapted parameters in climate models are essential to make accurate predictions
for future projections. In climate science, the record of precise and comprehensive obser-
vational data is rather short and parameters of climate models are often hand-tuned or
learned from artificially generated data. Due to limited and noisy data, one wants to use
Bayesian models to have access to uncertainties of the inferred parameters. Most popu-
lar algorithms for learning parameters from observational data like the Kalman inversion
approach only provide point estimates of parameters.
In this work, we compare two Bayesian parameter inference approaches applied to the
intermediate complexity model for the El Niño-Southern Oscillation by Zebiak & Cane. i)
The "Calibrate, Emulate, Sample" (CES) approach, an extension of the ensemble Kalman
inversion which allows posterior inference by emulating the model via Gaussian Processes
and thereby enables efficient sampling. ii) The simulation-based inference (SBI) approach
where the approximate posterior distribution is learned from simulated model data and
observational data using neural networks.
We evaluate the performance of both approaches by comparing their run times and the
number of required model evaluations, assess the scalability with respect to the number
of inference parameters, and examine their posterior distributions.

How to cite: Roeder, B., Schloer, J., and Goswami, B.: Parameter inference and uncertainty quantification for an intermediate complexity climate model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6479, https://doi.org/10.5194/egusphere-egu22-6479, 2022.

EGU22-6553 | Presentations | ITS2.6/AS5.1

Can simple machine learning methods predict concentrations of OH better than state of the art chemical mechanisms? 

Sebastian Hickman, Paul Griffiths, James Weber, and Alex Archibald

Concentrations of the hydroxyl radical, OH, control the lifetime of methane, carbon monoxide and other atmospheric constituents.  The short lifetime of OH, coupled with the spatial and temporal variability in its sources and sinks, makes accurate simulation of its concentration particularly challenging. To date, machine learning (ML) methods have been infrequently applied to global studies of atmospheric chemistry.

We present an assessment of the use of ML methods for the challenging case of simulation of the hydroxyl radical at the global scale, and show that several approaches are indeed viable.  We use observational data from the recent NASA Atmospheric Tomography Mission to show that machine learning methods are comparable in skill to state of the art forward chemical models and are capable, if appropriately applied, of simulating OH to within observational uncertainty.  

We show that a simple ridge regression model is a better predictor of OH concentrations in the remote atmosphere than a state of the art chemical mechanism implemented in a forward box model. Our work shows that machine learning may be an accurate emulator of chemical concentrations in atmospheric chemistry, which would allow a significant speed up in climate model runtime due to the speed and efficiency of simple machine learning methods. Furthermore, we show that relatively few predictors are required to simulate OH concentrations, suggesting that the variability in OH can be quantitatively accounted for by few observables with the potential to simplify the numerical simulation of atmospheric levels of key species such as methane. 

How to cite: Hickman, S., Griffiths, P., Weber, J., and Archibald, A.: Can simple machine learning methods predict concentrations of OH better than state of the art chemical mechanisms?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6553, https://doi.org/10.5194/egusphere-egu22-6553, 2022.

EGU22-6674 | Presentations | ITS2.6/AS5.1

The gravity wave parameterization calibration problem: A 1D QBO model testbed 

Ofer Shamir, L. Minah Yang, David S. Connelly, and Edwin P. Gerber

An essential step in implementing any new parameterization is calibration, where the parameterization is adjusted to work with an existing model and yield some desired improvement. In the context of gravity wave (GW) momentum transport, calibration is necessitated by the facts that: (i) Some GWs are always at least partially resolved by the model, and hence a parameterization should only account for the missing waves. Worse, the parameterization may need to correct for the misrepresentation of under-resolved GWs, i.e., coarse vertical resolution can bias GW breaking level, leading to erroneous momentum forcing. (ii) The parameterized waves depend on the resolved solution for both their sources and dissipation, making them susceptible to model biases. Even a "perfect" parameterization could then yield an undesirable result, e.g., an unrealistic Quasi-Biennial Oscillation (QBO).  While model-specific calibration is required, one would like a general "recipe" suitable for most models. From a practical point of view, the adoption of a new parameterization will be hindered by a too-demanding calibration process. This issue is of particular concern in the context of data-driven methods, where the number of tunable degrees of freedom is large (possibly in the millions). Thus, more judicious ways for addressing the calibration step are required. 

To address the above issues, we develop a 1D QBO model, where the "true" gravity wave momentum deposition is determined from a source distribution and critical level breaking, akin to a traditional physics-based GW parameterization. The control parameters associated with the source consist of the total wave flux (related to the total precipitation for convectively generated waves) and the spectrum width (related to the depth of convection). These parameters can be varied to mimic the variability in GW sources between different models, i.e., biases in precipitation variability. In addition, the model’s explicit diffusivity and vertical advection can be varied to mimic biases in model numerics and circulation, respectively. The model thus allows us to assess the ability of a data-driven parameterization to (i) extrapolate, capturing the response of GW momentum transport to a change in the model parameters and (ii) be calibrated, adjusted to maintain the desired simulation of the QBO in response to a change in the model parameters. The first property is essential for a parameterization to be used for climate prediction, the second, for a parameterization to be used at all. We focus in particular on emulators of the GW momentum transport based on neural network and regression trees, contrasting their ability to satisfy both of these goals.  

 

How to cite: Shamir, O., Yang, L. M., Connelly, D. S., and Gerber, E. P.: The gravity wave parameterization calibration problem: A 1D QBO model testbed, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6674, https://doi.org/10.5194/egusphere-egu22-6674, 2022.

All oceanic general circulation models (GCMs) include parametrizations of the unresolved subgrid-scale (eddy) effects on the large-scale motions, even at the (so-called) eddy-permitting resolutions. Among the many problems associated with the development of accurate and efficient eddy parametrizations, one problem is a reliable decomposition of a turbulent flow into resolved and unresolved (subgrid) scale components. Finding an objective way to separate eddies is a fundamental, critically important and unresolved problem. 
Here a statistically consistent correlation-based flow decomposition method (CBD) that employs the Gaussian filtering kernel with geographically varying topology – consistent with the observed local spatial correlations – achieves the desired scale separation. CBD is demonstrated for an eddy-resolving solution of the classical midlatitude double-gyre quasigeostrophic (QG) circulation, that possess two asymmetric gyres of opposite circulations and a strong meandering eastward jet, such as the Gulf Stream in the North Atlantic and Kuroshio in the North Pacific. CBD facilitates a comprehensive analysis of the feedbacks of eddies on the large-scale flow via the transient part of the eddy forcing. A  `product integral' based on time-lagged correlation between the diagnosed eddy forcing and the evolving large-scale flow, uncovers robust `eddy backscatter' mechanism. Data-driven augmentation of non-eddy-resolving ocean model by stochastically-emulated eddy fields allows to restore the missing eddy-driven features, such as the merging western boundary currents, their eastward extension and low-frequency variabilities of gyres.

  • N. Argawal, Ryzhov, E.A., Kondrashov, D., and P.S. Berloff, 2021: Correlation-based flow decomposition and statistical analysis of the eddy forcing, Journal of Fluid Mechanics, 924, A5. doi:10.1017/jfm.2021.604

  • N. Argawal, Kondrashov, D., Dueben, P., Ryzhov, E.A., and P.S. Berloff, 2021: A comparison of data-driven approaches to build low-dimensional ocean modelsJournal of Advances in Modelling Earth Systems, doi:10.1029/2021MS002537

 

How to cite: Kondrashov, D.: Towards physics-informed stochastic parametrizations of subgrid physics in ocean models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6859, https://doi.org/10.5194/egusphere-egu22-6859, 2022.

EGU22-7044 | Presentations | ITS2.6/AS5.1

Seismic Event Characterization using Manifold Learning Methods 

Yuri Bregman, Yochai Ben Horin, Yael Radzyner, Itay Niv, Maayan Kahlon, and Neta Rabin

Manifold learning is a branch of machine learning that focuses on compactly representing complex data-sets based on their fundamental intrinsic parameters. One such method is diffusion maps, which reduces the dimension of the data while preserving its geometric structure. In this work, diffusion maps are applied to several seismic event characterization tasks. The first task is automatic earthquake-explosion discrimination, which is an essential component of nuclear test monitoring. We also use this technique to automatically identify mine explosions and aftershocks following large earthquakes. Identification of such events helps to lighten the analysts’ burden and allow for timely production of reviewed seismic bulletins.

The proposed methods begin with a pre-processing stage in which a time–frequency representation is extracted from each seismogram while capturing common properties of seismic events and overcoming magnitude differences. Then, diffusion maps are used in order to construct a low-dimensional model of the original data. In this new low-dimensional space, classification analysis is carried out.

The algorithm’s discrimination performance is demonstrated on several seismic data sets. For instance, using the seismograms from EIL station, we identify arrivals that were caused by explosions at the nearby Eshidiya mine in Jordan. The model provides a visualization of the data, organized by its intrinsic factors. Thus, along with the discrimination results, we provide a compact organization of the data that characterizes the activity patterns in the mine.

Our results demonstrate the potential and strength of the manifold learning based approach, which may be suitable to other in other geophysics domains.

How to cite: Bregman, Y., Ben Horin, Y., Radzyner, Y., Niv, I., Kahlon, M., and Rabin, N.: Seismic Event Characterization using Manifold Learning Methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7044, https://doi.org/10.5194/egusphere-egu22-7044, 2022.

Accurate streamflow forecasts can provide guidance for reservoir managements, which can regulate river flows, manage water resources and mitigate flood damages. One popular way to forecast streamflow is to use bias-corrected meteorological forecasts to drive a calibrated hydrological model. But for cascade reservoirs, such approaches suffer significant deficiencies because of the difficulty to simulate reservoir operations by physical approach and the uncertainty of meteorological forecasts over small catchment. Another popular way is to forecast streamflow with machine learning method, which can fit a statistical model without inputs like reservoir operating rules. Thus, we integrate meteorological forecasts, land surface hydrological model and machine learning to forecast hourly streamflow over the Yantan catchment, which is one of the cascade reservoirs in the Hongshui River with streamflow influenced by both the upstream reservoir water release and the rainfall runoff process within the catchment.

Before evaluating the streamflow forecast system, it is necessary to investigate the skill by means of a series of specific hindcasts that isolate potential sources of predictability, like meteorological forcing and the initial condition (IC). Here, we use ensemble streamflow prediction (ESP)/reverse ESP (revESP) method to explore the impact of IC on hourly stream prediction. Results show that the effect of IC on runoff prediction is 16 hours. In the next step, we evaluate the hourly streamflow hindcasts during the rainy seasons of 2013-2017 performed by the forecast system. We use European Centre for Medium-Range Weather Forecasts perturbed forecast forcing from the THORPEX Interactive Grand Global Ensemble (TIGGE-ECMWF) as meteorological inputs to perform the hourly streamflow hindcasts. Compared with the ESP, the hydrometeorological ensemble forecast approach reduces probabilistic and deterministic forecast errors by 6% during the first 7 days. After integrated the long short-term memory (LSTM) deep learning method into the system, the deterministic forecast error can be further reduced by 6% in the first 72 hours. We also use historically observed streamflow to drive another LSTM model to perform an LSTM-only streamflow forecast. Results show that its skill sharply dropped after the first 24 hours, which indicates that the meteorology-hydrology modeling approach can improve the streamflow forecast.

How to cite: Liu, J. and Yuan, X.: Reservoir inflow forecast by combining meteorological ensemble forecast, physical hydrological simulation and machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7093, https://doi.org/10.5194/egusphere-egu22-7093, 2022.

EGU22-7113 | Presentations | ITS2.6/AS5.1 | Highlight

Coupling regional air quality simulations of EURAD-IM with street canyon observations - a machine learning approach 

Charlotte Neubacher, Philipp Franke, Alexander Heinlein, Axel Klawonn, Astrid Kiendler-Scharr, and Anne-Caroline Lange

State of the art atmospheric chemistry transport models on regional scales as the EURAD-IM (EURopean Air pollution Dispersion-Inverse Model) simulate physical and chemical processes in the atmosphere to predict the dispersion of air pollutants. With EURAD-IM’s 4D-var data assimilation application, detailed analyses of the air quality can be conducted. These analyses allow for improvements of atmospheric chemistry forecast as well as emission source strength assessments. Simulations of EURAD-IM can be nested to a spatial resolution of 1 km, which does not correspond to the urban scale. Thus, inner city street canyon observations cannot be exploited since here, anthropogenic pollution vary vastly over scales of 100 m or less.

We address this issue by implementing a machine learning (ML) module into EURAD-IM, forming a hybrid model that enable bridging the representativeness gap between model resolution and inner-city observations. Thus, the data assimilation of EURAD-IM is strengthened by additional observations in urban regions. Our approach of the ML module is based on a neural network (NN) with relevant environmental information of street architecture, traffic density, meteorology, and atmospheric pollutant concentrations from EURAD-IM as well as the street canyon observation of pollutants as input features. The NN then maps the observed concentration from street canyon scale to larger spatial scales.

We are currently working with a fully controllable test environment created from EURAD-IM forecasts of the years 2020 and 2021 at different spatial resolutions. Here, the ML model maps the high-resolution hourly NO2 concentration to the concentration of the low resolution model grid. It turns out that it is very difficult for NNs to learn the hourly concentrations with equal accuracy using diurnal cycles of pollutant concentrations. Thus, we develop a model that uses an independent NN for each hour to support time-of-day learning. This allows to reduce the training error by a factor of 102. As a proof of concept, we trained the ML model in an overfitting regime where the mean squared training error reduce to 0.001% for each hour. Furthermore, by optimizing the hyperparameters and introducing regularization terms to reduce the overfitting, we achieved a validation error of 9−12% during night and 9−16% during day.

How to cite: Neubacher, C., Franke, P., Heinlein, A., Klawonn, A., Kiendler-Scharr, A., and Lange, A.-C.: Coupling regional air quality simulations of EURAD-IM with street canyon observations - a machine learning approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7113, https://doi.org/10.5194/egusphere-egu22-7113, 2022.

EGU22-7135 | Presentations | ITS2.6/AS5.1 | Highlight

How to calibrate a climate model with neural network based physics? 

Blanka Balogh, David Saint-Martin, and Aurélien Ribes

Unlike the traditional subgrid scale parameterizations used in climate models, current neural network (NN) parameterizations are only tuned offline, by minimizing a loss function on outputs from high resolution models. This approach often leads to numerical instabilities and long-term biases. Here, we propose a method to design tunable NN parameterizations and calibrate them online. The calibration of the NN parameterization is achieved in two steps. First, some model parameters are included within the NN model input. This NN model is fitted at once for a range of values of the parameters, using an offline metric. Second, once the NN parameterization has been plugged into the climate model, the parameters included among the NN inputs are optimized with respect to an online metric quantifying errors on long-term statistics. We illustrate our method with two simple dynamical systems. Our approach significantly reduces long-term biases of the climate model with NN based physics.

How to cite: Balogh, B., Saint-Martin, D., and Ribes, A.: How to calibrate a climate model with neural network based physics?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7135, https://doi.org/10.5194/egusphere-egu22-7135, 2022.

EGU22-8279 | Presentations | ITS2.6/AS5.1

Using deep learning to improve the spatial resolution of the ocean model 

Ihor Hromov, Georgy Shapiro, Jose Ondina, Sanjay Sharma, and Diego Bruciaferri

For the ocean models, the increase of spatial resolution is a matter of significant importance and thorough research. Computational resources limit our capabilities of the increase in model resolution. This constraint is especially true for the traditional dynamical models, for which an increase of a factor of two in the horizontal resolution results in simulation times increased approximately tenfold. One of the potential methods to relax this limitation is to use Artificial Intelligence methods, such as Neural Networks (NN). In this research, NN is applied to ocean circulation modelling. More specifically, NN is used on data output from the dynamical model to increase the spatial resolution of the model output. The main dataset being used is Sea Surface Temperature data in 0.05- and 0.02-degree horizontal resolutions for Irish Sea. 

Several NN architectures were applied to address the task. Generative Adversarial Networks (GAN), Convolutional Neural Networks (CNN) and Multi-level Wavelet CNN. They are used in other areas of knowledge in problems related to the increase of resolution. The work will contrast and compare the efficiency of and present a provisional assessment of the efficiency of each of the methods. 

How to cite: Hromov, I., Shapiro, G., Ondina, J., Sharma, S., and Bruciaferri, D.: Using deep learning to improve the spatial resolution of the ocean model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8279, https://doi.org/10.5194/egusphere-egu22-8279, 2022.

EGU22-8334 | Presentations | ITS2.6/AS5.1

Information theory solution approach for air-pollution sensors' location-allocation problem 

Barak Fishbain, Ziv Mano, and Shai Kendler

Urbanization and industrialization processes are accompanied by adverse environmental effects, such as air pollution. The first action in reducing air pollution is the detection of its source(s). This is achievable through monitoring. When deploying a sensor array, one must balance between the array's cost and performance. This optimization problem is known as the location-allocation problem. Here, a new solution approach, which draws its foundation from information theory is presented. The core of the method is air-pollution levels computed by a dispersion model in various meteorological conditions. The sensors are then placed in the locations which information theory identifies as the most uncertain. The method is compared with two other heuristics typically applied for solving the location-allocation problem. In the first, sensors are randomly deployed, in the second, the sensors are placed according to the maximal cumulative pollution levels (i.e., hot spot). For the comparison two simulated scenes were evaluated, one contains point sources and buildings, and the other also contains line sources (i.e., roads). It shows that the Entropy method resulted in a superior sensors' deployment compared to the other two approaches in terms of source apportionment and dense pollution field reconstruction from the sensors' network measurements.

How to cite: Fishbain, B., Mano, Z., and Kendler, S.: Information theory solution approach for air-pollution sensors' location-allocation problem, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8334, https://doi.org/10.5194/egusphere-egu22-8334, 2022.

EGU22-8719 | Presentations | ITS2.6/AS5.1

Multi-station Multivariate Multi-step Convection Nowcasting with Deep Neural Networks 

Sandy Chkeir, Aikaterini Anesiadou, and Riccardo Biondi

Extreme weather nowcasting has always been a challenging task in meteorology. Many research studies have been conducted to accurately forecast extreme weather events, related to rain rates and/or wind speed thresholds, in spatio-temporal scales. Over decades, this field gained attention in the artificial intelligence community which is aiming towards creating more accurate models using the latest algorithms and methods.  

In this work, within the H2020 SESAR ALARM project, we aim to nowcast rain and wind speed as target features using different input configurations of the available sources such as weather stations, lightning detectors, radar, GNSS receivers, radiosonde and radio occultations data. This nowcasting task has been firstly conducted at 14 local stations around Milano Malpensa Airport as a short-term temporal multi-step forecasting. At a second step, all stations will be combined, meaning that the forecasting becomes a spatio-temporal problem. Concretely, we want to investigate the predicted rain and wind speed values using the different inputs for two case scenarios: for each station, and joining all stations together. 

The chaotic nature of the atmosphere, e.g. non-stationarity of the driving series of each weather feature, makes the predictions unreliable and inaccurate and thus dealing with these data is a very delicate task. For this reason, we have devoted some work to cleaning, feature engineering and preparing the raw data before feeding them into the model architectures. We have managed to preprocess large amounts of data for local stations around the airport, and studied the feasibility of nowcasting rain and wind speed targets using different data sources altogether. The temporal multivariate driving series have high dimensionality and we’ve  made multi-step predictions for the defined target functions.

We study and test different machine learning architectures starting from simple multi-layer perceptrons to convolutional models, and Recurrent Neural Networks (RNN) for temporal and spatio-temporal nowcasting. The Long Short-Term Memory (LSTM) encoder decoder architecture outperforms other models achieving more accurate predictions for each station separately.  Furthermore, to predict the targets in a spatio-temporal scale, we will deploy a 2-layer spatio-temporal stacked LSTM model consisting of independent LSTM models per location in the first LSTM layer, and another LSTM layer to finally predict targets for multi-steps ahead. And the results obtained with different algorithm architectures applied to a dense network of sensors are to be reported.

How to cite: Chkeir, S., Anesiadou, A., and Biondi, R.: Multi-station Multivariate Multi-step Convection Nowcasting with Deep Neural Networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8719, https://doi.org/10.5194/egusphere-egu22-8719, 2022.

EGU22-8852 | Presentations | ITS2.6/AS5.1

Time-dependent Hillshades: Dispelling the Shadow Curse of Machine Learning Applications in Earth Observation 

Freddie Kalaitzis, Gonzalo Mateo-Garcia, Kevin Dobbs, Dolores Garcia, Jason Stoker, and Giovanni Marchisio

We show that machine learning models learn and perform better when they know where to expect shadows, through hillshades modeled to the time of imagery acquisition.

Shadows are detrimental to all machine learning applications on satellite imagery. Prediction tasks like semantic / instance segmentation, object detection, counting of rivers, roads, buildings, trees, all rely on crisp edges and colour gradients that are confounded by the presence of shadows in passive optical imagery, which rely on the sun’s illumination for reflectance values.

Hillshading is a standard technique for enriching a mapped terrain with relief effects, which is done by emulating the shadow caused by steep terrain and/or tall vegetation. A hillshade that is modeled to the time of day and year can be easily derived through a basic form of ray tracing on a Digital Terrain Model (DTM) (also known as a bare-earth DEM) or Digital Surface Model (DSM) given the sun's altitude and azimuth angles. In this work, we use lidar-derived DSMs. A DSM-based hillshade conveys a lot more information on shadows than a bare-earth DEM alone, namely any non-terrain vertical features (e.g. vegetation, buildings) resolvable at a 1-m resolution. The use of this level of fidelity of DSM for hillshading and its input to a machine learning model is novel and the main contribution of our work. Any uncertainty over the angles can be captured through a composite multi-angle hillshade, which shows the range where shadows can appear throughout the day.

We show the utility of time-dependent hillshades in the daily mapping of rivers from Very High Resolution (VHR) passive optical and lidar-derived terrain data [1]. Specifically, we leverage the acquisition timestamps within a daily 3m PlanetScope product over a 2-year period. Given a datetime and geolocation, we model the sun’s azimuth and elevation relative to that geolocation at that time of day and year. We can then generate a time-dependent hillshade and therefore locate shadows in any given time within that 2-year period. In our ablation study we show that, out of all the lidar-derived products, the time-dependent hillshades contribute a 8-9% accuracy improvement in the semantic segmentation of rivers. This indicates that a semantic segmentation machine learning model is less prone to errors of commission (false positives), by better disambiguating shadows from dark water.

Time-dependent hillshades are not currently used in ML for EO use-cases, yet they can be useful. All that is needed to produce them is access to high-resolution bare-earth DEMs, like that of the US National 3D Elevation Program covering the entire continental U.S at 1-meter resolution, or creation of DSMs from the lidar point cloud data itself. As the coverage of DSM and/or DEM products expands to more parts of the world, time-dependent hillshades could become as commonplace as cloud masks in EO use cases.


[1] Dolores Garcia, Gonzalo Mateo-Garcia, Hannes Bernhardt, Ron Hagensieker, Ignacio G. Lopez-Francos, Jonathan Stock, Guy Schumann, Kevin Dobbs and Freddie Kalaitzis Pix2Streams: Dynamic Hydrology Maps from Satellite-LiDAR Fusion. AI for Earth Sciences Workshop, NeurIPS 2020

How to cite: Kalaitzis, F., Mateo-Garcia, G., Dobbs, K., Garcia, D., Stoker, J., and Marchisio, G.: Time-dependent Hillshades: Dispelling the Shadow Curse of Machine Learning Applications in Earth Observation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8852, https://doi.org/10.5194/egusphere-egu22-8852, 2022.

EGU22-9348 | Presentations | ITS2.6/AS5.1

Data-driven modelling of soil moisture: mapping organic soils 

Doran Khamis, Matt Fry, Hollie Cooper, Ross Morrison, and Eleanor Blyth

Improving our understanding of soil moisture and hydraulics is crucial for flood prediction, smart agriculture, modelling nutrient and pollutant spread and evaluating the role of land as a sink or source of carbon and other greenhouse gases. State of the art land surface models rely on poorly-resolved soil textural information to parametrise arbitrarily layered soil models; soils rich in organic matter – key to understanding the role of the land in achieving net zero carbon – are not well modelled. Here, we build a predictive data-driven model of soil moisture using a neural network composed of transformer layers to process time series data from point-sensors (precipitation gauges and sensor-derived estimates of potential evaporation) and convolutional layers to process spatial atmospheric driving data and contextual information (topography, land cover and use, location and catchment behaviour of water bodies). We train the model using data from the COSMOS-UK sensor network and soil moisture satellite products and compare the outputs with JULES to investigate where and why the models diverge. Finally, we predict regions of high peat content and propose a way to combine theory with our data-driven approach to move beyond the sand-silt-clay modelling framework.

How to cite: Khamis, D., Fry, M., Cooper, H., Morrison, R., and Blyth, E.: Data-driven modelling of soil moisture: mapping organic soils, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9348, https://doi.org/10.5194/egusphere-egu22-9348, 2022.

EGU22-9452 | Presentations | ITS2.6/AS5.1

Eddy identification from along track altimeter data using deep learning: EDDY project 

Adili Abulaitijiang, Eike Bolmer, Ribana Roscher, Jürgen Kusche, Luciana Fenoglio, and Sophie Stolzenberger

Eddies are circular rotating water masses, which are usually generated near the large ocean currents, e.g., Gulf Stream. Monitoring eddies and gaining knowledge on eddy statistics over a large region are important for fishery, marine biology studies, and testing ocean models.

At mesoscale, eddies are observed in radar altimetry, and methods have been developed to identify, track and classify them in gridded maps of sea surface height derived from multi-mission data sets. However, this procedure has drawbacks since much information is lost in the gridded maps. Inevitably, the spatial and temporal resolution of the original altimetry data degrades during the gridding process. On the other hand, the task of identifying eddies has been a post-analysis process on the gridded dataset, which is, by far, not meaningful for near-real time applications or forecasts. In the EDDY project at the University of Bonn, we aim to develop methods for identifying eddies directly from along track altimetry data via a machine (deep) learning approach.

At the early stage of the project, we started with gridded altimetry maps to set up and test the machine learning algorithm. The gridded datasets are not limited to multi-mission gridded maps from AVISO, but also include the high resolution (~6 km) ocean modeling simulation dataset (e.g., FESOM, Finite Element Sea ice Ocean Model). Later, the gridded maps are sampled along the real altimetry ground tracks to obtain the single-track altimetry data. Reference data, as the training set for machine learning, will be produced by open-source geometry-based approach (e.g., py-eddy-tracker, Mason et al., 2014) with additional constraints like Okubo-Weiss parameter and Sea Surface Temperature (SST) profile signatures.

In this presentation, we introduce the EDDY project and show the results from the machine learning approach based on gridded datasets for the Gulf stream area for the period 2017, and first results of single-track eddy identification in the region.

How to cite: Abulaitijiang, A., Bolmer, E., Roscher, R., Kusche, J., Fenoglio, L., and Stolzenberger, S.: Eddy identification from along track altimeter data using deep learning: EDDY project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9452, https://doi.org/10.5194/egusphere-egu22-9452, 2022.

DINCAE (Data INterpolating Convolutional Auto-Encoder) is a neural network to reconstruct missing data (e.g. obscured by clouds or gaps between tracks) in satellite data. Contrary to standard image reconstruction (in-painting) with neural networks, this application requires a method to handle missing data (or data with variable accuracy) already in the training phase. Instead of using a cost function based on the mean square error, the neural network (U-Net type of network) is optimized by minimizing the negative log likelihood assuming a Gaussian distribution (characterized by a mean and a variance). As a consequence, the neural network also provides an expected error variance of the reconstructed field (per pixel and per time instance).

 

In this updated version DINCAE 2.0, the code was rewritten in Julia and a new type of skip connection has been implemented which showed superior performance with respect to the previous version. The method has also been extended to handle multivariate data (an example will be shown with sea-surface temperature, chlorophyll concentration and wind fields). The improvement of this network is demonstrated in the Adriatic Sea. 

 

Convolutional networks work usually with gridded data as input. This is however a limitation for some data types used in oceanography and in Earth Sciences in general, where observations are often irregularly sampled.  The first layer of the neural network and the cost function have been modified so that unstructured data can also be used as inputs to obtain gridded fields as output. To demonstrate this, the neural network is applied to along-track altimetry data in the Mediterranean Sea. Results from a 20-year reconstruction are presented and validated. Hyperparameters are determined using Bayesian optimization and minimizing the error relative to a development dataset.

How to cite: Barth, A., Alvera-Azcárate, A., Troupin, C., and Beckers, J.-M.: A multivariate convolutional autoencoder to reconstruct satellite data with an error estimate based on non-gridded observations: application to sea surface height, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9578, https://doi.org/10.5194/egusphere-egu22-9578, 2022.

EGU22-9734 | Presentations | ITS2.6/AS5.1

High Impact Weather Forecasts in Southern Brazil using Ensemble Precipitation Forecasts and Machine Learning 

Cesar Beneti, Jaqueline Silveira, Leonardo Calvetti, Rafael Inouye, Lissette Guzman, Gustavo Razera, and Sheila Paz

In South America, southern parts of Brazil, Paraguay and northeast Argentina are regions particularly prone to high impact weather (intensive lightning activity, high precipitation, hail, flash floods and occasional tornadoes), mostly associated with extra-tropical cyclones, frontal systems and Mesoscale Convective Systems. In the south of Brazil, agricultural industry and electrical power generation are the main economic activities. This region is responsible for 35% of all hydro-power energy production in the country, with long transmission lines to the main consumer regions, which are severely affected by these extreme weather conditions. Intense precipitation events are a common cause of electricity outages in southern Brazil, which ranks as one of the regions in Brazil with the highest annual lightning incidence, as well. Accurate precipitation forecasts can mitigate this kind of problem. Despite improvements in the precipitation estimates and forecasts, some difficulties remain to increase the accuracy, mainly related to the temporal and spatial location of the events. Although several options are available, it is difficult to identify which deterministic forecast is the best or the most reliable forecast. Probabilistic products from large ensemble prediction systems provide a guide to forecasters on how confident they should be about the deterministic forecast, and one approach is using post processing methods such as machine learning (ML), which has been used to identify patterns in historical data to correct for systematic ensemble biases.

In this paper, we present a study, in which we used 20 members from the Global Ensemble Forecast System (GEFS) and 50 members from European Centre for Medium-Range Weather Forecasts (ECMWF)  during 2019-2021,  for seven daily precipitation thresholds: 0-1.0mm, 1.0mm-15mm, 15mm-40mm, 40mm-55mm, 55mm-105mm, 105mm-155mm and over 155mm. A ML algorithm was developed for each day, up to 15 days of forecasts, and several skill scores were calculated, for these daily precipitation thresholds. Initially, to select the best members of the ensembles, a gradient boosting algorithm was applied, in order to improve the skill of the model and reduce processing time. After preprocessing the data, a random forest classifier was used to train the model. Based on hyperparameter sensitivity tests, the random forest required 500 trees, a maximum tree depth of 12 levels, at least 20 samples per leaf node, and the minimization of entropy for splits. In order to evaluate the models, we used a cross-validation on a limited data sample. The procedure has a single parameter that refers to the number of groups that a given data sample is to be split into. In our work we created a twenty-six fold cross validation with 30 days per fold to verify the forecasts. The results obtained by the RF were evaluated through estimated value versus observed value. For the forecast range, we found values above 75% for the precision metrics in the first 3 days, and around 68% in the next days. The recall was also around 80% throughout the entire forecast range,  with promising results to apply this technique operationally, which is our intent in the near future. 

How to cite: Beneti, C., Silveira, J., Calvetti, L., Inouye, R., Guzman, L., Razera, G., and Paz, S.: High Impact Weather Forecasts in Southern Brazil using Ensemble Precipitation Forecasts and Machine Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9734, https://doi.org/10.5194/egusphere-egu22-9734, 2022.

EGU22-9833 | Presentations | ITS2.6/AS5.1

Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress 

Laura Laurenti, Elisa Tinti, Fabio Galasso, Luca Franco, and Chris Marone

Earthquakes forecasting and prediction have long, and in some cases sordid, histories but recent work has rekindled interest in this area based on advances in short-term early warning, hazard assessment for human induced seismicity and successful prediction of laboratory earthquakes.

In the lab, frictional stick-slip events provide an analog for the full seismic cycle and such experiments have played a central role in understanding the onset of failure and the dynamics of earthquake rupture. Lab earthquakes are also ideal targets for machine learning (ML) techniques because they can be produced in long sequences under a wide range of controlled conditions. Indeed, recent work shows that labquakes can be predicted from fault zone acoustic emissions (AE). Here, we generalize these results and explore additional ML and deep learning (DL) methods for labquake prediction. Key questions include whether improved ML/DL methods can outperform existing models, including prediction based on limited training, or if such methods can successfully forecast beyond a single seismic cycle for aperiodic failure. We describe significant improvements to existing methods of labquake prediction using simple AE statistics (variance) and DL models such as Long-Short Term Memory (LSTM) and Convolution Neural Network (CNN). We demonstrate: 1) that LSTMs and CNNs predict labquakes under a variety of conditions, including pre-seismic creep, aperiodic events and alternating slow and fast events and 2) that fault zone stress can be predicted with fidelity (accuracy in terms of R2 > 0.92), confirming that acoustic energy is a fingerprint of the fault zone stress. We predict also time to start of failure (TTsF) and time to the end of Failure (TTeF). Interestingly, TTeF is successfully predicted in all seismic cycles, while the TTsF prediction varies with the amount of fault creep before an event. We also report on a novel autoregressive forecasting method to predict future fault zone states, focusing on shear stress. This forecasting model is distinct from existing predictive models, which predict only the current state. We compare three modern approaches in sequence modeling framework: LSTM, Temporal Convolution Network (TCN) and Transformer Network (TF). Results are encouraging in forecasting the shear stress at long-term future horizons, autoregressively. Our ML/DL prediction models outperform the state of the art and our autoregressive model represents a novel forecasting framework that could enhance current methods of earthquake forecasting.

How to cite: Laurenti, L., Tinti, E., Galasso, F., Franco, L., and Marone, C.: Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9833, https://doi.org/10.5194/egusphere-egu22-9833, 2022.

EGU22-10157 | Presentations | ITS2.6/AS5.1

How land cover changes affect ecosystem productivity 

Andreas Krause, Phillip Papastefanou, Konstantin Gregor, Lucia Layritz, Christian S. Zang, Allan Buras, Xing Li, Jingfeng Xiao, and Anja Rammig

Historically, many forests worldwide were cut down and replaced by agriculture. While this substantially reduced terrestrial carbon storage, the impacts of land-use change on ecosystem productivity have not been adequately resolved yet.

Here, we apply the machine learning algorithm Random Forests to predict the potential gross primary productivity (GPP) of forests, grasslands, and croplands around the globe using high-resolution datasets of satellite-derived GPP, land cover, and 20 environmental predictor variables.

With a mean potential GPP of around 2.0 kg C m-2 yr-1 forests are the most productive land cover on two thirds of the global suitable area, while grasslands and croplands are on average 23 and 9% less productive, respectively. These findings are robust against alternative input datasets and algorithms, even though results are somewhat sensitive to the underlying land cover map.

Combining our potential GPP maps with a land-use reconstruction from the Land-Use Harmonization project (LUH2) we estimate that historical agricultural expansion reduced global GPP by around 6.3 Gt C yr-1 (4.4%). This reduction in GPP induced by land cover changes is amplified in some future scenarios as a result of ongoing deforestation but partly reversed in other scenarios due to agricultural abandonment.

Finally, we compare our potential GPP maps to simulations from eight CMIP6 Earth System Models with an explicit representation of land management. While the mean GPP values of the ESM ensemble show reasonable agreement with our estimates, individual Earth System Models simulate large deviations both in terms of mean GPP values of different land cover types as well as in their spatial variations. Reducing these model biases would lead to more reliable simulations concerning the potential of land-based mitigation policies.

How to cite: Krause, A., Papastefanou, P., Gregor, K., Layritz, L., Zang, C. S., Buras, A., Li, X., Xiao, J., and Rammig, A.: How land cover changes affect ecosystem productivity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10157, https://doi.org/10.5194/egusphere-egu22-10157, 2022.

EGU22-10519 | Presentations | ITS2.6/AS5.1 | Highlight

Adaptive Bias Correction for Improved Subseasonal Forecasting 

Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight, Maria Geogdzhayeva, Sam Levang, Ernest Fraenkel, and Lester Mackey

Improving our ability to forecast the weather and climate is of interest to all sectors of the economy and government agencies from the local to the national level. In fact, weather forecasts 0-10 days ahead and climate forecasts seasons to decades ahead are currently used operationally in decision-making, and the accuracy and reliability of these forecasts has improved consistently in recent decades. However, many critical applications require subseasonal forecasts with lead times in between these two timescales. Subseasonal forecasting—predicting temperature and precipitation 2-6 weeks ahead—is indeed critical for effective water allocation, wildfire management, and drought and flood mitigation. Yet, accurate forecasts for the subseasonal regime are still lacking due to the chaotic nature of weather.

While short-term forecasting accuracy is largely sustained by physics-based dynamical models, these deterministic methods have limited subseasonal accuracy due to chaos. Indeed, subseasonal forecasting has long been considered a “predictability desert” due to its complex dependence on both local weather and global climate variables. Nevertheless, recent large-scale research efforts have advanced the subseasonal capabilities of operational physics-based models, while parallel efforts have demonstrated the value of machine learning and deep learning methods in improving subseasonal forecasting.

To counter the systematic errors of dynamical models at longer lead times, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. We evaluate our adaptive bias correction method in the contiguous U.S. over the years 2011-2020 and demonstrate consistent improvement over standard meteorological baselines, state-of-the-art learning models, and the leading subseasonal dynamical models, as measured by root mean squared error and uncentered anomaly correlation skill. When applied to the United States’ operational climate forecast system (CFSv2), ABC improves temperature forecasting skill by 20-47% and precipitation forecasting skill by 200-350%. When applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 8-38% and precipitation forecasting skill by 40-80%.

Overall, we find that de-biasing dynamical forecasts with our learned adaptive bias correction method yields an effective and computationally inexpensive strategy for generating improved subseasonal forecasts and building the next generation of subseasonal forecasting benchmarks. To facilitate future subseasonal benchmarking and development, we release our model code through the subseasonal_toolkit Python package and our routinely updated SubseasonalClimateUSA dataset through the subseasonal_data Python package.

How to cite: Mouatadid, S., Orenstein, P., Flaspohler, G., Oprescu, M., Cohen, J., Wang, F., Knight, S., Geogdzhayeva, M., Levang, S., Fraenkel, E., and Mackey, L.: Adaptive Bias Correction for Improved Subseasonal Forecasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10519, https://doi.org/10.5194/egusphere-egu22-10519, 2022.

EGU22-10711 | Presentations | ITS2.6/AS5.1

A new approach toward integrated inversion of reflection seismic and gravity datasets using deep learning 

Mahtab Rashidifard, Jeremie Giraud, Mark Jessell, and Mark Lindsay

Reflection seismic data, although sparsely distributed due to the high cost of acquisition, is the only type of data that can provide high-resolution images of the crust to reveal deep subsurface structures and the architectural complexity that may vector attention to minerally prospective regions. However, these datasets are not commonly considered in integrated geophysical inversion approaches due to computationally expensive forward modeling and inversion. Common inversion techniques on reflection seismic images are mostly utilized and developed for basin studies and have very limited application for hard-rock studies. Post-stack acoustic impedance inversions, for example, rely a lot on extracted petrophysical information along drilling borehole for depth correction purposes which are not necessarily available. Furthermore, the available techniques do not allow simple, automatic integration of seismic inversion with other geophysical datasets. 

 

 We introduce a new methodology that allows the utilization of the seismic images within the gravity inversion technique with the purpose of 3D boundary parametrization of the subsurface. The proposed workflow is a novel approach for incorporating seismic images into the integrated inversion techniques which relies on the image-ray method for depth-to-time domain conversion of seismic datasets. This algorithm uses a convolutional neural network to iterate over seismic images in time and depth domains. This iterative process is functional to compensate for the low depth resolution of the gravity datasets. We use a generalized level-set technique for gravity inversion to link the interfaces of the units with the depth-converted seismic images. The algorithm has been tested on realistic synthetic datasets generated from scenarios corresponding to different deformation histories. The preliminary results of this study suggest that post-stack seismic images can be utilized in integrated geophysical inversion algorithms without the need to run computationally expensive full wave-form inversions.  

How to cite: Rashidifard, M., Giraud, J., Jessell, M., and Lindsay, M.: A new approach toward integrated inversion of reflection seismic and gravity datasets using deep learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10711, https://doi.org/10.5194/egusphere-egu22-10711, 2022.

EGU22-11043 | Presentations | ITS2.6/AS5.1

Framework for the deployment of DNNs in remote sensing inversion algorithms applied to Copernicus Sentinel-4 (S4) and TROPOMI/Sentinel-5 Precursor (S5P) 

Fabian Romahn, Victor Molina Garcia, Ana del Aguila, Ronny Lutz, and Diego Loyola

In remote sensing, the quantities of interest (e.g. the composition of the atmosphere) are usually not directly observable but can only be inferred indirectly via the measured spectra. To solve these inverse problems, retrieval algorithms are applied that usually depend on complex physical models, so-called radiative transfer models (RTMs). RTMs are very accurate, however also computationally very expensive and therefore often not feasible in combination with the strict time requirements of operational processing of satellite measurements. With the advances in machine learning, the methods of this field, especially deep neural networks (DNN), have become very promising for accelerating and improving the classical remote sensing retrieval algorithms. However, their application is not straightforward but instead quite challenging as there are many aspects to consider and parameters to optimize in order to achieve satisfying results.

In this presentation we show a general framework for replacing the RTM, used in an inversion algorithm, with a DNN that offers sufficient accuracy while at the same time increases the processing performance by several orders of magnitude. The different steps, sampling and generation of the training data, the selection of the DNN hyperparameters, the training and finally the integration of the DNN into an operational environment are explained in detail. We will also focus on optimizing the efficiency of each step: optimizing the generation of training samples through smart sampling techniques, accelerating the training data generation through parallelization and other optimizations of the RTM, application of tools for the DNN hyperparameter optimization as well as the use of automation tools (source code generation) and appropriate interfaces for the efficient integration in operational processing systems.

This procedure has been continuously developed throughout the last years and as a use case, it will be shown how it has been applied in the operational retrieval of cloud properties for the Copernicus satellite sensors Sentinel-4 (S4) and TROPOMI/Sentinel-5 Precursor (S5P).

How to cite: Romahn, F., Molina Garcia, V., del Aguila, A., Lutz, R., and Loyola, D.: Framework for the deployment of DNNs in remote sensing inversion algorithms applied to Copernicus Sentinel-4 (S4) and TROPOMI/Sentinel-5 Precursor (S5P), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11043, https://doi.org/10.5194/egusphere-egu22-11043, 2022.

EGU22-11420 | Presentations | ITS2.6/AS5.1

Histroy Matching for the tuning of coupled models: experiments on the Lorenz 96 model 

Redouane Lguensat, Julie Deshayes, and Venkatramani Balaji

The process of relying on experience and intuition to find good sets of parameters, commonly referred to as "parameter tuning" keeps having a central role in the roadmaps followed by dozens of modeling groups involved in community efforts such as the Coupled Model Intercomparison Project (CMIP). 

In this work, we study a tool from the Uncertainty Quantification community that started recently to draw attention in climate modeling: History Matching also referred to as "Iterative Refocussing". The core idea of History Matching is to run several simulations with different set of parameters and then use observed data to rule-out any parameter settings which are "implausible". Since climate simulation models are computationally heavy and do not allow testing every possible parameter setting, we employ an emulator that can be a cheap and accurate replacement. Here a machine learning algorithm, namely, Gaussian Process Regression is used for the emulating step. History Matching is then a good example where the recent advances in machine learning can be of high interest to climate modeling.

One objective of this study is to evaluate the potential for history matching to tune a climate system with multi-scale dynamics. By using a toy climate model, namely, the Lorenz 96 model, and producing experiments in perfect-model setting, we explore different types of applications of HM and highlight the strenghts and challenges of using such a technique. 

How to cite: Lguensat, R., Deshayes, J., and Balaji, V.: Histroy Matching for the tuning of coupled models: experiments on the Lorenz 96 model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11420, https://doi.org/10.5194/egusphere-egu22-11420, 2022.

EGU22-11465 | Presentations | ITS2.6/AS5.1

Quantile machine learning models for predicting European-wide, high resolution fine-mode Aerosol Optical Depth (AOD) based on ground-based AERONET and satellite AOD data 

Zhao-Yue Chen, Raul Méndez-Turrubiates, Hervé Petetin, Aleks Lacima, Albert Soret Miravet, Carlos Pérez García-Pando, and Joan Ballester

Air pollution is a major environmental risk factor for human health. Among the different air pollutants, Particulate Matter (PM) arises as the most prominent one, with increasing health effects over the last decades. According to the Global Burden of Disease, PM contributed to 4.14 million premature deaths globally in 2019, over twice as much as in 1990 (2.04 million). With these numbers in mind, the assessment of ambient PM exposure becomes a key issue in environmental epidemiology. However, the limited number of ground-level sites measuring daily PM values is a major constraint for the development of large-scale, high-resolution epidemiological studies.

In the last five years, there has been a growing number of initiatives estimating ground-level PM concentrations based on satellite Aerosol Optical Depth (AOD) data, representing a low-cost alternative with higher spatial coverage compared to ground-level measurements. At present, the most popular AOD product is NASA’s MODIS (Moderate Resolution Imaging Spectroradiometer), but the data that it provides is restricted to Total Aerosol Optical Depth (TAOD). Compared with TAOD, Fine-mode Aerosol Optical Depth (FAOD) better describes the distribution of small-diameter particles (e.g. PM10 and PM2.5), which are generally those associated with anthropogenic activity. Complementarily, AERONET (AErosol RObotic NETwork, which is the network of ground-based sun photometers), additionally provide Fine- and Coarse-mode Aerosol Optical Depth (FAOD and CAOD) products based on Spectral Deconvolution Algorithms (SDA).

Within the framework of the ERC project EARLY-ADAPT (https://early-adapt.eu/), which aims to disentangle the association between human health, climate variability and air pollution to better estimate the early adaptation response to climate change, here we develop quantile machine learning models to further advance in the association between AERONET FAOD and satellite AOD over Europe during the last two decades. Due to large missing data form satellite estimations, we also included the AOD estimates from ECMWF’s Copernicus Atmosphere Monitoring Service Global Reanalysis (CAMSRA) and NASA’s Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA-2), together with atmosphere, land and ocean variables such as boundary layer height, downward UV radiation and cloud cover from ECMWF’s ERA5-Land.

The models were thoroughly validated with spatial cross-validation. Preliminary results show that the R2 of the three AOD estimates (TAOD, FAOD and CAOD) predicted with quantile machine learning models range between 0.61 and 0.78, and the RMSE between 0.02 and 0.03. For the Pearson correlation with ground-level PM2.5, the predicted FAOD is highest (0.38), while 0.18, 0.11 and 0.09 are for Satellite, MERRA-2, CAMSRA AOD, respectively. This study provides three useful indicators for further estimating PM, which could improve our understanding of air pollution in Europe and open new avenues for large-scale, high-resolution environmental epidemiology studies.

How to cite: Chen, Z.-Y., Méndez-Turrubiates, R., Petetin, H., Lacima, A., Soret Miravet, A., Pérez García-Pando, C., and Ballester, J.: Quantile machine learning models for predicting European-wide, high resolution fine-mode Aerosol Optical Depth (AOD) based on ground-based AERONET and satellite AOD data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11465, https://doi.org/10.5194/egusphere-egu22-11465, 2022.

EGU22-11924 | Presentations | ITS2.6/AS5.1

Automated detection and classification of synoptic scale fronts from atmospheric data grids 

Stefan Niebler, Peter Spichtinger, Annette Miltenberger, and Bertil Schmidt

Automatic determination of fronts from atmospheric data is an important task for weather prediction as well as for research of synoptic scale phenomena. We developed a deep neural network to detect and classify fronts from multi-level ERA5 reanalysis data. Model training and prediction is evaluated using two different regions covering Europe and North America with data from two weather services. Due to a label deformation step performed during training we are able to directly generate frontal lines with no further thinning during post processing. Our network compares well against the weather service labels with a Critical Success Index higher than 66.9% and a Object Detection Rate of more than 77.3%. Additionally the frontal climatologies generated from our networks ouput are highly correlated (greater than 77.2%) to climatologies created from weather service data. Evaluation of cross sections of our detection results provide further insight in the characteristics of our predicted fronts and show that our networks classification is physically plausible.

How to cite: Niebler, S., Spichtinger, P., Miltenberger, A., and Schmidt, B.: Automated detection and classification of synoptic scale fronts from atmospheric data grids, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11924, https://doi.org/10.5194/egusphere-egu22-11924, 2022.

EGU22-12043 | Presentations | ITS2.6/AS5.1

A Domain-Change Approach to the Semantic Labelling of Remote Sensing Images 

Chandrabali Karmakar, Gottfried Schwartz, Corneliu Octavian Dumitru, and Mihai Datcu

For many years, image classification – mainly based on pixel brightness statistics – has been among the most popular remote sensing applications. However, during recent years, many users were more and more interested in the application-oriented semantic labelling of remotely sensed image objects being depicted in given images.


In parallel, the development of deep learning algorithms has led to several powerful image classification and annotation tools that became popular in the remote sensing community. In most cases, these publicly available tools combine efficient algorithms with expert knowledge and/or external information ingested during an initial training phase, and we often encounter two alternative types of deep learning approaches, namely Autoencoders (AEs) and Convolutional Neural Networks (CNNs). Both approaches try to convert the pixel data of remote sensing images into semantic maps of the imaged areas. In our case, we made an attempt to provide an efficient new semantic annotation tool that helps in the semantic interpretation of newly recorded images with known and/or possibly unknown content.


Typical cases are remote sensing images depicting unexpected and hitherto uncharted phenomena such as flooding events or destroyed infrastructure. When we resort to the commonly applied AE or CNN software packages we cannot expect that existing statistics, or a few initial ground-truth annotations made by an image interpreter, will automatically lead to a perfect understanding of the image content. Instead, we have to discover and combine a number of additional relationships that define the actual content of a selected image and many of its characteristics.

Our approach consists of a two-stage domain-change approach where we first convert an image into a purely mathematical ‘topic representation’ initially introduced by Blei [1]. This representation provides statistics-based topics that do not yet require final application-oriented labelling describing physical categories or phenomena and support the idea of explainable machine learning [2]. Then, during a second stage, we try to derive physical image content categories by exploiting a weighted multi-level neural network approach that converts weighted topics into individual application-oriented labels. This domain-changing learning stage limits label noise and is initially supported by an image interpreter allowing the joint use of pixel statistics and expert knowledge [3]. The activity of the image interpreter can be limited to a few image patches. We tested our approach on a number of different use cases (e.g., polar ice, agriculture, natural disasters) and found that our concept provides promising results.  


[1] D.M. Blei, A.Y. Ng, and M.I. Jordan, (2003). Latent Dirichlet Allocation, Journal of Machine Learning Research, Vol. 3, pp. 993-1022.
[2] C. Karmakar, C.O. Dumitru, G. Schwarz, and M. Datcu (2020). Feature-free explainable data mining in SAR images using latent Dirichlet allocation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14, pp. 676-689.
[3] C.O. Dumitru, G. Schwarz, and M. Datcu (2021). Semantic Labelling of Globally Distributed Urban and Non-Urban Satellite Images Using High-Resolution SAR Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 15, pp. 6009-6068.

How to cite: Karmakar, C., Schwartz, G., Dumitru, C. O., and Datcu, M.: A Domain-Change Approach to the Semantic Labelling of Remote Sensing Images, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12043, https://doi.org/10.5194/egusphere-egu22-12043, 2022.

EGU22-12489 | Presentations | ITS2.6/AS5.1

“Fully-automated” clustering method for stress inversions (CluStress) 

Lukács Kuslits, Lili Czirok, and István Bozsó

As it is well-known, stress fields are responsible for earthquake formation. In order to analyse stress relations in a study area using focal mechanisms’ (FMS) inversions, it is vital to consider three fundamental criteria:

(1)       The investigated area is characterized by a homogeneous stress field.

(2)       The earthquakes occur with variable directions on pre-existing faults.

(3)       The deviation of the fault slip vector from the shear stress vector is minimal (Wallace-Bott hypothesis).

The authors have attempted to develop a “fully-automated” algorithm to carry out the classification of the earthquakes as a prerequisite of stress estimations. This algorithm does not call for the setting of hyper-parameters, thus subjectivity can be reduced significantly and the running time can also decrease. Nevertheless, there is an optional hyper-parameter that is eligible to filter outliers, isolated points (earthquakes) in the input dataset.

In this presentation, they show the operation of this algorithm in case of synthetic datasets consisting of different groups of FMS and a real seismic dataset. The latter come from a survey area in the earthquake-prone Vrancea-zone (Romania). This is a relatively small region (around 30*70 km) in the external part of SE-Carpathians where the distribution of the seismic events is quite dense and heterogeneous.

It shall be noted that though the initial results are promising, further developments are still necessary. The source codes are soon to be uploaded to a public GitHub repository which will be available for the whole scientific community.

How to cite: Kuslits, L., Czirok, L., and Bozsó, I.: “Fully-automated” clustering method for stress inversions (CluStress), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12489, https://doi.org/10.5194/egusphere-egu22-12489, 2022.

EGU22-12549 | Presentations | ITS2.6/AS5.1

Joint calibration and mapping of satellite altimetry data using trainable variaitional models 

Quentin Febvre, Ronan Fablet, Julien Le Sommer, and Clément Ubelmann

Satellite radar altimeters are a key source of observation of ocean surface dynamics. However, current sensor technology and mapping techniques do not yet allow to systematically resolve scales smaller than 100km. With their new sensors, upcoming wide-swath altimeter missions such as SWOT should help resolve finer scales. Current mapping techniques rely on the quality of the input data, which is why the raw data go through multiple preprocessing stages before being used. Those calibration stages are improved and refined over many years and represent a challenge when a new type of sensor start acquiring data.

We show how a data-driven variational data assimilation framework could be used to jointly learn a calibration operator and an interpolator from non-calibrated data . The proposed framework significantly outperforms the operational state-of-the-art mapping pipeline and truly benefits from wide-swath data to resolve finer scales on the global map as well as in the SWOT sensor geometry.

 

How to cite: Febvre, Q., Fablet, R., Le Sommer, J., and Ubelmann, C.: Joint calibration and mapping of satellite altimetry data using trainable variaitional models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12549, https://doi.org/10.5194/egusphere-egu22-12549, 2022.

EGU22-12574 | Presentations | ITS2.6/AS5.1 | Highlight

SWIFT-AI: Significant Speed-up in Modelling the Stratospheric Ozone Layer 

Helge Mohn, Daniel Kreyling, Ingo Wohltmann, Ralph Lehmann, Peter Maass, and Markus Rex

Common representations of the stratospheric ozone layer in climate modeling are widely considered only in a very simplified way. Neglecting the mutual interactions of ozone with atmospheric temperature and dynamics has the effect of making climate projections less accurate. Although, more elaborate and interactive models of the stratospheric ozone layer are available, they require far too much computation time to be coupled with climate models. Our aim with this project was to break new ground and pursue an interdisciplinary strategy that spans the fields of machine learning, atmospheric physics and climate modelling.

In this work, we present an implicit neural representation of the extrapolar stratospheric ozone chemistry (SWIFT-AI). An implicitly defined hyperspace of the stratospheric ozone chemistry offers a continuous and even differentiable representation that can be parameterized by artificial neural networks. We analysed different parameter-efficient variants of multilayer perceptrons. This was followed by an intensive, as far as possible energy-efficient search for hyperparameters involving Bayesian optimisation and early stopping techniques.

Our data source is the Lagrangian chemistry and transport model ATLAS. Using its full model of stratospheric ozone chemistry, we focused on simulating a wide range of stratospheric variability that will occur in future climate (e.g. temperature and meridional circulation changes). We conducted a simulation for several years and created a data-set with over 200E+6 input and output pairs. Each output is the 24h ozone tendency of a trajectory. We performed a dimensionality reduction of the input parameters by using the concept of chemical families and by performing a sensitivity analysis to choose a set of robust input parameters.

We coupled the resulting machine learning models with the Lagrangian chemistry and transport model ATLAS, substituting the full stratospheric chemistry model. We validated a two-year simulation run by comparing to the differences in accuracy and computation time from both the full stratospheric chemistry model and the previous polynomial approach of extrapolar SWIFT. We found that SWIFT-AI consistently outperforms the previous polynomial approach of SWIFT, both in terms of test data and simulation results. We discovered that the computation time of SWIFT-AI is more than twice as fast as the previous polynomial approach SWIFT and 700 times faster than the full stratospheric chemistry scheme of ATLAS, resulting in minutes instead of weeks of computation time per model year – a speed-up of several orders of magnitude.

To ensure reproducibility and transparency, we developed a machine learning pipeline, published a benchmark dataset and made our repository open to the public.

In summary, we could show that the application of state-of-the-art machine learning methods to the field of atmospheric physics holds great potential. The achieved speed-up of an interactive and very precise ozone layer enables a novel way of representing the ozone layer in climate models. This in turn will increase the quality of climate projections, which are crucial for policy makers and of great importance for our planet.

How to cite: Mohn, H., Kreyling, D., Wohltmann, I., Lehmann, R., Maass, P., and Rex, M.: SWIFT-AI: Significant Speed-up in Modelling the Stratospheric Ozone Layer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12574, https://doi.org/10.5194/egusphere-egu22-12574, 2022.

Recently, an increase in forecast skill of the seasonal climate forecast for winter in Europe has been achieved through an ensemble subsampling approach by way of predicting the mean winter North Atlantic Oscillation (NAO) index through linear regression (based on the autumn state of the four predictors sea surface temperature, Arctic sea ice volume, Eurasian snow depth and stratospheric temperature) and the sampling of the ensemble members which are able to reproduce this NAO state. This thesis shows that the statistical prediction of the NAO index can be further improved via nonlinear methods using the same predictor variables as in the linear approach. This likely also leads to an increase in seasonal climate forecast skill. The data used for the calculations stems from the global reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5. The available time span for use in this thesis covered only 40 years from 1980 till 2020, hence it was important to use a method that still yields statistically significant and meaningful results under those circumstances. The nonlinear method chosen was k-nearest neighbor, which is a simple, yet powerful algorithm when there is not a lot of data available. Compared to other methods like neural networks it is easy to interpret. The resulting method has been developed and tested in a double cross-validation setting. While sea ice in the Barents-Kara sea in September-October shows the most predictive capability for the NAO index in the subsequent winter as a single predictor, the highest forecast skill is achieved through a combination of different predictor variables.

How to cite: Hauke, C., Ahrens, B., and Dalelane, C.: Prediction of the North Atlantic Oscillation index for the winter months December-January-February via nonlinear methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12628, https://doi.org/10.5194/egusphere-egu22-12628, 2022.

EGU22-12765 | Presentations | ITS2.6/AS5.1

Supervised machine learning to estimate instabilities in chaotic systems: computation of local Lyapunov exponents 

Daniel Ayers, Jack Lau, Javier Amezcua, Alberto Carrassi, and Varun Ojha

Weather and climate are well known exemplars of chaotic systems exhibiting extreme sensitivity to initial conditions. Initial condition errors are subject to exponential growth on average, but the rate and the characteristic of such growth is highly state dependent. In an ideal setting where the degree of predictability of the system is known in real-time, it may be possible and beneficial to take adaptive measures. For instance a local decrease of predictability may be counteracted by increasing the time- or space-resolution of the model computation or the ensemble size in the context of ensemble-based data assimilation or probabilistic forecasting.

Local Lyapunov exponents (LLEs) describe growth rates along a finite-time section of a system trajectory. This makes the LLEs the ideal quantities to measure the local degree of predictability, yet a main bottleneck for their real-time use in  operational scenarios is the huge computational cost. Calculating LLEs involves computing a long trajectory of the system, propagating perturbations with the tangent linear model, and repeatedly orthogonalising them. We investigate if machine learning (ML) methods can estimate the LLEs based only on information from the system’s solution, thus avoiding the need to evolve perturbations via the tangent linear model. We test the ability of four algorithms (regression tree, multilayer perceptron, convolutional neural network and long short-term memory network) to perform this task in two prototypical low dimensional chaotic dynamical systems. Our results suggest that the accuracy of the ML predictions is highly dependent upon the nature of the distribution of the LLE values in phase space: large prediction errors occur in regions of the attractor where the LLE values are highly non-smooth.  In line with classical dynamical systems studies, the neutral LLE is more difficult to predict. We show that a comparatively simple regression tree can achieve performance that is similar to sophisticated neural networks, and that the success of ML strategies for exploiting the temporal structure of data depends on the system dynamics.

How to cite: Ayers, D., Lau, J., Amezcua, J., Carrassi, A., and Ojha, V.: Supervised machine learning to estimate instabilities in chaotic systems: computation of local Lyapunov exponents, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12765, https://doi.org/10.5194/egusphere-egu22-12765, 2022.

EGU22-13228 | Presentations | ITS2.6/AS5.1 | Highlight

Developing a data-driven ocean forecast system 

Rachel Furner, Peter Haynes, Dan Jones, Dave Munday, Brooks Paige, and Emily Shuckburgh

The recent boom in machine learning and data science has led to a number of new opportunities in the environmental sciences. In particular, process-based weather and climate models (simulators) represent the best tools we have to predict, understand and potentially mitigate the impacts of climate change and extreme weather. However, these models are incredibly complex and require huge amounts of High Performance Computing resources. Machine learning offers opportunities to greatly improve the computational efficiency of these models by developing data-driven emulators.

Here I discuss recent work to develop a data-driven model of the ocean, an integral part of the weather and climate system. Much recent progress has been made with developing data-driven forecast systems of atmospheric weather, highlighting the promise of these systems. These techniques can also be applied to the ocean, however modelling of the ocean poses some fundamental differences and challenges in comparison to modelling the atmosphere, for example, oceanic flow is bathymetrically constrained across a wide range of spatial and temporal scales.

We train a neural network on the output from an expensive process-based simulator of an idealised channel configuration of oceanic flow. We show the model is able to learn well the complex dynamics of the system, replicating the mean flow and details within the flow over single prediction steps. We also see that when iterating the model, predictions remain stable, and continue to match the ‘truth’ over a short-term forecast period, here around a week.

 

How to cite: Furner, R., Haynes, P., Jones, D., Munday, D., Paige, B., and Shuckburgh, E.: Developing a data-driven ocean forecast system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13228, https://doi.org/10.5194/egusphere-egu22-13228, 2022.

EGU22-591 | Presentations | ITS2.7/AS5.2

Identifying precursors for extreme stratospheric polar vortex events  using an explainable neural network 

Zheng Wu, Tom Beucler, Raphaël de Fondeville, Eniko Székely, Guillaume Obozinski, William Ball, and Daniela Domeisen

The winter stratospheric polar vortex exhibits considerable variability in both magnitude and zonal wave structure, which arises in part from stratosphere-troposphere coupling associated with tropospheric precursors and can result in extreme polar vortex events. These extremes can subsequently influence weather in the troposphere and thus are important sources of surface prediction. However, the predictability limit of these extreme events is around 1-2 weeks in the state-of-the-art prediction system. In order to explore and improve the predictability limit of the extreme vortex events, in this study, we train an artificial neural network (ANN) to model stratospheric polar vortex anomalies and to identify strong and weak stratospheric vortex events. To pinpoint the origins of the stratospheric anomalies, we then employ two neural network visualization methods, SHapley Additive exPlanations (SHAP) and Layerwise Relevance Propagation (LRP), to uncover feature importance in the input variables (e.g., geopotential height and background zonal wind). The extreme vortex events can be identified by the ANN with an averaged accuracy of 60-80%. For the correctly identified extreme events, the composite of the feature importance of the input variables shows spatial patterns consistent with the precursors found for extreme stratospheric events in previous studies. This consistency provides confidence that the ANN is able to identify reliable indicators for extreme stratospheric vortex events and that it could help to identify the role of the previously found precursors, such as the sea level pressure anomalies associated with the Siberian high. In addition to the composite of all the events, the feature importance for each of the individual events further reveals the physical structures in the input variables (such as the locations of the geopotential height anomalies) that are specific to that event. Our results show the potential of explainable neural networks techniques in understanding and predicting the stratospheric variability and extreme events, and in searching for potential precursors for these events on subseasonal time scales. 

How to cite: Wu, Z., Beucler, T., de Fondeville, R., Székely, E., Obozinski, G., Ball, W., and Domeisen, D.: Identifying precursors for extreme stratospheric polar vortex events  using an explainable neural network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-591, https://doi.org/10.5194/egusphere-egu22-591, 2022.

EGU22-676 | Presentations | ITS2.7/AS5.2

A two-stage machine learning framework using global satellite data of cloud classes for process-oriented model evaluation 

Arndt Kaps, Axel Lauer, Gustau Camps-Valls, Pierre Gentine, Luis Gómez-Chova, and Veronika Eyring

Clouds play a key role in weather and climate but are quite challenging to simulate with global climate models as the relevant physics include non-linear processes on scales covering several orders of magnitude in both the temporal and spatial dimensions. The numerical representation of clouds in global climate models therefore requires a high degree of parameterization, which makes a careful evaluation a prerequisite not only for assessing the skill in reproducing observed climate but also for building confidence in projections of future climate change. Current methods to achieve this usually involve the comparison of multiple large-scale physical properties in the model output to observational data. Here, we introduce a two-stage data-driven machine learning framework for process-oriented evaluation of clouds in climate models based directly on widely known cloud types. The first step relies on CloudSat satellite data to assign cloud labels in line with cloud types defined by the World Meteorological Organization (WMO) to MODIS pixels using deep neural networks. Since the method is supervised and trained on labels provided by CloudSat, the predicted cloud types remain objective and do not require a posteriori labeling. The second step consists of a regression algorithm that predicts fractional cloud types from retrieved cloud physical variables. This step aims to ensure that the method can be used with any data set providing physical variables comparable to MODIS. In particular, we use a Random Forest regression that acts as a transfer model to evaluate the spatially relatively coarse output of climate models and allows the use of varying input features. As a proof of concept, the method is applied to coarse grained ESA Cloud CCI data. The predicted cloud type distributions are physically consistent and show the expected features of the different cloud types. This demonstrates how advanced observational products can be used with this method to obtain cloud type distributions from coarse data, allowing for a process-based evaluation of clouds in climate models.

How to cite: Kaps, A., Lauer, A., Camps-Valls, G., Gentine, P., Gómez-Chova, L., and Eyring, V.: A two-stage machine learning framework using global satellite data of cloud classes for process-oriented model evaluation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-676, https://doi.org/10.5194/egusphere-egu22-676, 2022.

EGU22-696 | Presentations | ITS2.7/AS5.2 | Highlight

Latent Linear Adjustment Autoencoder: a novel method for estimating dynamic precipitation at high resolution 

Christina Heinze-Deml, Sebastian Sippel, Angeline G. Pendergrass, Flavio Lehner, and Nicolai Meinshausen

A key challenge in climate science is to quantify the forced response in impact-relevant variables such as precipitation against the background of internal variability, both in models and observations. Dynamical adjustment techniques aim to remove unforced variability from a target variable by identifying patterns associated with circulation, thus effectively acting as a filter for dynamically induced variability. The forced contributions are interpreted as the variation that is unexplained by circulation. However, dynamical adjustment of precipitation at local scales remains challenging because of large natural variability and the complex, nonlinear relationship between precipitation and circulation particularly in heterogeneous terrain. 

In this talk, I will present the Latent Linear Adjustment Autoencoder (LLAAE), a novel statistical model that builds on variational autoencoders. The Latent Linear Adjustment Autoencoder enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high resolution and in a spatially coherent manner. To predict circulation-induced precipitation, the LLAAE combines a linear component, which models the relationship between circulation and the latent space of an autoencoder, with the autoencoder's nonlinear decoder. The combination is achieved by imposing an additional penalty in the cost function that encourages linearity between the circulation field and the autoencoder's latent space, hence leveraging robustness advantages of linear models as well as the flexibility of deep neural networks. 

We show that our model predicts realistic daily winter precipitation fields at high resolution based on a 50-member ensemble of the Canadian Regional Climate Model at 12 km resolution over Europe, capturing, for instance, key orographic features and geographical gradients. Using the Latent Linear Adjustment Autoencoder to remove the dynamic component of precipitation variability, forced thermodynamic components are expected to remain in the residual, which enables the uncovering of forced precipitation patterns of change from just a few ensemble members. We extend this to quantify the forced pattern of change conditional on specific circulation regimes. 

Future applications could include, for instance, weather generators emulating climate model simulations of regional precipitation, detection and attribution at subcontinental scales, or statistical downscaling and transfer learning between models and observations to exploit the typically much larger sample size in models compared to observations.

How to cite: Heinze-Deml, C., Sippel, S., Pendergrass, A. G., Lehner, F., and Meinshausen, N.: Latent Linear Adjustment Autoencoder: a novel method for estimating dynamic precipitation at high resolution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-696, https://doi.org/10.5194/egusphere-egu22-696, 2022.

EGU22-722 | Presentations | ITS2.7/AS5.2 | Highlight

Climate-Invariant, Causally Consistent Neural Networks as Robust Emulators of Subgrid Processes across Climates 

Tom Beucler, Fernando Iglesias-Suarez, Veronika Eyring, Michael Pritchard, Jakob Runge, and Pierre Gentine

Data-driven algorithms, in particular neural networks, can emulate the effects of unresolved processes in coarse-resolution Earth system models (ESMs) if trained on high-resolution simulation or observational data. However, they can (1) make large generalization errors when evaluated in conditions they were not trained on; and (2) trigger instabilities when coupled back to ESMs.

First, we propose to physically rescale the inputs and outputs of neural networks to help them generalize to unseen climates. Applied to the offline parameterization of subgrid-scale thermodynamics (convection and radiation) in three distinct climate models, we show that rescaled or "climate-invariant" neural networks make accurate predictions in test climates that are 8K warmer than their training climates. Second, we propose to eliminate spurious causal relations between inputs and outputs by using a recently developed causal discovery framework (PCMCI). For each output, we run PCMCI on the inputs time series to identify the reduced set of inputs that have the strongest causal relationship with the output. Preliminary results show that we can reach similar levels of accuracy by training one neural network per output with the reduced set of inputs; stability implications when coupled back to the ESM are explored.

Overall, our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes may improve their ability to generalize across climate regimes, while quantifying causal associations to select the optimal set of inputs may improve their consistency and stability.

How to cite: Beucler, T., Iglesias-Suarez, F., Eyring, V., Pritchard, M., Runge, J., and Gentine, P.: Climate-Invariant, Causally Consistent Neural Networks as Robust Emulators of Subgrid Processes across Climates, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-722, https://doi.org/10.5194/egusphere-egu22-722, 2022.

EGU22-1065 | Presentations | ITS2.7/AS5.2 | Highlight

Skilful US Soy-yield forecasts at pre-sowing lead-times 

Sem Vijverberg, Dim Coumou, and Raed Hamed

Soy harvest failure events can severely impact farmers, insurance companies and raise global prices. Reliable seasonal forecasts of mis-harvests would allow stakeholders to prepare and take appropriate early action. However, especially for farmers, the reliability and lead-time of current prediction systems provide insufficient information to justify for within-season adaptation measures. Recent innovations increased our ability to generate reliable statistical seasonal forecasts. Here, we combine these innovations to predict the 1-3 poor soy harvest years in eastern US. We first use a clustering algorithm to spatially aggregate crop producing regions within the eastern US that are particularly sensitive to hot-dry weather conditions. Next, we use observational climate variables (sea surface temperature (SST) and soil moisture) to extract precursor timeseries at multiple lags. This allows the machine learning model to learn the low-frequency evolution, which carries important information for predictability. A selection based on causal inference allows for physically interpretable precursors. We show that the robust selected predictors are associated with the evolution of the horseshoe Pacific SST pattern, in line with previous research. We use the state of the horseshoe Pacific to identify years with enhanced predictability. We achieve very high forecast skill of poor harvests events, even 3 months prior to sowing, using a strict one-step-ahead train-test splitting. Over the last 25 years, 90% of the predicted events in February were correct. When operational, this forecast would enable farmers (and insurance/trading companies) to make informed decisions on adaption measures, e.g., selecting more drought-resistant cultivars, invest in insurance, change planting management.

How to cite: Vijverberg, S., Coumou, D., and Hamed, R.: Skilful US Soy-yield forecasts at pre-sowing lead-times, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1065, https://doi.org/10.5194/egusphere-egu22-1065, 2022.

EGU22-1835 | Presentations | ITS2.7/AS5.2

Using Deep Learning for a High-Precision Analysis of Atmospheric Rivers in a High-Resolution Large Ensemble Climate Dataset 

Timothy Higgins, Aneesh Subramanian, Andre Graubner, Lukas Kapp-Schwoerer, Karthik Kashinath, Sol Kim, Peter Watson, Will Chapman, and Luca Delle Monache

Atmospheric rivers (ARs) are elongated corridors of water vapor in the lower Troposphere that cause extreme precipitation over many coastal regions around the globe. They play a vital role in the water cycle in the western US, fueling most extreme west coast precipitation and sometimes accounting for more than 50% of total annual west coast precipitation (Gershunov et al. 2017). Severe ARs are associated with extreme flooding and damages while weak ARs are typically more beneficial to our society as they bring much needed drought relief.

Precipitation is particularly difficult to predict in traditional climate models.  Predicting water vapor is more reliable (Lavers et al. 2016), allowing IVT (integrated vapor transport) and ARs to be a favorable method for understanding changing patterns in precipitation (Johnson et al. 2009).  There are a variety of different algorithms used to track ARs due to their relatively diverse definitions (Shields et al. 2018). The Atmospheric River Tracking Intercomparison Project (ARTMIP) organizes and provides information on all of the widely accepted algorithms that exist. Nearly all of the algorithms included in ARTMIP rely on absolute and relative numerical thresholds, which can often be computationally expensive and have a large memory footprint. This can be particularly problematic in large climate datasets. The vast majority of algorithms also heavily factor in wind velocity at multiple vertical levels to track ARs, which is especially difficult to store in climate models and is typically not output at the temporal resolution that ARs occur.

A recent alternative way of tracking ARs is through the use of machine learning. There are a variety of neural networks that are commonly applied towards identifying objects in cityscapes via semantic segmentation. The first of these neural networks that was applied towards detecting ARs is DeepLabv3+ (Prabhat et al. 2020). DeepLabv3+ is a state of the art model that demonstrates one of the highest performances of any present day neural network when tasked with the objective of identifying objects in cityscapes (Wu et al. 2019). We employ a light-weight convolutional neural network adapted from CGNet (Kapp-Schwoerer et al. 2020) to efficiently track these severe events without using wind velocity at all vertical levels as a predictor variable. When applied to cityscapes, CGNet's greatest advantage is its performance relative to its memory footprint (Wu et al. 2019). It has two orders of magnitude less parameters than DeepLabv3+ and is computationally less expensive. This can be especially useful when identifying ARs in large datasets. Convolutional neural networks have not been used to track ARs in a regional domain. This will also be the first study to demonstrate the performance of this neural network on a regional domain by providing an objective analysis of its consistency with eight different ARTMIP algorithms.

How to cite: Higgins, T., Subramanian, A., Graubner, A., Kapp-Schwoerer, L., Kashinath, K., Kim, S., Watson, P., Chapman, W., and Delle Monache, L.: Using Deep Learning for a High-Precision Analysis of Atmospheric Rivers in a High-Resolution Large Ensemble Climate Dataset, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1835, https://doi.org/10.5194/egusphere-egu22-1835, 2022.

EGU22-2012 | Presentations | ITS2.7/AS5.2

Gap filling in air temperature series by matrix completion methods 

Benoît Loucheur, Pierre-Antoine Absil, and Michel Journée

Quality control of meteorological data is an important part of atmospheric analysis and prediction, as missing or erroneous data can have a negative impact on the accuracy of these environmental products.

In Belgium, the Royal Meteorological Institute (RMI) is the national meteorological service that provide weather and climate services based on observations and scientific research. RMI collects and archives meteorological observations in Belgium since the 19th century. Currently, air temperature is monitored in Belgium in about 30 synoptic automatic weather stations (AWS) as well as in 110 manual climatological stations. In the latter stations, a volunteer observer records every morning at 8 o'clock the daily extreme air temperatures. All observations are routinely checked for errors, inconsistencies and missing values by the RMI staff. Misleading data are corrected and gaps are filled by estimations. This quality control tasks require a lot of human intervention. With the forthcoming deployment of low-cost weather stations and the subsequent increase in the volume of data to verify, the process of data quality control and completion should become as automated as much as possible.

In this work, the quality control process is fully automated by using mathematical tools. We present low-rank matrix completion methods (LRMC) that we used to solve the problem of completing missing data in daily minimum and maximum temperature series. We used a machine learning technique called Monte Carlo cross-validation to train our algorithms and then test them in a real case.

Among the matrix completion methods, some are regularised by graphs. In our case, it is then possible to represent the spatial and temporal component via graphs. By manipulating the construction of these graphs, we hope to improve the completion results. We were then able to compare our methods with what is done in the state of the art, such as the inverse distance weighting (IDW) method.

All our experiments were performed with a dataset provided by the RMI, including daily minimum and maximum temperature measurements from 100 stations over the period 2005-2019.

How to cite: Loucheur, B., Absil, P.-A., and Journée, M.: Gap filling in air temperature series by matrix completion methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2012, https://doi.org/10.5194/egusphere-egu22-2012, 2022.

EGU22-2248 | Presentations | ITS2.7/AS5.2

Exploring flooding mechanisms and their trends in Europe through explainable AI 

Shijie Jiang, Yi Zheng, and Jakob Zscheischler

Understanding the mechanisms causing river flooding and their trends is important to interpret past flood changes and make better predictions of future flood conditions. However,  there is still a lack of quantitative assessment of trends in flooding mechanisms based on observations. Recent years have witnessed the increasing prevalence of machine learning in hydrological modeling and its predictive power has been demonstrated in numerous studies. Machine learning makes hydrological predictions by recognizing generalizable relationships between inputs and outputs, which, if properly interpreted, may provide us further scientific insights into hydrological processes. In this study, we propose a new method using interpretive machine learning to identify flooding mechanisms based on the predictive relationship between precipitation and temperature and flow peaks. Applying this method to more than a thousand catchments in Europe reveals three primary input-output patterns within flow predictions, which can be associated with three catchment-wide flooding mechanisms: extreme precipitation, soil moisture excess, and snowmelt. The results indicate that approximately one-third of the studied catchments are controlled by a combination of the above mechanisms, while others are mostly dominated by one single mechanism. Although no significant shifts from one dominant mechanism to another are observed for the catchments over the past seven decades overall, some catchments with single mechanisms have become dominated by mixed mechanisms and vice versa. In particular, snowmelt-induced floods have decreased significantly in general, whereas rainfall has become more dominant in causing floods, and their effects on flooding seasonality and magnitude are crucial. ​Overall, this study provides a new perspective for understanding climatic extremes and demonstrates the prospect of artificial intelligence(AI)-assisted scientific discovery in the future.

How to cite: Jiang, S., Zheng, Y., and Zscheischler, J.: Exploring flooding mechanisms and their trends in Europe through explainable AI, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2248, https://doi.org/10.5194/egusphere-egu22-2248, 2022.

EGU22-2391 | Presentations | ITS2.7/AS5.2

Exploring cirrus cloud microphysical properties using explainable machine learning 

Kai Jeggle, David Neubauer, Gustau Camps-Valls, Hanin Binder, Michael Sprenger, and Ulrike Lohmann

Cirrus cloud microphysics and their interactions with aerosols remain one of the largest uncertainties in global climate models and climate change projections. The uncertainty originates from the high spatio-temporal variability and their non-linear dependence on meteorological drivers like temperature, updraft velocities, and aerosol environment. We combine ten years of CALIPSO/CloudSat satellite observations of cirrus clouds with ERA5 and MERRA-2 reanalysis data of meteorological and aerosol variables to create a spatial data cube. Lagrangian back trajectories are calculated for each cirrus cloud observation to add a temporal dimension to the data cube. We then train a gradient boosted tree machine learning (ML) model to predict vertically resolved cirrus cloud microphysical properties (i.e. observed ice crystal number concentration and ice water content). The explainable machine learning method of SHAP values is applied to assess the impact of individual cirrus drivers as well as combinations of drivers on cirrus cloud microphysical properties in varying meteorological conditions. In addition, we analyze how the impact of the drivers differs regionally, vertically, and temporally.

We find that the tree-based ML model is able to create a good mapping between cirrus drivers and microphysical properties (R² ~0.75) and the SHAP value analysis provides detailed insights in how different drivers impact the prediction of the microphysical cirrus cloud properties. These findings can be used to improve global climate model parameterizations of cirrus cloud formation in future works. Our approach is a good example for exploring unsolved scientific questions using explainable machine learning and feeding back insights to the domain science.

How to cite: Jeggle, K., Neubauer, D., Camps-Valls, G., Binder, H., Sprenger, M., and Lohmann, U.: Exploring cirrus cloud microphysical properties using explainable machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2391, https://doi.org/10.5194/egusphere-egu22-2391, 2022.

Global circulation models (GCMs) form the basis of a vast portion of earth system research and inform our climate policy. However, our climate system is complex and connected across scales. To simulate it, we must use parameterisations. These parameterisations, which are present in all models, can have a detectable influence on the GCM outputs.

GCMs are improving, but we need to use their current output to optimally estimate the risks of extreme weather. Therefore, we must debias GCM outputs with respect to observations. Current debiasing methods cannot correct both spatial correlations and cross-variable correlations. This limitation means current methods can produce physically implausible weather events - even when the single-location, single-variable distributions match the observations. This limitation is very important for extreme event research. Compound events like heat and drought, which drastically increase wildfire risk, and spatially co-occurring events like multiple bread-basket failures, are not well corrected by these current methods.

We propose using unsupervised image-to-image translations networks to perform bias correction of GCMs. These neural network architectures are used to translate (perform bias correction) between different image domains. For example, they have been used to translate computer-generated city scenes into real-world photos, which requires spatial and cross-variable correlations to be translated. Crucially, these networks learn to translate between image domains without requiring corresponding pairs of images. Such pairs cannot be generated between climate simulations and observations due to the inherent chaos of weather.

In this work, we use these networks to bias correct historical recreation simulations from the HadGEM3-A-N216 atmosphere-only GCM with respect to the ERA5 reanalysis dataset. This GCM has a known bias in simulating the South Asian monsoon, and so we focus on this region. We show the ability of neural networks to correct this bias, and show how combining the neural network with classical techniques produces a better bias correction than either method alone. 

How to cite: Fulton, J. and Clarke, B.: Correcting biases in climate simulations using unsupervised image-to-image-translation networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2988, https://doi.org/10.5194/egusphere-egu22-2988, 2022.

EGU22-3009 | Presentations | ITS2.7/AS5.2

Application of Machine Learning for spatio-temporal mapping of the air temperature in Warsaw 

Amirhossein Hassani, Núria Castell, and Philipp Schneider

Mapping the spatio-temporal distribution of near-surface urban air temperature is crucial to our understanding of climate-sensitive epidemiology, indoor-outdoor thermal comfort, urban biodiversity, and interactive impacts of climate change and urbanity. Urban-scale decision-making in face of future climatic uncertainties requires detailed information on near-surface air temperature at high spatio-temporal resolutions. However, reaching such fine resolutions cannot be currently realised by traditional observation networks, or even by regional or global climate models (Hamdi et al. 2020). Given the complexity of the processes affecting air temperature at the urban scale to the regional scale, here we apply Machine Learning (ML) algorithms, in particular, XGBoost gradient boosting method to build predictive models of near surface air temperature (Ta at 2-meter height). These predictive models establish data-driven relations between crowd-sourced measured Ta (data produced by citizens’ sensors) and a set of spatial and spatio-temporal predictors, primarily derived from Earth Observation satellite data including Modis Aqua/Landsat 8 Land Surface Temperature (LST), Modis Terra vegetative indices, and Sentinel-2 water vapour product. We use our models to predict sub-daily (at Modis Aqua satellite passing times) variation in urban scale Ta in city of Warsaw, Poland at spatial resolution of 1 km for the months July-September and the years 2016 to 2021. A 10-fold cross-validation of the developed models shows a root mean square error between 0.97 and 1.02 °C and a coefficient of determination between 0.96 and 0.98, which are satisfactory according to the literature (Taheri-Shahraiyni and Sodoudi 2017). The resulting maps allow us to identify regions of Warsaw that are vulnerable to heat stress. The strength of the method used here is that it can be easily replicated in other EU cities to achieve high resolution maps due to the accessibility and open-sourced nature of the training and predictor data. Contingent on data availability, the predictive framework developed also can be used for monitoring and downscaling of other urban governing climatic parameters such as relative humidity in the context of future climate uncertainties.

Hamdi, R., H. Kusaka, Q.-V. Doan, P. Cai, H. He, G. Luo, W. Kuang, S. Caluwaerts, F. Duchêne, B. J. E. S. Van Schaeybroek and Environment (2020). "The state-of-the-art of urban climate change modeling and observations." 1-16.

Taheri-Shahraiyni, H. and S. J. T. S. Sodoudi (2017). "High-resolution air temperature mapping in urban areas: A review on different modelling techniques."  21(6 Part A): 2267-2286.

How to cite: Hassani, A., Castell, N., and Schneider, P.: Application of Machine Learning for spatio-temporal mapping of the air temperature in Warsaw, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3009, https://doi.org/10.5194/egusphere-egu22-3009, 2022.

The interdisciplinary research project "BayTreeNet" investigates the reactions of forest ecosystems to current climate dynamics. In the mid-latitudes, local climatic phenomena often show a strong dependence on the large-scale climate dynamics, the weather types (WT), which significantly determine the climate of a region through frequency and intensity. In the topographically diverse region of Bavaria, different WT show various weather conditions at different locations.

The meaning of every WT is explained for the different forest regions in Bavaria and the results of the climate dynamics sub-project provide the physical basis for the "BayTreeNet" project. Subsequently, climate-growth relationships are established in the dendroecology sub-project to investigate the response of forests to individual WT at different forest sites. Complementary steps allow interpretation of results for the past (20th century) and projection into the future (21st century). One hypothesis to be investigated is that forest sites in Bavaria are affected by a significant influence of climate change in the 21st century and the associated change in WT.

The automated classification of large-scale weather patterns is presented by Self-Organizing-Maps (SOM) developed by Kohonen, which enables visualization and reduction of high-dimensional data. The poster presents the evaluation and selection of an appropriate SOM-setting and its first results. Besides, it is planned to show first analyses of the environmental conditions of the different WT and how these are represented in global climate models (GCMs) in the past and future.

How to cite: Wehrmann, S. and Mölg, T.: Classifying weather types in Europe by Self-Organizing-Maps (SOM) with regard to GCM-based future projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3105, https://doi.org/10.5194/egusphere-egu22-3105, 2022.

EGU22-3482 | Presentations | ITS2.7/AS5.2

Public perception assessment on climate change and natural disaster influence using social media big-data: A case study of USA 

SungKu Heo, Pouya Ifaei, Mohammad Moosazadeh, and ChangKyoo Yoo

Climate change is a global crisis to the world which influences the human race and society's development. Threatens of climate change have become increasingly recognized to the public and government in both environments, society, and economy across the globe; because the consequence of climate change is not only shown up as the increasing of global temperature, also expressed in an intensive natural hazard, such as floods, droughts, wildfires, and hurricanes. For the sustainability development in the globe, it is crucial to provide a response to mitigating climate change through the government’s policy and decision-making; however, the public's engagement in the actions towards the critical environmental crisis still needs to be largely promoted. Analyzing the relationship between the public awareness of climate change and natural disasters is an essential aspect in climate change mitigation and policymaking. In this study, based on the abundance of the text message in social media, especially Twitter, the public understanding and discussions upon climate change from the surrounding environment was recognized and analyzed through the human as the sensor which receiving information of climate change. Twitter content analysis and filed data impact analysis were conducted; text mining algorithms are implemented in the Twitter big-data information to find the similarity based on a cosine similarity score (CSS) between the climate change corpus and the natural events corpora. Then, the factors of nature disaster influence were predicted utilizing a multiple linear regression model and climate change tweets dataset. This research shows that the public is more pretend to link the natural events with climate change when they tweeting when serious natural disasters happened. The developed regression model indicated that natural events caused by the consequence of climate change influenced the people’s social media activity through messages on Twitter with having the awareness of climate change. From this study, the results indicated that the public experience of natural events including intensive disasters can lead them to link the climate change with the natural events easily; when compared with the people who rarely experience natural events.

Acknowledgment

This research was supported by the project (NRF-2021R1A2C2007838) through the National Research Foundation of Korea (NRF) and the Korea Ministry of Environment (MOE) as Graduate school specialized in Climate Change.

How to cite: Heo, S., Ifaei, P., Moosazadeh, M., and Yoo, C.: Public perception assessment on climate change and natural disaster influence using social media big-data: A case study of USA, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3482, https://doi.org/10.5194/egusphere-egu22-3482, 2022.

EGU22-4431 | Presentations | ITS2.7/AS5.2

Identification of Global Drivers of Indian Summer Monsoon using Causal Inference and Interpretable AI 

Deepayan Chakraborty, Adway Mitra, Bhupendranath Goswami, and Pv Rajesh

Indian Summer Monsoon Rainfall (ISMR) is a complex phenomenon that depends on several climatic phenomena at different parts of the word through teleconnections. Each season is characterized by extended periods of wet and dry spells (which may cause floods or droughts) which contribute to intra-seasonal variability. Tropical and extra-tropical drivers jointly influence the intra-seasonal variability. Although El Nino and Southern Oscillation (ENSO) is known to be a driver of ISMR, researchers have also found its relation with Indian Ocean Dipole (IOD), North Atlantic Oscillations (NAO), Atlantic Multi-decadal Oscillation (AMO). In this work, we use ideas from Causality Theory and Explainable Machine Learning to quantify the influence of different climatic phenomena on the intraseasonal variation of ISMR.

To identify such causal relations, we applied two statistically sound causal inference approaches, i.e., PCMCI+ Algorithm (Conditional Independence based) and Granger Causal test (Regression-based).  For the Granger causality test, we have examined separately for both linear and non-linear regression. In case of PCMCI+, conditional independence tests were used between pairs of variables at different "lag periods". It is worth pointing out that, till now “causality” is not properly quantified in the Climate Science community and only linear correlations are used as a basis to identify relationships like ENSO-ISMR and AMO-ISMR. We performed experiments on mean monthly rainfall anomaly data (during the monsoon months of June-September over India) along with six probable drivers (ENSO, AMO, North Atlantic Oscillation, Pacific Decadal Oscillation, Atlantic Nino, and Indian Ocean Dipole) for May, June, July, August, September months during the period 1861-2016. While the two approaches produced some contradictions, they also produced a common conclusion that ENSO and AMO are equally important and independent drivers of ISMR. 

Additionally, we have studied the contribution of the drivers on annual extremes of ISMR (years of deficient and excess rainfall) using Shapley values based on the concept of Game Theory to quantify the contributions of different predictors in a model. In this work, we train a XGBoost model to predict the ISMR anomaly from any values of the predictor variables. The experiment is carried out in two approaches. One approach involves analyzing the contribution of each driver for each of the ISMR months of any year on the mean seasonal rainfall anomaly of that year. Another approach focuses on the contribution of the seasonal mean value of each driver on the same. In both approaches, we analyze the distribution of each driver’s Shapley values for excess and deficient monsoon years for contrast. We find that while ENSO is indeed the dominant driving factor for a majority of these years, AMO is another major factor which frequently contributes to such deficiencies, while Atlantic Nino and Indian Ocean Dipole too sometimes contribute. On the other hand, Indian Ocean Dipole seems to be a major contributor for several years of excess rainfall. As future work, we plan to carry out a robustness analysis of these results, and also examine the drivers of regional extremes.

How to cite: Chakraborty, D., Mitra, A., Goswami, B., and Rajesh, P.: Identification of Global Drivers of Indian Summer Monsoon using Causal Inference and Interpretable AI, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4431, https://doi.org/10.5194/egusphere-egu22-4431, 2022.

EGU22-4534 | Presentations | ITS2.7/AS5.2

Spatial multi-modality as a way to improve both performance and interpretability of deep learning models to reconstruct phytoplankton time-series in the global ocean 

Joana Roussillon, Jean Littaye, Ronan Fablet, Lucas Drumetz, Thomas Gorgues, and Elodie Martinez

Phytoplankton plays a key role in the carbon cycle and fuels marine food webs. Its seasonal and interannual variations are relatively well-known at global scale thanks to satellite ocean color observations that have been continuously acquired since 1997. However, the satellite-derived chlorophyll-a concentrations (Chl-a, a proxy of phytoplankton biomass) time series are still too short to investigate phytoplankton biomass low-frequency variability. Machine learning models such as support vector regression (SVR) or multi-layer perceptron (MLP) have recently proven to be an alternative approach to mechanistic ones to reconstruct Chl-a past signals (including periods before the satellite era) from physical predictors, but they remain unsatisfactory. In particular, the relationships between phytoplankton and its physical surrounding environment are not homogeneous in space, and training such models over the entire globe does not allow them to capture these regional specificities. Moreover, if the global ocean is commonly partitioned into biogeochemical provinces into which phytoplankton growth is supposed to be governed by similar processes, their time-evolving nature makes it difficult to impose a priori spatial constraints to restrict the learning phase on specific areas. Here, we propose to overcome this limitation by introducing spatial multi-modalities into a convolutional neural network (CNN). The latter can learn with no particular supervision several spatially weighted modes of variability. Each of them is associated with a CNN submodel trained in parallel, standing for a mode-specific response of phytoplankton biomass to the physical forcing. Beyond improving performance reconstruction, we will show that the learned spatial modes appear physically consistent and may help to get new insights into physical-biogeochemical processes controlling phytoplankton repartition at global scale.

How to cite: Roussillon, J., Littaye, J., Fablet, R., Drumetz, L., Gorgues, T., and Martinez, E.: Spatial multi-modality as a way to improve both performance and interpretability of deep learning models to reconstruct phytoplankton time-series in the global ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4534, https://doi.org/10.5194/egusphere-egu22-4534, 2022.

EGU22-4584 | Presentations | ITS2.7/AS5.2

Super-Resolution based Deep Downscaling of Precipitation 

Sumanta Chandra Mishra Sharma and Adway Mitra

Downscaling is widely used to improve spatial resolution of meteorological variables. Broadly there are two classes of techniques used for downscaling i.e. dynamical downscaling and statistical downscaling. Dynamical downscaling depends on the boundary conditions of coarse resolution global models like General Circulation Models (GCMs) for its operation whereas the statistical model tries to interpret the statistical relationship between the high-resolution and low-resolution data (Kumar et. al. 2021). With the rapid development of deep learning techniques in recent years, deep learning based super-resolution (SR) models have been designed for image processing and computer vision, for increasing the resolution of a given image. But many researchers from other fields have also adapted these techniques and achieved state-of-the-art performance in various domains. To the best of our knowledge, only a few works exist that have used the super-resolution methods in climate domain, for deep downscaling of precipitation data.

These super-resolution approaches mostly use convolutional neural networks (CNN) to accomplish their task. In CNN when we increase the depth of the model then there is a chance of information loss and error propagation (Vandal et.al.2017). To reduce this information loss, we have introduced residual-based deep downscaling models. These models have multiple residual blocks and skip connections between similar types of convolutional layers. The long skip connections in the model helps to reduce information loss in the network. These models take as input, data that is pre-upsampled by linear interpolation, and then improve the estimates of the pixel values.

In our experiments, we have focused on downscaling of rainfall over Indian landmass (for Indian summer monsoon rainfall) and for a region in the USA spanning the southeast CONUS and parts of its neighboring states that are present between the longitude 700 W to 1000 W and latitude 240 N to 400 N. The precipitation data for this task is collected from the India Meteorological Department (IMD), Pune, India, and NOAA Physical Science Laboratory. We have examined our model's predictive behavior and compared it with the existing super-resolution models like SRCNN and DeepSD, which have been earlier used for precipitation downscaling. In the DeepSD model, we have used the GTOPO30 land elevation data provided by USGS along with the precipitation data as input. All these models are trained and tested in both the geographical regions separately and it is found that the proposed model performs better than the existing models on multiple accuracy measures like PSNR, Correlation Coefficient, etc. for the specific region and scaling factor.

How to cite: Mishra Sharma, S. C. and Mitra, A.: Super-Resolution based Deep Downscaling of Precipitation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4584, https://doi.org/10.5194/egusphere-egu22-4584, 2022.

EGU22-4853 | Presentations | ITS2.7/AS5.2

Can cloud properties provide information on surface wind variations using deep learning? 

Sebastiaan Jamaer, Jérôme Neirynck, and Nicole van Lipzig

Recent studies have shown that the increasing sizes of offshore wind farms can cause a reduced energy production through mesoscale interactions with the atmosphere. Therefore, accurate nowcasting of the energy yields of large offshore wind farms depend on accurate predictions of the large synoptic weather systems as well as accurate predictions of the smaller mesoscale weather systems. In general, global or regional forecasting models are very well suited to predict synoptic-scale weather systems. However, satellite or radar data can support the nowcasting of shorter, smaller-scale systems. 

In this work, a first step towards nowcasting of the mesoscale wind using satellite images has been taken, namely the coupling of the mesoscale wind component to cloud properties that are available from satellite images using a deep learning framework. To achieve this, a high-resolution regional atmospheric model (COSMO-CLM) was used to generate one year of high resolution cloud en hub-height wind data. From this wind data the mesoscale component was filtered out and used as target images for the deep learning model. The input images of the model were several cloud-related fields from the atmospheric model. The model itself was a Deep Convolutional Neural Network (a U-Net) which was trained to minimize the mean squared error. 

This analysis indicates that cloud information can be used to extract information about the mesoscale weather systems and could be used for nowcasting by using the trained U-Net as a basis for a temporal deep learning model. However, future validation with real-world data is still needed to determine the added value of such an approach.

How to cite: Jamaer, S., Neirynck, J., and van Lipzig, N.: Can cloud properties provide information on surface wind variations using deep learning?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4853, https://doi.org/10.5194/egusphere-egu22-4853, 2022.

EGU22-5058 | Presentations | ITS2.7/AS5.2

Can satellite images provide supervision for cloud systems characterization? 

Dwaipayan Chatterjee, Hartwig Deneke, and Susanne Crewell

With ever-increasing resolution, geostationary satellites are able to reveal the complex structure and organization of clouds. How cloud systems organize is important for the local climate and strongly connects to the Earth's response to warming through cloud system feedback.

Motivated by recent developments in computer vision for pattern analysis of uncurated images, our work aims to understand the organization of cloud systems based on high-resolution cloud optical depth images. We are exploiting the self-learning capability of a deep neural network to classify satellite images into different subgroups based on the distribution pattern of the cloud systems.

Unlike most studies, our neural network is trained over the central European domain, which is characterized by strong land surface type and topography variations. The satellite data is post-processed and retrieved at a higher spatio-temporal resolution (2 km, 5 min), enhanced by 66% compared to the current standard, equivalent to the future Meteosat third-generation satellite, which will be launched soon.

We show how recent advances in deep learning networks are used to understand clouds' physical properties in temporal and spatial scales. In a purely data-driven approach, we avoid the noise and bias obtained from human labeling, and with proper scalable techniques, it takes 0.86 ms and 2.13 ms to label an image at two different spatial configurations. We demonstrate explainable artificial intelligence (XAI), which helps gain trust for the neural network's performance.

To generalize the results, a thorough quantified evaluation is done on two spatial domains and two-pixel configurations (128x128, 64x64). We examine the uncertainty associated with distinct machine-detected cloud-pattern categories. For this, the learned features of the satellite images are extracted from the trained neural network and fed to an independent hierarchical - agglomerative algorithm. Therefore the work also explores the uncertainties associated with the automatic machine-detected patterns and how they vary with different cloud classification types.

How to cite: Chatterjee, D., Deneke, H., and Crewell, S.: Can satellite images provide supervision for cloud systems characterization?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5058, https://doi.org/10.5194/egusphere-egu22-5058, 2022.

Extreme weather events, such as droughts, floods or heatwaves, severely impact agricultural yield. However, crop yield failure may also be caused by the temporal or multivariate compounding of more moderate weather events. An example of such an occurrence is the phenomenon of 'false spring', where the combined effects of a warm interval in late winter followed by a period of freezing temperatures can result in severe damage to vegetation. Alternatively, multiple weather events may impact crops simultaneously, as with compound hot and dry weather conditions.

Machine learning techniques are able to learn highly complex and nonlinear relationships between predictors. Such methods have previously been used to explore the influence of monthly- or seasonally-aggregated weather data as well as predefined extreme event indicators on crop yield. However, as crop yield may be impacted by climatic variables at different temporal scales, interpretable machine learning methods that can extract relevant meteorological features from higher-resolution time series data are desirable.

In this study we test the ability of adaptations of random forest models to identify compound meteorological drivers of crop failure from simulated data. In particular, adaptations of random forest models capable of ingesting daily multivariate time series data and spatial information are used. First, we train models to extract useful features from daily climatic data and predict crop yield failure probabilities. Second, we use permutation feature importances and sequential feature selection to investigate weather events and time periods identified by the models as most relevant for crop yield failure prediction. Finally, we explore the interactions learned by the models between these selected meteorological drivers, and compare the outcomes for several global crop models. Ultimately, our goal is to present a robust and highly interpretable machine learning method that can identify critical weather conditions from datasets with high temporal and spatial resolution, and is therefore able to identify drivers of crop failure using relatively few years of data.

How to cite: Sweet, L. and Zscheischler, J.: Using interpretable machine learning to identify compound meteorological drivers of crop yield failure, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5464, https://doi.org/10.5194/egusphere-egu22-5464, 2022.

EGU22-5756 | Presentations | ITS2.7/AS5.2

The influence of meteorological parameters on wind speed extreme events:  A causal inference approach 

Katerina Hlavackova-Schindler (Schindlerova), Andreas Fuchs, Claudia Plant, Irene Schicker, and Rosmarie DeWit

Based on the ERA5  data of hourly  meteorological parameters [1], we investigate temporal effects of  12 meteorological parameters on  the extreme values occurring in  wind speed.  We approach the problem by using the Granger causal inference, namely by the heterogeneous graphical Granger model (HGGM) [2]. In contrary to the classical Granger model proposed for causal inference among Gaussian processes, the HGGM detects causal relations among time series with distributions from the exponential family, which includes a wider class of common distributions. In previous synthetic experiments, HGGM combined with the genetic algorithm search based on the minimum message length principle has been shown superior in precision over the baseline causal methods [2].  We investigate various experimental settings of all 12 parameters with respect to the wind extremes in various time intervals. Moreover, we compare the influence of various data preprocessing methods and evaluate the interpretability of the discovered causal connections based on meteorological knowledge.

[1] https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview

[2] Behzadi, S, Hlaváčková-Schindler, K., Plant, C. (2019) Granger causality for heterogeneous processes, In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, pp. 463-475.

[3] Hlaváčková-Schindler, K., Plant, C. (2020) Heterogeneous graphical Granger causality by minimum message length, Entropy, 22(1400). pp. 1-21 ISSN 1099-4300 MDPI (2020).

How to cite: Hlavackova-Schindler (Schindlerova), K., Fuchs, A., Plant, C., Schicker, I., and DeWit, R.: The influence of meteorological parameters on wind speed extreme events:  A causal inference approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5756, https://doi.org/10.5194/egusphere-egu22-5756, 2022.

EGU22-6093 | Presentations | ITS2.7/AS5.2

Machine learning to quantify cloud responses to aerosols from satellite data 

Jessenia Gonzalez, Odran Sourdeval, Gustau Camps-Valls, and Johannes Quaas

The Earth's radiation budget may be altered by changes in atmospheric composition or land use. This is called radiative forcing. Among the human-generated influences in radiative forcing, aerosol-cloud interactions are the least understood. A way to quantify a key uncertainty in this regard, the adjustment of cloud liquid water path (LWP), is by the ratio (sensitivity) of LWP to changes in cloud droplet number concentration (Nd). A key problem in quantifying this sensitivity from large-scale observations is that these two quantities are not retrieved by operational satellite products and are subject to large uncertainties. 

In this work, we use machine learning techniques to show that inferring LWP and Nd directly from satellite observation data may yield a better understanding of this relationship without using retrievals, which may lead to large and systematic uncertainties. In particular, we use supervised learning on the basis of available high-resolution ICON-LEM (ICOsahedral Non-hydrostatic Large Eddy Model) simulations from the HD(CP)² project (High Definition Clouds and Precipitation for advancing Climate Prediction) and forward-simulated radiances obtained from the radiative transfer modeling (RTTOV, Radiative Transfer for TOVS) which uses MODIS (Moderate Resolution Imaging Spectroradiometer) data as a reference. Usually, only two channels from the reflectance of MODIS can be used to estimate the LWP and Nd. However, having access to 36 bands allows us to exploit data and find other patterns to get these parameters directly from the observation space rather than from the retrievals. A machine learning model is used to create an emulator which approximates the Radiative Transfer Model, and another machine learning model to directly predict the sensitivity of LWP - Nd from the satellite observation data.

How to cite: Gonzalez, J., Sourdeval, O., Camps-Valls, G., and Quaas, J.: Machine learning to quantify cloud responses to aerosols from satellite data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6093, https://doi.org/10.5194/egusphere-egu22-6093, 2022.

Microclimate is a relatively recent concept in atmospheric sciences, which started drawing attention of engineers and climatologists after proliferation of the open thermal (infrared, middle- and near-infrared) remote sensing instruments and high-resolution emissivity datasets. Rarely mentioned in the context of global climate change reversing, efficient management of microclimates nevertheless can be considered as a possible solution. Their function is bi-directional; On one hand, they can perform as ‘buffers’ by smoothing out effects of the already altered global climate on people and ecosystems, whilst also acting as the structural contributors to perturbations in the higher layers of the atmosphere. 

In the most abstract terms, microclimates tend to manifest themselves via land surface temperature conditions, which in turn are highly sensitive to the underlying land cover and use decisions. Forests are considered as the most efficient terrestrial carbon sinks and climate regulators, and various forms, configurations and continuity of logging can substantially alter the patterns of local temperature fluxes, precipitation and ecosystems. In this study we propose a novel heteroskedastic machine learning method, which can attribute localised forest loss patches due to industrial mining activity and estimate the resulting change in dynamics of the surrounding microclimate(s). 

How to cite: Tkachenko, N. and Garcia Velez, L.: Global attribution of microclimate dynamics to industrial deforestation sites using thermal remote sensing and machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6466, https://doi.org/10.5194/egusphere-egu22-6466, 2022.

EGU22-6543 | Presentations | ITS2.7/AS5.2

High-resolution hybrid spatiotemporal modeling of daily relative humidity across Germany for epidemiological research: a Random Forest approach 

Nikolaos Nikolaou, Laurens Bouwer, Mahyar Valizadeh, Marco Dallavalle, Kathrin Wolf, Massimo Stafoggia, Annette Peters, and Alexandra Schneider

Introduction: Relative humidity (RH) is a meteorological variable of great importance as it affects other climatic variables and plays a role in plant and animal life as well as in human comfort and well-being. However, the commonly used weather station observations are inefficient to represent the great spatiotemporal RH variability, leading to exposure misclassification and difficulties to assess local RH health effects. There is also a lack of high-resolution RH spatial datasets and no readily available methods for modeling humidity across space and time. To tackle these issues, we aimed to improve the spatiotemporal coverage of RH data in Germany, using remote sensing and machine learning (ML) modeling.

Methods: In this study, we estimated German-wide daily mean RH at 1km2 resolution over the period 2000-2020. We used several predictors from multiple sources, including DWD RH observations, Ta predictions as well as satellite-derived DEM, NDVI and the True Color band composition (bands 1, 4 and 3: red, green and blue). Our main predictor for estimating the daily mean RH was the daily mean Ta. We had already mapped daily mean Ta in 1km2 across Germany through a regression-based hybrid approach of two linear mixed models using land surface temperature. Additionally, a very important predictor was the date, capturing the day-to-day variation of the response-explanatory variables relationship. All these variables were included in a Random Forest (RF) model, applied for each year separately. We assessed the model’s accuracy via 10-fold cross-validation (CV). First internally, using station observations that were not used for the model training, and then externally in the Augsburg metropolitan area using the REKLIM monitoring network over the period 2015-2019.

Results: Regarding the internal validation, the 21-year overall mean CV-R2 was 0.76 and the CV-RMSE was 6.084%. For the model’s external performance, at the same day, we found CV-R2=0.75 and CV-RMSE=7.051% and for the 7-day average, CV-R2=0.81 and CV-RMSE=5.420%. Germany is characterized by high relative humidity values, having a 20-year average RH of 78.4%. Even if the annual country-wide averages were quite stable, ranging from 81.2% for 2001 to 75.3% for 2020, the spatial variability exceeded 15% annually on average. Generally, winter was the most humid period and especially December was the most humid month. Extended urban cores (e.g., from Stuttgart to Frankfurt) or individual cities as Munich were less humid than the surrounding rural areas. There are also specific spatial patterns for RH distribution, including mountains, rivers and coastlines. For instance, the Alps and the North Sea coast are areas with elevated RH.

Conclusion: Our results indicate that the applied hybrid RF model is suitable for estimating nationwide RH at high spatiotemporal resolution, achieving a strong performance with low errors. Our method contributes to an improved spatial estimation of RH and the output product will help us understand better the spatiotemporal patterns of RH in Germany. We also plan to apply other ML techniques and compare the findings. Finally, our dataset will be used for epidemiological analyses, but could also be used for other research questions.

How to cite: Nikolaou, N., Bouwer, L., Valizadeh, M., Dallavalle, M., Wolf, K., Stafoggia, M., Peters, A., and Schneider, A.: High-resolution hybrid spatiotemporal modeling of daily relative humidity across Germany for epidemiological research: a Random Forest approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6543, https://doi.org/10.5194/egusphere-egu22-6543, 2022.

EGU22-6958 | Presentations | ITS2.7/AS5.2

Causal Discovery in Ensembles of Climate Time Series 

Andreas Gerhardus and Jakob Runge

Understanding the cause and effect relationships that govern natural phenomena is central to the scientific inquiry. While being the gold standard for inferring causal relationships, there are many scenarios in which controlled experiments are not possible. This is for example the case for most aspects of Earth's complex climate system. Causal relationships then have to be learned from statistical dependencies in observational data, a task that is commonly referred to as (observational) causal discovery.

When applied to time series data for learning causal relationships in dynamical systems, methods for causal discovery face additional statistical challenges. This is so because, as licensed by an assumption of stationarity, samples are taken in a sliding window fashion and hence autocorrelated rather than iid. Moreover, strong autocorrelations also often occlude other relevant causal links. The recent PCMCI algorithm (Runge et al., 2019) and its variants PCMCI+ (Runge, 2020) and LPCMCI (Gerhardus and Runge, 2020) address and to some extent alleviate theses issues.

In this contribution we present the Ensemble-PCMCI method, an adaption of PCMCI (and its variants PCMCI+ and LPCMCI) to cases in which the data comprises several time series, i.e., measurements of several instances of the same underlying dynamical system. Samples can then be taken from these different time series instead of a in a sliding window fashion, thus avoiding the issue of autocorrelation and also allowing to relax the stationarity assumption. In particular, this opens the possibility to analyze temporal changes in the underlying causal mechanisms. A potential domain of application are ensemble forecasts.

Related references:
Jakob Runge et al. (2019). Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances 5 eaau4996.

Jakob Runge (2020). Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). Proceedings of Machine Learning Research 124 1388–1397. PMLR.

Andreas Gerhardus and Jakob Runge (2020). High-recall causal discovery for autocorrelated time series with latent confounders. In Advances in Neural Information Processing Systems 33 12615–12625. Curran Associates, Inc.

How to cite: Gerhardus, A. and Runge, J.: Causal Discovery in Ensembles of Climate Time Series, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6958, https://doi.org/10.5194/egusphere-egu22-6958, 2022.

EGU22-6998 | Presentations | ITS2.7/AS5.2

Inferring the Cloud Vertical Distribution from Geostationary Satellite Data 

Sarah Brüning, Holger Tost, and Stefan Niebler

Clouds and their radiative feedback mechanisms are of vital importance for the atmospheric cycle of the Earth regarding global weather today as well as climate changes in the future. Climate models and simulations are sensitive to the vertical distribution of clouds, emphasizing the need for broadly accessible fine resolution data. Although passive satellite sensors provide continuous cloud monitoring on a global scale, they miss the ability to infer physical properties below the cloud top. Active instruments like radar are particularly suitable for this task but lack an adequate spatio-temporal resolution. Here, recent advances in Deep-Learning models open up the possibility to transfer spatial information from a 2D towards a 3D perspective on a large-scale.

By an example period in 2017, this study aims to explore the feasibility and potential of neural networks to reconstruct the vertical distribution of volumetric radar data along a cloud’s column. For this purpose, the network has been tested on the Full Disk domain of a geostationary satellite with high spatio-temporal resolution data. Using raw satellite channels, spectral indices, and topographic data, we infer the 3D radar reflectivity from these physical predictors. First results demonstrate the network’s capability to reconstruct the cloud vertical distribution. Finally, the ultimate goal of interpolating the cloud column for the whole domain is supported by a considerably high accuracy in predicting the radar reflectivity. The resulting product can open up the opportunity to enhance climate models by an increased spatio-temporal resolution of 3D cloud structures.

How to cite: Brüning, S., Tost, H., and Niebler, S.: Inferring the Cloud Vertical Distribution from Geostationary Satellite Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6998, https://doi.org/10.5194/egusphere-egu22-6998, 2022.

EGU22-7011 | Presentations | ITS2.7/AS5.2

Unlocking the potential of ML for Earth and Environment researchers 

Tobias Weigel, Frauke Albrecht, Caroline Arnold, Danu Caus, Harsh Grover, and Andrey Vlasenko

This presentation reports on support done under the aegis of Helmholtz AI for a wide range of machine learning based solutions for research questions related to Earth and Environmental sciences. We will give insight into typical problem statements from Earth observation and Earth system modeling that are good candidates for experimentation with ML methods and report on our accumulated experience tackling such challenges with individual support projects. We address these projects in an agile, iterative manner and during the definition phase, we direct special attention towards assembling practically meaningful demonstrators within a couple of months. A recent focus of our work lies on tackling software engineering concerns for building ML-ESM hybrids.

Our implementation workflow covers stages from data exploration to model tuning. A project may often start with evaluating available data and deciding on basic feasibility, apparent limitations such as biases or a lack of labels, and splitting into training and test data. Setting up a data processing workflow to subselect and compile training data is often the next step, followed by setting up a model architecture. We have made good experience with automatic tooling to tune hyperparameters and test and optimize network architectures. In typical implementation projects, these stages may repeat many times to improve results and cover aspects such as errors due to confusing samples, incorporating domain model knowledge, testing alternative architectures and ML approaches, and dealing with memory limitations and performance optimization.

Over the past two years, we have supported Helmholtz-based researchers from many subdisciplines on making the best use of ML methods along with these steps. Example projects include wind speed regression on GNSS-R data, emulation of atmospheric chemistry modeling, Earth System model parameterizations with ML, marine litter detection, and rogue waves prediction. The poster presentation will highlight selected best practices across these projects. We are happy to share our experience as it may prove useful to applications in wider Earth System modeling. If you are interested in discussing your challenge with us, please feel free to chat with us.

How to cite: Weigel, T., Albrecht, F., Arnold, C., Caus, D., Grover, H., and Vlasenko, A.: Unlocking the potential of ML for Earth and Environment researchers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7011, https://doi.org/10.5194/egusphere-egu22-7011, 2022.

EGU22-7034 | Presentations | ITS2.7/AS5.2

Developing a new emergent constraint through network analysis 

Lucile Ricard, Athanasios Nenes, Jakob Runge, and Fabrizio Falasca

Climate sensitivity expresses how average global temperature responds to an increase in greenhouse gas concentration. It is a key metric to assess climate change, and to formulate policy decisions, but its estimation from the Earth System Models (ESM) provides a wide range: between 2.5 and 4.0 K based on the sixth assessment report (AR6) of the Intergovernmental Panel on Climate Change (IPCC). To narrow down this spread, a number of observable metrics, called “emergent constraints” have been proposed, but often are based on relatively few parameters from a simulation – thought to express the “essence” of the climate simulation and its relationship with climate sensitivity. Many of the constraints to date however are model-dependent, therefore questionable in terms of their robustness.

We postulate that methods based on “holistic” consideration of the simulations and observations may provide more robust constraints; we also focus on Sea Surface Temperature (SST) ensembles as SST is a major driver of climate variability. To extract the essential patterns of SST variability, we use a knowledge discovery and network inference method, δ-Maps (Fountalis et al., 2016, Falasca et al, 2019), expanded to include a causal discovery algorithm (PCMCI) that relies on conditional independence testing, to capture the essential dynamics of the climate simulation on a functional graph and explore the true causal effects of the underlying dynamical system (Runge et al., 2019). The resulting networks are then quantitatively compared using network “metrics” that capture different aspects, including the regions of uniform behavior, how they alternate over time and the strength of association. These metrics are then compared between simulations, and observations and used as emergent constraints, called Causal Model Evaluation (CME).

We apply δ-Maps and CME to CMIP6 model SST outputs and demonstrate how the networks and related metrics can be used to assess the historical performance of CMIP models, and climate sensitivity. We start by comparing the CMIP6 simulations against CMIP5 models, by using the reanalysis dataset HadISST (Met Office Hadley Centre) as a proxy for observations. Each field is reduced to a network and then how similar they are with reanalysis SST. The CMIP6 historical networks are then compared against CMIP6 projected networks, build from the Shared Socio-Economic Pathway ssp245 (“Middle of the road”) scenario. Comparing past and future SST networks help us to evaluate the extent to which climate warming is encompassed in the change overlying dynamical system of our networks. A large distance from network build over the past period to network build over a future scenario could be tightly related to a large temperature response to an increase of greenhouse gas emission, that is the way we define climate sensitivity. We finally give a new estimation of the climate sensitivity with a weighting scheme approach, derived from a combination of its performance metrics.

How to cite: Ricard, L., Nenes, A., Runge, J., and Falasca, F.: Developing a new emergent constraint through network analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7034, https://doi.org/10.5194/egusphere-egu22-7034, 2022.

EGU22-7355 | Presentations | ITS2.7/AS5.2

Combining cloud properties and synoptic observations to predict cloud base height using Machine Learning 

Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic

Cloud base height (CBH) is an important geometric parameter of a cloud and shapes its radiative properties. The CBH is also further of practical interest in the aviation community regarding pilot visibility and aircraft icing hazards. While the cloud-top height has been successfully derived from passive imaging radiometers on satellites during recent years, the derivation of the CBH remains a more difficult challenge with these same retrievals.

In our study we combine surface observations and passive satellite remote-sensing retrievals to create a database of CBH labels and cloud properties to ultimately train a machine learning model predicting CBH. The labels come from the global marine meteorological observations dataset (UK Met Office, 2006) which consists of near-global synoptic observations made on sea. This data set provides information about CBH, cloud type, cloud cover and other meteorological surface quantities with CBH being the main interest here. The features based upon which the machine learning model is trained consist in different cloud-top and cloud optical properties (Level 2 products MOD06/MYD06 from the MODIS sensor) extracted on a 127km x 127km grid around the synoptic observation point. To study the large diversity in cloud scenes, an auto-encoder architecture is chosen. The regression task is then carried out in the modelled latent space which is output by the encoder part of the model. To account for the spatial relationships in our input data the model architecture is based on Convolutional Neural Networks. We define a study domain in the Atlantic ocean, around the equator. The combination of information from below and over the cloud could allow us to build a robust model to predict CBH and then extend predictions to regions where surface measurements are not available.

How to cite: Lenhardt, J., Quaas, J., and Sejdinovic, D.: Combining cloud properties and synoptic observations to predict cloud base height using Machine Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7355, https://doi.org/10.5194/egusphere-egu22-7355, 2022.

EGU22-8068 | Presentations | ITS2.7/AS5.2

Generative Adversarial Modeling of Tropical Precipitation and the Intertropical Convergence Zone 

Cody Nash, Balasubramanya Nadiga, and Xiaoming Sun

In this study we evaluate the use of generative adversarial networks (GANs) to model satellite-based estimates of precipitation conditioned on reanalysis temperature, humidity, wind, and surface latent heat flux.  We are interested in the climatology of precipitation and modeling it in terms of atmospheric state variables, in contrast to a weather forecast or precipitation nowcast perspective.  We consider a hierarchy of models in terms of complexity, including simple baselines, generalized linear models, gradient boosted decision trees, pointwise GANs and deep convolutional GANs. To gain further insight into the models we apply methods for analyzing machine learning models, including model explainability, ablation studies, and a diverse set of metrics for pointwise and distributional differences, including information theory based metrics.  We find that generative models significantly outperform baseline models on metrics based on the distribution of predictions, particularly in capturing the extremes of the distributions.  Overall, a deep convolutional model achieves the highest accuracy.  We also find that the relative importance of atmospheric variables and of their interactions vary considerably among the different models considered. 

How to cite: Nash, C., Nadiga, B., and Sun, X.: Generative Adversarial Modeling of Tropical Precipitation and the Intertropical Convergence Zone, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8068, https://doi.org/10.5194/egusphere-egu22-8068, 2022.

EGU22-8130 | Presentations | ITS2.7/AS5.2

A comparison of explainable AI solutions to a climate change prediction task 

Philine Lou Bommer, Marlene Kretschmer, Dilyara Bareeva, Kadircan Aksoy, and Marina Höhne

In climate change research we are dealing with a chaotic system, usually leading to huge computational efforts in order to make faithful predictions. Deep neural networks (DNNs) offer promising new approaches due to their computational efficiency and universal solution properties. However, despite the increase in successful application cases with DNNs, the black-box nature of such purely data-driven approaches limits their trustworthiness and therefore the useability of deep learning in the context of climate science.

The field of explainable artificial intelligence (XAI) has been established to enable a deeper understanding of the complex, highly-nonlinear methods and their predictions. By shedding light onto the reasons behind the predictions made by DNNs, XAI methods can serve as a support for researchers to reveal the underlying physical mechanisms and properties inherent in the studied data. Some XAI methods have already been successfully applied to climate science, however, no detailed comparison of their performances is available. As the number of XAI methods on the one hand, and DNN applications on the other hand are growing, a comprehensive evaluation is necessary in order to understand the different XAI methods in the climate context.

In this work we provide an overview of different available XAI methods and their potential applications for climate science. Based on a previously published climate change prediction task, we compare several explanation approaches, including model-aware (e.g. Saliency, IntGrad, LRP) and model-agnostic methods (e.g. SHAP). We analyse their ability to verify the physical soundness of the DNN predictions as well as their ability to uncover new insights into the underlying climate phenomena. Another important aspect we address in our work is the possibility to assess the underlying uncertainties of DNN predictions using XAI methods. This is especially crucial in climate science applications where uncertainty due to natural variability is usually large. To this end, we investigate the potential of two recently introduced XAI methods —UAI+ and NoiseGrad, which have been designed to include uncertainty information of the predictions into the explanations. We demonstrate that those XAI methods enable more stable explanations with respect to model noise and can further deal with uncertainties of network information. We argue that these methods are therefore particularly suitable for climate science application cases.

How to cite: Bommer, P. L., Kretschmer, M., Bareeva, D., Aksoy, K., and Höhne, M.: A comparison of explainable AI solutions to a climate change prediction task, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8130, https://doi.org/10.5194/egusphere-egu22-8130, 2022.

Despite the importance of the Atlantic Meridional Overturning Circulation (AMOC) to the climate on decadal and multidecadal timescales, Earth System Models (ESM) exhibit large differences in their estimation of the amplitude and spectrum of its variability. In addition, observational data is sparse and before the onset of the current century, many reconstructions of the AMOC rely on linear relationships to the more readily observed surface properties of the Atlantic rather than the less explored deeper ocean. Yet, it is conceptually well established that the density distribution is dynamically closely related to the AMOC, and in this contribution, we investigate this connection in model simulations to identify which density information is necessary to reconstruct the AMOC. We chose to establish these links in a data-driven approach. 

We use simulations from a historically forced large ensemble as well as abruptly forced long term simulations with varying strength of forcing and therefore comprising vastly different states of the AMOC. In a first step, we train uncertainty-aware neural networks to infer the state of the AMOC from the density information at different layers in the North Atlantic. In a second step, we compare the performance of the trained neural networks across depth and with their linear counterparts in simulations that were not part of the training process. Finally, we investigate how the networks arrived at their specific prediction using Layer-Wise-Relevance Propagation (LRP), a recently developed technique that propagates relevance backwards through the network to the input density field, effectively filtering out important from unimportant information and identifying regions of high relevance for the reconstruction of the AMOC.

Our preliminary results show that in general, the information provided by only one density layer between the surface and 1100 m is sufficient to reconstruct the AMOC with high precision, and neural networks are capable of generalizing to unseen simulations. From the set of these neural networks trained on different layers, we choose the surface layer as well as one subsurface layer close to 1000 m for further investigation of their decision-making process using LRP. Our preliminary investigation reveals that the LRP in the subsurface layer identifies regions of potentially high physical relevance for the AMOC. By contrast, the regions identified in the surface layer show little physical relevance for the AMOC.

How to cite: Mayer, B., Barnes, E., Marotzke, J., and Baehr, J.: Reconstructing the Atlantic Meridional Overturning Circulation in Earth System Model simulations from density information using explainable machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8411, https://doi.org/10.5194/egusphere-egu22-8411, 2022.

EGU22-8454 | Presentations | ITS2.7/AS5.2

Using Generative Adversarial Networks (GANs) to downscale tropical cyclone precipitation. 

Emily Vosper, Dann Mitchell, Peter Watson, Laurence Aitchison, and Raul Santos-Rodriguez

Fluvial flood hazards from tropical cyclones (TCs) are frequently the leading cause of mortality and damages (Rezapour and Baldock, 2014). Accurately modeling TC precipitation is vital for studying the current and future impacts of TCs. However, general circulation models at typical resolution struggle to accurately reproduce TC rainfall, especially for the most extreme storms (Murakami et al., 2015). Increasing horizontal resolution can improve precipitation estimates (Roberts et al., 2020; Zhang et al., 2021), but as these methods are computationally expensive there is a trade-off between accuracy and generating enough ensemble members to generate sufficient high impact, low probability events. Often, downscaling models are used as a computationally cheaper alternative. 

Here, we downscale TC precipitation data from 100 km to 10 km resolution using a generative adversarial network (GAN). Generative approaches have the potential to well reproduce the fine spatial detail and stochastic nature of precipitation (Ravuri et al., 2021). Using observational products for tracking (IBTrACS) and rainfall (MSWEP), we train our GAN over the historical period 1979 - 2020. We are interested in how well our model reproduces precipitation intensity and structure with a focus on the most extreme events, where models have traditionally struggled. 

Bibliography 

Murakami, H., et al., 2015. Simulation and Prediction of Category 4 and 5 Hurricanes in the High-Resolution GFDL HiFLOR Coupled Climate Model*. Journal of Climate, 28(23), pp.9058-9079. 

Ravuri, S., et al., 2021. Skilful precipitation nowcasting using deep generative models of radar. Nature, 597(7878), pp.672-677. 

Rezapour, M. and Baldock, T., 2014. Classification of Hurricane Hazards: The Importance of Rainfall. Weather and Forecasting, 29(6), pp.1319-1331. 

Roberts, M., et al., 2020. Impact of Model Resolution on Tropical Cyclone Simulation Using the HighResMIP–PRIMAVERA Multimodel Ensemble. Journal of Climate, 33(7), pp.2557-2583. 

Zhang, W., et al., 2021. Tropical cyclone precipitation in the HighResMIP atmosphere-only experiments of the PRIMAVERA Project. Climate Dynamics, 57(1-2), pp.253-273. 

How to cite: Vosper, E., Mitchell, D., Watson, P., Aitchison, L., and Santos-Rodriguez, R.: Using Generative Adversarial Networks (GANs) to downscale tropical cyclone precipitation., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8454, https://doi.org/10.5194/egusphere-egu22-8454, 2022.

EGU22-8499 | Presentations | ITS2.7/AS5.2 | Highlight

Matryoshka Neural Operators: Learning Fast PDE Solvers for Multiscale Physics 

Björn Lütjens, Catherine H. Crawford, Campbell Watson, Chris Hill, and Dava Newman

Running a high-resolution global climate model can take multiple days on the world's largest supercomputers. Due to the long runtimes that are caused by solving the underlying partial differential equations (PDEs), climate researchers struggle to generate ensemble runs that are necessary for uncertainty quantification or exploring climate policy decisions.

 

Physics-informed neural networks (PINNs) promise a solution: they can solve single instances of PDEs up to three orders of magnitude faster than traditional finite difference numerical solvers. However, most approaches in physics-informed machine learning learn the solution of PDEs over the full spatio-temporal domain, which requires infeasible amounts of training data, does not exploit knowledge of the underlying large-scale physics, and reduces model trust. Our philosophy is to limit learning to the hard-to-model parts. Hence, we are proposing a novel method called \textit{matryoshka neural operator} that leverages an old scheme called super-parametrizations developed in geophysical fluid dynamics. Using this scheme our proposed physics-informed architecture exploits knowledge of approximate large-scale dynamics and only learns the influence of small-scale dynamics onto large-scale dynamics, also called subgrid parametrizations.

 

Some work in geophysical fluid dynamics is conceptually similar, but fully relies on neural networks which can only operate on fixed grids (Gentine et al., 2018). We are the first to learn grid-independent subgrid parametrizations by leveraging neural operators that learn the dynamics in a grid-independent latent space. Neural operators can be seen as an extension of neural networks to infinite-dimensions: They encode infinite-dimensional inputs into a finite-dimensional representations, such as Eigen or Fourier modes, and learn the nonlinear temporal dynamics in the encoded state.

 

We demonstrate the neural operators for learning non-local subgrid parametrizations over the full large-scale domain of the two-scale Lorenz96 equation. We show that the proposed learning-based PDE solver is grid-independent, has quasilinear instead of quadratic complexity in comparison to a fully-resolving numerical solver, is more accurate than current neural network or polynomial-based parametrizations, and offers interpretability through Fourier modes.

 

Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G. (2018). Could machine learning break the convection parameterization deadlock? Geophysical Research Letters, 45, 5742– 5751. https://doi.org/10.1029/2018GL078202

How to cite: Lütjens, B., Crawford, C. H., Watson, C., Hill, C., and Newman, D.: Matryoshka Neural Operators: Learning Fast PDE Solvers for Multiscale Physics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8499, https://doi.org/10.5194/egusphere-egu22-8499, 2022.

EGU22-8649 | Presentations | ITS2.7/AS5.2

Physically Based Deep Learning Framework to Model Intense Precipitation Events at Engineering Scales 

Bernardo Teufel, Fernanda Carmo, Laxmi Sushama, Lijun Sun, Naveed Khaliq, Stephane Belair, Asaad Yahia Shamseldin, Dasika Nagesh Kumar, and Jai Vaze

The high computational cost of super-resolution (< 250 m) climate simulations is a major barrier for generating climate change information at such high spatial and temporal resolutions required by many sectors for planning local and asset-specific climate change adaptation strategies. This study couples machine learning and physical modelling paradigms to develop a computationally efficient simulator-emulator framework for generating super-resolution climate information. To this end, a regional climate model (RCM) is applied over the city of Montreal, for the summers of 2015 to 2020, at 2.5 km (i.e., low resolution – LR) and 250 m (i.e., high resolution – HR), which is used to train and validate the proposed super-resolution deep learning (DL) model. In the field of video super-resolution, convolutional neural networks combined with motion compensation have been used to merge information from multiple LR frames to generate high-quality HR images. In this study, a recurrent DL approach based on passing the generated HR estimate through time helps the DL model to recreate fine details and produce temporally consistent fields, resembling the data assimilation process commonly used in numerical weather prediction. Time-invariant HR surface fields and storm motion (approximated by RCM-simulated wind) are also considered in the DL model, which helps further improve output realism. Results suggest that the DL model is able to generate HR precipitation estimates with significantly lower errors than other methods used, especially for intense short-duration precipitation events, which often occur during the warm season and are required to evaluate climate resiliency of urban storm drainage systems. The generic and flexible nature of the developed framework makes it even more promising as it can be applied to other climate variables, periods and regions.

How to cite: Teufel, B., Carmo, F., Sushama, L., Sun, L., Khaliq, N., Belair, S., Shamseldin, A. Y., Nagesh Kumar, D., and Vaze, J.: Physically Based Deep Learning Framework to Model Intense Precipitation Events at Engineering Scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8649, https://doi.org/10.5194/egusphere-egu22-8649, 2022.

EGU22-8656 | Presentations | ITS2.7/AS5.2 | Highlight

Conditional normalizing flow for predicting the occurrence of rare extreme events on long time scales 

Jakob Kruse, Beatrice Ellerhoff, Ullrich Köthe, and Kira Rehfeld

The socio-economic impacts of rare extreme events, such as droughts, are one of the main ways in which climate affects humanity. A key challenge is to quantify the changing risk of once-in-a-decade or even once-in-a-century events under global warming, while leaning heavily on comparatively short observation spans. The predictive power of classical statistical methods from extreme value theory (EVT) often remains limited to uncorrelated events with short return periods. This is mainly due to their strong assumption of an underlying exponential family distribution of the variable in question. Standard EVT is therefore at odds with the rich and large-scale correlations found in various surface climate parameters such as local temperatures, as well as the more complex shape of empirical distributions. Here, we turn to recent developments in machine learning, namely to conditional normalizing flows, which are flexible neural networks for modeling highly-correlated unknown distributions. Given a short time series, we show how such networks can model the posterior probability of events whose return periods are much longer than the observation span. The necessary correlations and patterns can be extracted from a paired set of inputs, i.e. time series, and outputs, i.e. return periods. To evaluate this approach in a controlled setting, we generate synthetic training data by sampling temporally autoregressive processes with a non-trivial covariance structure. We compare the results to a baseline analysis using EVT. In this work, we focus on the prediction of return periods of rare statistical events. However, we expect the same potential for a wide range of statistical measures, such as the power spectrum and rate functions. Future work should also investigate its applicability to compound and spatially extended events, as well as changing conditions under warming scenarios.

How to cite: Kruse, J., Ellerhoff, B., Köthe, U., and Rehfeld, K.: Conditional normalizing flow for predicting the occurrence of rare extreme events on long time scales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8656, https://doi.org/10.5194/egusphere-egu22-8656, 2022.

EGU22-8848 | Presentations | ITS2.7/AS5.2 | Highlight

Defining regime specific cloud sensitivities using the learnings from machine learning 

Alyson Douglas and Philip Stier

Clouds remain a core uncertainty in quantifying Earth’s climate sensitivity due to their complex dynamical and microphysical  interactions with multiple components of the Earth system. Therefore it is pivotal to observationally constrain possible cloud changes in a changing climate in order to evaluate our current generation of Earth system models by a set of physically realistic sensitivities. We developed a novel observational regime framework from over 15 years of MODIS satellite observations, from which we have derived a set of regimes of cloud controlling factors. These regimes were established using the relationship strength, as measured by using the weights of a trained, simple machine learning model. We apply these as observational constraints on the ​​r1i1p1f1 and r1i1p1f3 historical runs from various CMIP6 models to test if CMIP6 climate models can accurately represent key cloud controlling factors.. Within our regime framework, we can compare the observed environmental drivers and sensitivities of each regime against the parameterization-driven, modeled outcomes. We find that, for almost every regime, CMIP6 models do not properly represent the global distribution of occurrence, raising into question how much we can trust our range of climate sensitivities when specific cloud controlling factors are so badly represented by these models. This is especially pertinent in southern ocean and marine stratocumulus regimes, as the changes in these clouds’ optical depths and cloud amount have increased the ECS from CMIP5 to CMIP6. Our results suggest that these uncertainties in CMIP6 cloud parameterizations propagate into derived cloud feedbacks and ultimately climate sensitivity, which is evident from a regimed based analysis of cloud controlling factors.

How to cite: Douglas, A. and Stier, P.: Defining regime specific cloud sensitivities using the learnings from machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8848, https://doi.org/10.5194/egusphere-egu22-8848, 2022.

EGU22-9112 | Presentations | ITS2.7/AS5.2

Causal Orthogonal Functions: A Causal Inference approach to temporal feature extraction 

Nicolas-Domenic Reiter, Jakob Runge, and Andreas Gerhardus

Understanding complex dynamical systems is a major challenge in many scientific disciplines. There are two aspects which are of particular interest when analyzing complex dynamical systems: 1) the temporal patterns along which they evolve and 2) the governing causal mechanisms.

Temporal patterns in a time-series can be extracted and analyzed through a variety of time-series representations, that is a collection of filters. Discrete Wavelet and Fourier Transforms are prominent examples and have been widely applied to investigate the temporal structure of dynamical systems.

Causal Inference is a framework formalizing questions of cause and effect. In this work we propose an elementary and systematic approach to combine time-series representations with Causal Inference. Hereby we introduce a notion of cause and effect with respect to a pair of arbitrary time-series filters. Using a Singular Value Decomposition we derive an alternative representation of how one process drives another over a specified time-period. We call the building blocks of this representation Causal Orthogonal Functions. Combining the notion of Causal Orthogonal Functions with a Wavelet or Fourier decomposition of a time-series yields time-scale specific Causal Orthogonal Functions. As a result we obtain a time-scale specific representation of the causal influence one process has on another over some fixed time-period. This allows to conduct causal effect analysis in discrete-time stochastic dynamical systems at multiple time-scales. We illustrate our approach by examining linear VAR processes.

How to cite: Reiter, N.-D., Runge, J., and Gerhardus, A.: Causal Orthogonal Functions: A Causal Inference approach to temporal feature extraction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9112, https://doi.org/10.5194/egusphere-egu22-9112, 2022.

Outliers detection generally aims at identifying extreme events and insightful changes in climate behavior. One important type of outlier is pattern outlier also called discord, where the outlier pattern detected covers a time interval instead of a single point in the time series. Machine learning contributes many algorithms and methods in this field especially unsupervised algorithms for different types of data time series. In a first submitted paper, we have investigated discord detection applied to climate-related impact observations. We have introduced the prominent discord notion, a contextual concept that derives a set of insightful discords by identifying dependencies among variable length discords, and which is ordered based on the number of discords they subsume. 

Following this study, here we propose a ranking function based on the length of the first subsumed discord and the total length of the prominent discord, and make use of the powerful matrix profile technique. Preliminary results show that our approach, applied to monthly runoff timeseries between 1902 and 2005 over West Africa, detects both the emergence of long term change with the associated former climate regime, and the regional driest decade (1982-1992) of the 20th century (i.e. climate extreme event). In order to demonstrate the genericity and multiple insights gained by our method, we go further by evaluating the approach on other impact (e.g. crop data, fires, water storage) and climate (precipitation and temperature) observations, to provide similar results on different variables, extract relationships among them and identify what constitutes a prominent discord in such cases. A further step will consist in evaluating our methodology on climate and impact historical simulations, to determine if prominent discords highlighted in observations can be captured in climate and impact models.

How to cite: El Khansa, H., Gervet, C., and Brouillet, A.: Prominent discords in climate data through matrix profile techniques: detecting emerging long term pattern changes and anomalous events , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9250, https://doi.org/10.5194/egusphere-egu22-9250, 2022.

EGU22-9281 | Presentations | ITS2.7/AS5.2

Machine learning-based identification and classification of ocean eddies 

Eike Bolmer, Adili Abulaitijiang, Jürgen Kusche, Luciana Fenoglio-Marc, Sophie Stolzenberger, and Ribana Roscher

The automatic detection and tracking of mesoscale ocean eddies, the ‘weather of the ocean’, is a well-known task in oceanography. These eddies have horizontal scales from 10 km up to 100 km and above. They transport water mass, heat, nutrition, and carbon and have been identified as hot spots of biological activity. Monitoring eddies is therefore of interest among others to marine biologists and fishery. 
Recent advances in satellite-based observation for oceanography such as sea surface height (SSH) and sea surface temperature (SST) result in a large supply of different data products in which eddies are visible. In radar altimetry observations are acquired with repeat cycles between 10 and 35 days and cross-track spacing of a few 10 km to a few 100 km. Therefore, ocean eddies are clearly visible but typically covered by only one ground track. In addition, due to their motion, eddies are difficult to reconstruct, which makes creating detailed maps of the ocean with a high temporal resolution a challenge. In general, they are considered a perturbation, and their influence on altimetry data is difficult to determine, which is especially limiting for the determination of an accurate time-averaged dynamic topography of the ocean.
Due to their spatio-temporal dynamic behavior the identification and tracking are challenging. There is a number of methods that have been developed to identify and track eddies in gridded maps of sea surface height derived from multi-mission data sets. However, these procedures have shortcomings since the gridding process removes information that is valuable in achieving more accurate results.
Therefore, in the project EDDY carried out at the University of Bonn we intend to use ground track data from satellite altimetry and - as a long-term goal - additional remote sensing data such as SST, optical imagery, as well as statistical information from model outputs. The combination of the data will serve as a basis for a multi-modal deep learning algorithm. In detail, we will utilize transformers, a deep neural network architecture, that originates from the field of Natural Language Processing (NLP) and became popular in recent years in the field of computer vision. This method shows promising results in terms of understanding temporal and spatial information, which is essential in detecting and tracking highly dynamic eddies.
In this presentation, we introduce the deep neural network used in the EDDY project and show the results based on gridded data sets for the Gulf stream area for the period 2017 and first results of single-track eddy identification in the region.

How to cite: Bolmer, E., Abulaitijiang, A., Kusche, J., Fenoglio-Marc, L., Stolzenberger, S., and Roscher, R.: Machine learning-based identification and classification of ocean eddies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9281, https://doi.org/10.5194/egusphere-egu22-9281, 2022.

EGU22-9461 | Presentations | ITS2.7/AS5.2

Data Driven Approaches for Climate Predictability 

Balasubramanya Nadiga

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate. In this context, the Linear Inverse Modeling (LIM) approach (closely related to Dynamic Mode Decomposition DMD), by helping capture a few essential interactions between dynamical components of the full system, has proven valuable in being able to give insights into the dynamical behavior of the full system. While nonlinear extensions of the LIM approach have been attempted none have gained widespread acceptance. We demonstrate that Reservoir Computing (RC), a form of machine learning suited for learning in the context of chaotic dynamics, by exploiting the phenomenon of generalized synchronization, provides an alternative nonlinear approach that comprehensively outperforms the LIM approach.  Additionally, the potential of the RC approach to capture the structure of the climatological attractor and to continue the evolution of the system on the attractor in a realistic fashion long after the ensemble average has stopped tracking the reference trajectory is highlighted. Finally, other dynamical systems based methods and probabilistic deep learning methods are considered and a broader perspective on the use of data-driven methods in understanding climate predictability is offered

How to cite: Nadiga, B.: Data Driven Approaches for Climate Predictability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9461, https://doi.org/10.5194/egusphere-egu22-9461, 2022.

EGU22-9877 | Presentations | ITS2.7/AS5.2

A Conditional Generative Adversarial Network for Rainfall Downscaling 

Marcello Iotti, Paolo Davini, Jost von Hardenberg, and Giuseppe Zappa

Predicting extreme precipitation events is one of the main challenges of climate science in this decade. Despite the continuously increasing computing availability, Global Climate Models’ (GCMs) spatial resolution is still too coarse to correctly represent and predict small-scale phenomena as convection, so that precipitation prediction is still imprecise. Indeed, precipitation shows variability on both spatial and temporal scales (much) smaller than the current state-of-the-art GCMs resolution. Therefore, downscaling techniques play a crucial role, both for the understanding of the phenomenon itself and for applications like e.g. hydrologic studies, risk prediction and emergency management. Seen in the context of image processing, a downscaling procedure has many similarities with super-resolution tasks, i.e. the improvement of the resolution of an image. This scope has taken advantage from the application of Machine Learning techniques, and in particular from the introduction of Convolutional Neural Networks (CNNs).

In our work we exploit a conditional Generative Adversarial Network (cGAN) to train a generator model to perform precipitation downscaling. This generator, a deep CNN, takes as input the precipitation field at the scale resolved by GCMs, adds random noise, and outputs a possible realization of the precipitation field at higher resolution, preserving its statistical properties with respect to the coarse-scale field. The GAN is being trained and tested in a “perfect model” setup, in which we try to reproduce the ERA5 precipitation field starting from an upscaled version of it.

Compared to other downscaling techniques, our model has the advantage of being computationally inexpensive at run time, since the computational load is mostly concentrated in the training phase. We are examining the Greater Alpine Region, upon which numerical models performances are limited by the complex orography. Nevertheless the approach, being independent of physical, statistical and empirical assumptions, can be easily extended to different domains.

How to cite: Iotti, M., Davini, P., von Hardenberg, J., and Zappa, G.: A Conditional Generative Adversarial Network for Rainfall Downscaling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9877, https://doi.org/10.5194/egusphere-egu22-9877, 2022.

EGU22-10120 | Presentations | ITS2.7/AS5.2

A Convolutional Neural Network approach for downscaling climate model data in Trentino-South Tyrol (Eastern Italian Alps) 

Alice Crespi, Daniel Frisinghelli, Tatiana Klisho, Marcello Petitta, Alexander Jacob, and Massimiliano Pittore

Statistical downscaling is a very popular technique to increase the spatial resolution of existing global and regional climate model simulations and to provide reliable climate data at local scale. The availability of tailored information is particularly crucial for conducting local climate assessments, climate change studies and for running impact models, especially in complex terrain. A crucial requirement is the ability to reliably downscale the mean, variability and extremes of climate data, while preserving their spatial and temporal correlations.

Several machine learning-based approaches have been proposed so far to perform such task by extracting non-linear relationships between local-scale variables and large-scale atmospheric predictors and they could outperform more traditional statistical methods. In recent years, deep learning has gained particular interest in geoscientific studies and climate science as a promising tool to improve climate downscaling thanks to its greater ability to extract high-level features from large datasets using complex hierarchical architectures. However, the proper network architecture is highly dependent on the target variable, time and spatial resolution, as well as application purposes and target domain.

This contribution presents a Deep Convolutional Encoder-Decoder Network (DCEDN) architecture which was implemented and evaluated for the first time over Trentino-South Tyrol in the Eastern Italian Alps to derive 1-km climate fields of daily temperature and precipitation from ERA-5 reanalysis. We will show that in-depth optimization of hyper-parameters, loss function choice and sensitivity analyses are essential preliminary steps to derive an effective architecture and enhance the interpretability of results and of their variability. The validation of downscaled fields of both temperature and precipitation confirmed the improved representation of local features for both mean and extreme values, even though lower performances were obtained for precipitation in reproducing small-scale spatial features. In all cases, DCEDN was found to outperform classical schemes based on linear regression and the bias adjustment procedures used as benchmarks. We will discuss in detail the advantages and recommendations for the integration of DCEDN as an efficient post-processing block in climate data simulations supporting local-scale studies. The model constraints in feature extraction, especially for precipitation, over the limited extent of the study domain will also be explained along with potential future developments of such type of networks for improved climate science applications.

How to cite: Crespi, A., Frisinghelli, D., Klisho, T., Petitta, M., Jacob, A., and Pittore, M.: A Convolutional Neural Network approach for downscaling climate model data in Trentino-South Tyrol (Eastern Italian Alps), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10120, https://doi.org/10.5194/egusphere-egu22-10120, 2022.

EGU22-10773 | Presentations | ITS2.7/AS5.2 | Highlight

Choose your own weather adventure: deep weather generation for “what-if” climate scenarios 

Campbell Watson, Jorge Guevara, Daniela Szwarcman, Dario Oliveira, Leonardo Tizzei, Maria Garcia, Priscilla Avegliano, and Bianca Zadrozny

Climate change is making extreme weather more extreme. Given the inherent uncertainty of long-term climate projections, there is growing need for rapid, plausible “what-if” climate scenarios to help users understand climate exposure and examine resilience and mitigation strategies. Since the 1980s, such “what-if” scenarios have been created using stochastic weather generators. However, it is very challenging for traditional weather generation algorithms to create realistic extreme climate scenarios because the weather data being modeled is highly imbalanced, contains spatiotemporal dependencies and has extreme weather events exacerbated by a changing climate.

There are few works comparing and evaluating stochastic multisite (i.e., gridded) weather generators, and no existing work that compares promising deep learning approaches for weather generation with classical stochastic weather generators. We will present the culmination of a multi-year effort to perform a systematic evaluation of stochastic weather generators and deep generative models for multisite precipitation synthesis. Among other things, we show that variational auto-encoders (VAE) offer an encouraging pathway for efficient and controllable climate scenario synthesis – especially for extreme events. Our proposed VAE schema selects events with different characteristics in the normalized latent space (from rare to common) and generates high-quality scenarios using the trained decoder. Improvements are provided via latent space clustering and bringing histogram-awareness to the VAE loss.

This research will serve as a guide for improving the design of deep learning architectures and algorithms for application in Earth science, including feature representation and uncertainty quantification of Earth system data and the characterization of so-called “grey swan” events.

How to cite: Watson, C., Guevara, J., Szwarcman, D., Oliveira, D., Tizzei, L., Garcia, M., Avegliano, P., and Zadrozny, B.: Choose your own weather adventure: deep weather generation for “what-if” climate scenarios, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10773, https://doi.org/10.5194/egusphere-egu22-10773, 2022.

EGU22-10888 | Presentations | ITS2.7/AS5.2

How to utilize deep learning to understand climate dynamics? : An ENSO example. 

Na-Yeon Shin, Yoo-Geun Ham, Jeong-Hwan Kim, Minsu Cho, and Jong-Seong Kug

Many deep learning technologies have been applied to the Earth sciences, including weather forecast, climate prediction, parameterization, resolution improvements, etc. Nonetheless, the difficulty in interpreting deep learning results still prevents their applications to studies on climate dynamics. Here, we applied a convolutional neural network to understand El Niño–Southern Oscillation (ENSO) dynamics from long-term climate model simulations. The deep learning algorithm successfully predicted ENSO events with a high correlation skill of 0.82 for a 9-month lead. For interpreting deep learning results beyond the prediction skill, we first developed a “contribution map,” which estimates how much each grid point and variable contribute to a final output variable. Furthermore, we introduced a “sensitivity,” which estimates how much the output variable is sensitively changed to the small perturbation of the input variables by showing the differences in the output variables. The contribution map clearly shows the most important precursors for El Niño and La Niña developments. In addition, the sensitivity clearly reveals nonlinear relations between the precursors and the ENSO index, which helps us understand the respective role of each precursor. Our results suggest that the contribution map and sensitivity would be beneficial for understanding other climate phenomena.

How to cite: Shin, N.-Y., Ham, Y.-G., Kim, J.-H., Cho, M., and Kug, J.-S.: How to utilize deep learning to understand climate dynamics? : An ENSO example., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10888, https://doi.org/10.5194/egusphere-egu22-10888, 2022.

EGU22-11111 | Presentations | ITS2.7/AS5.2

Machine learning based estimation of regional Net Ecosystem Exchange (NEE) constrained by atmospheric inversions and ecosystem observations 

Samuel Upton, Ana Bastos, Fabian Gans, Basil Kraft, Wouter Peters, Jacob Nelson, Sophia Walther, Martin Jung, and Markus Reichstein

Accurate estimates and predictions of the global carbon fluxes are critical for our understanding of the global carbon cycle and climate change. Reducing the uncertainty of the terrestrial carbon sink and closing the budget imbalance between sources and sinks would improve our ability to accurately project future climate change. Net Ecosystem Exchange (NEE), the net flux of biogenic carbon from the land surface to the atmosphere, is only directly measured at a sparse set of globally distributed eddy-covariance measurement sites. To estimate the terrestrial carbon flux at the regional and global scale, a global gridded estimate of NEE must be accurately upscaled from a model trained at the ecosystem level. In this study, the Fluxcom system* is used to train a site-level model on remotely-sensed and meteorological variables derived from site measurements, MODIS and ECMWF ERA5 atmospheric reanalysis data. The non-representative distribution of these site-level data along with missing disturbance histories impart known biases to current upscaling efforts. Observations of atmospheric carbon may provide important additional information, improving the accuracy of the upscaled flux estimate. 

This study adds an atmospheric observational operator to the model training process that connects the ecosystem-level flux model to top-down observations of atmospheric carbon by adding an additional term to the objective function. The target data are regionally integrated fluxes from an ensemble of atmospheric inversions corrected for fossil-fuel emissions and lateral fluxes.  Calculating the regionally integrated flux estimate at each training step is computationally infeasible. Our hypothesis is that the regional flux can be modeled with a limited set of points and that this sparse model preserves sufficient information about the phenomena to act as a constraint for the underlying ecosystem-level model, improving regional and global upscaled products.  Experimental results show improvements in the machine learning based regional estimates of NEE while preserving features such as the seasonal variability in the estimated flux.

 

*Jung, Martin, Christopher Schwalm, Mirco Migliavacca, Sophia Walther, Gustau Camps-Valls, Sujan Koirala, Peter Anthoni, et al. 2020. “Scaling Carbon Fluxes from Eddy Covariance Sites to Globe: Synthesis and Evaluation of the FLUXCOM Approach.” Biogeosciences 17 (5): 1343–65. 

 

How to cite: Upton, S., Bastos, A., Gans, F., Kraft, B., Peters, W., Nelson, J., Walther, S., Jung, M., and Reichstein, M.: Machine learning based estimation of regional Net Ecosystem Exchange (NEE) constrained by atmospheric inversions and ecosystem observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11111, https://doi.org/10.5194/egusphere-egu22-11111, 2022.

EGU22-11216 | Presentations | ITS2.7/AS5.2

Unsupervised clustering of Lagrangian trajectories in the Labrador Current 

Noémie Planat and Mathilde Jutras

Lagrangian studies are a widely-used and powerful way to analyse and interpret phenomenons in oceanography and atmospheric sciences. Such studies can be based on dataset either consisting of real trajectories (e.g. oceanic drifters or floats) or of virtual trajectories computed from velocity outputs from model or observation-derived velocities. Such data can help investigate pathways of water masses, pollutants or storms, or identify important convection areas to name a few. As many of these analyses are based on large volumes of data that can be challenging to examine, machine learning can provide an efficient and automated way to classify information or detect patterns.

Here, we present an application of unsupervised clustering to the identification of the main pathways of the shelf-break branch of the Labrador Current, a critical component of the North Atlantic circulation. The current flows southward along the Labrador Shelf and splits in the region of the Grand Banks, either retroflecting north-eastward and feeding the subpolar basin of the North Atlantic Ocean (SPNA) or continuing westward along the shelf-break, feeding the Slope Sea and the east coast of North America. The proportion feeding each area impacts their salinity and convection, as well as their biogeochemistry, with consequences on marine life.

Our dataset is composed of millions of virtual particle trajectories computed from the water velocities of the GLORYS12 ocean reanalysis. We implement an unsupervised Machine Learning clustering algorithm on the shape of the trajectories. The algorithm is a kernalized k-means++ algorithm with a minimal number of hyperparameters, coupled to a kernalized Principal Component Analysis (PCA) features reduction. We will present the pre-processing of the data, as well as canonical and physics-based methods for choosing the hyperparameters. 

The algorithm identifies six main pathways of the Labrador Current. Applying the resulting classification method to 25 years of ocean reanalysis, we quantify the relative importance of the six pathways in time and construct a retroflection index that is used to study the drivers of the retroflection variability. This study highlights the potential of such a simple clustering method for Lagrangian trajectory analysis in oceanography or in other climate applications.

How to cite: Planat, N. and Jutras, M.: Unsupervised clustering of Lagrangian trajectories in the Labrador Current, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11216, https://doi.org/10.5194/egusphere-egu22-11216, 2022.

EGU22-11388 | Presentations | ITS2.7/AS5.2 | Highlight

Learning ENSO-related Principal Modes of Vegetation via a Granger-Causal Variational Autoencoder 

Gherardo Varando, Miguel-Ángel Fernández-Torres, and Gustau Camps-Valls

Tackling climate change needs to understand the complex phenomena occurring on the Planet. Discovering  teleconnection patterns is an essential part of the endeavor. Events like El Niño Southern Oscillation (ENSO) impact essential climate variables at large distances, and influence the underlying Earth system dynamics. However, their automatic identification from the wealth of observational data is still unresolved. Nonlinearities, nonstationarities and the (ab)use of correlation analyses hamper the discovery of true causal patterns.  Classical approaches proceed by first, extracting principal modes of variability and second, by performing lag-correlations or Granger causal analysis to identify possible teleconnections. While the principal modes are an effective representation of the data, they could be causally not meaningful. 
To address this, we here introduce a deep learning methodology that extracts nonlinear latent representations from spatio-temporal Earth data that are Granger causal with the index altogether. The proposed algorithm consists of a variational autoencoder trained with an additional causal penalization that enforces the latent representation to be (partially) Granger-causally related to the considered signal. The causal loss term is obtained by training two additional autoregressive models to forecast some of the latent signals, one of them including the target signal as predictor. The causal penalization is finally computed by comparing the log variances of the two autoregressive models, similarly to the standard Granger causality approach. 

The major drawback of deep autoencoders with respect to the classical linear principal component approaches is the lack of a straightforward interpretability of the representations learned. 
To address this point we perform synthetic interventions in the latent space and analyse the differences in the recovered NDVI signal.
We illustrate the feasibility of the approach described to study the impact of ENSO on vegetation, which allows for a more rigorous study of impacts on ecosystems globally. The output maps show NDVI patterns which are consistent with the known phenomena induced by El Niño event. 

How to cite: Varando, G., Fernández-Torres, M.-Á., and Camps-Valls, G.: Learning ENSO-related Principal Modes of Vegetation via a Granger-Causal Variational Autoencoder, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11388, https://doi.org/10.5194/egusphere-egu22-11388, 2022.

EGU22-11451 | Presentations | ITS2.7/AS5.2

Time evolution of temperature profiles retrieved from 13 years of IASI data using an artificial neural network 

Marie Bouillon, Sarah Safieddine, Simon Whitburn, Lieven Clarisse, Filipe Aires, Victor Pellet, Olivier Lezeaux, Noëlle A. Scott, Marie Doutriaux-Boucher, and Cathy Clerbaux

The IASI remote sensor measures Earth’s thermal infrared radiation over 8461 channels between 645 and 2760 cm-1. Atmospheric temperatures at different altitudes can be retrieved from the radiances measured in the CO2 absorption bands (645-800 cm-1 and 2250-2400 cm-1) by selecting the channels that are the most sensitive to the temperature profile. The three IASI instruments on board of the Metop suite of satellites launched in 2006, 2012 and 2018, will provide a long time series for temperature, adequate for studying the long term evolution of atmospheric temperature. However, over the past 14 years, EUMETSAT, who processes radiances and computes atmospheric temperatures, has carried out several updates on the processing algorithms for both radiances and temperatures, leading to non-homogeneous time series and thus large difficulties in the computation of trends for temperature and atmospheric composition.

 

In 2018, EUMETSAT has reprocessed the radiances with the most recent version of the algorithm and there is now a homogeneous radiance dataset available. In this study, we retrieve a new temperature record from the homogeneous IASI radiances using an artificial neural network (ANN). We train the ANN with IASI radiances as input and the European Centre for Medium-Range Weather Forecasts reanalysis ERA5 temperatures as output. We validate the results using ERA5 and in situ radiosonde temperatures from the ARSA database. Between 750 and 7 hPa, where IASI has most of its sensitivity, a very good agreement is observed between the 3 datasets. This work suggests that ANN can be a simple yet powerful tool to retrieve IASI temperatures at different altitudes in the upper troposphere and in the stratosphere, allowing us to construct a homogeneous and consistent temperature data record.

 

We use this new dataset to study extreme events such as sudden stratospheric warmings, and to compute trends over the IASI coverage period [2008-2020]. We find that in the past thirteen years, there is a general warming trend of the troposphere, that is more important at the poles and at mid latitudes (0.5 K/decade at mid latitudes, 1 K/decade at the North Pole). The stratosphere is globally cooling on average, except at the South Pole as a result of the ozone layer recovery and a sudden stratospheric warming in 2019. The cooling is most pronounced in the equatorial upper stratosphere (-1 K/decade).

How to cite: Bouillon, M., Safieddine, S., Whitburn, S., Clarisse, L., Aires, F., Pellet, V., Lezeaux, O., Scott, N. A., Doutriaux-Boucher, M., and Clerbaux, C.: Time evolution of temperature profiles retrieved from 13 years of IASI data using an artificial neural network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11451, https://doi.org/10.5194/egusphere-egu22-11451, 2022.

Existing databases for extreme weather events such as floods, heavy rainfall events, or droughts are heavily reliant on authorities and weather services manually entering details about the occurrence of an event. This reliance has led to a massive geographical imbalance in the likelihood of extreme weather events being recorded, with a vast number of events especially in the developing world remaining unrecorded. With continuing climate change, a lack of systematic extreme weather accounting in developing countries can lead to a substantial misallocation of funds for adaptation measures. To address this imbalance, in this pilot study we combine socio-economic data with climate and geographic data and use several machine-learning algorithms as well as traditional (spatial) econometric tools to predict the occurrence of extreme weather events and their impacts in the absence of information from manual records. Our preliminary results indicate that machine-learning approaches for the detection of the impacts of extreme weather could be a crucial tool in establishing a coherent global disaster record system. Such systems could also play a role in discussions around future Loss and Damages.

How to cite: Schwarz, M. and Pretis, F.: Filling in the Gaps: Consistently detecting previously unidentified extreme weather event impacts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12165, https://doi.org/10.5194/egusphere-egu22-12165, 2022.

EGU22-12720 | Presentations | ITS2.7/AS5.2 | Highlight

Interpretable Deep Learning for Probabilistic MJO Prediction 

Hannah Christensen and Antoine Delaunay

The Madden–Julian Oscillation (MJO) is the dominant source of sub-seasonal variability in the tropics. It consists of an Eastward moving region of enhanced convection coupled to changes in zonal winds. It is not possible to predict the precise evolution of the MJO, so subseasonal forecasts are generally probabilistic. Ideally the spread of the forecast probability distribution would vary from day to day depending on the instantaneous predictability of the MJO. Operational subseasonal forecasting models do not have this property. We present a deep convolutional neural network that produces skilful state-dependent probabilistic MJO forecasts. This statistical model accounts for intrinsic chaotic uncertainty by predicting the standard deviation about the mean, and model uncertainty using a Monte-Carlo dropout approach. Interpretation of the mean forecasts from the neural network highlights known MJO mechanisms, providing confidence in the model, while interpretation of the predicted uncertainty indicates new physical mechanisms governing MJO predictability.

How to cite: Christensen, H. and Delaunay, A.: Interpretable Deep Learning for Probabilistic MJO Prediction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12720, https://doi.org/10.5194/egusphere-egu22-12720, 2022.

EGU22-12822 | Presentations | ITS2.7/AS5.2

Assessing model dependency in CMIP5 and CMIP6 based on their spatial dependency structure with probabilistic network models 

Catharina Elisabeth Graafland and Jose Manuel Gutiérrez Gutiérrez

Probabilistic network models (PNMs) are well established data-driven modeling and machine learning prediction techniques used in many disciplines, including climate analysis. These techniques can efficiently learn the underlying (spatial) dependency structure and a consistent probabilistic model from data (e.g. gridded reanalysis or GCM outputs for particular variables; near surface temperature in this work), thus constituting a truly probabilistic backbone of the system underlying the data. The complex structure of the dataset is encoded using both pairwise and conditional dependencies and can be explored and characterized using network and probabilistic metrics. When applied to climate data, it is shown that Bayesian networks faithfully reveal the various long‐range teleconnections relevant in the dataset, in particular those emerging in el niño periods (Graafland, 2020).

 

In this work we apply probabilistic Gaussian networks to extract and characterize most essential spatial dependencies of the simulations generated by the different GCMs contributing to CMIP5 and 6 (Eyring 2016). In particular we analyze the problem of model interdependency (Boe, 2018) which poses practical problems for the application of these multi-model simulations in practical applications (it is often not clear what exactly makes one model different from or similar to another model).  We show that probabilistic Gaussian networks provide a promising tool to characterize the spatial structure of GCMs using simple metrics which can be used to analyze how and where differences in dependency structures are manifested. The probabilistic distance measure allows to chart CMIP5 and CMIP6 models on their closeness to reanalysis datasets that rely on observations. The measures also identifies significant atmospheric model changes that underwent CMIP5 GCMs in their transition to CMIP6. 

 

References:

 

Boé, J. Interdependency in Multimodel Climate Projections: Component Replication and Result Similarity. Geophys. Res. Lett. 45, 2771–2779, DOI: 10.1002/2017GL076829 (2018).

 

Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model. Dev. 9, 1937–1958, DOI: 10.5194/gmd-9-1937-2016  (2016).

 

Graafland, C.E., Gutiérrez, J.M., López, J.M. et al. The probabilistic backbone of data-driven complex networks: an example in climate. Sci Rep 10, 11484 (2020). DOI: 10.1038/s41598-020-67970-y



Acknowledgement

 

The authors would like to acknowledge project ATLAS (PID2019-111481RB-I00) funded by MCIN/AEI (doi:10.13039/501100011033). We also acknowledge support from Universidad de Cantabria and Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabria via the “instrumentación y ciencia de datos para sondear la naturaleza del universo” project for funding this work. L.G. acknowledges support from the Spanish Agencia Estatal de Investigación through the Unidad de Excelencia María de Maeztu with reference MDM-2017-0765.



How to cite: Graafland, C. E. and Gutiérrez, J. M. G.: Assessing model dependency in CMIP5 and CMIP6 based on their spatial dependency structure with probabilistic network models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12822, https://doi.org/10.5194/egusphere-egu22-12822, 2022.

EGU22-12858 | Presentations | ITS2.7/AS5.2

Identifying drivers of extreme reductions in carbon uptake of forests with interpretable machine learning 

Mohit Anand, Gustau Camps-Valls, and Jakob Zscheischler

Forests form one of the major components of the carbon cycle and take up large amounts of carbon dioxide from the atmosphere, thereby slowing down the rate of climate change. Carbon uptake by forests is a highly complex process strongly controlled by meteorological forcing, mainly because of two reasons. First, forests have a large storage capacity acting as a buffer to short-duration changes in meteorological drivers. The response can thus be very complex and extend over a long time. Secondly, the responses are often triggered by combinations of multiple compounding drivers including precipitation, temperature and solar radiation. Effects may compound between variables and across time. Therefore, a large amount of data is required to identify the complex drivers of adverse forest response to climate forcing. Recent advances in machine learning offer a suite of promising tools to analyse large amounts of data and address the challenge of identifying complex drivers of impacts. Here we analyse the potential of machine learning to identify the compounding drivers of reduced carbon uptake/forest mortality. To this end, we generate 200,000 years of gross and net carbon uptake from the physically-based forest model FORMIND simulating a beech forest in Germany. The climate data is generated through a weather generator (AWEGEN-1D) from bias-corrected ERA5 reanalysis data.  Classical machine learning models like random forest, support vector machines and deep neural networks are trained to estimate gross primary product. Deep learning models involving convolutional layers are found to perform better than the other classical machine learning models. Initial results show that at least three years of weather data are required to predict annual carbon uptake with high accuracy, highlighting the complex lagged effects that characterize forests. We assess the performance of the different models and discuss their interpretability regarding the identification of impact drivers.



How to cite: Anand, M., Camps-Valls, G., and Zscheischler, J.: Identifying drivers of extreme reductions in carbon uptake of forests with interpretable machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12858, https://doi.org/10.5194/egusphere-egu22-12858, 2022.

EGU22-13345 | Presentations | ITS2.7/AS5.2

A novel approach to systematically analyze the error structure of precipitation datasets using decision trees 

Xinxin Sui, Zhi Li, Guoqiang Tang, Zong-Liang Yang, and Dev Niyogi
Multiple environmental factors influence the error structure of precipitation datasets. The conventional precipitation evaluation method over-simply analyzes how the statistical indicators vary with one or two factors via dimensionality reduction. As a result, the compound influences of multiple factors are superposed rather than disassembled. To overcome this deficiency, this study presents a novel approach to systematically and objectively analyze the error structure within precipitation products using decision trees. This data-driven method can analyze multiple factors simultaneously and extract the compound effects of various influencers. By interpreting the decision tree structures, the error characteristics of precipitation products are investigated. Three types of precipitation products (two satellite-based: ‘top-down’ IMERG and ‘bottom-up’ SM2RAIN-ASCAT, and one reanalysis: ERA5-Land) are evaluated across CONUS. The study period is from 2010 to 2019, and the ground-based Stage IV precipitation dataset is used as the ground truth. By data mining 60 binary decision trees, the spatiotemporal pattern of errors and the land surface influences are analyzed.
 
Results indicate that IMERG and ERA5-Land perform better than SM2RAIN-ASCAT with higher accuracy and more stable interannual patterns for the ten years of data analyzed. The conventional bias evaluation finds that ERA5-Land and SM2RAIN-ASCAT underestimate in summer and winter, respectively. The decision tree method cross-assesses three spatiotemporal factors and finds that underestimation of ERA5-Land occurs in the eastern part of the rocky mountains, and SM2RAIN-ASCAT underestimates precipitation over high latitudes, especially in winter. Additionally, the decision tree method ascribes system errors to nine physical variables, of which the distance to the coast, soil type, and DEM are the three dominant features. On the other hand, the land cover classification and the topography position index are two relatively weak factors.

How to cite: Sui, X., Li, Z., Tang, G., Yang, Z.-L., and Niyogi, D.: A novel approach to systematically analyze the error structure of precipitation datasets using decision trees, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13345, https://doi.org/10.5194/egusphere-egu22-13345, 2022.

NP5 – Predictability

EGU22-869 | Presentations | NP5.1 | Highlight

Machine learning for postprocessing ensemble forecasts of wind gusts with a focus on European winter storms 

Benedikt Schulz and Sebastian Lerch

Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings, e.g. in European winter storms. First, we provide a comprehensive review and systematic comparison of several statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing, then we assess the performance of selected methods within winter storms. The methods can be divided in three groups: State of the art postprocessing techniques from statistics (ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression), established machine learning methods (gradient-boosting extended EMOS, quantile regression forests) and neural network-based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The different approaches are systematically compared using six years of data from a high-resolution, convection-permitting ensemble prediction system run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts as well as estimating locally adaptive neural networks leads to significant improvements in forecast skill. Assessing the performance of EMOS and neural network-based postprocessing for selected winter storms, we find that the networks better adapt to the extreme conditions than the statistical benchmark and thus yield a superior predictive performance. However, results suggest that the performance can still be further improved, e.g. via regime-dependent postprocessing.

How to cite: Schulz, B. and Lerch, S.: Machine learning for postprocessing ensemble forecasts of wind gusts with a focus on European winter storms, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-869, https://doi.org/10.5194/egusphere-egu22-869, 2022.

EGU22-921 | Presentations | NP5.1

Generative machine learning methods for multivariate ensemble post-processing 

Jieyu Chen, Sebastian Lerch, and Tim Janke

Statistical post-processing of ensemble forecasts has become a common practice in research to correct biases and errors in calibration. While many of the developments have been focused on univariate methods that calibrate the marginal distributions, practical applications often require accurate modeling of spatial, temporal, and inter-variable dependencies. Copula-based multivariate post-processing methods, such as ensemble copula coupling, have been proposed to address this issue and proceed by reordering univariately post-processed ensembles with copula functions to retain the dependence structure. We propose a novel multivariate post-processing method based on generative machine learning where post-processed multivariate ensemble forecasts are generated from random noise, conditional on the inputs of raw ensemble forecasts. Moving beyond the two-step strategy of separately modeling marginal distributions and multivariate dependence structure, the generative modelling approach allows for directly obtaining multivariate probabilistic forecasts as output. The flexibility of the generative model also enables us to incorporate additional predictors straightforwardly and to generate an arbitrary number of post-processed ensemble members. In a case study on the surface temperature and wind speed forecasts from the European Centre of Medium-Range Weather Forecasts at weather stations in Germany, our generative model that incorporates additional weather predictors substantially improves upon the multivariate spatial forecasts from copula-based approaches. And the model shows competitive performance even with state-of-the-art neural network-based post-processing models applied for the marginal distributions.

How to cite: Chen, J., Lerch, S., and Janke, T.: Generative machine learning methods for multivariate ensemble post-processing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-921, https://doi.org/10.5194/egusphere-egu22-921, 2022.

EGU22-1201 | Presentations | NP5.1

Physics-constrained postprocessing of surface temperature and humidity 

Francesco Zanetta and Daniele Nerini

Traditional post-processing methods aim at minimizing forecast error. This often leads to predictions that violate physical principles and disregard dependencies between variables. However, for various impact-based applications such as hydrological forecasting or heat indices, it is important to provide forecasts that not only have high univariate accuracy, but also are physically consistent, in the sense of respecting physical principles and variable dependencies. Achieving physical consistency remains an open problem in the post-processing of weather forecasts, while this question has recently gained a lot of attention in the wider deep learning community and climate field. Recent contributions show that physical consistency may be pursued by applying different forms of constraints to deep learning models. The most widely used approaches are to incorporate physics via regularization, by defining physics-based losses in addition to common metrics such as mean absolute error, or to define custom-designed model architectures, such that the physical constraints are strictly enforced. Including constraints also has the potential to help the training procedure by restraining the hypothesis space of the model and improving generalization capabilities.

This work investigates the application of the aforementioned approaches for the postprocessing of a set of variables related to surface temperature and humidity, specifically temperature, dew point, surface pressure, relative humidity and water vapor mixing ratio. As baseline, we use an unconstrained fully connected neural network. We consider the simple case of postprocessing at a single location, and we show how it is possible to incorporate domain knowledge, specifically thermodynamic relationships, via analytic constraints, to obtain physically consistent postprocessed prediction. We compare different approaches and show that we can enforce physical consistency without degrading performance, or even improving it. Furthermore, we discuss additional advantages and disadvantages of these approaches in the context of post-processing, besides error reduction.

How to cite: Zanetta, F. and Nerini, D.: Physics-constrained postprocessing of surface temperature and humidity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1201, https://doi.org/10.5194/egusphere-egu22-1201, 2022.

EGU22-2176 | Presentations | NP5.1

Probabilistic power ramp forecasts using multivariate Gaussian regression 

Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Thorsten Simon, and Achim Zeileis

Efficient wind farm operation requires reliable probabilistic forecasts of power ramps. These are sudden fluctuations in power production which, if unanticipated, can lead to significant imbalances in the electrical grid.  The power produced by a turbine strongly depends on the wind speed at hub-height, making it is useful to base these forecasts on calibrated wind speed scenarios generated by statistically postprocessing numerical weather predictions (NWPs). Since the probability of a ramp event depends jointly on the wind speed distributions forecasted at multiple future times, postprocessing methods must not only calibrate the marginal forecasts for each lead time, but also estimate temporal dependencies among their errors.

We use new multivariate Gaussian regression (MGR) models to postprocess all next-day hourly 100m wind speeds near offshore wind farms in one step. The postprocessed forecast is a multivariate Gaussian distribution with mean vector μ — containing the 24 forecasted hourly mean wind speeds — and Σ — the 24 × 24 covariance matrix containing uncertainties of the individual forecasts as well as their temporal error correlations.  Joint distributions are estimated conditionally by flexibly linking the components of μ and parameters specifying Σ to predictors derived from an ECMWF ensemble using generalized additive models for each distributional parameter.

The joint distribution — predicted uniquely for each ECMWF initialization — can simulate postprocessed wind speed ensembles with any number of members. Subsequently, the forecasted ensembles are transformed into power space using an idealized turbine power curve and probabilities computed for different ramp events. Ramp forecasts from MGR outperform those obtained using reference methods which postprocess wind speed forecasts in two-steps: (i) first calibrating the marginal distributions with nonhomogeneous Gaussian regression before (ii) constructing temporal error dependencies using either the order statistics of the NWP ensemble (ensemble copula coupling, ECC) or those of raw observations (Schaake Shuffle).

How to cite: Muschinski, T., Lang, M. N., Mayr, G. J., Messner, J. W., Simon, T., and Zeileis, A.: Probabilistic power ramp forecasts using multivariate Gaussian regression, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2176, https://doi.org/10.5194/egusphere-egu22-2176, 2022.

EGU22-2311 | Presentations | NP5.1

Gaussian mixture models for clustering and calibration of ensemble weather forecasts 

Gabriel Jouan, Anne Cuzol, Valérie Monbet, and Goulven Monnier

Nowadays, most weather forecasting centers produce ensemble forecasts.  Ensemble forecasts provide information about probability distribution of the weather variables. They give a more complete description of the atmosphere than a unique run of the meteorological model. However, they may suffer from bias and under/over dispersion errors that need to be corrected. These distribution errors may depend on weather regimes. In this paper, we propose various extensions of the Gaussian mixture model and its associated inference tools for ensemble data sets.  The proposed models are then used to identify clusters which correspond to different types of distribution errors. Finally, a standard calibration method known as Non homogeneous Gaussian Regression (NGR)  is applied cluster by cluster in order to correct ensemble forecast distributions. It is shown that the proposed methodology is effective, interpretable and easy to use.  The clustering algorithms are illustrated on simulated and real data. The calibration method is applied to real data of temperature and wind medium range forecast for 3 stations in France. 

How to cite: Jouan, G., Cuzol, A., Monbet, V., and Monnier, G.: Gaussian mixture models for clustering and calibration of ensemble weather forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2311, https://doi.org/10.5194/egusphere-egu22-2311, 2022.

EGU22-5609 | Presentations | NP5.1

Postprocessing of gridded precipitation forecasts using conditional generative adversarial networks and quantile regression 

Stephan Hemri, Jonas Bhend, Christoph Spirig, Daniele Nerini, Lionel Moret, Reinhard Furrer, and Mark A. Liniger

Probabilistic predictions of precipitation call for rather sophisticated postprocessing approaches due to its low predictability, high spatio-temporal variability and highly positive skewness. Moreover, the large number of zeros makes the generation of physically realistic postprocessed forecast scenarios using standard approaches like ensemble copula coupling (ECC) rather difficult. In addition to classical statistical approaches, recently, machine learning based methods gained increasing popularity in the field of postprocessing of probabilistic weather forecasts.

In this study, we compare conditional generative adversarial network (cGAN) based postprocessing of daily precipitation with a quantile regression based approach. In principle, an appropriately trained cGAN model should be able to generate postprocessed forecast scenarios that improve forecast skill and cannot be distinguished from observed data in terms of spatial structure. While we use ECC to generate physically realistic forecast scenarios from quantile regression, cGAN does not need any additional ECC steps. For training and verification, we use COSMO-E ensemble forecasts with a grid resolution of about 2 km over Switzerland and the corresponding CombiPrecip observations, which are a gridded blend of radar and gauge observations. Preliminary results suggest that it is possible to generate realistic looking forecast scenarios using cGAN, but up to now, we have not been able to increase forecast skill. On the other hand, quantile regression seems to increase forecast skill at the expense of relying on an additional ECC step to generate forecast scenarios.

How to cite: Hemri, S., Bhend, J., Spirig, C., Nerini, D., Moret, L., Furrer, R., and Liniger, M. A.: Postprocessing of gridded precipitation forecasts using conditional generative adversarial networks and quantile regression, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5609, https://doi.org/10.5194/egusphere-egu22-5609, 2022.

EGU22-5797 | Presentations | NP5.1

News about the EUMETNET statistical postprocessing benchmark 

Jonathan Demaeyer

New postprocessing methods are sometimes introduced without proper comparison to other available techniques, and therefore the institutions responsible for the operational implementation of weather forecasts may struggle deciding the best choice for their particular usecase. With the goal of helping the weather community to make such decisions, the benchmark of different postprocessing methods on predefined datasets is an important topic and is a key deliverable of the current EUMETNET postprocessing module. This benchmark is also a collaborative effort from several meteorological institutions, members of EUMETNET, and academia to define common pratices and shape standards.

 

In this presentation, we will highlight the different aspects of the benchmark: (1) its current status and organization and (2) its objectives for the next 2 years. We will also detail the challenges ahead for this exercise, and the foreseen datasets and infrastructures needed to tackle them.

How to cite: Demaeyer, J.: News about the EUMETNET statistical postprocessing benchmark, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5797, https://doi.org/10.5194/egusphere-egu22-5797, 2022.

EGU22-7407 | Presentations | NP5.1

Temperature prediction with expert agregation 

Léo Pfitzner, Olivier Mestre, Olivier Wintenberger, and Eric Adjakossa

A lot of Numerical Weather Prediction (NWP) models and their associated Model Output Statistics (MOS) are available. Expert aggregation has a bunch of advantages to deal with all these models, like being online, adaptive to model changes and having theoretical guarantees. With a new expert aggregation algorithm - FSBOA - a combination of BOA (Wintenberger 2017) and FS (Herbster and Warmuth 1998), and the use of a sliding window, we improved the temperature prediction on average without loosing too much reactivity of the expert weights. We also tested several aggregation strategies in order to improve the prediction of  extrem temperature events like cold and heat waves. To do so, we added some biased experts of the Météo-France 35-member ensemble forecast (PEARP) to the set of models. We also tried out the SMH (Mourtada et al. 2017) algorithm which fits the sleeping experts framework.

How to cite: Pfitzner, L., Mestre, O., Wintenberger, O., and Adjakossa, E.: Temperature prediction with expert agregation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7407, https://doi.org/10.5194/egusphere-egu22-7407, 2022.

EGU22-8200 | Presentations | NP5.1

climpred: weather and climate forecast verification in python 

Aaron Spring

Predicting subseasonal to seasonal weather and climate yields numerous benefits for economic and environmental decision-making.
Forecasters verify the forecast quality of models by initializing large sets of retrospective forecasts to predict past variations and phenomena in hindcast studies.

Quantifying prediction skill for multi-dimensional geospatial model output is computationally expensive and a difficult coding challenge. The large datasets require parallel and out-of-memory computing to be analyzed efficiently. Further, aligning the many forecast initializations with differing observational products is a straight-forward, but exhausting and error-prone exercise for researchers.

To simplify and standardize forecast verification across scales from hourly weather to decadal climate forecasts, we built climpred: a python package for computationally efficient and methodologically consistent verification of ensemble prediction models. We rely on the python software ecosystem developed by the open pangeo geoscience community. We leverage NetCDF metadata using xarray and out-of-core computation parallelized with dask to scale analyses from a laptop to supercomputer.

With climpred, researchers can assess forecast quality from a large set of metrics (including cprs, rps, rank_histogram, reliability, contingency, bias, rmse, acc, ...) in just a few lines of code:

hind = xr.open_dataset('initialized.nc')
obs = xr.open_dataset('observations.nc')
he = climpred.HindcastEnsemble(hind).add_observations(obs)
# he = he.remove_bias(how='basic_quantile',
#                                       train_test_split='unfair', 
#                                       alignment='same_verif')
he.verify(metric='rmse',
                comparison='e2o',
                alignment='same_verif',
                dim='init',
                reference=['persistence', 'climatology'])

This simplified and standardized process frees up resources to tackle the large process-based unknowns in predictability research. Here, we perform a live and interactive multi-model comparison removing bias with different methodologies from NMME project hindcasts and compare against persistence and climatology reference forecasts.

Documentation: https://climpred.readthedocs.io

Repository: https://github.com/pangeo-data/climpred

Reference paper: Brady, Riley X. and Aaron Spring (Mar. 2021). “Climpred: Verification of Weather and Climate Forecasts”. en. Journal of Open Source Software 6.59, p. 2781. https://joss.theoj.org/papers/10.21105/joss.02781

How to cite: Spring, A.: climpred: weather and climate forecast verification in python, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8200, https://doi.org/10.5194/egusphere-egu22-8200, 2022.

EGU22-8424 | Presentations | NP5.1

Offline models for statistical post-processing of surface weather variables 

Zied Ben Bouallegue, Fenwick Cooper, and Matthew Chantry

Statistical post-processing based on machine learning (ML) methods aims to capture systematic forecasts errors, relying on information from various predictors. We explore the exclusive use of “offline” predictors for the bias correction and uncertainty estimation of 2m temperature and 10 m wind speed forecasts. Offline predictors are defined as predictors available before the start of the forecast-of-the-day. Offline predictors encompass model characteristics such as the model orography and the model vegetation cover as well as spatio-temporal markers such as the day of the year, the time of the day and the latitude. The resulting offline models are particularly simple to implement as no time-critical operations are involved. The benefits of offline models and performance compared with more complex approaches will be discussed. 

How to cite: Ben Bouallegue, Z., Cooper, F., and Chantry, M.: Offline models for statistical post-processing of surface weather variables, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8424, https://doi.org/10.5194/egusphere-egu22-8424, 2022.

EGU22-8706 | Presentations | NP5.1

IMPROVER : A probabilistic, multi-model post-processing system for meteorological forecasts 

Stephen Moseley, Fiona Rust, Gavin Evans, Ben Ayliffe, Katharine Hurst, Kathryn Howard, Bruce Wright, and Simon Jackson

The UK Met Office is developing an open-source probability-based post-processing system called IMPROVER to exploit convection permitting, hourly cycling ensemble forecasts. The system is tasked with blending these forecasts with both deterministic nowcast data, and coarser resolution global ensemble model data, to produce seamless probabilistic forecasts from the very short to medium range.

A majority of the post-processing within IMPROVER is performed on gridded forecasts, with site-specific forecasts extracted as a final step, helping to ensure consistency. IMPROVER delivers a wide range of probabilistic products to both operational meteorologists and as input to automated forecast production. and this presentation will detail some of the work that has been undertaken in the past year to prepare, with a focus on the use of statistical post-processing.

Statistical post-processing plays two complimentary roles within IMPROVER; ensuring forecasts better reflect reality, and in so doing, bringing different models into better alignment, which improves the seamlessness of model transitions. For a selection of diagnostics, the gridded forecasts from different source models are calibrated independently using ensemble model output statistics (EMOS). Results of experiments looking at the calibration of gridded forecasts will be discussed briefly.

More recently calibration of site forecasts has been introduced as a final step for temperature and wind speed forecasts. Results of experiments using EMOS to perform calibration in a variety of different ways will be presented, including justifications and trade-offs made in choosing a final approach.

  • This will include some discussion of the remaking of weather symbol products as period, rather than instantaneous, forecasts and the implications for their verification.

How to cite: Moseley, S., Rust, F., Evans, G., Ayliffe, B., Hurst, K., Howard, K., Wright, B., and Jackson, S.: IMPROVER : A probabilistic, multi-model post-processing system for meteorological forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8706, https://doi.org/10.5194/egusphere-egu22-8706, 2022.

EGU22-10869 | Presentations | NP5.1

Causality in long-term predictions, past-value problems and a stochastic-deterministic hybrid 

Lenin Del Rio Amador and Shaun Lovejoy

Over time scales between 10 days and 10-20 years – the macroweather regime – atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the fractional energy balance equation (fractional Gaussian noise) which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential.

The multivariate version of the model (m-StocSIPS), exploits the space-time statistics of the temperature field to produce realistic global simulations, including realistic teleconnection networks and El Niño events and indices. One of the implications of this model is the lack of Granger-causality: the optimal predictor at gridpoint i is obtained from the past of the timeseries i and cannot be improved using past temperatures from any other location j. This allows to treat predictions for long-memory processes as “past value” problems rather than the conventional initial value approach that uses the current state of the atmosphere to produce ensemble forecasts.

To improve the stochastic predictions, a zero-lag independent (non-stochastic) predictor is needed. Here we use the Canadian Seasonal to Interannual prediction System (CanSIPS), as a deterministic co-predictor. CanSIPS is a long-term multi-model ensemble (MME) system using two climate models developed by the Canadian Centre for Climate Modelling and Analysis (CCCma). The optimal linear combination of CanSIPS and StocSIPS (CanStoc) was based on minimizing the square error of the final predictor in the common hindcast period 1981-2010 using different out-of-sample validations. Global time series and regional maps at 2.5ºx2.5º resolution show that the skill of CanStoc is better than that of each individual model for most of the regions when non-overlapping training and verification periods are used.

How to cite: Del Rio Amador, L. and Lovejoy, S.: Causality in long-term predictions, past-value problems and a stochastic-deterministic hybrid, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10869, https://doi.org/10.5194/egusphere-egu22-10869, 2022.

EGU22-11689 | Presentations | NP5.1

Graphical Model Assessment of Probabilistic Forecasts 

Moritz N. Lang, Reto Stauffer, and Achim Zeileis

As a consequence of the growing importance of probabilistic predictions in various application fields due to a necessary functional risk management and strategy, there is an increasing demand for appropriate probabilistic model evaluation. Besides proper scoring rules, which can evaluate not only the expectation but the entire predictive distribution, graphical assessment methods are particularly advantageous to diagnose possible model misspecifications.

Probabilistic forecasts are often based on distributional regression models, whereby the computation of predictive distributions, probabilities, and quantiles is generally dependent on the software (package) being used. Therefore, routines to graphically evaluate probabilistic models are not always available and if so then only for specific types of models and distributions provided by the corresponding package. An easy to use unified infrastructure to graphical assess and compare different probabilistic model types does not yet exist. Trying to fill that gap, we present a common conceptual framework accompanied by a flexible and object-oriented software implementation in the R package topmodels (https://topmodels.R-Forge.R-project.org/).  

The package includes visualizations for PIT (probability integral transform) histograms, Q-Q (quantile-quantile) plots of (randomized) quantile residuals, rootograms, reliability diagrams, and worm plots. All displays can be rendered in base R as well as in ggplot2 and provide different options for, e.g., computing confidence intervals, scaling or setting graphical parameters. Using examples of post-processing precipitation ensemble forecasts, we further discuss how all theses types of graphics can be compared to each other and which types of displays are particularly useful for bringing out which types of model deficiencies.

How to cite: Lang, M. N., Stauffer, R., and Zeileis, A.: Graphical Model Assessment of Probabilistic Forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11689, https://doi.org/10.5194/egusphere-egu22-11689, 2022.

EGU22-13118 | Presentations | NP5.1

Calibration of wind speed ensemble forecasts for power generation 

Sándor Baran and Ágnes Baran

In 2020, 36.6 % of the total electricity demand of the world was covered by renewable sources, whereas in the EU (UK included) this share reached 49.3 %. A substantial part of green energy is produced by wind farms, where accurate short range power predictions are required for successful integration of wind energy into the electrical grid. Accurate predictions of the produced electricity require accurate forecasts of the corresponding weather quantity, where the state-of-the-art method is the probabilistic approach based on ensemble forecasts. However, ensemble forecasts are often uncalibrated and might also be biased, thus require some form of post-processing to improve their predictive performance.

To calibrate (hub height) wind speed ensemble forecasts we propose a novel flexible machine learning approach, which results either in a truncated normal or a log-normal predictive distribution (Baran and Baran, 2021). In a case study based on 100m wind speed forecasts of the operational AROME-EPS of the Hungarian Meteorological Service, the forecast skill of this method is compared with the predictive performance of three different ensemble model output statistics approaches and the raw ensemble predictions. We show that compared with the raw ensemble, post-processing always improves the calibration of probabilistic and accuracy of point forecasts, and from the five competing methods the novel machine learning based approaches result in the best overall performance. 

Reference

Baran, S., Baran, Á., Calibration of wind speed ensemble forecasts for power generation. Idöjárás 125 (2021), 609-624.

How to cite: Baran, S. and Baran, Á.: Calibration of wind speed ensemble forecasts for power generation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13118, https://doi.org/10.5194/egusphere-egu22-13118, 2022.

EGU22-13125 | Presentations | NP5.1

Restoration of temporal dependence in post-processed ensemble forecasts 

Mária Lakatos and Sándor Baran

An influential step in weather forecasting was the introduction of ensemble forecasts in operational use due to their capability to account for the uncertainties in the future state of the atmosphere. However, ensemble weather forecasts are often underdispersive and might also contain bias, which calls for some form of post-processing. A popular approach to calibration is the ensemble model output statistics (EMOS) resulting in a full predictive distribution for a given weather variable. However, this form of univariate post-processing may ignore the prevailing spatial and/or temporal correlation structures among different dimensions. Since many applications call for spatially and/or temporally coherent forecasts, multivariate post-processing aims to capture these possibly lost dependencies.

Our main objective is the comparison of different nonparametric multivariate approaches to modeling temporal dependence of ensemble weather forecasts with different forecast horizons. We investigate two-step methods, where after univariate post-processing, the EMOS predictive distributions corresponding to different forecast horizons are combined to a multivariate calibrated prediction using an (empirical) copula (Lerch et al, 2020). Based on global ensemble predictions of the European Centre for Medium-Range Weather Forecasts from January 2002 to March 2014 we investigate the forecast skill of different versions of Ensemble Copula Coupling and Schaake Shuffle. In general, compared with the raw and independently calibrated forecasts, multivariate post-processing substantially improves the forecast skill; however, there is no unique winner, the best-performing approach strongly depends on the weather variable at hand. 

Reference

Lerch, S., Baran, S., Möller, A., Groß, J., Schefzik, R., Hemri, S., Graeter, M., Simulation-based comparison of multivariate ensemble post-processing methods. Nonlinear Process. Geophys. 27 (2020), 349-371.

 

How to cite: Lakatos, M. and Baran, S.: Restoration of temporal dependence in post-processed ensemble forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13125, https://doi.org/10.5194/egusphere-egu22-13125, 2022.

EGU22-13205 | Presentations | NP5.1

Support Vector Machine Quantile Regression based ensemble postprocessing 

David Jobst, Annette Möller, and Jürgen Groß

Current practice in predicting future weather is the use of numerical weather prediction (NWP) models to produce ensemble forecasts. Despite of enormous improvements over the last few decades, they still tend to exhibit bias and dispersion errors and consequently lack calibration. Therefore, these forecasts need to be statistically postprocessed.

Support vector machines are often used for classification and regression tasks in a wide range of applications, as e.g. energy, ecology, hydrology and economics. In this study, ensemble forecasts of 2m surface temperature are post-processed using a quantile regression approach based on support vector machines (SVMQR). This approach will be compared to the benchmark postprocessing methods ensemble model output statistics (EMOS), boosted EMOS and quantile regression forests (QRF). Instead of only regarding temperature variables as predictors, other weather variables including time dependence are taken into account as independent variables. The considered dataset consists of observations and forecasts for five years which cover Germany including three different forecast horizons. Despite of a shorter training period for SVMQR in contrast to e.g. boosted EMOS or QRF, SVMQR yields more calibrated quantile ensemble forecasts than the other approaches. Additionally, a comparable performance in terms of CRPS to the benchmark methods and a great improvement in comparison to the raw ensemble forecasts could be detected.

How to cite: Jobst, D., Möller, A., and Groß, J.: Support Vector Machine Quantile Regression based ensemble postprocessing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13205, https://doi.org/10.5194/egusphere-egu22-13205, 2022.

EGU22-13388 | Presentations | NP5.1

Spatially adaptive Bayesian estimation for Probabilistic Temperature Forecasts 

Annette Möller, Thordis Thorarinsdottir, Alex Lenkoski, and Tilmann Gneiting

To account for forecast uncertainty in numerical weather prediction (NWP) models it has become common practice to employ ensemble prediction systems generating probabilistic forecast ensembles by multiple runs of the NWP model, each time with variations in the details of the numerical model and/or initial and boundary conditions. However, forecast ensembles typically exhibit biases and dispersion errors as they are not able to fully represent uncertainty in NWP models. Therefore, statistical postprocessing models are employed to correct ensembles for biases and dispersion errors in conjunction with recently observed forecast errors.

For incorporating dependencies in space, this work proposes a spatially adaptive extension of the state-of-the-art Ensemble Model Output Statistics (EMOS) model. The new approach, named Markovian EMOS (MEMOS), introduces a Markovian dependence structure on the model parameters by employing Gaussian Markov random fields. For fitting the MEMOS model in a Bayesian fashion the recently developed Integrated Nested Laplace Approximation (INLA) approach is utilized, allowing for fast and accurate approximation of the posterior distributions of the parameters. To obtain physically coherent forecasts the basic MEMOS model is provided with an additional spatial dependence structure induced by the Ensemble Copula Coupling (ECC) approach, which makes explicit use of the rank order structure of the raw ensemble.

The method is applied to temperature forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) over Europe, where it exhibits comparable or improved performance over univariate EMOS variants.

How to cite: Möller, A., Thorarinsdottir, T., Lenkoski, A., and Gneiting, T.: Spatially adaptive Bayesian estimation for Probabilistic Temperature Forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13388, https://doi.org/10.5194/egusphere-egu22-13388, 2022.

EGU22-13413 | Presentations | NP5.1

Multivariate post-processing of temporal dependencies with autoregressive and LSTM neural networks 

Daniel Tolomei, Sjoerd Dirksen, Kirien Whan, and Maurice Schmeits

We consider the problem of post-processing forecasts for multiple lead times simultaneously. In particular, we focus on post-processing wind speed forecasts for consecutive lead times (0 - 48h ahead) from the deterministic HARMONIE-AROME NWP model. Given the strong temporal dependency between forecasts at consecutive lead times, it is essential to model the problem as a multivariate statistical post-processing problem in order to take this temporal correlation into account.

A standard procedure in multivariate statistical post-processing is to produce multiple probabilistic forecasts independently for each lead time and introduce the dependency between them at a later stage using an empirical copula. For our specific problem, a successful example of this approach is to use EMOS to fit truncated normal marginal distributions at each lead time and then model the joint distribution by drawing samples from these distributions and reconstructing the temporal dependencies using the Schaake Shuffle.

Our aim is to explore alternative methods that can model and exploit temporal dependencies more explicitly with the goal of improving forecast performance and moving away from sample based distribution modelling. We develop two new methods that produce multivariate truncated normal probabilistic forecasts for all lead times simultaneously, by combining elements from time series analysis and artificial neural networks.

In our first method, we exploit the autoregressive dependencies in the residuals of the NWP wind speed forecasts to deduce an explicit multivariate model. By using a neural network to determine the parameters of this model, we arrive at our first method, which we coin ARMOSnet.

In our second method, we apply Long Short-Term Memory networks, which rank among the state-of-the-art tools for the forecasting of time series. We adapt the LSTM architecture to output a multivariate density that models the temporal dependencies between the consecutive lead times.

We compare our two methods to EMOS combined with the Schaake Shuffle for post-processing wind speed forecasts from the HARMONIE-AROME NWP model. Our new methods both outperform the EMOS-Schaake Shuffle approach in terms of the logarithmic, energy, and variogram scores. Among the two new methods, ARMOSnet exhibits the best performance.

 

How to cite: Tolomei, D., Dirksen, S., Whan, K., and Schmeits, M.: Multivariate post-processing of temporal dependencies with autoregressive and LSTM neural networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13413, https://doi.org/10.5194/egusphere-egu22-13413, 2022.

EGU22-336 | Presentations | NP5.2

Multilevel Monte Carlo estimation of background error covariances in ensemble variational data assimilation 

Mayeul Destouches, Paul Mycek, Jérémy Briant, Selime Gürol, Anthony Weaver, Serge Gratton, and Ehouarn Simon

In ensemble variational (EnVar) data assimilation systems, background error covariances are sampled from an ensemble of forecasts evolving with time. One possible way of generating this ensemble is by running an Ensemble of Data Assimilations (EDA) that samples all possible error sources (initial condition errors, boundary condition errors, model errors). Large ensemble sizes are desirable to minimize sampling errors, but generating a single ensemble member is usually expensive due to the cost of integrating the physical model. In practice, ensembles with coarser spatial resolutions are sometimes used, allowing for cheaper generation of individual members, and thus larger ensemble sizes.

Multilevel Monte Carlo (MLMC) methods propose to go beyond this usual trade-off between grid resolution and ensemble size, by expressing a fine-grid estimator as an astute combination of estimators computed on a hierarchy of spatial grids. Starting from a Monte Carlo covariance estimator on a coarse grid but with a large ensemble size, correction terms are added to form a quasi-telescopic sum. The correction terms come from EDAs of increasing spatial resolutions and decreasing ensemble sizes, with a pairwise stochastic coupling between EDAs of two successive resolutions. The expectation of this MLMC estimator is equal to the expectation of the Monte Carlo estimator on the finest grid, so that no bias is introduced by the coarse resolution forecasts. Without increasing the computational cost, MLMC effectively reduces the variance of the covariance estimator, i.e. reduces the sampling noise on covariances.

We first present the theoretical basis of MLMC and how it can apply to the estimation of covariance matrices. An illustration with a quasi-geostrophic model is then presented. For a given computational budget, we compare three equal-cost methods to estimate background error covariances: (1) the usual single-resolution ensemble estimate, (2) a combination of estimates of various resolutions based on Bayesian Model Averaging and (3) the MLMC estimate. The methods are compared in terms of mean square error of the covariance estimators, and in terms of quality of the resulting analyses for one assimilation cycle. The role of covariance localization in each case is also briefly discussed.

This work is partially supported by 3IA Artificial and Natural Intelligence Toulouse Institute, French "Investing for the Future- PIA3" program under the Grant agreement ANR-19-PI3A-0004.

This project has received financial support from the CNRS through the 80Prime program.

How to cite: Destouches, M., Mycek, P., Briant, J., Gürol, S., Weaver, A., Gratton, S., and Simon, E.: Multilevel Monte Carlo estimation of background error covariances in ensemble variational data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-336, https://doi.org/10.5194/egusphere-egu22-336, 2022.

EGU22-1164 | Presentations | NP5.2

On the physical nudging equations 

Giovanni Conti, Ali Aydoğdu, Silvio Gualdi, Antonio Navarra, and Joe Tribbia

In this work we show how it is possible to derive a new set of nudging equations, a tool still used in many data assimilation problems, starting from statistical physics considerations and availing ourselves of stochastic parameterizations that take into account unresolved interactions. The fluctuations used are thought of as Gaussian white noise with zero mean. The derivation is based on the conditioned Langevin dynamics technique. Exploiting the relation between the Fokker–Planck and the Langevin equations, the nudging equations are derived for a maximally observed system that converges towards the observations in finite time. The new nudging term found is the analog of the so called quantum potential of the Bohmian mechanics. In order to make the new nudging equations feasible for practical computations, two approximations are developed and used as bases from which extending this tool to non-perfectly observed systems. By means of a physical framework, in the zero noise limit, all the physical nudging parameters are fixed by the model under study and there is no need to tune other free ad-hoc variables. The limit of zero noise shows that also for the classical nudging equations it is necessary to use dynamical information to correct the typical relaxation term. A comparison of these approximations with a 3DVar scheme, that use a conjugate gradient minimization, is then shown in a series of four twin experiments that exploit low order chaotic models.

How to cite: Conti, G., Aydoğdu, A., Gualdi, S., Navarra, A., and Tribbia, J.: On the physical nudging equations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1164, https://doi.org/10.5194/egusphere-egu22-1164, 2022.

EGU22-1218 | Presentations | NP5.2

A fast, single-iteration ensemble Kalman smoother for sequential data assimilation 

Colin Grudzien and Marc Bocquet

Ensemble-variational methods form the basis of the state-of-the-art for nonlinear, scalable data assimilation, yet current designs may not be cost-effective for reducing prediction error in online, short-range forecast systems. We propose a novel, outer-loop optimization of the Bayesian maximum a posteriori formalism for ensemble-variational smoothing in applications for which the forecast error dynamics are weakly nonlinear, such as synoptic meteorology. In addition to providing a rigorous mathematical derivation our technique, we systematically develop and inter-compare a variety of ensemble-variational schemes in the Lorenz-96 model using the open-source Julia package DataAssimilationBenchmarks.jl. This high-performance numerical framework, supporting our mathematical results, produces extensive benchmarks that demonstrate the significant performance advantages of our proposed technique versus several similar estimator designs. In particular, our single-iteration ensemble Kalman smoother (SIEnKS) is shown both to improve prediction / posterior accuracy and to simultaneously reduce the leading order cost of iterative, sequential smoothers in a variety of relevant test cases for operational short-range forecasts.  These results are currently in open review in Geoscientific Model Development (Preprint gmd-2021-306) and the Journal of Open Source Software (Preprint #3976).

How to cite: Grudzien, C. and Bocquet, M.: A fast, single-iteration ensemble Kalman smoother for sequential data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1218, https://doi.org/10.5194/egusphere-egu22-1218, 2022.

EGU22-1497 | Presentations | NP5.2

A 4D-Localized Particle Filter Method for Regional Data Assimilation at DWD 

Nora Schenk, Anne Walter, and Roland Potthast

Nonlinear ensemble data assimilation methods like particle filters aim to improve the numerical weather prediction and the uncertainty quantification in a non-Gaussian setting. The localized adaptive particle filter (LAPF), introduced by R. Potthast, A. Walter and A. Rhodin in 2019, overcomes filter collapse in a high-dimensional framework. This particle filter was further developed by Walter et al. (2021) to the local mixture coefficients particle filter (LMCPF) which was tested within the global ICON model. In the LMCPF method the background distribution is approximated by Gaussian mixtures. After a classical resampling step, Bayes' formula is carried out explicitly under the assumption of a Gaussian distributed observation error. Furthermore, the particle uncertainty can be adjusted which affects the strength of the shift of the particles toward the observation. Lastly, Gaussian resampling is employed to increase the ensemble variability. All steps are carried out in ensemble space and observation localization is applied in the method.

Following a study of Kotsuki et al. (2021), we recently substituted the approximated particle weights in the LMCPF method with the exact Gaussian mixture weights which leads to an increase of the effective ensemble. Using the exact weights, Kotsuki et al. (2021) detected an improvement of  the stability of the LMCPF method with respect to the inflation parameters within the SPEEDY model.

Furthermore, we explore the potential of the LMCPF with the exact particle weights in the kilometre-scale ensemble data assimilation (KENDA) system with the limited area mode of the ICON model (ICON-LAM) and compare the particle filter method to the localized ensemble transform Kalman filter (LETKF) which is operationally used at the German Meteorological Service (DWD). Both methods describe four-dimensional data assimilation schemes if the observation operators are applied during the model forward integration at the exact observation times and not only at analysis time. This leads to four-dimensional background error covariance matrices at times and locations of the observations which are employed to derive the analysis ensemble.

In addition to a mathematical introduction of the LMCPF method, we present experimental results for the LMCPF in comparison with the LETKF method in KENDA used at DWD for the ICON-LAM model. Moreover, we discover the improvements of the LMCPF with exact particle weights over the method with approximated weights.

How to cite: Schenk, N., Walter, A., and Potthast, R.: A 4D-Localized Particle Filter Method for Regional Data Assimilation at DWD, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1497, https://doi.org/10.5194/egusphere-egu22-1497, 2022.

EGU22-1560 | Presentations | NP5.2 | Highlight

Performance of the multiscale alignment ensemble filter in reducing vortex position errors 

Yue Ying, Jeffrey Anderson, and Laurent Bertino

Position errors in coherent features have been a challenging problem for data assimilation (DA) due to their high nonlinearity. To effectively reduce position errors, a multiscale alignment (MSA) method was introduced to compute ensemble Kalman filter (EnKF) updates on a sequence of model states at low to high resolutions (large to small scales). Large-scale state has less nonlinearity due to position errors, therefore linear EnKF updates are optimal. The large-scale analysis increments are then utilized to compute the displacement vectors that warp the model grid, reduce position errors and precondition the state at smaller scales before the EnKF update is computed again. This study further tests the performance of the MSA method in an idealized vortex model. The asymptotic behavior is documented for a multiscale solution as number of scales (Ns) increases. We show that the optimal Ns depends on the degree of nonlinearity caused by the position errors. When feature-based observations (such as the vortex position) are used, the MSA performs well with Ns  3 no matter how large the position errors are. A challenging scenario is identified for the MSA method, when the large-scale background flow is incoherent with the small-scale vortex position error (deviation from coherence assumption). In cycling DA experiments, the MSA performs better than the traditional EnKF at equal cost (using decreased ensemble size for MSA to compensate for its increased cost when Ns >1), showing good scalability for real application and potential for improving prediction skill in many multiscale Earth systems.

How to cite: Ying, Y., Anderson, J., and Bertino, L.: Performance of the multiscale alignment ensemble filter in reducing vortex position errors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1560, https://doi.org/10.5194/egusphere-egu22-1560, 2022.

EGU22-1664 | Presentations | NP5.2

Inferring the instability of a dynamical system from the skill of data assimilation exercises 

Yumeng Chen, Alberto Carrassi, and Valerio Lucarini

Data assimilation (DA) aims at optimally merging observational data and model outputs to create a coherent statistical and dynamical picture of the system under investigation. Indeed, DA aims at minimizing the effect of observational and model error and at distilling the correct ingredients of its dynamics. DA is of critical importance for the analysis of systems featuring sensitive dependence on the initial conditions, as chaos wins over any finitely accurate knowledge of the state of the system, even in absence of model error. Clearly, the skill of DA is guided by the properties of dynamical system under investigation, as merging optimally observational data and model outputs is harder when strong instabilities are present. In this paper we reverse the usual angle on the problem and show that it is indeed possible to use the skill of DA to infer some basic properties of the tangent space of the system, which may be hard to compute in very high-dimensional systems. Here, we focus our attention on the first Lyapunov exponent and the Kolmogorov–Sinai entropy and perform numerical experiments on the Vissio–Lucarini 2020 model, a recently proposed generalization of the Lorenz 1996 model that is able to describe in a simple yet meaningful way the interplay between dynamical and thermodynamical variables.

How to cite: Chen, Y., Carrassi, A., and Lucarini, V.: Inferring the instability of a dynamical system from the skill of data assimilation exercises, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1664, https://doi.org/10.5194/egusphere-egu22-1664, 2022.

EGU22-2604 | Presentations | NP5.2

Koopman eigenfunctions estimation from reproducing kernel Hilbert space manifold, and ensemble data assimilation 

Gilles Tissot, Etienne Mémin, and Bérenger Hug

This study aims at proposing a new framework to perform ensemble-based estimations of dynamical trajectories of a geophysical fluid flow system. To perform efficient estimations, the ensemble members are embedded in a set of evolving reproducing kernel Hilbert spaces (RKHS) defining a manifold of spaces, we nicknamed Wonderland, due to its analytical properties.

The method proposed here is designed to deal with very large scale systems such as oceanic or meteorological flows, where it is out of the question to explore the whole attractor, neither to run very long time simulations. Instead, we propose to learn the system locally, in phase space, from an ensemble of trajectories.

The novelty of the present work relies on the fact that the feature maps between the native space and the RKHS manifold are transported by the dynamical system. This creates, at any time, an isometry between the tangent RKHS at time t and the initial conditions. This has several important consequences. First, the kernel evaluations are constant along trajectories, instead to be attached to a system state. By doing so, a new ensemble member embedded in the RKHS manifold at the initial time can be very simply estimated at a further time. This framework displays striking properties. The Koopman and Perron-Frobenius operators on such RKHS manifold are unitary, even though the system might be non invertible. They are furthermore uniformly continuous (with bounded generators) and diagonalizable. As such they can be rigourously expended in exponential forms. 

This set of analytical properties enables us to provide a practical estimation of the Koopman eigenfunctions. In the proposed strategy, evaluations of these Koopman eigenfunctions at the ensemble members are exact. To perform robust estimations, the finite-time Lyapunov exponents associated with each Koopman eigenfunction (which are easily accessible on the RKHS manifold as well) are determined. On this basis, we are able to filter the kernel by removing contributions of the Koopman modes that exceed the predictability time. We show that it leads to robust estimations of new unknown trajectories. This framework allows us to write an ensemble-based data assimilation problem, where constant-in-time linear combinations coefficients between ensemble members are sought in order to estimate the QG flow based on noisy swath observations.

The methodology is demonstrated on a barotropic quasi-geostrophic model of a double gyres. After comparing various kernels and provided guidelines to adapt the kernel with the spread of the ensemble, we show isometry and Koopman-filtered reconstructions. Finally, the data assimilation is presented.

How to cite: Tissot, G., Mémin, E., and Hug, B.: Koopman eigenfunctions estimation from reproducing kernel Hilbert space manifold, and ensemble data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2604, https://doi.org/10.5194/egusphere-egu22-2604, 2022.

EGU22-2698 | Presentations | NP5.2

Quality Control Methods in Ocean-Sea ice Coupled Data Assimilation 

Shastri Paturi, Alexandra Bozec, Eric Chassignet, Zulema Garraffo, Avichal Mehra, and Daryl Kleist

The purpose of employing data assimilation methods in operational ocean forecasting systems is to provide good initialization to the models and is dependent on good quality ocean observations being assimilated. Using or accepting erroneous data can result in an inaccurate analysis and alternatively, rejecting extreme or valid data can result in missing important events.

In this study two ocean-sea ice coupled systems are considered: HYCOM-CICE4 and MOM6-CICE6 at ¼-deg horizontal resolution and 41 vertical layers. The two coupled models are initialized from the World Ocean Atlas 2018 (WOA) temperature and salinity climatology for a period of 20 years. Both models are forced with GEFS (Global Ensemble Forecast System created by the National Centers for Environmental Prediction: NCEP). The data assimilation is performed on a 24-hr cycle using RTOFS-DA (Real-time Ocean Forecasting system-DA; 3DVAR) for HYCOM-CICE4 and SOCA (Sea ice Ocean Coupled Assimilation; 3DVAR) for MOM6-CICE6 to compare the data Quality Control (QC) methods. The ocean data being assimilated include satellite sea surface temperature (SST) and sea surface salinity (SSS), in-situ temperature & salinity, absolute dynamic topography (ADT), sea ice concentration.

The QC in RTOFS-DA and SOCA are fully automated and are performed through various filters applied (e.g., land-sea area fraction to eliminate satellite data near the coast, temperature inversion elimination in in-situ profile data, etc). The various QC methods in both DA systems are described. The results of the analysis and 24-forecast are compared against independent observations and statistics of the data accepted and rejected between the two DA systems are presented and discussed.

How to cite: Paturi, S., Bozec, A., Chassignet, E., Garraffo, Z., Mehra, A., and Kleist, D.: Quality Control Methods in Ocean-Sea ice Coupled Data Assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2698, https://doi.org/10.5194/egusphere-egu22-2698, 2022.

EGU22-2838 | Presentations | NP5.2

Bridging linear state estimation and machine learning 

Hristo G. Chipilski

Recent years have seen active efforts within the geophysical community to combine traditional Data Assimilation (DA) methods with emerging Machine Learning (ML) techniques. However, most of this past theoretical work has been centered on variational DA approaches due to their similarity with ML in terms of how the underlying optimization problem is formulated and solved. Here I will present a new and completely general nonlinear estimation theory that retains the flexibility of advanced sampling-based methods (e.g., the particle filter) and the analytical tractability of linear estimation algorithms (e.g., the ensemble Kalman filter). In particular, an alternative state space model will be constructed whose filtering and smoothing distributions remain closed under a wide class of nonlinear functions. Since these nonlinear functions are only required to be bijective and continuously differentiable, the new estimation theory serves an ideal framework for rigorously incorporating invertible neural networks in the DA design. There are two additional properties which make the proposed framework especially appealing. First, linear estimation results follow immediately upon substituting the invertible neural networks with the identity transformation. Second, the prior and posterior belong to the same distribution family, which implies that the correlation structure and the corresponding dynamical balances in the model state are preserved following the analysis step. During the upcoming EGU meeting, I will discuss the motivation behind the new estimation framework, place it in the context of existing nonlinear DA techniques and demonstrate some of its benefits through idealized numerical examples.

How to cite: Chipilski, H. G.: Bridging linear state estimation and machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2838, https://doi.org/10.5194/egusphere-egu22-2838, 2022.

EGU22-3065 | Presentations | NP5.2

A multi-model ensemble Kalman filter for forecasting and data assimilation 

Eviatar Bach and Michael Ghil

Data assimilation (DA) aims to optimally combine model forecasts and noisy observations. Multi-model DA generalizes the variational or Bayesian formulation of the Kalman filter, and we prove here that it is also the minimum variance linear unbiased estimator. However, previous implementations of this approach have not estimated the model error, and have therewith not been able to correctly weight the separate models and the observations. Here, we show how multiple models can be combined for both forecasting and DA by using an ensemble Kalman filter with adaptive model error estimation. This methodology is applied to the Lorenz-96 model and it results in significant error reductions compared to the best model and to an unweighted multi-model ensemble.

How to cite: Bach, E. and Ghil, M.: A multi-model ensemble Kalman filter for forecasting and data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3065, https://doi.org/10.5194/egusphere-egu22-3065, 2022.

EGU22-3616 | Presentations | NP5.2

A design of the optimal setup for a coupled data assimilation for decadal climate predictions 

Iuliia Polkova, Guokun Lyu, Detlef Stammer, Silke Schubert, Frank Lunkeit, and Armin Köhl

The need for reliable climate predictions is growing in demand for various socio-economic sectors. The predictability studies though show that Earth System Models (ESMs) can predict important climate variables, the predictions suffer from model errors and initialization shocks that limit predictability. The magnitude of this effect is difficult to quantify unless one could perform experiments in dynamically- and model-consistent settings to contrast against each other various sources of initialization shocks. This is the idea of our study, which concerns quantifying and understanding the impact of deriving dynamically balanced initial conditions on the decadal prediction skill.

Among a variety of coupled data assimilation (CDA) methods, the adjoint method is one of the promising because its result is dynamically consistent with the ESM equations; however, the method might also be one of the most demanding to design and maintain. Here, we use the coupled adjoint model developed for the ESM of intermediate complexity CESAM (Centrum für Erdsystemforschung und Nachhaltigkeit Erdsystem Assimilations-Modell) to produce a coupled ocean-atmosphere reanalysis. So far, we prepared and tested the forward and adjoint CESAM for upcoming decadal climate prediction experiments. We present the performance of the forward CESAM in terms of the 20th-century historical simulations, which are typically used as the benchmark for comparing initialized versus uninitiated climate simulations. We also present the setup for the adjoint CESAM as well as the initial CDA experiments. In the following, these CDA experiments will serve as a source of initial conditions for ensembles of retrospective decadal predictions. In a model-consistent approach, the study will compare initialization based on the coupled ocean-atmosphere reanalysis and based on the widespread strategy in decadal prediction studies of nudging toward ocean and atmosphere reanalyses, which are usually external to a prediction system as well as they are un-coupled. Results of this study aim to guideline future initialization developments for comprehensive ESMs.

How to cite: Polkova, I., Lyu, G., Stammer, D., Schubert, S., Lunkeit, F., and Köhl, A.: A design of the optimal setup for a coupled data assimilation for decadal climate predictions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3616, https://doi.org/10.5194/egusphere-egu22-3616, 2022.

The sensible heat flux and evapotranspiration couple the atmospheric boundary layer and the land surface together. It was shown that screen-level observations like the 2-metre-temperature contain information about land surface parameters such as the soil moisture. As model biases and parametrizations normally causes problems in operational land surface data assimilation, such screen-level observations are assimilated into the soil moisture with simplified data assimilation methods. Here, we will take another point of view onto the problem and show a potential of advanced ensemble data assimilation methods.

We ask what would happen, if we would have a perfect model and favorable conditions. With the limited-area TerrSysMP modelling framework, in a COSMO-CLM configuration, we perform idealized twin experiments for a seven-day period, where all differences between runs are only due to initial soil conditions or data assimilation. We assimilate sparsely-distributed and synthetic 2-metre-temperature observations from a nature run into the soil moisture. In these idealized experiments, we are able to prove that a localized ensemble transform Kalman filter, as similarly used for operational data assimilation in the mesoscale, can directly assimilate hourly instantaneous screen-level observations without the need of an additional optimal interpolation step. Here, we improve the soil moisture analysis by up to 50% compared to our open-loop run without data assimilation. Furthermore, taking temporal dependencies within a 24-hour window during the correction step into account and using a 4DEnVar-like localized ensemble Kalman smoother improves the analysis by a further 10%.

The approximation of the vertical covariances by the ensemble can nevertheless induce an overconfidence of the analysis, especially in ensemble smoothers where more observations are assimilated at once. Then, the potential of the observations cannot be fully used. An idea to circumvent such problems is to assimilate observational features instead of the raw observations to make the data assimilation problem simpler. We can explicitly construct such features by making use of characteristic fingerprints within the observations that point towards errors within the variable of interest; we term them fingerprint operators. Here, we will show two fingerprint operators for the 2-metre-temperature: the averaged temperature between 6 UTC and 18 UTC and the amplitude of a sine curve, fitted to 2-metre-temperature observations in a 24-hour window. These fingerprints represent that the soil moisture influences the daytime temperature and the diurnal cycle of the 2-metre-temperature. With these features, we retain useful information about the soil moisture and obtain similar results to the localized ensemble Kalman smoother. As our idealized experiments have by construction favorable conditions for ensemble Kalman smoothers, these results indicate a potential for fingerprint operators in coupled data assimilation across the atmosphere-land interface.

How to cite: Finn, T., Geppert, G., and Ament, F.: Ensemble data assimilation of screen-level observations across the atmosphere-land interface enhanced by fingerprint operators, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4845, https://doi.org/10.5194/egusphere-egu22-4845, 2022.

EGU22-5424 | Presentations | NP5.2 | Highlight

Two stage inversion method for microplastics emission estimation 

Ondřej Tichý, Nikolaos Evangeliou, and Václav Šmídl
The goal of this contribution is to explore two-stage inversion algorithm for spatio-temporal emission estimation (2D and time) from deposition measurements of microplastics and microfibers from Western USA. We consider the linear inversion model formulated as y = M x , where y is the measurement vector, M is source-receptor-sensitivity matrix computed using Lagrangian particle dispersion model FLEXPART, and x is the unknown emission vector from given spatial element. The inverse problem is typically ill-conditioned due to the measurements sparsity, hence, we propose two stage algorithm for inversion of this type. First, we run the inversion algorithm for the whole spatial domain, hence, we obtain averaged emission from each spatial element of the considered spatial domain. Second, we use the estimated emission from the first step (common for all spatial elements) as a prior emission in the second step where the inversion problem is considered for each spatial element separately. We demonstrate that this approach regularizes the inversion problem of spatio-temporal emission from sparse measurements, concretely on microplastics and microfibers emission estimation in Western USA.

How to cite: Tichý, O., Evangeliou, N., and Šmídl, V.: Two stage inversion method for microplastics emission estimation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5424, https://doi.org/10.5194/egusphere-egu22-5424, 2022.

EGU22-5692 | Presentations | NP5.2

Model error correction with data assimilation and machine learning 

Alban Farchi, Marc Bocquet, Patrick Laloyaux, Massimo Bonavita, Marcin Chrust, and Quentin Malartic

The idea of using machine learning (ML) methods to reconstruct the dynamics of a system is the topic of recent studies in the geosciences in which the key output is a surrogate model meant to emulate the dynamical model. In order to treat sparse and noisy observations in a rigorous way, ML can be combined with data assimilation (DA). This yields a class of iterative methods in which, at each iteration a DA step estimates the system's state, and alternates with a ML step to learn the system's dynamics from the DA analysis.

This framework can be used to correct the error of an existent, physical model. The resulting surrogate model is hybrid, with a physical and a statistical part. In practice, the correction can be added as an integrated term (i.e. in the model resolvent) or directly inside the tendencies of the physical model. The resolvent correction is easy to implement but is not suited for short-term predictions. The tendency correction is more technical since it requires the adjoint of the physical model, but also more flexible and can be used for any forecast lead time.

In this presentation, we start by a proof of concept for the use of joint DA and ML tools to correct model error. We use the resolvent correction with simple neural networks to correct the error of a two-dimensional, two layer quasi-geostrophic layer. The difference between the resolvent and the tendency correction is then illustrated with the two- scale Lorenz model. Finally, we show that the tendency correction opens the possibility to make online model error correction, i.e. improving the model progressively as new observations become available. We compare online and offline learning using the same twin experiment with the two-scale Lorenz model.

 

How to cite: Farchi, A., Bocquet, M., Laloyaux, P., Bonavita, M., Chrust, M., and Malartic, Q.: Model error correction with data assimilation and machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5692, https://doi.org/10.5194/egusphere-egu22-5692, 2022.

EGU22-7395 | Presentations | NP5.2

Effects of data assimilation on different fluxes of a fully coupled land surface/subsurface model 

Bastian Waldowski, Insa Neuweiler, and Emilio Sánchez-León

We test the improvement of flux predictions with data assimilation (DA) in a coupled land surface/subsurface model. We present results of DA experiments in an idealized testcase with an extent of 1km x 5km x 50m. Our model considers multiple heterogeneous soil units, different plant functional types and a sophisticated topographical design chosen to induce lateral flow and rivers at specific areas. We use TSMP-PDAF to couple the land-surface model CLM and the subsurface/surface flow model ParFlow with the DA framework PDAF. We use a Localized Ensemble Kalman Filter (LEnKF) with an ensemble of 93 members. We consider uncertainty in the atmosphere, soil properties and initial conditions by different atmospheric forcings, distinct heterogeneous soil parameter distributions and an individual spinup for each ensemble member. The ensemble, which has a horizontal grid resolution of 40m, is updated with virtual measurements from a high resolution (10m) reference model.
In the scope of this work, we address the impact of updating different state variables (soil moisture and pressure head) on groundwater recharge, lateral subsurface flow, surface runoff, and evapotranspiration. While surface runoff and evapotranspiration directly depend on pressure head and soil moisture, subsurface flow depends on pressure head gradients. For groundwater recharge, our estimate depends on groundwater storage changes (which can directly be enforced by the updates during DA) as well as subsurface flow. To investigate if DA can directly improve these fluxes, we run multiple experiments with different observation frequencies and localization radii. Further, we investigate if there are improvements in the fluxes during open loop forecasting periods subsequent to DA.

How to cite: Waldowski, B., Neuweiler, I., and Sánchez-León, E.: Effects of data assimilation on different fluxes of a fully coupled land surface/subsurface model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7395, https://doi.org/10.5194/egusphere-egu22-7395, 2022.

EGU22-7448 | Presentations | NP5.2

Model error covariance estimation in observation space weak-constraint 4DVar 

Anne Pein and Peter Jan Van Leeuwen
Weak-constraint 4DVar (WC-4DVar) not only takes errors in initial conditions into account but also assumes that the physical model itself is erroneous. As model errors, arising e.g. from unresolved processes, can be substantial in geoscience applications, the weak-constraint formulation yields more accurate results compared to its strong-constraint counterpart. Furthermore, accuracy in forecasting should be improved since the algorithm produces an optimal solution at the end of the assimilation window, instead of revised initial conditions. Finally, WC-4DVar allows for longer assimilation windows because of reduced sensitivity to initial conditions. 
 
However, for complex high-dimensional models, it is not simple to estimate the model error covariances, as needed in the WC-4DVar algorithm. A promising approach to address this challenge might look as follows: We start with a first-guess model error covariance, e.g. a scaled-down (in amplitude and length-scale) initial state (background) covariance (the so-called B-matrix) with added time correlation, and perform a WC-4DVar assimilation step. This yields, besides an optimised solution at the end of the assimilation window, estimates for the model errors. We then use these model errors to derive new model error covariances with which we perform the next assimilation step. This procedure is iterated.
 
In this talk, we present initial results of this approach applied to the Burgers’ equation and using an observation-space WC-4DVar algorithm (sometimes called PSAS). We outline the procedure, demonstrate its feasibility, and discuss extensions to real-world systems.

How to cite: Pein, A. and Van Leeuwen, P. J.: Model error covariance estimation in observation space weak-constraint 4DVar, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7448, https://doi.org/10.5194/egusphere-egu22-7448, 2022.

EGU22-8578 | Presentations | NP5.2

Data-driven data assimilation to better characterize both accuracy and uncertainty of climate projections: a case study with an idealized chaotic AMOC model 

Pierre Le Bras, Pierre Ailliot, Noémie Le Carrer, Juan Ruiz, Florian Sévellec, and Pierre Tandeo

The multi-model ensemble approach is applied in geosciences to provide better predictions or projections, by weighting the outputs from different dynamical models. Basically, the weighting procedure relies on the choice of a performance metric to measure the closeness of individual model outputs to actual observations. The highest weight is then given to the model that best matches the observations, and so forth. Model weights can be used to constrain both the mean and the uncertainty in future projections of climate models.

In this study, we seek to combine different parameterizations of an idealized three-dimensional chaotic model of the Atlantic Meridional Overturning Circulation. One of the parameterizations plays the role of the observations. Each parameterization is evaluated online in a data assimilation framework using the EnKF by comparing the forecasts with the observations.

Traditional data assimilation procedures require access to the model equations, resulting in significant computational costs to run multiple model simulations to obtain forecasts at each time step. Here, a machine learning approach is implemented to provide the forecasts (i.e., analog forecasting). For each parameterization, the classical way of producing the forecasts is, in our case, replaced by an already existing catalog of trajectory time evolutions (e.g., long-term simulations), allowing to statistically emulate the model dynamics. This data-driven methodology retains the benefits given by the classical EnKF (i.e., optimal initial conditions, uncertainties consideration), at low computational costs. For each model-parameterization, a local performance metric (namely, the contextual model evidence) is computed at each time step in order to compare observations and model forecasts. This metric, based on the innovation likelihood, is sensitive to differences in the model dynamics and takes into account both the uncertainties of the forecasts and of the observations. To validate the methodology, different case studies are performed with various sensitivity tests (e.g., changing the parameterization used for the observations).

The results of the proposed weighting scheme on projections are discussed considering different quality metrics compared to benchmark methodologies. These include the equally weighting approach (also called the “model democracy”) and the direct comparison between the climatological probability distributions of simulations and observations.

How to cite: Le Bras, P., Ailliot, P., Le Carrer, N., Ruiz, J., Sévellec, F., and Tandeo, P.: Data-driven data assimilation to better characterize both accuracy and uncertainty of climate projections: a case study with an idealized chaotic AMOC model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8578, https://doi.org/10.5194/egusphere-egu22-8578, 2022.

EGU22-8659 | Presentations | NP5.2

Asymptotic behavior of the forecast-assimilation process with unstable dynamics 

Dan Crisan and Michael Ghil

Extensive numerical evidence for real and/or simulated data shows that the assimilation of observations has a stabilizing effect on unstable dynamics in numerical weather prediction and elsewhere.  In this talk, I will discuss mathematically rigorous considerations showing why this is so. In particular we prove that the expected value of the Wasserstein distance between the forecast-assimilation (FA) process starting from the true initial conditions and FA process wrongly initialized can be controlled uniformly in time. Under suitable circumstances, the number of observations required to achieve this stabilization can be much smaller than the number of model variables. In particular, it suffices to observe the model's unstable degrees of freedom. 

How to cite: Crisan, D. and Ghil, M.: Asymptotic behavior of the forecast-assimilation process with unstable dynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8659, https://doi.org/10.5194/egusphere-egu22-8659, 2022.

EGU22-8869 | Presentations | NP5.2

Ensemble Kalman Filter based Data Assimilation for Tropical Waves in the MJO Skeleton Model 

Tabea Gleiter, Tijana Janjic, and Nan Chen

The Madden-Julian oscillation (MJO) is the dominant component of tropical intraseasonal variability with wide reaching impacts even on extratropical weather and climate patterns. However, predicting the MJO is challenging. One reason are suboptimal state estimates obtained with standard data assimilation (DA) approaches. Those are typically based on filtering methods with Gaussian approximations and do not consider physical properties that are specifically important for the MJO.

In our recent paper (Gleiter et al. 2022), a constrained ensemble DA method is applied to study the impact of different physical constraints on the state estimation and prediction of the MJO with the Skeleton model. The utilized quadratic programming ensemble (QPEns) algorithm extends the standard stochastic ensemble Kalman filter (EnKF) with specifiable constraints on the updates of all ensemble members. This allows to recover physically more consistent states and to respect possible associated non-Gaussian statistics. Our results demonstrate an overall improvement in the filtering and forecast skill when the model's total energy is conserved in the initial condition. The degree of benefit is found to be dependent on the observational setup and the strength of the model's nonlinear dynamics. It is also shown that even in cases where the statistical error in some waves remains comparable to the stochastic EnKF during the DA stage, their prediction is remarkably improved when using the initial state resulting from the QPEns.

Gleiter, T., T. Janjic, N. Chen, 2022, Ensemble Kalman Filter based Data Assimilation for Tropical Waves in the MJO Skeleton Model, QJR Meteorol Soc., https://doi.org/10.1002/qj.4245

How to cite: Gleiter, T., Janjic, T., and Chen, N.: Ensemble Kalman Filter based Data Assimilation for Tropical Waves in the MJO Skeleton Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8869, https://doi.org/10.5194/egusphere-egu22-8869, 2022.

EGU22-8918 | Presentations | NP5.2

The Relationship between Desroziers and Three-Cornered Hat Methods 

Ricardo Todling, Noureddine Semane, Rick Anthes, and Sean Healy

This note examines the relationship between what at first sight looks like two unrelated methods for estimating second order statistics of relevance to data assimilation. The first method is due to Desroziers et al. (2005) and relies on residual statistics readily available from data assimilation applications. The second method, due to Gray and Allan (1974), only recently making its appearance in atmospheric sciences, is generally formulated to use three data sets and seems in principle capable of deriving estimates of observation, background and analysis just as well. The usefulness of either method lies in them not requiring knowledge of the true value of the quantities at play. Desroziers derives its results by relying explicitly on the constraints associated with the data assimilation minimization problem; the 3CH method is general and its estimates hold as long as random errors in the three data sets of choice are independent. Establishing the relationship between the methods amounts to identifying the data sets of 3CH with be the observation, background, and analysis associated with Desroziers. The choice of observation and background for two of the data sets of 3CH is acceptable under the typical assumption of independence in their errors. Specifying the third data set of 3CH as the analysis seems unreasonable for analysis errors are by construction dependent on errors in both observations and background. This note finds that when the assumption of optimality required of Desroziers is applied to 3CH the latter method recovers the Desroziers error estimates for observation and background. More interestingly, in contrast with Desroziers estimate of errors in the analysis, the remaining corner of 3CH obtains the negative of the analysis error variance. An illustration of this finding is provided by deriving various uncertainties in bending angle.

 

How to cite: Todling, R., Semane, N., Anthes, R., and Healy, S.: The Relationship between Desroziers and Three-Cornered Hat Methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8918, https://doi.org/10.5194/egusphere-egu22-8918, 2022.

Assessing pesticide transfers and fate in agricultural catchments is a major challenge to protect water ressources and aquatic organisms. To do so, physically-based, spatialized hydrological models are useful tools as they can be used to set up relevant mitigation strategies. The PESHMELBA model (Rouzies et al. 2019) is one such model that focuses on accurately simulating water and pesticide transfers both in the surface and the subsurface compartments of the soil. This model also aims at explicitely integrating and assessing the impact of landscape structures such as hedges, vegetative filter strips or ditches on transfers. To do so, the PESHMELBA model is characterized by a highly modular structure that relies on various code units standing for different physical processes in the different soil compartments. Such code units are thus coupled in a dedicated framework to reach a complete representation of the catchment with interacting processes. The resulting structure is quite complex and leads to significant difficulties to quantify and reduce the uncertainties associated to the simulation outputs.

In this study, we aim at setting a relevant data assimilation framework to reduce the uncertainty into the PESHMELBA coupled surface / subsurface water flow and reactive solute transport model. To do so, we test several data assimilation methods on hydrological and pesticide variables describing the catchment behavior. At first, these methods are implemented by combining the PESHMELBA model and surface moisture satellite images, at the small catchment scale. Different filtering and smoothing stochastic assimilation methods are explored: the Ensemble Kalman Filter, the Ensemble Smoother with Multiple Data Assimilation and the iterative Ensemble Kalman Smoother. Their abilities to retrieve moisture and pesticide concentration in the observed surface compartment but also in the deeper soil, that is not observed, are assessed. Furthermore, the conducted experiments also aim at retrieving some input parameters that characterize such different soil compartments.

Preliminary results on this part show that all tested methods only succeed in retrieving surface moisture. The Ensemble Smoother is shown to particulary outperform the other methods as it fully integrates the system dynamics. However, its performances are much more limited to retrieve moisture and input parameters in the deeper compartment due to poor correlations between the surface and the subsurface compartments. To overcome such limitation, other sources of data are gradually integrated in the DA framework. The process is proven successfull and we explore how the corrections from the DA process can propagate to other compartments such as the river streamflow and pesticide related variables .

How to cite: Rouzies, E., Lauvernet, C., and Vidard, A.: Which data assimilation method and data source for a multi-compartment hydrology/water quality model? Application on the PESHMELBA model in a small agricultural catchment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10384, https://doi.org/10.5194/egusphere-egu22-10384, 2022.

EGU22-10717 | Presentations | NP5.2

Improved soil moisture-atmospheric boundary layer interactions by assimilation of Cosmic-Ray Neutron counts 

Amol Patil, Benjamin Fersch, Harrie-Jan Hendricks Franssen, and Harald Kunstmann

The Cosmic-Ray Neutron Sensing (CRNS) technology determines soil moisture for a few tens of hectares in a non-invasive way. These measurements, however, can be used to extend soil moisture characterization at regional scales using data assimilation. In the present study, we deployed the Ensemble Adjustment Kalman Filter (EAKF) to assimilate the CRNS neutron counts in order to update the spatial soil moisture, soil infiltration, and evapotranspiration parameters of the Noah-MP land surface model witch is also part of the WRF-Hydro modelling system. The study was conducted in the southern part of Germany, which includes the Rott and Ammer catchments within the TERENO Pre-Alpine observatory. The assimilation was carried out for both, a Noah-MP standalone scenario with observed rainfall as input and a coupled WRF-Hydro scenario with simulated rainfall to fully evaluate the added value of the assimilation. The assimilation performance was analysed at local and regional scale using independent soil moisture observations across the modelling domain. During the assimilation period, the Noah-MP standalone findings demonstrate a significant improvement in field scale soil moisture characterisation. The RMSE of simulated soil moisture was decreased by up to 66 % at field scale and up to 23 % at catchment scale. Additionally, the spatial patterns in the field scale soil moisture have showed improvement with reduction in spatial Bias by 0.025 cm3/cm3. The initial results from coupled WRF-Hydro scenario demonstrate that the soil moisture and parameter estimation experiment had a significant impact on estimated soil moisture and, humidity and evapotranspiration at regional scale. These findings support the use of the CRNS technique to improve the land surface and coupled hydro-atmospheric modelling.

How to cite: Patil, A., Fersch, B., Hendricks Franssen, H.-J., and Kunstmann, H.: Improved soil moisture-atmospheric boundary layer interactions by assimilation of Cosmic-Ray Neutron counts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10717, https://doi.org/10.5194/egusphere-egu22-10717, 2022.

EGU22-12279 | Presentations | NP5.2 | Highlight

Estimating states and parameters in earthquake sequence models in the presence of a parameter bias 

Arundhuti Banerjee, Ylona van Dinther, and Femke Vossepoel

Forecasting earthquake occurrence is a challenging endeavor, which will ultimately require a combination of observations and physics-based models. Data assimilation may help to combine these and their uncertainties in a statistically solid manner. To understand the potential of ensemble data assimilation, we investigate whether the fault stress state can be estimated and forecasted in the presence of a bias in a friction parameter. In a perfect model test, we introduce different degrees of bias in rate-and-state parameter b. b describes the evolution of frictional strength with fault slip velocity and thus impacts earthquake slip and the subsequent recurrence interval. Our forward model is a simplified, zero-dimensional (0D) Burridge-Knopoff spring-block system with a rate- and state-dependent friction formulation using a ‘slip law’. We assimilate synthetic observations of fault shear stress and slip rate variables and corresponding large uncertainties. We compare state estimation with joint state-parameter estimation using a sequential importance resampling particle filter by evaluating the quality of the estimated fault stress probability density functions (pdf’s).

The results of the study indicate that state estimation works well for systems with low (3%) to intermediate (15%) bias. This performance for the case of intermediate bias can be improved through increasing model error combined with double resampling in the particle filter. For a large friction-parameter bias (42 %), we show that state-parameter estimation is the only way to correct the bias. This is an important result, because it shows that state-parameter estimation is able to identify trade-offs and separate error contributions coming from stress state and friction parameters.  Furthermore, the results of this study can be applied to other data assimilation applications involving models that are particularly vulnerable to parameter biases.

How to cite: Banerjee, A., van Dinther, Y., and Vossepoel, F.: Estimating states and parameters in earthquake sequence models in the presence of a parameter bias, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12279, https://doi.org/10.5194/egusphere-egu22-12279, 2022.

EGU22-12361 | Presentations | NP5.2

Prediction of Subsurface Fluid Flow via Physics Informed Neural Networks 

Linus Walter, Francesco Parisio, and Víctor Vilarrasa

Geoenergies such as underground gas storage and energy storage, geothermal energy and geologic carbon storage are key technologies on the way to the foreseeable energy transition. The reservoir characterization in these projects remains challenging since predictive modeling approaches face limitations in identifying the spatial distribution of distinct lithologies and their hydro-mechanical properties from downwell testing procedures. Pumping tests are usually carried out to infer permeability, but offer only few observation points in space and require large extrapolation through inversion. The application of Physics Informed Neural Networks (PINN) offers a promising solution which can seamlessly incorporate field data, while enforcing the accordance with physical laws in the domain of study. This concept is implemented via two distinct loss terms for both the physical constraints and for the observational data in the loss function of an Artificial Neural Network (ANN). The physics-informed loss term contains a mass balance equation consisting of a storage and a diffusion component. The process is considered to be purely hydraulic and Darcy flow is assumed. The observational loss term compares the output of the ANN to a set of training data. This set consists of  the system’s initial fluid pressure, as well as of a fluid pressure time series at the domain boundaries and at the borehole location. Preliminary results suggest that our PINN model is able to forecast the spatiotemporal fluid pressure distribution in a 2D domain for a variety of pumping test schemes. In this way, we give a first impression of the opportunities that PINN applications offer in the field of reservoir modeling.

How to cite: Walter, L., Parisio, F., and Vilarrasa, V.: Prediction of Subsurface Fluid Flow via Physics Informed Neural Networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12361, https://doi.org/10.5194/egusphere-egu22-12361, 2022.

EGU22-473 | Presentations | CL5.3.2

Improving the parameterization of vegetation cover variability in land surface models based on satellite observations 

Fransje van Oorschot, Ruud van der Ent, Markus Hrachowitz, Franco Catalano, Souhail Boussetta, and Andrea Alessandri

Vegetation is highly dynamic at seasonal, inter-annual, decadal and longer timescales. These dynamics are strongly coupled with hydrological, biogeochemical and bio-physical processes. In global land surface models,  this coupling is controlled by  parameterizations of the effective sub-grid vegetation cover that controls amongst others modelled evapotranspiration, albedo and surface roughness. In this study we aim to explore the use of observational satellite datasets of LAI and Fraction of green vegetation Cover (FCover) for an improved model parameterization of effective vegetation cover.
The effective vegetation cover can be described by exponential functions resembling the Lambert Beer law of extinction of light under a vegetated canopy  (1-e-k*LAI), with k the canopy light extinction coefficient. In HTESSEL (i.e. the land surface model in EC-EARTH) k has been set to a constant value of 0.5 so far. However, k varies for different vegetation types as it represents the structure and the clumping of a vegetation canopy. For example tree canopies are more clumped than grasses, resulting in a larger effective coverage. In this study we optimize the canopy extinction coefficient k using the LAI and FCover satellite products for different vegetation types (ESA-CCI land cover), with FCover equivalent to the model effective vegetation cover.  
This effort results in a vegetation dependent relation between LAI and effective vegetation cover that is implemented in HTESSEL. The improved effective vegetation cover parameterization is evaluated using offline model simulations. To evaluate the sensitivity to the new parameterization, modelled evaporation, discharge and skin temperature are compared with station and satellite observations.

How to cite: van Oorschot, F., van der Ent, R., Hrachowitz, M., Catalano, F., Boussetta, S., and Alessandri, A.: Improving the parameterization of vegetation cover variability in land surface models based on satellite observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-473, https://doi.org/10.5194/egusphere-egu22-473, 2022.

EGU22-846 | Presentations | CL5.3.2

Investigating 25 years of coupled climate modeling 

Lukas Brunner, Ruth Lorenz, Erich M. Fischer, and Reto Knutti

The Coupled Model Intercomparison Project (CMIP) is an effort to compare model simulations of the climate system and its changes. In the quarter of a century since CMIP1 models have increased considerably in complexity and improved in how well they are able to represent historical climate compared to observations. Other aspects, such as the projected changes we have to expect in a warming climate, have remained remarkably stable. Here we track the evolution of climate models based on their output and discuss it in the context of 25 years of model development. 

We draw on temperature and precipitation data from CMIP1 to CMIP6 and calculate consistent metrics of model performance, inter-dependence, and consistency across multiple generations of CMIP. We find clear progress in model performance that can be related to increased resolution among other things. Our results also show that the models’ development history can be tracked using their output fields with models sharing parts of their source code or common ancestors grouped together in a clustering approach.

The global distribution of projected temperature and precipitation change and its robustness across different models is also investigated. Despite the considerable increase in model complexity across the CMIP generations driven, for example, by the inclusion of additional model components and the increase in model resolutions by several orders of magnitude, the overall structure of simulated changes remains stable, illustrating the remarkable skill of early coupled models.

How to cite: Brunner, L., Lorenz, R., Fischer, E. M., and Knutti, R.: Investigating 25 years of coupled climate modeling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-846, https://doi.org/10.5194/egusphere-egu22-846, 2022.

EGU22-1448 | Presentations | CL5.3.2

An analogue approach to predicting European climate 

Leonard Borchert, Matthew Menary, and Juliette Mignot

Decadal climate prediction is a scientific endeavour of potentially large societal impacts. Yet such predictions remain challenging, as they predict climate skilfully only under certain circumstances or in specific regions. Moreover, decadal climate prediction simulations rely on dedicated coupled climate model simulations that are particularly expensive. In this study, we build upon earlier research by Menary et al. (2021) in search of a method to make skilful and cheap decadal climate predictions by constructing predictions from existing climate model simulations using the so-called analogue method.

The analogue method draws on the idea that there is decadal memory in the climatic state at the start of a prediction. This method identifies the observed state of the climate system at the start of a prediction and then screens the archive of available model simulations for comparable climatic states. It then selects a number of modelled climate states that are similar to the observed situation, and uses the years after the selected simulated climate states as prediction. Using a simple analogue method based on temperature trends in the North Atlantic basin, Menary et al. (2021) demonstrated skilful prediction of North Atlantic SST on par with dynamical decadal prediction simulations. In this study, we refine the original method by using more sophisticated algorithms to select the analogues, and choosing decadal prediction of seasonal European climate as our target. These new selection algorithms include multivariate regression at different time lags as well as non-linear methods.

 

Menary, MB, J Mignot, J Robson (2021) Skilful decadal predictions of subpolar North Atlantic SSTs using CMIP model-analogues. Environ. Res. Lett. 16 064090. https://doi.org/10.1088/1748-9326/ac06fb

How to cite: Borchert, L., Menary, M., and Mignot, J.: An analogue approach to predicting European climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1448, https://doi.org/10.5194/egusphere-egu22-1448, 2022.

EGU22-1817 | Presentations | CL5.3.2

Identifying efficient ensemble perturbations for initializing subseasonal-to-seasonal prediction 

Jonathan Demaeyer, Stephen Penny, and Stéphane Vannitsem

The prediction of weather at subseasonal-to-seasonal (S2S) timescales is affected by both initial and boundary conditions, and as such is a complicated problem that the geophysical community is attempting to address in greater detail. One important question about this problem is how to initialize ensembles of numerical forecast models to produce reliable forecasts1, i.e. initialize each member of an ensemble forecast such that their statistical properties are consistent with the actual uncertainties of the future state of the physical system.

Here, we introduce a method to construct the initial conditions to generate reliable ensemble forecasts. This method is based on projections of the ensemble initial conditions onto the modes of the model's dynamic mode decomposition (DMD), which are related to the procedure used for forming Linear Inverse Models (LIMs). In the framework of a low-order ocean-atmosphere model exhibiting multiple different characteristic timescales, we compare the DMD-oriented method to other ensemble initialization methods based on Empirical Orthogonal Functions (EOFs) and the Lyapunov vectors of the model2, and we investigate the relations between these.

References:

1. Leutbecher, M., & Palmer, T.N. (2008). Ensemble forecasting. Journal of Computational Physics, 227, 3515–3539.

2. Vannitsem, S., & Duan, W. (2020). On the use of near-neutral Backward Lyapunov Vectors to get reliable ensemble forecasts in coupled ocean–atmosphere systems. Climate Dynamics, 55, 1125-1139.

How to cite: Demaeyer, J., Penny, S., and Vannitsem, S.: Identifying efficient ensemble perturbations for initializing subseasonal-to-seasonal prediction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1817, https://doi.org/10.5194/egusphere-egu22-1817, 2022.

The challenges of climate prediction are varied and complex. On the one hand they include conceptual and mathematical questions relating to the consequences of model error and the information content of observations and models. On the other, they involve practical issues of model and ensemble design, and the statistical processing of data.

A route to understanding the complexity of these challenges is to study them using low-dimensional nonlinear systems that encapsulate the key characteristics of climate and climate change. Doing so facilitates the fast generation of very large ensembles with a variety of designs and target goals. These idealised ensembles can provide a solid foundation for improving the design of ESM/GCM ensembles, making them better suited to evaluating the risks associated with climate change and to providing end-user support through climate services.

The ODESSS project - Optimizing the Design of Ensembles to Support Science and Society - is using low-dimensional nonlinear systems to provide solid foundations for the design of climate change ensembles with climate models. In this presentation I will introduce the project and the concepts behind it.

First I will discuss the essential characteristics required of a low dimensional nonlinear system to be able to capture the process of climate prediction. Results will then be presented from the coupled Lorentz ’84 - Stommel ’61 system; a low-dimensional nonlinear system which has these characteristics. These results will be used to illustrate the dangers of confounding natural variability with the consequences of initial condition uncertainty[1], and to demonstrate why risk assessments require much larger initial condition ensembles than are currently available with today’s ESMs/GCMs.

The difference between micro and macro initial condition ensembles [2,3] will then be introduced, along with an explanation of how this leads to a requirement for ensembles of ensembles: the former exploring macro-initial-condition-uncertainty, the latter micro-initial-conditional-uncertainty. The importance of this distinction will be illustrated with both new results from the Lorentz ‘84 - Stommel ‘61 system, and also a GCM[3]. I will highlight the challenges in designing these ensembles of ensembles to be most informative. These challenges relate closely to the problems of initialization and the optimal use of observations.

Finally the subject of model error, multi-model and perturbed-physics ensembles will be discussed. The impact of model error on climate predictions can only be studied effectively if climate change can be accurately quantified within each model. To begin to explore the consequences of model error for climate predictions therefore requires ensembles of ensembles of ensembles: perturbed-physics or multi-model ensembles which  themselves consist of both macro and micro initial condition ensembles. Some approaches will be presented for how low-dimensional systems can be used to optimise the design of such multi-layered ensembles with ESMs/GCMs where computational constraints are more restrictive.

[1] Daron and Stainforth, On predicting climate under climate change. ERL, 2013.

[2] Stainforth et al., Confidence, uncertainty and decision-support relevance in climate predictions. Phil. Trans Roy. Soc., 2007.

[3] Hawkins et al., Irreducible uncertainty in near-term climate projections. Climatic Change, 2015.

How to cite: Stainforth, D.: Ensembles of ensembles of ensembles: On using low-dimensional nonlinear systems to design climate prediction experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3885, https://doi.org/10.5194/egusphere-egu22-3885, 2022.

EGU22-5377 | Presentations | CL5.3.2

What can the last century teach us about climate models? 

André Düsterhus, Leonard Borchert, Björn Mayer, Vimal Koul, Holger Pohlmann, Sebastian Brune, and Johanna Baehr

Climate models are an important tool in our understanding of the climate system. Among other things, we use them together with initialisation procedures to predict the climate from a few weeks to more than a decade. While the community has demonstrated prediction skill for various climate modes on these time scales in the past years, we have also encountered problems. One is the non-stationarity of prediction skill over the past century in seasonal and decadal predictions. It was shown in multiple prediction systems and for multiple variables that prediction skill varies over time. Potential reasons for this non-stationarity was found in the changing state of the North Atlantic system on multi-decadal scales and the limited representation of physical processes within the model. While on the one side this feature of climate predictions leaves uncertainties for future predictions it also highlights windows of opportunity and challenges within climate models. 

We investigate the past century for this non-stationarity with a special focus on the North Atlantic Oscillation, and how the North Atlantic sector changes during these low prediction skill periods. We will demonstrate the limited predictability of features of the North Atlantic Oscillation, like the movement of its activity centres, as well as its implication for the Signal-to-Noise paradox. We also discuss the implications of non-stationarity model prediction skill for the development on future prediction systems and which processes are most likely the reason for the current challenges the community faces.

How to cite: Düsterhus, A., Borchert, L., Mayer, B., Koul, V., Pohlmann, H., Brune, S., and Baehr, J.: What can the last century teach us about climate models?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5377, https://doi.org/10.5194/egusphere-egu22-5377, 2022.

EGU22-6756 | Presentations | CL5.3.2

Seasonal-to-decadal variability and predictability of the Kuroshio Extension in the GFDL Coupled Ensemble Reanalysis and Forecasting system 

Youngji Joh, Thomas Delworth, Andrew Wittenberg, William Cooke, Xiasong Yang, Fanrong Zeng, Liwei Jia, Feiyu Lu, Nathaniel Johnson, Sarah Kapnick, Anthony Rosati, Liping Zhang, and Colleen McHugh

The Kuroshio Extension (KE), an eastward-flowing jet located in the Pacific western boundary current system, exhibits prominent seasonal-to-decadal variability, which is crucial for understanding climate variations in northern midlatitudes. We explore the representation, predictability, and prediction skill for the KE in the GFDL SPEAR (Seamless System for Prediction and EArth System Research) coupled model. Two different approaches are used to generate coupled reanalyses and forecasts: (1) restoring the coupled model’s SST and atmospheric variables toward existing reanalyses, or (2) assimilating SST and subsurface observations into the coupled model without atmospheric assimilation.  Both systems use an ocean model with 1o resolution and capture the largest sea surface height (SSH) variability over the KE region. Assimilating subsurface observations appears to be critical to reproduce the narrow front and related oceanic variability of the KE jet in the coupled reanalysis. We demonstrate skillful retrospective predictions of KE SSH variability in monthly (up to 1 year) and annual-mean (up to 5 years) KE forecasts in the seasonal and decadal prediction systems, respectively. The prediction skill varies seasonally, peaking for forecasts initialized in January and verifying in September due to the winter intensification of North Pacific atmospheric forcing. We show that strong large-scale atmospheric anomalies generate deterministic oceanic forcing (i.e., Rossby waves), leading to skillful long-lead KE forecasts. These atmospheric anomalies also drive Ekman convergence/divergence that forms ocean memory, by sequestering thermal anomalies deep into the winter mixed layer that re-emerge in the subsequent autumn. The SPEAR forecasts capture the recent negative-to-positive transition of the KE phase in 2017, projecting a continued positive phase through 2022.

How to cite: Joh, Y., Delworth, T., Wittenberg, A., Cooke, W., Yang, X., Zeng, F., Jia, L., Lu, F., Johnson, N., Kapnick, S., Rosati, A., Zhang, L., and McHugh, C.: Seasonal-to-decadal variability and predictability of the Kuroshio Extension in the GFDL Coupled Ensemble Reanalysis and Forecasting system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6756, https://doi.org/10.5194/egusphere-egu22-6756, 2022.

EGU22-6767 | Presentations | CL5.3.2 | Highlight

Long-term climate prediction for Ireland and its surrounding 

Stephen Ogungbenro, Catherine O'Beirne, and André Düsterhus

Ireland is bordering the North Atlantic, and its climate is dominated by its climate modes on short to longer timescales. The Atlantic low-pressure systems, Jetstream variabilities and airmasses are features of the atmospheric circulation, which also contribute to the climate this region.  So, a long-term climate prediction of Ireland is majorly controlled by the ocean, and by other atmospheric components.

The Ocean has shown good capabilities for decadal to multi-decadal climate predictions, hence, our study adapted a coupled model to investigate seasonal changes in the climate on annual to multi-annual timescales within the Max Planck Institute for Meteorology Earth System Model (MPI-ESM).  Initialized prediction is extended to multi-decadal timescale up onto twenty lead years, and we study prediction capabilities for common climate variables in and around , by identifying major drivers and documenting their prediction skills.  Our results have shown prediction skill for surface temperature over longer timescales, and we explore these capabilities for other variables of interest.  This study opens new opportunities for better long-term predictions of climate components in the region, and our results are relevant for strategic planning.

How to cite: Ogungbenro, S., O'Beirne, C., and Düsterhus, A.: Long-term climate prediction for Ireland and its surrounding, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6767, https://doi.org/10.5194/egusphere-egu22-6767, 2022.

EGU22-7037 | Presentations | CL5.3.2 | Highlight

Destabilizing the Earth’s thermostat: Riverine alkalinity responses to climate change 

Nele Lehmann, Tobias Stacke, Sebastian Lehmann, Hugues Lantuit, John Gosse, Chantal Mears, Jens Hartmann, and Helmuth Thomas

Alkalinity generation from rock weathering is thought to modulate the Earth’s climate at geological time scales. Here, we use global alkalinity data paired with consistent measurements of erosion rates to develop an empirically-based model for riverine alkalinity concentration, demonstrating the impact of both erosion (i.e. erosion rate) and climate (i.e. temperature) on alkalinity generation, globally. We show that alkalinity generation from carbonate rocks is very responsive to temperature and that the weathering flux to the ocean will be significantly altered by climate warming as early as the end of this century, constituting a sudden feedback of ocean CO2 sequestration to climate. While we anticipate that climate warming under a low emissions scenario will induce a reduction in terrestrial alkalinity flux for mid-latitudes (-1.3 t(bicarbonate) a-1 km-2) until the end of the century, resulting in a temporary reduction in CO2 sequestration, we expect an increase (+1.6 t(bicarbonate) a-1 km-2) under a high emissions scenario, causing an additional short-term CO2 sink at decadal timescales.

How to cite: Lehmann, N., Stacke, T., Lehmann, S., Lantuit, H., Gosse, J., Mears, C., Hartmann, J., and Thomas, H.: Destabilizing the Earth’s thermostat: Riverine alkalinity responses to climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7037, https://doi.org/10.5194/egusphere-egu22-7037, 2022.

EGU22-7652 | Presentations | CL5.3.2

Towards operational climate prediction: ENSO-related variability as simulated in a set of state-of-the-art seasonal prediction systems 

Roberto Suarez-Moreno, Lea Svendsen, Ingo Bethke, Martin P. King, Ping-Gin Chiu, and Tarkan A. Bilge

In the last decade, high demands from stakeholders and policymakers have driven unprecedented research efforts directed to improve climate predictability. Nevertheless, attempts to get operational climate predictions on seasonal time scales have been far from skillful for a long time. Based on sources of predictability from the ocean, atmosphere and land processes, current state-of-the-art prediction systems are approaching operational predictability. This work examines and compares the ability of different prediction systems to simulate the variability of sea surface temperatures (SSTs) associated with El Niño-Southern Oscillation (ENSO) and the ENSO-forced response of hydroclimate variability in the North Atlantic-Europe (NAE) region. Seasonal hindcasts derived from two generations of the Norwegian Earth System Model (NorESM1-ME and NorESM2-MM) are used in addition to C3S data to generate time series of year-to-year variability that are validated against observational data. Our results reveal both the advantages and the limitations of these prediction systems to simulate ENSO-related variability, identifying model biases that prevent skillful predictability. Further efforts must be aimed at mitigating these biases in order to achieve fully operational predictions of paramount importance for the benefit of society.

How to cite: Suarez-Moreno, R., Svendsen, L., Bethke, I., King, M. P., Chiu, P.-G., and Bilge, T. A.: Towards operational climate prediction: ENSO-related variability as simulated in a set of state-of-the-art seasonal prediction systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7652, https://doi.org/10.5194/egusphere-egu22-7652, 2022.

EGU22-8031 | Presentations | CL5.3.2

Multi-model comparison of carbon cycle predictability in initialized perfect-model simulations 

Aaron Spring, Hongmei Li, Tatiana Ilyina, Raffaele Bernardello, Yohan Ruprich-Robert, Etienne Tourigny, Juliette Mignot, Filippa Fransner, Jerry Tjiputra, Reinel Sospedra-Alfonso, Thomas Frölicher, and Michio Watanabe

Predicting carbon fluxes and atmospheric CO2 can constrain the expected next-year atmospheric CO2 growth rate and thereby allow to independently monitor total anthropogenic CO2 emission rates. Several studies have established predictive skill in retrospective forecasts of carbon fluxes. These studies are usually backed by perfect-model simulations of single models showing the origins of predictive skill in carbon fluxes and atmospheric CO2 concentration. Yet, a comprehensive multi-model comparison of perfect-model predictions, which can be valuable in explaining differences in retrospective predictions, is still lacking. Moreover, as of now, we don't have sufficient understanding of how well do the models predict their own integrated carbon cycles and how congruent this predictability is across models.

Here, we show the predictive skill of land and ocean carbon fluxes as well as atmospheric CO2 concentration in seven Earth-System-Models. Our first results indicate predictive skill of globally aggregated carbon fluxes of 2±1 years and atmospheric CO2 of 3±2 years. However, the regional patterns, hotspots and origins of predictive skill diverge among models. This heterogeneity explains the regional differences found in existing retrospective forecasts and backs the overall consistent predictability time-scales at global scale.

How to cite: Spring, A., Li, H., Ilyina, T., Bernardello, R., Ruprich-Robert, Y., Tourigny, E., Mignot, J., Fransner, F., Tjiputra, J., Sospedra-Alfonso, R., Frölicher, T., and Watanabe, M.: Multi-model comparison of carbon cycle predictability in initialized perfect-model simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8031, https://doi.org/10.5194/egusphere-egu22-8031, 2022.

EGU22-8038 | Presentations | CL5.3.2 | Highlight

Global carbon budget variations in emission-driven earth system model predictions 

Hongmei Li, Tatiana Ilyina, Tammas Loughran, and Julia Pongratz

Predictions of the variations in anthropogenic global carbon budget (GCB), i.e., CO2 emissions and their redistribution among the atmosphere, ocean, and land reservoirs, is crucial to constrain the global carbon cycle and climate change of the past and facilitate their prediction and projection into the future. Global carbon project assesses the GCB every year by taking into account available datasets and stand-alone model component simulations. The utilization of different data sources leads to an unclosed budget, i.e., budget imbalance. We propose a novel approach to assess the GCB in decadal prediction systems based on emission-driven earth system models (ESMs). Such a fully coupled prediction system enables a closed carbon budgeting and therefore provides an additional line of evidence for the ongoing assessments of the GCB.

As ESMs have their own mean state and internal variability, we assimilate ocean and atmospheric observational and reanalysis data into Max Planck Institute Earth system model (MPI-ESM) to reconstruct the actual evolution of climate and carbon cycle towards to the real world. In the emission-driven model configuration, the carbon cycle changes in response to the physical state changes, in the meanwhile, the feedback of atmospheric CO2 changes to physics are also considered via interactive carbon cycle. Our reconstructions capture the observed GCB variations in the past decades. They show high correlations relative to the assessments from the global carbon project of 0.75, 0.75 and 0.97 for atmospheric CO2 growth, air-land CO2 fluxes and air-sea CO2 fluxes, respectively. Retrospective predictions starting from the reconstruction show promising predictive skill for the global carbon cycle up to 5 years for the air-sea CO2 fluxes and up to 2 years for the air-land CO2 fluxes and atmospheric carbon growth rate. Furthermore, evolution in atmospheric CO2 concentration in comparing to satellite and in-situ observations show robust skill in reconstruction and next-year prediction.  

How to cite: Li, H., Ilyina, T., Loughran, T., and Pongratz, J.: Global carbon budget variations in emission-driven earth system model predictions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8038, https://doi.org/10.5194/egusphere-egu22-8038, 2022.

EGU22-8624 | Presentations | CL5.3.2 | Highlight

Seasonal prediction of North American wintertime cold extremes in GFDL SPEAR forecast system 

Liwei Jia, Thomas Delworth, Xiaosong Yang, William Cooke, Nathaniel Johnson, and Andrew Wittenberg

Skillful prediction of wintertime cold extremes on seasonal time scales is beneficial for multiple sectors. This study demonstrates that North American cold extremes, measured by the frequency of cold days in winter, are predictable several months in advance in Geophysical Fluid Dynamics Laboratory’s SPEAR seasonal (Seamless system for Prediction and EArth system Research) forecast system. Two predictable components of cold extremes over North American land areas are found to be skillfully predicted on seasonal scales. One is a trend component, which shows a continent-wide decrease in the frequency of cold extremes and is attributable to external radiative forcing. This trend component is predictable at least 9 months ahead. The other predictable component displays a dipole structure over North America, with negative signs in the northwest and positive signs in the southeast. This dipole component is predictable with significant correlation skill for 2 months and is a response to the central Pacific El Nino as revealed from SPEAR AMIP-like simulations. 

How to cite: Jia, L., Delworth, T., Yang, X., Cooke, W., Johnson, N., and Wittenberg, A.: Seasonal prediction of North American wintertime cold extremes in GFDL SPEAR forecast system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8624, https://doi.org/10.5194/egusphere-egu22-8624, 2022.

EGU22-9618 | Presentations | CL5.3.2

Processes of interannual internal variability of the CO2 flux at the air-sea interface in IPSLCM6A 

Matthew Menary, Juliette Mignot, Laurent Bopp, and Lester Kwiatkowski

In order to improve our ability to predict the near-term evolution of climate, it may be important to accurately predict the evolution of atmospheric CO2, and thus carbon sinks. Following on from process-driven improvements of decadal predictions in physical oceanography, we focus on improving our understanding of the internal processes and variables driving CO2 uptake by the North Atlantic ocean. Specifically, we use the CMIP6 model IPSLCM6A to investigate the drivers of ocean-atmosphere CO2 flux variability in the North Atlantic subpolar gyre (NA SPG) on seasonal to decadal timescales. We find that DpCO2 (CO2 partial pressure difference between atmosphere and ocean) variability dominates over sea surface temperature (SST) and sea surface salinity (SSS) variability on all timescales within the NA SPG. Meanwhile, at the ice-edge, there are significant roles for both ice concentration and surface winds in driving the overall CO2 flux changes. Investigating the interannual DpCO2 variability further, we find that this variability is itself driven largely by variability in simulated mixed layer depths in the northern SPG. On the other hand, SSTs show an important contribution to DpCO2 variability in the southern SPG and on longer (decadal) timescales. Initial extensions into a multi-model context show similar results. By determining the key regions and processes important for skilful decadal predictions of ocean-atmosphere CO2 fluxes, we aim to both improve confidence in these predictions as well as highlight key targets for climate model improvement. 

How to cite: Menary, M., Mignot, J., Bopp, L., and Kwiatkowski, L.: Processes of interannual internal variability of the CO2 flux at the air-sea interface in IPSLCM6A, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9618, https://doi.org/10.5194/egusphere-egu22-9618, 2022.

EGU22-9719 | Presentations | CL5.3.2

Seasonal Forecasting of Horn of Africa’s Long Rains Using Physics-Guided Machine Learning 

Victoria Deman, Akash Koppa, and Diego Miralles

The Horn of Africa is known to be prone to climate impacts; the frequent occurrence of droughts and floods creates vulnerable conditions in the region. Gaining knowledge on (sub-)seasonal weather prediction and generating more reliable long-term forecasts is an important asset in building resilience. Most of the region is characterized by a bimodal precipitation cycle with rainfall seasons in boreal spring (March–May), termed the long rains, and boreal autumn (October–November), termed the short rains. Previous studies on seasonal forecasting focused mostly on empirical linear regression methods using information from ocean–atmosphere modes. To date, the potential of more complex methods, such as machine learning approaches, in improving seasonal precipitation predictability in the Horn of Africa still remains understudied. 

 

In this study, machine learning models targeting precipitation during the long rains are developed. The focus on the long rains is motivated by the fact that it is the main rain season in the region and the sources of predictability have proven to be more difficult to pin down. The long rain season has a weak internal coherence and looking at the months separately has proven to enhance prediction skill. Therefore, machine learning models are constructed for the different months (March, April, and May) separately at lead times of 1–3 months. Following an extensive survey of literature, the predictors of the long rain precipitation at seasonal timescales selected in this study include coupled oceanic-atmospheric oscillation indices (such as MJO, ENSO and PDO), regions of zonal winds over 200mb and 850mb and sea-surface temperature (SST) regions with strong correlation to long rain precipitation. Further, a selection of additional terrestrial and oceanic predictors is guided by Lagrangian transport modeling, used to identify the regions sourcing moisture during the long rains. This set of predictors include soil moisture, land surface temperature, normalized vegetation index (NDVI), leaf area index (LAI) and SST, which are averaged over the climatological source region of long rain precipitation. Finally, we provide new insights into the predictability of long rain precipitation at seasonal timescales by analyzing the relative importance of the different predictors used for developing the machine learning model.

How to cite: Deman, V., Koppa, A., and Miralles, D.: Seasonal Forecasting of Horn of Africa’s Long Rains Using Physics-Guided Machine Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9719, https://doi.org/10.5194/egusphere-egu22-9719, 2022.

EGU22-9921 | Presentations | CL5.3.2

Understanding intermodel differences in land carbon sink projections 

Ryan S. Padrón, Lukas Gudmundsson, Vincent Humphrey, Laibao Liu, and Sonia I. Seneviratne

Over the last decades, land ecosystems have removed from the atmosphere approximately one third of anthropogenic carbon emissions, highlighting the importance of the evolution of the land carbon sink for projected climate change. Nevertheless, the latest land carbon sink projections from multiple Earth system models show large differences, even for a policy-relevant scenario with mean global warming by the end of the century below 2°C relative to preindustrial conditions. We hypothesize that this intermodel uncertainty originates from model differences in the sensitivities of annual net biome production (NBP) to (i) the CO2 fertilization effect, and to the annual anomalies in growing season (ii) air temperature and (iii) soil moisture, as well as model differences in long-term average (iv) air temperature and (v) soil moisture. Using multiple linear regression and a resampling technique we quantify the individual contributions of these five terms for explaining the cumulative NBP anomaly of each model relative to the ensemble mean. Differences in the three sensitivity terms contribute the most, however, differences in average temperature and soil moisture also have sizeable contributions for some models. We find that the sensitivities of NBP to temperature and soil moisture anomalies, particularly in the tropics, explain approximately half of the deficit relative to the ensemble mean for the two models with the lowest carbon sink (ACCESS-ESM1-5 and UKESM1-0-LL) and half of the surplus for the two models with the highest sink (CESM2 and NorESM2-LM). In addition, year-to-year variations in NBP are more related to variations in soil moisture than air temperature across most models and regions, although several models indicate a stronger relation totemperature variations in the core of the Amazon. Overall, our study advances our understanding of why land carbon sink projections from Earth system models differ globally and across regions, which can guide efforts to reduce the underlying uncertainties.

How to cite: Padrón, R. S., Gudmundsson, L., Humphrey, V., Liu, L., and Seneviratne, S. I.: Understanding intermodel differences in land carbon sink projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9921, https://doi.org/10.5194/egusphere-egu22-9921, 2022.

EGU22-10228 | Presentations | CL5.3.2

Near-term prediction of the global carbon cycle using EC-Earth3-CC, the Carbon Cycle version of the EC-Earth3 Earth System Model 

Etienne Tourigny, Raffaele Bernardello, Valentina Sicardi, Pablo Ortega, Yohan Ruprich Robert, Vladimir Lapin, Juan C. Acosta Navarro, Roberto Bilbao, Arndt Meier, Hongmei Li, and Tatiana Ilyina

Anthropogenic CO2 emissions are associated with global warming in the late 20th century and beyond. Climate-carbon feedbacks will likely result in a higher airborne fraction of emitted CO2 in the future. However, the variability in atmospheric CO2 growth rate is largely controlled by natural variability and is poorly understood. This can interfere with the attribution  of slowing CO2 growth rates  to reducing emissions during the implementation of the Paris Agreement. There is thus a need to both improve our understanding of the processes controlling the global carbon cycle and establish a near-term prediction system of the climate and carbon cycle.

As part of the 4C (Carbon Cycle Interactions in the Current Century) project, the Barcelona Supercomputing Center is implementing a new system for near-term prediction of the climate and carbon cycle interactions using EC-Earth3-CC, the Carbon Cycle version of the EC-Earth3 Earth System Model. This new system is based on the existing operational climate prediction system developed by the BSC, contributing to the WMO Global Annual to Decadal Climate Update. EC-Earth3-CC comprises the IFS atmospheric model, the NEMO ocean model, the PISCES ocean biogeochemistry model, the LPJ-GUESS dynamic vegetation model, the TM5 global atmospheric transport model and the OASIS3 coupler. The system uses initial conditions from in-house ocean biogeochemical and land/vegetation reconstructions based on global atmospheric/ocean reanalyses. By performing retrospective decadal predictions of ocean and land carbon uptake we are able to evaluate the performance of the system in predicting CO2 fluxes and atmospheric CO2 concentrations.

We will present results from the latest concentration- and emission-driven retrospective predictions (or hindcasts) using our system, highlighting the skill and biases of the carbon fluxes and atmospheric CO2. We will also present future predictions for 2022 and beyond, a prototype for the operational system for prediction of future atmospheric CO2.

How to cite: Tourigny, E., Bernardello, R., Sicardi, V., Ortega, P., Ruprich Robert, Y., Lapin, V., Acosta Navarro, J. C., Bilbao, R., Meier, A., Li, H., and Ilyina, T.: Near-term prediction of the global carbon cycle using EC-Earth3-CC, the Carbon Cycle version of the EC-Earth3 Earth System Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10228, https://doi.org/10.5194/egusphere-egu22-10228, 2022.

EGU22-10245 | Presentations | CL5.3.2

Drivers of the natural CO2 fluxes at global scale as simulated by CMIP6 simulations 

Veronica Martin-Gomez, Yohan Ruprich-Robert, Raffaele Bernardello, and Margarida Samso Cabre

The implementation of the Paris Agreement should translate into a decrease of the growth rate of atmospheric CO2 in the coming decades due to the reduction in emissions by signing countries. However, the detection of this decrease and its attribution to mitigation measures will be challenging for two reasons: 1) the internal variability of the Earth system may temporarily offset this signal and 2) countries may not maintain their promises. Unless absolute transparency on emissions is adopted by all signing parties, without a robust estimate of the impact of internal variability on the atmospheric CO2 changes, there is no independent way to verify their claims. 

Historical reconstructions and future predictions of global carbon cycle dynamics with predictive systems based on state-of-the-art Earth System Models (ESMs) represent an emerging field of research. With the continuous improvement of ESMs and of these predictive systems, these tools might have the potential of becoming skillful enough in their predictions to represent a useful instrument for policy makers in their effort to monitor and verify the progress of the Paris Agreement’s implementation. 

Here we analyze the main sources of the atmospheric CO2 concentration variability at inter-annual timescale due to internal climate processes in three ESMs, which are used in carbon cycle prediction systems: EC-Earth3-CC, IPSL-CM6A-LR, and MPI-ESM1-2-LR. These results are then compared to the available CMIP6 simulations database.

Investigating the surface CO2 fluxes, we find that land flux inter-annual variations are 10 times higher than ocean flux variations. This has direct consequences in terms of predictability since the land surface processes are generally less predictable than the ocean ones. The regions contributing the most to the variations are Australia, South America and sub-Saharan Africa, suggesting that those are the most important regions to simulate correctly in order to constrain the atmospheric CO2 variations. Interestingly, all those regions are linked to tropical SST variations resembling El Niño Southern Oscillation variability.

Investigating the ocean CO2 fluxes, we find that the regions contributing the most to the global CO2 variations are the Southern Ocean followed by the tropical Pacific.

Therefore, from the analysis of the CMIP6 simulations, we conclude that the main internal driver of the global atmospheric CO2 fluctuations is the tropical Pacific. If the ratio between land and ocean CO2 variations is realistically simulated by the CMIP6 ESMs, this implies that the predictability of the atmospheric CO2 variations due to internal climate processes is tied to the predictability of the tropical Pacific.

How to cite: Martin-Gomez, V., Ruprich-Robert, Y., Bernardello, R., and Samso Cabre, M.: Drivers of the natural CO2 fluxes at global scale as simulated by CMIP6 simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10245, https://doi.org/10.5194/egusphere-egu22-10245, 2022.

EGU22-10340 | Presentations | CL5.3.2 | Highlight

On the seasonal prediction and predictability of winter temperature swings over North America 

Xiaosong Yang, Tom delworth, Liwei Jia, Nathaniel Johnson, Feiyu Lu, and Colleen MacHugh

A novel temperature swing index (TSI) is formed to measure the extreme surface temperature variations associated with the winter extratropical storms. The seasonal prediction skill of the winter TSI over North America was assessed versus ERA5 data using GFDL’s new SPEAR seasonal prediction system. The location with the skillful TSI prediction shows distinctive geographic pattern from that with skillful seasonal mean temperature prediction, thus the skillful prediction of TSI provides additive predictable climate information beyond the traditional seasonal mean temperature prediction. The source of the seasonal TSI prediction can be attributed to year-to-year variations of ENSO, North Pacific Oscillation and NAO. These results point towards providing skillful prediction of higher-order statistical information related to winter temperature extremes, thus enriching the seasonal forecast products for the research community and decision makers beyond the seasonal mean.

How to cite: Yang, X., delworth, T., Jia, L., Johnson, N., Lu, F., and MacHugh, C.: On the seasonal prediction and predictability of winter temperature swings over North America, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10340, https://doi.org/10.5194/egusphere-egu22-10340, 2022.

In the Northwest Atlantic (NWA), including the Labrador Sea, interactions between the atmosphere, ocean circulation, and sea ice play a critical role in regulating the global climate system. The ocean and climate in this region observe rapid and unprecedented, anthropogenically forced changes to the physical environment and biosphere with downstream effects. Future projections of NWA circulation and sea ice can help address pressing questions about these changes and mitigate their potential impacts on the global carbon cycle, coastal communities, and transportation. However, the spatial resolution of current climate models is often insufficient to accurately represent important features in the NWA, such as the location and strength of the Gulf Stream and Labrador Current and their dynamical interactions. This can lead to biases in the model’s mean state, and a misrepresentation of the temporal and spatial scales of ocean variability, e.g., mesoscale eddies, deep convection. Regional ocean models with grid spacing <10 km, forced by global climate simulations, can be used to improve estimates of historical and future circulation and hydrography. However, given the limited spatial resolution and biases in global climate models, a challenge of downscaling their simulations is the appropriate reconstruction of the forcing fields.

Here, we present preliminary results of future projections of NWA circulation and sea ice based on downscaled global climate simulations. These projections are performed using an eddy-resolving, coupled circulation-sea ice model based on the Regional Ocean Modeling System (ROMS) and the Los Alamos Sea Ice Model (CICE). We will focus on the value of correcting biases in the mean and variance of the forcing. We further explore the need of including missing spatial and temporal scales in the atmospheric forcing that are not captured by the global models. Implications for the design of model experiments for future projections will be discussed.

How to cite: Renkl, C. and Oliver, E.: Bias Correction and Spatiotemporal Scales for Downscaling Future Projections of Northwest Atlantic Circulation and Sea Ice, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10467, https://doi.org/10.5194/egusphere-egu22-10467, 2022.

EGU22-10473 | Presentations | CL5.3.2

Proposal for an international effort aimed at quantifying the impact of a realistic representation of vegetation/land cover on seasonal climate forecasts (GLACE-VEG) 

Andrea Alessandri, Gianpaolo Balsamo, Souhail Boussetta, and Constantin Ardilouze

Several works have been showing the importance of vegetation/land cover in forcing interannual climate anomalies and in modulating the influence from soil moisture and/or snow. The aim of this initiative is to exploit the latest available observational data over land to improve the representation of vegetation and land cover that can positively contribute to skillful short-term (seasonal) climate predictions. However, the lack of observations in the past has often determined diverging representations of the processes related to land cover and vegetation among different land surface models. It is therefore fundamental to use the multi-model approach.

A coordinated multi-model prediction experiment will be designed to demonstrate the improvements of the predictions at seasonal time scale due to the enhanced representation of land cover and vegetation. Building from already established efforts (e.g. SNOWGLACE, LS3MIP, ESM-snowMIP, LS4P, CONFESS) we will involve the climate prediction community to develop a common experimental protocol for a multi-model coordinated experiment for the robust evaluation of the performance effects on state-of-the-art dynamical prediction systems. In addition, the verification of the coordinated multi-model predictions will provide understanding and guidance about the better approaches to pursue in the future to model land-vegetation processes.

The initial group of cooperative institutions include ISAC-CNR, ECMWF, Meteo France, while other relevant modeling groups already expressed interest to join. It is expected that a good representation of the centres previously involved in GLACE-2 initiative will participate in this coordinated effort.

The details of experimental protocol will be implemented during the second half of 2022. Simulations are expected to begin in 2023. To facilitate the spread of the initiative among the prediction community and the engagement with stakeholders, a proposal for a new Community Activity in the framework of GEO has been submitted. The initiative is also supported by the GEWEX-GLASS panel that will push it further within the related community.

How to cite: Alessandri, A., Balsamo, G., Boussetta, S., and Ardilouze, C.: Proposal for an international effort aimed at quantifying the impact of a realistic representation of vegetation/land cover on seasonal climate forecasts (GLACE-VEG), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10473, https://doi.org/10.5194/egusphere-egu22-10473, 2022.

EGU22-10621 | Presentations | CL5.3.2

Some key challenges for subseasonal to decadal prediction research 

William Merryfield, Johanna Baehr, Lauriane Batté, Asmerom Beraki, Leon Hermanson, Debra Hudson, Stephanie Johnson, June-Yi Lee, François Massonnet, Ángel Muñoz, Yvan Orsolini, Hong-Li Ren, Ramiro Saurral, Doug Smith, Yuhei Takaya, and Krishnan Raghavan

The practice of initialized subseasonal, seasonal and decadal climate prediction has matured considerably in recent years, with real-time subseasonal and decadal multi-system ensembles joining those established previously for the seasonal to multi-seasonal range. However, substantial scientific, modelling, and informational challenges remain that must be overcome in order to more fully realize the potential for such predictions to serve societal needs. This presentation will examine five such challenges that the World Climate Research Programme’s Working Group on Subseasonal to Interdecadal Prediction (WGSIP) has identified as crucial for further advancing capabilities for translating the inherent predictability of the Earth system into actionable predictive information. Surmounting these challenges will bring nearer an envisaged future in which global users have access to such information specific to individual needs, across Earth system components and on a continuum of time scales, with degrees of confidence, limitations and uncertainties clearly indicated, as well as tools to guide optimal actions.

How to cite: Merryfield, W., Baehr, J., Batté, L., Beraki, A., Hermanson, L., Hudson, D., Johnson, S., Lee, J.-Y., Massonnet, F., Muñoz, Á., Orsolini, Y., Ren, H.-L., Saurral, R., Smith, D., Takaya, Y., and Raghavan, K.: Some key challenges for subseasonal to decadal prediction research, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10621, https://doi.org/10.5194/egusphere-egu22-10621, 2022.

Over East Asia, reliable forecasts of boreal spring droughts and pluvials can provide time window of opportunities to mitigate their adverse effects. Here, we aim to assess the seasonal prediction skill of boreal spring droughts and pluvials over East Asia (EA), using NMME and atmospheric-only global climate model (AGCM) simulations. Results show that NMME models show a better prediction skill of pluvials than that of droughts, indicating asymmetry in the prediction skill. This asymmetric tendency is also found in the prediction skill of sea surface temperature (SST) during the corresponding drought and pluvial years. Results from the AGCM simulations show asymmetry in the prediction skills of spring droughts and pluvials, indicating the limited predictability of SST-teleconnections in the model physics. The findings of this study prioritize a need to improve the representation of sea-air interactions during drought years in the current climate models.

How to cite: Kim, B.-H. and Kam, J.: Asymmetry in the prediction skills of NMME models for springtime droughts and pluvials over East Asia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10950, https://doi.org/10.5194/egusphere-egu22-10950, 2022.

EGU22-11562 | Presentations | CL5.3.2

Effects of aerosols reduction on the Asian summer monsoon prediction: the case of summer 2020 

Annalisa Cherchi, Andrea Alessandri, Etienne Tourigny, Juan C Acosta Navarro, Pablo Ortega, Paolo Davini, Danila Volpi, Franco Catalano, and Twan van Noije

Northern Hemisphere anthropogenic aerosols influence Southeast and East Asian summer monsoon precipitation. In the late 20th century, both the East Asian and the South Asian summer monsoons weakened because of increased emissions of anthropogenic aerosols over Asia, counteracting the warming effect of increased greenhouse gases (GHGs). Changes in the anthropogenic aerosols burden in the Northern Hemisphere, and specifically over the Asian continent, may also have affected the sub-seasonal evolution of the summer monsoon. During the spring 2020, when restrictions to contain the spread of the coronavirus were implemented worldwide, reduced emissions of gases and aerosols were detected also over Asia.

Following on from the above and using the EC-Earth3 coupled model, a case-study forecast for summer 2020 (May 1st start date) has been designed and produced with and without the reduced atmospheric forcing due to covid-19 in the SSP2-4.5 baseline scenario, as estimated and adopted within CMIP6 DAMIP covidMIP experiments (hereinafter “covid-19 forcing”). The forecast ensembles (sensitivity and control experiments, meaning with and without covid-19 forcing) consist of 60 members each to better account for the internal variability (noise) and to maximize the capability to identify the effects of the reduced emissions.

The analysis focuses on  the effects of the covid-19 forcing, in particular the reduction of anthropogenic aerosols, on the forecasted evolution of the monsoon, with a specific focus on the performance in predicting the summer precipitation over India and over other parts of  South and East Asia. Changes in the performance of the prediction for specific aspects of the monsoon, like the onset and the length of the season, are evaluated as well.

How to cite: Cherchi, A., Alessandri, A., Tourigny, E., Acosta Navarro, J. C., Ortega, P., Davini, P., Volpi, D., Catalano, F., and van Noije, T.: Effects of aerosols reduction on the Asian summer monsoon prediction: the case of summer 2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11562, https://doi.org/10.5194/egusphere-egu22-11562, 2022.

EGU22-12989 | Presentations | CL5.3.2

Skillful Prediction of Barents Sea Phytoplankton Concentration 

Filippa Fransner, Marius Årthun, Ingo Bethke, François Counillon, Annette Samuelsen, Jerry Tjiputra, Are Olsen, and Noel Keenlyside

The predictability of phytoplankton abundance in the Barents Sea is explored in the CMIP6 decadal prediction runs with the Norwegian Climate Prediction Model (NorCPM1), together with satellite data and in situ measurements. The model successfully predicts a maximum in the observed phytoplankton abundance in 2007 up to five years in advance, which is associated with a strong predictive skill of 2007 minimum extent of the summer sea ice concentration. The underlying mechanism is an event of anomalously high heat transport into the Barents Sea that is seen both in the model and in situ observations. These results are an important step towards marine ecosystem predictions.

How to cite: Fransner, F., Årthun, M., Bethke, I., Counillon, F., Samuelsen, A., Tjiputra, J., Olsen, A., and Keenlyside, N.: Skillful Prediction of Barents Sea Phytoplankton Concentration, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12989, https://doi.org/10.5194/egusphere-egu22-12989, 2022.

EGU22-95 | Presentations | CL2.2

Predicting the occurrence of extreme El Nino events based on Schumann resonancemeasurements? 

Tamas Bozoki, Earle Williams, Gabriella Satori, Ciaran D. Beggan, Colin Price, Peter Steinbach, Anirban Guha, Yakun Liu, Anne Neska, Robert Boldi, and Mike Atkinson

Multi-station observations of Schumann resonance (SR) intensity document common behavior in the evolution of continental-scale lightning activity in two super El Niño events, occurring in 1997/98 and 2015/16. The vertical electric field component of SR at Nagycenk, Hungary and the two horizontal magnetic field components in Rhode Island, USA in 1997, and in 2014-2015, the two horizontal magnetic field components at Hornsund, Svalbard and Eskdalemuir, United Kingdom as well as in Boulder Creek, California and Alberta, Canada exhibit considerable increases in SR intensity from some tens of percent up to a few hundred percent in the transition months preceding the two super El Niño events. The UT time distribution of anomalies in SR intensity indicates that in 1997 the lightning activity increased mainly in Southeast Asia, the Maritime Continent and India, i.e. the Asian chimney region. On the other hand, a global response in lightning is indicated by the anomalies in SR intensity in 2014 and 2015. SR-based results are strengthened by comparison to independent lightning observations from the Optical Transient Detector and the World Wide Lightning Location Network, which also exhibit increased lightning activity in the transition months. The increased lightning is attributable to increased instability due to thermodynamic disequilibrium between the surface and the mid-troposphere during the transition. Our main conclusion is that variations in SR intensity may act as a precursor for the occurrence and magnitude of these extreme climate events, and in keeping with earlier findings, as a precursor to maxima in global surface air temperature. As a continuation of our research we plan to set up a webpage dedicated to monitor the actual state of global lightning activity based on SR measurements which may contribute to the early identification of increased instability preceding the next super El Niño event. 

How to cite: Bozoki, T., Williams, E., Satori, G., Beggan, C. D., Price, C., Steinbach, P., Guha, A., Liu, Y., Neska, A., Boldi, R., and Atkinson, M.: Predicting the occurrence of extreme El Nino events based on Schumann resonancemeasurements?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-95, https://doi.org/10.5194/egusphere-egu22-95, 2022.

EGU22-1347 | Presentations | CL2.2

ENSO Atmospheric Feedbacks under Global Warming 

Tobias Bayr and Mojib Latif

Two atmospheric feedbacks play an important role in the dynamics of the El Niño/Southern Oscillation (ENSO), the amplifying zonal wind feedback and the damping heat flux feedback. Here we investigate how and why both feedbacks change under global warming in climate models of 5th and 6th phase of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) under a “business-as-usual” scenario (RCP8.5 and SSP5-8.5, respectively). The amplifying wind feedback over the western equatorial Pacific (WEP) becomes stronger in most climate models (on average by 8 ± 8%) as well as the damping heat flux feedback over the eastern and central equatorial Pacific (EEP and CEP, respectively) (on average by 18 ± 11%). The simultaneous strengthening of both feedbacks can be explained by the stronger warming in the EEP relative to the WEP and the off-equatorial regions, which shifts the rising branch of the Pacific Walker Circulation to the east and increases mean convection and precipitation over the CEP. This in turn leads to a stronger vertical wind response during ENSO events over the CEP that strengthens both atmospheric feedbacks. Further, we separate the climate models into sub-ensembles with STRONG and WEAK ENSO atmospheric feedbacks, as 2/3 of the models underestimate both feedbacks under present day conditions by more than 40%, causing an error compensation. Despite both sub-ensembles show similar changes in the mean state and ENSO atmospheric feedbacks, the ENSO dynamics in WEAK remain weaker relative to STRONG under global warming. Due to the more realistic ENSO dynamics, we postulate that the ENSO predictions of the models in STRONG should be more reliable. Finally, we analyze the relation between changes in ENSO amplitude and ENSO atmospheric feedbacks. We find that models tending to simulate an eastward shift of the wind feedback and increasing precipitation response over the EEP during Eastern Pacific El Niño events also exhibit an increasing ENSO amplitude.

How to cite: Bayr, T. and Latif, M.: ENSO Atmospheric Feedbacks under Global Warming, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1347, https://doi.org/10.5194/egusphere-egu22-1347, 2022.

EGU22-1420 | Presentations | CL2.2

Changes in ENSO characteristics in CESM1 simulations with considerably altered background climate states 

Joke Lübbecke, Thea Siuts, and Tobias Bayr

Changes in the tropical Pacific background state can affect interannual variability, i.e. the El Niño-Southern Oscillation (ENSO) by altering feedbacks that control ENSO’s characteristics. Here, the sensitivity of ENSO to the background climate is investigated utilizing two Community Earth System Model version 1 (CESM1) simulations in which the solar constant is altered by ±25 W/m2. The resulting stable warm and cold climate mean state simulations differ in terms of ENSO characteristics such as amplitude, frequency, asymmetry and seasonality. Under warm mean state conditions, ENSO reveals a larger amplitude and occurs at higher frequencies than in the cold mean state and control run. The warm run also features an increased asymmetry and a stronger seasonal phase-locking. We relate these changes to the differences in the mean state and the amplifying and damping feedbacks. In the warm run, a shallower mean thermocline results in a stronger subsurface-surface coupling while the cold run reveals reduced ENSO variability due to a reduced Bjerknes Feedback in accordance with a deeper mean thermocline and enhanced mean surface wind stress. A strong zonal advective and Ekman feedback further contribute to the large ENSO amplitude in the warm mean state run. However, in light of the large temperature contrast between the simulations of up to 6 K in the tropical Pacific, the results also highlight the robustness of ENSO dynamics under vastly different climate mean states.

How to cite: Lübbecke, J., Siuts, T., and Bayr, T.: Changes in ENSO characteristics in CESM1 simulations with considerably altered background climate states, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1420, https://doi.org/10.5194/egusphere-egu22-1420, 2022.

Tropical cyclone (TC) can pump heat downward through inducing intense vertical mixing. Many efforts have been made to estimate the magnitude of TC-induced ocean heat uptake (OHU), but the spatiotemporal variability of TC-induced OHU remains unclear. This study uses satellite-observed sea surface temperature (SST), subsurface temperature profiles, and turbulent heat fluxes to investigate the spatiotemporal variability of TC-induced OHU and its potential impacts on ocean heat content (OHC) during the period 1985-2018. It is found that category 3-5 TCs dominate the TC-induced OHU, accounting for ~70% of overall amount of TC-induced OHU globally each year. The time series of TC-induced OHU in global and regional oceans exhibit evident interannual-to-interdecadal variability, which is closely related to the TC power dissipation index (PDI). We further decompose PDI into TC intensity, frequency, and duration and find that category 3-5 TC frequency, annually averaged TC intensities, and durations all contribute to the variability of TC-induced OHU except that the averaged TC intensities have no significant relations with the TC-induced OHU in the North Indian Ocean, South Indian Ocean, and Southwest Pacific. In addition, the TC-induced OHU is shown to be responsive to equatorial SSTs rather than tropical SSTs, implying that the TC-induced OHU is modulated by El Niño-Southern Oscillation (ENSO). The TC-induced OHU might have the potential to influence OHC variability, particularly in the equatorial Pacific, where there is significant TC-induced OHU convergence. It has an important implication that TC-induced OHU might have potential effects on ENSO evolution.

How to cite: Fan, K., Wang, X., and Shao, C.: Spatiotemporal Variability of Tropical Cyclone Induced Ocean Heat Uptake and Its Effect on Ocean Heat Content, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2966, https://doi.org/10.5194/egusphere-egu22-2966, 2022.

EGU22-3263 | Presentations | CL2.2

Asymmetries in the ENSO phase space 

Dietmar Dommenget and Maryam Al Ansari

El Niño Southern Oscillation (ENSO) dynamics are best described by the recharge oscillator model, in which the eastern tropical Pacific sea surface temperatures (T) and subsurface heat content (thermocline depth; h) have an out-of-phase relationship. This defines a 2-dimensional phase space diagram between T and h. In an idealized damped oscillator, the phase space diagram should be a perfectly symmetrical circle with a clockwise propagation over time. However, the observed phase space shows strong asymmetries in this diagram. In this study we will illustrate how the ENSO phase space can be used to discuss the phase-dependency of ENSO dynamics. The normalized spherical coordinate system allows to define a phase-depending ENSO growth rates and phase transition speeds. Based on these we discuss the implications of the observed asymmetries are for the dynamics and predictability of ENSO, with a particular focus on the variations in the growth rate and coupling of ENSO along the oscillation cycle.  Using linear and non-linear recharge oscillator models we will show how noise and internal dynamics are driving ENSO at different phases of the ENSO cycles. We will illustrate that a non-linear growth rate of T can explain most of the observed non-linear phase space characteristics.

How to cite: Dommenget, D. and Al Ansari, M.: Asymmetries in the ENSO phase space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3263, https://doi.org/10.5194/egusphere-egu22-3263, 2022.

EGU22-3833 | Presentations | CL2.2

Representation of extreme El Niño events and associated atmospheric moisture flux convergence patterns in observations and CMIP6 global climate models 

Janeet Sanabria, Pierluigi Calanca, Raphael Neukom, Nadine Salzmann, and Carlos Carrillo

Extreme precipitation in the western tropical Andes have significant socio-economic impacts in northern Peru and Ecuador. Previous investigations have shown that high impact episodes were caused by atmospheric moisture flux convergence associated with strong El Niño events in the eastern Pacific Ocean, identifying two patterns: the one emerging during the 1982/1983 and 1997/1998 events, and the one emerging during the 2015/2016 event.

In this contribution, we discuss the ability of CMIP6 global climate models to represent these two types of extreme El Niño events, by analyzing the associated atmospheric moisture transport patterns. Based on SST observations, we identified historical extreme El Niño events using the relative Niño34 index, an index recently proposed for addressing ENSO in a warming climate. We also use ERA5 to compare with the moisture flux of CMIP6. We compared 13 CMIP6 models with the historical record (1901-2014). We found the following: (1) six of the models simulated the two extremes El Niño patterns; (2) 62% of the models identify 4.5 extreme El Niño events; and (3) only 27% of the models represent the seasonality of the moisture flux convergence overestimating the moisture flux convergence branch located to the south (4° S) of its normal position (4° N).

Our results provide a starting point to investigate the impacts of climate change and its impacts on atmospheric dynamics and associated extreme events at the regional level in tropical South America.

How to cite: Sanabria, J., Calanca, P., Neukom, R., Salzmann, N., and Carrillo, C.: Representation of extreme El Niño events and associated atmospheric moisture flux convergence patterns in observations and CMIP6 global climate models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3833, https://doi.org/10.5194/egusphere-egu22-3833, 2022.

EGU22-4711 | Presentations | CL2.2

Energy Export from the Tropical Pacific via the Atmosphere - a Lagrangian Perspective 

Katharina Baier, Marina Duetsch, Lucie Bakels, Michael Mayer, Leopold Haimberger, and Andreas Stohl

The El Niño-Southern Oscillation (ENSO) is linked with energy exchange between the ocean, atmosphere and space. It has a global impact on weather, agriculture and the economic system. In association with ENSO, we analyse the atmospheric energy export from the Tropical Pacific with the particle dispersion model FLEXPART using meteorological input data from the ERA5 reanalysis. In this Lagrangian model, the atmosphere was filled homogeneously with five million particles, which were traced forward in time and represent the global atmospheric mass transport. From this Lagrangian reanalysis dataset covering the years 1979-2017, air masses residing within the Nino3.4 + Nino3 region and below 1 km are selected and followed 30 days forward in time. We found that some of these relatively warm air masses are transported to the Atlantic Ocean where they are mainly located at upper layers. Furthermore, we found strong correlations between the mass transport and the Nino3.4 Index, thus more air is exported to the Atlantic Ocean during El Niño conditions. This transported air further releases energy, as shown by a negative energy divergence. Even over the Sahel zone there is a significant signal, which indicates a direct atmospheric connection between West Africa and the Tropical Pacific. Based on our findings, the transported air might support drier surface conditions during El Niño in that region. In summary, the Lagrangian technique provides new insights into how energy is exported from the Tropical Pacific via the atmosphere and clarifies the relevance of atmospheric transport associated with ENSO.

How to cite: Baier, K., Duetsch, M., Bakels, L., Mayer, M., Haimberger, L., and Stohl, A.: Energy Export from the Tropical Pacific via the Atmosphere - a Lagrangian Perspective, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4711, https://doi.org/10.5194/egusphere-egu22-4711, 2022.

EGU22-4869 | Presentations | CL2.2

ENSO induced shifts of the Subarctic Front in the North Pacific over the past 700 ka: Evidence from planktic foraminiferal proxy data 

Lara Jacobi, Weng-si Chao, Dirk Nürnberg, Lester Lembke- Jene, and Ralf Tiedemann

The subarctic front (SAF) in the pelagic Pacific Ocean is the northernmost front that separates the Oyashio Current, which marks the southern boundary of the subpolar gyre, from the Kuroshio Current, the northern boundary of the subtropical gyre. Its strong sea surface temperature (SST) gradient is not a stable and permanent feature but shifts on timescales from interannual to glacial/interglacial. Yet the complex interplay of different driving mechanisms for this phenomenon is not yet entirely understood. In this study, we present newly retrieved data from the Emperor Seamount chain that reveals a link between long-term ENSO (El Niño /Southern Oscillation) dynamics in the tropics and shifts of the SAF. Here, we use marine sediment core SO264-45-2 (46°33.792’N, 169°36.072’E), recovered from the Emperor Seamount Chain during R/V SONNE Cruise SO264 in 2018 to reconstruct changes in (sub-) surface temperature and salinity via a combined Mg/Ca and δ18O analyses of the shells of the shallow living planktic foraminifera Globigerina bulloides and the near thermocline living Neogloboquadrina pachyderma. This reveals that SST and salinity do not show a clear glacial/interglacial pattern during the last 280 ka and thus we assume that the SAF was south of the core site during this time interval. Prior to 280 ka, SSTs were significantly higher and show greater amplitudes than after 280 ka, while the subsurface temperature stayed relatively constant. Such high SSTs together with the observed higher sea surface salinities prior to 280 ka indicate that water from the Kuroshio-Oyashio transition zone temporarily reached the core site in form of a warm surface water lens. This points to a northward displacement of the SAF of at least 5° so that it was located right above the core site. This way very small north and southward displacements e.g. in relation to glacial/interglacial periods would have caused SST changes as high as we observe them in the time interval 280-700 ka. Notably, this assumed shift of the SAF at 280 ka occurs simultaneously to a change from more La Niña-like to more El Niño-like conditions in the tropical Pacific. Moreover, warm phases in the time interval 280-700 ka seem to occur during times of more La Niña-like conditions in the tropics, while cold phases seem to be related to more El Niño-like conditions. As our study area is linked to the subtropical gyre via the Kuroshio Current, we assume that the observed shifts of the SAF at our study site were caused by the enhancement of the Kuroshio Current in time intervals of more La Niña-like like conditions.

How to cite: Jacobi, L., Chao, W., Nürnberg, D., Lembke- Jene, L., and Tiedemann, R.: ENSO induced shifts of the Subarctic Front in the North Pacific over the past 700 ka: Evidence from planktic foraminiferal proxy data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4869, https://doi.org/10.5194/egusphere-egu22-4869, 2022.

EGU22-5921 | Presentations | CL2.2

El Niño diversity during the Holocene in relation to mean state changes 

Isma Abdelkader Di Carlo, Pascale Braconnot, Olivier Marti, Matthieu Carré, and Mary Elliot

A consensus has not yet been reached when it comes to the long-term changes in ENSO diversity. Indeed, for models that simulate larger warming in the East Pacific, some studies show an increase of Eastern Pacific (EP) events, and a decrease in Central Pacific (CP) events, or the opposite. Similar apparent contradictions also emerge from analyses of the changes in EP versus CP El-Niño events in the Holocene. In this study, we consider the Holocene period as a means to study long-term El Niño variability in a context relatively close to the present. Indeed, the Holocene period allows studying the changes related to the long-term trend induced by the long-term evolution of the Earth’s orbit and seasonal evolution induced by the orbital forcing. We use two 6,000-year-long transient simulations of the IPSL model and two different indicators to characterize El Niño events. 

This study shows that we can have opposite results on the behavior of EP and CP events depending on the type of indicator used to characterize El Niño. We will discuss the reasons for these contrasting results, as seen in two previous studies. Moreover, we will test the extent to which the types of events are induced by changes in the tropical Pacific’s thermocline.

How to cite: Abdelkader Di Carlo, I., Braconnot, P., Marti, O., Carré, M., and Elliot, M.: El Niño diversity during the Holocene in relation to mean state changes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5921, https://doi.org/10.5194/egusphere-egu22-5921, 2022.

EGU22-6181 | Presentations | CL2.2

The ENSO-induced South Pacific Meridional Mode 

Boris Dewitte, Emilio Concha, Diego Sepulveda, Oscar Pizarro, Cristian Martinez-Villalobos, Marcel Ramos, and Aldo Montecinos

The meridional modes (MM) in the Pacific are the conduit by which mid to high-latitudes external forcing (NPO/SPO) can trigger or influence ENSO; While for the Northern Hemisphere the MM (NPMM) is considered a precursor of ENSO, the MM-ENSO relationship in the Southern Hemisphere (SH) is more uncertain. Here we show that, rather than acting as a precursor, strong MMs of the SH (SPMM) are dominantly (~66%) triggered by strong El Niño events in observations and the historical simulations of the Large Ensemble CESM (LENS-CESM). In the LENS-CESM simulations, strong ENSO-induced SPMMs are associated with a precursor signal (warm SST anomalies) of the coast off northern central Chile (20°S-35°S) resulting from the combined effect of the propagation of oceanic downwelling coastal Kelvin waves and the reduction in upwelling favorable winds due to an activated Pacific South American (PSA) pattern during the development of coincident ENSO cycle. The analysis of the simulations of the Coupled Intercomparison Project phases 5 and 6 (CMIP5/6) indicate a large diversity in terms of the ENSO-SPMM relationship, which can be interpreted as resulting from the spread in the meridional location of the center of action of the SPMM and of the seasonality of the SPO variance. We further discuss how ENSO-induced SPMM interferes with the coincident ENSO cycle and contributes to its asymmetry.

How to cite: Dewitte, B., Concha, E., Sepulveda, D., Pizarro, O., Martinez-Villalobos, C., Ramos, M., and Montecinos, A.: The ENSO-induced South Pacific Meridional Mode, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6181, https://doi.org/10.5194/egusphere-egu22-6181, 2022.

EGU22-6406 | Presentations | CL2.2

Two types of Coastal El Niño events 

Cristian Martinez-Villalobos, Boris Dewitte, René D. Garreaud, Leandra Loyola, and Emilio Concha

Coastal El Niño events —instances of anomalous surface ocean warming in the eastern Tropical Pacific not associated to basin-wide events— have received a great deal of attention following the strong coastal event of early 2017. This event was associated to large increases in precipitation and widespread damage in Ecuador and Northern Peru comparable to that during the 1997/98 El Niño event. Despite their importance, it is currently not well understood whether these events are essentially driven by local dynamics or are a local manifestation of large-scale modes of climate variability, a possibility that may increase their predictability prospects. We identify three Coastal El Niño events and 7 Coastal La Niña events occurring in the last 40 years. We show that these events are at least partially driven by large-scale processes and can be grouped in two types. The first type is driven by westerly wind bursts in the western Pacific and occur in the initial stages of the development of basin-wide El Niño events. The second type occurs in association with active phases of the North Pacific Meridional Mode and are characterized by large-scale positive wind-evaporation-SST (WES) feedback. We develop a simple model that provides theoretical underpinnings for the WES feedback-driven type of events. Finally, we show that these two types of events have counterparts in the CESM Large Ensemble and discuss their projected change under global warming.

How to cite: Martinez-Villalobos, C., Dewitte, B., Garreaud, R. D., Loyola, L., and Concha, E.: Two types of Coastal El Niño events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6406, https://doi.org/10.5194/egusphere-egu22-6406, 2022.

EGU22-6460 | Presentations | CL2.2 | Highlight

La Niña Came to Eden 

Michael J. McPhaden and Christina Karamperidou

In 1929, Dr Friedrich Ritter and his mistress Dore Strauch left their spouses and the turmoil of post-World War I Germany for the remote, rugged and uninhabited volcanic island of Floreana in the Galapagos archipelago.  Their dream was to live self-sufficiently in an idyllic tropical setting unspoiled by civilization. Yachts stopping at Floreana after Ritter and Strauch established a homestead reported on their pioneering enterprise to the outside world in the early 1930s. The news created a sensation that subsequently attracted other settlers to the island, one of whom, a mysterious Austrian faux baroness, vexed Ritter and Strauch to the point of open hostility. Not all the participants in this drama survived the experience of colonizing Floreana though. A prolonged drought that gripped the island from 1933 to 1935 led to food shortages and ultimately the death of Dr. Ritter, who unwittingly ate tainted chicken out of desperation. The bizarre intrigues, extraordinary adventures, and struggles to endure on Floreana were chronicled in Strauch’s 1936 memoir “Satan Came to Eden” and a 2013 Hollywood documentary based on it.  A story that has not been told is how climate variability, and in particular an extended period of cold La Niña conditions in 1933-35, led to the drought that caused food shortages on the island and the untimely demise of Dr. Ritter.  We will use atmospheric reanalyses, contemporaneous marine meteorological observations in the vicinity of islands, and historical accounts from the broader Pacific basin, to describe the evolution of the 1933-35 La Niña and how it affected the human drama as it unfolded on Floreana Island. This protracted La Niña event had impacts felt in other parts of the globe as well and in particular was a major influence on development of the 1930s Dust Bowl in the southern plains of the United States.

How to cite: McPhaden, M. J. and Karamperidou, C.: La Niña Came to Eden, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6460, https://doi.org/10.5194/egusphere-egu22-6460, 2022.

EGU22-6877 | Presentations | CL2.2

ENSO Impact on Summer and Fall Temperatures in Western Europe 

Maialen Martija-Diez, Belén Rodríguez-Fonseca, and Jorge López-Parages

El Niño-Southern Oscillation (ENSO) is the main predictor of global climate variability at interannual time scales. Its impact on European precipitation variability has been deeply studied, but not so much its impact on temperature. Recent studies suggest that the increasing intensity in heatwaves seems to be related to the interannual variability of the mean temperature. Therefore, the predictability of temperature could be very useful for the future adaptation to potentially severe heatwaves. In this study, we investigate the impact of ENSO on maximum and minimum temperature throughout the whole seasonal cycle with the aim of finding some predictability and trends. Due to the observed changing teleconnection between ENSO and remote regions, we consider the possible nonlinear and nonstationary relationship as well. For our study, we choose a region in western Europe that has experienced intense heatwaves, and which is also the main region of air temperature interannual variability in Europe. We found a nonseasonal, nonlinear and nonstationary impact. During decades prior to 1980s, warmer conditions are related to La Niña events in summer. Nevertheless, El Niño events seem to be linked to the increase in fall temperatures during decades after the 1980s. These warmer conditions are found to be correlated as well with ENSO characteristics from previous seasons, which suggest a potential source for improving the seasonal forecast. We analyze the underlying dynamical mechanisms of the detected teleconnection, and we found a circumglobal response for summer and an arching-like pattern in fall. Finally, we investigate the possible reasons explaining this variable impact among seasons and decades.

How to cite: Martija-Diez, M., Rodríguez-Fonseca, B., and López-Parages, J.: ENSO Impact on Summer and Fall Temperatures in Western Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6877, https://doi.org/10.5194/egusphere-egu22-6877, 2022.

EGU22-8519 | Presentations | CL2.2

Tropical Atlantic modulation of the ENSO teleconnection to the North Atlantic 

Jake W. Casselman, Bernat Jiménez-Esteve, and Daniela I.V. Domeisen

The El Niño-Southern Oscillation (ENSO) teleconnection towards the Tropical North Atlantic (TNA) represents a robust response, where sea surface temperatures (SST) are positively correlated with ENSO. Following the peak of TNA SST anomalies (SSTAs) in the decaying phase of ENSO, the TNA can influence the local Walker circulation, creating a Rossby Wave Source (RWS) over the Caribbean region in boreal spring and summer. Additionally, when combined with the Pacific SSTAs, this Walker cell perturbation forms the Pacific-Caribbean Dipole (PCD), acting predominantly in the developing phase of ENSO and impacting the North Atlantic European (NAE) region. However, the influence of the TNA SSTAs on the Caribbean RWS and resulting NAE perturbation in the decaying phase of ENSO remains unclear. Thus, we use a series of sensitivity experiments with a simplified atmospheric general circulation model to determine how the TNA modulates the inter-basin teleconnection and how this modulation can influence the NAE response. We find that the NAE region is modulated by the TNA SSTA and Caribbean region in the boreal spring and summer. In boreal spring, a propagating Rossby wave train modulates the NAE region, while in boreal summer, the influence is nonlinear and tends to strengthen ENSO’s influence in the NAE region. Overall, our analysis presents a deeper understanding of the inter-basin Walker cell interactions in the decaying phase of an ENSO event and the TNA’s modulation of the teleconnection to the NAE region.

How to cite: Casselman, J. W., Jiménez-Esteve, B., and Domeisen, D. I. V.: Tropical Atlantic modulation of the ENSO teleconnection to the North Atlantic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8519, https://doi.org/10.5194/egusphere-egu22-8519, 2022.

Atmospheric teleconnections are remote impacts associated with atmospheric processes transmitted through planetary-scale waves like the Rossby wave. Tropical heat sources like El Nino Southern Oscillation (ENSO) could force such planetary-scale wave responses. The El Nino events are classified into Non-OLR El Nino events and OLR El Nino events based on its convective signal over the central-eastern equatorial Pacific using an OLR based El Nino Index. The key purpose of this study is to analyse the difference in teleconnection patterns during these OLR based El Nino events and understand its baroclinic-to-barotropic mode responses using an intermediate complexity atmospheric circulation model called Quasi-equilibrium tropical circulation model (QTCM). The study analyses the difference in the distribution of atmospheric variables and Rossby wave source (RWS) anomalies during Non OLR El Nino events and OLR El Nino using QTCM experiments. It is seen that the OLR El Nino events have a larger barotropic contribution to the positive anomaly of SLP over the western Pacific and a larger baroclinic contribution to the negative anomaly of SLP over the eastern Pacific compared to Non-OLR El Nino events. This is due to stronger baroclinic Rossby waves from the eastern and central tropical Pacific that propagates towards western Pacific and force barotropic wave trains due to barotropic-baroclinic interactions. Also, on analysing the effective RWS forcing and its components over certain regions during OLR and Non OLR El Nino, we see a difference in their distribution due to contributions from the absolute vorticity advection by divergent wind flow and subtropics vortex stretching. We further investigates the baroclinic-to-barotropic interaction over the midlatitude and tropical teleconnection through baroclinic-barotropic interaction terms in barotropic Rossby wave during Non OLR El Nino and OLR El Nino. It was seen that among the barotropic Rossby wave source interaction terms, the shear advection term has the largest contribution and the mean baroclinic zonal wind that advects the baroclinic zonal wind anomaly due to tropospheric heating is the most relevant component. The effective RWS over the tropics and the subtropics arise from the mean state baroclinic flow that acts on the baroclinic wind structure arising due to the ENSO tropospheric heating that spreads over a scale of equatorial radius of deformation from the deep tropics to the subtropics. This baroclinic wind structure is stronger for OLR El Nino compared to Non OLR El Nino. The experiment is also extended to preindustrial and mid-Holocene periods using data from CESM. The mid-Holocene OLR El Nino has a weaker RWS response than the preindustrial OLR El Nino due to the relatively weaker tropospheric heating and temperature structure, resulting in a weaker baroclinic wind structure.

How to cite: Suresan, S. and Joseph Mani, N.: Understanding ENSO related tropical teleconnections using Quasi-equilibrium tropical circulation model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9861, https://doi.org/10.5194/egusphere-egu22-9861, 2022.

EGU22-10063 | Presentations | CL2.2

Is there an impact of resolving the stratosphere on ENSO? A first approach from EC-EARTH 

Mario Rodrigo, Javier García-Serrano, and Ileana Bladé

The European Consortium EC-EARTH climate model version 3.1 is used to assess the effects of a well-resolved stratosphere on the representation of El Niño-Southern Oscillation, and in particular on the simulation of extreme El Niño events, known as super El Niños. Three 100-year long experiments with fixed radiative forcing representative of present climate are compared: one with the top at 0.01hPa and 91 vertical levels (HIGH-TOP or HT), another with the top at 5hPa and 62 vertical levels (LOW-TOP or LT), and another high-top experiment but with the stratosphere nudged to the climatology of HT from 10hPa upwards (NUDG). The differences in vertical resolution between HT and LT start at around 100hPa. By comparing HT with LT we explore the influence of increased vertical resolution above the tropopause on ENSO, while by comparing HT with NUDG we evaluate the influence of stratospheric variability, with special emphasis on the Quasi-Biennial Oscillation (QBO). No extreme ENSO events occur in the two simulations without QBO (LT and NUDG), while HT is able to simulate such extreme events. These super El Niños coincide with a positive Indian Ocean Dipole (IOD) and the westerly phase of the QBO in the lower stratosphere during boreal summer and fall. Previous studies have proposed an interaction between El Niño and IOD-related sea surface temperature anomalies to explain the existence of super El Niños. Our work suggests that this interaction alone is not enough in our climate model to simulate super El Niños. We postulate that changes in the upper tropospheric circulation over the Indian Ocean-Maritime Continent during boreal summer and fall, related to the westerly phase of the QBO, establish favourable conditions for the development of El Niños, increasing the probability of having super El Niños.

How to cite: Rodrigo, M., García-Serrano, J., and Bladé, I.: Is there an impact of resolving the stratosphere on ENSO? A first approach from EC-EARTH, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10063, https://doi.org/10.5194/egusphere-egu22-10063, 2022.

EGU22-10456 | Presentations | CL2.2

Revisiting ENSO and IOD contributions to Australian Precipitation 

Giovanni Liguori, Shayne McGregor, Martin Singh, Julie Arblaster, and Emanuele Di Lorenzo

Tropical modes of variability, such as El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), exert a strong influence on the interannual variability of Australian precipitation. Nevertheless, commonly used indices of ENSO and IOD variability display significant co-variability that prevents a robust quantification of the independent contribution of each mode to precipitation anomalies. This co-variability issue is often addressed by statistically removing ENSO or IOD variability from the precipitation field before calculating teleconnection patterns. However, by performing a suite of coupled and uncoupled modelling experiments in which either ENSO or IOD variability is physically removed, we show that ENSO-only-driven precipitation patterns computed by statistically removing the IOD influence significantly underestimate the impact of ENSO on Australian precipitation variability. Inspired by this, we propose a conceptual model that allows one to effectively separate the contribution of each mode to Australian precipitation variability.

How to cite: Liguori, G., McGregor, S., Singh, M., Arblaster, J., and Di Lorenzo, E.: Revisiting ENSO and IOD contributions to Australian Precipitation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10456, https://doi.org/10.5194/egusphere-egu22-10456, 2022.

EGU22-10892 | Presentations | CL2.2

Mining Large Climate Model Datasets to Make Multi-Year Initialized ENSO Forecasts with Actionable Skill 

Matthew Newman, Hui Ding, Jiale Lou, Sam Lillo, Michael Alexander, Andrew Hoell, and Andrew Wittenberg

Seasonal to interannual forecasts made by coupled general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. The subsequent evolution of those “model-analogs” yields an ensemble forecast, without additional model integration. This technique is applied to CGCMs either used operationally by NCEP or as part of the CMIP6 dataset. By selecting from these long control runs those model states whose monthly SST and SSH anomalies best resemble the observations at initialization time, hindcasts are then made for leads of 1-36 months during 1958-2019. Deterministic and probabilistic skill measures of these model-analog hindcasts are comparable to, and in some regions better than, traditionally assimilation-initialized CGCM hindcasts after 1982, for both the individual models and the multi-model ensemble.

On average, ENSO skill of AC>0.5 exists for forecast leads of 18 months for forecasts initialized in summer. More important, we find that not only were some notable ENSO events predictable two years (or more) ahead of time, but that we can both identify forecast “hits” and avoid “false alarms” -- at the time of forecast -- by using a simple forecast signal-to-noise metric (SNR; root-mean-squared ensemble mean divided by ensemble spread), determined from the large (O(100) member) model-analog ensemble. That is, our analog ensemble approach can be used to make actionable ocean predictions, where forecasts of opportunity can be identified well in advance.

Since these long-lead hindcasts do not require full-field initialization, they have also been extended back prior to 1900. We find that while there has been considerable multi-decadal variation in seasonal ENSO skill, there has been no long-term trend for leads up to about 6-9 months. However, while multi-year ENSO skill appears to have also occurred in the past for a few large ENSO events, in the past thirty years it has occurred with considerably greater frequency, raising the possibility that it is a more recent phenomenon.

How to cite: Newman, M., Ding, H., Lou, J., Lillo, S., Alexander, M., Hoell, A., and Wittenberg, A.: Mining Large Climate Model Datasets to Make Multi-Year Initialized ENSO Forecasts with Actionable Skill, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10892, https://doi.org/10.5194/egusphere-egu22-10892, 2022.

Atmospheric moisture is perturbed during ENSO variation, as can be quantified using regression with ENSO indices. Seasonal and annual anomalies of the water column, horizontal moisture flux, surface evaporation, rainfall, and other basic variables, associated with the sea-surface temperature indices NINO34 and Pacific-Indian Dipole are evaluated from ERA5 reanalyses over 1980-2019. The skill in the corresponding regression coefficients (at one standard deviation) from historical climate simulations by the ten (only) CMIP6 models for which the vertically integrated flux was submitted is assessed, subject to the statistical uncertainty in ENSO from 40-year series. The ten-model mean fields are encouragingly realistic, although ENSO anomalies in the equatorial Pacific extend farther westward. The future change for the period 2040-2079 under the SSP585 scenario of rising greenhouse gases is evaluated. There is generally little change in the standard deviation in the two indices or in the SST and wind anomalies. The water column, moisture flux, and rainfall anomalies tend to be amplified in the low latitudes, but with limited change in the teleconnections to higher latitudes. The climatological changes in rainfall and moisture flux resemble those of ENSO in the tropical Indo-Pacific, in part linked to a small positive shift in both the indices. Elsewhere, widespread increases in water column, evaporation, midlatitude surface pressure, and, of course, temperature are not ENSO-like. Implications for the reliability of future projected means and variability will be considered. An obvious recommendation is that the vertically integrated moisture fluxes be routinely output by climate models and be a requested variable in future CMIPs.

How to cite: Watterson, I.: Atmospheric moisture anomalies associated with ENSO and future changes in CMIP6 simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10905, https://doi.org/10.5194/egusphere-egu22-10905, 2022.

EGU22-13182 | Presentations | CL2.2

Persistent discrepancies between observed and modeled trends in the tropical Pacific 

Richard Seager, Mark Cane, and Naomi Henderson

The trends over recent decades in tropical Pacific sea surface and upper ocean temperature are examined in observations, an ocean reanalysis and the latest models from the Coupled Model Intercomparison Project Six and the multimodel Large Ensembles archive.  Comparison is made using three metrics of SST trend - the east-west and north-south sea surface temperature (SST) gradients and a pattern correlation for the equatorial region - as well as change in thermocline depth.  It is shown that the latest generation of models persist in not reproducing the observed SST trends as a response to radiative forcing and that the latter are at the far edge or beyond the range of modeled internal variability.  The observed combination of thermocline shoaling and lack of warming in the equatorial cold tongue upwelling region is similarly at the extreme limit of modeled behavior.  The persistence over the last century and a half of the observed trend towards an enhanced east-west SST gradient, and in four of five observed datasets to an enhanced equatorial north-south SST gradient, is also at the limit of model behavior. It is concluded that it is extremely unlikely that the observed trends are consistent with modeled internal variability.  Instead, the results support the argument that the observed trends are a response to radiative forcing in which an enhanced east-west SST gradient and thermocline shoaling are key and that the latest generation of climate models continue to be unable to simulate this aspect of climate change.

How to cite: Seager, R., Cane, M., and Henderson, N.: Persistent discrepancies between observed and modeled trends in the tropical Pacific, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13182, https://doi.org/10.5194/egusphere-egu22-13182, 2022.

EGU22-13397 | Presentations | CL2.2

The response of ENSO teleconnections to future dynamical and thermal changes 

Nicholas Tyrrell and Alexey karpechko

Future climate change will lead to both dynamical and thermal changes to the atmosphere, and these changes will affect the transmission and impact of ENSO-related teleconnections. As the dynamical atmospheric changes are a response to the radiatively-forced temperature changes, it is difficult to separate these effects. In this study we use a novel nudging technique to separately apply the future thermal and dynamical changes from CMIP6 models to the ECHAM6 atmospheric model.

First there is a training stage where the atmospheric model is nudged to a chosen future climate, and the nudging tendencies are recorded. In the second stage the nudging tendencies for temperature and winds can be applied individually or together to replicate different aspects of the future climate. During the second stage the nudging tendencies are independent of the current model state. This means that idealised ENSO SST experiments can be performed within the constructed future climates, and the model can respond to those perturbations. The study focuses on the how ENSO teleconnections, particularly relating the northern hemisphere polar vortex, will respond to future thermal and dynamical changes.

How to cite: Tyrrell, N. and karpechko, A.: The response of ENSO teleconnections to future dynamical and thermal changes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13397, https://doi.org/10.5194/egusphere-egu22-13397, 2022.

EGU22-13513 | Presentations | CL2.2

Impacts of the ENSO cycle on climate and coffee production in Colombia 

Michael Sanderson, Cathryn fox, Katie Hodge, José Ricardo Cure, Daniel Rodríguez, Luigi Ponti, and Andrew Paul Gutierrez

Colombia is the world’s third largest coffee exporter. The high altitude and rich soils of Colombia’s mountains and valleys create ideal conditions for growing coffee plants. The coffee industry in Colombia mostly consists of small, family-owned farms, and provides many hundreds of thousands of jobs in rural areas. Climatic conditions during the growing season strongly influence the quality and overall yields of coffee beans. Links between the ENSO cycle and coffee production will be investigated. Additionally, coffee crops in Colombia face a variety of threats originating from climate change, including loss of quality and increased prevalence of pests (e.g., the coffee berry borer, Hypothenemus hampei) and diseases (e.g., the coffee leaf rust, Hemileia vastatrix). High resolution climate data are needed to assess how the climate of the coffee growing areas could change and assist growers to adapt to these changes. The ability of three regional climate models (RCA4, RegCM4.3 and CRCM5) to reproduce observed teleconnections between the ENSO cycle and climate in coffee-growing areas of Colombia is also assessed. These regional climate model simulations were produced for the Coordinated Regional Dynamical Experiment (CORDEX) for the Central America, Caribbean, and Mexico (CAM) domain. They represent the highest resolution climate data available for Colombia. Projected changes in the ENSO cycle and possible impacts on coffee production will also be investigated. This study is believed to be the first to explicitly use the CAM-CORDEX results for Colombia.

How to cite: Sanderson, M., fox, C., Hodge, K., Cure, J. R., Rodríguez, D., Ponti, L., and Gutierrez, A. P.: Impacts of the ENSO cycle on climate and coffee production in Colombia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13513, https://doi.org/10.5194/egusphere-egu22-13513, 2022.

EGU22-732 | Presentations | OS4.7

Sea-level modelling in the Mediterranean Sea using data assimilation 

Christian Ferrarin, Marco Bajo, and Georg Umgiesser

The correct reproduction of sea-level dynamics is crucial for forecasting floods and managing the associated risk. On the other hand, sea-level monitoring through observations can provide a description only of past events and it is challenging and costly, both of time and money. In this context, oceanographic models are increasingly used to describe the sea dynamics, providing a spatial/temporal extension to the observations. The best solution, which merges the observation accuracy and the model spatial/temporal resolution, is the data assimilation analysis, which is particularly important in coastal regions with scarce monitoring resources. In this study, we investigate the benefits of assimilating sparse observations from tide gauges in an unstructured hydrodynamic model for simulating the sea level in the Mediterranean Sea. We use the Ensemble Kalman filter, both to obtain an analysis of the past and to produce accurate forecasts. In the analysis we tested the assimilation in storm-surge simulations, only-tide simulations, and total-level simulations, using the observations in the stations. The results of storm-surge simulations were compared with those of total-level simulations, by adding the tide obtained from harmonic analysis of the observations. RMSE and correlation show improvements for all the components of the sea level and all the stations considered (not assimilated). The averaged-over-station RMSE reduces from 9.1 to 3.4 cm for the total level. The greatest improvements happen when the model without assimilation, due to an error of the wind-pressure forcing, did not reproduce some barotropic free modes of oscillation triggered by an initial surge. The preliminary forecast simulations of storm surge show improvements due to the data assimilation extending up to 5 days of forecasting. Even in this case, the longer improvements seem to happen when a free mode of oscillation is triggered. The results of this study will be used to improve the sea level forecasting system in the Adriatic Sea, developed within the framework of the Interreg Italy-Croatia STREAM project (Strategic development of flood management, project ID 10249186).

How to cite: Ferrarin, C., Bajo, M., and Umgiesser, G.: Sea-level modelling in the Mediterranean Sea using data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-732, https://doi.org/10.5194/egusphere-egu22-732, 2022.

EGU22-923 | Presentations | OS4.7

TOPAZ4b: a new version of the ocean and sea-ice Arctic reanalysis 

Jiping Xie and Laurent Bertino

The second version of the Arctic ocean and sea ice reanalysis is based on the coupled ensemble data assimilation system (TOPAZ4b). Compared to its predecessor (Xie et al. 2017) it has benefited from enhancements to observation, model vertical resolution, and forcing datasets. TOPAZ4 relies on version 2.2 of the HYCOM ocean model and the ensemble Kalman filter data assimilation using 100 dynamical members. A 30-years reanalysis of the Arctic ocean and sea ice has been completed starting in 1991, and made available as the multi-year physical product by the Arctic Marine Forecasting Center (ARC MFC) under the Copernicus Marine Environment Monitoring Service. Contrary to the previous version of the Arctic reanalysis, the systematic errors due to fragmented time series of assimilated observations have been removed by using consistent ESA CCI data. The comparison to in situ profiles shows that the temperature and salinity stratification has been considerably improved by the increased vertical resolution in HYCOM, for example in the East Greenland Sea, the temperature root mean square error (RMSE) from surface to 1400 m has been reduced by 50%. These improvements encourage the use of this Arctic reanalysis for climate studies.

How to cite: Xie, J. and Bertino, L.: TOPAZ4b: a new version of the ocean and sea-ice Arctic reanalysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-923, https://doi.org/10.5194/egusphere-egu22-923, 2022.

This study uses a variational method combined with satellite observations to reconstruct three-dimensional temperature and salinity profiles for the Northern Indian Ocean (NIO). Sensitivity experiments show that sea surface temperature (SST) dominantly improve the temperature reconstruction of upper 100 m; sea surface salinity (SSS) determines salinity estimation in the upper 100 m; sea surface height anomaly (SSHA) dominates the reconstruction of thermocline. The reconstructed temperature fields can be greatly improved in the thermocline by removing barotropic signal from the altimeter SSH data through a linear regression method. Ocean reanalysis and in situ temperature and salinity data are used to evaluate the results of reconstruction. Comparing with Simple Ocean Data Assimilation (SODA) in 2016, the spectral correlation between the reconstruction and the SODA density anomalies show that the reconstruction fields can retrieve mesoscale and large-scale signals better. Moreover, the reconstruction salinity is much more accurate than SODA salinity in the upper ocean over the Bay of Bengal (BoB). Compared with CTD section observations, the reconstruction fields can capture the mesoscale eddy structure in the Arabian Sea (AS) and BoB well, respectively. The long time series of reconstruction along Argo trajectory shows that the reconstruction fields can better reproduce the observed intraseasonal oscillations of thermocline/halocline in the BoB. Compared with the World Ocean Atlas 2013 (WOA13) climatology, the reconstruction fields can better characterize upper ocean water mass variability.

How to cite: He, Z., Wang, X., Wu, X., and Chen, J.: Projecting Three-dimensional Ocean Thermohaline Structure in the North Indian Ocean from the Satellite Sea Surface Data Based on a Variational Method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2107, https://doi.org/10.5194/egusphere-egu22-2107, 2022.

The objective of this study is to investigate if the assimilation of ocean color data into a complex marine ecosystem model can improve the hindcast of key biogeochemical variables in coastal seas. A localized Singular Evolutive Interpolated Kalman filter was used to make assimilation of the daily fully reprocessed product of Multi-Satellite chlorophyll observations into a three-dimensional ecosystem model of the Baltic Sea. Twin experiments are performed to evaluate the performance of the assimilation with respect to both satellite and in situ observations. Compared to the reference run, the assimilation was found to immediately and considerably reduce the bias, root mean square error, and increase the correlation with the spatial distributions of the assimilated chlorophyll data while this improvement is limited to the upper layer of the water column. This feature is explained by the weak correlation taken into account by the assimilation between the surface and deep phytoplankton. The assimilation scheme used is multivariate, updating all biogeochemical model state variables. The other variables were not degraded by the assimilation. More significantly, the skill metrics for non assimilated variables indicate that the hindcast of the mean data values at L4 was improved; however, improvements in the short-term forecast were not discernable. Our results provide general recommendations for the successful application of ocean color assimilation to hindcast key biogeochemical variables in coastal seas.

How to cite: Liu, Y. and Arneborg, L.: Assimilating the remote sensing ocean color data into a biogeochemical model of the Baltic Sea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2555, https://doi.org/10.5194/egusphere-egu22-2555, 2022.

Accurate knowledge of ocean surface currents is crucial for a gamut of applications. In this study, the way in which merging altimeters composited two-dimensional sea surface height (SSH, 1/4°) with remote sensing combined sea surface temperature (SST, 9km) image improves the surface current estimates is investigated. Based on the surface quasigeostrophic (SQG) theory, we reconstruct the surface current by resolving the large scale motions, the mesoscale dynamics, and the oceanic smaller processes. Its feasibility is validated using the altimeter-derived geostrophic current (GC) and drogued drifters in the South Indian Ocean (SIO) during 2011–2015. Results of the two cases show that the effective resolution of the reconstructed surface current (RSC) has improved to 30 km after merging the high-resolution SST information, compared to 70 km of the GC. Moreover, the RSC outperforms the altimeter-derived GC in reproducing the practical dynamical processes. Over the analyzed period, compared with 841 drifters, the statistical results indicate that the RSC reduces the reconstruction errors of zonal velocity, meridional velocity, and velocity phase by about 14.6%, 45.7%, 27.0% in the SIO relative to the GC, respectively. Our method particularly improves the meridional velocity and velocity phase along the Antarctic Circumpolar Current, Agulhas Retroflection, Greater Agulhas System, and South Equatorial Current. In addition, the lower Lagrangian separation distance and higher skill score of the RSC given by Lagrangian analysis also demonstrate that the proposed method is more promising to provide essential information on ocean surface currents applications, such as water property transports, search and rescue, etc.

How to cite: Chen, Z., Wang, X., and Chen, J.: Improving the Surface Currents from the Merging of Altimetry and Sea Surface Temperature Image in the South Indian Ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2731, https://doi.org/10.5194/egusphere-egu22-2731, 2022.

EGU22-4313 | Presentations | OS4.7

Improving High Resolution Ocean Reanalyses Using a Smoother Algorithm 

Bo Dong, Keith Haines, and Matthew Martin

We present a post-hoc smoothing algorithm for use with sequentially generated reanalysis products, utilizing the archive of “future” assimilation increments to update the “current” analysis. This is applied to the Lorenz 1963 model and then to the Met Office GloSea5 Global ¼° ocean reanalysis during 2016.  A decay time parameter is applied to the sequential increments which assumes that background error covariances remain spatially unchanged but decay exponentially away from analysis times. Only increments are smoothed so the reanalysis product retains modelled high-frequency variability, e.g., from atmospheric forcing. Results show significant improvement over the original reanalysis in the 3D temperature and salinity variability, as well as in the sea surface height (SSH) and ocean currents. Spatial gap filling from future data is particularly beneficial. The impact on the time variability of ocean heat and salt content, as well as kinetic energy and the Atlantic Meridional Overturning Circulation (AMOC), is demonstrated. 

How to cite: Dong, B., Haines, K., and Martin, M.: Improving High Resolution Ocean Reanalyses Using a Smoother Algorithm, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4313, https://doi.org/10.5194/egusphere-egu22-4313, 2022.

EGU22-4741 | Presentations | OS4.7

Variational data assimilation for advanced cross-scale ocean modelling. 

Marco Stefanelli, Eric Jansen, Ali Aydogdu, Ivan Federico, Giovanni Coppini, and Nadia Pinardi

Eight of the top ten most populated cities in the world are located by the coast. The improvement of the coastal ocean representation is a key topic to understand the  present and near-future ocean state and predict its evolution under climate change conditions.

The coastal ocean is difficult to model due to the presence of complex coastlines, interaction with inland waters, rapid changes in  topography and highly space-time variability of the phenomena involved. Unstructured-grid models are used to partially attenuate this source of errors in cross-scale (from open sea to coastal regions) oceanographic modelling. On the other hand, the data assimilation methodologies to improve the unstructured-grid models in the coastal seas is being developed only recently (e.g., Aydogdu et al., 2018; Bajo et al., 2019) and needs more advancements.  

Here, we show preliminary results from the coastal ocean forecasting system SANIFS (Southern Adriatic Northern Ionian coastal Forecasting System, Federico et al., 2017) based on SHYFEM fully-baroclinic unstructured-grid model (Umgiesser et al., 2004)  interfaced with OceanVar (Dobricic and Pinardi, 2008; Storto et al., 2014), a state-of-art variational data assimilation scheme, adopted for several systems based on structured grid (e.g. regional CMEMS for Mediterranean and Black Seas, marine.cmems.eu).

In OceanVar, Empirical Orthogonal Functions (EOFs) method is used to reduce the dimensionality of computation removing the statistically less significant modes and to correlate observations and model background in the water column;  while the increments are spread horizontally using the recursive filter method. While this method is typically only used to model covariances between neighbouring points in a structured grid, the algorithm has now been generalised and successfully implemented also for unstructured grids.

Preliminary results show that temperature and salinity observations from Argo profilers improve the ocean state. Future steps will also include sea level assimilation. 

This work is a starting point in order to improve our forecast of local extreme events (e.g. heat waves and storm surge) which are statistically increasing in number and intensity in the Mediterranean region due to climate change.

How to cite: Stefanelli, M., Jansen, E., Aydogdu, A., Federico, I., Coppini, G., and Pinardi, N.: Variational data assimilation for advanced cross-scale ocean modelling., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4741, https://doi.org/10.5194/egusphere-egu22-4741, 2022.

EGU22-5698 | Presentations | OS4.7

Bivariate sea-ice assimilation for Global Ocean Analysis/Reanalysis 

Andrea Cipollone, Deep Sankar Banerjee, Ali Aydogdu, Doroteaciro Iovino, and Simona Masina

Recent intercomparison studies among ocean/sea-ice Reanalyses (such as ORA-IP) have shown large discrepancies in many sea-ice-related fields, despite a rather general agreement in the sea-ice extension. The low accuracy of sea-ice thickness measurements together with the highly non-gaussian distributions of related uncertainty, made multivariate sea-ice data assimilation (DA) strategies still at an early stage, although nearly twenty years of thickness observations are now available. In a standard multivariate scheme, the break of Gaussianity can generate un-realistic corrections due to the poor linear relationship driven by the B matrix.

One approach to solve the problem is the implementation of anamorphous transformations that modify the probability density functions of ice anomalies into Gaussian ones (Brankart et al. 2012). In this study, a 3DVar DA scheme (called OceanVar), employed in the routinely production of global/regional ocean reanalysis CGLORS (Storto et al, 2016), has been recently extended to ingest sea-ice concentration (SIC) and thickness (SIT) data. An anamorphous operator, firstly developed and made freely available within the SANGOMA project (http://www.data-assimilation.net/), has been updated and adapted for the bivariate assimilation of SIC/SIT within the OceanVar framework.

We present the comparison among several sensitivity experiments that were performed assimilating different observation datasets and using different DA configurations at 1/4 degree global resolution. Specifically, we assess the impact of ingesting different SIT products, such as SMOS and CRYOSAT-2 data or the merged product CS2SMOS.

We show that the sole assimilation of SIC improves the spatial representation of SIT with respect to a free run. The inclusion of thickness correction, determined by empirical relations, appears to improve the sea ice characteristics in the Atlantic sector and degrade them in the Siberian region; therefore a refined tuning could probably be beneficial. The spatial error reduces sharply only once CRYOSAT-2 data are assimilated jointly with SIC data. In the present set up, all the experiments generally tend to overestimate the sea-ice volume in the case SMOS data are not assimilated. However, observational errors associated with SMOS data are generally too small, leading to jumps in the volume time series at the beginning of the accretion period if not calibrated correctly.

The proposed approach is suitable to be used for covarying ocean/sea-ice variables in future coupled ocean/sea-ice DA.

Storto, A. and Masina, S. (2016), Earth Syst. Sci. Data, 8, 679, doi: 0.5194/essd-8-679-2016

Brankart, et al. (2012), Ocean Sci., 8, 121, doi: 10.5194/os-8-121-2012

 

How to cite: Cipollone, A., Banerjee, D. S., Aydogdu, A., Iovino, D., and Masina, S.: Bivariate sea-ice assimilation for Global Ocean Analysis/Reanalysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5698, https://doi.org/10.5194/egusphere-egu22-5698, 2022.

EGU22-6451 | Presentations | OS4.7

Measurement and modeling of small-scale to mesoscale ocean circulation in the Straits of Florida 

Breanna Vanderplow, John Kluge, Alexander Soloviev, Richard Dodge, Jon Wood, Johanna Evans, William Venezia, and Michael Ferrar

Predicting ocean circulation in strong currents remains challenging because of limits in modelling capabilities such as resolution. Coastal ocean circulation models typically have horizontal resolution starting from 1 km. To address this matter, we have developed a high resolution three-dimensional computational fluid dynamics (CFD) model for strong ocean currents such as the Gulf Stream. Our model domain contains three inlets and an outlet and has been verified with field data from the Straits of Florida. For model verification, a 6 ADCP mooring array in a rectangular shape was deployed 8 miles offshore on the Miami Terrace. The data from 5 ADCP moorings were used to produce the inlet boundary conditions, which were updated every 1 minute. The sixth ADCP in the center of the outlet was used for model verification. This approach has demonstrated good predictive ability for ocean circulation in the challenging environment of a strong western boundary current. We anticipate our work to be a starting point for the development of sophisticated prediction models applicable to western boundary currents in the range from small-scales to sub-mesoscales, based on advanced data assimilation techniques.

How to cite: Vanderplow, B., Kluge, J., Soloviev, A., Dodge, R., Wood, J., Evans, J., Venezia, W., and Ferrar, M.: Measurement and modeling of small-scale to mesoscale ocean circulation in the Straits of Florida, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6451, https://doi.org/10.5194/egusphere-egu22-6451, 2022.

EGU22-6848 | Presentations | OS4.7

Effects of inclusion of adjoint sea ice rheology on estimating ocean-sea ice state 

Guokun Lyu and Meng Zhou

As part of the ongoing development of a data assimilation system for reconstructing the Arctic ocean-sea ice state, we incorporated an adjoint of sea ice rheology, which was approximated by free drift assumption due to stability problem, into an adjoint model of a coupled ocean-sea ice model. The adjoint sensitivity experiments show that the internal stress effect, represented by the adjoint rheology, induced remarkable differences in the sensitivities to ice drift and wind stress in the central Arctic Ocean. In contrast, ice is mostly free drift in the marginal ice zone. The assimilation experiments reveal that including the adjoint of ice rheology helps extract observational information, especially the ice drift observations, which improves the estimation of the sea ice decline process in 2012. The results suggested great potentials for further improving the Arctic ocean-ice state estimation in the framework of the adjoint method with the adjoint sea ice rheology included. 

How to cite: Lyu, G. and Zhou, M.: Effects of inclusion of adjoint sea ice rheology on estimating ocean-sea ice state, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6848, https://doi.org/10.5194/egusphere-egu22-6848, 2022.

EGU22-147 | Presentations | AS1.3

Study of Deep Convection with Presence of Overshooting Tops During RELAMPAGO Campaign 

Inés Cecilia Simone, Paola Salio, Juan Ruiz, and Luciano Vidal

Thunderstorms in southeastern South America (SESA) often reach extreme intensity, duration, and vertical extension. Diverse techniques have been proposed to identify severe storm signatures in satellite images, such as overshooting tops (OTs). Previous studies have shown a large correlation between OTs and the occurrence of severe weather such as large hail, damaging winds, and tornadoes. In particular, in SESA, deep convection systems initiation is sometimes related to elevated topography such as Sierras de Córdoba and the Andes mountain range. These unique meteorological and geographical conditions motivated the RELAMPAGO-CACTI field campaign, which was conducted to study the storms in this region.

This study aims to characterize the occurrence of OTs in SESA through their spatial distribution as well as their diurnal and seasonal cycles.  An OT analysis is presented using an OT detection algorithm (known as OT-DET) applied to GOES16 satellite data from October 2018 to March 2019. OT-DET sensitivity is evaluated considering two alternatives of tropopause temperature determination and different cloud anvil temperature thresholds. OT-DET is validated against an OT occurrence database generated through an expert detection of OTs using GOES16 visible and IR images. The results of this validation as well as the OT characterization will be described at the conference. 

How to cite: Simone, I. C., Salio, P., Ruiz, J., and Vidal, L.: Study of Deep Convection with Presence of Overshooting Tops During RELAMPAGO Campaign, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-147, https://doi.org/10.5194/egusphere-egu22-147, 2022.

EGU22-317 | Presentations | AS1.3

Identification of ZDR columns for early detection of severe convection in southern England 

Chun Hay Brian Lo, Thorwald H. M. Stein, Chris D. Westbrook, Robert W. Scovell, Timothy Darlington, and Humphrey W. Lean

Various studies in the UK, Great Plains and Southeastern USA have highlighted the presence of certain radar signatures prior to the onset of or during severe convection. One type of such radar signature is a differential reflectivity (ZDR) column, which is defined as a vertical columnar region of enhanced ZDR that extends above the freezing level. Several field campaigns synthesising radar and in-situ measurements confirmed that such columns contain large supercooled millimetre-sized droplets lofted into convective storms and are in, or near strong updrafts. Recent work using a single research radar in Oklahoma also exploited the usefulness of detecting ZDR columns for informing nowcasters of severe convection.

The goal of this study is to identify potential severe convective events in the UK mostly for cases over the summer season using polarimetric radar measurements. The UK Met Office has fully upgraded all 18 C-band radars since January 2018 with full dual-polarisation operational capability. From this network, we derive a 3D radar composite, which provides large coverage on the order of 1000 km for monitoring potentially hazardous weather. Environmental conditions are also investigated prior to and during the onset of convection to understand the effectiveness in ZDR columns as precursors of severe convection depending on synoptic regime.

Using past cases, we track storm cells using maximum reflectivity in the column and identify whether the cells contain ZDR columns, where a ZDR column is identified based on a 3D volume thresholded by reflectivity (ZH) and ZDR. For nowcasting of severe storms, with ZH > 50 dBZ, we find optimal ZH and ZDR thresholds of around 30 dBZ and 2.0 dB respectively existing within ZDR columns. This agrees with past literature and physical understanding indicating a low concentration of large super-cooled water droplets within ZDR columns explained by condensation-coalescence processes, especially during early stages of convective development. In contrast, other works may show ZDR columns associated with areas of high ZH, suggesting detection of such columns in more mature stages of a storm. Algorithm performance in identifying ZDR columns for early detection of severe convection and its optimal parameters vary with synoptic regime.

How to cite: Lo, C. H. B., Stein, T. H. M., Westbrook, C. D., Scovell, R. W., Darlington, T., and Lean, H. W.: Identification of ZDR columns for early detection of severe convection in southern England, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-317, https://doi.org/10.5194/egusphere-egu22-317, 2022.

EGU22-742 | Presentations | AS1.3

Ensemble forecast of the Madden Julian Oscillation using a stochastic weather generator based on analogs of  Z500 

Meriem Krouma, Pascal Yiou, and Riccardo Silini

Skillful forecast of the Madden Julian Oscillation (MJO) has an important scientific interest because the MJO represents one of the most important sources of  sub-seasonal predictability. Proxies of the MJO can be derived from the first principal components of wind speed and outgoing longwave radiation (OLR) in the Tropics (RMM1 and RMM2). The challenge is to forecast these two indices. This study aims at providing ensemble forecasts MJO indices  from analogs of the atmospheric circulation, mainly the geopotential at 500 hPa (Z500) by using a stochastic weather generator. We generate an ensemble of 100 members for the amplitude and the RMMs for sub-seasonal lead times (from 2 to 4 weeks). Then we evaluate the skill of the ensemble forecast and the ensemble mean using respectively probabilistic and deterministic skill scores. We found that a reasonable forecast could reach 40 days for the different seasons. We compared our SWG forecast with other forecasts of the MJO.

How to cite: Krouma, M., Yiou, P., and Silini, R.: Ensemble forecast of the Madden Julian Oscillation using a stochastic weather generator based on analogs of  Z500, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-742, https://doi.org/10.5194/egusphere-egu22-742, 2022.

In front of determinism limitations, ensemble forecasting provides competitive advantage assessing uncertainty and helping weather information users in decision-making. Analog ensemble method (AnEn) is one of the most intuitive and computationally cheap ensemble methods that leverages a single deterministic model integration to produce probabilistic information. This method builds an ensemble forecast from a set of past observations of the target variable, neatly selected from a historical training dataset. For a given location, the most similar past forecasts to the current prediction are identified and the associated  past observations are nominated  as members of the analog ensemble forecast. However, The  AnEn forecasting quality is tightly affected by the process of skillful analogs selection in the training data which depends on predictor’s weighting among other factors. This work presents a new weighting strategy based on machine learning techniques (XGBoost, Random Forest and Linear regression) and assesses the impact of its application on the AnEn performance  for 10m wind speed  and 2m temperature forecasting over 13 Moroccan airports in the short term forecasting framework (24 hours). To achieve this, hourly forecasts from the operational mesoscale AROME model and the verifying observations covering 5 year period (2016-2020) are used.  The predictors include 2m temperature, 2m relative humidity, 10m wind speed and direction, mean sea level pressure and surface pressure,  meridonal and zonal components of 10m wind. The basic configuration of Delle Monache et al. (2013) -DM13- where all the predictor’s weights are equal to one is used here as a benchmark. The best weights are computed independently from one airport to another. Since the proposed predictor-weighting strategies can accomplish both the selection of relevant predictors as well as finding their optimal weights, and hence preserve physical meaning and correlations of the used weather variables, the AnEn performances are improved by up to 50 % for bias and by 30% for RMSE for most airports. This improvement varies as function of lead-times and seasons compared to AROME and DM13’s configuration. Results show also that AnEn performance is geographically dependent where a slight worsening is found for some airports.

 

Keywords : Analog Ensemble,  Machine Learning, Predictors Weighting Strategies, 2m Temperature, 10m Wind Speed, XGBoost, Linear Regression, Random Forest, Ensemble Forecasting.

How to cite: Alaoui, B., Bari, D., and Ghabbar, Y.: New AI based weighting strategy for 2m temperature and 10m wind speed forecasting over Moroccan airports  using the analog ensemble method., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2450, https://doi.org/10.5194/egusphere-egu22-2450, 2022.

EGU22-2471 | Presentations | AS1.3

Characterization and warnings for mountain waves using HARMONIE-AROME 

Javier Díaz Fernández, Pedro Bolgiani, Daniel Santos Muñoz, Mariano Sastre, Francisco Valero, Jose Ignacio Farrán, Juan Jesús González Alemán, and María Luisa Martín Pérez

Mountain lee waves are a kind of gravity waves often associated with adverse weather phenomena, such as turbulence that can affect the aviation safety. Not surprisingly, turbulence events have been related with numerous aircraft accidents reports. In this work, several mountain lee wave events in the vicinity of the Adolfo Suarez Madrid-Barajas airport (Spain) are simulated and analyzed using HARMONIE-AROME, the high-resolution numerical model linked to the international research program ACCORD-HIRLAM. Brightness temperature from the Meteosat Second Generation (MSG-SEVIRI) has been selected as observational variable to validate the HARMONIE-AROME simulations of cloudiness associated with mountain lee wave events. Subsequently, a characterization of the atmospheric variables related with the mountain lee wave formation (wind direction and speed, static stability and liquid water content) has been carried out in several grid points placed in the windward, leeward and over the summits of the mountain range close to the airport. The characterization results are used to develop a decision tree to provide a warning method to alert both mountain lee wave events and associated lenticular clouds. Both HARMONIE-AROME brightness temperature simulations and the warnings associated with mountain lee wave events were satisfactory validated using satellite observations, obtaining a probability of detection and percent correct above 60% and 70%, respectively.  

How to cite: Díaz Fernández, J., Bolgiani, P., Santos Muñoz, D., Sastre, M., Valero, F., Farrán, J. I., González Alemán, J. J., and Martín Pérez, M. L.: Characterization and warnings for mountain waves using HARMONIE-AROME, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2471, https://doi.org/10.5194/egusphere-egu22-2471, 2022.

EGU22-7026 | Presentations | AS1.3

Scale-dependent blending of ensemble rainfall nowcasts with NWP in the open-source pySTEPS library 

Ruben Imhoff, Lesley De Cruz, Wout Dewettinck, Carlos Velasco-Forero, Daniele Nerini, Edouard Goudenhoofdt, Claudia Brauer, Klaas-Jan van Heeringen, Remko Uijlenhoet, and Albrecht Weerts

Radar rainfall nowcasting, an observation-based rainfall forecasting technique that statistically extrapolates current observations into the future, is increasingly used for short-term forecasting (<6 hours ahead). These first hours ahead are a key time scale for e.g. (flash) flood warnings and they are generally not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models.

A recent development in nowcasting is the transition to more community-driven, open-source models. The Python library pySTEPS is an example of this. One of its main features is an efficient Python implementation of the probabilistic nowcasting scheme STEPS. pySTEPS generates an ensemble of rainfall forecasts by perturbing a deterministic extrapolation nowcast with spatially and temporally correlated stochastic noise. It considers the dynamical scaling of the rainfall predictability by decomposing the rainfall fields into a multiplicative cascade and applies different stochastic perturbations for each scale. This results in large-scale features that evolve more slowly than the small-scale features.

Despite pySTEPS' representation of the uncertainty associated with growth and decay of rainfall in the first 1-2 hours of the nowcast, it quickly loses skill after 2 hours, or even less for convective rainfall events or small radar domains. To extend the skillful lead time to the desired time scale of 6 hours or more, a blending with NWP rainfall forecasts is necessary. We have implemented an adaptive scale-dependent blending in pySTEPS based on earlier work in the STEPS scheme. In this blending implementation, the blending of the extrapolation nowcast, NWP and noise components is performed level-by-level, which means that the blending weights vary per cascade level. These scale-dependent blending weights are computed from the recent skill of the forecast components, and converge to a climatological value, which is computed from a 1-month rolling window and can be adjusted to the (operational) needs of the user. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy.

We present a validation of the blending approach in a hydrometeorological testbed using Belgian radar and NWP data for the Belgian and Dutch catchments Dommel, Geul and Vesdre. We compare the resulting ensemble rainfall and discharge forecasts of the blending implementation with ensemble nowcasts from pySTEPS, ALARO (NWP) forecasts and a linear blending strategy.

How to cite: Imhoff, R., De Cruz, L., Dewettinck, W., Velasco-Forero, C., Nerini, D., Goudenhoofdt, E., Brauer, C., van Heeringen, K.-J., Uijlenhoet, R., and Weerts, A.: Scale-dependent blending of ensemble rainfall nowcasts with NWP in the open-source pySTEPS library, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7026, https://doi.org/10.5194/egusphere-egu22-7026, 2022.

Ensemble forecasts are calculated to give insight into the range of possible future outcomes and potential risks, but it is challenging for operational forecasters to deal with large ensemble data sets and to distil pertinent information from them, especially during high-impact events where forecasts and warnings must be issued and updated quickly with a high degree of accuracy and consistency.  Therefore, it is important to streamline this process by reducing the amount of data an operational forecaster must digest while still maintaining the necessary accuracy.  To do this, a novel clustering technique has been developed for use on ensemble forecasts to extract likely scenarios in real-time.  This technique uses k-medoids clustering and the spatial separation between frontal regions in ensemble members to group similar members together.  Frontal regions are often associated with heavy rain and strong winds, common high-impact events in the UK.  A single representative member is then extracted from each cluster to present to the forecaster as a potential weather scenario.  The method is illustrated with the UK Met Office operation ensemble forecasting system, MOGREPS-G.

How to cite: Boykin, K.: Extracting likely scenarios from high resolution ensemble forecasts in real-time, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7391, https://doi.org/10.5194/egusphere-egu22-7391, 2022.

EGU22-10595 | Presentations | AS1.3

Evaluation of radar rainfall nowcasting techniques to forecast synthetic storms of different processes 

Ahmed Abdelhalim, Miguel Rico-Ramirez, and Dawei Han

Early hydrological hazard warning demands precise weather forecasts to accurately predict the timing and the location of intense precipitation events which can cause severe floods/landslides and present risks to urban and natural environments. Extrapolation of precipitation by radar rainfall products at high space and time scales with short lead times outperforms forecasts of numerical weather prediction. Therefore, developing and improving of rainfall nowcasts systems are essential. Rainfall nowcasting is the process of forecasting precipitation field movement and evolution at high spatial and temporal resolutions with short lead times(<6h) in which the advection of the precipitation fields is estimated by extrapolating real-time remotely sensed observations. Radar rainfall nowcasting is increasingly applied because of the high potential of radar products in short-term rainfall forecasting due to their high spatiotemporal resolutions (typically, 1 km and 5 min). It consists of two procedures in tracking precipitation features to calculate the velocity from a series of consecutive radar images and propagating the most recent precipitation observation into the future using the obtained velocity. Optical flow represents one of the most used methods for tracking the motion fields from consecutive images. Deep learning techniques are those machine learning methods that utilise deep artificial neural networks. Deep learning has become one of the most popular and rapidly spreading methods in different scientific disciplines including water-related research. Deep learning applications in radar-based precipitation nowcasting is still in its early stage with many knowledge gaps and their full potential in rainfall nowcasting requires more investigation. This work evaluates the performance of a deep convolutional neural network (called rainnet) and three optical flow algorithms (called Rainymotion Sparse, Rainymotion Dense, Rainymotion DenseRotation) compared with Eulerian Persistence to assess their predictive skills in nowcasting. Synthetic precipitation scenarios have been created with different motion fields (linear and rotational motions), velocities, intensities, sizes, and locations. The models have been evaluated to forecast different precipitation processes that contribute mainly to model errors such as constant and accelerated linear and rotational motions, growth and decay in both size and intensity. Different verification metrics have been used to evaluate the skill of the forecasts.

 

Keywords: radar rainfall nowcasting; deep learning; optical flow; extrapolation; rainnet; rainymotion

How to cite: Abdelhalim, A., Rico-Ramirez, M., and Han, D.: Evaluation of radar rainfall nowcasting techniques to forecast synthetic storms of different processes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10595, https://doi.org/10.5194/egusphere-egu22-10595, 2022.

EGU22-11143 | Presentations | AS1.3

Predicting Rainfall using Data-Driven Time Series Approaches 

Faisal Baig, Mohsen Sherif, Luqman Ali, Wasif Khan, and Muhammad Abrar Faiz

Rainfall plays a significant role in agricultural farming and is considered one of the major natural sources for all living things.  The increase in greenhouse emissions and change in climatic conditions have an adverse effect on the rainfall patterns. Therefore, it becomes crucial to analyze the changing patterns and to forecast rainfall  to mitigate natural disasters that could be caused by the unexpected heavy rainfalls. This paper aims to compare the performance of seven states of the art time series models namely Moving Average(MA), Naïve Forecast(NF), Simple Exponential(SE), Holt’s Linear(HL), Holt’s Linear Additive(HLA), Autoregressive Integrated Moving Average(ARIMA), Seasonal Autoregressive Integrated Moving Average(SARIMA) for the prediction of rainfall. The historical monthly rainfall data from six different stations in United Arab Emirates (UAE) was obtained to assess the performance of seven techniques. Experimental results show that ARIMA outperforms all the prediction models with a mean square error (RMSE) of 9.49 followed by Holt’s Linear model with an RMSE value of 9.91. The performance of all the models is comparable and shows promising performance in rainfall prediction. This also shows the ability of these models to predict the rainfall in arid regions like the UAE

How to cite: Baig, F., Sherif, M., Ali, L., Khan, W., and Faiz, M. A.: Predicting Rainfall using Data-Driven Time Series Approaches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11143, https://doi.org/10.5194/egusphere-egu22-11143, 2022.

EGU22-11240 | Presentations | AS1.3

High-frequency ensemble wind speed forecasting using deep learning 

Irene Schicker, Petrina Papazek, and Rosmarie DeWit

In this study, we present a deep learning-based method to provide seamless high-frequency wind speed forecasts for up to 30 hours ahead. For each selected site, our method generates an ensemble forecast with an update frequency of 10 to 15 minutes(depending on the observation site’s update-frequency). The main objective in this machine learning based post-processing method is to optimally exploit highly resolved NWP models and particularly utilize their multi-level meteorological parameters to integrate the three-dimensionality of weather processes. Further key objectives of this research are to consider different spatial and temporal resolutions and different topographic characteristics of the selected sites. We evaluate the best praxis for efficiently post-processing both the 10-meter wind speed at selected Austrian meteorological observation sites and wind speed on hub height of wind turbines in wind farms.

The method is based on an artificial neural network (ANN), particularly a long-short-term-memory (LSTM) adopted to process several differently structured inputs simultaneously (i.e., different gridded inputs along with observed time-series) and generate ensemble output. An LSTM layer models recurrent steps in the ANN and is, thus, useful for time-series, such as meteorological observations.

Our ensemble forecast method is evaluated for a case study in 2021 using several years of training, including extreme weather event for the selection of sites. The utilized data includes the meteorological observations, gridded nowcasting data as well as NWP data from ECMWF IFS and AROME at several pressure/altitude levels. Hourly runs for 12 test locations (selected TAWES sites covering different topographic situations in Austria) and two wind turbine sites in different seasons are conducted. The obtained results indicate that the model succeeds in learning from inputs while remaining computationally efficient. In most cases the ANN method yields high forecast-skills and is compared to available methods such as the raw NWP model output, climatology, and persistence.

How to cite: Schicker, I., Papazek, P., and DeWit, R.: High-frequency ensemble wind speed forecasting using deep learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11240, https://doi.org/10.5194/egusphere-egu22-11240, 2022.

EGU22-12086 | Presentations | AS1.3 | Highlight

GAN-based video prediction model for precipitation nowcasting 

Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, Karim Mache, Martin Schultz, and Xiefei Zhi

Detecting and predicting heavy precipitation for the next few hours is of great importance in weather related decision-making and early warning systems. Although great progress has been achieved in convective-permitting numerical weather prediction (NWP) over the past decades, video prediction models based on deep neural networks have become increasingly popular over the last years for precipitation nowcasting where NWP models fail to capture the quickly varying precipitation patterns. However, previous video prediction studies for precipitation nowcasting showed that heavy precipitation events are barely captured. This has been attributed to the optimization on pixel-wise losses which fail to properly handle the inherent uncertainty.  Hence, we present a novel video prediction model, named CLGAN, embedding the adversarial loss is proposed in this study which aims to generate improved heavy precipitation nowcasting. The model applies a Generative Adversarial Network (GAN) as the backbone. Its generator is a u-shaped encoder decoder network (U-Net) equipped with recurrent LSTM cells and its discriminator constitutes a fully connected network with 3-D convolutional layers. The Eulerian persistence, an optical flow model DenseRotation and an advanced video prediction model PredRNN-v2 serve as baseline methods for comparison. The models performance are evaluated in terms of application-specific scores including root mean square error (RMSE), critical success index (CSI), fractions skill score (FSS) and the method of object-based diagnostic evaluation (MODE). Our model CLGAN is superior to the baseline models for dichotomous events, i.e. the CSI, with a threshold of heavy precipitation (8mm/h), is significantly higher, thus revealing improvements in accurately capturing heavy precipitation events. Besides, CLGAN outperforms in terms of spatial scores such as FSS and MODE. We conclude that the predictions of our CLGAN architecture match the stochastic properties of ground truth precipitation events better than those of previous video prediction methods. The results encourage the applications of GAN-based video prediction architectures for extreme precipitation forecasting.

How to cite: Ji, Y., Gong, B., Langguth, M., Mozaffari, A., Mache, K., Schultz, M., and Zhi, X.: GAN-based video prediction model for precipitation nowcasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12086, https://doi.org/10.5194/egusphere-egu22-12086, 2022.

EGU22-12252 | Presentations | AS1.3

Stochastic downscaling of the 2m temperature with a generative adversarial network (GAN) 

Michael Langguth, Bing Gong, Yan Ji, Mozaffari Amirpasha, Karim Mache, and Martin G. Schultz

Inspired by the success of superresolution applications in computer vision, deep neural networks have recently been recognized as an appealing approach for statistical downscaling of meteorological fields. While further increasing the resolution of numerical weather prediction models is computationally very expensive, statistical downscaling models can accomplish this task much cheaper once they have been trained.

In this study, we apply a generative adversarial network (GAN) to downscale the 2m temperature over Central Europe where complex terrain introduces a high degree of spatial variability. GANs are considered superior to purely convolutional networks since the model is encouraged to generate data whose statistical properties are similar to real data. Here, the generator consists of an u-shaped encoder decoder network which is capable of extracting features on various spatial scales. As a quasi-realistic test suite, we map data from the ERA5 reanalysis dataset onto a 0.1°-grid with the help of short-range forecasts from the Integrated Forecasting System (IFS) model. To increase the complexity of the downscaling task, the ERA5 reanalysis data is coarsened beforehand onto a 0.8°-grid, thus increasing the downscaling factor to 8. We evaluate our statistical downscaling model in terms of several evaluation metrics which measure the error on grid point-level as well as the quality of the downscaled product in terms of spatial variability and produced probability function. We also investigate the importance of static and dynamic predictors such as the surface elevation and the temperature on different pressure levels, respectively. Our results motivate further development of deep neural networks for statistical downscaling of meteorological fields. This includes downscaling of other, inherently uncertain variables such as precipitation, operations on spatial resolutions at kilometer-scale and ultimately targets an operational application on output data from global NWP models.

How to cite: Langguth, M., Gong, B., Ji, Y., Amirpasha, M., Mache, K., and Schultz, M. G.: Stochastic downscaling of the 2m temperature with a generative adversarial network (GAN), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12252, https://doi.org/10.5194/egusphere-egu22-12252, 2022.

EGU22-12384 | Presentations | AS1.3

AI-based blending of conventional nowcasting with a convection-permitting NWP model 

Alexander Kann, Aitor Atencia, Phillip Scheffknecht, and Apostolos Giannakos

For hydrological runoff simulations in hydropower applications, accurate analyses and short-term forecasts of precipitation are of utmost importance. Traditionally, radar-based extrapolations are used for very short-term time scales (approx. 0 - 2 hours ahead). However, during recent years, convection-permitting NWP models have become better at very high spatial and temporal resolution forecasts (e.g. through radar assimilation, RUC configurations). Such models have the advantage of capturing the complex and non-linear evolution of precipitation systems like fronts or thunderstorms in a more physically accurate way than extrapolations, but they are also prone to inaccuracies in precipitation distribution. The aim of this paper is to employ machine learning to combine the strengths of the conventional radar extrapolation (localization and movement of existing storms) with the benefit of the model’s ability to predict storm evolution.  Results show that even a relatively simple sequential deep neural network is able to outperform both, the operational nowcasting and NWP model forecasts. However, the results are highly sensitive to variable selection, loss function, and localization features have a large impact on performance, which is also discussed.

How to cite: Kann, A., Atencia, A., Scheffknecht, P., and Giannakos, A.: AI-based blending of conventional nowcasting with a convection-permitting NWP model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12384, https://doi.org/10.5194/egusphere-egu22-12384, 2022.

EGU22-12529 | Presentations | AS1.3

Project IMA: Building the Belgian Seamless Prediction System 

Lesley De Cruz, Alex Deckmyn, Daan Degrauwe, Idir Dehmous, Laurent Delobbe, Wout Dewettinck, Edouard Goudenhoofdt, Ruben Imhoff, Maarten Reyniers, Geert Smet, Piet Termonia, Joris Van den Bergh, Michiel Van Ginderachter, and Stéphane Vannitsem

Thanks to recent advances in multisensory observation systems and high-resolution numerical weather prediction (NWP) models, a wealth of information is available to feed and improve operational weather forecasting systems. At the same time, end users such as the renewable energy sector and hydrological services require increasingly detailed and timely weather forecasts that take into account the latest observations.

However, data assimilation in NWP models cannot yet leverage the full spatial or temporal resolution of today's observation systems. Moreover, the combined assimilation and model run takes significantly more time than an extrapolation-based nowcast, and cannot match its accuracy at short lead times. Therefore, many National Meteorological Services (NMSs) are moving towards seamless prediction systems. Seamless prediction aims to make optimal use of today’s rapidly available, high-resolution multisensory observations, nowcasting algorithms and state-of-the-art convection-permitting NWP models. This approach integrates multiple data and model sources to provide a single, frequently updating deterministic or probabilistic forecast for lead times from minutes to days.

We present the seamless ensemble prediction system of the Royal Meteorological Institute of Belgium, called Project IMA (Japanese for "now" or "soon"). It provides rapidly updating seamless forecasts for the next 5 minutes to 24 hours. The nowcasting component is based on two systems: (1) the open-source probabilistic precipitation nowcasting scheme pySTEPS, which now features a scale-dependent blending with NWP ensemble forecasts (also presented in this session) and (2) an ensemble of INCA-BE nowcasts using two different NWP models, for other meteorological variables. The short-range NWP component consists of a multimodel lagged Mini-EPS of two convection-permitting configurations of the ACCORD system: AROME and ALARO, running at 1.3km resolution. It features a 3-hourly DA cycle and provides high-frequency precipitation output to facilitate the blending of precipitation nowcasts and forecasts. The system runs robustly using our NodeRunner tool based on EcFlow, ECMWF's operational work-flow package. We will give an overview of the development (past and future), some lessons learned, and use cases for Project IMA.

How to cite: De Cruz, L., Deckmyn, A., Degrauwe, D., Dehmous, I., Delobbe, L., Dewettinck, W., Goudenhoofdt, E., Imhoff, R., Reyniers, M., Smet, G., Termonia, P., Van den Bergh, J., Van Ginderachter, M., and Vannitsem, S.: Project IMA: Building the Belgian Seamless Prediction System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12529, https://doi.org/10.5194/egusphere-egu22-12529, 2022.

Terrain with different shapes and ground surface properties has extremely complex impacts on atmospheric motion, and the forecast uncertainty and complexity caused by terrain brings great challenges to disaster prevention and mitigation. Therefore, it is essential to design a new-style model topography disturbance model for ensemble prediction system specifically to solve the prediction uncertainty caused by complex terrain. In this paper, on the basis of combing the current models and methods for dealing with different terrain uncertainty, and considering the non-uniformity of terrain gradient, the key element of describing terrain complexity, an orthogonal terrain disturbance method based on terrain gradient is designed and proposed, and the obtained high-resolution orthogonal terrain disturbance is superimposed on the static terrain height of the model to generate different ensemble members, so as to describe the uncertainty in the terrain generation process of high-resolution numerical model. At the same time, a comparative study is carried out with the ensemble forecast of model terrain disturbance between using the new-style method and using different terrain interpolation schemes or smoothing schemes. The preliminary test shows that: first of all, the ensemble dispersion of terrain height disturbance based on the new-style method is closely related to the terrain gradient. The area with small terrain gradient has smaller terrain disturbance ensemble dispersion, while the area with large terrain gradient has larger ensemble dispersion, which shows that the new scheme is more reasonable. Furthermore, compared with the model terrain disturbance schemes with different interpolation or smoothing methods, the dispersion of the new-style method is larger, and the skill of the new-style method becomes more and more obvious with the increase of model resolution. Thirdly, from the comparative study of the forecast effect of high-level and low-level weather elements, the new-style method ensemble forecast has obvious improvement on the forecast effect of low-level variables, especially in areas with complex terrain or large terrain gradient. The possible reason is that the new method can more objectively describe the terrain uncertainty. Fourthly, compared with the ensemble forecast results of different interpolation and smoothing methods, the new-style terrain disturbance scheme can improve the precipitation probability forecast skill and reduce the ensemble average root mean square error, and improve the ensemble average forecast of upper-air elements and near-surface elements. Lastly, the test of the number of ensemble members shows that the prediction effect of new-style terrain disturbance scheme with less members is equivalent or better than that of the interpolation or smoothing terrain disturbance scheme with more members. In summary, the new-style terrain perturbation theory based on terrain gradient in this paper provides a technical reference for the development of complex terrain convection-allowing scale ensemble forecast, which has important theoretical value and application prospect.

Key words: complex terrain,ensemble prediction,convection-allowing scale,topographic perturbation,topographic gradient

How to cite: Chaohui, C., Yi, L., Hongrang, H., Kan, L., and Yongqiang, J.: Preliminary study of a new-style terrain disturbance method based on gradient inhomogeneity in convection-allowing scale ensemble prediction system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13244, https://doi.org/10.5194/egusphere-egu22-13244, 2022.

EGU22-13532 | Presentations | AS1.3

An Assessment Method of Squall Line Intensity Based on Cold Pool 

Ru Yang, Yongqiang Jiang, Chaohui Chen, Hongrang He, Yi Li, and Hong Huang

To quantify the intensity of squall line in mid-latitudes, the author recently proposed a squall line intensity assessment method based on cold pool, which provides a measure of squall line intensity.

The disturbance potential temperature density is calculated by using the potential temperature, water vapor and all kinds of water condensate output from the numerical weather forecast model, and the boundary of the cold pool is judged according to the disturbance potential temperature density less than -2K. Based on the contour surface buoyancy, the high surface buoyancy is calculated according to the disturbance potential temperature density, and then the strength of the cold pool is calculated. In this method, the intensity of squall line is analyzed comprehensively by principal component analysis, combined with the weather phenomena accompanied by squall line occurrence, such as cold pool intensity, surface wind speed, ground pressure variation, surface temperature variation, simulated radar echo and so on. The above analysis is the local intensity on different grid points when the squall line occurs, and the overall squall line intensity is obtained by accumulating the local intensity in the squall line range.

The method is verified by the model output data of a squall line process occurred in northern Jiangsu on May 16, 2013. The results show that the distribution of the local squall line intensity is coupled with the surface wind field and heavy precipitation. The intensity evolution of the overall squall line reaches the peak in a short time and then decreases, which corresponds to the life history of the birth, development, maturity and dissipation of the squall line, and also reflects the characteristics of the short life history of the squall line developing rapidly and then dissipating. This method provides technical support for the forecast of squall line and the emergency plan issued by meteorological department.

Acknowledgements. This research was supported by the National Natural Science Foundation of China (Grant Nos. 41975128 and 42075053).

Keywords: squall line, intensity, assessment method, disturbance potential temperature density

How to cite: Yang, R., Jiang, Y., Chen, C., He, H., Li, Y., and Huang, H.: An Assessment Method of Squall Line Intensity Based on Cold Pool, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13532, https://doi.org/10.5194/egusphere-egu22-13532, 2022.

NP6 – Turbulence, Transport and Diffusion

EGU22-501 | Presentations | NP6.1 | Highlight

Thermohaline response of the upper ocean to tropical cyclones. Observations and modelling. 

Pavel Pivaev, Vladimir Kudryavtsev, Nicolas Reul, and Bertrand Chapron

An impact of the upper ocean response to tropical cyclones (TC) is usually considered as a negative feedback mechanism between cooling of the mixed layer (ML) and intensity of a TC. Influence of TCs on the upper ocean is manifested as anomalies in sea surface temperature (SST) and sea surface salinity (SSS) in wakes of hurricanes, that can vary significantly along tracks of TCs (Reul et al. 2021). Proper modelling of ML dynamics is still vital to explain surface cooling observed in satellite and in situ data. Although numerous models of the ML evolution have been developed (e.g., Zilitinkevich et al. 1979, Gillian et al. 2020, and works cited therein including many schemes incorporated in numerical models), there is still a controversy as to turbulent closure schemes and simplified approaches that could allow for a quick and high quality assessment of ML parameters.

The purpose of the this work is to apply a simplified model of the upper ocean response to TCs suggested by Kudryavtsev et al. 2019 with barotropic and baroclinic modes resolved. To describe ML dynamics, results of Zilitinkevich and Esau (2003) are applied. The cases studied are those of hurricanes passing over the Amazon-Orinoco river plume: Igor (Reul et al. 2014), Katia (Grodsky et al. 2012) and Irma (Balaguru et al. 2020).

Best track parameters of the TCs are obtained from the IBTrACKS archive. Multi-source GHRSST data on SST as well as SMOS and SMAP satellite data on SSS are used to compare the observed ocean responses to the simulated ones. ISAS20 in situ archive data are used to provide vertical profiles of temperature and salinity as an input to the model. Precipitation and evaporation data are obtained from TRMM measurements and ERA5 reanalysis, respectively. Subsets of IBTrACKS, GHRSST, ISAS20, TRMM and ERA5 data specific to domain of a TC’s wake were produced by the Centre de Recherche et d'Exploitation Satellitaire (CERSAT), at IFREMER, Plouzane (France) for ESA funded project MAXSS (Marine Atmosphere eXtreme Satellite Synergy). Model simulations are consistent with the observations and provide a deeper insight in the physics of relationship between SST and SSS anomalies in TC wakes. On the basis of analysis of the observations and model results, a semi-empirical expressions to predict SSS and SST anomalies using TC parameters (radius, wind speed and translation velocity) and prestorm stratification are suggested.

The work was supported by the Russian Science Foundation through the Project No. 21-47-00038, by Ministry of Science and Education of the Russian Federation under State Assignment No. 0555-2021-0004 at MHI RAS, and State Assignment No. 0763-2020-0005 at RSHU (P.P. and V.K.). The ESA/MAXSS project support is also gratefully acknowledged (N.R. and B.C.).

How to cite: Pivaev, P., Kudryavtsev, V., Reul, N., and Chapron, B.: Thermohaline response of the upper ocean to tropical cyclones. Observations and modelling., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-501, https://doi.org/10.5194/egusphere-egu22-501, 2022.

EGU22-1687 | Presentations | NP6.1

Numerical investigation of cell formation in a 2-dimensional differentially heated shell utilizing thermo-electrohydrodynamics 

Yann Gaillard, Peter Haun, Peter Szabo, Yaraslau Sliavin, and Christoph Egbers

Today models of our atmosphere to study climate change become more and more important not only from a meteorological point of view but also from a global perspective to understand the large-scale motion of planetary waves that transport a large amount of energy. This study investigates numerically such large-scale flows in a simplified 2-dimensional model that is aligned to the AtmoFlow experiment. This experiment is the legacy of the GeoFlow experiment, which investigated planet mantle convection. The AtmoFlow experiment is a spherical shell that mimics a planet at a small scale, where terrestrial gravity is artificially induced by an equivalent electric central force field. This small planet can rotate synchronized or differentially by moving the inner and outer boundaries to simulated planetary rotation. Analogous to a real planet, the poles are cooled and the equator heated. The fluid used in the numerical simulation to mimic a planetary atmosphere is a dielectric fluid with an electric permittivity sensitive to temperature to induce convection similar to a terrestrial buoyancy. While the fluid is also sensitive to the temperate-dependent density, the spherical shell experiments are performed in free space and thus the experiment is planned to be operated on the International Space Station (ISS) after 2024. Flow patterns are retrieved using a Wollaston Shear Interferometry (WSI) and sent back to Earth's ground station.


To be able to investigate the flow structures recorded by the experiment, a numerical model is built. Here we only show 2-dimensional results of the shell in the equatorial plane without rotation. The boundary conditions in these simulations are set to an ideal fixed temperature where the inner shell is heated, and the outer is cooled. To induce thermo-electro-hydrodynamics convection, an electric voltage is applied at the inner shell whereas the outer is grounded. The resulting flow patterns evolve in time and are stationary, quasi-stationary, or chaotic structures. The arising convection cells can be classified using a time-averaged spatial Fast Fourier Transformation (FFT) of the temperature along the mid-gap of the domain to quantify a mode number. The heat transfer is expressed with the Nusselt number and increases with the Rayleigh number. This is reflected by the mode number increasing to a maximum before it decreases when the flow becomes unstable while maintaining a clear structure and mode shape with detaching plumes at the tangent cylinder.

How to cite: Gaillard, Y., Haun, P., Szabo, P., Sliavin, Y., and Egbers, C.: Numerical investigation of cell formation in a 2-dimensional differentially heated shell utilizing thermo-electrohydrodynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1687, https://doi.org/10.5194/egusphere-egu22-1687, 2022.

EGU22-2071 | Presentations | NP6.1

: Collapses in the oceanic Ekman boundary layer 

Victor Shrira and Joseph Oloo

Mixing in the uppermost part of the water column is crucial for modelling air-sea interaction, yet it remains poorly understood, especially the processes under strong wind conditions. The Ekman boundary layers are a salient feature of the air-sea interface. In the the Ekman boundary layers the current velocity vectors always rotates, making two components of the basic flow vorticity comparable and, thus, the boundary layer three dimensional. Linear instabilities of the homogeneous steady Ekman layers were examined and  found to occur for sufficiently large turbulent Reynolds numbers.  Here, we derive a model of  nonlinear instabilities of 3d Ekman layer  in deep ocean taking into account also a possible weak stratification of the boundary layer caused by air entrainment due to wave breaking or solar heating. The model exploits the observation that the corresponding linearized boundary value problem  always supports a “vorticity wave” mode which is often decaying. Employing an asymptotic procedure utilizing smallness of the boundary layer thickness to the characteristic wavelength of perturbations  scaled as  inverse Reynolds number squared we derive a novel nonlinear evolution equation with a pseudo-differential dispersion.  We take into  account viscosity and weak stratification  in the boundary layer. Within the framework of this equation a  wide class of initial conditions, which we a priori specify, leads to `collapses’ of localized perturbations, that is an initial perturbation becomes more and more localised and its amplitude becomes infinite in finite time forming a point singularity. We derived a self-similar solution describing these collapses. The mechanism of collapse is essentially nonlinear. A new insight into linear instabilities has been also  obtained.  The collapses are expected to result in intense mixing and even temporary destruction of the boundary layer.

How to cite: Shrira, V. and Oloo, J.: : Collapses in the oceanic Ekman boundary layer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2071, https://doi.org/10.5194/egusphere-egu22-2071, 2022.

EGU22-4286 | Presentations | NP6.1

Mechanisms of the polar low development 

Alexandra Kuznetsova, Evgeny Poplavsky, and Yuliya Troitskaya

In the recent researches, the disagreement on the issue of the mechanisms of the polar low development is observed. Thus, in [1], on the basis of numerical experiments, the dominant role of baroclinic instability during the development of polar low at the initial stage of atmospheric vortex formation is noted, but then the polar low was maintained due to the sensible heat flux from the surface. At the same time, in [2], the dominant role of condensational heating was noted with a minor role of sensible and latent heat fluxes on the ocean surface. It was shown in [3] that sensible and latent heat fluxes between the ocean and the atmosphere play a decisive role both at the stage of baroclinic intensification of the polar low and at the stage of its maintenance; later experiments [4] showed that an increase in the ocean surface temperature leads to the emergence of more prolonged and long-lived polar lows. In this work, the simulations to elucidate the mechanisms of the development of polar low were carried out within the framework of the WRF atmosphere model. As a control experiment, an experiment with certain physical processes available in WRF was used. To assess the sensitivity of a polar low to convective processes in the model, calculations were carried out that were completely identical to the control experiment, but with the shutdown of certain physical processes. When the heat generated by condensation was turned off, the role of latent heat was studied. This was done by turning off the contribution of heat to the temperature profile in the module responsible for the microphysics of clouds. To assess the sensitivity to heat fluxes on the surface, a numerical experiment was carried out with such switching off. To reveal the role of the baroclinic growth as a mechanism for intensifying the atmospheric vortex, both heat fluxes on the surface and the release of latent heat during condensation were turned off. The role of energy fluxes on the ocean surface during the development of the polar low was demonstrated, which forms new directions for the study of this issue.

This work was supported by a RSF grant № 21-77-00076.

References

  • Føre, I., Kristjánsson, J. E., Kolstad, E. W., Bracegirdle, T. J., Saetra, Ø. and Røsting, B. (2012), A ‘hurricane-like’ polar low fuelled by sensible heat flux: high-resolution numerical simulations. Q.J.R. Meteorol. Soc., 138: 1308–1324. doi:10.1002/qj.1876
  • Watanabe, S.I. and H. Niino, 2014: Genesis and Development Mechanisms of a Polar Mesocyclone over the Japan Sea. Mon. Wea. Rev., 142, 2248–2270, https://doi.org/10.1175/MWR-D-13-00226.1
  • Kolstad, E. W., T. J. Bracegirdle, and M. Zahn (2016), Re-examining the roles of surface heat flux and latent heat release in a “hurricane-like” polar low over the Barents Sea, J. Geophys. Res. Atmos., 121, 7853–7867, doi:10.1002/2015JD024633
  • Kolstad, E. W. and Bracegirdle, T. J. (2017), Sensitivity of an apparently hurricane-like polar low to sea-surface temperature. Q.J.R. Meteorol. Soc, 143: 966–973. doi:10.1002/qj.2980

How to cite: Kuznetsova, A., Poplavsky, E., and Troitskaya, Y.: Mechanisms of the polar low development, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4286, https://doi.org/10.5194/egusphere-egu22-4286, 2022.

EGU22-4409 | Presentations | NP6.1

Updated approximation formulas for the radius and temperature of saline droplets 

Dmitry Kozlov and Yuliya Troitskaya

The number of spume droplets increases rapidly with wind speed [1], [2], so that under hurricane conditions the spray-mediated heat and momentum fluxes can have a significant impact on the exchanging processes between the ocean and the atmosphere. The estimation of the additional enthalpy flux, as well as latent and sensible heat fluxes, is based on the solution of the microphysics equations for a single saline droplet, detailed in [3]. In theoretical studies [4]-[6] it was shown that the evolution of the radius and temperature of a droplet can be described accurate enough using the following formulas:

T(t)=Twb+(Tw-Twb)e-t/τT,

r(t)=req+(r0-req)e-t/τr,

where Twb is the wet bulb temperature, req is the equilibrium radius, τT and τr is the e-folding time to reach that temperature Twb and radius req, T(0)=Tw is the temperature of the water, r(0)=r0 is the initial radius of the drop. However, the numerical solution of the microphysical equations of the droplet’s thermodynamics showed that for the characteristic conditions of a tropical cyclone at the initial stage evaporation occurs much more intensively than after reaching the wet bulb temperature, and the characteristic time of this change is the same as for a change in temperature. In the present study, we propose an updated parameterization of the evolution of the radius and temperature of a single saline droplet, which provides more accurate describing of the droplet’s thermodynamics. On its basis we obtained estimations of enthalpy, latent and sensible heat fluxes caused by droplets generated by bag break-up instability (the main source of spume droplets at extreme wind speeds [7]).

 

[1]      E. L. Andreas. A review of the sea spray generation function for the open ocean // Atmos. Interact. - 2002. - V. 1. - P. 1–46.

[2]      D. H. Richter and F. Veron. Ocean spray: An outsized influence on weather and climate // Phys. Today - 2016. - V. 69. - №11. - P. 34–39.

[3]      H. R. Pruppacher and J. D. Klett. Microphysics of clouds and precipitation, D. Reidel. Norwell: Mass., 2010.

[4]      E. L. Andreas. Time constants for the evolution of sea spray droplets // Tellus, Ser. B - 1990. - V. 42 B. - №5. - P. 481–497.

[5]      E. L. Andreas. Sea spray and the turbulent air-sea heat fluxes // J. Geophys. Res. - 1992. - V. 97. - №C7. - P. 11429–11441.

[6]      E. L. Andreas. Approximation formulas for the microphysical properties of saline droplets // Atmos. Res. - 2005. - V. 75. - №4. - P. 323–345.

[7]      Y. Troitskaya, A. Kandaurov, O. Ermakova, D. Kozlov, D. Sergeev, and S. Zilitinkevich. The “bag breakup” spume droplet generation mechanism at high winds. Part I: Spray generation function // J. Phys. Oceanogr. - 2018. - V. 48. - №9. - P. 2168–2188.

How to cite: Kozlov, D. and Troitskaya, Y.: Updated approximation formulas for the radius and temperature of saline droplets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4409, https://doi.org/10.5194/egusphere-egu22-4409, 2022.

EGU22-4807 | Presentations | NP6.1

On the interaction of small-scale turbulence and internal waves in the framework of the semi-empirical turbulence model in a stratified fluid 

Irina Soustova, Lev Ostrovsky, Yuliya Troitskaya, Daria Gladskikh, and Evgeny Mortikov

The interaction of small-scale turbulence with internal and surface waves is an urgent problem of hydrology and oceanology. In particular, this issue is especially important for the properties of the upper layer of the ocean and the inland waters.

Small-scale processes that exist against the background of average profiles of various hydrophysical quantities (temperature, velocity, density, and large-scale currents caused, in particular, by wind forcing) are usually nonlinear and therefore effectively interact with each other. We consider some aspects of the interaction of internal waves and turbulence in the upper layer of the ocean and inland waters within the framework of the semi-empirical theory of turbulence in a stratified fluid. The model used in this study takes into account  mutual transformation of the kinetic and potential energies of turbulent fluctuations [Ostrovsky&Troitskaya, 1987; Zilitinkevich et al., 2013]. The effects of amplification and maintenance of turbulence by low-frequency and high-frequency internal waves, quasi-stationary distributions of turbulent energy in the presence of a shear caused by a low-frequency internal wave are investigated; the role of the transformation of energies on the indicated processes is analyzed.

A modification of the k-epsilon mixing scheme is also proposed, which removes the limitation on the existence of turbulence at large values of the gradient Richardson number. Within the framework of the modification, the parameterization of the Prandtl number is used, which makes it possible to take into account the influence of density stratification and velocity shear on mixing processes.

A numerical study of the influence of vertical mixing schemes on the transfer processes of biochemical fields in an internal reservoir was also carried out. The modified scheme was implemented into a three-dimensional model of thermo-hydrodynamics and biochemistry of an inland water body [Gladskikh et al., 2021], and a series of numerical experiments was conducted.

The work was supported by the RFBR (20-05-00776; 20-05-00322; 21-05-52005), and by Moscow Center of Fundamental and Applied Mathematics (agreement with the Ministry of Science and Higher Education 075-15-2019-1621).

Ostrovsky LA, Troitskaya YuI (1987) A model of turbulent transfer and dynamics of turbulence in a stratified shear flow. Izv Akad Nauk SSSR, Fiz Atmos Okeana. 3:101–104.
Zilitinkevich SS, Elperin T, Kleeorin N, Rogachevskii I, Esau I (2013) A hierarchy of Energyand Flux-Budget (EFB) turbulence closure models for stably-stratified geophysical flow. Boundary-Layer Meteorol. 146:341–373
Gladskikh DS, Stepanenko VM, Mortikov EV (2021) The Effect of the Horizontal Dimensions of Inland Water Bodies on the Thickness of the Upper Mixed Layer. Water Resour 48:226–234

How to cite: Soustova, I., Ostrovsky, L., Troitskaya, Y., Gladskikh, D., and Mortikov, E.: On the interaction of small-scale turbulence and internal waves in the framework of the semi-empirical turbulence model in a stratified fluid, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4807, https://doi.org/10.5194/egusphere-egu22-4807, 2022.

Recently, we demonstrated that the temporal fetch-dependent wind-wave growth under abruptly applied wind forcing can be accurately described by considering a stochastic ensemble of multiple unstable harmonics (submitted to PRL). In that study, the two-phase viscous shear flow instability at the air-water interface was examined using the energy growth rates β and the group velocities cg of the unstable harmonics obtained by solving the coupled Orr-Sommerfeld (OS) equations in air and water with appropriate boundary and initial conditions. The predictions of this unidirectional model compare well with measurements of random time-and space-dependent wave field performed in our laboratory (JFM 828, 459, 2017). The eigenvalues of the model equations determine β and cg of each harmonic defined by its wavenumber; the suggested model then allows computation of variation with time and with fetch of the statistical wave field parameters such as the characteristic wave amplitude and the instantaneous dominant frequency. The eigenvalues of the OS system however depend strongly on the adopted mean vertical velocity profile in air and in water. The water velocity is assumed to decay exponentially with depth from the maximum value corresponding to the drift velocity at the interface. In air, we assumed the lin-log suggested by Miles that consists of a linear segment in the viscous sublayer connected smoothly to a logarithmic turbulent velocity profile over smooth water surface. The assumption of smooth water surface is reasonable at the onset of wind. However, emerging wind-waves render the surface rough; the surface roughness becomes more pronounced at higher wind forcing and larger fetches. In the present study, we extend our previous study and apply the developed OS solver to investigate the dependence of the viscous shear-flow stability on the shape of air velocity profile. We take advantage of the detailed wind-velocity profiles measured in our facility at various wind velocities and a number of fetches (JGR 117, C00J19, 2012)that demonstrated the significant deviations of the actual air velocity profiles over waves from the shape corresponding to smooth-surface. The surface drift velocities under different operational conditions were also measured. The effect of the evolving wind-wave field on eigenvalues of the OS system of equation and thus on the domains of instability, the energy growth rates β and the group velocities cg is studied. These results extend our understanding of the interrelation between the varying in time and space wind-wave field and the turbulent airflow above the water surface and shed light on momentum and energy exchange between air and water.

How to cite: Geva, M. and Shemer, L.: Viscous shear instability at air-water interface as a function of wind velocity profile, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6007, https://doi.org/10.5194/egusphere-egu22-6007, 2022.

EGU22-6245 | Presentations | NP6.1

On the dynamics of a drift flow under low wind 

Oleg Druzhinin

The dynamics of a drift flow in the near-surface water layer driven by a turbulent air wind is investigated by direct numerical simulation (DNS). Comparatively low (up to 2×104) bulk Reynolds numbers of the air-flow are considered when the air boundary layer is turbulent but velocity fluctuations in the water are sufficiently small and the water surface remains aerodynamically smooth. It is shown that a drift flow develops in the near-surface water layer, and its velocity grows monotonically with time. At long times there develops an instability which leads to a saturation of the growth of the drift-velocity. A threshold Reynolds number is defined in DNS under which the drift flow becomes unstable, and a parameterization of the surface drift velocity is formulated in terms of the air-flow friction velocity.

How to cite: Druzhinin, O.: On the dynamics of a drift flow under low wind, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6245, https://doi.org/10.5194/egusphere-egu22-6245, 2022.

EGU22-7649 | Presentations | NP6.1

Experimental investigation of microwave signal scattered by the breaking waves 

Nikita Rusakov, Georgy Baidakov, Alexander Kandaurov, Yuliya Troitskaya, Evgeny Poplavsky, and Olga Ermakova

The work is concerned with the study of the breaking surface wave impact on the scattered radar signal in laboratory conditions using optical methods for analyzing the state of the water surface.

The experiments were carried out on the reconstructed TSWiWaT wind wave flume of the IAP RAS. The channel is 12 m long, the channel cross-section varies from 0.7 x 0.7 m at the entrance to 0.7 x 0.9 m in the working section at a distance of 9 m. The airflow speed on the axis is 3-35 m/s, which corresponds to the values of the wind speed U10 of 11-50 m/s.

At the beginning of the channel, a wavemaker was installed, operating in a pulsed mode and generating a train of three long waves every 15 seconds. In front of the area under study, an inclined plate was installed under the water, simulating shallow water and stimulating the breaking of waves in the zone of optical and radar measurements. In parallel, wind waves were generated. Due to the design features of the experimental setup, the distance from the beginning of the channel to the inclined plate in the case of optical measurements was 884 cm, and for radar measurements - 781 cm.

Radar measurements were carried out using a Doppler scatterometer operating at a wavelength of 3.2 cm, with the ability to simultaneously receive two direct and two cross-polarizations (VV, HH, VH, HV). The dimensions of the observation window on the water surface varied depending on the selected incidence angles (30, 40, 50 degrees). Optical measurements were carried out independently of radar measurements using three cameras with a shooting frequency of 50 Hz. Using a specially developed algorithm based on threshold processing of the image brightness, the time dependences of the fraction of breakers on the area of the investigated water surface during the passage of a train of three waves were calculated.  Due to the different configuration of the experiments, the data of radar and optical measurements are separated in time, their synchronization was performed using correlation analysis.  Comparison of the data made it possible to find that, on cross-polarization, the received power monotonically increases with an increase in the fraction of breakers, while on direct polarization, the change in power remains within the values observed during collapses of wind waves. Further comparison of the values of the radar cross-section of the water surface and the relative area of the wave breaking will make it possible to determine the influence of the breaking on the formation of the scattered signal.

This work was supported by the Russian Science Foundation (RSF) project No. 21-17-00214.

How to cite: Rusakov, N., Baidakov, G., Kandaurov, A., Troitskaya, Y., Poplavsky, E., and Ermakova, O.: Experimental investigation of microwave signal scattered by the breaking waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7649, https://doi.org/10.5194/egusphere-egu22-7649, 2022.

EGU22-8909 | Presentations | NP6.1

Assessment of the sea aerosol production including the "bag breakup" effect in the spray generation function 

Evgeny Poplavsky, Alexandra Kuznetsova, Alexander Dosaev, and Yuliya Troitskaya

Marine aerosol has a large impact on the earth system, including the physics and chemistry of the atmosphere over the oceans. It is a suspension in the air, consisting mainly of droplets injected from the ocean surface as a result of wave breaking in the coastal zone or during strong winds. A marine aerosol production model is an element of great importance in climate change and forecasting models.

In this work, the calculation of the production of sea aerosol using the developed parameterization of the sea spray generation function is carried out taking into account the contribution of the «bags breakup» spume droplet generation mechanism. For example, the work [1] show the decisive contribution of this type of spray to the sea spray generation function. The calculation is carried out both on the basis of reanalysis data on the global distribution of wind speed (CFSv2 [2]) and wave parameters (WAVEWATCH III® Hindcast and Reanalysis Archives [3]), and on the basis of calculation data within the WRF atmospheric model and the WAVEWATCH III wave model. An assessment of the production of sea aerosol is carried out using the example of hurricane Irma. The wind data in the calculations in the WRF model is obtained using the Large Eddy Simulation (LES) technique of the planetary boundary layer (PBL) with the boundary conditions from the CFSv2 reanalysis. Wave parameters data is obtained from calculations within the WAVEWATCH III wave model. A comparison of the resulting sea spray generation function obtained using the WRF LES and WAVEWATCH III data and the distribution obtained using the reanalysis data is carried out. Conclusions are made about the advantages of using computational models of high spatial resolution.

The work is supported by President Grant for young scientists MK-2489.2022.1.5.

[1] Troitskaya, Y., Kandaurov, A., Ermakova, O., Kozlov, D., Sergeev, D., & Zilitinkevich, S. (2018). The “bag breakup” spume droplet generation mechanism at high winds. Part I: Spray generation function. Journal of physical oceanography48(9), 2167-2188.

[2] Saha, S., et al. 2011, updated monthly. NCEP Climate Forecast System Version 2 (CFSv2) Selected Hourly Time-Series Products. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/D6N877VB. Accessed 14 December 2021.

[3] https://polar.ncep.noaa.gov/waves/hindcasts/

How to cite: Poplavsky, E., Kuznetsova, A., Dosaev, A., and Troitskaya, Y.: Assessment of the sea aerosol production including the "bag breakup" effect in the spray generation function, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8909, https://doi.org/10.5194/egusphere-egu22-8909, 2022.

EGU22-10841 | Presentations | NP6.1

Scaling of spacing between surface streaming on non-breaking and breaking wind waves 

Wu-ting Tsai and Guan-hung Lu

The high-speed, wind-aligned streaks on the wind waves are geometrically similar to the low-speed streaks observed in the turbulent wall layer. It is generally accepted that the spanwise spacing between the low-speed streaks in wall-bounded turbulent flow, when scaled by the viscous length, exhibit probability distribution conforming to lognormal behavior with a universal mean value of 100 independent on the wall friction velocity. Analyses of thermal images from wind-wave flume experiments, however, reveal that the scaling between the mean streak spacing and the surface friction velocity is different from that of wall-bounded flow. For non-breaking waves, the scaled mean streak spacing becomes notably narrower than that between low-speed streaks next to the solid wall. Comparative numerical simulations reveal that the presence of surface waves intensifies the generation of quasi-streamwise vortices that form the elongated streaks, and reduces the streak spacings. For breaking wind waves, analyses of the consecutive image sequences reveal that the breakers wipe out the existing surface streaks. After the passage of the breakers, the wind-aligned streaks reform immediately, which are then destructed again by the next breaking waves. In contrast to the streaks on the non-breaking waves, the scaled mean streak spacing in the wake of breakers is close to the canonical value of 100, which approximately follows the wall-flow scaling.

How to cite: Tsai, W. and Lu, G.: Scaling of spacing between surface streaming on non-breaking and breaking wind waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10841, https://doi.org/10.5194/egusphere-egu22-10841, 2022.

A series of experiments was carried out on the Thermostratified Wind Wave Tank of IAP RAS, to study the processes of secondary generation of spray due to the fall of the droplets onto a rough surface. The general scheme of the experiments was similar to [1]. The range of equivalent wind speed from U10 is from 21 to 34 m/s. Initially, high-speed filming with shadow visualization of the rough surface from above was performed, followed by detection, marking and calculating the number of events during image processing using special programs. It has been demonstrated that the number of phenomena of falling drops into water per unit time per unit area, leading to the formation of spray, significantly exceeds the similar values for previously studied mechanisms of spray generation: liquid ligaments fragmentation type, bubbles rupture and bag-breakup fragmentation type. Further, detailed studies of this phenomenon were carried out with a higher resolution filming. Two main scenarios of this event: with the formation of a “crown” at large angles of drop incidence, and the so-called "jet" at small angles were identified by analogy with [2]. The number droplets, size and velocity distributions were obtained for different wind speeds. These results can be used to design a spray generation function due to this phenomenon.

Investigations were supported by Russian Science Foundation project 21-19-00755 (carrying out experiments), Russian Foundation Basic Research project 21-55-52005 (data processing), work of A.A. Kandaurov was partially supported by the President's grant for young scientists МК-5503.2021.1.5.

  • Troitskaya, A. Kandaurov, O. Ermakova, D. Kozlov, D. Sergeev, and S. Zilitinkevich, The ‘Bag Breakup’ Spume Droplet Generation Mechanism at High Winds. Part I: Spray Generation Function, J. Phys. Oceanogr., vol. 48, no. 9, pp. 2167–2188, 2018.
  • V. Gielen, P. Sleutel, J. Benschop, M. Riepen, V. Voronina, C. W. Visser, D. Lohse, J. H. Snoeijer, M. Versluis, andH. Gelderblom, Oblique drop impact onto a deep liquid pool, Phys. Rev. Fluids, vol. 2, pp. 083602, 2017.

How to cite: Kandaurov, A., Ermakova, O., Troitskaya, Y., and Sergeev, D.: Investigation of the mechanism of production of spray due to falling drops on the water surface in the framework of laboratory modeling of wind-wave interaction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11007, https://doi.org/10.5194/egusphere-egu22-11007, 2022.

EGU22-12026 | Presentations | NP6.1 | Highlight

Evaluating the evolution of cyclone IDAI using the physically based PASM air-sea flux model 

Royston Fernandes, Jean-Luc Redelsperger, and Marie-Noelle Bouin

Earth System Models (ESM) and Numerical Weather Prediction (NWP) systems often have large biases in their representation of surface-atmosphere fluxes when compared to observations. Over sea, these biases are more pronounced due to dynamic non-linear interactions between atmosphere and sea surface waves. This non-linearity is not accurately represented by traditional semi-emiprical models like COARE. To this end, the PASM (Physically derived Air-Sea Momentum flux) model, developed by us, is the first attempt to represent air-sea exchanges by considering the two-way interaction between the ocean-waves and the atmospheric flow. It can simulate (i) the main turbulent eddies of the air-flow, and (ii) the wind-wave interactions including wave growth, transport and breaking. This model has been previously demonstrated to better predict the air-sea fluxes under 10m high wind speeds greater than 20m/s, where traditional approaches like COARE fail. In this study, we evaluate for the first time, the evolution of cyclone IDAI off the coast of Madagascar, using PASM and COARE approaches, to demonstrate the efficiency of our physically based model in better simulating the evolution and trajectory of cyclones, and thus its usefulness in ESM and NWP models.

How to cite: Fernandes, R., Redelsperger, J.-L., and Bouin, M.-N.: Evaluating the evolution of cyclone IDAI using the physically based PASM air-sea flux model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12026, https://doi.org/10.5194/egusphere-egu22-12026, 2022.

EGU22-51 | Presentations | NP6.2

Novel Coccolithophores from the Lower Deep Photic Zone Off Bermuda 

Josue Millan, Amos Winter, Richard W Jordan, and Leocadio Blanco-Bercial

Coccolithophores are a ubiquitous oceanic phytoplankton group. Their unique ability to acquire carbon from different environmental sources and to produce calcareous body scales (coccoliths) make them an integral functional group in the biogeochemical cycling of carbon. Despite this, their vertical distribution, particularly in the lower photic zone (LPZ), species composition, and life cycles, are still poorly understood. Discrete water samples were examined from the LPZ during the 2020 fall overturn event occurring from October to November at hydrostation S of the Bermuda Atlantic Time-Series (BATS). This provided an opportunity to compare our results with a previous BATS survey of coccolithophore population dynamics taken 28 to 26 years earlier (1992-1994). This latter study demonstrated that coccolithophores exhibit seasonal changes in their vertical and horizontal distribution and showed that the coccolithophore population transition of the LPZ occurs primarily at overturn events. Here, we place particular emphasis on those LPZ coccolithophore species adapted to live between the deep chlorophyll maximum and the upper mesopelagic zone because of their potential for mixotrophic activity. We discovered numerous unidentified taxa in this region, which may be either new to science or alternate phases of already described species. Some of the holococcolithophores appear to be associated with the Papposphaeraceae, with similarities to the Turrisphaera-phase. In addition, we provide the first unquestionable evidence of Florisphaera profunda combination coccospheres, featuring both heterococcolith and holococcolith phases in the same sample.

How to cite: Millan, J., Winter, A., Jordan, R. W., and Blanco-Bercial, L.: Novel Coccolithophores from the Lower Deep Photic Zone Off Bermuda, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-51, https://doi.org/10.5194/egusphere-egu22-51, 2022.

EGU22-895 | Presentations | NP6.2

Gyrotactic plankton cells in turbulence: the effects of motility, shape, fluid acceleration and inertia 

Eric Climent, Jingran Qiu, Zhiwen Cui, and Lihao Zhao

A detailed understanding of the physical mechanisms driving gyrotactic species to migrate vertically towards the surface allows better quantification of biogeochemical fluxes across the ocean. We focus on marine phytoplankton cells that are motile under gyrotactic forcing. Some species spontaneously swim in the direction opposite to gravity [1]. Gyrotaxis is originating either from morphological aspects (elongated shape, density heterogeneity) or the coupled effect of swimming and settling which results in an inertial torque. Indeed, fluid inertial torque may have a potential impact on the gyrotaxis for elongated planktonic swimmers, especially for those forming long chains and thus having large swimming and settling speeds
Based on numerical simulations of hundreds of thousands of micro-organisms swimming in homogeneous isotropic turbulence, we will comment on the different sources of gyrotactic induced spatial clustering [2, 3] and vertical migration [4]. Some specific configurations lead to the accumulation of elongated plankton cells in upwelling flow regions enhancing their ability to move across turbulence through the water column.  

[1] Kessler J.O. (1985), Nature - 313, 218–220.
[2] Durham W. M., et al. (2013) Nat. Commun. - 4, 2148.
[3] De Lillo F., et al. (2014) Phys. Rev. Lett. – 112, 044502
[4] Lovecchio S., et al. (2019) Sci. Adv. - 5: eaaw7879

How to cite: Climent, E., Qiu, J., Cui, Z., and Zhao, L.: Gyrotactic plankton cells in turbulence: the effects of motility, shape, fluid acceleration and inertia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-895, https://doi.org/10.5194/egusphere-egu22-895, 2022.

EGU22-897 | Presentations | NP6.2

Phytoplankton-zooplankton dynamics in three-dimensional turbulent flows behind an idealized island 

Stefano Berti, Alice Jaccod, Enrico Calzavarini, and Sergio Chibbaro

Plankton constitutes the productive base of aquatic ecosystems and plays a key role in climate dynamics, by taking part in the global carbon budget. Understanding how turbulent flows affect the distributions of planktonic species is a complex problem that has attracted considerable interest in the past, with particular emphasis on the scaling behavior of plankton variance spectra. The issue is relevant to assess the relative importance of fluid and biological dynamics, and to quantify the patchiness of plankton spatial distributions. Indeed, were the spectra of the, reactive, planktonic fields different from those of a passive (non-reactive) scalar, this would point to predominant biological activity in the corresponding range of scales. Furthermore, spectral slopes give information on the scale-by-scale intensity of the fluctuations of biological population densities and, hence, could allow to quantify the typical size of structures of highest plankton concentration.

Previous numerical studies provided interesting insight into plankton bloom formation and patchiness. However, they relied on simplified kinematic flow settings or on turbulence parametrizations. By means of direct numerical simulations, in this work we investigate the dynamics of interacting phytoplankton and zooplankton populations in two and three-dimensional turbulent wakes behind a cylinder. We mainly aim at identifying the minimal flow ingredients needed to sustain a bloom, and at characterizing how the latter could be affected by multiscale fluid properties. Notwithstanding its idealized character, the system we consider allows us to avoid any bias possibly coming from the modeling of small-scale fluid motions. Our analysis focuses on the impact of the space dimensionality of the advecting velocity field on the variance spectra, and spatial distributions, of the planktonic species.

In spite of the different statistical properties of the two-dimensional and three-dimensional carrying flows, we find that the qualitative biological dynamics in the two cases share important common features, mostly independent of the space dimensionality. This observation suggests that, in both cases, the emergence of persistent blooms is controlled by the ratio between the typical timescales of the biological activity, and of the fluid flow at large length scales. Similarly, in both two and three dimensions, we find that the spectral properties of the planktonic populations are essentially indistinguishable from those of an inert tracer. This result then hints at the prevailing role of turbulent transport over biological mechanisms in the generation of plankton patchiness. The main difference, instead, that arises from the comparison of our two and three-dimensional configurations concerns the local spatial distribution of plankton density fields. In fact, the three-dimensional turbulent dynamics tend to destroy the localized coherent structures characterizing the two-dimensional flow, in which the planktonic species are mostly concentrated, thus reducing the phytoplankton average biomass in the system.

How to cite: Berti, S., Jaccod, A., Calzavarini, E., and Chibbaro, S.: Phytoplankton-zooplankton dynamics in three-dimensional turbulent flows behind an idealized island, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-897, https://doi.org/10.5194/egusphere-egu22-897, 2022.

EGU22-2386 | Presentations | NP6.2

Accumulation and alignment of elongated gyrotactic swimmers in turbulence 

Linfeng Jiang, Zehua Liu, and Chao Sun

Understanding the dynamics and transport of elongated gyrotactic swimmers in a flow is essential for the ecology of aquatic plankton. We study their dynamics in turbulence, whose orientation is governed by gravitational torque and local fluid velocity gradient. The gyrotaxis strength is measured by the ratio of the Kolmogorov time scale to the reorientation time scale due to gravity, and a large value of this ratio means the gyrotaxis is strong. By means of direct numerical simulations, we investigate the effects of swimming velocity and gyrotactic stability on spatial accumulation and alignment. Three-dimensional Voronoi analysis is used to study the spatial distribution and time evolution of the particle concentration. We study spatial distribution by examing the overall preferential sampling and where clusters and voids (subsets of particles that have small and large Voronoi volumes respectively) form. Compared with the ensemble particles, the preferential sampling of clusters and voids is found to be more pronounced. The clustering of fast swimmers lasts much longer than slower swimmers when the gyrotaxis is strong and intermediate, but an opposite trend emerges when the gyrotaxis is weak. In addition, we study the preferential alignment with the Lagrangian stretching direction, with which passive slender rods have been known to align. We show that the Lagrangian alignment is reduced by the swimming velocity when the gyrotaxis is weak, while the Lagrangian alignment is enhanced for the regime in which gyrotaxis is strong.

How to cite: Jiang, L., Liu, Z., and Sun, C.: Accumulation and alignment of elongated gyrotactic swimmers in turbulence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2386, https://doi.org/10.5194/egusphere-egu22-2386, 2022.

EGU22-3718 | Presentations | NP6.2

Role of large-scale advection and small-scale turbulence on vertical migration of gyrotactic swimmers 

Cristian Marchioli, Harshit Bhatia, Gaetano Sardina, Luca Brandt, and Alfredo Soldati

Using DNS-based Eulerian-Lagrangian simulations, we investigate the dynamics of small gyrotactic swimmers in free-surface turbulence. We consider open channel flow turbulence in which bottom-heavy swimmers are dispersed. Swimmers are characterized by different vertical stability, so that some realign to swim upward with a characteristic time smaller than the Kolmogorov time scale, while others possess a re-orientation time longer than the Kolmogorov time scale. We cover one order of magnitude in the flow Reynolds number, and two orders of magnitude in the stability number, which is a measure of bottom heaviness. We observe that large-scale advection dominates vertical motion when the stability number, scaled on the local Kolmogorov time scale of the flow, is larger than unity: This condition is associated to enhanced migration towards the surface, particularly at low Reynolds number, when swimmers can rise through surface renewal motions that originate directly from the bottom-boundary turbulent bursts. Conversely, small-scale effects become more important when the Kolmogorov-based stability number is below unity: Under this condition, migration towards the surface is hindered, particularly at high Reynolds, when bottom-boundary bursts are less effective in bringing bulk fluid to the surface. In an effort to provide scaling arguments to improve predictions of models for motile micro-organisms in turbulent water bodies, we demonstrate that a Kolmogorov-based stability number around unity represents a threshold beyond which swimmer capability to reach the free surface and form clusters saturates.

How to cite: Marchioli, C., Bhatia, H., Sardina, G., Brandt, L., and Soldati, A.: Role of large-scale advection and small-scale turbulence on vertical migration of gyrotactic swimmers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3718, https://doi.org/10.5194/egusphere-egu22-3718, 2022.

EGU22-3902 | Presentations | NP6.2

Lagrangian connectivity of marine plankton under thermal constraints 

Darshika Manral, Linda Amaral-Zettler, and Erik van Sebille

The biogeographic distribution of marine planktonic communities in the global ocean and its drivers has been a topic of great interest in the scientific community. Some of these drivers can be abiotic: ocean currents, temperature, salinity, nutrients, and others biotic: presence of predators and competitive species. In our study, we focus on the distribution mediated by ocean currents and temperature. Combining Lagrangian modeling and network theory approaches, we estimate the pathways and timescales that establish the surface connectivity for passive i.e., freely floating plankton between stations in the Atlantic Ocean where plankton have been sampled during Tara Oceans & Tara Oceans Polar Circle (2009-2013) and Tara Pacific (2016-2018) expeditions.

We obtain these estimates using a transition matrix approach derived from surface ocean simulations. Given the high rates of reproduction of many planktonic species and that only a few organisms are needed to establish connectivity, we make use of the minimum time path between different stations. To obtain plankton connectivity, two types of constraints are applied on the passive connectivity model: thermal niche and thermal adaptation rate, based on data for a given planktonic species from the literature. From the preliminary analysis, we find that, using minimum time paths, passive particles representative of foraminifera can connect all the stations in less than 3 years. Application of thermal niche constraints increases the minimum connectivity time between stations by approximately 10%, suggesting that plankton can keep to within their favorable thermal conditions by advecting via slightly longer paths. Main pathways of connectivity between these stations are also highlighted in this study. The developed approach can be applied for other plankton species, for any location in the Atlantic and can also be further expanded to derive seasonal connectivity.

How to cite: Manral, D., Amaral-Zettler, L., and van Sebille, E.: Lagrangian connectivity of marine plankton under thermal constraints, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3902, https://doi.org/10.5194/egusphere-egu22-3902, 2022.

EGU22-4812 | Presentations | NP6.2

Active gyrotactic stability of microswimmers using hydromechanical signals 

Kristian Gustavsson, Jingran Qiu, Navid Mousavi, and Lihao Zhao

Many plankton species undergo daily vertical migration to large depths in the turbulent ocean. To do this efficiently, the plankton can use a gyrotactic mechanism, aligning them with gravity to swim downwards, or against gravity to swim upwards. Many species show passive mechanisms for gyrotactic stability. For example, bottom-heavy plankton tend to align upwards. This is efficient for upward migration in quiescent flows, but it is often sensitive to turbulence which upsets the alignment. In this presentation we suggest a simple, robust active mechanism for gyrotactic stability, which is only lightly affected by turbulence and allows alignment both along and against gravity.

 

How to cite: Gustavsson, K., Qiu, J., Mousavi, N., and Zhao, L.: Active gyrotactic stability of microswimmers using hydromechanical signals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4812, https://doi.org/10.5194/egusphere-egu22-4812, 2022.

EGU22-7020 | Presentations | NP6.2

Effect of Light and Upwelling Intensity on the Phytoplankton Community Composition in the Peruvian Upwelling System 

Jacqueline Behncke, Mar Fernández-Méndez, and Ulf Riebesell

The coast of Perú hosts the largest and most productive Eastern Boundary Upwelling System. Climate change is predicted to increase stratification, thereby increasing light availability and lowering nutrient concentrations at the surface. Moreover, the winds causing upwelling in this area are predicted to change their intensity and migrate polewards.

To better understand and predict the response of phytoplankton to changes in light and nutrient conditions, we recreated different light and nutrient scenarios in 9 off-shore mesocosms during the KOSMOS-Peru-2020 experiment in March-April 2020 off the coast of Callao (Perú). We recreated two light scenarios: high light (HL) and low light (LL); and four levels of upwelling by adding deep water (DW) in different proportions (0, 15, 30, 45 and 60 %). We monitored the phytoplankton composition every two days for 36 days. Photosynthetic pigments were measured using HPLC and the phytoplankton community composition was estimated using CHEMTAX and taxonomically determined by microscopic analyses, whereas chlorophyll-a (Chla) as a proxy for bulk phytoplankton biomass and particulate organic carbon, nitrogen and phosphorus (POC, PON and POP) provided information about biomass and stoichiometry of the total suspended matter.

The enclosed initial community was dominated by the red-tide forming raphidophyte Fibrocapsa japonica, detected for the first time off the coast of Perú during this experiment.

After an initial phase, during which F. japonica consumed the nutrients available, the DW was added and a second bloom, dominated by diatoms developed. As expected, more phytoplankton accumulated under HL and in higher DW treatments. The phytoplankton community under LL increased its Chla content per cell to maximize photosynthetic performance, whereas HL caused a significant increase in the POC:PON ratio.

Diatoms, coccolithophores and Phaeocystis were positively affected by HL, whereas the LL phytoplankton assemblage was dominated by smaller groups such as cryptophytes, prasinophytes, Synechococcus and especially the pelagophyte Octactis octonaria. F. japonica became more abundant under LL during the initial phase. Higher upwelling intensity favored diatoms as well as pelagophytes and chlorophytes under LL, whereas low nutrients conditions favored prasinophytes. Upwelling events were accompanied by high contributions of diatoms, whereas nutrient-depleted conditions were dominated by small phytoplankton groups and dinoflagellates.

From our results we conclude that although upwelling intensity did not affect stoichiometry significantly for the duration of the experiment, an intensification of stratification causing greater exposure to HL conditions might decrease the nutritional value of phytoplankton for upper trophic levels. Changes in light and nutrient availability caused by climate change will trigger a shift in the phytoplankton community composition. HL and intense upwelling areas might be dominated by diatoms and LL and low nutrient areas might be dominated by prasinophytes with distinct consequences for the trophic transfer and export efficiency of the Peruvian upwelling system.

How to cite: Behncke, J., Fernández-Méndez, M., and Riebesell, U.: Effect of Light and Upwelling Intensity on the Phytoplankton Community Composition in the Peruvian Upwelling System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7020, https://doi.org/10.5194/egusphere-egu22-7020, 2022.

EGU22-7195 | Presentations | NP6.2

Numerical study of collisions between settling non-spherical particles in turbulence 

Anđela Grujić, Luca Brandt, and Gaetano Sardina

The dynamics of microplastics in the ocean can be modeled similarly to natural particles such as sediment grains, marine snow, phyto- and zooplankton. The settling of the particle is important not only for the individual particle motion, but it also affects the encounter rate, which is important for several physical processes such as nutrient uptake, biofouling, the degradation of microplastics and transport of pollutants into the food chain in the marine environment.

Some of the factors that determine the collision and accumulation of the particles are the level of turbulence, buoyancy, particle shape and diffusivity. Microplastics are often elongated in shape, whereas phytoplankton often form long chains colonies and filaments, even if these are unicellular, which makes the investigation as nonspherical particles in turbulent flows relevant. The objective of this study is to quantify how turbulence affects collision kernels of the nonspherical settling particles. This work is motivated by recent studies in laminar flows showing how collisions between fiber-like particles are much more frequent than those between spherical particles, even in the presence of turbulence (Slomka, J., Stocker, R., 2020. On the collision of rods in a quiescent fluid, Proceedings of the National Academy of Sciences 117, 3372-3374). To this end, we shall consider particles as elongated spheroids. Given the low-density ratios, close to 1, and the size, order of microns, inertia can be neglected, and the particle velocity is assumed to be equal to the sum of the fluid velocity at the particle position and the settling speed. The settling speed is taken to be the Stokes settling velocity for oblate spheroids, function of the object orientation and aspect ratio; note that this is not parallel to gravity for any general orientation. We report results from simulations of sinking inertia-less elongated spheroids in homogeneous isotropic turbulence (HIT). The velocity field is assumed to be incompressible and to obey the Navier-Stokes and continuity equation. To maintain the turbulent velocity in a statistically steady state, a random forcing field is needed. The elongated spheroids studied here are small compared to the Kolmogorov length scale of the turbulence and have different aspect ratios: 1 (spheres), 2, 5, 10 and 20.  We will present results for two different settling velocities – equal to 1 Kolmogorov and 3 times the Kolmogorov velocity, velocity scale of the smallest vortices in the flow. In order to quantify clustering in fully three-dimensional isotropic turbulent flows, the radial pair distribution function (r.d.f.) is used, which provides information about the collision rates when combined with the relative particle velocity at distances of the order of the particle size.

We show that the effect of the different collisional relative velocity has a greater impact than the patchiness on the increase of the collision rate. For larger settling velocities, i.e. larger particle sizes, the collision rates of elongated particles increase with the aspect ratio, an increase however smaller than that observed in quiescent flows. Results obtained for the collision of particles of different buoyancy will also be presented.

How to cite: Grujić, A., Brandt, L., and Sardina, G.: Numerical study of collisions between settling non-spherical particles in turbulence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7195, https://doi.org/10.5194/egusphere-egu22-7195, 2022.

EGU22-7710 | Presentations | NP6.2

Copepods counter dispersion to maintain high mating-encounter rates 

Ron Shnapp and Markus Holzner

Finding mating partners can be challenging for copepods: the ocean is vast and turbulent, the average animal's concentration is sparse, and their swimming ability is limited. Therefore, the probability for locating a mate assuming a homogeneous distribution of animals and a random motion leads to low mating encounter rates. However, zooplankton distribution is not homogeneous; field observations since the 1950s have shown that plantkon have patchy distributions over multiple scales - from thousand of kilometers down to the millimeter scale1. Of relevance to mating is patchiness at small scales (on the order of the animal's size), leading to increased probability for sexual encounters due to higher local concentrations. Indeed, such mating clusters have been identified in ship transect observations2. However, how such clusters form in the diffusive turbulent environment is not fully understood.

In certain species, males actively search for females to achieve sexual encounters. When a male locates a female it pursuits her to achieve contact3, and this behavior is thought to drive small-scale clustering4. Nevertheless, the details of this process are not so straightforward. Specifically, the random swimming pattern males perform in their search, and the (super) diffusive nature of turbulence5, both increases the animals' dispersion, thus opposing patch formation. Therefore, the existence of mating clusters requires a detailed balance between diffusion and pair-interactions. However, this equilibrium in zooplankton patch formation was not examined in the past.

Our study examines the equilibrium between diffusion and pair-interactions in zooplankton. Specifically, we have formulated a numerical framework, the pair-interaction model, which allows to study patch formation. Remarkably, we observe that pair-interactions can lead to patches of numerous particles, similar to the field observations2. We thus explore the model's parameter space, to reveal what is required for patchiness to be sustained. Furthermore, we compare our model's results with laboratory measurements of calanoid copepod trajectories3 and show good agreement between the model and the experiment. Our results support the hypothesis that small-scale patchiness is driven by the animal's behavior and thus explain the details of how zooplankton achieve high mating encounter rates in their complex environment.

 

1 B. Pinel-Alloul and A. Ghadouani (2007). Spatial heterogeneity of planktonic microorganisms in aquatic systems, Springer Netherlands, Dordrecht.

2 C. S. Davis, S. M. Gallager and A. R. Solow (1992). Science 257, 230-232.

3 F.-G. Michalec et al. (2017). Proc. Natl. Acad. Sci. U.S.A. 114 ; F.-G. Michalec et al. (2020). eLife 9, e62014.

4 C. L. Folt and C. W. Burns (1999). Trends in Ecology and Evolution, 14, 300–305.

5 J. P. Salazar and L. R. Collins (2009). Ann. Rev. Fluid Mech. 41, 405-432.

How to cite: Shnapp, R. and Holzner, M.: Copepods counter dispersion to maintain high mating-encounter rates, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7710, https://doi.org/10.5194/egusphere-egu22-7710, 2022.

Marine micro-organisms must cope with complex flow patterns and even turbulence as they navigate the ocean. To survive they must avoid predation and find efficient energy sources. A major difficulty in analysing possible survival strategies is that the time series of environmental cues in non-linear flow is complex, and that it depends on the decisions taken by the organism. One way of determining and evaluating optimal strategies is reinforcement learning. In a proof-of-principle study, Colabrese et al. [Phys. Rev. Lett. (2017)] used this method to find out how a micro-swimmer in a vortex flow can navigate towards the surface as quickly as possible, given a fixed swimming speed.  The swimmer measured its instantaneous swimming direction and the local flow vorticity in the laboratory frame, and reacted to these cues by swimming either left, right, up, or down. However, usually a motile micro-organism measures the local flow rather than global information, and it can only react in relation to the local flow, because in general it cannot access global information (such as up or down in the laboratory frame). Here we analyse optimal strategies with local signals and actions that do not refer to the laboratory frame. We demonstrate that symmetry-breaking is required to find such strategies. Using reinforcement learning we analyse the emerging  strategies for different sets of environmental cues that micro-organisms are known to measure. This talk is based on "Navigation of micro-swimmers in steady flow: the importance of symmetries" by Jingran Qiu, [Opens in a new win Navid Mousavi, Kristian Gustavsson[Opens in a new window], Chunxiao Xu, Bernhard Mehlig, and Lihao Zhao, Journal of Fluid Mechanics 932, A10. doi:10.1017/jfm.2021.978

How to cite: Mehlig, B.: Navigation of micro-swimmers in steady flow: the importance of symmetries, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7811, https://doi.org/10.5194/egusphere-egu22-7811, 2022.

EGU22-8638 | Presentations | NP6.2

The Agiturb laboratory turbulence generation system and its application to plankton studies: zooplankton and phytoplankton 

François G. Schmitt, Clotilde Le Quiniou, Yongxiang Huang, Enrico Calzavarini, Emilie Houliez, and Urania Christaki
Plankton species live in a turbulent flow and are fully adapted to it. They have specific behaviour and responses related to turbulence characteristics and intensities, that are still largely unknown. Turbulence systems in the laboratory are needed to perform controled experiments with different zooplankton and phytoplankton species. Here we present the Agiturb turbulence generation system and some first results using different plankton species.
 
In the Agiturb system, the turbulent flow is produced using four contra-rotating agitators that are place under a cubic tank. The model for such flow is the so-called “four-roll mill” proposed by G.I. Taylor in 1934 to generate a statistically stationary, spatially inhomogeneous flow with compression and stretching. In our experiment,  the flow close to the agitators is a free flow similar to the four-roll mill, without the cylindrical rolls. The injection of the energy in the flow is produced by 4 stirring bars activated by 4 magnetic stirrers situated at symmetric positions, the centers being placed at one-fourth of the width of the tank. The cubic tank is almost half-full with 15 liters of sea water. For each experiment, the magnitude of the rotation rate of each agitator was identical, with two agitators rotating clockwise and two anti-clockwise, the same directions being along the diagonal. Different values of the rotation rate were chosen to reach different turbulence levels, characterized by the microscale Reynolds number Rλ  going from 130 to 360.
 
We present the result of two different experiments: the first one is a record, using a high speed camera in the infrared, of copepods trajectories, at different turbulent intensities, in order to see an optimal Reynolds number for copepods swimming activities (Acartia tonsa). The second one is a systematic study of the proliferation of diatoms under different turbulent intensities (Pseudo-nitzschia). In both cases different rotation rates of the system are considered, and an optimal turbulence level has been found, with maximum swimming activity for copepods and maximum growth rate for diatoms.

How to cite: Schmitt, F. G., Le Quiniou, C., Huang, Y., Calzavarini, E., Houliez, E., and Christaki, U.: The Agiturb laboratory turbulence generation system and its application to plankton studies: zooplankton and phytoplankton, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8638, https://doi.org/10.5194/egusphere-egu22-8638, 2022.

EGU22-12875 | Presentations | NP6.2

Impact of the winter convective event on gelatinous zooplankton in the open southern Adriatic 

Mirna Batistić, Rade Garić, and Marijana Hure

The southern Adriatic is the deepest part of the Adriatic Sea (1242 m) and one of three sites of open-sea deep convection in the Mediterranean. By analyzing zooplankton samples taken in the open southern Adriatic in winter and spring/summer 2021 we investigated effect of winter vertical mixing on distribution of gelatinous zooplankton. During the convection time in winter, gelatinous zooplankton abundance was low and unusual vertical distribution for some species was occurred. In the spring-summer time an increase in gelatinous zooplankton abundance in upper and deeper layer was registered. This is probably related to the early spring phytoplankton bloom enhanced by nutrient input into euphotic zone due to winter mixing phase. As a consequence of this event, there is also availability of more food for deep-sea gelatinous organisms.

 

How to cite: Batistić, M., Garić, R., and Hure, M.: Impact of the winter convective event on gelatinous zooplankton in the open southern Adriatic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12875, https://doi.org/10.5194/egusphere-egu22-12875, 2022.

EGU22-13031 | Presentations | NP6.2

Orientation of anisotropic particles in stratified turbulent flows 

Alessandro Sozza and Alain Pumir

Small-scale turbulence and density stratification are two major ingredients shaping the life of marine micro-organisms in the pycnocline. Such tiny particles are rarely spherical, ranging from flat disks to elongated rods. Particle orientation with respect to the flow or to density gradients plays a crucial role in many aspects of phytoplankton's life, e.g. light harvesting for photosynthesis, enhancement of nutrient uptake, optimal navigation and vertical migration. However, it's still unclear how anisotropic particles align in a turbulent pycnocline and how they are able to cope with density stratification.

In the present work, we aim to characterize the effects of stratification on the orientation of inertialess non-spherical particles. To achieve this purpose, we performed direct numerical simulations of a mixed Eulerian-Lagrangian model. The flow is described by the Boussinesq equations, which evolve fluid velocity and density fluctuations in a triply periodic cubic domain. The space is initially seeded with spheroidal particles of different shapes (from rods to disks) transported by the flow as passive tracers. Particle orientation evolves in response to velocity gradients according to Jeffery’s dynamics.

We have explored different configurations of the parameters' space by changing particle shape, density stratification and turbulence intensity. The statistical properties of orientation are then unveiled by characterizing the particles' distributions and their mean behavior. Moreover, we have inspected the alignment of particles with respect to the flow and to the iso-density surfaces. We have analyzed rotation rates of the particles and compared our results with the case of spherical particles and homogeneous isotropic turbulence. Such outcomes provide a clear picture of the influence of stratification on the orientational dynamics and on its transition from non-stratified to strongly stratified turbulence. Finally, we conclude by discussing the implications of our results for oceanic applications.

How to cite: Sozza, A. and Pumir, A.: Orientation of anisotropic particles in stratified turbulent flows, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13031, https://doi.org/10.5194/egusphere-egu22-13031, 2022.

EGU22-13293 | Presentations | NP6.2

Orientation of swimmers in turbulent flows 

Filippo De Lillo, Matteo Borgnino, Guido Boffetta, Kristian Gustafsson, Bernhard Mehlig, and Massimo Cencini

Many phytoplankters are able to swim, and are thus not passively transported by the flow. Although usually weak, ocean turbulence can affect the motion of one-celled organisms in nontrivial ways. It is known that an ellipsoidal body can be rotated by the fluid gradients, depending on its aspect ratio.  On the other hand, directed swimming (e.g. following chemical or physiscal cues, in any form of taxis) can play an important role in determining the fitness of an individual, whether for finding food, light or escaping from predators.

By means of theoretical and numerical investigation [1,2], we show how a microswimmer's orientation can be influenced by different scales of the flow and in what condition relevant correlation with the orientation of the flow can be expected.   

 

[1] Alignment of nonspherical active particles in chaotic flows M Borgnino, K Gustavsson, F De Lillo, G Boffetta, M Cencini, B Mehlig, Physical review letters 123 (13), 138003

[2] M Borgnino, K Gustavsson, F De Lillo, G Boffetta, M Cencini (2021) in preparation.

How to cite: De Lillo, F., Borgnino, M., Boffetta, G., Gustafsson, K., Mehlig, B., and Cencini, M.: Orientation of swimmers in turbulent flows, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13293, https://doi.org/10.5194/egusphere-egu22-13293, 2022.

EGU22-13437 | Presentations | NP6.2

A litre-scale turbulence facility for microorganism-flow interactions 

Jeanette D. Wheeler, Aaron C. True, François-Gaël Michalec, Markus Holzner, Roman Stocker, and John P. Crimaldi

Bacteria and phytoplankton are abundant in aquatic environments, forming the base of the food web and mediating elemental cycling at a global scale. Understanding the interactions these microorganisms have with their turbulent fluid environments is an active area of research, largely conducted in laboratory-based flow experiments. In this work, we provide an open-source design and rigorous flow characterization for a 1L, dual oscillating grid turbulence facility, the smallest volume facility to date which produces near-isotropic, homogeneous turbulence. We optimized the tank geometry (grid-to-grid and grid-to-wall spacing), the grid geometry (for both classical and fractal grids: effective mesh size, blockage ratio, and fractal grid parameters), and the grid forcing regimes (for both coupled, antiphase and decoupled, randomized forcing: frequency range, stroke range, and randomized forcing parameters) to minimize mean flows and to produce acceptably homogeneous and isotropic turbulence within the unique constraints of a litre-scale volume. We acquired particle image velocimetry (PIV) measurements for both classical and fractal grids across a wide range of grid forcing regimes. We discuss the resulting length- and timescales relevant to microorganism-flow interactions, from the integral to the Kolmogorov scales. Finally, we discuss how the range of turbulent kinetic energy (TKE) dissipation rates achieved across the operational space of the facility mimics oceanographic turbulence in a range of in situ conditions, from the nearshore to the open ocean. This facility meets a long-standing need in the oceanography community in which feasible experimental working volumes are constrained by labor-intensive culturing requirements for large volumes of aquatic bacteria and phytoplankton.

How to cite: Wheeler, J. D., True, A. C., Michalec, F.-G., Holzner, M., Stocker, R., and Crimaldi, J. P.: A litre-scale turbulence facility for microorganism-flow interactions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13437, https://doi.org/10.5194/egusphere-egu22-13437, 2022.

EGU22-915 | Presentations | NP6.3

Quantifying the electron scattering by electrostatic fluctuations in the Earth’s bow shock 

Sergey Kamaletdinov, Ivan Vasko, Anton Artemyev, and Rachel Wang

Collisionless shocks are known to be natural sources of suprathermal particles, but the mechanism resulting in acceleration of thermal electrons to suprathermal energies still remains elusive. The problem is, that the Diffusive Shock Acceleration (DSA) becomes efficient only for suprathermal electrons, which fluxes in the far upstream region are relatively low. Recent studies have shown that the so-called Stochastic Shock Drift Acceleration (SSDA) mechanism can potentially provide the necessary pre-acceleration of incoming thermal electrons to suprathermal energies. In this mechanism, electrons are temporarily kept trapped in the shock transition region due to magnetic mirror reflection by the magnetic ramp and pitch-angle scattering of electrons trying to escape upstream by wave turbulence. Spacecraft measurements showed that broadband electrostatic turbulence is always present in the Earth’s bow shock, but its efficiency in scattering suprathermal electrons has not been estimated up to date. In this study we have quantified the electron scattering by the broadband electrostatic turbulence and, specifically, by electrostatic solitary waves (ion holes) substantially contributing to this turbulence in the Earth’s bow shock. Adopting the solitary wave and turbulence parameters typical of the Earth’s bow shock, we obtain quasi-linear scattering rates and compare these scattering rates to the results of test-particle simulations. This analysis showed that scattering of suprathermal electrons by the osberved electrostatic turbulence is relatively well estimated by the quasi-linear approach. We estimated the quasi-linear scattering rates at various energies and pitch-angles and demonstrated that the electrostatic turbulence in the Earth’s bow shock can provide pre-acceleration of thermal electrons from a few tens of eV to a few hundred eV via the SSDA mechanism.

This work was supported by the Russian Scientific Foundation, Project No. 19–12-00313

How to cite: Kamaletdinov, S., Vasko, I., Artemyev, A., and Wang, R.: Quantifying the electron scattering by electrostatic fluctuations in the Earth’s bow shock, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-915, https://doi.org/10.5194/egusphere-egu22-915, 2022.

EGU22-1485 | Presentations | NP6.3

Plasmoid-dominated turbulent reconnection in symmetric and asymmetric systems 

Seiji Zenitani, Momoka Yamamoto, and Takahiro Miyoshi

In magnetohydrodynamics (MHD), magnetic reconnection has been discussed by three theoretical models: Sweet--Parker reconnection, Petschek reconnection, and plasmoid-dominated turbulent reconnection. Among these models, properties of plasmoid-dominated reconnection remain unclear, because it was discovered only recently. In this talk, we explore basic properties of plasmoid-dominated reconnection in a low-beta plasma such as in a solar corona, by using large-scale MHD simulations [1]. We have found that the system becomes highly complex due to repeated formation of plasmoids and shocks. We have further found that the reconnection rate goes higher than previously thought. Next we explore influence of asymmetry in background plasma densities in plasmoid-dominated reconnection. We have found that the average reconnection rate follows Cassak-Shay's hybrid relation [2]. Many signatures become asymmetric across the reconnection layer, and plasmas inside the plasmoids start to swirl in specific directions. Formation processes of these vortices and a potential extension of our numerical survey will be discussed.

References:
[1] S. Zenitani and T. Miyoshi, Astrophys. J. Lett., 894, L7 (2020)
[2] P. A. Cassak and M. A. Shay, Phys. Plasmas, 14, 102114 (2007)

How to cite: Zenitani, S., Yamamoto, M., and Miyoshi, T.: Plasmoid-dominated turbulent reconnection in symmetric and asymmetric systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1485, https://doi.org/10.5194/egusphere-egu22-1485, 2022.

EGU22-1577 | Presentations | NP6.3

Physical Regimes of 2D MHD turbulent reconnection in different Lundquist numbers 

Haomin Sun, Yan Yang, Quanming Lu, San Lu, Minping Wan, and Rongsheng Wang

Using two-dimensional (2D) MHD simulations in different Lundquist numbers , we investigate physical regimes of turbulent reconnection and the role of turbulence in enhancing the reconnection rate. Turbulence is externally injected into the system with varying strength. External driven turbulence contributes to the conversion of magnetic energy to kinetic energy flowing out of the reconnection site and thus enhances the reconnection rate. The plasmoids formed in high Lundquist numbers contribute to the fast reconnection rate as well. Moreover, an analysis of the power of turbulence implies its possible association with the generation of plasmoids. Additionally, the presence of turbulence has great impact on the magnetic energy conversion and may be favorable for the Kelvin-Helmholtz (K-H) instability in the magnetic reconnection process.

How to cite: Sun, H., Yang, Y., Lu, Q., Lu, S., Wan, M., and Wang, R.: Physical Regimes of 2D MHD turbulent reconnection in different Lundquist numbers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1577, https://doi.org/10.5194/egusphere-egu22-1577, 2022.

EGU22-1740 | Presentations | NP6.3

Local slope of magnetic field power spectrum in inertial and kinetic ranges of solar wind turbulence 

Alexander Pitna, Jana Safrankova, Zdenek Nemecek, Gilbert Pi, Luca Franci, and Byeongseon Park

Solar wind, a supersonic flow of plasma embedded in the magnetic field, exhibits turbulent behavior. The character of turbulent fluctuations has been investigated through low cadence measurements of particle distribution function and high cadence magnetic field measurements. One of the most frequently adopted approach in the analysis of the ‘measured’ time series of any particular quantity is the estimation of its power spectral density (PSD). The shape of the PSD then may infer which physical mechanisms govern the evolution of turbulent fluctuations. Generally, every ‘measured’ time series is ‘noisy’ and it differs from the ‘true’ one (measured by an ideal instrument). In turn, the shape of PSD is affected as well. In this paper, we focus on a special case where the signal and noise are independent, i.e., the noise is additive and therefore, the PSD of measured signal can be expressed as a sum of ‘true’ and ‘noise’ PSDs. Moreover, we define a so-called local slope in the framework of continuous wavelet transform as the finite difference derivative between the two consequent values of a global PSD. Employing this technique, we show that the noise of magnetic field measurements of the MFI instrument on board the Wind spacecraft is additive. Finally, we applied the technique to measurements of the Parker Solar Probe close to the Sun. Our preliminary results suggest that our technique may lead to a more accurate estimations of the kinetic range spectral indices.

How to cite: Pitna, A., Safrankova, J., Nemecek, Z., Pi, G., Franci, L., and Park, B.: Local slope of magnetic field power spectrum in inertial and kinetic ranges of solar wind turbulence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1740, https://doi.org/10.5194/egusphere-egu22-1740, 2022.

The measurements of the longitudinal velocity were performed in an open-circuit suction wind tunnel installed at the laboratory of the Max-Planck Institute for Dynamics and Self-Organization in Gottingen, using hot wire anemometer at different positions in turbulent flow generated by a traditional fractal square grid (FSG) and by a spaced fractal square grid (SFSG) with similar physical properties have shown that the self-similarity is present. The statistical description of this complex turbulent system was performed using Extended Self Similarity (ESS). We propose a complementary methodology suitable for non-homogeneous turbulence based on the analysis of the energy transfer hierarchy. The signature of the non-homogeneous characteristics of a turbulent field, indicated by nonlocal dynamics, is separated from those usually assigned as being only due to the intermittency. We propose a physical interpretation of the observed scale independence of the relative scaling exponents in such non-homogeneous flows by means of the compensation effect of the energy transfer on the difference between the strong coherent turbulent events and the background less intense turbulence. This procedure is able to distinguish whether the intermittency arises from the small scales or is linked to coherent structures. The practical interest of this type of turbulent excitation concerns several fields of aeronautical and space application and energy or environmental problems of noise reduction of mixers in combustion or for the numerical models of prediction of the dispersion of pollutants in the atmosphere.

How to cite: Ben Mahjoub, O. and Ouadoud, A.: Intermittency in turbulence generated by traditional fractal square grid and spaced fractal square grid, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1997, https://doi.org/10.5194/egusphere-egu22-1997, 2022.

Turbulence, the self-generated turbulence by plasmas and magnetic field collective interaction, has been found to play an essential role in energizing charged particles in the large-scale reconnecting current sheet in the major solar eruption.

The typical large-scale CME/Flare events involve sudden bursts of particle acceleration from the sudden release of magnetic energy in a few minutes to a few tens of minutes. The X-rays emission and gamma rays burst produced by the combined result from the interactions of electrons, hydrogen, helium, and other heavier ions. Space and laboratory researchers are more inclined to believe that turbulence acceleration is belonged to shock acceleration. Solar and astrophysics researchers are more inclined to believe that turbulence acceleration is an independent acceleration mechanism that belongs to the flare acceleration. The evidence in both theories and observations from solar atmosphere activities shows that the acceleration is related to nonlinear resonant wave-particle interaction (e.g., Landau acceleration). So far, many-particle acceleration models consider turbulence acceleration as an effective way of generating energetic electrons, protons, and heavier ions. However, the detailed role of turbulence in this process remains unclear. More effort needs to invest in looking into particle accelerations by turbulence that occurs over a large range of the scale in space from the inertial scale of individual particles to the MHD scale.

In this work, applying the statistical treatment of plasma physics, combing with filter theory of turbulence, the actual ratio of the proton mass to the electron mass, and mass-to-charge ratios, we investigate the interaction of charged particles with the turbulent electric field and magnetic field in the large-scale CME/flare current sheet by applying the

We found the significant Langmuir turbulence acceleration (LTA) through the nonlinear resonant wave-particle interaction in the diffusion region via tracking the trajectories and analyzing the energy spectrum of energetic protons and electrons. The results show that protons and electrons could be efficiently accelerated simultaneously and that the way of LTA is similar to that of the shock acceleration}} but is much more efficient than the shock acceleration. This indicates that large-scale reconnection is a good candidate for the mechanism for the efficient acceleration of protons and electrons in the major solar eruption.

The acceleration of heavy ion considered Helium (3He/4He) and other heavy elements in 3He-rich flares burst would explore in the follow-up work series.

URL: https://pan.cstcloud.cn/s/drEdcjIaT8E

How to cite: Zhu, B., Li, Y., and Lin, J.: Investigations of Particle Accelerations by Turbulent Magnetic Reconnection in Large-Scale CME/Flare Current Sheet: I. Protons and Electrons, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2116, https://doi.org/10.5194/egusphere-egu22-2116, 2022.

EGU22-2649 | Presentations | NP6.3

Electromagnetic energy conversion by various processes in turbulent plasmas observed by MMS 

Thanapon Aiamsai, Peera Pongkitiwanichakul, Rungployphan Kieokaew, David Ruffolo, and Theerasarn Pianpanit

A key issue in space plasma physics is how electromagnetic energy is converted to plasma particle energy and heat. Electromagnetic energy conversion generally involves turbulence and/or instabilities. With the Magnetospheric Multiscale (MMS) mission data, we investigate such energy conversion in turbulent plasmas, separating the plasma currents from various drift motions and other processes and assessing their contributions. For example, we have explored the roles of curvature drift, gradient drift, particle inertia drift and perpendicular magnetization currents. We will discuss their roles and related mechanisms in turbulent plasmas. This research has been supported in part by grant RTA6280002 from Thailand Science Research and Innovation, by DPST scholarship grant ,and by grant RGNS 63-045 from Office of the Permanent Secretary, Ministry of Higher Education, Science, Research and Innovation.

How to cite: Aiamsai, T., Pongkitiwanichakul, P., Kieokaew, R., Ruffolo, D., and Pianpanit, T.: Electromagnetic energy conversion by various processes in turbulent plasmas observed by MMS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2649, https://doi.org/10.5194/egusphere-egu22-2649, 2022.

EGU22-3198 | Presentations | NP6.3

Instabilities and Turbulence in Two-Dimensional Magnetohydrodynamics 

Jonathan Tessier, Francis J. Poulin, and David W. Hughes

The solar tachocline is a dynamically important thin region in the Sun, located between the convective and radiative zones and characterised by strong shear in both the radial and latitudinal directions. Furthermore, it is believed to play a key role in the solar dynamo process through the shearing of a poloidal field into a stronger toroidal component. Motivated by the dynamics of the tachocline we have conducted a detailed numerical exploration of the dynamics of sheared MHD turbulence.

Specifically, we have implemented a parallelized numerical model using the "shenfun" Python library to solve the nonlinear two-dimensional Magnetohydrodynamic (MHD) equations to study the dynamics of unstable jets and turbulence in astrophysical plasmas. In particular, we study details of how the jet becomes unstable and the resulting cascade of energy in the case of MHD turbulence. In addition to studying the evolution of the physical quantities, we also investigate the evolution of the spectral slopes and spectral fluxes. As has been found in previous studies of MHD turbulence, a very weak large-scale magnetic field can play a key dynamical role through its amplification on small scales. For extremely weak fields, the behaviour is essentially hydrodynamic. However, once the field is dynamic, the nature of the resulting MHD solution is very different. We are able to classify the various flows and quantify the nature of the solutions in the two regimes.

How to cite: Tessier, J., Poulin, F. J., and Hughes, D. W.: Instabilities and Turbulence in Two-Dimensional Magnetohydrodynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3198, https://doi.org/10.5194/egusphere-egu22-3198, 2022.

EGU22-3354 | Presentations | NP6.3

High-Speed Jets in Earth’s Magnetosheath Downstream of the Quasi-Parallel Shock: A Two-Dimensional Global Hybrid Simulation 

Jin Guo, San Lu, Quanming Lu, Yu Lin, Xueyi Wang, Yufei Hao, Kai Huang, Rongsheng Wang, and Xinliang Gao

High-speed jets (HSJs) occur frequently in Earth’s magnetosheath downstream of the quasi-parallel bow shock. They have great impacts on the magnetosheath and the magnetosphere. Using a two-dimensional global hybrid simulation, we investigate the formation and evolution of the HSJs with an IMF cone angle of 0°. The quasi-parallel shock is near the subsolar point, and the HSJs begin to appear in the quasi-parallel magnetosheath with a parallel (perpendicular) scale size of about 1RE (0.2RE). These HSJs then converge, leading to the formation of a large-scale HSJ with a parallel (perpendicular) scale size of 6RE (1.2RE). Some long HSJs, with a large parallel but small perpendicular scale size, are formed at the quasi-parallel bow shock and extend toward the quasi-perpendicular magnetosheath along with the background magnetosheath flow. Moreover, these long HSJs can cause filamentary structures in the magnetosheath.

How to cite: Guo, J., Lu, S., Lu, Q., Lin, Y., Wang, X., Hao, Y., Huang, K., Wang, R., and Gao, X.: High-Speed Jets in Earth’s Magnetosheath Downstream of the Quasi-Parallel Shock: A Two-Dimensional Global Hybrid Simulation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3354, https://doi.org/10.5194/egusphere-egu22-3354, 2022.

EGU22-3546 | Presentations | NP6.3

Overshoot dependence on the shock parameters 

Natalia Borodkova, Olga Sapunova, Victor Eselevich, Georgy Zastenker, and Yury Yermolaev

The structure of the solar wind plasma flow downstream of the ramp of the interplanetary and bow shocks was studied based on the BMSW plasma spectrometer installed onboard the SPEKTR-R spacecraft. Particular attention was paid to the overshoot region, where correlated oscillations of the ion flux and magnetic field are observed. They are formed by two populations of ions: the inflowing solar wind and the beam of coherent gyrating ions. Based on the statistical analysis it was shown that overshoots form both in supercritical and subcritical shocks. It is found that maximum values of the overshoot amplitudes are significantly influenced by the angle between the shock normal and magnetic field vectors, Mach number, plasma and magnetic compression at the shock front. It was established that the oscillation wavelength determined from the magnetic field measurements onboard the WIND spacecraft, on average, coincides with the oscillation wavelength determined from the ion flux on the SPEKTR-R, while the rates of relaxation of these oscillations can greatly differ. It was also shown that the estimates of the overshoot wavelength good correlate with the convected ion gyroradius.

How to cite: Borodkova, N., Sapunova, O., Eselevich, V., Zastenker, G., and Yermolaev, Y.: Overshoot dependence on the shock parameters, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3546, https://doi.org/10.5194/egusphere-egu22-3546, 2022.

Previous numerical works on electron/ion foreshocks observed upstream of a curved shock have been already performed within a self-consistent approach based on 2D PIC simulation (Savoini et Lembege, 2010, 2013, 2015), but are restricted to a supercritical regime only. Present two dimensional PIC (Particle in cell) simulations are used in order to analyze the features of a curved shock and associated foreshocks in a subcritical regime. In order to investigate the dynamic of each electron and ion backstreaming populations, we used test-particles in a pre-computed electromagnetic field (issued from 2D PIC simulations) which allows us to define precisely the characteristic of each population in terms of initial velocity and/or their upstream position to the  θBn angle (angle between the local shock normal and the interplanetary magnetic field IMF). Then, results allow to clarify the following questions: what is the impact of the subcritical regime (i) on the persistence of each electron/ion foreshock respectively ?, (ii) in the case the persistence is confirmed,  how the location (along the curved front) and the angular direction of each foreshock edge are affected ?, and (iii) how the mapping of upstream local  distribution functions are impacted ? Preliminary results will be presented and compared with those already obtained for a supercritical shock.

How to cite: Savoini, P. and Lembege, B.: Analysis of a curved shock front microstructures and associated electron/ion foreshock for a subcritical shock regime, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3631, https://doi.org/10.5194/egusphere-egu22-3631, 2022.

EGU22-3868 | Presentations | NP6.3

Model of KAW contribution to cross-magnetopause ion transport 

Alexander Lukin, Anton Artemyev, and Anatoly Petrukovich

Magnetosheath ion transport across the night-side magnetopause can be contributed by ion cross-field diffusion due to wave-particle scattering. In this presentation we focus on such scattering mechanism for the most intense magnetosheath wave emission, kinetic Alfven waves (KAWs). These waves carry a finite field-aligned electric field and potentially can accelerate particles along magnetic field lines. In the fast plasma flows these waves are usually observed as a wide Doppler-shifted electromagnetic spectrum characterized by strong electric fields in high wave-number range. Dense frequency spectrum leads to overlapping of particles resonances with waves and causes particle diffusion in pitch-angle and energy space. We investigate particles diffusion caused by interactions with KAW turbulence in a realistic model of the Earth flank magnetopause with nonuniform ambient magnetic field fitting the tangential discontinuity. The KAW spectrum is determined by a sum of a several thousand plane waves with different frequencies and propagation angles. We estimate diffusion coefficients as function of ion pitch-angle and energy for different distances from the magnetopause and discuss the expected cross-field transport rate for this model.

How to cite: Lukin, A., Artemyev, A., and Petrukovich, A.: Model of KAW contribution to cross-magnetopause ion transport, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3868, https://doi.org/10.5194/egusphere-egu22-3868, 2022.

EGU22-4040 | Presentations | NP6.3

Spectral features and energy cascade of kinetic scale plasma turbulence 

Giuseppe Arrò, Francesco Califano, and Giovanni Lapenta

Solar wind (SW) in situ observations of plasma turbulence show that the turbulent magnetic field spectrum follows a Kolmogorov-like scaling ∼k-5/3 at large MHD scales and steepens at ion scales where a different power law develops with a scaling exponent varying between -2 and -4, depending on SW conditions. Recent satellite measurements revealed the presence of a second spectral break around electron scales where the magnetic field spectrum shows an exponential falloff described by the so called exp model ∼k-8/3exp(-ρek), where ρe is the electron gyroradius. This model was tested on a large number of magnetic spectra at various distances from the Sun (from 0.3 to 1 AU) and appears to be a solid feature of turbulent magnetic field fluctuations at kinetic scales [1]. 

Using a fully kinetic energy conserving particle-in-cell (PIC) simulation of freely decaying plasma turbulence we study the spectral properties of the turbulent cascade at kinetic scales. Consistently with satellite observations, we find that the magnetic field spectrum follows the kexp(-λk) law at sub-ion scales, with an exponential range developing around kρe≈1. The same exponential falloff is observed also in the electron velocity spectrum but not in the ion velocity spectrum that drops like a power law without reaching electron scales. We investigate the development of these spectral features by analyzing the high-pass filtered electromagnetic work J·E and pressure-strain interaction -P:∇u of both the ions and the electrons. Our analysis shows that the magnetic field dynamics at kinetic scales is mainly driven by the electrons that are responsible for the formation of the exponential range. In particular, we see that at fully developed turbulence the magnetic field energy is dissipated by a two-stage mechanism lead by the electrons that first subtract energy from the magnetic field and then convert it into internal energy at electron scales through the pressure-strain interaction, that accounts for the electron heating [2].

 

References

[1] Alexandrova, O., Jagarlamudi, V. K., Hellinger, P., Maksimovic, M., Shprits, Y., & Mangeney, A. (2021). Spectrum of kinetic plasma turbulence at 0.3–0.9 astronomical units from the Sun. Physical Review E, 103(6), 063202.

[2] Arrò, G., Califano, F., & Lapenta, G. (2021). Spectral properties and energy cascade at kinetic scales in collisionless plasma turbulence. arXiv preprint arXiv:2112.12753.

How to cite: Arrò, G., Califano, F., and Lapenta, G.: Spectral features and energy cascade of kinetic scale plasma turbulence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4040, https://doi.org/10.5194/egusphere-egu22-4040, 2022.

EGU22-4447 | Presentations | NP6.3

Electron heating scales in quasi-perpendicular shocks 

Andreas Johlander, Andrew Dimmock, Yuri Khotyaintsev, Daniel Graham, and Ahmad Lalti

Collisionless shock waves are important for particle heating and acceleration in space. Electron heating at shocks is a combination of adiabatic heating due to large-scale electric and magnetic fields and scattering by high-frequency oscillations. Electron heating and scattering at the shock is still poorly understood but the scales at which heating happens can hint to which physical processes are taking place. Here, we study electron heating scales with the Magnetospheric Multiscale (MMS) spacecraft at Earth’s quasi-perpendicular bow shock. We utilize the small tetrahedron formation and rapid plasma measurements of MMS to directly measure the electron temperature gradient inside the shock. From this, we reconstruct the electron temperature profile inside the shock ramps of a number of shock crossings with varying shock parameters. We find that most of the electron temperature increase takes place on a scale of tens of electron inertial lengths. Further, we investigate the electron distribution functions and attempt to disentangle the effects of the large-scale adiabatic heating and scattering by high-frequency waves.

How to cite: Johlander, A., Dimmock, A., Khotyaintsev, Y., Graham, D., and Lalti, A.: Electron heating scales in quasi-perpendicular shocks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4447, https://doi.org/10.5194/egusphere-egu22-4447, 2022.

EGU22-5068 | Presentations | NP6.3

Measurement of short  (Debye length) scale electrostatic waves with MMS EDP instrument:  Problems and possible mitigation 

Ahmad Lalti, Yuri Khotyaintsev, Daniel Graham, Konrad Steinvall, and Andreas Johlander

High frequency electrostatic oscillations are one of the most fundamental players in energy conversion in collisionless plasmas. Whether at collisionless shocks, turbulence energy cascades or reconnection, small scale Debye length processes  are at the heart of irreversible energy exchange between particles and fields. MMS is one of the most advanced still active spacecraft, with high resolution field and particle instruments. The electric field instrument (EDP) on board of MMS is formed of 3 axial double probes positioned in a perpendicular configuration allowing for the measurement of the 3D electric field. In this study we probe the limitations of the EDP instrument in measuring Debye-scale electrostatic oscillations. In particular we show that at such small wavelengths the electric field is attenuated due to the finite probe-to-probe separation. Furthermore, we propose a method to correct for the electric field attenuation based on the single spacecraft interferometry technique which will allow us to properly determine the observed wave modes.

How to cite: Lalti, A., Khotyaintsev, Y., Graham, D., Steinvall, K., and Johlander, A.: Measurement of short  (Debye length) scale electrostatic waves with MMS EDP instrument:  Problems and possible mitigation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5068, https://doi.org/10.5194/egusphere-egu22-5068, 2022.

EGU22-5192 | Presentations | NP6.3

Identifying active magnetic reconnection in simulations and in situ observations of plasma turbulence using magnetic flux transport 

Tak Chu Li, Yi-Hsin Liu, Yi Qi, and Christopher T. Russell

For decades, magnetic reconnection has been suggested to play an important role in the dynamics and energetics of plasma turbulence by spacecraft observations, numerical simulations and theory. Reliable approaches to study reconnection in turbulence are essential to advance this frontier topic of plasma physics. A new method based on magnetic flux transport (MFT) has been recently developed to identify reconnection activity in turbulent plasmas. Applications to gyrokinetic simulations of two- and three-dimensional (2D and 3D) plasma turbulence, and MMS observations of reconnection events in the magnetosphere have demonstrated the capability and accuracy of MFT in identifying active reconnection in turbulence. In 2D, MFT identifies multiple active reconnection X-lines; two of them have developed bi-directional electron and ion outflow jets, observational signatures for reconnection, while one of the X-line does not have bi-directional electron or ion outflow jets, beyond the category of electron-only reconnection recently discovered in the turbulent magnetosheath. In 3D, plentiful reconnection X-lines are identified through MFT, and a new picture of reconnection in turbulence results. In space, MMS observations have provided first evidence for MFT signatures of active reconnection under varying plasma conditions throughout the Earth's magnetosphere. MFT is applicable to in situ measurements by spacecraft missions, including PSP and Solar Orbiter, and laboratory experiments.

How to cite: Li, T. C., Liu, Y.-H., Qi, Y., and Russell, C. T.: Identifying active magnetic reconnection in simulations and in situ observations of plasma turbulence using magnetic flux transport, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5192, https://doi.org/10.5194/egusphere-egu22-5192, 2022.

EGU22-5222 | Presentations | NP6.3

Fluctuations of the solar wind ion flux near the Earth bow shock 

Olga Sapunova, Natalia Borodkova, Yuri Yermolaev, and Georgii Zastenker

In our study we analyzing fluctuations of the solar wind ion flux associated with the Earth bow shock using data obtained by the BMSW experiment, installed onboard the SPEKTR-R satellite. The high time resolution of the spectrometer (0.031 s for the plasma flux magnitude and direction and 1.5 s for velocity, temperature, and density) makes it possible to study fine structures in detail.

From 2011 to 2019 SPEKTR-R satellite crossed the Earth bow shock many times. In our work we analyzed more than 200 bow shock crossings including multiple ones. More than half of them had fluctuations near the Earth bow shock front.

It was shown that in 25% of events the frequencies of ion flux fluctuations were in the range of 3-4 Hz. In 5-7% of events the frequencies of ion flux fluctuations lay in the interval of 5-6 Hz. Just few cases had frequencies of ion flux fluctuations equal or more than 7 Hz. In other cases the frequencies of ion flux fluctuations were lower than 3 Hz or no fluctuations were observed at all.

We also observed low-frequencies fluctuations about 0.1 Hz and lower. These fluctuations were also visible by the 1.5 s plasma parameters: protons density and velocity; He++ (alpha particles) density and velocity (including helium abundance).

How to cite: Sapunova, O., Borodkova, N., Yermolaev, Y., and Zastenker, G.: Fluctuations of the solar wind ion flux near the Earth bow shock, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5222, https://doi.org/10.5194/egusphere-egu22-5222, 2022.

EGU22-5560 | Presentations | NP6.3

Statistical study of the ripples and reformation in the collisionless shocks using MMS observation 

Ajay Lotekar, Yuri Khotyaintsev, Daniel Graham, Andrew Dimmock, Ahmad Lalti, and Andreas Johlander

Collisionless shocks are ubiquitous throughout the universe in near-Earth and astrophysical plasma environments. The behavior of collisionless shocks in terms of their structure and energy dissipation has been the subject of extensive research over many decades, but many open questions remain. Recent studies have demonstrated that the Earth's bow shock can exhibit ripples that propagate along the shock surface. However, their occurrence, dependence on shock parameters, and their role in shock dynamics is still under investigation. One signature of rippling is the presence of phase space holes in reduced ion distribution (integrated along the tangential plane of the shock). Such ion phase space holes are also observed in association with the shock reformation. It is unclear at what part of the parametric space these ion phase space holes are expected. In this study, we have focused on characterizing ion phase space holes at the Earth’s bow shock using MMS observations. We analyze more than 500 shock crossings observed by the MMS spacecraft and establish a systematic procedure to find the shocks exhibiting phase space holes. We investigate the key shock physical processes responsible for the existence of these phase space holes (e.g. ripples and reformation) and study the association to shock parameters such as Mach number and geometry. We present the first statistical study of this nature, and these results are important to understanding the non-stationary behavior of collisionless shocks. 

How to cite: Lotekar, A., Khotyaintsev, Y., Graham, D., Dimmock, A., Lalti, A., and Johlander, A.: Statistical study of the ripples and reformation in the collisionless shocks using MMS observation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5560, https://doi.org/10.5194/egusphere-egu22-5560, 2022.

EGU22-6084 | Presentations | NP6.3

Suprathermal populations and small scale fluctuations in the solar wind 

Marian Lazar, Rodrigo Lopez, Hamd Shaaban, Stefaan Poedts, Horst Fichtner, and Peter Yoon

In the last decade, studies of solar wind plasma have shown that suprathermal populations (up to a few keV) are closely linked to wave turbulence and fluctuations at small (or kinetic) scales. We aim to identify those types of wave fluctuations observed at these scales scales, for which existing theories predict a major implication in particle acceleration and formation of suprathermal tails in the velocity distributions of plasma particles. On the other hand, it is currently believed that fluctuation power (magnetic, density, velocity) measured at ion scales and lower are generated by the turbulent cascade but also wave instabilities. Therefore, we also intend to discuss a number of recent results describing the kinetic instabilities driven by the anisotropy of velocity distributions (e.g., temperature anisotropy, field-aligned drifts), and how are these instabilities influenced by the suprathermal populations. These results help to understand the energy exchanges between particles and electromagnetic fields, not only in the solar wind but also in the coronal plasma ejections, with consequences for the space weather and terrestrial magnetosphere.

How to cite: Lazar, M., Lopez, R., Shaaban, H., Poedts, S., Fichtner, H., and Yoon, P.: Suprathermal populations and small scale fluctuations in the solar wind, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6084, https://doi.org/10.5194/egusphere-egu22-6084, 2022.

EGU22-6378 | Presentations | NP6.3 | Highlight

Kinetic signatures of magnetic reconnection in the global hybrid-Vlasov and local particle-in-cell simulations.  

Ivan Zaitsev, Andrey Divin, Urs Ganse, Yann Pfau-Kempf, Markus Battarbee, Markku Alho, Jonas Suni, Maxime Grandin, Lucile Turc, Giulia Cozzani, Maarja Bussov, Maxime Dubart, Harriet George, Konstantinos Horaites, Konstantinos Papadakis, Talgat Manglayev, Vertti Tarvus, Honyang Zhou, and Minna Palmroth

Magnetic reconnection is the energy converter in space plasma that releases magnetic energy into the kinetic energy of particles. We study the magnetotail reconnection in the first 3D global magnetospheric hybrid-Vlasov simulation performed with Vlasiator code. We also performed a simulation of symmetric magnetic reconnection in particle-in-cell technique with the iPIC3D code to compare ion kinetic signatures of reconnection for both hybrid-Vlasov and fully-kinetic approaches. Despite the relatively coarse spatial resolution in the global 3D hybrid-Vlasov model, we are able to recognize the most distinguished reconnection features: ion demagnetization, non-gyrotropic ion acceleration and energy dissipation. Using the well-known signatures of the different subregions of symmetric magnetic reconnection we are able to identify ion diffusion regions, separatrices and reconnection jet fronts in the global simulation. Guided by the measure of the ion perpendicular slippage, we identify ion diffusion regions where ion non-gyrotropic crescent-type distributions are formed. These distinguishable features are nicely visible in the PIC simulation data as well. Separatrix regions are visible as the layers containing the potential Hall electric field at the boundaries of accelerated outflow. Reconnection jet fronts in the global simulation are highlighted at the positions where the energy dissipation peaks. Three-dimensional effects affecting the extending of the reconnection characteristics in the equatorial plane are discussed.

How to cite: Zaitsev, I., Divin, A., Ganse, U., Pfau-Kempf, Y., Battarbee, M., Alho, M., Suni, J., Grandin, M., Turc, L., Cozzani, G., Bussov, M., Dubart, M., George, H., Horaites, K., Papadakis, K., Manglayev, T., Tarvus, V., Zhou, H., and Palmroth, M.: Kinetic signatures of magnetic reconnection in the global hybrid-Vlasov and local particle-in-cell simulations. , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6378, https://doi.org/10.5194/egusphere-egu22-6378, 2022.

EGU22-6722 | Presentations | NP6.3

Asymmetric magnetic reconnection between two coalescing flux ropes as squeezed by convergent flows 

Qiaowen Luo, Jiansen He, and Jun Cui

We identify two coalescing flux ropes as squeezed by convergent ion flows near the magnetopause from the MMS observations. According to the electron distributions, we find that one flux rope is closer to the magnetosphere, while the other is closer to the magnetosheath. A current sheet with magnetic field reversal is found to sit at the interface between the two colliding flux ropes, and have magnetic reconnection occurring in the ion diffusion region (IDR). Due to the density asymmetry of flux ropes, the embedded magnetic reconnection event with a significant guide field component shows a large asymmetry in energy conversion across the reconnection site. On the side where the flux rope is closer to the magnetosphere with low density, we find that electrons gained energy from electromagnetic fields resulting in a parallel heating effect. In contrast, ions are found to obtain the energy from electromagnetic fields on the other side of the reconnection current sheet, where the flux rope is near the magnetosheath with high density.

How to cite: Luo, Q., He, J., and Cui, J.: Asymmetric magnetic reconnection between two coalescing flux ropes as squeezed by convergent flows, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6722, https://doi.org/10.5194/egusphere-egu22-6722, 2022.

EGU22-7069 | Presentations | NP6.3

Interplay between magnetic reconnection and flapping instabilities in the magnetotail: global hybrid-Vlasov simulation of the Earth’s magnetosphere 

Giulia Cozzani, Urs Ganse, Yann Pfau-Kempf, Markku Alho, Jonas Suni, Maxime Grandin, Lucile Turc, Ivan Zaitsev, Maarja Bussov, Maxime Dubart, Harriet George, Konstantinos Horaites, Talgat Manglayev, Konstantinos Papadakis, Vertti Tarvus, Honyang Zhou, and Minna Palmroth

Magnetic reconnection is a fundamental process in plasma and a major cause of energy conversion and transport by means of magnetic field topology reconfiguration. It takes place in thin plasma sheets, where energy is often explosively converted from the magnetic field to plasma heating and particle energization. Magnetic reconnection in Earth’s magnetotail is thought to play a crucial role in geomagnetic storms and substorms, one of the most explosive phenomena in the context of Earth’s magnetosphere. Several other current sheet-related processes, such as the ballooning instability, tearing instability, and a variety of flapping instabilities, can occur in the magnetotail, and the interplay between magnetic reconnection and these current sheet instabilities is largely unexplored. In this study, we investigate the interplay between magnetic reconnection and other instabilities taking place in the magnetotail current sheet, using a hybrid-Vlasov simulation that provides a three-dimensional description of the global coupled solar wind-magnetosphere system down to the ion-kinetic scale. In particular, we identify and characterize the flapping instability that develops in the magnetotail midnight sector and we discuss its dynamics in relation to magnetotail magnetic reconnection.

How to cite: Cozzani, G., Ganse, U., Pfau-Kempf, Y., Alho, M., Suni, J., Grandin, M., Turc, L., Zaitsev, I., Bussov, M., Dubart, M., George, H., Horaites, K., Manglayev, T., Papadakis, K., Tarvus, V., Zhou, H., and Palmroth, M.: Interplay between magnetic reconnection and flapping instabilities in the magnetotail: global hybrid-Vlasov simulation of the Earth’s magnetosphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7069, https://doi.org/10.5194/egusphere-egu22-7069, 2022.

EGU22-7354 | Presentations | NP6.3

Hybrid-Vlasov simulations of ion velocity distribution functions within Kelvin-Helmholtz vortices 

Vertti Tarvus, Lucile Turc, Hongyang Zhou, Giulia Cozzani, Urs Ganse, Yann Pfau-Kempf, Markku Alho, Markus Battarbee, Maarja Bussov, Maxime Dubart, Harriet George, Maxime Grandin, Konstantinos Horaites, Talgat Manglayev, Konstantinos Papadakis, Jonas Suni, Ivan Zaitsev, and Minna Palmroth

The Kelvin-Helmholtz instability (KHI) is a ubiquitous fluid instability in space plasmas. At the flanks of Earth's magnetopause, the KHI can typically develop during periods of northward interplanetary magnetic field, and it drives the solar wind-magnetosphere mass/energy transfer in the absence of dayside magnetic reconnection. We use local 2D-3V hybrid-Vlasov simulations to study the ion velocity distribution functions (VDFs) associated with the KHI in a magnetopause-like setup. Our results indicate that when the KHI enters the non-linear stage, the ion VDFs in the region perturbed by the instability become increasingly non-Maxwellian. The degree of non-Maxwellianity increases along with the magnitude of the density jump across the KHI boundary. We assess the impact of the non-Maxwellian ion VDFs on the development of the KHI, and compare the simulated VDFs with those observed by the Magnetospheric Multiscale Mission.

How to cite: Tarvus, V., Turc, L., Zhou, H., Cozzani, G., Ganse, U., Pfau-Kempf, Y., Alho, M., Battarbee, M., Bussov, M., Dubart, M., George, H., Grandin, M., Horaites, K., Manglayev, T., Papadakis, K., Suni, J., Zaitsev, I., and Palmroth, M.: Hybrid-Vlasov simulations of ion velocity distribution functions within Kelvin-Helmholtz vortices, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7354, https://doi.org/10.5194/egusphere-egu22-7354, 2022.

EGU22-7371 | Presentations | NP6.3

Uniturbulence due to non-linear damping of surface Alfvén waves 

Rajab Ismayilli and Tom Van Doorsselaere

We consider a simple 1-D planar equilibrium model with piece-wise constant density. We completed analytical computations in incompressible MHD. First, we derived mathematical formulas for the wave energy density, the rate of energy dissipation, and the energy cascade damping time. Following that, we developed an analytical model to estimate the damping time for the evolution of uniturbulence in surface Alfvén waves. According to the derived equation, the damping time is inversely proportional to the perpendicular wavenumber and the amplitude of the surface Alfvén waves. Next, we determined the numerical energy dissipation rate using the Fourier transform through numerical simulations. Finally, we approximated the damping time using the fundamental mode of a perpendicular wavenumber.
Consequently, we compared our theoretical model to a series of 3D ideal MHD simulations and observed a remarkable resemblance. The numerical findings demonstrate, in particular, that the damping time is inversely related to the density contrast and amplitude of surface Alfvén waves. Besides, we studied third-order structure-function (Yaglom's law) for Uniturbulence. We compared Yaglom's law (predicted energy dissipation) statistics obtained through simulation with our analytical model. In addition, we estimated the inertial range of the turbulent flow.

How to cite: Ismayilli, R. and Van Doorsselaere, T.: Uniturbulence due to non-linear damping of surface Alfvén waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7371, https://doi.org/10.5194/egusphere-egu22-7371, 2022.

EGU22-8160 | Presentations | NP6.3

Exploring 2.5D magnetic reconnection due to Rayleigh-Taylor induced turbulence in solar prominences 

Madhurjya Changmai and Rony Keppens

The internal dynamics of solar prominences have been observed for many decades to be highly complex, many of which also indicate the possibility of turbulence. Prominences represent large-scale, dense condensations suspended against gravity at great heights within the solar atmosphere. It is therefore of no surprise that the fundamental process of the Rayleigh-Taylor (RT) instability has been suggested as the potential mechanism for driving the dynamics and turbulence remarked upon within observations. We use the open-source MPI-AMRVAC code to construct an extremely high-resolution, 2.5D fully-resistive magnetohydrodynamic model, and employ it to explore the turbulent nature of RT-induced magnetic reconnection processes within solar prominences. The intermittent events of heating and energy dissipation are caused by magnetic reconnection. Furthermore, the strength of the mean magnetic field directed into the 2D plane, and its alignment with the plane itself, creates a system with varying turbulent behaviour. Based on low plasma beta (magnetic pressure dominant) evolution near the chromosphere and a higher value (plasma pressure dominant) evolution within the corona, the stratified numerical model generates different fluctuation statistics. Hence, we find the turbulent dynamics and prominence reconnection events to differ distinctly from those elsewhere within the solar corona.

How to cite: Changmai, M. and Keppens, R.: Exploring 2.5D magnetic reconnection due to Rayleigh-Taylor induced turbulence in solar prominences, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8160, https://doi.org/10.5194/egusphere-egu22-8160, 2022.

EGU22-8705 | Presentations | NP6.3

Fully kinetic simulations of the near-Sun solar wind plasma: turbulence, reconnection, and particle heating 

Luca Franci, Emanuele Papini, Alfredo Micera, Lorenzo Matteini, Julia Stawarz, Giovanni Lapenta, David Burgess, Petr Hellinger, Simone Landi, Andrea Verdini, and Victor Montagud-Camps

We model the development of plasma turbulence in the near-Sun solar wind with high-resolution fully-kinetic particle-in-cell (PIC) simulations, initialised with plasma conditions measured by Parker Solar Probe during its first solar encounter (ion and electron plasma beta ≤ 1 and a large amplitude of the turbulent fluctuations). The power spectra of the plasma and electromagnetic fluctuations are characterized by multiple power-law intervals, with a transition and a considerable steepening in correspondence of the electron scales. In the same range of scales, the kurtosis of the magnetic fluctuations is observed to further increase, hinting at a higher level of intermittency. We observe a number of electron-only reconnection events, which are responsible for an increase of the electron temperature in the direction parallel to the ambient field. The total electron temperature, however, exhibits only a small increase due to the cooling of electrons in the perpendicular direction, leading to a strong temperature anisotropy. We also analyse the power spectra of the different terms of the electric field in the generalised Ohm’s law, their linear and nonlinear components, and their alignment, to get a deeper insight on the nature of the turbulent cascade. Finally, we compare our results with those from hybrid simulations with the same parameters, as well as with spacecraft observations.

How to cite: Franci, L., Papini, E., Micera, A., Matteini, L., Stawarz, J., Lapenta, G., Burgess, D., Hellinger, P., Landi, S., Verdini, A., and Montagud-Camps, V.: Fully kinetic simulations of the near-Sun solar wind plasma: turbulence, reconnection, and particle heating, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8705, https://doi.org/10.5194/egusphere-egu22-8705, 2022.

In supercritical shocks a substantial fraction of ions is reflected at the steep shock ramp. The beam of reflected ions carries a considerable amount of energy and momentum. As a consequence, different plasma populations can co-exist within the same foot region, which constitutes a source of micro-instabilities excited by the relative drifts between incoming ions, reflected ions, and electrons across the ambient magnetic field B. With the help of a spectral periodic 2D PIC code, we investigate the resulting micro-turbulence. Three different waves with different frequency/wave number ranges can be excited simultaneously: Bernstein waves and whistler waves near the lower-hybrid frequency as well as the electron cyclotron frequency. The present work is a 2D extension of a previous analysis (Muschietti et Lembege, Ann. Geophys. 2017) and allows to self-consistently include the mutual interaction between the different instabilities/waves which propagate in different directions with respect to Bo and are at different stages of their respective linear/nonlinear phases. In order to clarify their intricate synergies, a new filtering procedure (low or high pass filter of a given wave number range) has been developed. Taking thus advantage of the spectral nature of the code, we can include/exclude at will the impact of a given instability on the other ones. We have performed several times the simulation with exactly the same initial conditions yet with different filtering ranges. The procedure allows us to illuminate the role played by each instability in the scenario when all are included. Recent results will be presented. 

How to cite: Muschietti, L., Lembege, B., and Decyk, V.: How to define the interplay between different instabilities excited within the foot of a supercritical shock : 2D PIC simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8962, https://doi.org/10.5194/egusphere-egu22-8962, 2022.

The Jovian magnetosphere is loaded internally with material from the volcanic moon of Io, which is ionised and brought into co-rotation forming the Io plasma torus. Plasma is removed from the torus mainly via ejection as energetic neutrals and by bulk transport into sink regions in the outer magnetosphere.

There are two physical processes that are implicated in the bulk transport process, these are diffusion and the radial-interchange (RI) instability. The latter is analogous to the Rayleigh-Taylor instability, but with centrifugal force replacing gravity. This allows magnetic flux tubes containing hot, tenuous plasma to exchange places with tubes containing cool, dense plasma, moving material from the inner to outer magnetosphere whilst returning magnetic flux to the planet. Observational data does not currently provide strong evidence to favour either process and indeed they may be non-linearly coupled. Furthermore, current state-of-the-art simulations do not permit an understanding of non-linear phases of the instability nor the effect of magnetosphere-ionosphere coupling on small length scales.

In order to examine the bulk transport process we have developed a full hybrid kinetic ion, fluid-electron plasma model in 2.5-dimensions, JERICHO. The technique of hybrid modelling allows for probing of plasma motions from the scales of planetary-radii down to the ion-inertial length, considering constituent ion species kinetically as charged particles and forming the electrons into a single magnetised fluid continuum. This allows for insights into particle motions on spatial scales below the size of the magnetic flux tubes. We are particularly interested in exploring a) bulk transport on spatial scales not currently accessible with other state-of-the-art models; b) the relative contributions from diffusive motions against those from RI instabilities; and c) non-linear effects generated by RI instabilities and the impact of these on plasma transport from the inner to outer magnetosphere. In this presentation we will examine the latest simulation results from JERICHO, initialised with a range of Jovian parameters, examining the evolution of the RI instability on differing spatial and temporal scales.

How to cite: Wiggs, J. and Arridge, C.: Examining Radial-Interchange in the Jovian Magnetosphere using JERICHO: a Kinetic-Ion, Fluid-Electron Hybrid Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9788, https://doi.org/10.5194/egusphere-egu22-9788, 2022.

EGU22-9885 | Presentations | NP6.3

MMS bow shock crossings database 

Yuri Khotyaintsev, Ahmad Lalti, Andrew P. Dimmock, Andreas Johlander, and Daniel B. Graham

Identifying collisionless shock crossings in data sent from spacecraft has so far been done manually. It is a tedious job that shock physicists have to go through if they want to conduct case studies or perform statistical studies. We use a machine learning approach to automatically identify shock crossings from the Magnetospheric Multiscale (MMS) spacecraft. We compile a database of those crossings including various spacecraft related and shock related parameters for each event. Furthermore, we show that the shocks in the database have properties that are spread out both in real space and parameter space. We also present a possible science application of the database by looking for correlations between ion acceleration efficiency at shocks and different shock parameters such as the shock geometry and the Mach number.

How to cite: Khotyaintsev, Y., Lalti, A., Dimmock, A. P., Johlander, A., and Graham, D. B.: MMS bow shock crossings database, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9885, https://doi.org/10.5194/egusphere-egu22-9885, 2022.

EGU22-10339 | Presentations | NP6.3

The combined effect of electron and proton firehose instabilities for the solar wind plasma conditions 

Rodrigo A. López, Alfredo Micera, Marian Lazar, Shaaban M. Shaaban, Stefaan Poedts, and Giovanni Lapenta

In the absence of collision, kinetic instabilities triggered by velocity space anisotropies of plasma particles play an essential role in limiting the deviations from isotropy. For example, in the solar wind, firehose instabilities may inhibit the growth of the temperatures in the direction parallel to the background magnetic field, counterbalancing the effect of the expansion. Electron and proton firehose instabilities can be triggered depending on the plasma parameters and the different branches within (periodic and aperiodic). Despite the significant difference between electron and proton spatial and temporal scales, both modes can work together to alter the dynamic of the plasma.
We use a fully kinetic 2D semi-implicit particle-in-cell simulation, iPic3D, to study the evolution and interplay of firehose instabilities triggered by electrons and protons when both species are anisotropic. The aperiodic electron firehose instability remains largely unaffected by the proton anisotropy and saturates rapidly at low-level fluctuations. On the other hand, the presence of anisotropic electrons has a considerable impact on the proton firehose modes, especially on the aperiodic branch, shifting the onset of the instability and boosting the saturation levels of the fluctuations. Anisotropic electrons contribute to more effective regulation of the proton anisotropy.

How to cite: López, R. A., Micera, A., Lazar, M., Shaaban, S. M., Poedts, S., and Lapenta, G.: The combined effect of electron and proton firehose instabilities for the solar wind plasma conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10339, https://doi.org/10.5194/egusphere-egu22-10339, 2022.

EGU22-10688 | Presentations | NP6.3

Multi-Spacecraft Observations of Interplanetary Shocks 

Oksana Kruparova, Vratislav Krupar, and Adam Szabo

Interplanetary (IP) shocks provide us with a unique opportunity to extensively investigate properties of collisionless shocks using in situ measurements under a wide range of upstream conditions. Here we report a case study of several IP shock crossings observed by the Wind, Solar and Heliospheric Observatory (SOHO), Advanced Composition Explorer (ACE), and Deep Space Climate Observatory (DSCOVR) spacecraft. By applying a simple timing method to multipoint measurements, we are able to investigate their characteristic spatiotemporal features. We assume that an IP shock can be represented by a moving plane with a constant velocity, when observed at closely separated points in space and time. We compared IP shock parameters obtained with the timing method with those obtained using the magnetic coplanarity, the mixed mode methods, and Rankine-Hugoniot jump relations.

 

How to cite: Kruparova, O., Krupar, V., and Szabo, A.: Multi-Spacecraft Observations of Interplanetary Shocks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10688, https://doi.org/10.5194/egusphere-egu22-10688, 2022.

EGU22-11028 | Presentations | NP6.3

Enernization of Alpha Particles in the Solar Wind Magnetic Reconnection 

Die Duan, Jiansen He, Xingyu Zhu, Rui Zhuo, Ziqi Wu, and Liu Yang
The acceleration and heating of solar wind particles by energy dissipation in the magnetic reconnection is an important problem in space physics. Although alpha particles are the second most abundant element in the solar wind, their dynamical behavior in the magnetic reconnection of the solar wind is not well understood. Using the high energy (1500~3000 eV) part of the SWA/PAS instrument on board the Solar Orbiter, we study the kinetic behavior of alpha particles in a magnetic-reconnetion exhaust region within a heliospheric current sheet. In this event, protons and alpha particles have similar bulk velocities. Alpha particles are accelerated and form a jet in the exhaust region. The counter-stream distribution of alpha particles is observed inside the exhuast region, which changes the direction from parallel to perpendicular to the magnetic field direction when the magnetic field is reversed. In addition, a pair of the slow shock/rotational discontinuity is observed in the exhuast region. The exhaust region is heated and bounded by the slow shocks , while the accelerated plasma jet is bounded by the rotational discontinuities.

How to cite: Duan, D., He, J., Zhu, X., Zhuo, R., Wu, Z., and Yang, L.: Enernization of Alpha Particles in the Solar Wind Magnetic Reconnection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11028, https://doi.org/10.5194/egusphere-egu22-11028, 2022.

EGU22-11524 | Presentations | NP6.3

Turbulence-driven magnetic reconnection and the magnetic correlation length in collisionless plasma turbulence 

Julia E. Stawarz, Jonathan P. Eastwood, Tai Phan, Imogen L. Gingell, Prayash S. Pyakurel, Michael A. Shay, Sadie L. Robertson, Christopher T. Russell, and Olivier Le Contel

Observations of Earth’s magnetosheath from the Magnetospheric Multiscale (MMS) mission have provided an unprecedented opportunity to examine the detailed structure of the multitude of thin current sheets that are generated by plasma turbulence, revealing that a novel form of magnetic reconnection, which has come to be known as electron-only reconnection, can occur within magnetosheath turbulence. These electron-only reconnection events occur at thin electron-scale current sheets and have super-Alfvénic electron jets that can approach the electron Alfvén speed; however, they do not appear to have signatures of ion jets. It is thought that electron-only reconnection can occur when the length of the reconnecting current sheets along the outflow direction is short enough that the ions cannot fully couple to the newly reconnected magnetic field lines before they fully relax. In this work, we examine how the correlation length of the magnetic fluctuations in a turbulent plasma, which constrains the length of the current sheets that can be formed by the turbulence, impacts the nature of turbulence-driven magnetic reconnection. Using observations from MMS, we systematically examine 60 intervals of magnetosheath turbulence – identifying 256 small-scale reconnection events, both with and without ion jets. We demonstrate that the properties of the reconnection events transition to become more consistent with electron-only reconnection when the magnetic correlation length of the turbulence is below ~20 ion inertial lengths. We further discuss the implications of the results in the context of other turbulent plasmas by considering observations of turbulent fluctuations in the solar wind.

How to cite: Stawarz, J. E., Eastwood, J. P., Phan, T., Gingell, I. L., Pyakurel, P. S., Shay, M. A., Robertson, S. L., Russell, C. T., and Le Contel, O.: Turbulence-driven magnetic reconnection and the magnetic correlation length in collisionless plasma turbulence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11524, https://doi.org/10.5194/egusphere-egu22-11524, 2022.

EGU22-11660 | Presentations | NP6.3

Magnetosheath jets at the magnetopause: reconnection onset conditions 

Adrian LaMoury, Heli Hietala, Jonathan Eastwood, Laura Vuorinen, and Ferdinand Plaschke

Magnetosheath jets are localised pulses of high dynamic pressure plasma observed in Earth’s magnetosheath. They are believed to form from the interaction between the solar wind and ripples in Earth’s collisionless bow shock, before propagating into the turbulent magnetosheath. Upon impacting the magnetopause, jets can influence magnetospheric dynamics. In particular, previous studies have suggested that, by virtue of their internal magnetic field orientations, jet impacts may be able to trigger local magnetic reconnection at the magnetopause. This is most notable during traditionally unfavourable solar wind conditions, such as intervals of northward interplanetary magnetic field. This idea has been supported by a small number of case studies and simulations. We present a large statistical study into the properties of jets near the magnetopause. We examine the components of the magnetic reconnection onset condition – the competing effects of magnetic shear angle and plasma beta – to determine how jets may affect magnetopause reconnection in a statistical sense. We find that, due to their increased beta, jet plasma is typically not favourable to reconnection, often more so than the non-jet magnetosheath. Most jets do contain some reconnection-favourable plasma, however, suggesting that jets may be able to both trigger and suppress magnetopause reconnection. We complement this with new case studies of jets interacting with the magnetopause.

How to cite: LaMoury, A., Hietala, H., Eastwood, J., Vuorinen, L., and Plaschke, F.: Magnetosheath jets at the magnetopause: reconnection onset conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11660, https://doi.org/10.5194/egusphere-egu22-11660, 2022.

EGU22-11807 | Presentations | NP6.3 | Highlight

Agyrotropy patterns in 3D small-scall turbulent reconnection 

Jeffersson Andres Agudelo Rueda, Daniel Verscharen, Robert T. Wicks, Christopher J. Owen, Andrew P. Walsh, and Kai Germaschewski
Turbulence and magnetic reconnection are at the core of the long-standing problem of energy dissipation in collisionless plasmas. More than two decades of research on magnetic reconnection have led us to understand the characteristic plasma flows and particle agyrotropy patterns present in collisionless reconnection events. However, it is still not clear what the agyrotropy patterns associated with reconnection events are that form in a turbulent cascade. In this work, we use an explicit fully kinetic particle-in-cell code to study the plasma particles’ agyrotropy associated with three-dimensional small-scale magnetic reconnection events generated by anisotropic and Alfvénic decaying turbulence. We select one reconnection event involving two reconnecting flux ropes. Although we observe similarities with agyrotropy patterns known from two-dimensional steady-state reconnection events, the agyrotropy patterns in our event are more complex. This has further implications for the energy transfer channels available in three-dimensional turbulent reconnection.
 

How to cite: Agudelo Rueda, J. A., Verscharen, D., Wicks, R. T., Owen, C. J., Walsh, A. P., and Germaschewski, K.: Agyrotropy patterns in 3D small-scall turbulent reconnection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11807, https://doi.org/10.5194/egusphere-egu22-11807, 2022.

EGU22-11945 | Presentations | NP6.3 | Highlight

Turbulence modified by velocity shear in coronal mass ejection sheaths 

Juska Soljento, Simon Good, Adnane Osmane, and Emilia Kilpua

Fast coronal mass ejections (CMEs) drive shock waves ahead of them. The turbulent sheath region between the shock and the CME itself contains magnetic field and velocity fluctuations on a broad spectrum of frequencies. In this work we aim to characterise the direction and source of solar wind fluctuations at MHD fluid scales in CME-driven sheaths near Earth. One possible source for these fluctuations is velocity shear, which are common occurrences in CME-driven sheaths. Here we first identify velocity shear as it occurs and then relate that to signatures of new fluctuations being created locally in the sheath. Turbulence parameters such as cross helicity, residual energy, Elsasser ratio, and Alfvén ratio are calculated, and they are correlated against large-scale signatures of velocity shear. Findings indicate a clear association between velocity shear and locally generated fluctuations, as well as a balance in the directionality of these new fluctuations, i.e., they tend to propagate equally towards and away from the Sun. In contrast, most solar wind is typically dominated by anti-sunward fluctuations.

How to cite: Soljento, J., Good, S., Osmane, A., and Kilpua, E.: Turbulence modified by velocity shear in coronal mass ejection sheaths, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11945, https://doi.org/10.5194/egusphere-egu22-11945, 2022.

The kinetic turbulence generated by accelerated particles in a reconnecting current sheet (RCS) with X- and O-nullpoints is considered. The simulations of magnetic reconnection using particle-in-cell (PIC) approach is carried out in a thin current sheet with 3D magnetic field topology affected by tearing instability that leads to a formation of two large magnetic islands . The model utilises a strong guiding field that leads to separation of the particles of opposite charges, generation of a strong polarisation electric field across the RCS and suppression of kink instability in the ’out-of-plane’ direction. The accelerated particles of the same charge entering an RCS from the opposite edges are shown accelerated to different energies forming the ‘bump-in-tail’ velocity distributions that, in turn, can generates plasma turbulence in different locations. The turbulence produced by either electron or proton beams is identified from the energy spectra of electromagnetic field fluctuations in the phase and frequency domains.

The spectral index of the power spectrum In a wavenumber space of the turbulent magnetic field near the ion inertial length approaches -2.7. The collective turbulence power spectra are consistent with the high-frequency fluctuations of perpendicular electric field, or upper hybrid waves, to occur in a vicinity of X-nullpoints, with the Langmuir waves  generated by accelerated electrons which can be converted to  Bernstein waves when electron beams become moving across the magnetic field lines. The frequency spectra of high and low-frequency waves are explored in the kinetic turbulence in parallel and perpendicular directions to the local magnetic field showing noticeable lower hybrid turbulence. The implication of finding for observations is also discussed.

How to cite: Zharkova, V. and Xia, Q.: Kinetic turbulence generated by accelerated particles in a reconnecting current sheet with magnetic islands, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12159, https://doi.org/10.5194/egusphere-egu22-12159, 2022.

EGU22-12840 | Presentations | NP6.3

Studying multi-beam ion temperature inside a collisionless reconnection plasmoid by means of Gaussian Mixture Model 

Igor Paramonik, Andrey Divin, Ivan Zaitsev, and Vladimir Semenov

Being ubiquitous energy converter is space plasmas, magnetic reconnection releases stored magnetic energy into kinetic energy of particles. Magnetic reconnection involves several particle acceleration mechanisms which form beams directed parallel to the magnetic field. It was recently demonstrated analytically that in the presence of complicated velocity space structures, the definition of higher moments (like thermal pressure) should be extended to cover such multibeam distributions. In practice, the number of beams at each spatial point of interest is not know a priori. With the aim to automatically reveal the information about the beams generated in the reconnection process, we applied an unsupervised machine learning algorithm (Gaussian Mixture Model, GMM) to the 2.5D Particle-in-Cell simulations of collisionless magnetic reconnection. We studied the ion distributions inside a plasmoid and found that the multibeam ion temperature  within the reconnected outflow deviates significantly from the standard ion temperature (calculated as the 2nd moment of the ion distribution function). In particular, the regions of the strong parallel heating contain in fact relatively cold counterstreaming beams and the overestimation of parallel temperature in this case could be as high as 10. In the current study, we make an attempt to figure out how long the multi-beam regime exists without significant thermalization inside a plasmoid formed by two adjacent X-lines.

How to cite: Paramonik, I., Divin, A., Zaitsev, I., and Semenov, V.: Studying multi-beam ion temperature inside a collisionless reconnection plasmoid by means of Gaussian Mixture Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12840, https://doi.org/10.5194/egusphere-egu22-12840, 2022.

NP7 – Nonlinear Waves

The main source of generation of short-period internal waves (SIW’s) is the dissipation of the internal tide on the roughness of the bottom relief. Differences in the slope of the bottom and geographical latitude have an impact on the propagation of internal tide and on the generation of SIW’s. Based on the synthesis of the results of contact, remote observations, and modeling, the characteristics of the SIW’s in the Barents Sea on a wide shelf with small bottom slopes, and in the Avacha Bay with a narrow shelf and significant bottom slopes are considered.

In the Barents Sea, in situ observations were carried out in August 2016 near the Kharlov island. Measurements in the Avacha Bay were carried out in August – September 2018 near the Cape Shipunsky. The height and period of the SIW’s were estimated. The SIW’s spectrum was calculated and compared with the Garrett-Munk spectrum.

Sentinel-1A/B, ALOS-2 PALSAR-2, Sentinel-2A/B, and Landsat-8 images were used to analyze the manifestations of SIW’s. To identify the centers of internal tide generation, the tidal body force criterium for harmonics M2 and K1 was used, calculated using Copernicus reanalysis data and the OTIS tidal model.

On the records in the Avacha Bay, long-period fluctuations of isotherms due to semi-daily tidal dynamics are traced. Against the background of semi-daily fluctuations, SIW’s with a period of about 15 minutes and a height of up to 8 meters are distinguished. On the record, during the low tide period, a SIW’s train with heights of up to 15 meters was recorded. In the Barents Sea, the long-period variability of isotherms is less pronounced, short-period fluctuations with a period of about 10 minutes and a height of up to 5 meters are dominant.

The Ursell parameter demonstrates that waves about 8 meters high in the Barents Sea are weakly nonlinear, and waves about 15 meters high in the Avacha Bay are strongly nonlinear. Spectrum calculations show that the oscillation energy in the Barents Sea at all frequencies is lower than in the Avacha Bay, while it does not exceed the energy of the Garrett-Monk spectrum. In Avacha Bay, the oscillation energy at almost all frequencies is higher than the energy of the Garrett-Monk spectrum.

93 manifestations of SIW’s were detected in the Barents Sea, and 72 ones were detected in the Avacha Bay. Most of the manifestations are in the areas of high values of the tidal body force criterium, which may indicate the generation of SIW’s under the influence of the decay of the internal tide.

It was shown that both in the Barents Sea, close to the critical latitude for the semidiurnal tide, and in the Avacha Bay beyond the critical latitude for the diurnal tide, SIW’s are generated under the influence of an internal tide. However, the energy of short-period oscillations in the Avacha Bay is higher than in the Barents Sea.

The study was supported by RFBR grant No. 20-35-90054.

How to cite: Svergun, E. and Zimin, A.: Short-period internal waves in tidal seas on various types of shelf according to in situ and satellite observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-59, https://doi.org/10.5194/egusphere-egu22-59, 2022.

EGU22-1228 | Presentations | NP7.1

Mode-2 internal waves and inter-mode resonance in late winter lakes 

Marek Stastna

In late winter/early spring temperate and northern lakes often experience a so-called weak, inverse stratification.  This occurs since: i) fresh water experiences a maximum density at around four degrees Centigrade, ii) the lake is iced over and thus mechanically isolated from the overlying atmosphere, iii) the increasing solar insolation heats the water column according to the Beer-Lambert-Bouguer law; thereby producing a region of instability that mixes a portion of the water column.  This classical scenario fits some lakes, but the small density differences due to the thermal forcing also imply that very small amounts of dissolved salts could create a more complex, combined solute-thermal stratification.  We explore the behaviour of nonlinear internal waves for one such measured stratification. For mode-1 we find well defined internal solitary waves. For mode-2 the coupling between pycnoclines is weaker leading to a more complex dynamics that we quantify in detail. 

How to cite: Stastna, M.: Mode-2 internal waves and inter-mode resonance in late winter lakes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1228, https://doi.org/10.5194/egusphere-egu22-1228, 2022.

EGU22-2365 | Presentations | NP7.1 | Highlight

The Interaction of Internal Solitary Waves and Sea Ice in the laboratory 

Sam Hartharn-Evans and Magda Carr

Internal Waves are commonly observed along density interfaces across the world’s oceans. In the Arctic Ocean, the internal wave field is much less energetic than at lower latitudes, but due to relative quiescence of the region, nonlinear internal waves are particularly important for mixing there. This mixing is responsible for bringing heat from warm Atlantic Water at intermediate depth towards the surface where it has ramifications for the formation and melt of sea ice, as well as the general circulation of the Arctic Ocean. In the rapidly changing Arctic Ocean, as sea ice extent declines, understanding how internal waves interact with sea ice, and how sea ice affects them is crucial, particularly in the marginal ice zone.

Using laboratory experiments of internal solitary waves (ISWs) propagating under model ice the interaction of ice and internal solitary waves is investigated. Specifically, (i) Particle Tracking Velocimetry is used to measure the motion of floating discs (with the same density as sea ice ρ = 910kg/m³), to determine how ice moves in response to the near-surface internal wave-induced flow using is quantified. Additionally, (ii) Particle Image Velocimetry is used to determine how the near-surface internal wave-induced flow dynamics are impacted by the presence and motion of the model sea-ice, which acts as a rough upper boundary condition and moves with the flow.

How to cite: Hartharn-Evans, S. and Carr, M.: The Interaction of Internal Solitary Waves and Sea Ice in the laboratory, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2365, https://doi.org/10.5194/egusphere-egu22-2365, 2022.

EGU22-3184 | Presentations | NP7.1

Korteweg-de Vries equation family in the theory of nonlinear internal waves 

Tatiana Talipova and Efim Pelinovsky

As it is known, the canonical Korteweg-de Vries equation is applied to describe nonlinear long internal waves in the first approximation on parameters of nonlinearity and dispersion. To compare with surface gravity waves, the coefficient of quadratic nonlinearity can have either sign and to be zero. In this case, the asymptotic procedure should take into account higher terms of nonlinearity. Generalized Korteweg-de Vries equation called the Gardner equation is now a popular model to analyze nonlinear internal waves in the ocean with complicated density and shear flow stratification. If the density stratification is almost linear, the number of nonlinear terms is increased. The family of the Korteweg-de Vries-like equations for internal waves in the form ut+ [F(u)]x + uxxx = 0 is discussed in this presentation. In leading order the nonlinear term is F(u) ~ qub  with b > 0. The steady-state travelling solitary waves is analyzed.

            For q > 0 and b > 1 the analysis re-confirmed that all travelling solitons have “light” exponentially decaying tails and propagate to the right. If q < 0 and b < 1, the travelling solitons (so called compactons) have a compact support (and thus vanishing tails) and propagate to the left. For more complicated F(u) and b > 1 (e.g., the Gardner equation and higher-order generalizations) standing algebraic solitons with “heavy” power-law tails may appear. If the leading term of F(u) is negative, the set of solutions may include wide or table-top solitons (similar to the solutions of the Gardner equation), including algebraic solitons and compactons with any of the three types of tails. The solutions usually have a single-hump structure but if F(u) represents a higher-order polynomial, the generalized KdV equation may support multi-humped pyramidal solitons.

Study is supported by RFBR Grant No 21-55-15008.

How to cite: Talipova, T. and Pelinovsky, E.: Korteweg-de Vries equation family in the theory of nonlinear internal waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3184, https://doi.org/10.5194/egusphere-egu22-3184, 2022.

EGU22-3255 | Presentations | NP7.1

Dynamics Insight of  Internal Tide Radiation in the Kuroshio 

Zhenhua Xu

Ocean circulation strongly influences how internal tides radiate and break and stimulates the spatial inhomogeneity and temporal variation of internal tidal mixing. Qualitative and quantitative characterizations of interactions between internal tides and general circulation are critical to multi-scale circulation dynamics. Based on significant progress in regional circulation simulation, we obtain an observation-supported internal tide energy field around Luzon Strait by deterministically resolving the dynamics of the radiating paths of the internal tide energy. These paths are created when the known most powerful internal tide of Luzon Strait interacts with the Kuroshio Current. We found that the radiating tidal pattern, local dissipation efficiency, and energy field respond differently to the leaping, looping, and leaking Kuroshio paths within Luzon Strait. Our new insights into the dynamics and our clarifying the controlling refraction mechanism within the general circulation create the potential for internal tides to be represented better in climate models. 

How to cite: Xu, Z.: Dynamics Insight of  Internal Tide Radiation in the Kuroshio, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3255, https://doi.org/10.5194/egusphere-egu22-3255, 2022.

EGU22-3426 | Presentations | NP7.1

Geographical inhomogeneity and temporal variability in mixing property and driving mechanism in the Arctic Ocean 

Jia You, ZhenHua Xu, Robin Robertson, Qun Li, and BaoShu Yin

   Upper ocean mixing plays a key role in the atmosphere-ocean heat transfer and sea ice extent and thickness via modulating the upper ocean temperatures in the Arctic Ocean. Observations of diffusivities in the Arctic that directly indicate the ocean mixing properties are sparse. Therefore, the spatiotemporal pattern and magnitude of diapycnal diffusivities and kinetic energy dissipation rates in the upper Arctic Ocean are important for atmosphere-ocean heat transfers and sea ice changes. These were first estimated from the Ice-Tethered Profilers dataset (2005–2019) using a strain-based fine-scale parameterization. The resultant mixing properties showed significant geographical inhomogeneity and temporal variability. Diapycnal diffusivities and dissipation rates in the Atlantic sector of the Arctic Ocean were stronger than those on the Pacific side. Mixing in the Atlantic sector increased significantly during the observation period; whereas in the Pacific sector, it weakened before 2011 and then strengthened. Potential impact factors include wind, sea ice, near inertial waves, and stratification, while their relative contributions vary between the two sectors of the Arctic Ocean. In the Atlantic sector, turbulent mixing dominated, while in the Pacific sector, turbulent mixing was inhibited by strong stratification prior to 2011, and is able to overcome the stratification gradually after 2014. The vertical turbulent heat flux constantly increased in the Atlantic sector year by year, while it decreased in the Pacific sector post 2010. The estimated heat flux variability induced by enhanced turbulent mixing is expected to continue to diminish sea ice in the near future. 

How to cite: You, J., Xu, Z., Robertson, R., Li, Q., and Yin, B.: Geographical inhomogeneity and temporal variability in mixing property and driving mechanism in the Arctic Ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3426, https://doi.org/10.5194/egusphere-egu22-3426, 2022.

EGU22-3451 | Presentations | NP7.1

The Three-Dimensional Internal Tide Radiation andDissipation in the Mariana Arc-Trench System 

Chen Zhao, Zhenhua Xu, Robin Robertson, Qun Li, Yang Wang, and Baoshu Yin

  Internal tides are energetic in the Mariana arc, but their three-dimensional radiation and dissipation remain unexplored, particularly the trench-arc-basin impacts. Here, the generation, propagation and dissipation of M2 internal tides over the Mariana area are examined using a series of observation-supported high-resolution simulations. The M2 barotropic to baroclinic conversion rate amounts to 8.35 GW, of which two arc-shaped ridges contribute ~81% of the generated energy. The contributions to generation by the Mariana basin and deep trench are weak. Nevertheless, they are important in modulating the energy radiation and dissipation, since tidal beams can spread to these areas. The Mariana ridges radiate the westward-focused and eastward-spreading tidal beams. This is very consistent with the altimetric measurements. The resonance in the ridge center enhances the westward converging beam, which can travel across the Palau Ridge, 800 km away. In contrast, the eastward beams propagate over a limited lateral range, but can radiate and dissipate significant energy in the deep water column, reaching even to the abyssal Mariana trench. The direct estimation from the model results reveals the dissipation’s multilayer vertical profile in the entire water column, and is well consistent with the finescale parameterization estimate based on vertical strain. However, the estimate of an oft-used energy balance method, which typically assumes an exponentially decaying vertical structure function for the dissipation rate based on distance above the seafloor, is largely inconsistent with the measurements. Our findings highlight the complexity of three-dimensional radiation paths and dissipation map of internal tides in the Mariana area.

How to cite: Zhao, C., Xu, Z., Robertson, R., Li, Q., Wang, Y., and Yin, B.: The Three-Dimensional Internal Tide Radiation andDissipation in the Mariana Arc-Trench System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3451, https://doi.org/10.5194/egusphere-egu22-3451, 2022.

EGU22-3460 | Presentations | NP7.1

Temporal variability of Multimodal Internal Tides at the East China Sea Shelf 

Weidong Wang, Robin Robertson, Yang Wang, Chen Zhao, Jia You, Zhenhua Xu, and Baoshu Yin

Internal tide variations and mixing properties are important to shelf dynamics and mass exchange. In the present study, spatiotemporal variability of internal tides and their modulation factors on the southern East China Sea (ECS) shelf are examined using a three-month mooring observation. Semidiurnal and diurnal internal tides are found to exhibit distinct varying trends. Specifically, the semidiurnal internal tides are quite weak at the early stage, but greatly enhanced in the last three spring-neap cycles. In contrast, the diurnal internal tides follow quasi spring-neap variability except for the strengthening in two specific periods. The enhancement of semidiurnal internal tides in late July and August can be attributed to the strengthened stratifications shelf-slope area northeast of Taiwan Island, which is identified as the generation source. While the diurnal internal tides are modulated by background circulation through the effective critical latitude. The weak critical latitude effect corresponds to the intermittent enhancement of diurnal internal tides in two specific periods. In addition, the circulation also affects the vertical modal structures of the internal tides. The proportion of higher modes internal tides increases during robust eddy activities.  The high-frequency and high-mode internal tides are of crucial significance for turbulent mixing on the shelf region.

Key word: Internal Tides; Mooring Observation; Spatiotemporal variation; Shelf dynamics

How to cite: Wang, W., Robertson, R., Wang, Y., Zhao, C., You, J., Xu, Z., and Yin, B.: Temporal variability of Multimodal Internal Tides at the East China Sea Shelf, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3460, https://doi.org/10.5194/egusphere-egu22-3460, 2022.

EGU22-4274 | Presentations | NP7.1

Estimation of energy loss of internal solitary waves over an isolated obstacle 

Kateryna Terletska and Vladimir Maderich

Simulations of internal solitary waves (ISW) of the first mode over the isolated obstacles of various shapes:  triangles, semicircle and rectangles of different lengths are presented.  The influence of height, length and shape of the obstacle on the transformation of ISW and energy dissipation was investigated. A two-layer free surface water system with upper and bottom layer thicknesses h1 and h2 and densities ρ1 and ρ2, respectively, and water depth H was considered.  It was carried out set of 42 numerical experiments with both ISW of elevation and depression types. The results of simulation were compared with the results of laboratory experiments. It is shown that the blocking parameter B  [1] (that is a dimensionless parameter equal to the ratio of the lower layer above the obstacle to the wave amplitude) is useful for describing the type of interaction and estimation of energy loss.  The transformation of large amplitude ISW over a triangular obstacle differs from the corresponding interaction with the semicircle obstacle. Internal boluses formed in the case of semicircle or rectangle obstacle are 1.5 - 2 times larger than in the case of a triangular obstacle. As a result, energy dissipation and corresponding mixing in the case of ISW transformation over semicircle and a rectangular obstacle is greater than in the case of a triangular ones. Maximum energy losses can reach 42% in the case of a rectangular obstacle. Energy losses increase with increasing length of the obstacle. Thus, we can conclude that topographic effects, namely the influence of shape and geometric characteristics of underwater obstacles have a significant impact on the dissipation of mechanical energy. 

 

[1]  T. Talipova , K. Terletska, V. Maderich, I. Brovchenko, K. T. Jung, E. Pelinovsky and R. Grimshaw  Internal solitary wave transformation over the bottom step: loss of energy. // Phys. Fluids, 2013, 25, 032110

How to cite: Terletska, K. and Maderich, V.: Estimation of energy loss of internal solitary waves over an isolated obstacle, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4274, https://doi.org/10.5194/egusphere-egu22-4274, 2022.

EGU22-5790 | Presentations | NP7.1

Mixing contributions from resonant trapped internal waves generated by bottom topography in an estuary 

Tess Wegman, Julie Pietrzak, Wouter Kranenburg, Robert Jan Labeur, and Martin Verlaan

The Rotterdam Waterway is part of the Rhine-Meuse estuary, which is characterized a salt wedge estuary. Therefore, it is persistently strongly stratified. Field observations in the Rotterdam Waterway, described in earlier literature, reveal internal waves (IWs) generated by resonance over undular bottom topography. IWs are widely found in estuarine and coastal regions, and can contribute to mixing in stratified bodies of water. In this study we explore the generation of IWs over a series of sinusoidal bed forms and their potential of mixing.

An idealised 2D stretch of an estuary, containing sinusoidal bottom topography, is modelled in the non-hydrostatic finite element numerical model FINLAB. The effects of varying wavelength and wave height of the undular topographic features on internal wave generation and vertical mixing are evaluated.

From the model results we find that the generation of the resonant internal wave modes are in correspondence with an analytical analysis based on linear theory. Our results show that in the case of bed form induced internal waves, vertical mixing in the short 2D stretch increases, compared to a flat bed. This is predominantly caused by an increase in bottom friction. This suggests that the trapped internal waves only give a relatively small contribution to this increase in vertical mixing in the area of generation.

Further investigations are required to quantify the contribution from internal waves to vertical mixing, once the waves start to propagate through the domain. Furthermore, the model results will be compared to recent observations of internal waves in the Rotterdam Waterway. Internal wave characteristics and the generation mechanism will be compared to the model results.

How to cite: Wegman, T., Pietrzak, J., Kranenburg, W., Labeur, R. J., and Verlaan, M.: Mixing contributions from resonant trapped internal waves generated by bottom topography in an estuary, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5790, https://doi.org/10.5194/egusphere-egu22-5790, 2022.

EGU22-6793 | Presentations | NP7.1

Numerical simulations of an internal solitary wave evolution beneath an ice keel 

Peiwen Zhang, zhenhua Xu, qun Li, jia You, baoshu Yin, robin Robertson, and quanan Zheng

The deformation and evolution of internal solitary waves (ISWs) beneath an ice keel can enable potential diapycnal mixing and facilitate upper ocean heat transport, despite a poor understanding of the underlying physics and energetics of ISWs in Polar environments. This study aims to understand the dynamic processes and mixing properties during the evolution of ISWs beneath ice keels (undersea portion of ice cover) in the Arctic Ocean using high-resolution, non-hydrostatic simulations. Ice keels can destabilize ISWs through overturning events. Consequently, the initial ISW disintegrates and transfers its energy into secondary smaller-scale waves. During the ISW-ice interaction, ISW-induced turbulent mixing can reach O(10-3) W/kg with a magnitude of resultant heat flux of O(10)W/m. Sensitivity experiments demonstrated that the ISW-ice interaction weakened as the ice keel depth decreased, and consequently, the resultant turbulent mixing and upward heat transfer also decreased. The ice keel depth was critical to the evolution and disintegration of an ISW beneath the ice keel, while the approximate ice keel shape had little effect. Our results provide an important but previously overlooked energy source for upper ocean heat transport in the Arctic Ocean.

How to cite: Zhang, P., Xu, Z., Li, Q., You, J., Yin, B., Robertson, R., and Zheng, Q.: Numerical simulations of an internal solitary wave evolution beneath an ice keel, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6793, https://doi.org/10.5194/egusphere-egu22-6793, 2022.

EGU22-7582 | Presentations | NP7.1

Energy exchanges between two-dimensional front and internal waves. 

Subhajit Kar and Roy Barkan

Fronts and near-inertial waves (NIWs) are energetic motions in the upper ocean that are thought to interact and provide a possible route for kinetic energy dissipation of mesoscale balanced flows. To date, the theoretical explanations for such interactions rely on the fronts being geostrophic, with a weak ageostrophic secondary circulation (ASC) and a small Rossby number. We develop a quasilinear model to study the interactions between NIW vertical modes and a 2D front undergoing semigeostrophic frontogenesis. In our model, frontal sharpening is divided into two stages: an exponential stage, that is characterized by a low Rossby number and is driven by geostrophic strain; and a super-exponential stage, that is characterized by an O(1) Rossby number and is driven by the convergence of the ASC. We identify a new mechanism, the convergence production, through which NIWs can efficiently extract energy from the front during the super-exponential stage. It is shown that the convergence production can dominate the known mechanism of energy extraction during the exponential stage, the deformation shear production, for a relatively strong geostrophic strain field.

How to cite: Kar, S. and Barkan, R.: Energy exchanges between two-dimensional front and internal waves., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7582, https://doi.org/10.5194/egusphere-egu22-7582, 2022.

EGU22-8278 | Presentations | NP7.1

Radiation Path of Diurnal Internal Tide in the Northwestern Pacific Controlled by Refraction and Interference 

Yang Wang, Zhenhua Xu, Toshiyuki Hibiya, Baoshu Yin, and Fan Wang

The diurnal internal tides contribute nearly a quarter of global baroclinic tidal energy, while their roles in shaping spatiotemporal inhomogeneity of tidal energy field are not well known. Here, based on a combination of observation-supported numerical simulation and theoretical analyses, we clarify the combined and relative contributions of β refraction, subtidal circulation refraction and multi-wave interference to the long-range radiation and dissipation maps of diurnal internal tides in the northwestern Pacific. The diurnal tidal beams are primarily emanated from the Luzon Strait (LS) and Talaud-Halmahera Passage (THP). The β refraction effect, which is more pronounced at higher latitudes, refracts the mean path of LS tidal beam equatorward by ~40° when it arrives at the deep basin, consistent with previous altimeter observations. A second refraction effect by subtidal circulation with seasonal variability deflects the mean beam path by ~10°. Multi-wave interference of tidal beams from the LS and THP further enhances the inhomogeneous pattern, resulting in enhanced and reduced energy flux beam branches with distinct vertical structures in the west Mariana basin. A modified line-source model and theoretical ray-tracing analysis can well explain the effects of refraction and interference. Internal tidal dissipation map in the deep basin coincides well with the inhomogeneous and spreading radiation paths. The mechanism characterization of the world’s most energetic diurnal internal tides in the northwestern Pacific could improve our understanding of global baroclinic tidal energy redistribution and associated tidal mixing parameterization in climate-scale ocean models.

How to cite: Wang, Y., Xu, Z., Hibiya, T., Yin, B., and Wang, F.: Radiation Path of Diurnal Internal Tide in the Northwestern Pacific Controlled by Refraction and Interference, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8278, https://doi.org/10.5194/egusphere-egu22-8278, 2022.

Oceanic nonlinear internal waves (NLIWs) play an important role in regional circulation, marine biogeochemistry, energetics, vertical mixing, underwater acoustics, marine engineering, and submarine navigation, most commonly generated by the interaction between barotropic tides and bathymetry. However, our understanding of their characteristics, generation, and propagation is still far from complete in many water bodies. Here, we present the characteristics of NLIWs observed from moored and underway observation in the northern East China Sea during May 15-28, 2015 and discuss their generation and propagation. The NLIWs observed during the experiment were characterized by an amplitude ranging from 4 to 16 m, width ranging from 380 to 600 m, and propagated southwestward at a speed of 0.64–0.72 m s−1. Groups of NLIWs were predominantly observed during, or a couple of days after, the period of spring tides, with a time interval 24–96 min shorter than the canonical semidiurnal period (12.42 h; M2); this is in contrast to those found in many other regions that have a phase-locking to the barotropic semidiurnal tides. The remote generation and propagation of the NLIWs from potential generation sites into the study area under time-varying stratification support the fact that the time interval departed from the semidiurnal period. Our results have substantial implications for turbulent mixing and ocean circulation in regions where the shelf is broad and shallow. The NLIWs generated from multiple sources propagate in multiple directions with propagating speeds varying over days depending on stratification. 

How to cite: Lee, S.-W. and Nam, S.: Characteristics, Generation, and Propagation of Nonlinear Internal Waves Observed in the Northern East China Sea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8974, https://doi.org/10.5194/egusphere-egu22-8974, 2022.

EGU22-567 | Presentations | NP7.2 | Highlight

Digital Modeling of the Mechanical Processes of Hydraulic Fracturing in Poro-Elastic-Plastic Artificial Materials 

Elizaveta Grebenshchikova, Victor Nachev, and Sergey Turuntaev

This work presents the numerical simulation results of porous-elastic-plastic materials' mechanical behavior, reproducing the filtration-capacitance properties of reservoir rocks. The authors perform numerical modelings of laboratory experiments conducted earlier at the Sadovsky Institute of Geospheres Dynamics of the Russian Academy of Science on an installation that allows conducting studies on fracture propagation under triaxial loading conditions. This work aims to study the dynamics of fracture propagation under various loading conditions using numerical modelings. For this purpose, we take into account the porous and elastoplastic properties of the medium under study.

The authors prepared a mathematical model to study the propagation trajectory and fracture shape of hydraulic fracturing in poroelastic plastic artificial materials: set a system of defining equations and fracture criteria. Then we prepared numerical models using a mechanical software package. We built a three-dimensional numerical elastoplastic model of the rock based on the geometry of the sample. Modeling includes setting a set of mechanical parameters: Young's modulus, Poisson's ratio, internal friction angle, dilatancy angle, and deformation criterion of failure. In the study, we used a ready-made physical and mathematical mechanical model depending on the pressure of Mohr-Coulomb and pore pressure. Next, a series of numerical mechanical calculations were performed using the extended finite element method.

As a result of numerical modeling using a software package, the authors obtain that in the poroelastic model of the sample, a plasticity zone appears in the region of the central well before the fracture begins to form. Then, as the fracture spreads, the plasticity zone along the fracture propagation path is preserved. Modeling the stress-strain state along the fracture trajectory shows asymmetric distributions of stresses, pressures, and porosity relative to the central well. Different pressure values caused it in the injection and production wells used in a laboratory experiment to create pore pressure. Also, it leads to the formation of different fracture lengths towards the production and injection wells, which we see during laboratory experiments.

How to cite: Grebenshchikova, E., Nachev, V., and Turuntaev, S.: Digital Modeling of the Mechanical Processes of Hydraulic Fracturing in Poro-Elastic-Plastic Artificial Materials, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-567, https://doi.org/10.5194/egusphere-egu22-567, 2022.

EGU22-1586 | Presentations | NP7.2

The abnormal 1/f2 spectral falloff caused by asymmetric friction 

Rui Xiang Wong, Elena Pasternak, and Arcady Dyskin

Asymmetric friction refers to different friction forces that resist sliding in opposing directions. Asymmetric friction can for instance be induced by an anisotropic block with an anisotropy axis inclined to the direction of sliding progressing in a constraint environment, (Bafekrpour et al., 2015). This study examines a Burridge-Knopoff-type model of multiple blocks connected through springs. Each block is connected to an external oscillating surface through a leaf spring; some blocks slide with asymmetric friction, while others experience conventional symmetric friction. Asymmetric friction favours slip in the direction of low friction. This increases spring forces to counteract the slip within the assembly, creating regions of tension and compression. When this model represents geological faults, regions of tension and compression can produce fractures oriented normal (in the tensile phase) or parallel (in the compressive phase) to the sliding surface.

Velocity spectra produced by the model excited by an external oscillating surface reveal that the presence of asymmetric friction creates spectra with a frequency falloff of 1/f2, where f is the frequency. This is in contrast with the case of only symmetric friction blocks where oscillations result in velocity spectra with a frequency falloff of 1/f.

Recent triaxial compression of rock samples have shown slip events over shear fracture produce velocity spectra with frequency falloff that approximates 1/f2(Beeler et al., 2020). Using the results found from the model, a hypothesis on the mechanism that produces the 1/f2falloff is proposed: the shear fracture in the compression test is produced by formation of vertical micro-cracks within the rock samples. This effectively creates an anisotropic material with axes of symmetry inclined to the shear fracture, which explains the 1/f2spectra.

Acknowledgement. EP and AVD acknowledge support from the Australian Research Council through project DP210102224.

References

BAFEKRPOUR, E., DYSKIN, A., PASTERNAK, E., MOLOTNIKOV, A. & ESTRIN, Y. 2015. Internally architectured materials with directionally asymmetric friction. Scientific reports, 5, 10732-10732.

BEELER, N. M., MCLASKEY, G. C., LOCKNER, D. & KILGORE, B. 2020. Near‐Fault Velocity Spectra From Laboratory Failures and Their Relation to Natural Ground Motion. Journal of geophysical research. Solid earth, 125, n/a.

How to cite: Wong, R. X., Pasternak, E., and Dyskin, A.: The abnormal 1/f2 spectral falloff caused by asymmetric friction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1586, https://doi.org/10.5194/egusphere-egu22-1586, 2022.

EGU22-2006 | Presentations | NP7.2 | Highlight

A Hydro-Mechanical Coupling Test System for Simulating Rock Masses in High Dam Reservoir Operations 

Yuxin Ban, Qiang Xie, Xiang Fu, and Aiqing Wu

A meter-level direct shear test system and a true triaxial test system were designed by placing the traditional test apparatus into sealed cabins subjected to high water pressure. The influences of three-dimensional seepage water pressure on the shear and compression deformation of rock mass in Xiluodu Hydropower Station were studied. The test results showed that the changes in water pressure caused obvious shear deformation of the interlayer dislocation zone and tensile deformation and reduction in the triaxial compression strength of the fractured rock mass. The effect of water pressure on shear displacement and tensile displacement had a hysteresis effect. This was consistent with deformation data collected through field monitoring. The deformation mechanism in the reservoir valley was the coupling of the stress and seepage fields caused by reservoir impoundment. The effective stress was reduced, the mechanical parameters were weakened, and the change of the initial stress field led to the slightly overall shear slip and tensile deformation of the bank slope.

How to cite: Ban, Y., Xie, Q., Fu, X., and Wu, A.: A Hydro-Mechanical Coupling Test System for Simulating Rock Masses in High Dam Reservoir Operations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2006, https://doi.org/10.5194/egusphere-egu22-2006, 2022.

EGU22-2171 | Presentations | NP7.2

Theoretical Research on the Pendulum-type Wave in Nonlinear Block-rock Mass Based on Hyperbolic Elastic Model 

Kuan Jiang, Cheng-zhi Qi, and Ze-fan Wang

Pendulum-type wave is a kind of sign-alternating wave with discontinuous and nonlinear characteristics found in deep rock mass, which is different from the traditional continuum elastic waves. The dynamic engineering disasters such as rockbrusts and anomalously low friction phenomenon occur frequently in surrounding deep level tunnels rock mass, and the study of pendulum-type wave is of great significance for explaining the mechanism and prevention of these engineering disasters. The presence of multiple fractures makes geomaterials nonlinear. Therefore, it is unreasonable to use linear model to study pendulum-type wave in block-rock mass. With the consideration of the nonlinear deformation characteristics of deep rock mass, a nonlinear dynamic model of pendulum-type wave in block-rock mass is established by introducing hyperbolic elastic model of interlayers of geoblocks, and the time-domain characteristics, frequency-domain characteristics and the law of energy transfer are studied. Furthermore, the influence of initial geostress on pendulum-type wave propagation in nonlinear block-rock mass is studied. The research shows that the improved nonlinear model can not only shows the nonlinear deformation of block-rock mass, but also limits the maximum compression deformation of cracks between rock blocks. Under the action of strong impact loading, the acceleration amplitude of blocks far away from the impact point increases significantly, and is the largest, which is not conducive to the structure safety of rock mass. With the increase of impact loading, the frequency response of blocks tends to move to high-frequency domain, and the frequency center increases continuously. Kinetic energy and potential energy are constantly transformed to each other in block-rock mass, and in the free vibration stage, they are in inverse phase, i.e. when the kinetic energy reaches the maximum, the elastic potential energy is the minimum, and vice versa. Relative to the initial geostress, the hyperbolic elastic model can be grouped into three categories: low stress state, high stress state and ultra-high stress state. Under different initial geostress states, the dynamic response of the block-rock mass is very different. With the increase of initial geostress, the displacement amplitude decrease approximately exponentially. In ultra-high stress state, the displacement amplitude of rock blocks decreases by more than 95% compared with that without initial geostress. Therefore, we conclude that in the ultra-high stress state, the pendulum-type wave phenomenon will not occur in block-rock mass, and the wave propagation is close to the longitudinal wave. This paper provides a reference for further study on nonlinear pendulum-type wave in block-rock mass under the conditions of strong impact and high initial geostress.

How to cite: Jiang, K., Qi, C., and Wang, Z.: Theoretical Research on the Pendulum-type Wave in Nonlinear Block-rock Mass Based on Hyperbolic Elastic Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2171, https://doi.org/10.5194/egusphere-egu22-2171, 2022.

Specimens of quasi-brittle materials under Brazilian disc testing usually fracture abruptly, with load carrying capacities dropping almost instantly from peak to zero. This abrupt failure in a split second is known due to the excess storage of strain energy at peak load that is released through the creation of new fracture surfaces and in the form of kinetic energy. This kind of dynamics and bursting is hard or impossible to control in laboratory testing using direct vertical displacement. We present our innovative technology, registered as an innovation patent in Australia, to control the dynamic splitting of Brazilian disc specimens so that the failure process is not abrupt, indicated by the snapback load-vertical displacement responses. Thanks to the use of lateral strain to control the loading, the time to complete the whole fracturing process increases significantly from a split second to a few hours, sufficient to enable advanced instrumentation using image-based techniques.  Acoustic Emission (AE) is used to monitor the fracturing process to make sure that the snapback response observed is not unloading. The proposed technology has been applied with largely success to a wide range of quasi-brittle materials, including sandstone, granite, and even 3D-printed rock-like materials with inherent weak discontinuities. This presentation reports the obtained results and challenges in controlling the dynamic splitting of Brazilian disc specimens using the proposed technology.

How to cite: Nguyen, G. D., Verma, R., and Karakus, M.: Controlling dynamic splitting of Brazilian disc specimens using Adelaide University Snapback Indirect Tensile test (AUSBIT), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3242, https://doi.org/10.5194/egusphere-egu22-3242, 2022.

EGU22-4482 | Presentations | NP7.2

Fractures in geomaterials driven by spatial stress fluctuations 

Arcady Dyskin and Elena Pasternak

Stress field in geomaterials is not uniform; in particular due to the presence of spatial fluctuations. These are induced under external loading due to multiple randomly located heterogeneities and small-scale defects/fractures or by fluid transport. Stress fluctuations can be a form of residual stresses caused for instance by phase transformation (e.g. magma solidification), or as a result of strong seismic events. Subsequently, the stress field can be represented as a superposition of slowly changing large scale stress and small scale self-equilibrating spatial stress fluctuations (random stress field with zero average).

At the first glance, the field of spatial stress fluctuation taking alone is not supposed to cause any large scale development of fractures considered as linear objects (fracture opening linearly depending upon the applied load) since the average stress is zero. However, often the fractures are not independent of the stress field but produced in the zone where the stress fluctuations exhibit high tensile stress. The fracture initial size is of the order of the characteristic size of the zone of high tensile stress, that is the correlation length of the random field of stress fluctuations. The fracture will then be able to propagate further. The simplest model of such a fracture is a disc-like crack opened in the centre by a pair of concentrated forces with magnitude equal to the total force of the tensile stress in that zone [1].

Model [1] predicts the extensive fracture growth to be stable that is further increase in its size would require increase in the concentrated forces that is increase in the amplitude of stress fluctuations. In the case when the stress fluctuations represent residual stress, it is not possible as the residual stress can only decrease [2]. Yet, self-equilibrating residual stresses can cause macroscopic failure. This requires a new paradigm of crack/fracture growth in self-equilibrating field of stress fluctuations. To this end we accept that (1) the fracture opening is bilinear such that the local compressive stress just closes the fracture and hence cannot equilibrate the corresponding local tensile stress; (2) the fracture growth is not planar as passing through zones of compression is not possible leading to local overlapping.

The simplest model that accommodates the above features of fracture growth is a fracture with distributed bridges (fracture with constraint opening represented as a crack with Winkler layer [4]). We show that such a fracture will exhibit unstable growth forming a mechanism of both breakage due to residual stress and large (e.g. geological) scale fracture formation.

  • Dyskin, A.V. 1999. On the role of stress fluctuations in brittle fracture. J. Fracture, 100, 29-53.
  • Dyskin V. and E. Pasternak, 2019. Residual strain mechanism of aftershocks and exponents of modified Omori’s law. J. Geophys. Research: Solid Earth, 10.1029/2018JB016148.
  • He, J., E. Pasternak and A.V. Dyskin, 2020. Bridges outside fracture process zone: Their existence and effect. Engineering Fracture Mechanics, 225, 106453.

Acknowledgement. The authors acknowledge support from the Australian Research Council through project DP210102224.

How to cite: Dyskin, A. and Pasternak, E.: Fractures in geomaterials driven by spatial stress fluctuations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4482, https://doi.org/10.5194/egusphere-egu22-4482, 2022.

EGU22-4550 | Presentations | NP7.2

Growth of fractures with constricted opening 

Elena Pasternak and Arcady Dyskin

Growth of real fractures is characterised by interruptions and local overlapping caused by rock heterogeneities. The interruptions and loci of overlapping can work as bridges distributed all over the fracture that connect opposite surfaces of the fracture [1]. These bridges constrain the crack opening and thus mitigate the dependence of the Mode I stress intensity factor of the fracture length (radius). In the case of hydraulic fractures, constraining the opening also affects the fluid (e.g. fracturing fluid) flow through the fracture.

The effect of bridges can be characterised by a characteristic length – the constriction length. When the fracture size is small compared to the constriction length, the fracture behaves similarly to a conventional Model I crack of the same configuration. Alternatively, when the fracture size is much greater than the constriction length both the  average fracture opening and the Mode I stress intensity factor become constant. This restricts the ability of the fracture to growth in unstable manner or dynamically.

The effect of constriction is even more pronounced in the case when the fracture gets open in the displacement-controlled mode. In this case the dependence of the stress intensity factor of disc-like fracture on the fracture radius is no longer monotonic. The stage of decrease of the stress intensity factor with the fracture radius leads to the emergence of stable fracture growth when increase in the displacement is required to maintain fracture propagation. It is important that without taking the constriction into account the corresponding stage of stable fracture propagation can be taken for the effect of rock heterogeneity.

The theory developed is essential for predicting and monitoring growth of Mode I fractures, in particular hydraulic fractures.

  • He, J., Pasternak, E. and A.V. Dyskin, 2020. Bridges outside fracture process zone: Their existence and effect. Engineering Fracture Mechanics, 225, 106453.

Acknowledgement. The authors acknowledge support from the Australian Research Council through project DP190103260.

How to cite: Pasternak, E. and Dyskin, A.: Growth of fractures with constricted opening, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4550, https://doi.org/10.5194/egusphere-egu22-4550, 2022.

EGU22-5669 | Presentations | NP7.2 | Highlight

Experimental study of saturated porous medium fracturing by abrupt pressure drop 

Sergey Turuntaev and Evgeny Zenchenko

It has been proposed that rock fracturing can be obtained by the powerful and fast pressure discharge at some boundary of the rock (fracture, borehole). The fracturing related with rapid pore pressure discharge looks as a “fracturing wave”.  Laboratory experiments allow to evaluate the main characteristics of the phenomena and to estimate conditions for the fracturing. Experimental data on the fracturing process were obtained with the help of transparent pressure-drop setup (plexiglass tube with length 455 mm, inner diameter 60 mm, wall thickness 5 mm) using the weak-cohesive porous samples made from the sand wetted by glycerol. The tube ends were closed by brass covers equipped by pressure transducers. Additionally, nipples for the air pumping and decompression were mounted at one of the covers. The pressure was increased by air pumping in up to 0.35 MPa, then the pressure was released through solenoid valve with flow section 15 mm. Decompression rate was controlled by the diaphragms with different diameters: 2.8, 3.0, 3.4, 4.0, 4.8 mm. To observe the fracturing, high-speed camera with frame rate 1200 frames per second was used. The dependencies of maximum depth of the fracture formations and mean distance between the fractures on decompression rate were obtained. It was found that the number of fractures and the last fracture depth grow with the pressure drop rate, while the inter-fracture distance decreases.

How to cite: Turuntaev, S. and Zenchenko, E.: Experimental study of saturated porous medium fracturing by abrupt pressure drop, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5669, https://doi.org/10.5194/egusphere-egu22-5669, 2022.

EGU22-8272 | Presentations | NP7.2

Modeling of capillary and thermal nonequilibrium flows in porous media 

Oleg Izvekov, Andrey Konyukhov, and Ivan Cheprasov

Complex structure of a skeleton of saturated porous medium can have a great influence on the processes of heat and mass transfer. 

There are various approaches to the description of  two-phase flow: direct numerical calculation of fluid flow in the pore space, multicontinuous models with the laws of mass exchange between continua, single-continuum models of non-equilibrium flow. In the family of isothermal non-equilibrium filtration models, the relative phase permeabilities and capillary pressure are functions not of current saturation but of their change history.

In this work we generalize the relaxation model of capillary nonequilibrium to the non-isothermal case. We introduce two internal thermodynamic parameters (capillary and thermal nonequilibrium) which depend on change history of saturation and temperature. In the model relative phase permeabilities and capillary pressure are functions of saturation, temperature, and current values of these internal parameters. Based on the analysis of the dissipation inequality, thermodynamically consistent kinetic equations for the evolution of these parameters are proposed. The parameters of the single-continuum model are clarified with double-porosity model of porous media with special structure. Structure of the penetration front of fluid hot (or cold) compared to the skeleton was investigated.

This work was supported by the Russian Foundation for Basic Research: grant N19-01-00592. 

 

How to cite: Izvekov, O., Konyukhov, A., and Cheprasov, I.: Modeling of capillary and thermal nonequilibrium flows in porous media, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8272, https://doi.org/10.5194/egusphere-egu22-8272, 2022.

EGU22-9482 | Presentations | NP7.2

Local biaxial loading induced by end friction in uniaxial compression 

Hongyu Wang, Arcady Dyskin, Elena Pasternak, and Phil Dight

Rock samples tested in uniaxial compression tests are often observed to have spallation failure at the lateral surface of the sample caused by buckling of layers developing parallel to the direction of loading. These layers are caused by extensive crack growth. Yet, the experiments show that extensive crack growth in real 3D situations requires the presence of intermediate compressive principal stress (biaxial loading). We check the hypothesis that the intermediate principal stress is generated by the direct contact with metal loading platens, where the “frictional” boundary conditions are developed at the sample ends, creating non-uniform stress distributions near the sample ends. By the use of the finite element method (FEM), we analyse the stress distributions at the immediate lateral surface of the cylindrical sample in uniaxial compression in the polar coordinate system. It is found that near the ends the sample is actually biaxially loaded: the circumferential stress could be induced to play the role of the intermediate principal stress. The sizes of the zones and the maximum magnitude of the circumferential stress depend upon the friction coefficient and the Poisson’s ratio of the rock. The biaxial load ratio is defined by the ratio between the circumferential compressive stress and the axial stress. Comparing the biaxial load ratio determined in numerical models with the critical biaxial load ratio inducing extensive crack growth, the spallation of rock samples in uniaxial compression tests can be interpreted from a new perspective.

How to cite: Wang, H., Dyskin, A., Pasternak, E., and Dight, P.: Local biaxial loading induced by end friction in uniaxial compression, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9482, https://doi.org/10.5194/egusphere-egu22-9482, 2022.

EGU22-9747 | Presentations | NP7.2 | Highlight

Seismic sliding of the single fault under fluid injection 

Vasily Riga and Sergey Turuntaev

Seismicity associated with fluid injection into the subsurface is one of the most important issues worldwide. Fluid injection near a fault could lead to seismic sliding of the fault and as a consequence to significant seismic events. In the presented research, we study the single fault behavior under the action of a single well injection near the fault. Various cases of fluid injection and friction properties of the fault are considered. To describe the friction on the fault we use two-parameter rate-and-state law. The fault has zones characterized by velocity-weakening and velocity-strengthening friction behavior. We analyze how injection rate and volume and parameters of the friction law influence the fault sliding dynamics and seismicity level. As the result, we get conditions that are favorable for the occurrence of noticeable seismicity and dependence of seismicity parameters on injection parameters.

How to cite: Riga, V. and Turuntaev, S.: Seismic sliding of the single fault under fluid injection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9747, https://doi.org/10.5194/egusphere-egu22-9747, 2022.

EGU22-10978 | Presentations | NP7.2

Oscillations induced by crack growth in compression and morphology of fracture surface 

Broadus Jeffcoat-Sacco, Arcady Dyskin, Elena Pasternak, Phil Dight, and Hongyu Wang

Growth of internal cracks in compression is a primary mechanism of catastrophic rock failures.  Since the cracks are internal, only indirect methods are currently available for investigation and monitoring of failures of this kind.  In order to understand the fundamentals of 3D (internal) crack growth in compression, special physical models using transparent polymer prisms with embedded penny-shaped flaws were performed.  A biaxial stress field was applied, to simulate conditions near the face or walls of deep tunnels.  Previously, such experiments were conducted within a fully-enclosed polyaxial testing machine, preventing observation of the growing crack itself.  Recent experiments at UWA utilized a plane-strain restraint device, which develops the secondary principal stress (σ2) by limiting the lateral expansion that would otherwise result from the imposition of the primary principal stress (σ1).  This device leaves clear line-of-sight to the sample free face, allowing visual observation and the installation of other instrumentation. 

As expected, the biaxial stress field resulted in growth of an extensive, nearly planar crack, parallel to the σ12 plane.  Growth of this crack was observed using high-speed video.  The crack surface morphology shows similarity to many natural and excavation-induced fractures in geomaterials.  This similarity justifies the usage of the transparent materials for investigating rock failure. Furthermore, the observed morphological features can be linked to specific events in the crack growth process.

In addition, experiments incorporated acoustic emission sensors, both on the sample, and in the air near the sample.  Two categories of vibration were observed at the sample’s rear free face: a short-duration wavelet, representing the crack initiation, and a long-duration high-amplitude “ringing” waveform.  The “ringing” was also observed in air (as audible sound) and in video (as movement of the whole sample). The observed vibrations could be utilized for monitoring dangerous rock failures.

How to cite: Jeffcoat-Sacco, B., Dyskin, A., Pasternak, E., Dight, P., and Wang, H.: Oscillations induced by crack growth in compression and morphology of fracture surface, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10978, https://doi.org/10.5194/egusphere-egu22-10978, 2022.

EGU22-11759 | Presentations | NP7.2

Investigation of 3D Fracture Propagation in Complex Reservoirs Rocks at Microscale 

Victor Nachev and Sergey Turuntaev

This work aims to study the propagation of fractures during hydraulic fracturing operations to determine the conditions that lead to the most extensive network of secondary fractures along with the main fractures at the microlevel. The occurrence and propagation of 3D fractures were studied, considering the granular composition and complex structure of elastic-plastic rock samples. The simulation allowed us to calculate fracture networks for various loading conditions. We propose a method that will enable us to calculate the pressure of hydraulic fracturing fluid injection into the formation from the obtained numerical conditions of loading and regional stress in the reservoir rock. Based on the results of numerical modeling and recalculation of the obtained loading conditions into the pressure of the injected fluid, geomechanical engineers will choose the necessary conditions that will provide the stress-strain states that lead to the most significant degree of fracturing of the formation.

How to cite: Nachev, V. and Turuntaev, S.: Investigation of 3D Fracture Propagation in Complex Reservoirs Rocks at Microscale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11759, https://doi.org/10.5194/egusphere-egu22-11759, 2022.

EGU22-13042 | Presentations | NP7.2 | Highlight

Nonlinear seismic phenomena as recorded by distributed acoustic sensors 

Boris Gurevich, Alexey Yurikov, Konstantin Tertyshnikov, Maxim Lebedev, Roman Isaenkov, Evgenii Sidenko, Sinem Yavuz, Valeriya Shulakova, Julia Correa, Stanislav Glubokovskikh, Barry Freifeld, and Roman Pevzner

Due to their granular nature and presence of fluids, elastic moduli of most crustal rocks show a strong stress dependency. This means that the relationship between stress and strain is nonlinear, which should cause nonlinear wave phenomena. In particular, interaction of seismic waves of different frequencies should generate higher harmonics and combinational frequencies. Analysis of these effects in field data can potentially help find areas of anomalous nonlinear properties, such as fractured zones, mixed saturation or overpressure. To better understand the potential of nonlinear seismology, we observed and analyzed nonlinear seismic effects in field and laboratory experiments. The field experiment was performed using two seismic vibrators generating monochromatic signals of different frequencies. The wavefield was recorded with a fiber optic distributed acoustic sensing (DAS) cable cemented in a 900 m deep borehole. The signals recorded both on the surface and in the borehole show combinational frequencies, harmonics, and other intermodulation products of the fundamental frequencies. The laboratory experiment, which was designed to replicate the setup of the field experiment, shows similar nonlinear products of the fundamental frequencies. Furthermore, the nonlinear effects show a dependency on the saturating fluid. These tests confirm that nonlinear components of the wavefield propagate in a form of body waves, are likely to be generated in rock formations, and have the potential for reservoir fluid characterization.

How to cite: Gurevich, B., Yurikov, A., Tertyshnikov, K., Lebedev, M., Isaenkov, R., Sidenko, E., Yavuz, S., Shulakova, V., Correa, J., Glubokovskikh, S., Freifeld, B., and Pevzner, R.: Nonlinear seismic phenomena as recorded by distributed acoustic sensors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13042, https://doi.org/10.5194/egusphere-egu22-13042, 2022.

EGU22-326 | Presentations | OS4.2

Feature of Surface Waves Generated by Polar Lows 

Vahid Cheshm Siyahi, Vladimir Kudryavtsev, and Maria Yurovskaya

A parametric wave model developed by Kudryavtsev et al. (2021) is adapted for Arctic conditions, to help simulate surface waves generated by non-stationary and non-uniform wind fields, to study extreme events in the Norwegian and Barents seas. The ERA-5 reanalysis wind field is used as the input parameter. The model equations are solved using method of characteristics and solutions are then presented as hourly fields of wave parameters (significant wave height, SWH, wavelength, and direction) on the regular grid. The satellite altimeter data are used to validate the model results. Model outputs can then be readily compared with all available satellite observations, including Sentinel-3, Altika and CryoSat-2 measurements.

Observations and analysis of model simulations reveal appearance of abnormal high surface waves, resulting from a resonant fetch-enhancement associated to travelling wind fields.  In other words, the generation of waves in the “spiral-shaped” PLs is most likely associated with the generation of waves in the TCs. However, in PLs with a “comma-shape”, the resonance effect occurs when the strong wind zone inside the PL is located in the right sector, where the direction of the wind velocity coincides with the movement of the front. That is, the surface wave group velocity enters in resonance with moving wind field features, leading to abnormal wave development.

ACKNOWLEDGMENT

The results presented in this work were obtained with the financial support of the Russian Science Foundation, Grant No. 21-47-00038, State Assignment of the Ministry of Science and Education No. 0763-2020-0005 at RSHU, and No. 0555-2021-0004 at MHI RAS.

Reference

Kudryavtsev, V., Yurovskaya M. , Chapron, B., 2021. “2D parametric model for surface wave development in wind field varying in space and time”, Journal of Geophysical Research: Oceans, Vol. 126.

How to cite: Cheshm Siyahi, V., Kudryavtsev, V., and Yurovskaya, M.: Feature of Surface Waves Generated by Polar Lows, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-326, https://doi.org/10.5194/egusphere-egu22-326, 2022.

EGU22-2036 | Presentations | OS4.2

The structure of waves during Geostrophic Adjustment on the mid-latitude β-plane 

Itamar Yacoby, Nathan Paldor, and Hezi Gildor

The theory of the transition from an unbalanced initial state to a geostrophically balanced state, referred to as geostrophic adjustment, is a fundamental theory in geophysical fluid dynamics. The theory originated in the 1930s on the f-plane and since then the theory was barely advanced to the β-plane. The present study partially fills the gap by extending the geostrophic adjustment theory to the β-plane in the case of resting fluid with a step-like initial height distribution η0. In the presentation, we focus on the one-dimensional adjustment theory in a zonally-invariant, finite, meridional domain of width L where η0 = η0(y). By solving the linearized rotating shallow water equations numerically, the effect of β on the adjustment process is examined primarily from the wave perspective while the spatial structure of the geostrophic steady-state will be addressed only briefly. The gradient of η0(y) is aligned perpendicular to the domain walls in our zonally-invariant set-up which implies that the geostrophic state only represents the time-averaged solution over many wave periods rather than a steady-state that is reached by the system at long times. We found that: (i) the effect of β on the geostrophic state is significant only for b = cot(φ0)Rd/R ≥ 0.5 (where Rd is the radius of deformation, R is Earth's radius and φ0 is the central latitude of the domain). (ii) In wide domains the effect of β on the waves is significant even for small b (e.g. b=0.005). EOF analysis demonstrates that for b=0.005 and in narrow domains (e.g. L = 4Rd) harmonic wave theory provides an accurate approximation for the waves, while in wide domains (e.g. L = 60Rd) accurate approximations are provided by the trapped wave theory. Preliminary results derived in the two-dimensional case, where η0 = η0(x) is symmetric, imply that the results outlined in item (ii) above hold in this case too. 

How to cite: Yacoby, I., Paldor, N., and Gildor, H.: The structure of waves during Geostrophic Adjustment on the mid-latitude β-plane, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2036, https://doi.org/10.5194/egusphere-egu22-2036, 2022.

EGU22-2064 | Presentations | OS4.2

Wave-induced tracer dispersion by ocean surface waves 

Joey Voermans, Alexander Babanin, Cagil Kirezci, Alex Skvortsov, Petra Heil, Luciano Pezzi, and Marcelo Santini

Material tracers at the ocean surface disperse under the influence of the quasi-random forces that act on the ocean surface. These forces may include ocean turbulence, wind, and surface waves. Currently, wind and ocean turbulence are assumed to be the important drivers of dispersion of the floating tracer particles. Despite some theoretical results and laboratory experiments, the experimental proof of the significant contribution of wave induced dispersion in overall transport of large-scale geophysical systems remains elusive. This is mainly due to a lack of practical observations.

In this study we aim to estimate the contribution of wave-induced dispersion in comparison with conventional mechanisms of dispersion due to ocean turbulence. We do so through the analysis of in-situ observations of surface drifters deployed across the seas and oceans.  The experimental dataset include data from the Global Drifter Program and newly obtained data through cluster deployment of Spotter wave buoys. The results suggest that waves during marine storm conditions may be a critical driver of surface tracer dispersion during the first ten days after the storm and at horizontal length scales up to the order of 10 km. Our results imply that accurate information of wave conditions is required for accurate prediction of tracer dispersion at short to intermediate time and length scales.

How to cite: Voermans, J., Babanin, A., Kirezci, C., Skvortsov, A., Heil, P., Pezzi, L., and Santini, M.: Wave-induced tracer dispersion by ocean surface waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2064, https://doi.org/10.5194/egusphere-egu22-2064, 2022.

Bubble plumes within the two-phase flow generated by sufficiently energetic surface breaking waves (whitecaps) enhance the exchange of gas, mass and heat between the atmosphere and ocean. The bubbles formed inside whitecaps range in size from order tens of microns to centimetres, and accurate measurements of the space- and time-evolving bubble size distribution is central to achieving a better understanding of air-sea gas exchange and aerosol production flux.

In the present study, we describe the measurements of time- and space-evolving bubble size distribution in 2-D laboratory breaking waves. The bubbles were measured with high resolution digital images using a range of novel image processing and object detection techniques. A wide range of breaking waves were considered by altering the underlying scale, nonlinearity and spectral bandwidth of the dispersively-focused wave groups. The experiments were initially conducted in the absence of wind, and again under influence of direct wind shear stress. A variety of wind speeds were examined to replicate the effects of varying wave age on the breaking process, air entrainment and resulting bubble size distribution.

Our experimental results demonstrate that underlying wave scale, non-linearity, spectral bandwidth and wind speed (wave age) all have a measurable influence on the evolution of the two-phase flow and bubble size distributions within the breaking waves studied here, highlighting the complexity of the air entrainment over the breaking process. The relative magnitude and importance of these influences will be discussed in detail in this work. For instance, compared to breaking waves without wind stress, wave in the presence of wind tend to break at lower wave steepness, resulting in a reduction of total air entrainment and significantly different spatial distribution of bubbles.

How to cite: Cao, R. and Callaghan, A.: The effects of wave scale, non-linearity, spectral bandwidth and direct wind shear stress on air entrainment and bubble size distributions in laboratory breaking waves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2741, https://doi.org/10.5194/egusphere-egu22-2741, 2022.

EGU22-3991 | Presentations | OS4.2

Global sea state variability from new multivariate multi-mission satellite altimeter products, reanalyses and wave buoys 

Ben Timmermans, Christine Gommenginger, and Andrew Shaw

Accurate knowledge and understanding of the sea state and its variability is crucial to numerous oceanic and coastal engineering applications, but also to climate change and related impacts including coastal inundation from extreme weather and ice-shelf break-up. An increasing duration of multi-decadal altimeter observations of the sea state motivates a range of global analyses, including the examination of changes in ocean climate. For ocean surface waves in particular, the recent development and release of products providing observations of altimeter-derived significant wave height make long term analyses fairly straightforward. In addition, advances in imaging SAR processing for some missions have made available multivariate observations of sea state including wave period and sea state partition information such as swell wave height. Records containing multivariate information from both Envisat and Sentinel-1 are included in the version 3 release of the European Space Agency Climate Change Initiative (CCI) for Sea State data product.

 

In this study, long term trends and variability in significant wave height spanning the continuous satellite record are intercompared across high-quality global datasets using a consistent methodology. We make use of products presented by Ribal et al. (2019), and the recently released products developed through Sea State CCI. In particular, making use of long term and continuous time series from moored data buoys, we demonstrate the impact of steadily increasing altimeter sample density on trend estimation. In addition to wave height, global climatologies for wave period are also intercompared between the recent Sea State CCI product, ERA 5 reanalysis and in situ observations. Results reveal good performance of the CCI products but also raise questions over methodological approach to multivariate sea state analysis. For example, differences in computational approach to the derivation of higher order summaries of wave period, such as the zero-crossing period, lead to apparent discrepancy between satellite products and reanalysis and modelled data. It is clear that the broadening diversity of reliable sea state observations from satellite, such as provided by the Sea State CCI project, thus motivates new intercomparisons and analyses, and in turn elucidates inconsistencies that have been previously overlooked.

 

We discuss these results in the context of both the current state of knowledge of the changing wave climate, and the on-going development of CCI Sea State altimetry and imaging SAR products.

How to cite: Timmermans, B., Gommenginger, C., and Shaw, A.: Global sea state variability from new multivariate multi-mission satellite altimeter products, reanalyses and wave buoys, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3991, https://doi.org/10.5194/egusphere-egu22-3991, 2022.

EGU22-4007 | Presentations | OS4.2

Detection and tracking of individual surface breaking waves from a fixed stereo video system 

Joseph Peach, Adrian Callaghan, Filippo Bergamasco, Alvise Benetazzo, and Francesco Barbariol

Sea surface wave breaking is the dominant process that results in dissipation of ocean surface wave energy. During the breaking process, wave energy is converted into turbulent kinetic energy, and if significantly energetic, entrains air which facilitates air-sea gas transfer and scatters light to create the signature whitecap. Exploiting the broadband scattering of light by the surface whitecaps, this study uses a fixed stereo video system to detect and track individual air-entraining surface breaking waves at wind speeds of up to 16 m/s. The sea surface foam (whitecap) from a breaking event is detected in grayscale images using a brightness thresholding technique based on the image pixel intensity histogram. The movement of individual whitecaps is estimated with optical flow and is used to track whitecaps between consecutive frames. Once breaking events have been tracked through their lifetime, fundamental properties of the whitecap such as the time-evolving foam area [m2], breaking speed [m/s], average crest length [m] and foam area growth and decay timescales [s] are extracted and subsequently aggregated into whitecap statistics. The geometric, kinematic and dynamic quantities obtained for individual whitecaps via this tracking method are used in conjunction with the volume-time-integral method developed in Callaghan et al 2016 to estimate the energy dissipated by each individual whitecap and to then develop an empirical frequency-dependent whitecap energy dissipation source term.

How to cite: Peach, J., Callaghan, A., Bergamasco, F., Benetazzo, A., and Barbariol, F.: Detection and tracking of individual surface breaking waves from a fixed stereo video system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4007, https://doi.org/10.5194/egusphere-egu22-4007, 2022.

EGU22-6647 | Presentations | OS4.2

Laboratory experimental study on wave-turbulence interactions 

Hongyu Ma, Dejun Dai, Shumin Jiang, Chuanjiang Huang, and Fangli Qiao

Surface gravity waves play an important role in the mixing process of upper ocean. How wave energy is transferred to ocean turbulence through the wave-turbulence interactions remains an open question. Here, laboratory experiments were designed and performed in a wave tank to investigate wave-turbulence interactions in detail. The turbulence intensities before and after the wave-turbulence interactions were compared quantitatively based on their power spectra, and the experimental results indicate that the background turbulence increased approximately by 23.3% through wave-turbulence interaction between 7 and 20 Hz of the power spectrum. Using the Holo-Hilbert spectral analysis method, the results clearly show that the turbulence was modulated by surface waves and then enhanced through the wave-turbulence interaction process. When the wave height was 3 cm and 5 cm, the modulation mainly occurred in the wave trough phase which is consistent with previous literatures. However, the modulation occurred in both the wave trough and crest phases when the surface wave was strong with a wave height of 7 cm. In addition, the intensity of the wave-turbulence interaction increases with the wave height and is proportional to .

How to cite: Ma, H., Dai, D., Jiang, S., Huang, C., and Qiao, F.: Laboratory experimental study on wave-turbulence interactions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6647, https://doi.org/10.5194/egusphere-egu22-6647, 2022.

In the presentation, wave-induced influences at the ocean side will be discussed. While the role of breaking waves in producing turbulence is well appreciated, the turbulence produced by wave orbital motion at the vertical scale of wavelength – is not. Such mixing, however, produces feedbacks to the ocean circulation at scales from weather to climate. In order to account for the wave-turbulence effects, large-scale air-sea interaction models need to be coupled with wave models. Theory and practical applications for the wave-induced turbulence are reviewed in the presentation.

 

Analytical approaches for the wave turbulence include viscous and instability theories which appear to be linked. This was verified through direct numerical simulations with fully nonlinear wave model coupled with three-dimensional (LES) model for turbulence. Furthermore, dedicated laboratory experiments and field observations, both in situ and by means of remote sensing, confirmed and validated the conclusions of theory and academic simulations and tests. Finally implementations of the wave-turbulence modules in models for Tropical Cyclones, ocean circulation and sea ice will be demonstrated.

How to cite: Babanin, A.: Wave-induced turbulence, and its role in connecting small- and large-scale ocean processes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6746, https://doi.org/10.5194/egusphere-egu22-6746, 2022.

EGU22-6778 | Presentations | OS4.2

Sea Spray Generation Function in Major Tropical Cyclones 

Alexander Soloviev, Breanna Vanderplow, Roger Lukas, Brian Haus, Muhammad Sumi, and Isaac Ginis

Sea spray is a factor in thermodynamics, intensity, and intensification of tropical cyclones. However, the sea spray generation function under major tropical cyclone conditions is still virtually unknown and the scatter of data between different field experiments is significant. In this work we have conducted a computational fluid dynamics experiment using the approach that has been partially verified with data from the air-sea interaction facility SUSTAIN. In the computational model, the sea spray generation function has been studied using the Volume of Fluid (VOF) method. This method is enhanced with a Volume of Fluid to Discrete Phase transition model (VOF to DPM). Due to dynamic remeshing, VOF to DPM resolves spray particles ranging in size from tens of micrometers to a few millimeters (spume). The water particles that satisfy the condition of asphericity are converted into Lagrangian particles involved in a two-way interaction with the airflow. The size distribution of non-spherical spray particles is represented by the equivalent radii calculated from the particle mass. The sea spray generation function has been calculated for category 1, 3, and 5 tropical cyclones. A comparison with the data available from literature for a category 1 tropical cyclone shows that our sea spray generation function is close to those found by Zhao et al. (2006) and Troitskaya et al. (2018) for the radius range of spume. Our sea spray generation function results in the spray-induced stress exceeding the interfacial wind stress at approximately 60 m/s wind speed. Connection of spray-induced enthalpy flux to the sea spray generation function is more complicated due to the suspension and evaporation of small-size particles in the turbulent boundary layer (Richter’s and Peng 2019 effect of negative feedback).

 

How to cite: Soloviev, A., Vanderplow, B., Lukas, R., Haus, B., Sumi, M., and Ginis, I.: Sea Spray Generation Function in Major Tropical Cyclones, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6778, https://doi.org/10.5194/egusphere-egu22-6778, 2022.

The Zakharov equation is a fundamental equation of water waves that is used as a dynamical model for wind wave growth/decay. A nearby Lax integrable version of the Zakharov equation is studied and subsequently a Hamiltonian perturbation provides a close approximation of the Zakharov equation itself.  Theorems of Kuksin, and Baker and Mumford are used to develop the algebraic-geometric solutions of the Zakharov equation in terms of the associated Its-Matveev formula. A subsequent derivation of a multiply periodic Fourier series solution is found which includes the coherent structure solutions (breathers) and cascading. The correlation function is computed and the space/time evolution of the Power spectrum is given analytic form, including a wind-wave transfer function appropriate for multiply periodic Fourier series. Some advantages of this method over classical kinetic equations are that the modulational instability is included together with coherent structure breather solutions. Furthermore, the weak interaction assumptions are no longer necessary in this new formulation, which retains the full nonlinear interactions of the Zakharov equation. 

How to cite: Osborne, A.: The Zakharov Equation as a Model for Wind Waves: Nearby Integrability, Hamiltonian Perturbations and Multiply Periodic Fourier Series, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7300, https://doi.org/10.5194/egusphere-egu22-7300, 2022.

EGU22-7495 | Presentations | OS4.2

On the wave boundary layer above wind waves: influence of surfactants 

Katja Schultz, Martin Gade, Marc P. Buckley, and Janina Tenhaus

This study aims to investigate the wave boundary layer and the turbulent
airflow above wind waves on slick-free and slick-covered water surfaces. To realize
this, we carried out laboratory measurements of the airflow in a wind-wave
tank, where we deployed three surfactants of different visco-elastic properties,
each at five wind speeds ranging from 4 ms−1 to 8 ms−1. For measurements
over slick-free water surfaces, we chose wind speeds, at which we observed the
same peak wave frequencies as in the presence of the surfactants. We measured
high-resolution single-point profiles of the horizontal and vertical velocity
components at different heights above the water surface using a Laser-Doppler-
Velocimeter (LDV), wave heights using a wire gauge, and wave slopes using
a laser slope gauge. Both wave field parameters were recorded simultaneously
with the airflow measurements to investigate the influences of the small-scale
wave field on the wave boundary layer. In the airflow turbulence spectra, we
found a clear maximum corresponding to the dominant wave frequencies reflecting
the influence of the waves on the airflow. However, depending on wind
speed and the surfactants’ damping behaviour, the maximum differs in both its
strength and its height above the wavy surface, the latter being interpreted as
the wave boundary layer height. The LDV achieved mean data rates exceeding
2 kHz; hence, it resolved the small-scale turbulence, which manifests in the
high-frequency part of the turbulence spectra. For the slick-free cases, we observed
a linear decrease in turbulence with increasing height above the surface,
and increasing turbulence with increasing friction velocity u∗, which depends
on the wind speed and wind-wave interactions. However, we did not find clear
trends at any wind speed when the water surface was covered by a surfactant.
Here, the turbulence increases with increasing height above the water surface for
higher friction velocities. Thus, the surfactants dampen not only the waves, but
they also reduce the turbulence in the airflow directly above the waves, within
the wave boundary layer.

How to cite: Schultz, K., Gade, M., Buckley, M. P., and Tenhaus, J.: On the wave boundary layer above wind waves: influence of surfactants, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7495, https://doi.org/10.5194/egusphere-egu22-7495, 2022.

EGU22-7723 | Presentations | OS4.2

Attenuation of surface waves in the Antarctic marginal ice zone from in-situ measurements 

Stina Wahlgren, Sebastiaan Swart, Louise Biddle, Jim Thomson, and Lucia Hošeková

Antarctic sea ice has an important impact on the global climate, affecting albedo, global circulation and heat- and gas exchange between the ocean and the atmosphere. Wave energy propagating into the sea ice can affect the quality and extent of the sea ice, and wave attenuation in sea ice is therefore an important factor for understanding changes in the ice cover. Yet in-situ observations of wave activity in the Antarctic marginal ice zone are scarce, due to the extreme conditions of the region.

We estimate attenuation of significant wave height in the Antarctic marginal ice zone using in-situ data from two drifting Surface Wave Instrument Float with Tracking (SWIFT) buoys deployed in the Southern Ocean for two days in the Antarctic winter and two weeks in the Antarctic spring. The buoy location ranges from open water to more than 200 km into the sea ice. The extent of the sea ice coverage is determined using satellite sea ice concentration from AMSR-E and SAR imagery from Sentinel-1. Waves were observed more than 150 km into the sea ice, and in higher than 85 % sea ice concentration. Significant wave height and wave direction measured by the buoys in open water agreed well with ERA5 reanalysis data. We find that the significant wave height decayed exponentially in sea ice, which is consistent with physical experiments and other field observations in the Arctic and Antarctic marginal ice zones. 

How to cite: Wahlgren, S., Swart, S., Biddle, L., Thomson, J., and Hošeková, L.: Attenuation of surface waves in the Antarctic marginal ice zone from in-situ measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7723, https://doi.org/10.5194/egusphere-egu22-7723, 2022.

EGU22-8355 | Presentations | OS4.2

On the global assessment of the coastal wave storminess 

Hector Lobeto, Alvaro Semedo, Melisa Menendez, Gil Lemos, Roshanka Ranasinghe, and Ali Dastgheib

Coastal storms represent powerful and damaging episodes involving climatic variables such as wind, precipitation, sea level and ocean wind waves. Particularly, ocean wind wave storms (or simply wave storms) have a high potential for coastal damage by acting as a major driver of impacts like shoreline erosion and flooding. Wave storms represent extreme wave events significantly exceeding the mean local wave climate conditions, hence impacting the coast by altering the mean equilibrium. This study assesses, for the first time, the global wave storminess based on a high resolution hindcast covering a 42-year period (1979-2020) with hourly time resolution, forced with wind fields from ERA5 reanalysis.

Here, wave events are classified as wave storms by using a unique global criterion based on exceedances over the 95th percentile of the significant wave height. This threshold is selected due to its widespread use in the scientific literature and its flexibility to adapt to local wave conditions, a basic requirement for working at global scale. Additionally, a minimum storm duration of 12 hours and a wave storm independence interval of 48 hours are considered to define the storms. For completeness, an independent analysis of the most severe wave storms reaching the coast is performed. For that matter, wave storms are classified as severe wave storms if the significant wave height exceeds the 99th percentile for more than 6 hours.  

The computation of several statistics and indices allows the analysis of the main characteristics of wave storms, such as frequency, duration and intensity. In addition, the mean significant wave height, mean wave direction and energy flux during wave storms are analyzed. Other secondary storm characteristics, such as swell and wind-sea dominance of the storm energy, and wave height and wave period dominance in the energy transport are also examined to complete the analysis. Results show a global coastal wave storminess pattern strongly characterized by a latitudinal gradient in which the coasts at higher latitudes are stormier than those at lower ones. The higher latitudes show the greatest mean wave heights during storms, reaching over 6 meters in western Ireland or southernmost Chile, and a high number of events per year. The tropical coasts are characterized by lower wave heights and longer storm durations, even exceeding 4 days in some stretches bordering the Arabian Sea. The most relevant exceptions to this behavior in the tropical region are the areas affected by TCs, which can be impacted by storms with very high wave heights.

How to cite: Lobeto, H., Semedo, A., Menendez, M., Lemos, G., Ranasinghe, R., and Dastgheib, A.: On the global assessment of the coastal wave storminess, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8355, https://doi.org/10.5194/egusphere-egu22-8355, 2022.

Air-sea interactions are important for weather and climate predictions since they control the momentum and energy transfer between the atmosphere and the ocean. In current models, the momentum flux in the atmospheric boundary layer is estimated by turbulence closure models which were developed heavily based on measurements over land. However, those turbulence closure models often fail to capture the momentum flux and wind profile in the marine atmospheric boundary layer due to wave impacts. In this study, we proposed a new turbulence closure model to estimate the wind stress in the wave boundary layer from viscous stress, shear-induced turbulent stress, wind-sea induced stress, and swell-induced upward stress, separately. The misalignment between the wind stress and wind is also considered in the model. Single-column simulations indicate that 1) the swell-induced upward momentum flux increases the surface wind and changes the wind direction, 2) the misalignment between the upward momentum flux and wind has a more significant impact on the wind profile than that from the downward momentum flux, and 3) the impact of swell-induced upward momentum flux decreases with atmospheric convection. The proposed closure scheme was implemented into an atmosphere-wave coupled model. A month-long simulation over the ocean off California shows that the surface wind can be altered up to 5% by ocean surface gravity waves.

How to cite: Wu, L. and Qiao, F.: A turbulence closure scheme in the wave boundary layer and its application in a coupled model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9640, https://doi.org/10.5194/egusphere-egu22-9640, 2022.

EGU22-9869 | Presentations | OS4.2

Space-time statistics of extreme ocean waves in crossing sea conditions during a tropical cyclone 

Silvio Davison, Alvise Benetazzo, Francesco Barbariol, and Guillaume Ducrozet

In recent years, the study of extreme ocean waves has gained considerable interest and several theoretical approaches have been developed for their statistical prediction. However, a full understanding of the main mechanisms responsible for the occurrence of extreme waves has not yet been reached in the relatively common case of a crossing sea, where a local wind sea system coexists with a system of swell. In this context, we investigate how the space-time extreme-value statistics of realistic crossing sea states differs from the statistics of the corresponding short-crested wind sea and long-crested swell partitions during tropical cyclone Kong Rey (2018) in the Northwestern Pacific Ocean (Yellow Sea and East China Sea). The investigation is carried out using an ensemble of numerical simulations obtained from a phase-resolving wave model based on the high-order spectral method (HOSM) and focuses on the maximum sea surface elevation (crest height). The reliability of the numerical model outputs has been assessed with space-time measurements of the 3D sea surface elevation field collected from a fixed offshore platform in the area of interest. Our results highlight the different roles that linear and nonlinear effects have in the formation of extreme waves for different combinations of wind sea and swell systems.

How to cite: Davison, S., Benetazzo, A., Barbariol, F., and Ducrozet, G.: Space-time statistics of extreme ocean waves in crossing sea conditions during a tropical cyclone, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9869, https://doi.org/10.5194/egusphere-egu22-9869, 2022.

EGU22-10814 | Presentations | OS4.2

Ocean surface wave and turbulence characteristics from direct measurements with a velocity sensor deployed in a buoy 

Francisco J. Ocampo-Torres, Pedro Osuna, Bernardo Esquivel-Trava, Nicolas Rascle, and Héctor García-Nava

There is great interest in acquiring directional ocean surface wave direct measurements in order to better determine sea state conditions in open waters as well as in harbors and nearshore sites. Typical applications range widely over coastal and oceanic engineering, naval architecture and safety at sea, for design and construction of vessels and infrastructure, as well as for maintenance and marine operations. In this work we explore the influence of the buoy motion and we are able to detect some turbulence characteristics of the near surface flow. Full motion of the buoy structure is recorded by an Inertial Motion Unit within the velocimeter case, and after applying motion corrections directional wave and some turbulence characteristics are analyzed. The buoy responde is readily defined and the final results are compared with corresponding measurements from a bottom fixed acoustic Doppler current profiler. Details of the groupinness behaviour of the wave field in a nearshore site are given, showing some enhancement of turbulence intensity during the passage of relatively high wave groups. Some attempts to quantify the kinetic energy dissipation rate are explained. Final results show similar turbulence intensity values from the buoys measurements when compared with those from the fixed ADCP.

How to cite: Ocampo-Torres, F. J., Osuna, P., Esquivel-Trava, B., Rascle, N., and García-Nava, H.: Ocean surface wave and turbulence characteristics from direct measurements with a velocity sensor deployed in a buoy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10814, https://doi.org/10.5194/egusphere-egu22-10814, 2022.

EGU22-11376 | Presentations | OS4.2

Experimental study of wave-turbulence interaction 

Benjamin K. Smeltzer, R. Jason Hearst, and Simen Å. Ellingsen

Turbulence is ubiquitous in the uppermost layer of the ocean, where it interacts with surface waves. Theoretical, numerical, and experimental works (e.g. [1,2,3] respectively) predict that motion of non-breaking waves will increase turbulent energy, in turn leading to a dissipation of waves. Waves are believed to contribute significantly to the turbulence in the ocean mixed layer, yet additional measurements are needed to validate and distinguish between models and theories [4].

In this work we study the modification of turbulence by surface waves using experimental measurements of turbulent flows in the presence of waves. The measurements were performed in the water channel laboratory at NTNU Trondheim [5], able to mimic the water-side flow in the ocean surface layer under a range of conditions. An active grid at the inlet allowed the turbulence intensity and length scale to be varied while maintaining an approximately constant mean flow. The flow field was measured in the spanwise-vertical plane by stereo particle image velocimetry for various background turbulence cases with waves propagating against the current. The turbulence characteristics are compared to cases without waves, and the turbulence level is found to be increased after the passage of wave groups. The results are discussed considering predictions from rapid distortion theory [1].

 

[1] Teixeira M. and Belcher S. 2002 “On the distortion of turbulence by a progressive surface wave” J. Fluid Mechanics 458 229-267.

[2] McWilliams J. C., Sullivan P. P. and Moeng C-H. 1997 “Langmuir turbulence in the ocean” J. Fluid Mechanics 334 1-30.

[3] Thais L. and Magnaudet J. 1996 “Turbulent structure beneath surface gravity waves sheared by the wind” J. Fluid Mechanics 328 313-344.

[4] D’Asaro E.A. 2014 “Turbulence in the upper-ocean mixed layer” Annual Review of Marine Sciences 101-115.

[5] Jooss Y., et al. 2021 “Spatial development of a turbulent boundary layer subjected to freestream turbulence” Journal of Fluid Mechanics 911 A4.

How to cite: Smeltzer, B. K., Hearst, R. J., and Ellingsen, S. Å.: Experimental study of wave-turbulence interaction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11376, https://doi.org/10.5194/egusphere-egu22-11376, 2022.

EGU22-11598 | Presentations | OS4.2 | Highlight

On the improvement of surface currents from ocean/waves coupled simulations : Sensitivity to wave forcing 

Lotfi Aouf, Stephane Law-Chune, Daniele Hauser, and Bertrand Chapron

The climate is evolving rapidly and there is a strong need of better description on momentum and heat fluxes exchanges between the ocean and the atmosphere. Recently directional wave observations from CFOSAT shed ligth on the improvement of dominant wave direction and better scaling of wind-wave growth in critical ocean areas such as the Southern Ocean (Aouf et al. 2021). This work examines the validation of coupled simulations between the ocean model NEMO and the wave model MFWAM including assimilation of directional wave observations. The coupling experiments have been performed for austral summer and fall seasons during 2020 and 2021. The objective of this work is on the one hand to assess the impact of waves on key parameters describing the ocean circulation and on the other hand to evaluate the contributions of different processes of the wave forcing (stress, Stokes drift and wave breaking inducing turbulence) on the mixing in upper ocean layers. The outputs of the coupled simulations have been validated with in situ observations of ocean surface currents, temperature and salinity. The results clearly reveals an improvement in the estimation of the Antarctic Circumpolar Current (ACC) with an increase in the intensity of the current for example in the region between Tasmania and Antarctica. We also observed a significant improvement of the surface currents in the tropics, for instance the ascending brazilian current. In other respects, we have examined the contribution of improved surface stress on inertial oscillations of the current in the Southern Ocean.

Comparison of the surface currents from the coupled simulations with those provided by altimeters showed an increase in current intensity and a better description for small scales in regions of strong currents such as the Agulhas, ACC and Kuroshio regions. We also investigated the impact of wave forcing depending on the mixing layer length.

Further discussions and conclusions will be presented in the final paper.

How to cite: Aouf, L., Law-Chune, S., Hauser, D., and Chapron, B.: On the improvement of surface currents from ocean/waves coupled simulations : Sensitivity to wave forcing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11598, https://doi.org/10.5194/egusphere-egu22-11598, 2022.

EGU22-11717 | Presentations | OS4.2

Impacts of Sea Spray in a coupled ocean-wave-atmosphere model : Mediterranean Sea case studies 

Sophia Brumer, Marie-Noelle Bouin, Marie Cathelain, Fabien Leckler, Hubert Branger, Jacques Piazolla, Fabrice Veron, Nicolas Michelet, Jean-François Filipot, and Jean-Luc Redelsperger

With the flourishing of offshore wind projects there is a new socio-economic interest to better our knowledge and forecasting ability of winds within the coastal marine atmospheric boundary layer (MABL). Air-sea fluxes of enthalpy and momentum greatly influence the turbulent and mean winds in the MABL. Already at moderate but certainly at high winds, wave breaking is a key driver of air-sea fluxes and the sea spray generated by whitecaps is thought to be a crucial component when modelling air-sea interactions. Most studies so far have focused on the role of sea spray in enhancing tropical cyclone intensity.  Here we investigate its impacts on the MABL under strong orographic wind forcing. A coupled model framework was developed within the scope of the CASSIOWPE project aiming at characterizing the physical environment in the Gulf of Lion (NW Mediterranean Sea) in the prospective of future floating wind farms development. It consists of the non-hydrostatic mesoscale atmospheric model of the French research community Meso-NH, the 3rd generation wave model WAVEWATCH III®, and the oceanic model CROCO. Sea-spray physics were incorporated into the Meso-NH’s surface model SURFEX. Added parametrizations will be detailed and a series of test cases will be presented to illustrate how sea spray alters the MABL under Mistral and Tramontane winds. Several sea-state dependent sea spray generation functions (SSGF) are considered in the present study. The variability in simulated fields linked to the choice of wave forcing or coupling will be showcased to evaluate their suitability in varying fetch conditions. Sea spray production remains to be adequately quantified. Existing measurement derived SSGFs span several orders of magnitude resulting in uncertainties in simulated fields which will be discussed.

How to cite: Brumer, S., Bouin, M.-N., Cathelain, M., Leckler, F., Branger, H., Piazolla, J., Veron, F., Michelet, N., Filipot, J.-F., and Redelsperger, J.-L.: Impacts of Sea Spray in a coupled ocean-wave-atmosphere model : Mediterranean Sea case studies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11717, https://doi.org/10.5194/egusphere-egu22-11717, 2022.

EGU22-11735 | Presentations | OS4.2

Historical Simulation of Global Wave Climate using Anthropogenic and Natural Forcings Derived from Multimodel Ensemble of CMIP6 

Anindita Patra, Guillaume Dodet, and Mickaël Accensi

Wind-waves are of paramount importance for shoreline stability, offshore and coastal activities, and renewable energy generation. There is sufficient evidence of climate-driven trends in historical wave heights. It is important to quantify the relative contributions of natural and anthropogenic forcings to historical changes in wave height in order to produce more reliable future projections and adopt appropriate adaptation strategies. Historical wave climate is simulated using numerical model WAVEWATCH-III ® (WW3) forced by multi-model CMIP6 simulations corresponding to natural forcing only (NAT), greenhouse gas forcing only (GHG), aerosol forcing only (AA), combined all forcings (ALL), and preindustrial control conditions (CTL). Surface wind at 3-hourly temporal resolution, and sea-ice area fraction at monthly frequency, from each CMIP6 model is derived to force spectral wave model WW3 over the global ocean at 1° grid resolution for 1950-2020. Other specification such as spectral discretization and parameterizations is same as the recent WW3 hindcast implemented at Ifremer. The ALL simulations generally ended in 2014, but simulations are extended to 2020 with the SSP (Shared Economic Pathway) 2-4.5 scenario. The preindustrial control (CTL) simulations is used to estimate internal climate variability. Model validation is done using altimeter data set produced by European Space Agency Climate Change Initiative (ESA-CCI), and recent ERA-5 reanalysis. Numerically simulated wave parameters time-series for different external forcing is not available yet. This study produces a novel database particularly useful for investigating the link between wave and climate variability.

How to cite: Patra, A., Dodet, G., and Accensi, M.: Historical Simulation of Global Wave Climate using Anthropogenic and Natural Forcings Derived from Multimodel Ensemble of CMIP6, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11735, https://doi.org/10.5194/egusphere-egu22-11735, 2022.

EGU22-698 | Presentations | AS1.7

Energy flux quantification in the oceanic internal wavefield 

Giovanni Dematteis, Kurt Polzin, and Yuri Lvov

The rate of diapycnal mixing, largely due to internal-wave breaking, is a key ingredient to understanding upwelling and horizontal circulation in the ocean. Here, we show a first-principles quantification of the downscale energy flux in the internal wavefield, that ultimately feeds the wave-breaking, shear-instability energy sink responsible for mixing. The approach is based on the wave kinetic equation that describes the inter-scale energy transfers via 3-wave nonlinear resonant interactions. Our results compare favorably with the phenomenological ‘Finescale Parameterization’ formula, by which deep ocean mixing is commonly implemented in the global models, and provide novel insights in the complex problem of oceanic energy transfers.

How to cite: Dematteis, G., Polzin, K., and Lvov, Y.: Energy flux quantification in the oceanic internal wavefield, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-698, https://doi.org/10.5194/egusphere-egu22-698, 2022.

EGU22-2813 | Presentations | AS1.7

Wave-eddy interactions in the Gulf of Lion: Bridging ocean general circulation models and process ocean simulations 

Mariona Claret, M.-Pascale Lelong, Kraig B. Winters, and Yann Ourmières

Near-inertial waves (NIWs) are of major relevance to the global ocean circulation as they inject wind energy from the surface to the ocean interior and represent a primary source  of energy to the internal wave continuum. Eddies and fronts play a significant role in the downward penetration of NIW energy (from generation to propagation) and subsurface dissipation. Much of our understanding of NIW interactions with submeso- and mesoscale flows comes from limited observations as well as idealized theoretical and numerical processes, but these do not typically consider the presence of temporally evolving larger-scale flows. On the other hand, more realistic and time-evolving eddy fields from submesoscale-resolving Ocean General Circulation Models (OGCMs) forced with winds show truncated spectra at the subsurface due to the lack of vertical resolution -the subgrid vertical scale is 1-2 orders of magnitude larger than the scale at which dissipation occurs.  Since OGCMs are indeed very attractive tools to quantify global-regional impacts of small-scale phenomena, we propose to gain understanding of their biases in terms of wave-eddy interactions by using a novel approach.


This approach consists of nesting a non-hydrostatic Boussinesq model (Flow_Solve) into an OGCM configuration (NEMO-GLAZUR64) for the Gulf of Lion with O(1 km) horizontal and  O(30 m) vertical resolution. Preliminary analysis of NEMO-GLAZUR64 output reveals a highly energetic NIW field with intriguing distribution patterns relative to the eddies. We zoom into these patterns by following eddies with our nesting approach. The Boussinesq model provides a magnifying glass into dynamical processes that are either parameterized or fully unresolved in the OGCM. Wave energy budgets inferred from high-resolution process studies with Flow_Solve and NEMO-GLAZUR64 are then compared in order to better constrain model uncertainty in OGCMs due to NIW dynamics. 

How to cite: Claret, M., Lelong, M.-P., Winters, K. B., and Ourmières, Y.: Wave-eddy interactions in the Gulf of Lion: Bridging ocean general circulation models and process ocean simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2813, https://doi.org/10.5194/egusphere-egu22-2813, 2022.

EGU22-3214 | Presentations | AS1.7

Inertia-gravity wave diffusion by geostrophic turbulence: the impact of flow time dependence 

Michael Cox, Jacques Vanneste, and Hossein Kafiabad

The scattering of short inertia-gravity waves by large-scale geostrophic turbulence in the atmosphere and ocean can be described as a diffusion of wave action in wavenumber space. When the time dependence of the turbulent flow is neglected, waves conserve their frequency, which restricts the diffusion of energy to the constant-frequency cone. We relax the assumption of time independence and consider scattering by a flow that evolves slowly compared with the wave periods, consistent with a small Rossby number. The weak diffusion across the constant-frequency cone introduced by time dependence leads to a stationary energy spectrum that remains localised around the cone (specifically decaying as 1/σ5 with σ the angular deviation from the cone) corresponding to a small frequency broadening. We contrast our results with unbounded frequency broadening that arises for surface- or shallow-water waves.

How to cite: Cox, M., Vanneste, J., and Kafiabad, H.: Inertia-gravity wave diffusion by geostrophic turbulence: the impact of flow time dependence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3214, https://doi.org/10.5194/egusphere-egu22-3214, 2022.

EGU22-3884 | Presentations | AS1.7

Detection of internal gravity waves by high-pass filtering 

Zuzana Procházková, Christopher Kruse, Aleš Kuchař, Petr Pišoft, and Petr Šácha

Terrestrial atmosphere supports propagation of various wave types. An important component of the dynamics especially in the middle atmosphere are the internal gravity waves (GWs) that are incessantly being generated from initial perturbations in a stably stratified atmosphere. Horizontal GW wavelengths range from a few to thousands of kilometres. Together with a wide range of temporal and vertical scales, this complicates their global observations and modeling, requiring high resolution model simulations. Subsequent analyses, nevertheless, contain a significant margin of uncertainty originating in the separation of GWs from the background flow, which is often performed by statistical means. In our work, we explore properties of a Gaussian high-pass filter method, using a deep WRF simulation with the horizontal resolution of 3 km in the region of the Drake Passage. Due to the revealed sensitivity of momentum flux and drag estimates to a filter cutoff parameter, we propose a new method that sets the value of the parameter on the basis of the horizontal spectra of horizontal kinetic energy.

How to cite: Procházková, Z., Kruse, C., Kuchař, A., Pišoft, P., and Šácha, P.: Detection of internal gravity waves by high-pass filtering, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3884, https://doi.org/10.5194/egusphere-egu22-3884, 2022.

EGU22-4192 | Presentations | AS1.7

Spectral variations of the cancellation factor for temperature investigation in the mesospheric nightglow layer 

Christophe Bellisario, Pierre Simoneau, Alain Hauchecorne, Philippe Keckhut, Fabrice Chane-Ming, and Constantino Listowski

The infrared emission lines observed between 80 and 100 km known as nightglow allow the investigation of dynamic phenomena such as gravity waves. These perturbations act on local temperature and density. However, the observation of the local perturbations in the nightglow layer is mainly performed by spectrally broad cameras. Swenson and Gardner (1998) introduced the cancellation factor linking relative variations of intensity with relative variations of temperature. The cancellation factor is a function of the perturbation vertical wavelength estimated from simulation that do not include spectral variations. In this study, we intend to estimate the spectral variability of the cancellation factor, in particular within the range 0.9-1.7 µm corresponding to infrared InGaAs camera, used during measurement campaigns. We describe briefly the model that resolves the vibrational states of the nightglow main source (OH). Then vertically propagating gravity waves are applied on a 1D scheme and the cancellation factor is computed based on the impact on both temperature and intensity. Spectral variations of the cancellation factor are observed and compared along the variation of the vertical wavelength.

How to cite: Bellisario, C., Simoneau, P., Hauchecorne, A., Keckhut, P., Chane-Ming, F., and Listowski, C.: Spectral variations of the cancellation factor for temperature investigation in the mesospheric nightglow layer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4192, https://doi.org/10.5194/egusphere-egu22-4192, 2022.

Resolving inertia-gravity (IG, or gravity) waves poses a real challenge for the formulation of numerical schemes for numerical weather prediction (NWP) and climate models due to different time scales of Rossby wave dynamics and fast-propagating IG waves. With ever increasing emphasize placed on high-resolution simulations, the importance of the issue is growing due to the implications of Courant-Friedrich-Levy (CFL) stability criterium. It is especially prominent in the tropical atmosphere, where a significant part of variability is associated with divergence-dominated dynamics. Detangling gravity and Rossby wave dynamics in the tropics is a challenging problem due to a lack of sepaartion between the Rossby and gravity regime that is present in the extra-tropics.   

TIGAR (Transient Inertia Gravity and Rossby wave dynamics) targets this problem by employing the eigensolutions of the linearized primitive equations on the sphere as the basis functions for the numerical representation of dynamical variables. This leads to the description of dynamics in terms of physically identifiable structures, i.e. the Rossby and gravity waves, which are fully dynamically separated at the linearization level. The benefits of such approach can be reaped on analytical, modelling and computational sides. As a research tool, TIGAR allows to study wave-wave interections directly in the model, without the need of intermediate software for wave filtering. Simplified models aimed at particular dynamical regime can be obtained from a full model with a simple configuration change. For instance, retaining only the Rossby modes in the spectral expansion will result in the quasi-geostrophic model, while additionally keeping the Kelvin and mixed Rossby-gravity waves will reproduce essential features of tropical circulation. 

Numerically, high precision computation is achieved in TIGAR through the use of higher order exponential time-differencing schemes, which take advantage of the normal modes framework, leading to the major increase in computational efficiency and stability. The comparison with classical time-stepping schemes in the horizontal component of the model shows accuracy improvements of several orders of magnitude at the same computational cost. In our testing on multiscale flows, the stability gains associated with the enhanced representation of gravity wave dynamics raise CFL time-step bound for explicit schemes by a factor of 4-6. 

We present TIGAR solutions of some classical steady and time-dependent problems including barotropic and baroclinic instability tests.

How to cite: Vasylkevych, S. and Žagar, N.: TIGAR - a new global atmospheric model for the simulation of Transient Inertia-Gravity And Rossby wave dynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6185, https://doi.org/10.5194/egusphere-egu22-6185, 2022.

EGU22-6323 | Presentations | AS1.7

Comparing gravity waves in a kilometer scale run of the IFS to AIRS satellite observations 

Emily Lear, Corwin Wright, Neil Hindley, and Inna Polichtchouk

Gravity waves impact the large scale circulation, and increasing our understanding of them is important to improve weather and climate models. This presentation focusses on atmospheric gravity waves in the stratosphere using data from the ECMWF ERA5 reanalysis, AIRS (Atmospheric Infrared Sounder) on NASA’s Aqua satellite and a high resolution run of the IFS operated at a km-scale spatial resolution. Data was examined during the first 2 weeks of November, as the high resolution model was initialized on the 1st of this month. Asia and surrounding regions are investigated, because preliminary studies of AIRS data suggested strong gravity wave activity in this region during this time period. Waves can also be seen in the ERA5 data at the same times and locations. The high resolution model also shows significant gravity wave activity in similar areas to where it is seen in the AIRS data, particularly over Russia. The 2D+1 S-Transform was used to find wave amplitudes, horizontal and vertical wavelengths and momentum flux for all three datasets. Weather models are advancing rapidly and km scales such as the experimental IFS run could become operational in next decade. At these grid scales, gravity waves must be resolved instead of parameterized so the models need to be tested to see if they do this correctly. This work provides information on how a cutting edge model resolves gravity waves compared to observations.

How to cite: Lear, E., Wright, C., Hindley, N., and Polichtchouk, I.: Comparing gravity waves in a kilometer scale run of the IFS to AIRS satellite observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6323, https://doi.org/10.5194/egusphere-egu22-6323, 2022.

Observations with high vertical resolution have shown that vertical wavenumber (m) power spectra of horizontal wind and temperature fluctuations have a universal shape with a steep slope that is roughly proportional to ~m–3. Several theoretical models explaining the universal spectra were proposed based on the assumption of gravity wave (GW) saturation. However, it has not yet been sufficiently confirmed that such characteristic spectra are fully composed of GWs. Thus, in the present study, we examine whether the m–3 spectra are due to GWs, using a GW-permitting general circulation model with a high top in the lower thermosphere. The model-simulated spectra have steep spectral slopes, which is consistent with observations. GWs are extracted as fluctuations having total horizontal wavenumbers of 21–639. From the comparison between spectra of the GWs and those of all simulated fluctuations, it is shown that GWs are dominant only at high ms, while disturbances other than the GWs largely contribute to the spectra at low ms even in the m–3 range. In addition, we examine vertical and geographical distributions of the characteristic wavenumbers, slopes, and amplitudes of GW spectra. The slopes of GW spectra are particularly steep near the eastward and westward jets in the middle atmosphere. It is theoretically shown that strong vertical shear below the jets is responsible for the formation of steep GW spectral slopes.

 

Reference:

Okui, H., Sato, K., and Watanabe, S., Contribution of gravity waves to the universal vertical wavenumber (m–3) spectra revealed by a gravity-wave permitting general circulation model, submitted to Journal of Geophysical Research Atmospheres.

How to cite: Okui, H., Sato, K., and Watanabe, S.: Contribution of gravity waves to the universal vertical wavenumber (m–3) spectra revealed by a gravity-wave permitting general circulation model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6694, https://doi.org/10.5194/egusphere-egu22-6694, 2022.

We investigate the influence of a barotropic geostrophic current on
the internal wave (IW) generation over a shelf slope.
It is well known that most of the energy in the tide-topography
generated waves lies in waves with tidal frequency $\sigma_T$. 
Here we restrict our attention on the frequencies other than the dominant frequency $\sigma_T$. 
The current $V_g(x)$ is modeled as an idealized Gaussian function centered at
$x_0$ with width $x_r$ and maximum velocity $V_{max}$.
The bathymetry is modelled as a linear slope with smoothed corners.
Since the center of the current lies on the slope, there will always
be a region on the slope where the effective frequency $f_{eff}$ is
greater than the Coriolis parameter $f$ and another region where
$f_{eff} < f$. Parametric subharmonic instability (PSI) occurs where
waves with approximately half of the primary wave frequency, in this
case $\sigma_T/2$, are generated. In the presence of a large current,
PSI can occur where $f_{eff} < \sigma_T/2 < f$. This could not
happen without a current, i.e. $f_{eff} = f > \sigma_T/2$. Other interesting
interactions, including interharmonics and strong tidal harmonics, are also observed.

How to cite: He, Y. and Lamb, K.: Tide-topography interactions: the influence of an along-shelf current on the internal wave spectrum, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6715, https://doi.org/10.5194/egusphere-egu22-6715, 2022.

EGU22-6834 | Presentations | AS1.7

Effects of viscosity on internal wave focusing by an oscillating torus. 

Natalia Shmakova, Bruno Voisin, Joel Sommeria, and Jan-Bert Flor

An experimental study of the focused internal waves generated by a horizontally oscillating torus in a linearly stratified fluid is presented for a large range of Stokes numbers from 100 to 6000. For low Stokes number the waves are unimodal, i.e. in each propagation direction they diffuse to form a single wave beam, after their emission at the critical locations where the wave rays are tangential to the torus boundary. In that regime, the waves amplify in amplitude in a single focal zone. With increasing Stokes number the waves become bimodal, forming dual wave beams in each propagation direction and focusing in four zones of amplitude amplification.

Comparison of the experimental results at small oscillation amplitude with an original linear theory gives excellent agreement over the entire Stokes number range. As the oscillation amplitude increases the wave amplitude saturates in the focal zone. This saturation only appears at large oscillation amplitude for low Stokes number and is present already at moderate oscillation amplitude for high Stokes number.

Fourier analysis reveals triadic interactions of the fundamental wave with two subharmonic waves owing to focusing. This triadic resonance is visible only at large oscillation amplitude when viscous effects are high, i.e. for low Stokes number, but with increasing Stokes number it manifests itself at smaller oscillation amplitude. For high Stokes numbers, above 1800, and large oscillation amplitudes, greater than or equal to the minor radius of the torus, wave turbulence is observed.

The Stokes drift, calculated theoretically, appears as the key to understand the generation of vertical mean flow in the focal zone. At low and moderate Stokes numbers the mean flow is almost exactly opposed by the Stokes drift, while for higher Stokes numbers perturbations of this flow start to appear with time, possibly due to the generation of subharmonics.

How to cite: Shmakova, N., Voisin, B., Sommeria, J., and Flor, J.-B.: Effects of viscosity on internal wave focusing by an oscillating torus., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6834, https://doi.org/10.5194/egusphere-egu22-6834, 2022.

EGU22-6993 | Presentations | AS1.7

Quantification of oblique orographic gravity wave propagation deduced from a mountain wave model 

Sebastian Rhode, Peter Preusse, Manfred Ern, Lukas Krasauskas, Markus Geldenhuys, and Martin Riese

Observations and high resolution models suggest a high potential for gravity waves (GW) to propagate horizontally, which is usually not considered in current parameterizations of general circulation models (GCM). For a quantification of the oblique propagation of orographic GWs and their transport of momentum throughout the atmosphere, we present a mountain wave model (MWM) that describes the terrain induced GW sources, propagation and momentum flux. Being aware of horizontal wind gradients, the model also allows for GW refraction which leads to a turning of the wave vector.

The MWM we present here is a simplified model identifying orographic GW sources from topography data. It is similar to the one presented in Bacmeister et.al. (1994). First, the topography is smoothed using a Gaussian bandpass filter, which allows to consider the different scales of generated MWs separately. This smoothed topography is afterwards reduced to the inherent ridge structure (i.e. to the arêtes of mountains) by employing edge and line detection algorithms from computer vision. Using this underlying arête structure in combination with a fit of idealized Gaussian-shaped mountain ridges to the topography gives us a straightforward way of determining MW parameters for launching a ray, i.e. source location, orientation and size of the wave vector as well as the displacement amplitude. These parameters are then used to calculate the propagation in space and time in given atmospheric backgrounds (determined from smoothed ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation) data) with the ray tracer GROGRAT. The results can then be binned in terms of momentum flux and drag or used for a reconstruction of 3D temperature perturbations for a given time.

The MWM presented here has been validated against global satellite data as well as local measurements to a new quality compared to previous studies. The validation has been performed by applying an instrument-specific observational filter to the model data before considering global maps of momentum flux distributions and horizontal cross-sections of temperature perturbations. Comparisons of these to satellite data and limb measurement retrievals respectively will be shown in this presentation.

How to cite: Rhode, S., Preusse, P., Ern, M., Krasauskas, L., Geldenhuys, M., and Riese, M.: Quantification of oblique orographic gravity wave propagation deduced from a mountain wave model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6993, https://doi.org/10.5194/egusphere-egu22-6993, 2022.

In contrast to the kinetic energy spectrum of the horizontal motions, the spectrum of kinetic energy of vertical motions (vertical kinetic energy spectrum) is poorly known because the vertical velocity is not an observed quantity of the global observing system. The vertical kinetic energy spectra can be simulated by non-hydrostatic models but are difficult to validate. Furthermore, contributions to the vertical kinetic energy spectrum from the Rossby and gravity waves have traditionally been treated separately using the quasi-geostrophic omega equations and the polarization relations for the stratified Boussinesq fluid, respectively. This approach is difficult to apply in the tropics, where the Rossby and gravity wave regimes are nonseparable and the frequency gap between the Rossby and gravity waves, present in the extra-tropics, is filled with the Kelvin and mixed Rossby-gravity waves.  

We apply a unified framework for the derivation of vertical velocities of the Rossby and inertia-gravity waves and associated kinetic energy spectra using the eigensolutions of the linearized primitive equations. It can be considered applicable to the hydrostatic atmosphere with horizontal scales up to around 10 km.  The derivation involves the analytical evaluation of divergence of the horizontal wind associated with the Rossby and inertia-gravity modes. The new framework is applied to the ECMWF analysis in August 2016 and August 2018. Latitude and altitude dependence of the horizontal wind divergence and vertical kinetic energy spectra within the tropics are discussed and compared with observations over the tropical Atlantic. In particular, we discuss the slope of the vertical kinetic energy spectra for the two dynamical regimes.

How to cite: Neduhal, V., Žagar, N., and Zaplotnik, Ž.: Zonal wavenumber spectra of the vertical velocity and horizontal wind divergence associated with the Rossby and non-Rossby waves in the tropics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8251, https://doi.org/10.5194/egusphere-egu22-8251, 2022.

With an aim of understanding the role of internal waves to oceanic mixing, various mechanisms have been cited as a possible explanation for how they transfer energy across the wavenumber and frequency spectra and eventually contribute to small-scale turbulence. Triadic Resonance Instability (TRI) has become increasingly recognised as potentially one of these mechanisms. This talk will summarise experimental work that examines the long-term temporal and spatial evolution of this instability in the more realistic scenario of a finite-width internal wave beam. Experiments have been conducted using a new generation of wave maker, featuring a flexible horizontal boundary driven by an array of independently controlled actuators. We present experimental results exploring the role the finite-width of a wave beam has on the evolution of TRI. Experimentally, we find that the approach to a saturated equilibrium state for the three triadic waves is not monotonic, rather their amplitudes continue to oscillate without reaching a steady equilibrium. A detailed study into the structure of the secondary waves shows that this behaviour is also witnessed in Fourier space. We show how a spectrum of these resonant frequencies in the flow ‘beat’ to cause interference patterns which manifest throughout the instability as slow amplitude modulations.

How to cite: Grayson, K., Dalziel, S., and Lawrie, A.: Experimental Investigation into the long-term spatial and temporal development of Triadic Resonance Instability in a finite-width internal wave beam, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8254, https://doi.org/10.5194/egusphere-egu22-8254, 2022.

EGU22-10138 | Presentations | AS1.7

Simulating Convective GWs forced by Radar-Based, Neural-Network-Predicted Diabatic Heating 

Christopher Kruse, M. Joan Alexander, Martina Bramberger, Padram Hassanzadeh, Ashesh Chattopadhyay, Brian Green, and Alison Grimsdell

Convection, both observed and modeled, generates gravity waves (GWs) that significantly impact large-scale circulations in the stratosphere and above. However, models that permit convection and resolve the GWs they generate cannot reproduce the timing, location, and intensity of the actual convective cells that generate the observed convective GWs. This issue prevents comparison of observed and modeled convective GWs and model validation/evaluation. 

Here, convective latent heating is predicted based on radar observations and provided to an idealized version of WRF, allowing WRF’s dynamics to generate convective updrafts/downdrafts and generated convective GWs both mechanically and diabatically. Two methods are used to predict convective latent heating: the composited lookup table of Bramberger et al. 2020 and neural networks (NNs) using the same, and additional, input variables. Offline performance of the NN-predicted latent heating can be improved over the previous method when more input variables are used. Preliminary comparisons of modeled and observed (via superpressure-balloon and satellite) convective GWs will be presented. 

How to cite: Kruse, C., Alexander, M. J., Bramberger, M., Hassanzadeh, P., Chattopadhyay, A., Green, B., and Grimsdell, A.: Simulating Convective GWs forced by Radar-Based, Neural-Network-Predicted Diabatic Heating, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10138, https://doi.org/10.5194/egusphere-egu22-10138, 2022.

EGU22-10645 | Presentations | AS1.7

The global reach of gravity waves at the stratospheric speed limit from the 2022 Hunga Tonga volcanic eruption 

Neil Hindley, Lars Hoffmann, M. Joan Alexander, Cathryn Mitchell, Scott Osprey, Cora Randall, Corwin Wright, and Jia Yue

At around 04:14 UTC on the 15th January 2022, a major volcanic eruption began beneath the Tongan islands of Hunga Tonga and Hunga Ha’apai (175.4W, 20.5S). Located under only a shallow depth of water, the volcano rapidly launched a plume of super-heated ash and vapourised water upwards into the atmosphere. Over the next few hours, satellite observations reveal unprecedented large-scale concentric waves in the mid-stratosphere (near 40km altitude) radiating away from the eruption across the entire Pacific Ocean. In this presentation, we show brightness temperature perturbations in the 4.3 micron bands of the AIRS/Aqua, CrIS/Suomi-NPP and CrIS/JPSS-1 instruments that reveal three groups of atmospheric waves of special interest. First, an initial concentric wave is found travelling near the stratospheric speed of sound, likely to be an acoustic compression wave. There then follows a gap, which corresponds to phase speeds not permitted by theory, then a second group of waves likely to be gravity waves. These gravity waves are shown to be travelling near the maximum phase speed permitted, and there is a suggestion that some may travel the whole way around the globe in the tropics. Third, we observe small-scale gravity waves that pervade many thousands of kilometres across almost the entire Pacific Ocean, suggesting an extremely consistent heating source. All three of these wave observations are unprecedented in more than 20 years of stratospheric satellite observations, and this eruption may potentially have produced the first observations of an acoustic wave in the mid-stratosphere that can be measured from space. Now that we have space-borne instruments to observe it, this volcanic eruption provides a unique test of theoretical predictions of atmospheric wave phase speeds on some of the largest scales possible.

How to cite: Hindley, N., Hoffmann, L., Alexander, M. J., Mitchell, C., Osprey, S., Randall, C., Wright, C., and Yue, J.: The global reach of gravity waves at the stratospheric speed limit from the 2022 Hunga Tonga volcanic eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10645, https://doi.org/10.5194/egusphere-egu22-10645, 2022.

EGU22-10667 | Presentations | AS1.7

Atmospheric Gravity Wave Observations from a Special Aeolus Campaign over the Southern Andes during Winter 2021 

Timothy Banyard, Corwin Wright, Neil Hindley, and Gemma Halloran

As the first Doppler wind lidar in space, ESA’s flagship Aeolus satellite provides us with a unique opportunity to study the propagation of gravity waves (GWs) from near the surface to the tropopause and UTLS. Existing space-based measurements of GWs in this altitude range are spatially limited and, where available, use temperature as a proxy for wind perturbations. Recent research confirms Aeolus’ ability to measure GWs, and so this and future spaceborne wind lidars have the potential to transform our understanding of these critically-important dynamical processes.

Here, we present results from a special campaign onboard Aeolus, involving a change to the satellite’s range-bin settings designed to allow better observations of orographic GWs over the Southern Andes during winter 2021. In line with recent research, we expect to see GW wind structures extending down to near the wave sources, enabling detailed measurements of vertical and horizontal wavelength, pseudo-momentum flux and wave intermittency. Such parameters will feed into the next generation of NWP and global circulation models, which will resolve waves at higher resolutions and employ more advanced parametrization schemes.

How to cite: Banyard, T., Wright, C., Hindley, N., and Halloran, G.: Atmospheric Gravity Wave Observations from a Special Aeolus Campaign over the Southern Andes during Winter 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10667, https://doi.org/10.5194/egusphere-egu22-10667, 2022.

EGU22-13062 | Presentations | AS1.7

First measurements of fine-vertical-scale wave impacts on the tropical lower stratosphere 

Martina Bramberger, M. Joan Alexander, Sean M. Davis, Aurelien Podglajen, Albert Hertzog, Lars Kalnajs, Terry Deshler, J. Douglas Goetz, and Sergey Khaykin

Atmospheric waves in the tropical tropopause layer are recognized as a significant influence on processes that impact global climate. For example, waves drive the quasi-biennial oscillation (QBO) in equatorial stratospheric winds and modulate occurrences of cirrus clouds. However, the QBO in the lower stratosphere and thin cirrus have continued to elude accurate simulation in state-of-the-art climate models and seasonal forecast systems. We use first-of-their-kind profile measurements deployed beneath a long-duration balloon to provide new insights into impacts of fine-scale waves on equatorial cirrus clouds and the QBO just above the tropopause. Analysis of these balloon-borne measurements reveals previously uncharacterized fine-vertical-scale waves (<1km) with large horizontal extent (>1000km) and multiday periods. These waves affect cirrus clouds and QBO winds in ways that could explain current climate model shortcomings in representing these stratospheric influences on climate. Accurately simulating these fine-vertical-scale processes thus has the potential to improve sub-seasonal to near-term climate prediction.

How to cite: Bramberger, M., Alexander, M. J., Davis, S. M., Podglajen, A., Hertzog, A., Kalnajs, L., Deshler, T., Goetz, J. D., and Khaykin, S.: First measurements of fine-vertical-scale wave impacts on the tropical lower stratosphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13062, https://doi.org/10.5194/egusphere-egu22-13062, 2022.

EGU22-13076 | Presentations | AS1.7

On gravity wave parameterisation in vicinity of low-level blocking... 

Markus Geldenhuys

The current orographic gravity wave drag parameterisation in the vicinity of low-level blocking is inadequate. Reducing the gravity wave amplitude (and thereby reducing the gravity wave drag) by assuming an effective mountain height dependent on the blocking depth is not realistic, yet this is implemented in most orographic gravity wave drag parameterisation schemes. The blocking layer acts as a sloped dynamic barrier that uplifts the air similarly to the mountain slope. Through a variety of mechanisms low-level blocking can induce more gravity waves or gravity waves with a higher momentum flux (compared to the current representation by parameterisation schemes). One possible solution is to modify the parameterisation scheme to not reduce the gravity wave momentum flux by the blocking depth. More realistic parameterisation schemes are likely to improve the models' performance.

How to cite: Geldenhuys, M.: On gravity wave parameterisation in vicinity of low-level blocking..., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13076, https://doi.org/10.5194/egusphere-egu22-13076, 2022.

EGU22-13505 | Presentations | AS1.7

Gravity wave generation by shear instability of balanced flow 

Manita Chouksey, Carsten Eden, and Dirk Olbers
  • The generation of internal gravity waves from an initially geostrophically balanced flow is diagnosed in non-hydrostatic numerical simulations of shear instabilities for varied dynamical regimes. A non-linear decomposition method up to third order in the Rossby number Ro is used as the diagnostic tool for a consistent separation of the balanced and unbalanced motions in the presence of their non-linear coupling. Wave emission is investigated in an Eady-like and a jet-like flow. For the jet-like case, geostrophic and ageostrophic unstable modes are used to initialize the flow in different simulations. Gravity wave emission is in general very weak over a range of values for Ro. At sufficiently high Ro, however, when the condition for symmetric instability is satisfied with negative values of local potential vorticity, significant wave emission is detected even at the lowest order. This is related to the occurrence of fast ageostrophic instability modes, generating a wide spectrum of waves. Thus, gravity waves are excited from the instability of the balanced mode to lowest order only if the condition of symmetric instability is satisfied and ageostrophic unstable modes obtain finite growth rates.

How to cite: Chouksey, M., Eden, C., and Olbers, D.: Gravity wave generation by shear instability of balanced flow, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13505, https://doi.org/10.5194/egusphere-egu22-13505, 2022.

NP8 – Emergent Phenomena in Geosciences

EGU22-1482 | Presentations | CL3.2.4

Pathways of resilience in complex systems. 

Max Rietkerk

The concept of tipping points and critical transitions helps inform our understanding of the catastrophic effects that global change may have on ecosystems, Earth system components, and the whole Earth system. The search for early warning indicators is ongoing, and spatial self-organization has been interpreted as one such signal. Here, we review how spatial self-organization can aid complex systems to evade tipping points and can therefore be a signal of resilience instead. Evading tipping points through various pathways of spatial pattern formation may be relevant for many ecosystems and Earth system components that hitherto have been identified as tipping prone, including for the entire Earth system.

M. Rietkerk, R. Bastiaansen, S. Banerjee, J. van de Koppel, M. Baudena and A. Doelman. 2021. Evasion of tipping in complex systems through spatial pattern formation. Science 374 (169): abj0359.

How to cite: Rietkerk, M.: Pathways of resilience in complex systems., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1482, https://doi.org/10.5194/egusphere-egu22-1482, 2022.

EGU22-2198 | Presentations | CL3.2.4 | Highlight

Partial tipping in a spatially heterogeneous world 

Robbin Bastiaansen, Henk Dijkstra, and Anna von der Heydt

Many climate subsystems are thought to be susceptible to tipping - and some might be close to a tipping point. The general belief and intuition, based on simple conceptual models of tipping elements, is that tipping leads to reorganization of the full (sub)system. Here, we explore tipping in conceptual, but spatially extended and spatially heterogenous models. These are extensions of conceptual models taken from all sorts of climate system components on multiple spatial scales. By analysis of the bifurcation structure of such systems, special stable equilibrium states are revealed: coexistence states with part of the spatial domain in one state, and part in another, with a spatial interface between these regions. These coexistence states critically depend on the size and the spatial heterogeneity of the (sub)system. In particular, in these systems a tipping point might lead to a partial tipping of the full (sub)system, in which only part of the spatial domain undergoes reorganization, limiting the impact of these events on the system's functioning.

How to cite: Bastiaansen, R., Dijkstra, H., and von der Heydt, A.: Partial tipping in a spatially heterogeneous world, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2198, https://doi.org/10.5194/egusphere-egu22-2198, 2022.

EGU22-3830 | Presentations | CL3.2.4 | Highlight

Cascading tipping in a coupled cryosphere-ocean model 

Sacha Sinet, Anna S. von der Heydt, and Henk A. Dijkstra
In the climate system, many different large-scale components have been identified as tipping elements, i.e., components that may pass a tipping point, with a substantial and definitive impact on earth and societies. These climate components do not stand on their own, but are dynamically coupled, which leads to the issue of cascading tipping. One important example of cascading involves the Greenland Ice Sheet (GIS), the West Antarctica Ice Sheet (WAIS) and the Atlantic Meridional Overturning Circulation (AMOC). While the destabilizing effect of a GIS decline on the AMOC is well established, the effect of a tipping WAIS is still unclear.
 
In this project, we aim at getting a better understanding of the global behaviour of this connected system, at a conceptual level. Accounting for the different nature of both ice sheets, we use two models including their most important feedbacks, namely, the marine ice sheet instability for the WAIS and the height-accumulation feedback for the GIS. The AMOC, depicted by the Rooth model, is coupled to both ice sheets through meltwater fluxes. Finally, we consider the Southern Ocean temperature as the main driver of the marine ice sheet instability.
With this conceptual interhemispheric model, we study the role of the AMOC as mediator of this potential cascading in hosing and/or climate change experiments, as well as the involved time scales. As a new result we find that, in this model, the stability of the AMOC depends on the ratio between the GIS and WAIS tipping rates, as well as their delay in time.

How to cite: Sinet, S., von der Heydt, A. S., and Dijkstra, H. A.: Cascading tipping in a coupled cryosphere-ocean model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3830, https://doi.org/10.5194/egusphere-egu22-3830, 2022.

EGU22-4425 | Presentations | CL3.2.4 | Highlight

Tipping risks due to temperature overshoots within the Paris range 

Nico Wunderling, Ricarda Winkelmann, Johan Rockström, Sina Loriani, David A. McKay, Paul Ritchie, Boris Sakschewski, and Jonathan F. Donges

Climate tipping elements potentially lead to accelerated and irreversible climate change once their critical temperature threshold is passed. Some of their critical thresholds (tipping points) are at risk to be transgressed already within the temperature guardrails of 1.5-2.0°C above pre-industrial levels. However, it has been suggested at the same time that global mean temperature levels are likely to temporarily overshoot these boundaries.

Therefore, we investigate the tipping risk for a set of four interacting climate tipping elements using a conceptual model. To this end, we study the impact of different peak and long-term saturation temperatures on the Greenland Ice Sheet, the West Antarctic Ice Sheet, the Atlantic Meridional Overturning Circulation (AMOC) and the Amazon rainforest.

We find that overshoot peak temperatures between 2.5-4.0°C increase the risk by 10-55% even if long-term global mean temperature levels are stabilized between 1.5-2.0°C. Furthermore, the interactions between the tipping elements increase tipping risks significantly already at modest to intermediate levels of interaction. Therefore our conceptual study suggests that safe overshoots are only possible for low peak temperatures of the overshoot as well as final saturation temperatures at or below today’s global warming levels.

How to cite: Wunderling, N., Winkelmann, R., Rockström, J., Loriani, S., McKay, D. A., Ritchie, P., Sakschewski, B., and Donges, J. F.: Tipping risks due to temperature overshoots within the Paris range, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4425, https://doi.org/10.5194/egusphere-egu22-4425, 2022.

EGU22-4970 | Presentations | CL3.2.4

Planetary limits to soil degradation 

Clarisse Kraamwinkel, Anne Beaulieu, Teresa Dias, and Ruth Howison

Soils are essential to life on Earth but are rapidly degrading worldwide due to unsustainable human activities. We argue that soil degradation constitutes a key Earth system process that should be added as 10th Earth system process to the planetary boundaries framework.

Soil degradation shares all key traits with the nine Earth system processes already present in the planetary boundaries framework. It is caused by human activity, has the potential to cause unacceptable environmental change, shows tipping point behavior when forced beyond a critical level, is relevant on both local and global scales, and is strongly interrelated with the other Earth system processes. 

Healthy soils have a level of resilience against disturbances but once forced beyond a critical level, they are at risk of entering into a downward spiral of degradation fuelled by strong positive feedback loops. Well-documented examples include the local feedback between loss of soil structure and soil biota and the large-scale feedback loop between soil erosion and climate change. The final degraded state of the soil is unable to sustain human life on earth. The fall of past civilizations has been related to their inability to protect the soil. At present, ~33% of the global soils are moderately to severely degraded as a direct result of human activities such as unsustainable agricultural practices, urban expansion, and industrialization. Estimates show that by 2050, 90% of our soils will be degraded, the majority of our ecosystems will be compromised and the entire human population will be affected.

Soils are essential to life on Earth through the provision of soil functions and ecosystem services such as biomass production (including ~95% of the food we eat), climate regulation, water storage and purification, habitat provision, and nutrient cycling. They play a key role in achieving many of the Sustainable Development Goals (SDGs) including SDG 15: life on land, SDG2: zero hunger, and SDG6: clean water and sanitation. Soil degradation leads to critical disruptions to biosphere integrity, biogeochemical flows, climate change, and land-system change, all processes that have already crossed their planetary boundaries. Hence, in order to improve the planetary boundaries framework and clearly signal the need to protect the soil, we call for soil degradation to be considered the 10th Earth system process in the planetary boundaries framework. 

How to cite: Kraamwinkel, C., Beaulieu, A., Dias, T., and Howison, R.: Planetary limits to soil degradation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4970, https://doi.org/10.5194/egusphere-egu22-4970, 2022.

EGU22-5176 | Presentations | CL3.2.4

Reversibility experiments of present-day Antarctic grounding lines 

Benoit Urruty, Emily A. Hill, Ronja Reese, Julius Garbe, Olivier Gagliardini, Gael Durand, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, Ricarda Winkelmann, Mondher Chekki, David Chandler, and Petra Langebroek

The stability of the grounding lines of Antarctica is a fundamental question in glaciology, because current grounding lines are in some locations at the edge of large marine basins, and have been hypothesized to potentially undergo irreversible retreat in response to climate change. This could have global consequences and raise sea levels by several metres. However, their reversibility for the current geometry has not yet been questioned, i.e. if pushed very slightly, are they able to recover their former position? 


Here we approach this question using three state-of-the-art ice sheet models (Elmer\Ice, Úa and PISM) which we initialise to closely replicate the current state of Antarctic ice sheet using inverse methods or spin-up approaches and the latest observations. To assess the reversibility of the Antarctic grounding lines in their current position, we apply a small amplitude perturbation in ice shelf melt rates for 20 years, which leads to a numerically significant grounding line retreat, but does not fundamentally alter it. After reversing the forcing we examine the grounding line evolution over the following 80 to 480 years, which allows us to see the direction of the ice sheet trajectory after removing the perturbation, i.e. recovery or further retreat. However, since ice dynamics adjust over long timescales of millennia, in some cases up to 500 years are not sufficient for the grounding lines to fully recover to their initial positions. To complement these experiments and to investigate the long-term response to small perturbations, we run the lower resolved Parallel Ice Sheet Model towards equilibrium. In this case, the perturbation is the increase from 1850 to present-day climate, and the experiments indicate whether present-day climate can cause Antarctic grounding lines to retreat on the long-term.


This work is part of the TiPACCs project and complements two presentations focusing on the short-term (EGU22-7802) and long-term (EGU22-7885) reversibility experiments of present-day Antarctic grounding lines in more detail.

How to cite: Urruty, B., Hill, E. A., Reese, R., Garbe, J., Gagliardini, O., Durand, G., Gillet-Chaulet, F., Gudmundsson, G. H., Winkelmann, R., Chekki, M., Chandler, D., and Langebroek, P.: Reversibility experiments of present-day Antarctic grounding lines, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5176, https://doi.org/10.5194/egusphere-egu22-5176, 2022.

EGU22-5370 | Presentations | CL3.2.4

Revealing hidden tipping in spatially-resolved Earth system analysis 

Sina Loriani, Boris Sakschewski, Jesse F. Abrams, Markus Drüke, Timothy Lenton, Nico Wunderling, Caroline Zimm, and Ricarda Winkelmann
The assessment of potential tipping elements in the Earth system and their associated tipping thresholds is essential for understanding long-term Earth system change and describing a safe operating space. However, their identification in model outputs and observational data typically requires making assumptions about the spatial extent of individual elements. While the resulting regional to continental aggregates allow for the study of collective time series, they are potentially based on subjective judgement and could mask non-linear behaviour on smaller scales.

In this work, we present a novel method based on a timescale- and variable-independent metric to automatically identify potential tipping elements in the Earth system with a few or no free parameters. Gridded datasets are scanned for abrupt shifts on the grid-cell level, which are subsequently automatically clustered in space and time. This allows for the creation of maps with areas grouped and classified by their dynamical behaviour without an a-priori definition of connected regions.

Applying the presented method to various Earth System model outputs, we detect clusters with different nonlinear responses to future emission scenarios which are otherwise masked. Consequently, our bottom-up approach provides insight into the spatial structures and temporal processes of large-scale tipping elements, and sheds light on ‘hidden’ tipping of their subsystems.

 

How to cite: Loriani, S., Sakschewski, B., Abrams, J. F., Drüke, M., Lenton, T., Wunderling, N., Zimm, C., and Winkelmann, R.: Revealing hidden tipping in spatially-resolved Earth system analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5370, https://doi.org/10.5194/egusphere-egu22-5370, 2022.

EGU22-6786 | Presentations | CL3.2.4

Estimating nonlinear stability from time series data 

Adrian van Kan, Jannes Jegminat, and Jonathan Donges

Basin stability (BS) is a measure of nonlinear stability in multistable dynamical systems. BS has previously been estimated using Monte-Carlo simulations, which requires the explicit knowledge of a dynamical model. We discuss the requirements for estimating BS from time series data in the presence of strong perturbations, and illustrate our approach for two simple models of climate tipping elements: the Amazon rain forest and the thermohaline ocean circulation. We discuss the applicability of our method to observational data as constrained by the relevant time scales of total observation time, typical return time of perturbations and internal convergence time scale of the system of interest and other factors.

How to cite: van Kan, A., Jegminat, J., and Donges, J.: Estimating nonlinear stability from time series data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6786, https://doi.org/10.5194/egusphere-egu22-6786, 2022.

EGU22-7064 | Presentations | CL3.2.4 | Highlight

Identification and management of climate change induced socio-economic tipping points 

Kees van Ginkel, Marjolijn Haasnoot, Elco Koks, and Wouter Botzen

Global warming may cause abrupt and non-linear climate tipping points, with large impacts to established socio-economic systems [1]. The socio-economic system itself also exhibits many non-linear change processes, and therefore may experience manifold unintentional climate-change induced socio-economic tipping points (SETPs) that could already follow from relatively small changes in climatic conditions. Examples are the gentrification of vulnerable groups or abrupt unplanned retreat from areas of increasing climate risk, abrupt transitions in financial markets, large-scale systematic malfunction of critical infrastructure networks during weather extremes, sudden reconfigurations of insurance markets and house price collapses. Such SETPs are defined as ‘a climate change induced, abrupt change of a socio-economic system, into a new, fundamentally different state’ [2]. It is important for spatial-economic planners and capital investors to know if and under what conditions SETPs may happen, and what can be done to anticipate and manage their causes and effects.

With three model-based case studies we demonstrate a stepwise approach to identify SETPs and to support adaptation and mitigation policy. The first is a house price collapse and radical transformation of long-term flood risk policy in a coastal city like Rotterdam, following rapid sea level rise due to Antarctic ice-sheet instability. Using a model that simulates flood risk, house prices and adaption integrally, we identify abrupt house price collapses in hundred-thousands possible futures spanning the uncertainty in sea level rise, storm surge and house market scenarios. We explicitly explore the long-term impacts of four dynamic adaptive strategies to anticipate flood risk and their successfulness in avoiding a SETP [3]. The second case is the financial collapse of the winter sports industry in the European Alps following a gradually retreating snowline [4]. The third is a large-scale systematic malfunction of national road networks of European countries due to increasing river flood hazards. The focus of our contribution is on showing how decision making can be supported despite the large uncertainties around SETPs. Finally, we discuss how the SETP-concept aligns with socio-ecological regime shifts [5] and deliberate positive social tipping points to achieve large mitigation and adaptation challenges [6,7].

Types of tipping points along the cause-effect chain from increasing GHG, to biophysical changes, to socioeconomic impacts and transformative adaptation and mitigation response. Source [2], CC-BY3.0 license.

Refs (doi): [1] 10.1073/pnas.2103081118; [2] 10.1088/1748-9326/ab6395; [3] 10.2139/ssrn.3935775; [4] 10.1016/j.envsci.2021.09.005; [5] 10.1088/1748-9326/aaaa75; [6] 10.1073/pnas.1900577117 [7] v10.1016/j.ecolecon.2021.107242

How to cite: van Ginkel, K., Haasnoot, M., Koks, E., and Botzen, W.: Identification and management of climate change induced socio-economic tipping points, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7064, https://doi.org/10.5194/egusphere-egu22-7064, 2022.

Soils are a key component of the Critical Zone of continental surfaces, ranging from the atmosphere to bedrock, guaranteeing the functioning of the Earth's ecosystems and ensuring the continuity of life on Earth. Our assumption is that highly biodiverse and functional soils provide the underpinning of indispensable services that ensure the basis for sustainable economic livelihoods and societies. Soils are susceptible to degradation through misuse, leading to a reduction in their functional diversity and redundancy. The adoption of a systemic approach, such as the social-ecological systems (SES) framework, may contribute to the identification of the adaptive capacities of societies to this expected reduction in soil functioning. In a SES framework, humans are embedded in natural systems and are understood to profoundly affect these system’s functions/services, interacting through feedbacks and cascading dynamics at different spatial and temporal scales. A SES framework is a suitable analytical tool that can provide insight on sensitive components and constellations of them, which likely may led to the crossing of a tipping point (TP), resulting in undesired alternative steady states of the system.

We aim to identify potential TPs, via an in-depth characterization and understanding of the SESs in the tri-national MAP region (Southwestern Amazon). For this purpose, we have delimited key underlying interconnected subsystems within the study region: the soil ecosystem, the livelihood system, the regional social system and the regional climate system. In our SES framework, we focus on relevant component’s functions for the tipping dynamics relating land use change and loss of ecosystem services. Our objective is to provide a set of early warning indicators of the impact and legacy damage of disturbances and the regulatory feedback dynamics between the different subsystems. Our hypothesis is that the crossing of a TP as consequence of reduced soil functions may exert pressure on livelihoods, as people shift to a new level of welfare or adapt their land use or income-generating activities. If this process leads to additional deforestation, it will likely lead to the amplification of regional drought events due to the loss of moisture convection that forests provide. Increasing drought due to the loss of forests will (self)amplify and lead to increased forest wildfires and more opportunities for illegal deforestation and land use change. Further, increasing livelihood and income insecurity, combined with insufficient provision of state services and regulation, as well as weak law enforcement, may exert pressure on social systems by e.g. making illegal and criminal activities more attractive, ultimately undermining social cohesion. In addition, a central aspect of our research is to investigate options for counteracting this cascade of detrimental/harmful and potentially self-amplifying positive feedbacks. This might be achieved by interfering with self-enhancing positive feedback loops, the stimulation of negative, stabilizing feedbacks, e.g. forest recovery or reflexive governance, especially on the local to regional level in order to prevent the crossing of TPs or even to stimulate non-linear dynamics towards positive TPs.

How to cite: Andrino, A. and the Prodigy Team: Exploring the emergence of tipping points in the social-ecological system at the border of Peru, Brazil and Bolivia (MAP region), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7867, https://doi.org/10.5194/egusphere-egu22-7867, 2022.

EGU22-11441 | Presentations | CL3.2.4

Detecting ecosystem-relevant crossings of thresholds 

Friederike Fröb, Timothée Bourgeois, Nadine Goris, Jörg Schwinger, and Christoph Heinze

With ongoing climate change, multiple stressors including ocean warming, deoxygenation, ocean acidification and limited nutrient availability are expected to lead to considerable regime shifts within marine ecosystems [1]. However, distinguishing such abrupt shifts from long-term trends in physical and biogeochemical ocean variables may not only be obscured by the natural variability of the system, but also the complexity of the ecosystem itself. Moreover, species-dependent physiological tolerances are likely going to limit the detectability of crossing of thresholds or tipping points of the whole ecosystem. The metabolic index describes temperature-dependent hypoxic tolerances with respect to the oxygen supply [2]. Critical values of the metabolic index indicate the geographical limits of marine species, therefore it is a useful metric to describe the extent of a potential habitat. Here, we assess the spatio-temporal detectability of abrupt changes in such a potential habitat for selected marine species using an environmental time series changepoint detection routine developed by [3]. We compare the number and timing of these abrupt changes in different Shared Socioeconomic Pathways (SSPs) run with the fully coupled Norwegian Earth System Model version 2 (NorESM2), i.e., analysing the SSP1-26, SSP-5-34-OS, and SSP5-85 scenarios. Preliminary results reveal global, regional and local abrupt changes of lost metabolically viable potential habitat in relation to environmental stressors under different evolving climates.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820989 (project COMFORT). The work reflects only the authors’ view; the European Commission and their executive agency are not responsible for any use that may be made of the information the work contains.

 

[1] Heinze et al., 2020, The quiet crossing of tipping points, PNAS, 118(9)

[2] Deutsch et al., 2020, Metabolic trait diversity shapes marine biogeography, Nature, 585, 557-562

[3] Beaulieu and Killick, 2018, Distinguishing trends and shifts from memory in climate data, Journal of Climate, 31(23), 9519-9543

How to cite: Fröb, F., Bourgeois, T., Goris, N., Schwinger, J., and Heinze, C.: Detecting ecosystem-relevant crossings of thresholds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11441, https://doi.org/10.5194/egusphere-egu22-11441, 2022.

Concerns are rising that the earth system may reach some critical tipping points in the coming decades. Though, growing evidence also supports the potential of positive social tipping points that could propel transformative changes towards global sustainability. The recently approved ERC Starting Grant “StoRes” (Spatial-Temporal Dynamics of Flood Resilience) proposed a systematic analysis on unique cases of flood resilience, which is expected to demonstrate such a positive perspective over various spatial and temporal scales.

The ERC project focuses on the historical Tea Horse Road area (THR), a mountainous region of the Southeast Tibetan Plateau with well-documented history going back over 600 years. The study first sets up a theoretical framework on the multi-spatial-temporal features of flood resilience at the THR region, which covers the spatial differences (household, community, city and region) over the past 600 years regarding the governance, technology, society, and culture perspectives of flood resilience. A set of quantitative proxy data, historical archives, literature re-analysis, statistical data, observation data and field survey data are integrated into both the empirical study in the case areas and the agent-based modelling across the cases. Preliminary results indicated that, various strong and smart social regulations (governance, institutions, plans, management, motivations, orders, donations, dedication, etc.) enabled a wise development of many water conservancy projects that consequently enhanced the resilience of local communities to hydrological hazards.

The study aims to further 1) establish a theoretical understanding of the spatial-temporal scales of flood resilience; 2) investigate the spatial patterns and temporal evolution of flood resilience at the THR cases; 3) model the spatial-temporal dynamics of flood resilience using agent-based models; 4) transfer and generalize the research findings of the THR cases to the Mekong River basin and beyond. By doing so, the project will present pioneering work to shape the emerging research field of flood resilience, offering new and multi-dimensional knowledge on the dynamic nature of flood-society relations, and providing crucial missing links to understand how flood resilience develops within complex human-environment contexts.

How to cite: Yang, L. E.: Spatial-temporal dynamics of positive social resilience to flood hazards, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12359, https://doi.org/10.5194/egusphere-egu22-12359, 2022.

EGU22-12865 | Presentations | CL3.2.4

Towards a green water planetary boundary 

Lan Wang-Erlandsson, Arne Tobian, Ruud van der Ent, Ingo Fetzer, Sofie te Wierik, Miina Porkka, Arie Staal, Fernando Jaramillo, Heindriken Dahlmann, Chandrakant Singh, Peter Greve, Dieter Gerten, Patrick Keys, Tom Gleeson, Sarah Cornell, Will Steffen, Xuemei Bai, and Johan Rockström

Green water - i.e., land precipitation, evaporation and soil moisture - is fundamental for the functioning of the biosphere and the Earth System, but is increasingly perturbed by continental-to-planetary scale human pressures on land, water and climate systems. The planetary boundaries (PB) framework demarcates a global safe operating space for humanity, but does hitherto not explicitly account for green water. Here, we propose a green-water boundary within the existing PB framework, of which a control variable could be defined as "the percentage of ice-free land area on which root-zone soil moisture deviates from Holocene variability for any month of the year". We provide provisional estimates of baseline departures based on CMIP6 data, and review the literature on soil-moisture induced deterioration in Earth System functioning. The evidences taken together suggest that the green water PB is already transgressed, implying that human modifications of green water need to come to a halt and be reversed. Future research needs to advance our understanding of root-zone water dynamics, including associated large-scale and potentially non-linear interactions with ecohydrology, hydroclimate, biogeochemistry and societies.

How to cite: Wang-Erlandsson, L., Tobian, A., van der Ent, R., Fetzer, I., te Wierik, S., Porkka, M., Staal, A., Jaramillo, F., Dahlmann, H., Singh, C., Greve, P., Gerten, D., Keys, P., Gleeson, T., Cornell, S., Steffen, W., Bai, X., and Rockström, J.: Towards a green water planetary boundary, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12865, https://doi.org/10.5194/egusphere-egu22-12865, 2022.

EGU22-13474 | Presentations | CL3.2.4

Global blue and green water cycles exit from pre-industrial variation – freshwater change planetary boundary exceeded? 

Miina Porkka, Vili Virkki, Lan Wang-Erlandsson, Chinchu Mohan, Tom Gleeson, Dieter Gerten, and Matti Kummu
Cycling of water supports a wide array of Earth system functions ranging from ecosystem provision to regulating greenhouse gas fluxes. While justifiably included in the planetary boundaries framework, the current freshwater planetary boundary fails in recognising the interplay between local and global drivers modifying the water cycle. Building on recent conceptual work and considering an extended selection of Earth system functions, we propose quantitative indicators for blue and green water to measure and monitor water cycle modifications. These indicators can capture changes at local, regional, or planetary scales, offering a robust and easily measurable way of determining alterations in the water cycle.
 
Our data consisted of discharge (blue water) and root-zone soil moisture (green water) simulated by state-of-the-art gridded global hydrological models in ISIMIP 2b. Initiating our analysis at the 30-arcmin grid scale, we set cell-wise dry (5th percentile) and wet (95th percentile) local bounds based on pre-industrial (1681–1860) data, separately for blue and green water. We then determined cell-wise exits from these local bounds of baseline variability and aggregated them at the global scale. This resulted in a time series of the percentage of global land area where blue or green water anomalies exit local bounds of baseline variability. The 95th percentile of these global baseline departures was then set as the safe limit of water cycle modifications. Finally, to estimate the state of the water cycle, we compared the recent past (1881–2005) blue and green water conditions to the pre-industrial conditions. First, we determined cell-wise exits from the local bounds and then aggregated the global baseline departures to compare those with the safe limits.
 
We show that in all aspects - blue and green water and dry and wet anomalies - the global water cycle has undergone substantial changes and transgressed the safe limits. This is a result of a gradual change throughout the 20th century. For blue water, drying conditions dominate along the mid-latitudes, whereas for green water, large-scale wetting prevails in the Northern Hemisphere boreal regions. Major changes in both blue and green water conditions co-occur commonly around regions with the highest anthropogenic pressures. Overall, global changes especially towards drier blue water conditions and wetter green water conditions have gone far beyond the pre-industrial levels - therefore placing the water cycle in a state unknown to modern societies.
 
Our results underline the necessity and urgency to update the freshwater change planetary boundary. As both blue and green water cycles have entered an unprecedented state following a long and gradual change, Earth system functions upkept by the water cycle may already be or become compromised. While further studies are required to assess the status of the freshwater change planetary boundary alongside other boundaries to provide a comprehensive analysis on total Earth system resilience, our results clearly show that the global water cycle is changing towards the unknown.

How to cite: Porkka, M., Virkki, V., Wang-Erlandsson, L., Mohan, C., Gleeson, T., Gerten, D., and Kummu, M.: Global blue and green water cycles exit from pre-industrial variation – freshwater change planetary boundary exceeded?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13474, https://doi.org/10.5194/egusphere-egu22-13474, 2022.

EGU22-13516 | Presentations | CL3.2.4 | Highlight

Ten new insights in climate science 2021 – a horizon scan 

Maria A. Martin

Since 2017, the 10 new insights in climate science (10NICS, https://10insightsclimate.science/) annually summarize a set of the most critical aspects of Earth’s complex climate system – including physical, biogeochemical and socioeconomic/sociocultural dimensions.

Here we set the context of the 10NICS series as a joint project between Future Earth, the Earth League and the World Climate Research Programme (WCRP), and briefly visit each of the ten insights from the 2021 edition (Martin et al., 2021):  (1) the options to still keep global warming below 1.5 °C; (2) the impact of non-CO2 factors in global warming; (3) a new dimension of fire extremes forced by climate change; (4) the increasing pressure on interconnected climate tipping elements; (5) the dimensions of climate justice; (6) political challenges impeding the effectiveness of carbon pricing; (7) demandside solutions as vehicles of climate mitigation; (8) the potentials and caveats of nature-based solutions; (9) how building resilience of marine ecosystems is possible; and (10) that the costs of climate change mitigation policies can be more than justified by the benefits to the health of humans and nature.

The 10NICS topics are not intended to form a comprehensive scientific assessment. Intentionally limited to 10, each insight is succinct and does not try to cover entire fields.

Martin, M. A., Alcaraz Sendra, O., Bastos, A., Bauer, N., Bertram, C., Blenckner, T., … Woodcock, J. (2021). Ten new insights in climate science 2021: a horizon scan. Global Sustainability, 4(e25), 1–20. https://doi.org/10.1017/sus.2021.25

How to cite: Martin, M. A.: Ten new insights in climate science 2021 – a horizon scan, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13516, https://doi.org/10.5194/egusphere-egu22-13516, 2022.

EGU22-13540 | Presentations | CL3.2.4

Conceptualizing World-Earth System resilience: Exploring transformation pathways towards a safe and just operating space for humanity 

John M. Anderies, Wolfram Barfuss, Jonathan F. Donges, Ingo Fetzer, Jobst Heitzig, and Johan Rockström

We develop a framework within which to conceptualize World-Earth System resilience.  Our notion of World-Earth System resilience emphasizes the need to move beyond the basin of attraction notion of resilience as we are not in a basin we can stay in. We are on a trajectory to a new basin and we have to avoid falling into undesirable basins.  We thus focus on `pathway resilience', i.e. the relative number of paths that allow us to move from the transitional operating space we occupy now as we leave the Holocene basin  to a safe and just operating space in the Anthropocene. We develop a mathematical model to formalize this conceptualization and demonstrate how interactions between earth system resilience  (biophysical processes) and world system resilience (social processes) impact pathway resilience.  Our findings show that building earth system resilience is probably our only chance to reach a safe and just operating space.  We also illustrate the importance of world system dynamics by showing how the notion of fairness coupled with regional inequality affects pathway resilience. 

How to cite: Anderies, J. M., Barfuss, W., Donges, J. F., Fetzer, I., Heitzig, J., and Rockström, J.: Conceptualizing World-Earth System resilience: Exploring transformation pathways towards a safe and just operating space for humanity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13540, https://doi.org/10.5194/egusphere-egu22-13540, 2022.

EGU22-74 | Presentations | GI2.2

Experimental assessment of corrosion influence in reinforced concrete by GPR 

Salih Artagan, Vladislav Borecky, Özgür Yurdakul, and Miroslav Luňák

Corrosion is one of the most critical issues leading to damage in reinforced concrete structures. In most cases, the detection of corrosion damage is performed by visual inspection. Other techniques (drilling cores with petrography or chemical examination, potential measurements, and resistivity measurements) require minimum destruction since they can be utilized by reaching the reinforcement bar [1]. Recently, there has been an increasing trend to use Ground Penetrating Radar (GPR) as one of the emerging non-destructive testing (NDT) techniques in the diagnosis of corrosion [2].

This paper focuses on a series of GPR tests on specimens constructed from poor-quality concrete and plain round bar. These specimens were subjected to accelerated corrosion tests under laboratory conditions. The corrosion intensity of those specimens is non-destructively assessed with GPR, by collecting data before and after corrosion tests. For GPR tests, the IDS Aladdin system was used with a double polarized 2 GHz antenna. Based on GPR measurement, Relative Dielectric Permittivity (RDP) values of concrete, are calculated based on the known dimension of specimens and two-way travel time (twt) values obtained from A-scans. The change in RDP values of specimens before and after exposure to corrosion is then computed. Moreover, amplitude change and variation in frequency spectrum before and after corrosion exposure are analyzed.

The results of this experimental study thus indicate that corrosion damage in reinforced concrete can be determined by using several GPR signal attributes. More laboratory tests are required for better quantification of the impact of the corrosion phenomenon in reinforced concrete.

All GPR tests were conducted in Educational and Research Centre in Transport; Faculty of Transport Engineering; University of Pardubice. This work is supported by the University of Pardubice (Project No: CZ.02.2.69/0.0/0.0/18_053/0016969).

[1]        V. Sossa, V. Pérez-Gracia, R. González-Drigo, M. A. Rasol, Lab Non Destructive Test to Analyze the Effect of Corrosion on Ground Penetrating Radar Scans, Remote Sensing. 11 (2019) 2814. https://doi.org/10.3390/rs11232814.

[2]        K. Tešić, A. Baričević, M. Serdar, Non-Destructive Corrosion Inspection of Reinforced Concrete Using Ground-Penetrating Radar: A Review, Materials. 14 (2021) 975. https://doi.org/10.3390/ma14040975.

How to cite: Artagan, S., Borecky, V., Yurdakul, Ö., and Luňák, M.: Experimental assessment of corrosion influence in reinforced concrete by GPR, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-74, https://doi.org/10.5194/egusphere-egu22-74, 2022.

EGU22-1544 | Presentations | GI2.2

Dielectric Constant Estimation through Alpha Angle with a Polarimetric GPR System 

Lilong Zou, Fabio Tosti, and Amir M. Alani

As a recognised non-destructive testing (NDT) tool, Ground Penetrating Radar (GPR) is becoming increasingly common in the field of environmental engineering [1]-[3]. GPR uses electromagnetic (EM) waves which travel at specific velocity determined by the permittivity of the material. With the development of new GPR signal processing methodologies, finding information on the physical properties of hidden targets has become a key target. Currently, only three types of approach could be applied for the quantitative estimation of permittivity from GPR data, i.e., hyperbola curve fitting, common middle point (CMP) velocity analysis and full-waveform inversion. However, the main challenges for the estimation of permittivity from GPR backscattered signals are to provide effective and accurate strategy for prediction.

In this research, we used a dual-polarimetric GPR system to estimate the dielectric constant of targets. The system is equipped with two 2GHz antennas polarised perpendicularly each to one another (HH and VV). The dual polarisation enables deeper surveying, providing images of both shallow and deeper subsurface features. Polarimetry is a property of EM waves that generally refers to the orientation of the electric field vector, which plays here an important role as it allows either direct or parameterisation permittivity effects within the scattering problem in the remote sensing [4].

The aim of this research is to provide a novel and more robust approach for dielectric constant prediction using a dual-polarimetric GPR system. To this extent, the relationship between the relative permittivity and the polarimetric alpha angle have been investigated based on data collected by a GPR system with dual-polarised antennas. The approach was then assessed by laboratory experiments where different moisture sand targets (simulating the effect of different relative permittivity targets) were measured. After signal processing, a clear relationship between the alpha angle and the relative permittivity was obtained, proving the viability of the proposed method.

 

Acknowledgements

The authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.

 

References

[1] Zou, L. et al., 2020. Mapping and Assessment of Tree Roots using Ground Penetrating Radar with Low-Cost GPS. Remote Sensing, vol.12, no.8, pp:1300.

[2] Zou, L. et al., 2020. On the Use of Lateral Wave for the Interlayer Debonding Detecting in an Asphalt Airport Pavement Using a Multistatic GPR System. IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 6, pp. 4215-4224.

[3] Zou, L. et al., 2021. Study on Wavelet Entropy for Airport Pavement Debonded Layer Inspection by using a Multi-Static GPR System. Geophysics, vol. 86, no. 3, pp. WB69-WB78.

[4] J. Lee and E. Pottier, Polarimetric Imaging: From Basics to Applications, FL, Boca Raton: CRC Press, 2009.

How to cite: Zou, L., Tosti, F., and Alani, A. M.: Dielectric Constant Estimation through Alpha Angle with a Polarimetric GPR System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1544, https://doi.org/10.5194/egusphere-egu22-1544, 2022.

EGU22-1849 | Presentations | GI2.2

On the use of Artificial Intelligence for classification of road pavements based on mechanical properties using ground-penetrating radar and deflection-based non-destructive testing data 

Fateme Dinmohammadi, Luca Bianchini Ciampoli, Fabio Tosti, Andrea Benedetto, and Amir M. Alani

Road pavements play a crucial role in the development of any construction as they provide safe surface on which vehicles can travel comfortably [1]. Pavements are multi-layered structures of processed and compacted materials in different thicknesses and in both unbound and bound forms with the function of supporting vehicle loads as well as providing a smooth riding quality. The condition of road pavement structures is susceptible to the impact of uncertain environmental factors and traffic loads, resulting in pavement deterioration over time. Therefore, the mechanical properties of pavements (such as strength, stiffness, etc.) need to be monitored on a regular basis to make sure that the pavement condition meets its prescribed threshold. The ground-penetrating radar (GPR) and deflection-based methods (e.g., the falling weight deflectometer (FWD)) are the most popular non-destructive testing (NDT) methods in pavement engineering science that are often used in combination to evaluate the damage and strength of pavements [2-4]. The layer thickness data from GPR scans are used as an input for deflection-based measurements to back-calculate the elastic moduli of the layers [2]. During the recent years, problems concerning the automatic interpretation of data from NDTs have received good attention and have simulated peer to peer interests in many industries like transportation. The use of Artificial Intelligence (AI) and Machine Learning (ML) techniques for the interpretation of NDT data can offer many advantages such as the improved speed and accuracy of analysis, especially for large-volume datasets. This study aims to train a dataset collected from GPR (2 GHz horn antenna) and the Curviameter deflection-based equipment using AI and ML algorithms to classify road flexible pavements based on their mechanical properties. Curviameter data are used as ground-truth measurements of pavement stiffness, whereas the GPR data provide geometric and physical attributes of the pavement structure. Several methods such as support vector machine (SVM), artificial neural network (ANN), and k nearest neighbours (KNN) are proposed and their performance in terms of accuracy of estimation of the strength and deformation properties of pavement layers are compared with each other as well as with the classical statistical methods. The results of this study can help road maintenance officials to identify and prioritise pavements at risk and make cost-effective and informed decisions for maintenance.

References

[1] Tosti, F., Bianchini Ciampoli, L., D’Amico, F. and Alani, A.M. (2019). Advances in the prediction of the bearing capacity of road flexible pavements using GPR. In: 10th International Workshop on Advanced GPR, European Association of Geoscientists & Engineers, pages 1-5.

[2] Plati, C., Loizos, A. & Gkyrtis, K. Assessment of Modern Roadways Using Non-destructive Geophysical Surveying Techniques. Surv Geophys 41, 395–430 (2020). 

[3] A. Benedetto, F. Tosti, Inferring bearing ratio of unbound materials from dielectric properties using GPR, in: Proceedings of the 2013 Airfield and Highway Pavement Conference: Sustainable and Efficient Pavements, June 2013, pp. 1336–1347.

[4] Tosti, F., Bianchini Ciampoli, L., D’Amico, F., Alani, A.M., Benedetto, A. (2018). An experimental-based model for the assessment of the mechanical properties of road pavements using GPR. Construction and Building Materials, Volume 165, pp. 966-974.

How to cite: Dinmohammadi, F., Bianchini Ciampoli, L., Tosti, F., Benedetto, A., and Alani, A. M.: On the use of Artificial Intelligence for classification of road pavements based on mechanical properties using ground-penetrating radar and deflection-based non-destructive testing data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1849, https://doi.org/10.5194/egusphere-egu22-1849, 2022.

EGU22-2166 | Presentations | GI2.2

Attenuation-compensated reverse-time migration of waterborne GPR based on attenuation coefficient estimation 

Ruiqing Shen, Yonghui Zhao, Hui Cheng, and Shuangcheng Ge

To the waterborne ground-penetrating radar detection, reverse-time migration (RTM) method can image the structure of the bottom of the water and locate the buried bodies. However, the image quality is limited by the attenuation of electromagnetic waves. How to compensate the attenuation becomes a critical problem. Some RTM methods related to the attenuation-compensated have been developed in recent years. We use the attenuation-compensated RTM based on the minus conductivity. However, the method is limited by the estimation of the attenuation coefficient. Here, we propose an attenuation-coefficient estimation method based on the centroid frequency downshift method (CFDS). In EM attenuation tomography, the centroid frequency downshift method works for attenuation estimation. Compared with the CFDS method in tomography, our proposal is based on the centroid frequency of the bottom-interface of water instead of the source wavelet. Thus, we can avoid the problem of the unknown source wavelet. The method is based on two assumptions: 1) GPR data can be regarded as zero-offset records. 2) Reflections from underwater interfaces are independent of frequency. In addition, the formula about the attenuation coefficient shows when the ratio between the conductivity and the product of the dielectric constant and the angular frequency is greater than one, the attenuation coefficient tends to be a constant. This does not meet the assumption that the attenuation coefficient is linearly related to frequency. We will select a proper frequency range to meet the linear relation by the spectral ratio method. Because the ratio of the signal spectrum of the bottom interface to the spectrum of the underwater interface is consistent with the change of the attenuation coefficient with frequency. Then, the CFDS method will acquire a linear attenuation coefficient with the frequency. Finally, we choose half of the central frequency to acquire the estimated attenuation coefficient. We design a layered waterborne GPR detection model, the conductivity of the silt layer varies between 0.1 and 0.01. The error of the conductivity estimation is below 10%. After acquiring the attenuation coefficient, the attenuation-compensated RTM works correctly and effectively.

How to cite: Shen, R., Zhao, Y., Cheng, H., and Ge, S.: Attenuation-compensated reverse-time migration of waterborne GPR based on attenuation coefficient estimation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2166, https://doi.org/10.5194/egusphere-egu22-2166, 2022.

EGU22-2253 | Presentations | GI2.2

An approach to integrate GPR thickness variability and roughness level into pavement performance evaluation 

Christina Plati, Andreas Loizos, and Konstantina Georgouli

It is a truism that pavements deteriorate due to the combined effects of traffic loads and environmental conditions. The manner or ability of a road to meet the demands of traffic and the environment and to provide at least an acceptable level of performance to road users throughout its life is referred to as pavement performance. An important indicator of pavement performance is ride quality. This is a rather subjective measure of performance that depends on (i) the physical properties of the pavement surface, (ii) the mechanical properties of the vehicle, and (iii) the acceptance of the perceived ride quality by road users. Due to the subjectivity of ride quality assessment, many researchers have worked in the past to develop an objective indicator of pavement quality. The International Roughness Index (IRI) is considered a good indicator of pavement performance in terms of road roughness. It was developed to be linear, transferable, and stable over time and is based on the concept of a true longitudinal profile. Following the identification and quantification of ride quality by the IRI, pavement activities include the systematic collection of roughness data in the form of the IRI using advanced laser profilers, either to "accept" an as-built pavement or to monitor and evaluate the functional condition of an in-service pavement.

On the other hand, pavement performance can vary significantly due to variations in layer thickness, primarily due to the construction process and quality control methods used. Even if a uniform design thickness is specified for a road section, the actual thickness may vary. It is expected that the layer thickness will have some probability distribution, with the highest density being around the target thickness. Information on layer thickness is usually obtained from as-built records, from coring or from Ground Penetrating Radar (GPR) surveys. GPR is a powerful measurement system that provides pavement thickness estimates with excellent data coverage at travel speeds. It can significantly improve pavement structure estimates compared to data from as-built plans. In addition, GPR surveys are fast, cost effective, and non-destructive compared to coring.

The present research developed a sensing approach that extends the capability of GPR beyond its ability to estimate pavement thickness. Specifically, the approach links GPR thickness to IRI based on the principle that a GPR system and a laser profiler are independent sensors that can be combined to provide a more complete image of pavement performance. To this end, field data collected by a GPR system and a laser profiler along highway sections are analyzed to evaluate pavement performance and predict future condition. The results show that thickness variations are related to roughness levels and specify the deterioration of the pavement throughout its lifetime.

How to cite: Plati, C., Loizos, A., and Georgouli, K.: An approach to integrate GPR thickness variability and roughness level into pavement performance evaluation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2253, https://doi.org/10.5194/egusphere-egu22-2253, 2022.

EGU22-2341 | Presentations | GI2.2 | Highlight

Monitoring of Bridges by Satellite Remote Sensing Using Multi-Source and Multi-Resolution Data Integration Techniques: a Case Study of the Rochester Bridge 

Valerio Gagliardi, Luca Bianchini Ciampoli, Fabrizio D’Amico, Maria Libera Battagliere, Sue Threader, Amir M. Alani, Andrea Benedetto, and Fabio Tosti

Monitoring of bridges and viaducts has become a priority for asset owners due to progressive infrastructure ageing and its impact on safety and management costs. Advancement in data processing and interpretation methods and the accessibility of Synthetic Aperture Radar (SAR) datasets from different satellite missions have contributed to raise interest for use of near-real-time bridge assessment methods. In this context, the Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) space-borne monitoring technique has proven to be effective for detection of cumulative surface displacements with a millimetre accuracy [1-3].

This research aims to investigate the viability of using satellite remote sensing for structural assessment of the Rochester Bridge in Rochester, Kent, UK. To this purpose, high-resolution SAR datasets are used as the reference information and complemented by additional data from different sensing technologies (e.g., medium-resolution SAR datasets and ground-based (GB) non-destructive testing (NDT)). In detail, high-resolution SAR products of the COSMO-SkyMed (CSK) mission (2017-2019) provided by the Italian Space Agency (ASI) in the framework of the Project “Motib - ID 742”, approved by ASI, are processed using a MT-InSAR approach.

The method allowed to identify several Persistent Scatterers (PSs) – which have been associated to different structural elements (e.g., the bridges piers) over the four main bridge decks – and monitor bridge displacements during the observation time. The outcomes of this study demonstrate that information from the use of high-resolution InSAR data can be successfully integrated to datasets of different resolution, scale and source technology. Compared to stand-alone technologies, a main advantage of the proposed approach is in the provision of a fully-comprehensive (i.e., surface and subsurface) and dense array of information with a larger spatial coverage and a higher time acquisition frequency. This results in a more effective identification and monitoring of decays at reduced costs, paving the way for implementation into next generation Bridge Management Systems (BMSs).

Acknowledgements: This research is supported by the Italian Ministry of Education, University and Research under the National Project “EXTRA TN”, PRIN2017, Prot. 20179BP4SM. Funding from MIUR, in the frame of the“Departments of Excellence Initiative 2018–2022”,attributed to the Department of Engineering of Roma Tre University, is acknowledged.Authors would also like to acknowledge the Rochester Bridge Trust for supporting research discussed in this paper. The COSMO-SkyMed (CSK) products - ©ASI- are provided by the Italian Space Agency (ASI) under a license to use in the framework of the Project “ASI Open-Call - Motib (ID 742)” approved by ASI.

References

[1] Gagliardi V., Bianchini Ciampoli L., D'Amico F., Alani A. M., Tosti F., Battagliere M. L., Benedetto A., “Bridge monitoring and assessment by high-resolution satellite remote sensing technologies”, Proc. SPIE 11525, SPIE Future Sensing Technologies. 2020. doi: 1117/12.2579700

[2] Jung, J.; Kim, D.-j.; Palanisamy Vadivel, S.K.; Yun, S.-H. "Long-Term Deflection Monitoring for Bridges Using X and C-Band Time-Series SAR Interferometry". Remote Sens. 2019

[3] Gagliardi V., Bianchini Ciampoli L., D'Amico F., Tosti F., Alani A. and Benedetto A. “A Novel Geo-Statistical Approach for Transport Infrastructure Network Monitoring by Persistent Scatterer Interferometry (PSI)”. In: 2020 IEEE Radar Conference, Florence, Italy, 2020, pp. 1-6, doi: 10.1109/RadarConf2043947.2020.9266336

How to cite: Gagliardi, V., Bianchini Ciampoli, L., D’Amico, F., Battagliere, M. L., Threader, S., Alani, A. M., Benedetto, A., and Tosti, F.: Monitoring of Bridges by Satellite Remote Sensing Using Multi-Source and Multi-Resolution Data Integration Techniques: a Case Study of the Rochester Bridge, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2341, https://doi.org/10.5194/egusphere-egu22-2341, 2022.

EGU22-2533 | Presentations | GI2.2

Monitoring of Airport Runways by Satellite-based Remote Sensing Techniques: a Geostatistical Analysis on Sentinel 1 SAR Data 

Valerio Gagliardi, Sebastiano Trevisani, Luca Bianchini Ciampoli, Fabrizio D’Amico, Amir M. Alani, Andrea Benedetto, and Fabio Tosti

Maintenance of airport runways is crucial to comply with strict safety requirements for airport operations and air traffic management [1]. Therefore, monitoring pavement surface defects and irregularities with a high temporal frequency, accuracy and spatial density of information becomes strategic in airport asset management [2-3]. In this context, Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets, proving their viability for the long-term evaluation of ground scatterers. However, the implementation of C-band SAR data as a routine tool in Airport Pavement Management Systems (APMSs) for the accurate measurement of differential displacements on runways is still an open challenge [4]. This research aims to demonstrate the viability of using medium-resolution (C-band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, Sentinel-1A SAR products, available through the European Space Agency (ESA) Copernicus Program, were acquired and processed to monitor displacements on “Runway n.3” of the “L. Da Vinci International Airport” in Fiumicino, Rome, Italy.A geostatistical study is performed for exploring the spatial data structure and for the interpolation of the Sentinel-1A SAR data in correspondence of ground control points.The analysis provided ample information on the spatial continuity of the Sentinel 1 data, also in comparison with the high-resolution COSMO-SkyMed and the ground-based topographic levelling data, taken as the benchmark.Furthermore, a comparison between the MT-InSAR outcomes from the Sentinel-1A SAR data, interpolated by means of Ordinary Kriging, and the ground-truth topographic levelling data demonstrated the accuracy of the Sentinel 1 data. Results support the effectiveness of using medium-resolution InSAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways. Outcomes of this study can pave the way for the development of more efficient and sustainable maintenance strategies for inclusion in next-generation APMSs.  

Acknowledgments and fundings: The authors acknowledge the European Space Agency (ESA), for providing the Sentinel 1 SAR products for the development of this research. The COSMO-SkyMed Products—©ASI (Italian Space Agency)- are delivered by ASI under the license to use.This research falls within the National Project “EXTRA TN”, PRIN 2017, supported by MIUR. The authors acknowledge funding from the MIUR, in the frame of the “Departments of Excellence Initiative 2018–2022”, attributed to the Department of Engineering of Roma Tre University

 References

[1]Gagliardi V., Bianchini Ciampoli L., D'Amico F., Tosti F., Alani A. and Benedetto A. “A Novel Geo-Statistical Approach for Transport Infrastructure Network Monitoring by Persistent Scatterer Interferometry (PSI)”. In: 2020 IEEE Radar Conference, Florence, Italy, 2020, pp. 1-6

[2]Gagliardi V, Bianchini Ciampoli L, Trevisani S, D’Amico F, Alani AM, Benedetto A, Tosti F. "Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis". 2021; 21(17):5769. https://doi.org/10.3390/s21175769

[3]Gao, M.; Gong, H.; Chen, B.; Zhou, C.; Chen, W.; Liang, Y.; Shi, M.; Si, Y. "InSAR time-series investigation of long-term ground displacement at Beijing Capital International Airport, China". Tectonophysics 2016, 691, 271–281.

[4]Department of Transportation Federal Aviation Administration (FAA), Advisory Circular 150/5320-6F, Airport Pavement Design and Evaluation, 2016

How to cite: Gagliardi, V., Trevisani, S., Bianchini Ciampoli, L., D’Amico, F., Alani, A. M., Benedetto, A., and Tosti, F.: Monitoring of Airport Runways by Satellite-based Remote Sensing Techniques: a Geostatistical Analysis on Sentinel 1 SAR Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2533, https://doi.org/10.5194/egusphere-egu22-2533, 2022.

EGU22-2712 | Presentations | GI2.2

Quality assessment in railway ballast by integration of NDT methods and remote sensing techniques: a study case in Salerno, Southern Italy 

Luca Bianchini Ciampoli, Valerio Gagliardi, Fabrizio D'Amico, Chiara Clementini, Daniele Latini, and Andrea Benedetto

Maintenance and rehabilitation policies represent a task of paramount importance for managers and administrators of railway networks to maintain the highest standards of transport safety while limiting as much as possible the costs of maintenance operations.

To this effect, high-productivity survey methods become crucial as they allow for timely recognition of the quality of the asset elements, among which the ballast layers are the most likely to undergo rapid deterioration processes. Particularly, Ground Penetrating Radar (GPR) has received positive feedback from researchers and professionals due to the capability of detecting signs of deterioration within ballasted trackbeds that are not recognizable by a visual inspection at the surface, through high-productivity surveys. On the other hand, satellite-based surveys are nowadays being increasingly applied to the monitoring of transport assets. Techniques such as Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) allows evaluating potential deformations suffered by railway sections and their surroundings by analyzing phase changes between multiple images of the same area acquired at progressive times. 

For both of these techniques, despite the wide recognition by the field-related scientific literature, survey protocols and data processing standards for the detection and classification of the quality of ballast layers are still missing. In addition, procedures of integration and data fusion between GPR and InSAR datasets are still very rare.

The present study aims at demonstrating the viability of the integration between these two survey methodologies for a more comprehensive assessment of the condition of ballasted track-beds over a railway stretch. Particularly, a traditional railway section going from Cava de’ Tirreni to Salerno, Campania (Italy), was subject to both GPR and MT-InSAR inspections. An ad hoc experimental setup was realized to fix horn antennas with different central frequencies to an actual inspection convoy that surveyed the railway stretch in both the travel directions. Time-frequency methods were applied to the data to detect subsections of the railway affected by the poor quality of ballast (i.e. high rate of fouling). In parallel, a two-years MT-InSAR analysis was conducted to evaluate possible deformations that occurred to the railway line in the period before the GPR test. In addition, results from both the analyses were compared to the reports from visual inspections as provided by the railway manager.

The results of the surveys confirm the high potential of GPR in detecting the fouling condition of the ballast layers at various stages of severity. The integration of this information to the outcomes of InSAR analysis allows for identifying whether the deterioration of the track-beds is related to poorly bearing subgrades or rather to excessive stresses between the aggregates resulting in their fragmentation.

Acknowledgments

This research is supported by the Italian Ministry of Education, University, and Research under the National Project “EXTRA TN”, PRIN2017, Prot. 20179BP4SM. Funding from MIUR, in the frame of the“Departments of Excellence Initiative 2018–2022”, attributed to the Department of Engineering of Roma Tre University, is acknowledged. The authors would also like to express their gratitude to RFI S.p.a. in the person of Eng. Pasquale Ferraro for the valuable support to the tests.

How to cite: Bianchini Ciampoli, L., Gagliardi, V., D'Amico, F., Clementini, C., Latini, D., and Benedetto, A.: Quality assessment in railway ballast by integration of NDT methods and remote sensing techniques: a study case in Salerno, Southern Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2712, https://doi.org/10.5194/egusphere-egu22-2712, 2022.

Detecting decay in tree trunks is essential in considering tree health and safety. Continual monitoring of tree trunks is possible using a digital model, which can contain incremental assessment data on tree health. Researchers have previously employed non-destructive techniques, for instance, laser scanning, acoustics, and Ground Penetrating Radar (GPR) to study both the external and internal physical dimensions of objects and structures [1], including tree trunks [2]. Light Detection and Ranging (LiDAR) technology is also continually employed in infrastructure and asset management to generate models and to detect surface displacements with millimeter accuracy [3]. Nevertheless, the scanning of structures using these existing state-of-the-art technologies can be time consuming, technical, and expensive.

This work investigates the design and implementation of a smartphone app for scanning tree trunks to generate a 3D digital model for later visualization and assessment. The app uses LiDAR technology, which has recently become available in smart devices, for instance, the Apple iPhone 12+ and the iPad Pro. With the prevalence of internet-of-things (IoT) sensors, digital twins are being increasingly used in a variety of industries, for example, architecture and manufacturing. A digital twin is a digital representation of an existing physical object or structure. With our app, a digital twin of a tree can be developed and maintained by continually updating data on its dimensions and internal state of decay. Further, we can situate and visualize tree trunks as digital objects in the real world using augmented reality, which is also possible in modern smart devices. We previously investigated tree trunks using GPR [2] to generate tomographic maps, to denote level of decay. We aim to adopt a data integration and fusion approach, using such existing (and incremental GPR data) and an external LiDAR scan to gain a full 3D ‘picture’ of tree trunks.

We intend to validate our app against state-of-the-art techniques, i.e., laser scanning and photogrammetry. With the ability to scan tree trunks within reasonable parameters of accuracy, the app can provide a relatively low-cost environmental modelling and assessment solution for researchers and experts.

 

Acknowledgments: Sincere thanks to the following for their support: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.

 

References

[1] Alani A. et al., Non-destructive assessment of a historic masonry arch bridge using ground penetrating radar and 3D laser scanner. IMEKO International Conference on Metrology for Archaeology and Cultural Heritage Lecce, Italy, October 23-25, 2017.

[2] Tosti et al., "The Use of GPR and Microwave Tomography for the Assessment of the Internal Structure of Hollow Trees," in IEEE Transactions on Geoscience and Remote Sensing, Doi: 10.1109/TGRS.2021.3115408.

[3] Lee, J et al., Long-term displacement measurement of bridges using a LiDAR system. Struct Control Health Monit. 2019; 26:e2428.

How to cite: Uzor, S., Tosti, F., and Alani, A. M.: Low-cost scanning of tree trunks for analysis and visualization in augmented reality using smartphone LiDAR and digital twins, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3247, https://doi.org/10.5194/egusphere-egu22-3247, 2022.

The need to monitor and evaluate the impact of natural phenomena on structures, infrastructures, as well as on the natural environment, in recent years, plays a role of considerable importance for society also due to the continuous occurrence of "catastrophic events" which recently faster change our Planet.

Innovation and research have allowed a profound change in the data acquisition and acquisitions methodology coming to develop increasingly complex and innovative technologies. From an application point of view, remote sensing gives the possibility to easily manage the layer information which is indispensable for the best characterization of the environment from a numerical and a chemical-physical point of view.

NeMeA Sistemi srl, observant to the environment and its protection for years, began to study it using RADAR / SAR (Synthetic Aperture RADAR) data thanks to the opportunity to use in the best way the COSMO-SkyMed data through the tender Open Call for SMEs (Small and Medium Enterprises) of the Italian Space Agency in 2015.

Since then, NeMeA Sistemi srl has started a highly focused and innovative training that led us to observe the Earth in a new way. The path undertaken in NeMeA Sistemi srl is constantly growing and allowed us to know the RADAR / SAR data and the enormous potential.

The COSMO-SkyMed data provided is treated, processed and transformed by providing various information, allows you to identify changes, classify objects and artifacts measuring them.

In this context, NeMeA Sistemi srl in 2016 proposed a first project for the monitoring of illegal buildings in the Municipality of Ventimiglia (Liguria), with positive results. In this context, the final product was obtained with classic standard classification techniques of the SAR data.

 Following this positive experience, NeMeA Sistemi srl applied also to the regional call issued by Sardegna Ricerche for the Sardinia Region where the source of funding is the European Regional Development Fund (ERDF) 2014-2020.

The SardOS project (Sardinia Observed from Space), proposed by NeMeA Sistemi srl, aims to monitor and safeguard environmental and anthropogenic health in the territory of 4 Sardinian municipalities (Alghero, Capoterra, Quartu and Arzachena), also identifying the coast profiles, the evolutionary trend of sediments in the riverbed and buildings not present in the land registry. For environmental monitoring purposes, COSMO-SkyMed data are exploited and combined with bathymetric measurements acquired using the Hydra aquatic drone owned by NeMeA Sistemi srl. SAR data were processed using innovative specific territorial analysis algorithms in urban environment.

After these successful cases studies, which allowed the development of new services for the territorial monitoring and control, NeMeA Sistemi srl is working on a new project, 3xA (Creation of Machine Learning and Deep Learning algorithms dedicated to pattern recognition in SAR data). By exploiting Artificial Intelligence, the implemented algorithms use innovative unsupervised techniques to identify any changes.

The objective of this document is to provide an overview of the experience gained in NeMeA Sistemi srl, the value-added products and innovative services developed in the company aimed at environmental monitoring, the prevention of dangers and natural risks.

How to cite: Pennino, I.: A strategy of territorial control: from the standard comparison techniques to the Advanced Unsupervised Deep Learning Change Detection in high resolution SAR images, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3799, https://doi.org/10.5194/egusphere-egu22-3799, 2022.

EGU22-4437 | Presentations | GI2.2

Rebar corrosion monitoring with a multisensor non-destructive geophysical techniques. 

Enzo Rizzo, Giacomo Fornasari, Luigi Capozzoli, Gregory De Martino, and Valeria Giampaolo

Rebar Corrosion is one of the main causes of deterioration of engineering reinforced structure. This degradation reduces the service life and durability of the structures. Such degradation can result in the collapse of engineering structures. When the first cracks are noticed on the concrete surface, corrosion has generally reached an advanced stage and maintenance action is required. The early detection of rebar corrosion of bridges, tunnel, buildings and other civil engineering structures is important to reduce the expensive cost to repair the deteriorated structure. Several techniques have been developed for understanding the mechanism and kinetics of the corrosion of rebar, but the paper defines the interest of combining several NDT for field inspection to overcome the limitation of measuring instantaneous corrosion rates and to improve the estimation of the service life of RC structures. Non-destructive testing and evaluation of the rebar corrosion is a major issue for predicting the service life of reinforced concrete structures.

This paper introduces a laboratory test, that was performed at Geophysical Laboratory of Ferrara University. The test consisted in a multisensor application concerning rebar corrosion monitoring using different geophysical methods on a concrete sample of about 50 x 30 cm with one steel rebar of 10 mm diameter. An accelerating reinforcement bar corrosion using direct current (DC) power supply with 5% sodium chloride (NaCl) solution was used to induce rebar corrosion. The 2GHz GPR antenna by IDS, the ERT with Abem Terrameter and Self-Potential with Keithley multivoltmeter at high impedance were used for rebar corrosion monitoring. A multisensor approach should reduce the errors resulting from measurements, and improve synergistically the estimation of service life of the RC.

Each technique provided specific information, but a data integration method used in the operating system will further improve the overall quality of diagnosis. The collected data were used for an integration approach to obtain an evolution of the phenomenon of corrosion of the reinforcement bar. All the three methods were able to detect the physical parameter variation during the corrosion phenomena, but more attention is necessary on natural corrosion, that is a slow process and the properties of the experimental steel–concrete interface may not be representative of natural corrosion. However, each of these geophysical methods possesses certain advantages and limitations, therefore a combination of these geophysical techniques, with an multisensor approach is recommended to use to obtain the corrosion condition of steel and the condition of concrete cover.  Moreover, extrapolating laboratory results performed with a single rebar to a large structure with interconnected rebars thus remains challenging. Therefore, during the next experiments, special care must be taken regarding the design and preparation of the samples to obtain meaningful information for field application.

How to cite: Rizzo, E., Fornasari, G., Capozzoli, L., De Martino, G., and Giampaolo, V.: Rebar corrosion monitoring with a multisensor non-destructive geophysical techniques., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4437, https://doi.org/10.5194/egusphere-egu22-4437, 2022.

EGU22-4826 | Presentations | GI2.2

A 24 GHz MIMO radar for the autonomous navigation of unmanned surface vehicles 

Giovanni Ludeno, Gianluca Gennarelli, Carlo Noviello, Giuseppe Esposito, Ilaria Catapano, and Francesco Soldovieri

In the last years, unmanned surface vehicles (USVs) in marine environment have attracted considerable interest since they are flexible observation platforms suitable to operate in remote areas on demand. Accordingly, their usage has been proposed in several contexts such as research activities, military operations, environmental monitoring and oil exploration [1]. However, most of current USV remote control techniques are based on human-assisted technology thus a fully autonomous USV system is still an open issue [2].

The safety of the vehicle and the ability to complete the mission depends crucially on the capability of detecting objects on the sea surface, which is necessary for collision avoidance. Anti-collision systems for USVs typically require measurements collected from multiple sensors (e.g. Lidar, cameras, etc.), where each sensor has its own advantages and disadvantages in terms of resolution, field of view (FoV), operative range and so on [3].

Among the available sensing technologies, radar is capable of operating regardless of weather and visibility conditions, has moderate costs and can be easily adapted to operate within the marine environment. Furthermore, radar is characterized by an excellent coverage and high resolution along the range coordinate and it is also able to guarantee a 360° FoV in the horizontal plane.

Nautical radars are the most popular solutions to detect floating targets on the sea surface; however, they are bulky and not always effective in detecting small objects located very close to the radar.

This contribution investigates the applicability of a compact and lightweight 24 GHz multiple-input multiple-output (MIMO) radar originally developed for automotive applications to localize floating targets at short ranges (from tens to few hundreds of meters). In this frame, we propose an ad-hoc signal processing strategy combining MIMO technology, detection, and tracking algorithms to achieve target localization and tracking in a real-time mode. A validation of the proposed signal processing chain is firstly performed thanks to numerical simulations. After, preliminary field tests carried out in the marine environment are presented to assess the performance of the radar prototype and of the related signal processing.

 

References

  • [1] Zhixiang et al. "Unmanned surface vehicles: An overview of developments and challenges", Annual Reviews in Control, vol. 41, pp. 71-93, 2016
  • [2] Caccia, M. Bibuli, R. Bono, G. Bruzzone, “Basic navigation, guidance and control of an unmanned surface vehicle”, Autonomous Robots, vol. 25, no. 4, pp. 349-365, 2008
  • [3] Robinette, M. Sacarny, M. DeFilippo, M. Novitzky, M. R. Benjamin, “Sensor evaluation for autonomous surface vehicles in inland waterways”, Proc. IEEE OCEANS 2019, pp. 1-8, 2019.

How to cite: Ludeno, G., Gennarelli, G., Noviello, C., Esposito, G., Catapano, I., and Soldovieri, F.: A 24 GHz MIMO radar for the autonomous navigation of unmanned surface vehicles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4826, https://doi.org/10.5194/egusphere-egu22-4826, 2022.

EGU22-4912 | Presentations | GI2.2

Multiples suppression scheme of waterborne GPR data 

Yonghui Zhao, Ruiqing Shen, and Hui Cheng

Ground penetrating radar (GPR) is a geophysical method that uses high frequency electromagnetic waves to detect underground or internal structures of objects. It has been widely used in the Geo-engineering and environment detection. In recent years, GPR has played an increasingly important role in shallow underwater structure survey due to its advantages of economy, high efficiency and high accuracy. However, due to the strong reflection coefficients of water surface and bottom for electromagnetic waves, there are multiples in the GPR profile acquired in waters, which will reduce the signal-to-noise ratio of the data and even lead to false imaging, finally seriously affect the reliability of the interpretation result. With the increasing requirement of high-precise GPR detection in waters, multiple suppression has become an essential issue in expanding the application fields of GPR. In order to suppress multiple waves in waterborne GPR profile, a novel multiple wave suppression method based on the combination scheme of the predictive deconvolution and free surface multiple wave suppression (SRME). Based on the validity test of one-dimensional data, the adaptive optimizations of these two methods are carried out according to the characteristics of GPR data in waters. First, the prediction step of predictive deconvolution can be determined by picking up the bottom reflection signal. Second, the water layer information provided by the bottom reflection is used in continuation from the surface to the bottom to suppress the internal multiples. The numerical model and real data test results show that each single method can suppress most of the multiples of the bottom interface and the combination strategy can further remove the additional residues. The research provides a basis for the precise interpretation of GPR data in hydro-detection.

How to cite: Zhao, Y., Shen, R., and Cheng, H.: Multiples suppression scheme of waterborne GPR data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4912, https://doi.org/10.5194/egusphere-egu22-4912, 2022.

EGU22-4914 | Presentations | GI2.2

Sensing roadway surfaces for a non-destructive assessment of pavement damage potential 

Konstantinos Gkyrtis, Andreas Loizos, and Christina Plati

Modern roadways provide road users with both a comfortable and safe ride to their destinations. Increases in traffic demands and maximum allowable loads imply that roadway authorities should also care for the structural soundness of pavements. In parallel, budgetary limitations and frequent road closures for rehabilitation activities, especially in heavy-duty motorways, might guide the related authorities to focus their strategies on the preservation of pavements functional performance. However, structural issues concerning pavement damage remain on the forefront, as pavement’s service life extends beyond its design life; thus structural condition assessment is required to ensure pavement sustainability in the long-term.

 

Non-Destructive Testing (NDT) has played a major role during condition monitoring and evaluation of rehabilitation needs. Together with input from visual inspections and/or sample destructive testing (e.g. coring), NDT data help to define indicators and threshold values that assist the related decision-making for pavement condition assessment. The most indicative tool for structural evaluation is the Falling Weight Deflectometer (FWD) that senses roadway surfaces through geophones recording load-induced deflections at various locations. Additional geophysical inspection data with the Ground Penetrating Radar (GRP) is used to estimate pavement’s stratigraphy. Integrating the above sensing data enables the estimation of pavement’s performance and its damage potential.

 

To this end, a major challenge that pavement engineers face, concerns the assumptions made about the mechanical characterization of pavement materials. Asphalt mixtures, located on the upper pavement layers, behave in a viscoelastic mode because of temperature- and loading frequency- dependency, whereas in the contrary, simplified assumptions for linear elastic materials are most commonly made during the conventional NDT analysis. In this research, an integration of mainly NDT data and sample data from cores extracted in-situ is followed to comparatively estimate the long-term pavement performance through internationally calibrated damage models considering different assumptions for asphalt materials. Two damage modes are considered including bottom-up and top-down fatigue cracks that are conceptually perceived as alligator cracks and longitudinal cracks respectively alongside a roadway’s surface. As part of an ongoing research for the long-term pavement condition monitoring, data from a new pavement was considered at this stage indicating a promising capability of NDT data towards damage assessment.

 

Overall, this study aims to demonstrate the power of pavement sensing data towards structural health monitoring of roadways pinpointing the significance of database development for a rational management throughout a roadway’s service life. Furthermore, data from limited destructive testing enriches the pavement evaluation processes with purely mechanistic perspectives thereby paving the way for developing integrated protocols with improved accuracy for site investigations, especially at project-level analysis, where rehabilitation design becomes critical.

How to cite: Gkyrtis, K., Loizos, A., and Plati, C.: Sensing roadway surfaces for a non-destructive assessment of pavement damage potential, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4914, https://doi.org/10.5194/egusphere-egu22-4914, 2022.

EGU22-5731 | Presentations | GI2.2

Ultrasonic Scattering and Absorption Imaging for the Reinforced Concrete using Adjoint Envelope Tomography 

Tuo Zhang, Christoph Sens-Schönfelder, Niklas Epple, and Ernst Niederleithinger

Seismic and ultrasound tomography can provide rich information about spatial variations of elastic properties inside a material rendering this method ideal for non-destructive testing. These tomographic methods primarily use direct and reflected waves, but are also strongly affected by waves scattering at small-scale structures below the resolution limit. As a consequence, conventional tomography has the ability to unveil the deterministic large-scale structure only, rendering scattered waves imaging noise. To image scattering and absorption properties, we presented the adjoint envelope tomography (AET) method that is based on a forward simulation of wave envelopes using Radiative Transfer Theory and an adjoint (backward) simulation of the envelope misfit, in full analogy to full-waveform inversion (FWI). In this algorithm, the forward problem is solved by modelling the 2-D multiple nonisotropic scattering in an acoustic medium with spatially variable heterogeneity and attenuation using the Monte-Carlo method. The fluctuation strength ε and intrinsic quality factor Q-1 in the random medium are used to describe the spatial variability of scattering and absorption, respectively. The misfit function is defined as the differences between the full squared observed and modelled envelopes. We derive the sensitivity kernels corresponding to this misfit function that is minimized during the iterative adjoint inversion with the L-BFGS method. This algorithm has been applied in some numerical tests (Zhang et al., 2021). In the present work, we show real data results from an ultrasonic experiment conducted in a reinforced concrete specimen. The later coda waves of the envelope processed from the 60 KHz ultrasonic signal are individually used for intrinsic attenuation inversion whose distribution has similarity to the temperature distribution of the concrete block. Based on the inversion result of intrinsic attenuation, scattering strength is inverted from early coda waves separately, which successfully provides the structure of the small-scale heterogeneity in the material. The resolution test shows that we recover the distribution of heterogeneity reasonably well.

How to cite: Zhang, T., Sens-Schönfelder, C., Epple, N., and Niederleithinger, E.: Ultrasonic Scattering and Absorption Imaging for the Reinforced Concrete using Adjoint Envelope Tomography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5731, https://doi.org/10.5194/egusphere-egu22-5731, 2022.

EGU22-6168 | Presentations | GI2.2

An investigation into road trees’ root systems through geostatistical analysis of GPR data 

Livia Lantini, Sebastiano Trevisani, Valerio Gagliardi, Fabio Tosti, and Amir M. Alani

Street trees are a critical asset for the urban environment due to the variety of environmental and social benefits provided [1]. However, the conflicting coexistence of tree root systems with the built environment, especially with road infrastructure, frequently results in extensive damage, such as the uplifting and cracking of sidewalks and curbs, endangering pedestrians, cyclists, and road drivers’ safety.

Within this context, ground penetrating radar (GPR) is gaining recognition as an accurate non-destructive testing (NDT) method for tree roots’ assessment and mapping [2]. Nevertheless, the investigation methods developed so far are often inadequate for application on street trees, as these are often difficult to access. Recent studies have focused on implementing new survey and processing techniques for rapid tree root assessment based on combined time-frequency analyses of GPR data [3].  

This research also explores the adoption of a geostatistical approach for the spatial data analysis and interpolation of GPR data. The radial development of roots and the complexity of root network constitute a challenging setting for the spatial data analysis and the recognition of specific spatial features.

Preliminary results are therefore presented based on a geostatistical analysis of GPR data. To this end, 2-D GPR outputs (i.e., B-scans and C-scans) were analysed to quantify the spatial correlation amongst radar amplitude reflection features and their anisotropy, leading to a more reliable detection and mapping of tree roots. The proposed processing system could be employed for investigating trees difficult to access, such as road trees, where more comprehensive analyses are difficult to implement. Results' interpretation has shown the viability of the proposed analysis and will pave the way to further investigations.

 

Acknowledgements

The authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.

 

References

[1]         Tyrväinen, L., Pauleit, S., Seeland, K., & de Vries, S., 2005. "Benefits and uses of urban forests and trees". In: Urban Forests and Trees. Springer, Berlin, Heidelberg.

[2]         Lantini, L., Tosti, F., Giannakis, I., Zou, L., Benedetto, A. and Alani, A. M., 2020. "An Enhanced Data Processing Framework for Mapping Tree Root Systems Using Ground Penetrating Radar," Remote Sensing 12(20), 3417.

[3]         Lantini, L., Tosti, F., Zou, L., Ciampoli, L. B., & Alani, A. M., 2021. "Advances in the use of the Short-Time Fourier Transform for assessing urban trees’ root systems." Earth Resources and Environmental Remote Sensing/GIS Applications XII. Vol. 11863. SPIE, 2021.

How to cite: Lantini, L., Trevisani, S., Gagliardi, V., Tosti, F., and Alani, A. M.: An investigation into road trees’ root systems through geostatistical analysis of GPR data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6168, https://doi.org/10.5194/egusphere-egu22-6168, 2022.

EGU22-6251 | Presentations | GI2.2

Algorithms fusion for near-surface geophysical survey 

Yih Jeng, Chih-Sung Chen, and Hung-Ming Yu

The near-surface geophysical methods have been widely applied to investigations of shallow targets for scientific and engineering research. Various data processing algorithms are available to help visualize targets, data interpretation, and finally, achieve research goals.

Most of the available algorithms are Fourier-based with linear stationary assumptions. However, the real data are rarely the case and should be treated as nonlinear and non-stationary. In recent decades, a few newer algorithms are proposed for processing non-stationary, or nonlinear and non-stationary data, for instance, wavelet transform, curvelet transform, full-waveform inversion, Hilbert-Huang transform, etc. This progress is encouraging, but conventional algorithms still have many advantages, like strong theoretical bases, fast, and easy to apply, which the newer algorithms are short of.

In this study, we try to fuse both conventional and contemporary algorithms in near-surface geophysical methods. A cost-effective ground-penetrating radar (GPR) data processing scheme is introduced in shallow depth structure mapping as an example. The method integrates a nonlinear filtering technique, natural logarithmic transformed ensemble empirical mode decomposition (NLT EEMD), with the conventional pseudo-3D GPR data processing methods including background removal and migration to map the subsurface targets in 2D profile. The finalized pseudo-3D data volume is constructed by conventional linear interpolation. This study shows that the proposed technique could be successfully employed to locate the buried targets with minimal survey effort and affordable computation cost. Furthermore, the application of the proposed method is not limited to GPR data processing, any geophysical/engineering data with the similar data structure are applicable.

How to cite: Jeng, Y., Chen, C.-S., and Yu, H.-M.: Algorithms fusion for near-surface geophysical survey, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6251, https://doi.org/10.5194/egusphere-egu22-6251, 2022.

EGU22-7009 | Presentations | GI2.2

Geoelectric data modeling using Mimetic Finite Difference Method 

Deepak Suryavanshi and Rahul Dehiya

Nondestructive imaging and monitoring of the earth's subsurface using the geoelectric method require reliable and versatile numerical techniques for solving differential equation that govern the method's physic. The discrete operator should encompass fundamental properties of the original continuum model and differential operator for a robust numerical algorithm. In geoelectric modeling, critical model properties are anisotropy, irregular geometry, and discontinuous physical properties, whereas vital continuum operator properties are symmetry, the positivity of solutions, duality, and self-adjointness of differential operators and exact mathematical identities of the vector and tensor calculus. In this study, to simulate the response, we use the Mimetic Finite Difference Method (MFDM), where the discrete operator is constructed based on the support operator [1]. The MFDM operator mimics the properties mentioned above for structured and unstructured grids [2]. It is achieved by enforcing the integral identities of the continuum divergence and gradient operator to satisfy the integral identities by discrete analogs. 

The developed algorithm's accuracy is benchmarked using the analytical responses of dyke models of various conductivity contrasts for pole-pole configuration. After verifying the accuracy of the scheme, further tests are conducted to check the robustness of the algorithm involving the non-orthogonality of the grids, which is essential for simulating response for rugged topography. The surface potential is simulated using structured grids for a three-layer model. Subsequently, the orthogonal girds are distorted using pseudo-random numbers, which follow a uniform distribution. To quantify the distortion, we calculated the angles at all grid nodes. The node angles emulate a Gaussian distribution. We characterize those grids as highly distorted, for which the angle at the grid node is outside 20 to 160 degrees interval. The numerical tests are conducted by varying degrees of grid distortion, such that the highly distorted cells are from 1% to 10% of the total cells. The maximum error in surface potential stays below 1.5% in all cases. Hence, the algorithm is very stable with grid distortion and consequently can model the response of a very complex model. Thus, the developed algorithm can be used to analyze geoelectrical data of complex geological scenarios such as rugged topography and anisotropic subsurface. 

[1] Winters, Andrew R., and Mikhail J. Shashkov. Support Operators Method for the Diffusion Equation in Multiple Materials. No. LA-UR-12-24117. Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2012.

[2] Lipnikov, Konstantin, Gianmarco Manzini, and Mikhail Shashkov. "Mimetic finite difference method." Journal of Computational Physics 257 (2014): 1163-1227.

How to cite: Suryavanshi, D. and Dehiya, R.: Geoelectric data modeling using Mimetic Finite Difference Method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7009, https://doi.org/10.5194/egusphere-egu22-7009, 2022.

EGU22-7547 | Presentations | GI2.2

Assessing Deformation Monitoring Systems For Supporting Structural Rehabilitation under Harsh Conditions 

Hans Neuner, Victoria Kostjak, Finn Linzer, Walter Loderer, Christian Seywald, Alfred Strauss, Matthias Rigler, and Markus Polt

This paper deals with the evaluation of four measuring systems for the detection of potential deformations that can occur during structural rehabilitation measures. For this purpose, a test object resembling the shape of a tunnel structure was constructed. The structural properties of this test object are discussed in the related paper by Strauss et. al submitted for the same session.

In the paper, the installed measuring systems are presented first. These are a lamella system based on fibre optics, an array of accelerometers, a digital image correlation system and a profile laser scanner based system. The operating principles of the systems are briefly introduced.

A long-term measurement on the object in an unloaded state, which extended over several weeks, enables statements about the capturing of temperature-related deformations, the temperature dependence of the measured values and drift effects of the investigated systems. Selective loading of the test object was generated via four screw rods and applied both in the elastic as well as in the plastic deformation range. This enabled knowledge gain regarding the precision and the sensitivity of the analysed measuring systems.

Environmental conditions may have a strong influence on the measurement values. The former can be determined by permanent installations on the structure and its operating conditions as well as by the undertaken rehabilitation measures. Representative for the first category we investigated the influence of magnetic fields and light conditions on the measuring systems. For the second category, strong dust formation and increased humidity were generated during a test procedure.

An assessment regarding data handling, including storage, transfer and processing, completes the investigation of the four measuring systems. A summarising evaluation concludes the article.

How to cite: Neuner, H., Kostjak, V., Linzer, F., Loderer, W., Seywald, C., Strauss, A., Rigler, M., and Polt, M.: Assessing Deformation Monitoring Systems For Supporting Structural Rehabilitation under Harsh Conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7547, https://doi.org/10.5194/egusphere-egu22-7547, 2022.

EGU22-8512 | Presentations | GI2.2

Verification of the performance of reinforced concrete profiles of alpine infrastructure systems assisted by innovative monitoring 

Alfred Strauss, Hans Neuner, Matthias Rigler, Markus Polt, Christian Seywald, Victoria Kostjak, Finn Linzer, and Walter Loderer

The verification of the structural behaviour of existing structures and its materials characteristics requires the application of tests and monitoring to gather information about the actual response. The comparison of the actual performance and the designed performance enables the verification of the design assumptions in terms of implied loads and materials resistance. In case of non-compliance of the designed with the current performance, the design assumptions need to be updated. The objective of this contribution is to provide a guidance for the verification of the performance of reinforced concrete profiles of alpine infrastructure systems like tunnels assisted by monitoring, testing and material testing.

The application of defined loads to a structure to verify its load carrying capacity is a powerful tool for evaluating existing structures. In particular, in this research different types of load tests are employed depending on the limit state which is being investigated on tunnel profiles, on the other hand, the system responses to validate the structural performance are recorded with monitoring systems innovative in tunnel systems, such as accelerometer arrays, fibre optic sensors, laser distance sensors and digital image correlation system, see also the related paper by Neuner et. al. In these studies we also pay special attention to the capabilities of Digital Image Correlation and Nonlinear Finite Element Analysis. Digital Image Correlation (often referred to as "DIC") is an easy-to-use optical method for measuring deformations on the surface of an object. The method tracks changes in the grayscale pattern in small areas called subsets) during deformation. 

Finally, we will present the process for the implementation and validation of proof loading concepts based on the mentioned monitoring information in order to derive the existing safety level by using advanced digital twin models.  

How to cite: Strauss, A., Neuner, H., Rigler, M., Polt, M., Seywald, C., Kostjak, V., Linzer, F., and Loderer, W.: Verification of the performance of reinforced concrete profiles of alpine infrastructure systems assisted by innovative monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8512, https://doi.org/10.5194/egusphere-egu22-8512, 2022.

EGU22-8594 | Presentations | GI2.2

Analysis of low-frequency drone-borne GPR for soil surface electrical conductivity mapping 

Kaijun Wu and Sébastien Lambot

In the VHF frequencies, the sensitivity of the reflection coefficient at the air-soil interface with respect to the soil electromagnetic properties, i.e., the dielectric permittivity and electrical conductivity, varies with frequency. The lower the frequency is, the lower the sensitivity to permittivity is and the larger the sensitivity to conductivity is. In this study, we investigated low-frequency drone-borne ground-penetrating radar (GPR) and full-wave inversion for soil surface electrical conductivity characterization. In order to have a good sensitivity to electrical conductivity, we operated in the 15-45 MHz frequency range. We conducted both numerical and field experiments, under the assumptions that the soil magnetic permeability is equal to the magnetic permeability of free space, and that the soil permittivity and conductivity are frequency-independent. Through the numerical experiments, we analyzed the sensitivity of the soil permittivity and electrical conductivity by plotting the objective function in the inverse problem. In addition, we analyzed the effects of modelling errors on the retrieval of the permittivity and conductivity. The results show that the soil electrical conductivity is sensitive enough to be characterized by the low-frequency drone-borne GPR. The depth of sensitivity was found to be around 0.5-1 m in the 15-45 MHz frequency range. Yet, the effects of permittivity cannot be neglected totally, especially for relatively wet soils. For validating our approach, we conducted field measurements with the drone-borne GPR and we compared results with electromagnetic induction (EMI) measurements considering two different offsets, i.e., 0.5 and 1 m, respectively. The lightweight GPR system consists of a handheld vector network analyzer (VNA), a 5-meter half-wave dipole antenna, a micro-computer stick, a GPS receiver, and a power bank. The good agreement in terms of absolute values and field structures between the GPR and EMI maps demonstrated the feasibility of the proposed low-frequency drone-borne GPR method, which appears thereby to be promising for precision agriculture applications.

How to cite: Wu, K. and Lambot, S.: Analysis of low-frequency drone-borne GPR for soil surface electrical conductivity mapping, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8594, https://doi.org/10.5194/egusphere-egu22-8594, 2022.

EGU22-8712 | Presentations | GI2.2

Estimation of point spread function for unmixing geological spectral mixtures 

Maitreya Mohan Sahoo, Arun Pattathal Vijayakumar, Ittai Herrmann, Shibu K. Mathew, and Alok Porwal

Geological materials are mixtures of different endmember constituents with most of them having particles smaller in size than the path length of incident light. The obtained spectral response (reflectance) from such mixtures is nonlinear which can be attributed to multiple scattering of light and the receiver sensor’s height from the incident surface. Assuming a sensor’s fixed instantaneous field of view (IFOV), variation in its field of view (FOV) by shifting its height affects the spatial resolution of acquired spectra. We propose to estimate the point spread function (PSF) for which the spectral responses of fine-resolution pixels acquired by a sensor are mixed to produce a coarse-resolution pixel obtained by the same. Our approach is based on the sensor’s unchanged IFOV obtaining spectral information from a smaller ground resolution cell (GRC) at a lower FOV and a larger GRC with an increased sensor’s FOV. The larger GRC producing a coarse resolution pixel can be modeled as a gaussian PSF of its corresponding center and neighboring fine-resolution subpixels with the center exerting the maximum influence. Extensive experiments performed using a point-based sensor and a push broom scanner revealed such variational effects in PSF that are dependent on the sensor’s FOV, the spatial interval of acquisition, and optical properties. The coarse-resolution pixels’ spectra were regressed with their corresponding fine-resolution subpixels to provide estimates of the PSF values that assumed the shape of a two-dimensional Gaussian function. Constraining these values as sum-to-one introduced sparsity and explained variability in the spectral acquisition by different sensors.  The estimated PSFs were further validated through the linear spectral unmixing technique. It was observed that the fractional abundances obtained for the fine-resolution subpixels convolved with our estimated PSF to produce its corresponding coarse-resolution counterpart with minimal error. The obtained PSFs using different sensors also explained spectral mixing at different scales of observation and provided a basis for nonlinear unmixing integrating spatial as well as spectral effects and addressing endmember variability. We performed our experiments with various coarse-grained and fine-grained igneous and sedimentary rocks under laboratory conditions to validate our results which were compared with available literature. 

How to cite: Sahoo, M. M., Pattathal Vijayakumar, A., Herrmann, I., Mathew, S. K., and Porwal, A.: Estimation of point spread function for unmixing geological spectral mixtures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8712, https://doi.org/10.5194/egusphere-egu22-8712, 2022.

EGU22-9441 | Presentations | GI2.2

Water use efficiency (WUE) Modeling at Leaf level of Cotton (Gossypium hirsutum L.) in Telangana, India 

Shreedevi Moharana and Phanindra BVN Kambhammettu

Water use efficiency (WUE) plays a vital role in planning and management of irrigation strategies. Considering the spatial scale, WUE can be quantified at scales ranging from leaf to whole-plant to ecosystem to region. However, the inter-relation and their associate is poorly understood. This study is aimed at stimulating WUE of irrigated cotton at leaf () and further investigate the role of environmental and biophysical conditions on WUE dynamics. This study was conducted in an agricultural croplands located in Sangareddy district, about 70 km west of Hyderabad, the capital city of southern state Telangana, India. Ground based observation were made such as soil moisture, photosynthetic parameters and meteorological parameters. Modelling leaf water use efficiency has been established. The stomatal conductance  and  of cotton leaves exposed to ambient CO2 were simulated using Ball-Berry (mBB) model. Moreover, the stomatal conductance  and  of Cotton leaves exposed to ambient CO2 is simulated using modified Ball-Berry model, with instantaneous gas exchanges measured around noon used to parameterize and validate the model. We observed a large diurnal (4.3±1.9 mmolCO2 mol-1H2O) and seasonal (5.16±1.51 mmolCO2 mol-1H2O) variations in  during the crop period. Model simulated  and  are in agreement with the measurements (R2>0.5, RMSE<0.3). Our results conclude that WUE is ruled by climatic as well as vegetative factors respectively, and are largely controlled by changes in transpiration over photosynthesis. This needs further investigation with extensive analysis by building library of in-situ measurements.

 

Keywords: Cotton, WUE, Irrigation, Stomatal conductance, Ball Berry Model

How to cite: Moharana, S. and Kambhammettu, P. B.: Water use efficiency (WUE) Modeling at Leaf level of Cotton (Gossypium hirsutum L.) in Telangana, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9441, https://doi.org/10.5194/egusphere-egu22-9441, 2022.

EGU22-9845 | Presentations | GI2.2

Implementation of an interoperable platform integrating BIM and GIS information for network-level monitoring and assessment of bridges 

Luca Bertolini, Antonio Napolitano, Jhon Diezmos Manalo, Valerio Gagliardi, Luca Bianchini Ciampoli, and Fabrizio D'Amico

Monitoring of critical civil engineering infrastructures, and especially viaducts and bridges, has become a priority nowadays as the ageing of construction materials may cause damages and collapses with dramatic consequences. Following recent bridge collapses, specific guidelines on risk classification and management, safety assessment and monitoring of existing bridges have been issued in Italy, by the Minister of Infrastructure as a mandatory code [1]. Accordingly, several laws and regulations have been issued on the same topic, emphasizing the crucial role of BIM-based procedures for the design and management of civil infrastructures [2, 3]. Within this context, monitoring operations are generally conducted by on-site inspections and specialized operators, and rarely by high-frequency ground-based Non-Destructive Testing methods (NDTs). Furthermore, the implementation of satellite-based remote sensing techniques, have been increasingly and effectively used for the monitoring of bridges in the last few years [4]. Generally, these crucial pieces of information are analyzed separately, and the implementation of a multi-scale and multi-source interoperable BIM platform is still an open challenge [5].

This study aims at investigating the potential of an interoperable and upgradeable BIM platform supplemented by non-destructive survey data, such as Mobile Laser Scanner (MLS), Ground Penetrating Radar (GPR) and Satellite Remote Sensing Information (i.e. InSAR). The main goal of the research is to contribute to the state-of-the-art knowledge on BIM applications, by testing an infrastructure management platform aiming at reducing the limits typically associated to the separate observation of these assessments, to the advantage of an integrated analysis including both the design information and the routinely updated results of monitoring activities.

The activities were conducted in the framework of the Project “M.LAZIO”, approved by the Lazio Region, with the aim to develop an informative BIM platform of the investigated bridges interoperable within a Geographic Information System (GIS). As on-site surveys are carried out , a preliminary multi-source database of information  is created, to be operated as the starting point for the integration process and the development of  the infrastructure management platform. Preliminary results have shown promising viability of the data management model for supporting asset managers in the various management phases, thereby proving this methodology to be worthy for implementation in infrastructure integrated monitoring plans.

Acknowledgements

This research is supported by the Project “M.LAZIO”, accepted and funded by the Lazio Region, Italy. Funding from MIUR, in the frame of the “Departments of Excellence Initiative 2018–2022”, attributed to the Department of Engineering of Roma Tre University, is acknowledged.

References

[1] MIT, 2020. Ministero delle Infrastrutture e dei Trasporti, DM 578/2020

[2] EU, 2014. Directive 2014/24/EU of the European Parliament and of the Council of 26 February 2014 on public procurement and repealing Directive 2004/18/EC.

[3] MIMS, 2021. Ministero delle Infrastrutture e della Mobilità Sostenibile, DM 312/2021

[4] Gagliardi, V. et al., “Bridge monitoring and assessment by high-resolution satellite remote sensing technologies”. In SPIE Future Sensing Technologies; https://doi.org/10.1117/12.2579700

[5] D'Amico F. et al., "A novel BIM approach for supporting technical decision-making process in transport infrastructure management", Proc. SPIE 11863;  https://doi.org/10.1117/12.2600140

How to cite: Bertolini, L., Napolitano, A., Diezmos Manalo, J., Gagliardi, V., Bianchini Ciampoli, L., and D'Amico, F.: Implementation of an interoperable platform integrating BIM and GIS information for network-level monitoring and assessment of bridges, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9845, https://doi.org/10.5194/egusphere-egu22-9845, 2022.

Knowledge of the monument for its conservation is the result of a multidisciplinary work based on the integration of different data sources obtainable from historical research, architectural survey, the use of different imaging technologies. The latter are increasingly within the reach of conservators, architects and restoration companies thanks to the reduction of costs and to the effort to produce increasingly user-friendly imaging technologies both in terms of data acquisition and processing. The critical element is the interpretation of the results on which depends the effectiveness of these technologies in answering various questions that the restoration poses. Scientific literature suggests different approaches aimed at making the interpretation of imaging diagnostics easier, particularly by means of : i) the comparison between direct data (carrots, visual inspection) and results from non-invasive tests; ii) the use of specimens or laboratory test beds; iii) Virtual and Augmented reality (VR/AR) to be used as a work environment to facilitate the interpretation of non invasive imaging investigations. In particular, the reading and visualization of multiparametric information using VR/AR contents increases the standard modes for the transmission of knowledge of physical characteristics and state of conservation of the architectural heritage. This approach represents an effective system for storing and analysing heterogeneous data derived from a number of diverse non invasive imaging techniques, including Ground Penetrating radar (GPR) at high frequency, Infrared Thermography (IRT), Seismic tomography and other diagnostics techniques. In the context of Heritage Within Project, a VR/AR platform to interrelate heterogeneous data derived from GPR, IRT, Ultrasonic and sonic measurements along with  results finite element computations has been developed and applied to the Convent of Our Lady of Mount Carmel  in Lisbon to understand cause-and-effect mechanisms between the constructive characteristics, degradation pathologies and stress/deformation maps.

References

Gabellone F., Leucci G., Masini N., Persico R., Quarta G., Grasso F. 2013. Non-destructive prospecting and virtual reconstruction of the chapel of the Holy Spirit in Lecce, Italy. Near Surface Geophysics, doi: 10.3997/1873-0604.2012030

Gabellone F., Chiffi M., “Linguaggi digitali per la valorizzazione”, in F. Gabellone, M. T. Giannotta, M. F. Stifani, L. Donateo (a cura di), Soleto Ritrovata. Ricerche archeologiche e linguaggi digitali per la fruizione. Editrice Salentina, 2015. ISBN 978-88-98289-50-9

Masini N., Nuzzo L., Rizzo E., GPR investigations for the study and the restoration of the Rose Window of Troia Cathedral (Southern Italy), Near Surface Geophysics, 5 (5)(2007), pp. 287-300, ISSN: 1569-4445; doi: 10.3997/1873-0604.2007010 

Masini N., Soldovieri F. (Eds) (2017). Sensing the Past. From artifact to historical site. Series: Geotechnologies and the Environment, Vol. 16. Springer International Publishing, ISBN: 978-3-319-50516-9, doi: 10.1007/978-3-319-50518-3, pp. 575

Javier Ortega, Margarita González Hernández, Miguel Ángel García Izquierdo, Nicola Masini, et al. (2021). Heritage Within. European Research Project, ISBN: 978-989-54496-6-8, Braga 2021.

How to cite: Masini, N., Gabellone, F., and Ortega, J.: VR/AR based approach for the diagnosis of the state of conservation of the architectural heritage. The case of the Convento do Carmo in Lisbon, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10538, https://doi.org/10.5194/egusphere-egu22-10538, 2022.

EGU22-11201 | Presentations | GI2.2

DIARITSup: a framework to supervise live measurements, Digital Twins modelscomputations and predictions for structures monitoring. 

Jean Dumoulin, Thibaud Toullier, Mathieu Simon, and Guillermo Andrade-Barroso

DIARITSup is a chain of various softwares following the concept of ”system of systems”. It interconnects hardware and software layers dedicated to in-situ monitoring of structures or critical components. It embeds data assimilation capabilities combined with specific Physical or Statistical models like inverse thermal and/or mechanical ones up to the predictive ones. It aims at extracting and providing key parameters of interest for decision making tools. Its framework natively integrates data collection from local sources but also from external systems [1, 2]. DIARITSup is a milestone in our roadmap for SHM Digital Twins research framework. Furthermore, it intends providing some useful information for maintenance operations not only for surveyed targets but also for deployed sensors.

Thanks to its Model-view-controller (MVC) design pattern, DIARITSup can be extended, customized and connected to existing applications. Its core component is made of a supervisor task that handles the gathering of data from local sensors and external sources like the open source meteorological data (observations and forecasts) from Météo-France Geoservice [4] for instance. Meanwhile, a recorder manage the recording of all data and metadata in the Hierarchical Data Format (HDF5) [6]. HDF5 is used to its full potential with its Single-Writer-Multiple-Readers feature that enables a graphical user interface to represent the saved data in real-time, or the live computation of SHM Digital Twins models [3] for example. Furthermore, the flexibility of HDF5 data storage allows the recording of various type of sensors such as punctual sensors or full field ones. Finally, DIARITSup is able to handle massive deployment thanks to Ansible [5] automation tool and a Gitlab synchronization for automatic updates. An overview of the developed software with a real application case will be presented. Perspectives towards improvements on the software with more component integrations (Copernicus Climate Data Store, etc.) and a more generic way to configure the acquisition and model configuration will be finally discussed.


References
[1] Nicolas Le Touz, Thibaud Toullier, and Jean Dumoulin. “Infrared thermography applied to the study of heated and solar pavement: from numerical modeling to small scale laboratory experiments”. In: SPIE - Thermosense: Thermal Infrared Applications XXXIX. Anaheim, United States, Apr. 2017. url: https://hal.inria.fr/hal-01563851.
[2] Thibaud Toullier, Jean Dumoulin, and Laurent Mevel. “Study of measurements bias due to environmental and spatial discretization in long term thermal monitoring of structures by infrared thermography”. In: QIRT 2018 - 14th Quantitative InfraRed Thermography Conference. Berlin, Germany, June 2018. url: https://hal.inria.fr/hal-01890292.
[3] Nicolas Le Touz, Thibaud Toullier, and Jean Dumoulin. “Study of an optimal heating command law for structures with non-negligible thermal inertia in varying outdoor conditions”. In: Smart Structures and Systems 27.2 (2021), pp. 379–386. doi: 10.12989/sss.2021.27.2.379. url: https://hal.inria.fr/hal-03145348.
[4] Météo France. Données publiques Météo France. 2022. url: https://donneespubliques.meteofrance.fr.
[5] Red Hat & Ansible. Ansible is Simple IT Automation. 2022. url: https://www.ansible.com/.
[6] The HDF Group. Hierarchical Data Format, version 5. 1997-2022. url: https://www.hdfgroup.org/HDF5/.

How to cite: Dumoulin, J., Toullier, T., Simon, M., and Andrade-Barroso, G.: DIARITSup: a framework to supervise live measurements, Digital Twins modelscomputations and predictions for structures monitoring., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11201, https://doi.org/10.5194/egusphere-egu22-11201, 2022.

EGU22-12743 | Presentations | GI2.2

Integrating Remote Sensing data to assess the protective effect of forests on rockfall:The case study of Monte San Liberatore (Campania, Italy) 

Alessandro Di Benedetto, Antonella Ambrosino, and Margherita Fiani

In recent years, great interest has been paid to the risk that hydrogeological instability causes to the territory, especially in densely populated and geologically fragile areas. 
The forests, exerting a natural restraint, play an important protective function for the infrastructures and settlements underneath from the danger of falling rocks that fall from the rocky walls. This protective action is influenced not only by issues related to the vegetation itself but also by the morphology of the terrain, as a steeply sloping land surface can significantly increase the momentum of the rolling rock.
The aim of our work is to design a methodology based on the integration of remote sensing data, in detail optical satellite images and LiDAR data acquired by UAVs, to identify areas most prone to natural rockfall retention [1]. The results could then be used to identify areas that need to be reinforced artificially (rockfall nets) and naturally (protective forests).
The test area is located near Monte San Liberatore in the Campania region (Italy), which was affected in 1954 by a disastrous flood, in which heavy rains induced the triggering of a few complex landslides in a region that was almost geomorphologically susceptible.  Indeed, there are several areas subject to high risk of rockfalls, whose exposed value is represented by a complex infrastructural network of viaducts, tunnels, and galleries along the north-west slope of the mountain, which is partly covered by thick vegetation, which reduces the rolling velocity of rocks detaching from the ridge. 
According to the Carta della Natura, the vegetation most present in the area is the holm oak (Quercus Ilex), an evergreen, long-lived, medium-sized tree. Its taproot makes it resistant and stable, able to survive in extremely severe environments such as rocky soils or vertical walls, so it is ideal for slope protection.
The first processing step involved the multispectral analysis on Pleiades 1A four-band (RGB +NIR) high-resolution satellite images (HRSI). The computed vegetation indices (NDVI, RVI and NDWI) were used to assess the vegetation health status and its presumed age; thus, the most resilient areas of the natural compartment in terms of robustness and vigor were identified. The average plant height was determined using the normalized digital surface model (nDSM).
Next, starting from the Digital Terrain Model (DTM), we derived the morphometric features suitable for the description of the slope dynamics: slope gradient, exposure with respect to the North direction, plane, and convexity profile. The DTM and the DSM were created by interpolating on a grid the LiDAR point cloud acquired via UAV. Classification of areas having similar characteristics was made using SOM (Self-Organizing Maps), based on unsupervised learning.
The classified maps obtained delimit the similar areas from a morphological and vegetation point of view; in this way, all those areas that tend to have a higher propensity for rock roll reduction were identified.

[1] Fanos, Ali Mutar, and Biswajeet Pradhan. "Laser scanning systems and techniques in rockfall source identification and risk assessment: a critical review." Earth Systems and Environment 2.2 (2018): 163-182.

How to cite: Di Benedetto, A., Ambrosino, A., and Fiani, M.: Integrating Remote Sensing data to assess the protective effect of forests on rockfall:The case study of Monte San Liberatore (Campania, Italy), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12743, https://doi.org/10.5194/egusphere-egu22-12743, 2022.

EGU22-13153 | Presentations | GI2.2

Integration of multiple geoscientific investigation methods for a better understanding of a water system: the example of Chimborazo glaciers melting effects on the Chambo aquifer, Ecuador 

Andrea Scozzari, Paolo Catelan, Francesco Chidichimo, Michele de Biase, Benito G. Mendoza Trujillo, Pedro A. Carrettero Poblete, and Salvatore Straface

The identification of the processes underlining natural systems often requires the adoption of multiple investigation techniques for the assessment of the sites under study. In this work, the combination of information derived from non-invasive sensing techniques, such as geophysics, remote sensing and hydrogeochemistry, highlights the possible influence of global climate change on the future water availability related to an aquifer in a peculiar glacier context, located in central Ecuador. In particular, we show that the Chambo aquifer, which supplies potable water to the region, does not contain fossil water, and it’s instead recharged over time. Indeed, the whole Chambo river basin is affected by the Chimborazo volcano, which is a glacerised mountain located in the inner tropics, one of the most critical places  to be observed in the frame of climate impact on water resources. Thanks to the infomation gathered by the various surveying techniques, numerical modelling permitted an estimate of the recharge, which can be fully originated by the runoff from Chimborazo melting glaciers. Actually, the retreat of the glaciers on top of the Chimborazo is an ongoing process presumably related to global climate change.

How to cite: Scozzari, A., Catelan, P., Chidichimo, F., de Biase, M., Mendoza Trujillo, B. G., Carrettero Poblete, P. A., and Straface, S.: Integration of multiple geoscientific investigation methods for a better understanding of a water system: the example of Chimborazo glaciers melting effects on the Chambo aquifer, Ecuador, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13153, https://doi.org/10.5194/egusphere-egu22-13153, 2022.

EGU22-13401 | Presentations | GI2.2

Tunnel deformation rate analysis based on PS-InSAR technique and stress-area method  

Long Chai, Xiongyao Xie, Pan Li, Biao Zhou, and Li Zeng

The permanent scatterer synthetic aperture radar interferometry (PS-InSAR) technique can detect the permanent scatterers(PSs) on the ground. But the deformation of PSs can’t be used to analyze the deformation of underground buildings below the ground surface directly, such as tunnels. In this paper, the process of tunnel deformation analysis using PSs data and stress-area method is proposed. The deformation data of PSs are used to fit the surface deformation of tunnel by kriging interpolation method. The stress area method is used to calculate the deformation of the soil above the tunnel, then the deformation of tunnel can be acquired. This process was applied to calculate the deformation of a tunnel in Shanghai, China. The results show that the fitted surface deformation rate data are accurate, with the maximum absolute difference of 1.45mm/y and the minimum difference of 0.11mm/y compared with the level monitoring data. The tunnel deformation rate calculated by this process is close to the measured deformation rate of the tunnel with error level in millimeters. The surface and tunnel deformation rate curves are similar in the tunnel extension direction. PS-InSAR technique has the advantages of acquiring large area, historical data of surface deformation. Combined with the process proposed in this paper, Large-scale tunnel deformation analysis can be achieved.

How to cite: Chai, L., Xie, X., Li, P., Zhou, B., and Zeng, L.: Tunnel deformation rate analysis based on PS-InSAR technique and stress-area method , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13401, https://doi.org/10.5194/egusphere-egu22-13401, 2022.

EGU22-13441 | Presentations | GI2.2

Collaborative use of ground monitoring and GPR data for the control of ground settlement in shield tunnel in soft soil 

Kang Li, Xiongyao Xie, Xiaobin Zhang, Biao Zhou, Tenfei Qu, and Li Zeng

In recent years, China's construction demand for shield tunnel in soft soil continues to increase, and the control of ground settlement in tunnel boring process affects the safety of the tunnel itself and its superstructure directly. Paying close attention to controlling the strata loss and the ground settlement by multiple means is important to ensure construction safety. In this paper, the intelligent real-time monitoring system with dual-frequency ground penetrating radar (GPR) is used to detect the quality of back-fill grouting of shield tunnel, while monitoring points are arranged on the ground surface to acquire the settlement values in real time. The collaborative analysis of ground and underground monitoring results reveals the relationship between grouting and settlement values, and realizes the dynamic guidance on grouting operation, which helps to achieve the purpose of controlling ground settlement better. Last but not least, this paper proposes an outlook on a multiple-data fusion system based on cloud computing platform to adapt to more complex and multiple data in the future, so as to achieve the higher accuracy, efficiency and intelligence of monitoring data analysis.

How to cite: Li, K., Xie, X., Zhang, X., Zhou, B., Qu, T., and Zeng, L.: Collaborative use of ground monitoring and GPR data for the control of ground settlement in shield tunnel in soft soil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13441, https://doi.org/10.5194/egusphere-egu22-13441, 2022.

EGU22-13515 | Presentations | GI2.2

Application of ground penetrating radar (GPR) in look-ahead detection of slurry balance shield machine 

Weiwei Duan, Xiongyao Xie, Yong Yang, Kun Zeng, Huiming Wu, Li Zeng, and Kang Li

The shield machine has become the mainstream of subway tunnels construction because of its safety and efficiency. But with the continuous development of urban construction, the environment of subway tunnel construction is becoming more and more complex. In the process of shield tunnels construction in southern cities of China, slurry balance shield machines often encounter various obstacles, such as large diameter boulders and concrete pile foundations, which result in accidents of shield machine sticking. Therefore, it is necessary to quickly and accurately detect the distribution of obstacles in front of shield excavation face in advance so that operators can in time take measures to reduce the occurrence of such accidents. Ground penetrating radar (GPR) is a method widely used in engineering geological exploration. It has advantages of small working space, high efficiency and no damage compared with other detecting methods. When the GPR antenna is mounted on the cutter head of the shield machine, the obstacles in the stratum ahead of the shield machine can be detected in real time. Under this condition the GPR antenna’s real work mode is that it will rotate with the cutter head to form a circumferential survey line. Based on Finite-Difference-Time-Domain-Method (FDTD), authors use the common numerical simulation software (GPRMAX) to make simulations of GPR circumferential detection under the antenna array rotating with the cutter head, which verifies the theoretical feasibility of this method. By simulating radar emission and reflection pattern of electromagnetic wave, we study the propagation pattern of the reflect wave after encountering the obstacles and conclude the image pattern to establish the foundation for image recognition of obstacles. Due to the radar wave being susceptible to electromagnetic interference, GPR is still lack of engineering practice in shield advanced detection. To reduce the interference of the surrounding metal cutter head, a new strip radar antenna with a shielding shutter is designed to improve the directivity of electromagnetic wave propagation. Several antennas are fixed at several slurry openings of the cutter head of slurry balance shield machine to form radar antenna array and improve detection efficiency and accuracy.

How to cite: Duan, W., Xie, X., Yang, Y., Zeng, K., Wu, H., Zeng, L., and Li, K.: Application of ground penetrating radar (GPR) in look-ahead detection of slurry balance shield machine, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13515, https://doi.org/10.5194/egusphere-egu22-13515, 2022.

EGU22-50 | Presentations | G3.3

Towards an improved understanding of vertical land motion and sea-level change in eastern North America 

Soran Parang, Glenn A. Milne, Makan A. Karegar, and Lev Tarasov

Many coastal cities are an early casualty in climate-related coastal flooding because of processes resulting in land subsidence and thus enhanced relative sea-level (RSL) rise. Much of the Atlantic coast of North America has been sinking for thousands of years, at a maximum rate of ~20 cm per century as a consequence of solid Earth deformation in response to deglaciation of the Laurentide ice sheet (between ~18,000 and ~7,000 years ago) [e.g. Love et al., Earth's Future, 4(10), 2016]. Karegar et al. [Geophysical Research Letters, 43(7), 2016] have shown that vertical land motion along the Atlantic coast of the USA is an important control on nuisance flooding. A key finding in this study is that while glacial isostatic adjustment (GIA) is the dominant process driving land subsidence in most areas, there can be large deviations from this signal due to the influence of anthropogenic activity impacting hydrological processes. For example, between Maine (45°N) and New Hampshire (43°N), the GPS data show uplift while geological data show long-term subsidence. The cause of this discrepancy is not clear, but one hypothesis is increasing water mass associated with the James Bay Hydroelectric Project in Quebec [Karegar et al., Scientific Reports, 7, 2017].

The primary aim of this study is to better constrain and understand the processes that contribute to contemporary and future vertical land motion in this region to produce improved projections of mean sea-level change and nuisance flooding. The first step towards achieving these aims is to determine a GIA model parameter set that is compatible with observations of past sea-level change for this region. We make use of two regional RSL data compilations: Engelhart and Horton [Quaternary Science Reviews, 54, 2012] for northern USA and Vacchi et al. [Quaternary Science Reviews, 201, 2018] for Eastern Canada, comprising a total of 1013 data points (i.e., sea level index points and limiting data points) over 38 regions distributed throughout our study region. These data are well suited to determine optimal GIA model parameters due to the magnitude of other signals being much smaller, particularly in near-field regions such as Eastern Canada. We consider a suite of 32 ice history models that is comprised mainly of a subset from Tarasov et al. [Earth and Planetary Science Letters, 315–316, 2012] as well as the ICE-6G and ANU models. We have computed RSL for these ice histories using a state-of-the-art sea-level calculator and 440 1-D Earth viscosity models per each ice history model to identify a set of Earth model parameters that is compatible with the observations.

How to cite: Parang, S., Milne, G. A., Karegar, M. A., and Tarasov, L.: Towards an improved understanding of vertical land motion and sea-level change in eastern North America, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-50, https://doi.org/10.5194/egusphere-egu22-50, 2022.

EGU22-852 | Presentations | G3.3

The inclusion of ice model uncertainty in 3D Glacial Isostatic Adjustment modelling: a case study from the Russian Arctic 

Tanghua Li, W. Richard Peltier, Gordan Stuhne, Nicole Khan, Alisa Baranskaya, Timothy Shaw, Patrick Wu, and Benjamin Horton

The western Russian Arctic was partially covered by the Eurasian ice sheet complex during the Last Glacial Maximum (~26 ka BP) and is a focus area for Glacial Isostatic Adjustment (GIA) studies. However, there have been few GIA studies conducted in the Russian Arctic due to the lack of high quality deglacial relative sea-level (RSL) data. Recently, Baranskaya et al. (2018) released a quality-controlled deglacial RSL database for the Russian Arctic that consists of ~400 sea-level index points and ~250 marine and terrestrial limiting data that constrain RSL since 20 ka BP. Here, we use the RSL database to constrain the 3D Earth structure beneath the Russian Arctic, with consideration of the uncertainty in ice model ICE-7G_NA, which is assessed via iteratively refining the ice model with fixed 1D Earth model to achieve a best fit with the RSL data. Also, the uncertainties in 3D Earth parameters and RSL predictions are investigated.

 

We find an optimal 3D Earth model (Vis3D) improves the fit with the deglacial RSL data compared with the VM7 1D model when fixed with the ICE-7G_NA ice model. Similarly, we show improved fit in the White Sea area, where 1D model shows notable misfits, with the refined ice model ICE-7G_WSR when fixed with VM7 Earth model. The comparable fits of ICE-7G_NA (Vis3D) and ICE-7G_WSR (VM7) implies that the uncertainty in the ice model might be improperly mapped into 3D viscosity structure when a fixed ice model is employed. Furthermore, fixed with refined ice model ICE-7G_WSR, we find an optimal 3D Earth model (Vis3D_R), which fits better than ICE-7G_WSR (VM7), and the magnitude of lateral heterogeneity decreases significantly from Vis3D to Vis3D_R.  We conclude that uncertainty in the ice model needs to be considered in 3D GIA studies.

How to cite: Li, T., Peltier, W. R., Stuhne, G., Khan, N., Baranskaya, A., Shaw, T., Wu, P., and Horton, B.: The inclusion of ice model uncertainty in 3D Glacial Isostatic Adjustment modelling: a case study from the Russian Arctic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-852, https://doi.org/10.5194/egusphere-egu22-852, 2022.

EGU22-918 | Presentations | G3.3

Regional GIA: modelling choices and community needs 

Riccardo Riva

GIA is a global process, because of gravitational effects, its interplay with earth rotation, and the large spatial extent of ice-sheet and ocean loading. However, mainly due to the presence of heterogeneities in the structure of crust and upper mantle, modelling of GIA signals often requires a regional approach. This is particularly true in the light of continuous advances in earth observation techniques, that allow increasingly accurate determination of land deformation, coastal sea level change, and mass balance of glaciers and ice sheets.

This talk will address a number of open issues related to regional GIA models, such as the effect of transient and non-linear rheologies, and the complementary role of forward and semi-empirical approaches, with an eye on the needs of the geodetic, sea level and cryosphere communities.

How to cite: Riva, R.: Regional GIA: modelling choices and community needs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-918, https://doi.org/10.5194/egusphere-egu22-918, 2022.

EGU22-1343 | Presentations | G3.3

Resolving the Influence of Ice Stream Instability on Postglacial Relative Sea-Level Histories: the case of the St Lawrence River Channel Ice Stream 

Richard Peltier, Tanghua Li, Gordan Stuhnne, Jesse Velay-Vitow, Matteo Vacchi, Simon Englehart, and Benjamin Horton

A challenge to understanding Late Quaternary glaciation history is the mechanism(s) responsible for the asymmetry in an individual glaciation cycle between the slow pace of glaciation and the more rapid pace of deglaciation (e.g., Broecker and Van Donk, 1970). It is increasingly clear that a major contributor to the rate of global deglaciation is the instability of marine terminating ice streams. Recent analyses by Velay-Vitow et al. (2020) suggest that these instabilities were often triggered by ocean tides of anomalously high amplitude. Examples include the Hudson Strait Ice Stream responsible for Heinrich Event 1 (H1) and the Amundsen Gulf Ice Stream. Here, we analyse the instability of the Laurentian Channel and St Lawrence River Channel ice stream system. Our analysis begins with the recognition of highly significant misfits of up to 60 m at ~9,000 calendar years ago between deglacial relative sea-level histories inferred by Vacchi et al. (2018) at sites along the St Lawrence River Channel and those predicted by the ICE-6G_C (VM5a) and ICE-7G_NA (VM7) models of the Glacial Isostatic Adjustment process. We suggest that these disagreements between models and data may be due to the St Lawrence River Channel ice stream becoming unstable during the deglaciation of the Laurentide Ice Sheet (LIS) due to the hypothesized tidal mechanism for ice stream destabilization. We investigate a sequence of scenarios designed to provide a best estimate of the timing of this event. Since this ice stream penetrated deeply into the interior of the LIS and was connected to the Laurentian Channel ice stream, the instability of the latter was required in order for destabilization of the St Lawrence River channel ice stream to be possible. We explore the consistency of the implied sequence of events with the observational constraints.

How to cite: Peltier, R., Li, T., Stuhnne, G., Velay-Vitow, J., Vacchi, M., Englehart, S., and Horton, B.: Resolving the Influence of Ice Stream Instability on Postglacial Relative Sea-Level Histories: the case of the St Lawrence River Channel Ice Stream, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1343, https://doi.org/10.5194/egusphere-egu22-1343, 2022.

EGU22-1447 | Presentations | G3.3 | Highlight

Benchmark of numerical GIA codes capable of laterally heterogeneous earth structures 

Volker Klemann, Jacky Austermann, Meike Bagge, Natasha Barlow, Jeffrey Freymueller, Pingping Huang, Erik R. Ivins, Andrew Lloyd, Zdeněk Martinec, Glenn Milne, Alessio Rovere, Holger Steffen, Rebekka Steffen, Wouter van der Wal, Maryam Yousefi, and Shijie Zhong

During the last decade there has been an increasing demand to improve models of present-day loading processes and glacial-isostatic adjustment (GIA). This is especially important when modelling the GIA process in tectonically active regions like the Pacific Northwest, Patagonia or West Antarctica. All these regions are underlain by zones of low-viscosity mantle. Although one-dimensional earth models may be sufficient to model local-scale uplift within these regions, modeling of the wider-scale deformation patterns requires consideration of three-dimensional viscosity structure that is consistent with other geophysical and laboratory findings. It is this wider-scale modeling that is necessary for earth-system model applications as well as for the validation or reduction of velocity fields determined by geodetic observation networks based on GNSS, for improving satellite gravimetry, and for present-day sea-level change as paleo sea-level reconstructions.

There are a number of numerical GIA codes in the community, which can consider lateral variations in viscoelastic earth structure, but a proper benchmark focusing on lateral heterogeneity is missing to date. Accordingly, ambiguity remains when interpreting the modelling results. The numerical codes are based on rather different methods to solve the respective field equations applying, e.g., finite elements, finite volumes, finite differences or spectral elements. Aspects like gravity, compressibility and rheology are dealt with differently. In this regard, the set of experiments to be performed has to be agreed on carefully, and we have to accept that not all structural features can be considered in every code.

We present a tentative catalogue of synthetic experiments. These are designed to isolate different aspects of lateral heterogeneity of the Earth's interior and investigate their impact on vertical and horizontal surface displacements, geocenter and polar motion, gravity, sea-level change and stress. The study serves as a follow up of the successful benchmarks of Spada et al. (2011) and Martinec et al. (2018) on 1D earth models and the sea-level equation. The study was initiated by the PALSEA-SERCE Workshop in 2021 (Austermann and Simms, 2022) and benefits from discussions inside different SCAR-INSTANT subcommittees, the IAG Joint Study Group 3.1 “Geodetic, Seismic and Geodynamic Constraints on Glacial Isostatic Adjustment", the IAG Subcommission 3.4 “Cryospheric Deformation" and PALSEA.

References:

Austermann, J., Simms, A., 2022 (in press). Unraveling the complex relationship between solid Earth deformation and ice sheet change. PAGES Mag., 30(1). doi:10.22498/pages.30.1.14

Martinec, Z., Klemann, V., van der Wal, W., Riva, R. E. M., Spada, G., Sun, Y., Melini, D., Kachuck, S. B., Barletta, V., Simon, K., A, G., James, T. S., 2018. A benchmark study of numerical implementations of the sea level equation in GIA modelling. Geophys. J. Int., 215:389-414. doi:10.1093/gji/ggy280

Spada, G., Barletta, V. R., Klemann, V., Riva, R. E. M., Martinec, Z., Gasperini, P., Lund, B., Wolf, D., Vermeersen, L. L. A., King, M. A. (2011). A benchmark study for glacial isostatic adjustment codes. Geophys. J. Int., 185:106-132. doi:10.1111/j.1365-246X.2011.04952.x

How to cite: Klemann, V., Austermann, J., Bagge, M., Barlow, N., Freymueller, J., Huang, P., Ivins, E. R., Lloyd, A., Martinec, Z., Milne, G., Rovere, A., Steffen, H., Steffen, R., van der Wal, W., Yousefi, M., and Zhong, S.: Benchmark of numerical GIA codes capable of laterally heterogeneous earth structures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1447, https://doi.org/10.5194/egusphere-egu22-1447, 2022.

EGU22-1479 | Presentations | G3.3

Peripheral and near field relative sea-level predictions using GIA models with 3D and regionally adapted 1D viscosity structures 

Meike Bagge, Volker Klemann, Bernhard Steinberger, Milena Latinovic, and Maik Thomas

Glacial isostatic adjustment (GIA) describes the viscoelastic response of the solid Earth to ice-sheet and ocean loading. GIA models determine the relative sea-level based on the viscoelastic deformations of the Earth interior including self-gravitation due to the loading of the water redistribution between ocean and ice and rotational effects. Choosing an Earth structure that adequately reflects the viscoelastic behavior of a region remains a challenge. For a specific region, the viscosity stratification can be inferred from present-day geodetic measurements like sea-level, gravity change and surface displacements or from paleo observations of former sea level. Here, we use a suite of geodynamically constrained 3D Earth structures that are derived from seismic tomography models and create regionally adapted 1D Earth structures to investigate to what extent regional, radially symmetric structures are able to reproduce the solid Earth response of a laterally varying structure. We discuss sea-level variations during the deglaciation in the near field (beneath the former ice sheet) and peripheral regions (surrounding the ice sheet) with focus on North America and Antarctica as well as Oregon and Patagonia. The suite of 3D Earth structures vary in transfer functions from seismic velocity to viscosity, i.e., in Arrhenius law and viscosity contrast between upper mantle and transition zone. We investigate how the relative sea-level predictions of the model suite members are affected due to the simplification of the Earth structure from 3D to 1D.

In general, our results support previous studies showing that 1D models in peripheral regions are not able to reproduce the 3D models’ predictions, because the response depends on the deformational behavior beneath the adjacent ice sheet and the local structure (superposition). Furthermore, the analysis of the model suite members shows different response behaviors for the 1D and 3D cases, e.g., suite members with weaker dependence of viscosity on seismic velocity can predict lowest RSL for the 3D case, but largest RSL for the 1D case. This indicates the relevance of the 3D structure in peripheral regions. 1D models in the near field are more capable to reproduce 3D model response behavior. But also here, deviations indicate that the lateral variations in the Earth structure beneath the ice sheet influence local relative sea-level predictions. 

How to cite: Bagge, M., Klemann, V., Steinberger, B., Latinovic, M., and Thomas, M.: Peripheral and near field relative sea-level predictions using GIA models with 3D and regionally adapted 1D viscosity structures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1479, https://doi.org/10.5194/egusphere-egu22-1479, 2022.

Further understanding of Antarctic Ice Sheet responses to global climate changes requires an accurate and continuous reconstruction of the AIS changes. However, the erosive nature of ice-sheet expansion and sea-level drop and the difficulty of accessing much of Antarctica make it difficult to obtain field-based evidence of ice-sheet and sea-level changes before the Last Glacial Maximum. Limited sedimentary records from the Indian Ocean sector of East Antarctica demonstrate that the sea level of Marine Isotope Stage 3 was close to the present level despite the global sea-level drop lower than −40 m. Although previous GIA-derived sea levels hardly explain these sea-level observations, we demonstrate glacial isostatic adjustment modeling with refined Antarctic Ice Sheet loading histories. Our experiments reveal that the Indian Ocean sector of the Antarctic Ice Sheet would have been required to experience excess ice loads before the Last Glacial Maximum in order to explain the observed sea-level highstands during Marine Isotope Stage 3. We also conduct a sensitivity test of the small Northern American Ice Sheet during Marine Isotope Stage 3, suggesting that this small ice sheet is not enough to achieve sea-level highstands during Marine Isotope Stage 3 in the Indian Ocean sector of East Antarctica. As such, we suggest that the Indian Ocean sector of the East Antarctic Ice Sheet reached its maximum thickness before the global Last Glacial Maximum.
 
Reference
Ishiwa, T., Okuno, J., and Suganuma, Y., 2021. Excess ice loads in the Indian Ocean sector of East Antarctica during the last glacial period. Geology, 49, 1182–1186. https://doi.org/10.1130/g48830.1

How to cite: Ishiwa, T., Okuno, J., and Suganuma, Y.: Excess ice loads prior to the Last Glacial Maximum in the Indian Ocean sector of East Antarctica derived from sea-level observations and GIA modeling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1568, https://doi.org/10.5194/egusphere-egu22-1568, 2022.

EGU22-1807 | Presentations | G3.3

Three-dimensional velocity variations due to ice mass changes in Greenland – Insights from a compressible glacial isostatic adjustment model 

Rebekka Steffen, Holger Steffen, Pingping Huang, Lev Tarasov, Kristian K. Kjeldsen, and Shfaqat A. Khan

The lithospheric thickness beneath and around Greenland varies from a few tens of kilometres in offshore regions to several tens of kilometres (up to 200 – 250 km) in land areas. But, due to different datasets and techniques applied in geophysical studies, there are large differences between the different geophysical lithosphere models. As an example, lithosphere models from seismological datasets show generally larger values (above 100 km), while models using gravity or thermal datasets tend to be thinner (values mostly below 100 km). To model the deformation associated with the melting of the Greenland Ice Sheet a detailed lithosphere model is required. Nevertheless, seismologically obtained lithosphere models are the ones usually applied in these so-called glacial isostatic adjustment (GIA) models. Besides, GIA models can be used to provide additional constraints on the lithospheric thickness.

Results from most 3D GIA models are compared to observed vertical velocities only, while horizontal velocities are known to be sensitive to the lateral variations of the Earth (e.g., lithospheric thickness). But, horizontal velocities from incompressible GIA models, which are commonly used, are not suitable due to the neglect of material parameter changes related to the dilatation. Compressible GIA models in turn can provide more accurate estimates of the horizontal and vertical viscoelastic deformations induced by ice-mass changes. Here, we use a variety of lithospheric thickness models, obtained from gravity, thermal, and seismological datasets, in a three-dimensional compressible GIA Earth model. The GIA model will be constructed using the finite-element software ABAQUS (Huang et al., under review in GJI) and applying recent ice history models Huy3 and GLAC-GR2a for Greenland in combination with the Little Ice Age deglaciation model by Kjeldsen et al. (2015). We will compare various lithosphere models, including their impact on the modelled 3D velocity field, and compare these against independent GNSS (Global Navigation Satellite System) observations.

References:

Huang, P., Steffen, R., Steffen, H., Klemann, V., van der Wal, W., Reusen, J., Wu, P., Tanaka, Y., Martinec, Z., Thomas, M. (under review in GJI): A finite element approach to modelling Glacial Isostatic Adjustment on three-dimensional compressible earth models. Geophysical Journal International. Under review.

Kjeldsen, K., Korsgaard, N., Bjørk, A. et al. (2015): Spatial and temporal distribution of mass loss from the Greenland Ice Sheet since AD 1900. Nature 528, 396–400, https://doi.org/10.1038/nature16183.

How to cite: Steffen, R., Steffen, H., Huang, P., Tarasov, L., Kjeldsen, K. K., and Khan, S.: Three-dimensional velocity variations due to ice mass changes in Greenland – Insights from a compressible glacial isostatic adjustment model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1807, https://doi.org/10.5194/egusphere-egu22-1807, 2022.

EGU22-4475 | Presentations | G3.3

The effect of uncertain historical ice information on GIA modelling 

Reyko Schachtschneider, Jan Saynisch-Wagner, Volker Klemann, Meike Bagge, and Maik Thomas

When inferring mantle viscosity by modelling the effects of glacial isostatic adjustment (GIA) a necessary constraint is the external forcing by surface loading. Such forcing is usually provided by a glaciation history, where the mass-conserving sea-level changes are considered solving the sea-level equation. The uncertainties of glaciation history reconstructions are quite large and the choice of a specific history strongly influences the deformation response obtained by GIA modelling. The reason is that any history is usually based on a certain Earth rheology, and mantle viscosity inversions using such models tend to resemble the viscosity structure used for the glaciation history (Schachtschneider et al., 2022, in press). Furthermore, uncertainties of glaciation histories propagate into the respective GIA modelling results. However, to quantify the impact of glaciation history on GIA modelling remains a challenge.

In this study we investigate the effect of uncertainties in glaciation histories on GIA modelling. Using a particle-filter approach we study the effect of spatial and temporal variations in ice distribution as well as the effect of total ice mass. We quantify the effects on a one-dimensional viscosity stratification and derive measures to which extent changes in sea-level pattern and surface deformation depend on variations in ice loading.

 

References:

Schachtschneider, R., Saynisch-Wagner, J., Klemann, V., Bagge, M., Thomas, M. 2021. Nonlin. Proc. Geophys., https://doi.org/10.5194/npg-2021-22

How to cite: Schachtschneider, R., Saynisch-Wagner, J., Klemann, V., Bagge, M., and Thomas, M.: The effect of uncertain historical ice information on GIA modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4475, https://doi.org/10.5194/egusphere-egu22-4475, 2022.

EGU22-4969 | Presentations | G3.3 | Highlight

Sea level response to Quaternary erosion and deposition in Scandinavia 

Gustav Pallisgaard-Olesen, Vivi Kathrine Pedersen, Natalya Gomez, and Jerry X. Mitrovica

The landscape in western Scandinavia has undergone dramatic changes through numerous glaciations during the Quaternary. These changes in topography and in the volumes of offshore sediment deposition, have caused significant isostatic adjustments and local sea-level changes, owning to erosional unloading and de- positional loading of the lithosphere. This geomorphic mass redistribution also has the potential to perturb the geoid, resulting in additional sea-level changes. However, the combined sea-level response from these processes is yet to be investigated in detail for Scandinavia.

In this study we estimate the total sea-level change from i) late Pliocene- Quaternary onshore bedrock erosion and erosion of sediments on the coastal shelf and ii) the subsequent deposition in the Norwegian Sea, northern North Sea and the Danish region. We use a gravitationally self-consistent global sea- level model that includes the full viscoelastic response of the solid Earth to surface loading and unloading. In addition to total late Pliocene-Quaternary geomorphic mass redistribution, we also estimate transient sea-level changes related specifically to the two latest glacial cycles.

We utilize existing observations of offshore sediment thicknesses of glacial origin, and combine these with estimates of onshore glacial erosion and of erosion on the inner shelf. Based on these estimates, we define mass redistribution and construct a preglacial landscape setting as well as approximate a geomorphic history of the last two glacial cycles.

Our results show that erosion and deposition has caused a sea-level fall of ∼50-100 m along the southern coast of Norway during the last two glacial cycles reaching ∼120 m in the offshore Skagerak region. The total relative sea-level fall during the Quaternary reach as much as ∼350 m in Skagerak. This highlights the importance of accounting for geomorphic sediment redistribution in glacial isostatic-adjustment modelling when interpreting ice sheet histories and glacial rebound.

How to cite: Pallisgaard-Olesen, G., Pedersen, V. K., Gomez, N., and Mitrovica, J. X.: Sea level response to Quaternary erosion and deposition in Scandinavia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4969, https://doi.org/10.5194/egusphere-egu22-4969, 2022.

EGU22-5146 | Presentations | G3.3

The use of Non-Linear Geometry (NLGEOM) and gravity loading in flat and spherical Finite Element models of Abaqus for Glacial Isostatic Adjustment (GIA) 

Jesse Reusen, Pingping Huang, Rebekka Steffen, Holger Steffen, Caroline van Calcar, Bart Root, and Wouter van der Wal

In geodynamic studies, most Finite-Element (FE) models in the commercial FE software Abaqus use elastic foundations at internal boundaries. This method works well for incompressible and so-called material-compressible material parameters but it is unclear if it works sufficiently well for implementing compressibility, especially in a 3D spherical model. The latter is of importance in investigations of glacial isostatic adjustment (GIA). A possible alternative method is based on a combination of explicit gravity loading with non-linear geometry (NLGEOM parameter in Abaqus) (Hampel et al., 2019). This method would remove the need to make a stress transformation to get the correct GIA stresses, and automatically accounts for the change in internal buoyancy forces that arises when allowing for compression, according to the Abaqus Documentation. We compared the method for (in)compressible flat (~half-space) FE models with existing numerical half-space and spherical (in)compressible codes and tested the applicability of this method in a spherical FE model. We confirm that this method works for multi-layer incompressible flat FE models. We furthermore notice that horizontal displacement rates of incompressible flat FE models match those of spherical incompressible GIA models below the current GNSS (Global Navigation Satellite System) measurement accuracy of 0.2-0.3 mm/a, but only for ice sheets that are smaller than 450 km in extent. For compressible models, disagreements in the vertical displacement rates are found between the flat NLGEOM model and the compressible Normal Mode code ICEAGE (Kaufmann, 2004). An extension of the NLGEOM-gravity method to a spherical FE model, where gravity must be implemented in the form of body forces combined with initial stress, leads to a divergence of the solution when viscous behaviour is turned on. We thus conclude that the applicability of the NLGEOM method is so far limited to flat FE models, and in GIA investigations for flat models the applicability further depends on the size of the load (ice sheet, glacier).

References:

Hampel, A., Lüke, J., Krause, T., & Hetzel, R., 2019. Finite-element modelling of glacial isostatic ad-
justment (GIA): Use of elastic foundations at material boundaries versus the geometrically non-linear
formulation, Computers & geosciences, 122, 1–14.

Kaufmann, G. (2004). Program Package ICEAGE, Version 2004. Manuscript. Institut für Geophysik der Universität Göttingen.

How to cite: Reusen, J., Huang, P., Steffen, R., Steffen, H., van Calcar, C., Root, B., and van der Wal, W.: The use of Non-Linear Geometry (NLGEOM) and gravity loading in flat and spherical Finite Element models of Abaqus for Glacial Isostatic Adjustment (GIA), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5146, https://doi.org/10.5194/egusphere-egu22-5146, 2022.

EGU22-6013 | Presentations | G3.3 | Highlight

A finite element approach to modelling Glacial Isostatic Adjustment on three-dimensional compressible earth models 

Pingping Huang, Rebekka Steffen, Holger Steffen, Volker Klemann, Wouter van der Wal, Jesse Reusen, Yoshiyuki Tanaka, Zdeněk Martinec, and Maik Thomas

A new finite element method called FEMIBSF is presented that is capable of modelling Glacial Isostatic Adjustment (GIA) on compressible earth models with three-dimensional (3D) structures. This method takes advantage of the classical finite element techniques to calculate the deformational and gravitational responses to the driving forces of GIA (including body forces and pressures on Earth’s surface and core-mantle boundary, namely CMB). Following Wu (2004) and Wong & Wu (2019), we implement the GIA driving forces in the commercial finite element software Abaqus and solve the equation of motion in an iterative manner. Different from those two studies, all formulations and calculations in this study are not associated with spherical harmonics but are performed in the spatial domain. Due to this, FEMIBSF is free from expanding the load, displacement, and potential into spherical harmonics with the short-wavelength components (of high degree and order) neglected. We compare the loading Love numbers (LLNs) generated by FEMIBSF with their analytical solutions for homogeneous models and numerical solutions for layered models calculated by the normal-mode approach/code, ICEAGE (Kaufmann, 2004), the iterative body force approach/code, IBF (Wong & Wu, 2019) and the spectral-finite element approach/code, VILMA-C (Martinec, 2000; Tanaka et al., 2011). We find that FEMIBSF agrees well with analytical and numerical LLN results of these codes. In addition, we show how to compute the degree-1 deformation directly in the spatial domain with the finite element approach and how to implement it in a GIA model using Abaqus. Finally, we demonstrate that the CMB pressure related to the gravitational potential change in the fluid core only influences the long-wavelength surface displacement and potential such as the degree-2 component.

 

References

 

Kaufmann, G. (2004). Program Package ICEAGE, Version 2004. Manuscript. Institut für Geophysik der Universität Göttingen.

 

Martinec, Z. (2000). Spectral–finite element approach to three-dimensional viscoelastic relaxation in a spherical earth. Geophysical Journal International142(1), 117-141.

 

Tanaka, Y., Klemann, V., Martinec, Z. & Riva, R. E. M. (2011). Spectral-finite element approach to viscoelastic relaxation in a spherical compressible Earth: application to GIA modelling. Geophysical Journal International184(1), 220-234.

 

Wong, M. C. & Wu, P. (2019). Using commercial finite-element packages for the study of Glacial Isostatic Adjustment on a compressible self-gravitating spherical earth–1: harmonic loads. Geophysical Journal International217(3), 1798-1820.

 

Wu, P. (2004). Using commercial finite element packages for the study of earth deformations, sea levels and the state of stress. Geophysical Journal International, 158(2), 401-408.

 
 
 

How to cite: Huang, P., Steffen, R., Steffen, H., Klemann, V., van der Wal, W., Reusen, J., Tanaka, Y., Martinec, Z., and Thomas, M.: A finite element approach to modelling Glacial Isostatic Adjustment on three-dimensional compressible earth models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6013, https://doi.org/10.5194/egusphere-egu22-6013, 2022.

EGU22-6236 | Presentations | G3.3

Identifying geographical patterns of transient deformation in the geological sea level record 

Karen M. Simon, Riccardo E. M. Riva, and Taco Broerse

In this study, we examine the effect of transient mantle creep on the prediction of glacial isostatic adjustment (GIA) signals. Specifically, we compare predictions of relative sea level change from GIA from a set of Earth models in which transient creep parameters are varied in a simple Burgers model to a reference case with a Maxwell viscoelastic rheology. The model predictions are evaluated in two ways: first, relative to each other to quantify the effect of parameter variation, and second, for their ability to reproduce well-constrained sea level records from selected locations. Both the resolution and geographic location of the relative sea level observations determine whether the data can distinguish between model cases. Model predictions are most sensitive to the inclusion of transient mantle deformation in regions that are near-field and peripheral relative to former ice sheets. This sensitivity appears particularly true along the North American west coast in the region of the former Cordilleran Ice Sheet, which experienced rapid sea-level fall following deglaciation between 14-12 kyr BP. Relative to the Maxwell case, Burgers models better reproduce this rapid phase of regional postglacial sea level fall. As well, computed goodness-of-fit values in this region show a clear preference for models where transient deformation is present in the whole or lower mantle, and for models where the rigidity of the Kelvin element is weakened relative to the rigidity of the Maxwell element. In contrast, model predictions of relative sea-level change in the far-field show little or weak sensitivity to the inclusion of transient deformation.

How to cite: Simon, K. M., Riva, R. E. M., and Broerse, T.: Identifying geographical patterns of transient deformation in the geological sea level record, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6236, https://doi.org/10.5194/egusphere-egu22-6236, 2022.

EGU22-6829 | Presentations | G3.3

Dependence of GIA-induced gravity change in Antarctica on viscoelastic Earth structure 

Yoshiya Irie, Jun'ichi Okuno, Takeshige Ishiwa, Koichiro Doi, and Yoichi Fukuda

The Antarctic ice mass loss is accelerating due to recent global warming. Changes in Antarctic ice mass have been observed as the gravity change by GRACE (Gravity Recovery and Climate Experiment) satellites. However, the gravity signal includes both the component of the ice mass change and the component of the solid Earth response to surface mass change (Glacial Isostatic Adjustment, GIA). Evaluating the GIA-induced gravity change requires viscoelastic Earth structure and ice history from the last deglaciation.

Antarctica is characterized by lateral heterogeneity of seismic velocity structure. West Antarctica shows relatively low seismic velocities, suggesting low viscosity regions in the upper mantle. On the other hand, East Antarctica shows relatively high seismic velocities, suggesting thick lithosphere. Here we examine the sensitivities of GIA-induced gravity change in Antarctica to upper mantle viscosity and lithosphere thickness using spherically symmetric Earth models.

Results indicate that the gravity field change depends on both the upper mantle viscosity profile and the lithosphere thickness. In particular, the long-wavelength gravity field changes become dominant in the adoption of viscoelastic models with a low viscosity layer beneath the elastic lithosphere. The same trend is also shown in the adoption of viscoelastic models with a thick lithosphere, and there is a trade-off between the structure of the low viscosity layer and the thickness of the lithosphere. This trade-off may reduce the effect of the lateral variations in Earth structure beneath Antarctica on the estimate of Antarctic ice sheet mass change.

How to cite: Irie, Y., Okuno, J., Ishiwa, T., Doi, K., and Fukuda, Y.: Dependence of GIA-induced gravity change in Antarctica on viscoelastic Earth structure, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6829, https://doi.org/10.5194/egusphere-egu22-6829, 2022.

EGU22-7609 | Presentations | G3.3

Deglaciation of the Antarctic Ice Sheet modeled with the coupled solid Earth – ice sheet model system PISM-VILMA 

Torsten Albrecht, Ricarda Winkelmann, Meike Bagge, and Volker Klemann

The Antarctic Ice Sheet is the largest and most uncertain potential contributor to future sea level rise. Understanding involved feedback mechanisms require physically-based models. Confidence in future projections can be improved by models that can reproduce past ice sheet changes, in particular over the last deglaciation. The complex interaction between ice, bedrock and sea level plays an important role in ice sheet instability with a large variety of characteristic response time scales dependent on the heterogeneous Earth structure underneath Antarctica and the ice sheet dynamics.

We have coupled the VIscoelastic Lithosphere and MAntle model (VILMA) to the Parallel Ice Sheet Model (PISM v2.0, www.pism.io) and ran simulations over the last two glacial cycles. In this framework, VILMA considers both viscoelastic deformations of the solid Earth by considering a three-dimensional rheology and a gravitationally self-consistent mass redistribution in the ocean by solving for the sea-level equation. PISM solves for the stress balance for a changing bed topography, which is updated in 100 years coupling intervals and which can directly affect ice sheet flow and grounding line dynamics.

Here, we show first results of coupled PISM-VILMA simulations scored against a database of geological constraints including sea level index points. We discuss sensitivities of model parameters and climatic forcing in preparation for a larger parameter ensemble study. This project is part of the German Climate Modeling Initiative PalMod.

 

How to cite: Albrecht, T., Winkelmann, R., Bagge, M., and Klemann, V.: Deglaciation of the Antarctic Ice Sheet modeled with the coupled solid Earth – ice sheet model system PISM-VILMA, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7609, https://doi.org/10.5194/egusphere-egu22-7609, 2022.

EGU22-7906 | Presentations | G3.3

Glacial Isostatic Adjustment in Antarctica : a rheological study 

Alexandre Boughanemi and Anthony Mémin

 The Antarctic Ice Sheet (AIS) is the largest ice sheet on Earth that has known important mass 
 changes during the last 20 kyrs. These changes deform the Earth and modify its gravity field, 
 a process known as Glacial Isostatic Adjustment (GIA). GIA is directly influenced by the mechanical
 properties and internal structure of the Earth, and is monitored using Global Navigation Satellite 
 System positioning or gravity measurements. However, GIA in Antarctica remains poorly constrained  
 due to the cumulative effect of past and present ice-mass changes, the unknown history of the past
 ice-mass change, and the uncertainties of the mechanical properties of the Earth. The viscous 
 deformation due to GIA is usually modeled using a Maxwell rheology. However, other geophysical
 processes employ Andrade (tidal deformation) or Burgers (post-seismic deformation) laws that could 
 result in a more rapid response of the Earth. We investigate the effect of using these
 different rheology laws to model GIA-induced deformation in Antarctica.  

Employing the ALMA and TABOO softwares, we use the Love number and Green functions formalism to
compute the surface motion and the gravity changes induced by the past and present ice-mass redistributions.
We use the elastic properties and the radial structure of the preliminary reference Earth model (PREM) and the
viscosity profile given by Hanyk (1999). The deformation is computed for the three rheological laws mentioned
above using ICE-6G and elevation changes from ENVISAT (2002-2010) to represent the past and present changes
of the AIS, respectively. 

We obtain that the three rheological laws lead to significant Earth response within a 20 kyrs time interval since
the beginning of the ice-mass change. The differences are the largest between Maxwell and Burgers rheologies
during the 500 years following the beginning of the surface-mass change. Regarding the response to present
changes in Antarctica, the largest discrepancies are obtained in regions with the greatest current melting rates,
namely Thwaites and Pine Island Glacier in West Antarctica. Uplift rates computed twelve years after the end of
the present melting using Burgers and Andrade rheologies are five and two times larger than those obtained
using Maxwell, respectively. 

How to cite: Boughanemi, A. and Mémin, A.: Glacial Isostatic Adjustment in Antarctica : a rheological study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7906, https://doi.org/10.5194/egusphere-egu22-7906, 2022.

EGU22-8112 | Presentations | G3.3

Investigating the Sensitivity of North Sea Glacial Isostatic Adjustment during the Last Interglacial to the Penultimate Deglaciation of Global Ice Sheets 

Oliver Pollard, Natasha Barlow, Lauren Gregoire, Natalya Gomez, Víctor Cartelle, Jeremy Ely, and Lachlan Astfalck

The Last Interglacial (LIG; MIS 5e) period (130 - 115 ka) saw the last time in Earth’s history that polar temperatures reached 3 - 5 °C above pre-industrial values causing the Greenland and Antarctic ice sheets to shrink to sizes smaller than those of today. Similar polar temperature increases are predicted in the coming decades and the LIG period could therefore help to shed light on ice-sheet and sea-level responses to a warming world. 

LIG estuarine sediments preserved in the North Sea region are promising study sites for identification of the Antarctic ice sheet's relative contribution to LIG sea level, as well as for the reconstruction of both the magnitude and rate of LIG sea-level change during the interglacial. For these purposes, sea-level records in the region must be corrected for the impacts of glacial isostatic adjustment (GIA) which is primarily a consequence of two components: the evolution of terrestrial ice masses during the Penultimate Deglaciation (MIS 6), predominantly the near-field Eurasian ice sheet, and the viscoelastic structure of the solid Earth. 

The relative paucity of geological constraints on characteristics of the MIS 6 Eurasian ice sheet makes it challenging to evaluate its effect on sea level in the North Sea region. In order to model the Eurasian ice extent, thickness, and volume during the Penultimate Deglaciation we use a simple ice sheet model (Gowan et al. 2016), calibrated against models of the Last Glacial Maximum. By employing a gravitationally consistent sea-level model (Kendall et al. 2005), we generate a large ensemble of GIA outputs that spans the uncertainty in parameters controlling both the viscoelastic earth model and the evolution of global ice sheets during the Penultimate Deglaciation. By performing spatial sensitivity analysis with this ensemble, we are able to demonstrate the relative importance of each parameter in controlling North Sea GIA. Our comprehensive approach to exploring uncertainties in both the global ice sheet evolution and solid earth response provides significant advances in our understanding of LIG sea level.

How to cite: Pollard, O., Barlow, N., Gregoire, L., Gomez, N., Cartelle, V., Ely, J., and Astfalck, L.: Investigating the Sensitivity of North Sea Glacial Isostatic Adjustment during the Last Interglacial to the Penultimate Deglaciation of Global Ice Sheets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8112, https://doi.org/10.5194/egusphere-egu22-8112, 2022.

EGU22-8350 | Presentations | G3.3 | Highlight

Reconstructing large scale differential subsidence in the Netherlands using a spatio-temporal 3D paleo-groundwater level interpolation 

Kim de Wit, Roderik S.W. van de Wal, and Kim M. Cohen

Subsidence is a land use problem in the western and northern Netherlands, especially where both shallow soft soil subsidence and deeper subsidence components, including glacio-isostatic adjustment (GIA), add up. The aim of this study is to improve the estimation of the GIA component within the total subsidence signal across the Netherlands during the Holocene, using coastal plain paleo-water level markers. Throughout the Holocene, the GIA induced subsidence in the Netherlands has been spatially and temporally variant, as shown by previous studies that used GIA modelling and geological relative sea-level rise reconstructions. From the latter work, many field data points are available based on radiocarbon dated coastal basal peats of different age and vertical position. These reveal Holocene relative sea-level rise to have been strongest in the Wadden Sea in the Northern Netherlands. This matches post-glacial GIA subsidence (forebulge collapse) as modelled for the Southern North Sea, being located in the near-field of Scandinavian and British former ice masses.

In this study, geological data analysis of RSL and other paleo-water level data available from the Dutch coastal plain for the Holocene period is considered in addition. The analysis takes the form of designing and executing a 3D interpolation (kriging with a trend: KT), where paleo-water level Z(x,y,age) is predicted and the field-data points are the observations (Age, X, Y and Z as knowns). We use a spatio-temporal 3D grid that covers the Dutch coastal plain, and reproduces and unifies earlier constructed sea level curves and high-resolution sampled individual sites (e.g. Rotterdam). The function describing the trend part of the interpolation separates linear and non-linear components of relative water level rise, i.e.: long-term background subsidence and shorter-term GIA subsidence signal and postglacial water level rise. The kriging part then processes remaining subregional patterns. The combined reconstruction thus yields a spatially continuous parameterization of regional trends that (i) allows to separate subsidence from water level rise terms, and (ii) is produced independently of GIA modelling to enable cross-comparison. Results are presented for the coastal plain of the Netherlands ([SW] Zeeland – Rotterdam – Holland – Wadden Sea – Groningen [NE]). The percentage of the total coastal-prism accommodation space that appears due to subsidence, from the south to the north of the study area increases by 20%. Holocene-averaged subsidence rates from the first analysis ranged from ca. 0.1 m/kyr (Zeeland) to 0.4 m/kyr (Groningen), which is 5-10 times larger than present-day GPS/GNSS-measured rates.

The research presented in this abstract is part of the project Living on soft soils: subsidence and society (grantnr.: NWA.1160.18.259). 

How to cite: de Wit, K., van de Wal, R. S. W., and Cohen, K. M.: Reconstructing large scale differential subsidence in the Netherlands using a spatio-temporal 3D paleo-groundwater level interpolation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8350, https://doi.org/10.5194/egusphere-egu22-8350, 2022.

EGU22-9485 | Presentations | G3.3

An adaptive-triangular fully coupled 3D ice-sheet–sea-level model 

Jorjo Bernales, Tijn Berends, and Roderik van de Wal

Regional sea-level change and the deformation of the solid Earth can lead to important feedbacks on the long- and short-term evolution and stability of ice sheets. A rigorous manner of accounting for these feedbacks in model-based ice-sheet reconstructions and projections, is to establish a two-way coupling between an ice-sheet and a sea-level model. However, the individual requirements of each of these two components such as a global, long ice sheet load history or a high ice-model resolution over critical sectors of an ice sheet are at present not easy to combine in terms of computational feasibility. Here, we present a coupling between the ice-sheet model UFEMISM, which solves a range of approximations of the stress balance on a dynamically adaptive irregular triangular mesh, and the gravitationally self-consistent sea-level model SELEN, which incorporates the glacial isostatic adjustment for a radially symmetric, viscoelastic and rotating Earth, including coastline migration. We show global simulations over glacial cycles, including the North American, Eurasian, Greenland, and Antarctic ice sheets, and compare its performance and results against commonly used alternatives.

How to cite: Bernales, J., Berends, T., and van de Wal, R.: An adaptive-triangular fully coupled 3D ice-sheet–sea-level model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9485, https://doi.org/10.5194/egusphere-egu22-9485, 2022.

EGU22-9968 | Presentations | G3.3

Interacting melt-elevation and glacial isostatic adjustment feedbacks allow for distinct dynamic regimes of the Greenland Ice Sheet 

Maria Zeitz, Jan M. Haacker, Jonathan F. Donges, Torsten Albrecht, and Ricarda Winkelmann

Interacting feedbacks play an important role in governing the stability of the Greenland Ice Sheet under global warming. Here we study the interaction between the positive melt-elevation feedback and the negative feedback from glacial isostatic adjustment (GIA), and how they affect the ice volume of the Greenland Ice Sheet on long time scales. We therefore use the Parallel Ice Sheet Model (PISM) coupled to a simple solid Earth model (Lingle-Clark) in idealized step-warming experiments. Our results suggest that for warming levels above 2°C, Greenland could become essentially ice-free on the long-term, mainly as a result of surface melting and acceleration of ice flow. The negative GIA feedback can mitigate ice losses and promote a partial recovery of the ice volume.

Exploring the full factorial parameter space which determines the relative strength of the two feedbacks reveals that four distinct dynamic regimes are possible: from stabilization, via recovery and self-sustained oscillations to the irreversible collapse of the Greenland Ice Sheet. In the recovery regime an initial ice loss is reversed and the ice volume stabilized at 61-93% of the present day volume. For certain combinations of temperature increase, atmospheric lapse rate and Earth mantle viscosity, the interaction of the GIA feedback and the melt-elevation feedback leads to self-sustained, long-term oscillations in ice-sheet volume with oscillation periods of tens to hundreds of thousands of years and oscillation amplitudes between 15-70% of present-day ice volume. This oscillatory regime reveals a possible mode of internal climatic variability in the Earth system on time scales on the order of 100,000 years that may be excited by or synchronized with orbital forcing or interact with glacial cycles and other slow modes of variability.

How to cite: Zeitz, M., Haacker, J. M., Donges, J. F., Albrecht, T., and Winkelmann, R.: Interacting melt-elevation and glacial isostatic adjustment feedbacks allow for distinct dynamic regimes of the Greenland Ice Sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9968, https://doi.org/10.5194/egusphere-egu22-9968, 2022.

Geodetic time series from autonomous GNSS systems distributed across Antarctica are revealing unexpected patterns and startling rates of crustal deformation due to GIA.  Linked with seismic mapping and derived rheological properties of the Antarctic crust and mantle, and with new modeling capabilities, our understanding of the timescales of GIA response to ice sheet change is swiftly advancing.  Rapid GIA response allows for cryosphere-solid earth interactions that can alter ice sheet behavior on decadal and centennial timescales.  Continued progress in understanding how such feedbacks may influence future contributions of polar ice sheets to global sea level change requires continuing and expanding our geodetic observations. What frameworks can lead to implementation of this goal?  U.S. and international science vision documents pertaining to geodynamics, the changing cryosphere and sea level, all point to international collaborative efforts as the way to achieve ambitious science goals and extend observational capacities in polar regions.  SCAR research programmes facilitated the network vision and collaborative relations that led to the POLENET (POLar Earth observing NETwork) network of geophysical and geodetic instruments during the International Polar Year 2007-08. Can the SCAR INSTANT programme provide a framework for collaborative initiatives between national Antarctic programs to form a sustainable model to support acquisition of the observations required to meet community science objectives?  Let’s consider the ‘grass roots’ actions by the science community needed to push international, interdisciplinary science frameworks forward.

How to cite: Wilson, T. J.: GNSS Observations of Antarctic Crustal Deformation – International Framework for Future Networks?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10610, https://doi.org/10.5194/egusphere-egu22-10610, 2022.

EGU22-10884 | Presentations | G3.3

Effect of Icelandic hotspot on Mantle viscosity in southeast Greenland 

Valentina R. Barletta, Wouter van der Wal, Andrea Bordoni, and Shfaqat Abbas Khan

Recent studies suggest the hotspot currently under Iceland was located beneath eastern Greenland at ~40 Ma BP and that the upwelling of hot material from the Iceland plume towards Greenland is ongoing. A warm upper mantle has a low viscosity, which in turn causes the solid Earth to rebound much faster to deglaciation. In the area of the Kangerlussuaq glacier, a large GPS velocities residual after removing predicted purely elastic deformations caused by present-day ice loss suggests the possibility of such fast rebound to little ice age (LIA) deglaciation. Here we investigate the lithospheric thickness and the mantle viscosity structure beneath SE-Greenland by means of model predictions of solid Earth deformation driven by a low viscosity mantle excited by the LIA deglaciation to the present day. From the comparison of such modeled deformations with the GPS residual, we conclude that 1) a rather thick lithosphere is preferred (90-100 km) 2) and the upper mantle most likely has a viscosity that changes with depth. Assuming a two layer upper mantle, it is not well constrained which part of the upper mantle has to be low, with a preference for low viscosity in the deeper upper mantle.

To understand such results we implemented forward modelling with more realistic earth models, relying on improvements in seismic models, petrology and gravity data. This yields 3D viscosity maps that can be compared to inferences based on the 1D model and forms the basis for 3D GIA models. The conclusion based on the 1D model can be explained with 3D Earth models. In the area of the Kangerlussuaq glacier the seismic derived viscosities prefer a higher viscosity layer above a lower viscosity one. This stems from the slow decrease in viscosity with depth. The layer that is characterized as shallow upper mantle still contains shallow regions with low temperatures, while the deeper upper mantle reaches low viscosities. Generally, for GIA earth models the “higher above lower” viscosity layering is unusual. However, the analysis of the 1D model clearly shows this to be one of the preferred model regions, in combination with a large lithosphere thickness of 100 km. This is a notable result that draws attention to the importance of shallow layering in GIA models. 

How to cite: Barletta, V. R., van der Wal, W., Bordoni, A., and Khan, S. A.: Effect of Icelandic hotspot on Mantle viscosity in southeast Greenland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10884, https://doi.org/10.5194/egusphere-egu22-10884, 2022.

EGU22-10942 | Presentations | G3.3

Separating of Glacial Isostatic Adjustment (GIA) across Antarctica from GRACE/GRACE-FO observations via Independent Component Analysis (ICA) 

Tianyan Shi, Yoichi Fukuda, Koichiro Doi, and Jun’ichi Okuno

The redistribution of the near-surface solid Earth due to glacial isostatic adjustment (GIA), which is the ongoing response of the solid Earth due to changes in the ice-ocean load following the Last Glacial Maximum, has a direct impact on the inferred Antarctic Ice Sheet (AIS) mass balance from gravimetric data acquired during the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions.

However, sparse in-situ observation networks across Antarctica have led to the inability to effectively constrain the GIA effect. Here, we analyze the mass change patterns across Antarctica via independent component analysis (ICA), a statistics-based blind source separation method to extract signals from complex datasets, in an attempt to reduce uncertainties in the glacial isostatic adjustment (GIA) effects and improve understanding of AIS mass balance.

The results reveal that GIA signal could be directly separated from GRACE/GRACE-FO observations without introducing any external model.  Although the GIA signal cannot be completely isolated, the correlation coefficients between ICA-separated GIA, and the ICE-5G and ICE-6G models are 0.692 and 0.691, respectively. The study demonstrates the possibility of extracting GIA effects directly from GRACE/GRACE-FO observations.

How to cite: Shi, T., Fukuda, Y., Doi, K., and Okuno, J.: Separating of Glacial Isostatic Adjustment (GIA) across Antarctica from GRACE/GRACE-FO observations via Independent Component Analysis (ICA), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10942, https://doi.org/10.5194/egusphere-egu22-10942, 2022.

EGU22-11569 | Presentations | G3.3

The influence of Earth’s hypsometry on global sea level through a glacial cycle and into the future 

Vivi Kathrine Pedersen, Natalya Gomez, Gustav Pallisgaard-Olesen, Julius Garbe, Andy Aschwanden, Ricarda Winkelmann, and Jerry Mitrovica

Earth’s topography and bathymetry is shaped by a complex interplay between solid-Earth processes that deform the Earth from within and the surface processes that modify the outer shape of the Earth. At the surface, an ultimate baselevel set by global sea level marks the defining transition from erosion to deposition. Over geological time scales, this baselevel has resulted in a distinct hypsometric distribution (distribution of surface area with elevation), with the highest concentration of surface area focused in a narrow elevation range near present-day sea level.

This particular feature in Earth’s hypsometry makes the global land fraction very sensitive to changes in sea level. Indeed, a sea-level change will result in a significant change in the land fraction as dictated by the hypsometric distribution, thereby modulating the very same sea-level change. However, it remains unexplored exactly how sea-level changes have modified the global land fraction over past glacial cycles and into the future.

Here we analyse how Earth’s hypsometry has changed over the last glacial cycle as large ice sheets waxed and waned particularly in Scandinavia and North America. These changes in global ice volume resulted in a significant global excursion in sea level, modulated regionally by solid-Earth deformation, gravitational effects, and effects from Earth’s rotation. These changes modified Earth’s hypsometry, and therefore the global land fraction at any given time. Consequently, we can map out how Earth’s hypsometry has influenced global mean sea level (GMSL) over time. To examine this relationship between Earth’s hypsometry and sea level further, we look to the deep future, to a scenario where both the Greenland Ice Sheet and the Antarctic Ice Sheets will melt away completely over multi-millennial timescales. This scenario is not meant to represent a realistic future scenario per se, but it allows us to define the hypsometric GMSL correction needed for any GMSL that the Earth has experienced recently or will experience in the future.

How to cite: Pedersen, V. K., Gomez, N., Pallisgaard-Olesen, G., Garbe, J., Aschwanden, A., Winkelmann, R., and Mitrovica, J.: The influence of Earth’s hypsometry on global sea level through a glacial cycle and into the future, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11569, https://doi.org/10.5194/egusphere-egu22-11569, 2022.

EGU22-12689 | Presentations | G3.3

Improving past and future relative sea-level constraints for the Norwegian coast 

Thomas R. Lakeman, F. Chantel Nixon, Anders Romundset, Matthew J.R. Simpson, John Inge Svendsen, Kristian Vasskog, Stein Bondevik, Glenn Milne, and Lev Tarasov

New research aims to improve relative sea-level (RSL) projections for the Norwegian coast. The main objectives are to: i) collect observations of past RSL changes, ranging from the end of the last ice age to the last century, ii) develop a high-quality database of post-glacial sea-level index points (SLIPs) for the Norwegian coast, and to iii) improve our understanding of past and future vertical land motion using glacial isostatic adjustment (GIA) modelling. To now, our collection of new empirical data has focussed on three significant, but enigmatic RSL histories that are not adequately reproduced in existing GIA models: very recent stillstands and transgressions documented by historical tide gauge records, rapid transgressions during the early- to mid-Holocene Tapes period, and abrupt transgressions during the latest Pleistocene Younger Dryas chronozone. Ongoing field sampling is focussed on developing high-resolution RSL trends from salt marshes, isolation basins, and raised beaches, using multiple biostratigraphic and geochemical proxies (i.e. micropaleontology, macrofossils, x-ray fluorescence, C/N) and dating techniques (i.e. Pb-210, Cs-137, C-14, tephrochronology, geochemical markers). Results from various localities spanning the Norwegian coast provide robust constraints for the timing and rate of RSL change during the Younger Dryas and Tapes chronozones. Additional results providing new estimates of very recent RSL trends in southwest Norway are presented by Holthuis et al. (Late Holocene sea-level change and storms in southwestern Norway based on new data from intertidal basins and salt marshes; Session CL5.2.2). These new and emerging constraints are being integrated into a post-glacial RSL database that incorporates high-quality data from the entire Norwegian coastline. Over 1000 SLIPs have been assembled from published studies. These existing data were updated using current radiocarbon calibration curves, high-resolution digital elevation models, new field observations, and new quantitative estimates of relevant uncertainties. Ongoing GIA modelling is utilizing the new RSL database, a glaciological model that freely simulates ice sheet changes, as well as geodetic and ice margin chronology constraints, to develop rigorous uncertainty estimates for present and future GIA along the Norwegian coast.

How to cite: Lakeman, T. R., Nixon, F. C., Romundset, A., Simpson, M. J. R., Svendsen, J. I., Vasskog, K., Bondevik, S., Milne, G., and Tarasov, L.: Improving past and future relative sea-level constraints for the Norwegian coast, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12689, https://doi.org/10.5194/egusphere-egu22-12689, 2022.

Uncertainty in present-day glacial isostatic adjustment (GIA) rates represent at least 44% of the total gravity-based ice mass balance signal over Antarctica. Meanwhile, physical couplings between solid Earth, sea level and ice dynamics enhance the dependency of the spatiotemporally varying GIA signal on 3D rheology. For example, the presence of low-viscosity mantle beneath melting marine-based ice sheet sectors such as the Amundsen Sea Embayment may delay or even prevent unstable grounding line retreat. Improved knowledge of upper mantle thermomechanical structure is therefore required to refine estimates of current and projected ice mass balance.

Here, we present a Bayesian inverse method for mapping shear wave velocities from high-resolution adjoint tomography into thermomechanical structure using a calibrated parameterisation of anelasticity at seismic frequency. We constrain the model using regional geophysical data sets containing information on upper mantle temperature, attenuation and viscosity structure. The Globally Adaptive Scaling Within Adaptive Metropolis (GASWAM) modification of the Metropolis-Hastings algorithm is utilised to allow efficient exploration of the multi-dimensional parameter space. Our treatment allows formal quantification of parameter covariances, and naturally permits us to propagate uncertainties in material parameters into uncertainty in thermomechanical structure.

We find that it is possible to improve agreement on steady state viscosity structure between tomographic models by approximately 30%, and reduce its uncertainty by an order of magnitude as compared to a forward-modelling approach. Direct access to temperature structure allows us to estimate lateral variations in lithospheric thickness, geothermal heat flow, and their associated uncertainties.

How to cite: Hazzard, J., Richards, F., Roberts, G., and Goes, S.: Reducing Uncertainty in Upper Mantle Rheology, Lithospheric Thickness and Geothermal Heat Flow Using a Bayesian Inverse Framework to Calibrate Experimental Parameterisations of Anelasticity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12967, https://doi.org/10.5194/egusphere-egu22-12967, 2022.

This article presents a comprehensive benchmark study for the newly updated and publicly available finite element code CitcomSVE for modeling dynamic deformation of a viscoelastic and incompressible planetary mantle in response to surface and tidal loading. A complete description of CitcomSVE’s finite element formulation including calculations of the sea-level change, polar wander, apparent center of mass motion, and removal of mantle net rotation is presented. The 3-D displacements and displacement rates and the gravitational potential anomalies are solved with CitcomSVE for three benchmark problems using different spatial and temporal resolutions: 1) surface loading of single harmonics, 2) degree-2 tidal loading, and 3) the ICE-6G GIA model. The solutions are compared with semi-analytical solutions for error analyses. The benchmark calculations demonstrate the accuracy and efficiency of CitcomSVE. For example, for a typical ICE-6G GIA calculation with a 122-ky glaciation-deglaciation history, time increment of 100 years, and ~50 km (or ~0.5 degree) surface horizontal resolution, it takes ~4.5 hours on CPU 96 cores to complete with about 1% and 5% errors for displacements and displacement rates, respectively. Error analyses shows that CitcomSVE achieves a second order accuracy, but the errors are insensitive to temporal resolution. CitcomSVE achieves the parallel computation efficiency >75% for using up to 6,144 CPU cores on a parallel supercomputer. With its accuracy, computing efficiency and its open-source public availability, CitcomSVE is a powerful tool for modeling viscoelastic deformation of a planetary mantle in response to surface and tidal loading problems. 

How to cite: Zhong, S., Kang, K., Aa, G., and Qin, C.: CitcomSVE: A Three-dimensional Finite Element Software Package for Modeling Planetary Mantle’s Viscoelastic Deformation in Response to Surface and Tidal Loads, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13136, https://doi.org/10.5194/egusphere-egu22-13136, 2022.

EGU22-13323 | Presentations | G3.3

Mantle viscosity derived from geoid and different land uplift data in Greenland 

Mohammad Bagherbandi, Hadi Amin, Linsong Wang, and Masoud Shirazian

The Earth’s mass redistribution due to deglaciation and recent ice sheet melting causes changes in the Earth’s gravity field and vertical land motion in Greenland. The changes are because of ongoing mass redistribution and related elastic (on a short time scale) and viscoelastic (on time scales of a few thousands of years) responses. These signatures can be used to determine the mantle viscosity. In this study, we infer the mantle viscosity associated with the glacial isostatic adjustment (GIA) and long-wavelength geoid beneath the Greenland lithosphere. The viscosity is determined based on a spatio-spectral analysis of the Earth’s gravity field and the land uplift rate in order to find the GIA-related gravity field. We used and evaluated different land uplift data, i.e. the vertical land motions obtained by the Greenland Global Positioning System (GPS) Network (GNET), GRACE and Glacial Isostatic Adjustment (GIA) data. In addition, a  combined land uplift rate using the Kalman filtering technique is presented in this study. We extract the GIA-related gravity signals by filtering the other effects due to the deeper masses i.e. core-mantle (related to long-wavelengths) and topography (related to short-wavelengths). To do this, we applied correlation analysis to detect the best harmonic window. Finally, the mantle viscosity using the obtained GIA-related gravity field is estimated. Using different land uplift rates, one can obtain different GIA-related gravity fields. For example, different harmonic windows were obtained by employing different land uplift datasets, e.g. the truncated geoid model with a harmonic window between degrees 10 to 39 and 10 to 25 showed a maximum correlation with the GIA model ICE-6G (VM5a) and the combined land uplift rates, respectively. As shown in this study, the mantle viscosities of 1.6×1022 Pa s and 0.9×1022 Pa s for a depth of 200  to 650  km are obtained using ICE-6G (VM5a) model and the combined land uplift model, respectively, and the GIA-related gravity potential signal.

How to cite: Bagherbandi, M., Amin, H., Wang, L., and Shirazian, M.: Mantle viscosity derived from geoid and different land uplift data in Greenland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13323, https://doi.org/10.5194/egusphere-egu22-13323, 2022.

EGU22-2188 | Presentations | SM2.1

Locating Nearby Explosions in Fürstenfeldbruck, Germany, Combining 8 Rotational Sensors 

Gizem Izgi, Eva P.S. Eibl, Frank Krüger, and Felix Bernauer

The seismic wavefield can only be completely described by the combination of translation, rotation and strain. Direct measurement of rotational motions in combination with the translational motions allow observing the complete seismic ground motion. Portable blueSeis-3A (iXblue) sensors allow to measure 3 components of rotational motions. We co-located Nanometrics Horizon seismometers with blueSeis-3A sensors and measured the full wavefield.

An active source experiment was performed in Fürstenfeldbruck, Germany in November 2019, in order to further investigate the performance of multiple rotational instruments in combination with seismometers. Within the scope of the experiment 5 explosions took place. For the first two explosions, all 8 rotational sensors were located inside of a bunker while for the rest of explosions, 4 sensors each were located at 2 different sites of the field. One group was co-located with translational seismometers. This is the first time the recordings of 8 rotational sensors are combined for event analysis and location. We calculate and intersect the back azimuths and derive phase velocities of the five explosions.

We discuss the reliability of the data recorded by the rotational sensors for further investigations in other environments.

How to cite: Izgi, G., Eibl, E. P. S., Krüger, F., and Bernauer, F.: Locating Nearby Explosions in Fürstenfeldbruck, Germany, Combining 8 Rotational Sensors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2188, https://doi.org/10.5194/egusphere-egu22-2188, 2022.

EGU22-2455 | Presentations | SM2.1

Understanding surface-wave modal content for high-resolution imaging with ocean-bottom distributed acoustic sensing 

Zack Spica, Loïc Viens, Mathieu Perton, Kiwamu Nishida, Takeshi Akuhara, Masanao Shinohara, and Tomoaki Yamada

Ocean Bottom Distributed Acoustic Sensing (OBDAS) is emerging as a new measurement method providing dense, high-fidelity, and broadband seismic observations from fiber-optic cables. Here, we use ~40 km of a telecommunication cable located offshore the Sanriku region, Japan, and apply ambient seismic field interferometry to obtain an extended 2-D high-resolution shear-wave velocity model. In some regions of the array, we observe and invert more than 20 higher modes and show that the accuracy of the retrieval of some modes strongly depends on the processing steps applied to the data. In addition, numerical simulations suggest that the number of modes that can be retrieved is proportional to the local velocity gradient under the cable. Regions with shallow low-velocity layers tend to contain more modes than those located in steep bathymetry areas, where sediments accumulate less. Finally, we can resolve sharp horizontal velocity contrasts under the cable suggesting the presence of faults and other sedimentary features. Our results provide new constraints on the shallow submarine structure in the area and further demonstrate the potential of OBDAS for offshore geophysical prospecting.

How to cite: Spica, Z., Viens, L., Perton, M., Nishida, K., Akuhara, T., Shinohara, M., and Yamada, T.: Understanding surface-wave modal content for high-resolution imaging with ocean-bottom distributed acoustic sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2455, https://doi.org/10.5194/egusphere-egu22-2455, 2022.

EGU22-2563 | Presentations | SM2.1

On the Multi-component Information of DAS for Near-Surface Seismic: A Pilot Field Experiment in the Groningen Area 

Musab Al Hasani, Guy Drijkoningen, and Kees Wapenaar

In a surface-seismic setting, Distributed Acoustic Sensing (DAS) is still not a widely adopted method for near-surface characterisation, especially for reflection seismic imaging, despite the dense spatial sampling it provides over long distances. This is mainly due to the decreased broadside sensitivity that DAS suffers from when buried horizontally in the ground (that is when the upgoing wavefield (e.g. reflected wavefield) is perpendicular to the optical fibre). This is unlike borehole settings (e.g. zero-offset Vertical Seismic Profiling), where DAS has been widely adopted for many monitoring applications. Advancements in the field, like shaping the fibre to a helix, commonly known as helically wound fibre, allow better sensitivity for the reflections.

The promise of spatially dense seismic data over long distances is an attractive prospect for retrieving the local variations of near-surface properties. This is particularly valuable for areas impacted by induced seismicity, as is the case in the Groningen Province in the north of The Netherlands,  where near-surface properties, mostly composed of clays and peats, play an essential role on the amount of damage on the very near-surface and the structures built on it. Installing fibre-optic cables for passive and active measurements is valuable in this situation. We installed multiple cables containing different fibre configurations of straight and helically wound fibres, buried in a 2-m deep trench. The combination of the different fibre configurations allows us to obtain multi-component information. We observe differences in the amplitude and phase information, suggesting that these differences can be used for separating the different components of the wave motion. We also see that using enhanced backscatter fibres, reflection images can be obtained for the helically wound fibre as well as the straight fibre, despite the decreased broadside sensitivity for the latter.

How to cite: Al Hasani, M., Drijkoningen, G., and Wapenaar, K.: On the Multi-component Information of DAS for Near-Surface Seismic: A Pilot Field Experiment in the Groningen Area, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2563, https://doi.org/10.5194/egusphere-egu22-2563, 2022.

EGU22-3404 | Presentations | SM2.1 | Highlight

Fibre-optic observation of volcanic tremor through floating ice sheet resonance 

Andreas Fichtner, Sara Klaasen, Sölvi Thrastarson, Yesim Cubuk-Sabuncu, Patrick Paitz, and Kristin Jonsdottir

We report on the indirect observation of low-frequency tremor at Grimsvötn, Iceland, via resonance of an ice sheet, floating atop a volcanically heated subglacial lake.

Entirely covered by Europe’s largest glacier, Vatnajökull, Grimsvötn is among Iceland’s largest and most active volcanoes. In addition to flood hazards, ash clouds pose a threat to settlements and air traffic, as direct interactions between magma and meltwater cause Grímsvötn to erupt explosively. To study the seismicity and structure of Grimsvötn in detail, we deployed a 12.5 km long fibre-optic cable around and inside the caldera, which we used for Distributed Acoustic Sensing (DAS) measurements in May 2021.

The experiment revealed a previously unknown level of seismicity, with nearly 2’000 earthquake detections in less than one month. Furthermore, the cable segment within the caldera recorded continuous and nearly monochromatic oscillations at 0.23 Hz. This corresponds to the expected fundamental-mode resonance frequency of flexural waves within the floating ice sheet, which effectively acts as a damped harmonic oscillator with Q around 15.

In spite of the ice sheet being affected by ambient noise at slightly lower frequencies, the resonance amplitude does not generally correlate with the level of ambient noise throughout southern Iceland. It follows that an additional and spatially localised forcing term is required to explain the observations. A linear inversion reveals that the forcing acts continuously, with periods of higher or lower activity alternating over time scales of a few days.

A plausible explanation for the additional resonance forcing is volcanic tremor, most likely related to geothermal activity, that shows surface expressions in the form of cauldrons and fumaroles along the caldera rim. Being largely below the instrument noise at channels outside the caldera, the ice sheet resonance acts as a magnifying glass that increases tremor amplitudes to an observable level, thereby providing a new and unconventional form of seismic volcano monitoring.

How to cite: Fichtner, A., Klaasen, S., Thrastarson, S., Cubuk-Sabuncu, Y., Paitz, P., and Jonsdottir, K.: Fibre-optic observation of volcanic tremor through floating ice sheet resonance, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3404, https://doi.org/10.5194/egusphere-egu22-3404, 2022.

EGU22-3728 | Presentations | SM2.1

Detecting earthen dam defects using seismic interferometry monitoring on Distributed Acoustic Sensing data 

Aurelien Mordret, Anna Stork, Sam Johansson, Anais Lavoue, Sophie Beaupretre, Romeo Courbis, Ari David, and Richard Lynch

Earthen dams and embankments are prone to internal erosion, their most significant source of failure. Standard monitoring techniques often measure erosion effects when they appear at the surface, reducing the potential response time to address the problem before failure. Through their integrative sensitivity along their propagation, seismic signals are well suited to assess mechanical changes in the bulk of a dam. Moreover, seismic velocities are strongly sensitive to porosity, pore pressure, and water saturation, physical properties that vary the most for internal erosion. Here, we used fiber optics and a Distributed Acoustic Sensing (DAS) array installed on an experimental dam with built-in defects to record the ambient seismic wavefield for one month while the dam reservoir is gradually filled up. The position and nature of the dam defects are unknown to us, to allow an actual blind-detection experiment. We computed cross-correlations between equidistant channels along the dam every 15 minutes and monitored the relative seismic velocity changes at each location for the whole month. The results show a strong correlation of the velocity changes with the water level in the reservoir at all locations along the dam. We also observe systematic deviations from the average velocity change trend. We interpret these anomalies as the effects of the built-in defects placed at different positions in the bulk of the dam. The careful analysis of the residual velocity changes allows us to hypothesize on the position and nature of the defects. 

How to cite: Mordret, A., Stork, A., Johansson, S., Lavoue, A., Beaupretre, S., Courbis, R., David, A., and Lynch, R.: Detecting earthen dam defects using seismic interferometry monitoring on Distributed Acoustic Sensing data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3728, https://doi.org/10.5194/egusphere-egu22-3728, 2022.

EGU22-3729 | Presentations | SM2.1

The Potential of DAS on Underwater Suspended Cables for Oceanic Current Monitoring and Failure Assessment of Fiber Optic Cables 

Daniel Mata, Jean-Paul Ampuero, Diego Mercerat, Diane Rivet, and Anthony Sladen

Distributed Acoustic Sensing (DAS) enables the use of existing underwater telecommunication cables as multi-sensor arrays. The great majority of underwater telecommunication cables are deployed from the water surface and the coupling between the cable and the seafloor is not fully controlled. This implies that there exists many poorly coupled cable segments less useful for seismological research. In particular, underwater cables include segments that are suspended in the water column across seafloor valleys or other bathymetry irregularities. However, it might be possible to use DAS along the suspended sections of underwater telecommunication cables for other purposes. A first one investigated here is the ability to monitor deep-ocean currents. It is common to observe that some particular sections of a cable oscillate with great amplitudes. These oscillations are commonly interpreted as due to vortex shedding induced by the currents. We investigate this hypothesis by estimating the oceanic current speeds from vortex frequencies measured in two underwater fiber optic cables located at Methoni, Greece, and another in Toulon, France. Our results in Greece are in agreement with in-situ historical measurements of seafloor currents while our estimations in Toulon are compatible with synchronous measurements of a nearby current meter. These different measurements therefore point to the possibility to exploit DAS measurements as a tool to monitor the activity of seafloor currents. A second possible application of DAS is to estimate how the cable is coupled to the seafloor, even in the absence of the strong oscillations associated to vortex shedding. For that, we have analyzed the spectral signature of the different cables. Some sections feature fundamental frequencies as expected from a theoretical model of in-plane vibration of hanging cables. By analyzing how the fundamental frequencies change along the cable, we are potentially inferring the contact points of the cable with the seafloor, which will promote fatigue of the cable and potential failure. This mapping of the coupling characteristics of the cable with the seafloor could also be useful to better interpret other DAS signals.

How to cite: Mata, D., Ampuero, J.-P., Mercerat, D., Rivet, D., and Sladen, A.: The Potential of DAS on Underwater Suspended Cables for Oceanic Current Monitoring and Failure Assessment of Fiber Optic Cables, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3729, https://doi.org/10.5194/egusphere-egu22-3729, 2022.

EGU22-4014 | Presentations | SM2.1

Near-field observations of snow-avalanches propagating over a fiber-optic array 

Patrick Paitz, Pascal Edme, Andreas Fichtner, Nadja Lindner, Betty Sovilla, and Fabian Walter

We present and evaluate array processing techniques and algorithms for the characterization of snow avalanche signals recorded with Distributed Acoustic Sensing (DAS).

Avalanche observations rely on comprehensive measurements of sudden and rapid snow mass movement that is hard to predict. Conventional avalanche sensors are limited to observations on or above the surface. Recently, seismic sensors have increased in their popularity for avalanche monitoring and characterization due to their avalanche detection and characterization capabilities. To date, however, seismic instrumentation in avalanche terrain is sparse, thereby limiting the spatial resolution significantly.

As an addition to conventional seismic instrumentation, we propose DAS to measure avalanche-induced ground motion. DAS is a technology using backscattered light along a fiber-optic cable to measure strain (-rate) along the fiber with unprecedented spatial and temporal resolution - in our example with 2 m spatial sampling and a sampling rate of 1kHz.

We analyze DAS data recorded during winter 2020/2021 at the Valleé de la Sionne avalanche test site in the Swiss Alps, utilizing an existing 700 m long fiber-optic cable. Our observations include avalanches propagating on top of the buried cable, delivering near-field observations of avalanche-ground interactions. After analyzing the properties of near-field avalanche DAS recordings, we discuss and evaluate algorithms for (1) automatic avalanche detection, (2) avalanche surge propagation speed evaluation and (3) subsurface property estimation.

Our analysis highlights the complexity of near-field DAS data, as well as the suitability of DAS-based monitoring of avalanches and other hazardous granular flows. Moreover, the clear detectability of avalanche signals using existing fiber-optic infrastructure of telecommunication networks opens the opportunity for unrivalled warning capabilities in Alpine environments.

How to cite: Paitz, P., Edme, P., Fichtner, A., Lindner, N., Sovilla, B., and Walter, F.: Near-field observations of snow-avalanches propagating over a fiber-optic array, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4014, https://doi.org/10.5194/egusphere-egu22-4014, 2022.

EGU22-4478 | Presentations | SM2.1

Non-linear ground response triggered by volcanic explosions at Etna Volcano, Italy 

Philippe Jousset, Lucile Costes, Gilda Currenti, Benjamin Schwarz, Rosalba Napoli, Sergio Diaz, and Charlotte Krawczyk

Volcanic explosions produce energy that propagates both in the subsurface as seismic waves and in the atmosphere as acoustic waves. We analyse thousands of explosions which occurred at different craters at Etna volcano (Italy) in 2018 and 2019. We recorded signals from infrasound sensors, geophones (GPH), broadband seismometers (BB) and Distributed Acoustic Sensing (DAS) with fibre optic cable. The instruments were deployed at Piano delle Concazze at about 2 to 2.5 km from the active craters, within (or onto) a ~300,000 m2 scoria layer deposited by recent volcanic eruptions. The DAS interrogator was setup inside the Pizzi Deneri Volcanic Observatory (~2800 m elevation). Infrasonic explosion records span over a large range of pressure amplitudes with the largest one reaching 130 Pa (peak to peak), with an energy of ca. 2.5x1011 J. In the DAS and the BB records, we find a 4-s long seismic “low frequency” signal (1-2 Hz) corresponding to the seismic waves, followed by a 2-s long “high-frequency” signal (16-21 Hz), induced by the infrasound pressure pulse. The infrasound sensors contain a 1-2 Hz infrasound pulse, but surprisingly no high frequency signal. At locations where the scoria layer is very thin or even non-existent, this high frequency signal is absent from both DAS strain-rate records and BB/GPH velocity seismograms. These observations suggest that the scoria layer is excited by the infrasound pressure pulse, leading to the resonance of lose material above more competent substratum. We relate the high frequency resonance to the layer thickness. Multichannel Analysis of Surface Wave from jumps performed along the fibre optic cable provide the structure of the subsurface, and confirm thicknesses derived from the explosion analysis. As not all captured explosions led to the observation of these high frequency resonance, we systematically analyze the amplitudes of the incident pressure wave versus the recorded strain and find a non-linear relationship between the two. This non-linear behaviour is likely to be found at other explosive volcanoes. Furthermore, our observations suggest it might also be triggered by other atmospheric pressure sources, like thunderstorms. This analysis can lead to a better understanding of acoustic-to-seismic ground coupling and near-surface rock response from natural, but also anthropogenic sources, such as fireworks and gas explosions.

How to cite: Jousset, P., Costes, L., Currenti, G., Schwarz, B., Napoli, R., Diaz, S., and Krawczyk, C.: Non-linear ground response triggered by volcanic explosions at Etna Volcano, Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4478, https://doi.org/10.5194/egusphere-egu22-4478, 2022.

EGU22-4583 | Presentations | SM2.1

Dynamic weakening in carbonate-built seismic faults: insights from laboratory experiments with fast and ultra-localized temperature measurements 

Stefano Aretusini, Arantzazu Nuñez Cascajero, Chiara Cornelio, Xabier Barrero Echevarria, Elena Spagnuolo, Alberto Tapetado, Carmen Vazquez, Massimo Cocco, and Giulio Di Toro

During earthquakes, seismic slip along faults is localized in < 1 cm-thick principal slipping zones. In such thin slipping zones, frictional heating induces a temperature increase which activates deformation processes and chemical reactions resulting in dramatic decrease of the fault strength (i.e., enhanced dynamic weakening) and, in a negative feedback loop, in the decrease of the frictional heating itself.

In the laboratory, temperature measurements in slipping zones are extremely challenging, especially at the fast slip rates and large slip displacements typical of natural earthquakes. Recently, we measured the temperature evolution in the slipping zone of simulated earthquakes at high acquisition rates (∼ kHz) and spatial resolutions (<< 1 mm2). To this end, we used optical fibres, which convey IR radiation from the hot rubbing surfaces to a two color pyrometer, equipped with photodetectors which convert the radiation into electric signals. The measured signals were calibrated into temperature and then synchronized with the mechanical data (e.g., slip rate, friction coefficient, shear stress) to relate the dynamic fault strength to the temperature evolution and eventually constrain the deformation processes and associated chemical reactions activated during seismic slip.

Here, we reproduce earthquake slip via rotary shear experiments performed on solid cylinders (= bare rock surfaces) and on gouge layers both made of 99.9% calcite. The applied effective normal stress is 20 MPa. Bare rock surfaces are slid for 20 m with a trapezoidal velocity function with a target slip rate of 6 m/s. Instead, the gouge layers are sheared imposing a trapezoidal (1 m/s target slip rate for 1 m displacement) and Yoffe (3.5 m/s peak slip rate, and 1.5 m displacement) velocity function. The temperature measured within the slipping zone, which in some experiments increases up to 1000 °C after few milliseconds from slip initiation, allow us to investigate the deformation mechanisms responsible for fault dynamic weakening over temporal (milliseconds) and spatial (contact areas << 1 mm2) scales which are impossible to detect with traditional techniques (i.e., thermocouples or thermal cameras).

Importantly, thanks to FE numerical simulations, these in-situ temperature measurements allow us to quantify the partitioning of the dissipated energy and power between frictional heating (temperature increase) and wear processes (e.g., grain comminution), to probe the effectiveness of other energy sinks (e.g., endothermic reactions, phase changes) that would buffer the temperature increase, and to determine the role of strain localization in controlling the temperature increase. The generalization of our experimental data and observations will contribute to shed light on the mechanics of carbonate-hosted earthquakes, a main hazard in the Mediterranean and other areas worldwide.

How to cite: Aretusini, S., Nuñez Cascajero, A., Cornelio, C., Barrero Echevarria, X., Spagnuolo, E., Tapetado, A., Vazquez, C., Cocco, M., and Di Toro, G.: Dynamic weakening in carbonate-built seismic faults: insights from laboratory experiments with fast and ultra-localized temperature measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4583, https://doi.org/10.5194/egusphere-egu22-4583, 2022.

EGU22-4963 | Presentations | SM2.1

A real-time classification method for pipeline monitoring combining Distributed Acoustic Sensing and Distributed Temperature and Strain Sensing 

Camille Huynh, Camille Jestin, Clément Hibert, Jean-Philippe Malet, Vincent Lanticq, and Pierre Clément

Distributed Fiber Optic Systems (DFOSs) refer to an ensemble of innovative technology that turns an optical fiber into a vast network of hundreds to thousands equally spaced sensors. According to the nature of the sensor, one can be sensitive to acoustic vibration (Distributed Acoustic Sensing, DAS) or to strain and temperature variation (Distributed Temperature and Strain Sensing, DTSS). DAS systems are well suited to detect short-term events in contrast to DTSS systems, which are intended to prevent long-term events. A combination of these two systems appears to be a good way to prevent against most possible events that can appear along an infrastructure. Furthermore, DFOS systems allow the interrogation of long profiles with very dense spatial and temporal sampling. Handling such amounts of data then appears as a challenge when trying to identify a threat along the structure. Machine learning solutions then proves their relevance for robust, fast and efficient acoustical event classification.

The goal of our study is to develop a method to handle, in real time, acquired data from these two DFOSs, classify them according to the nature of their origin and trigger an alarm if required. We mainly focus on major threats that jeopardize the integrity of pipelines. Our database contains leaks, landslides, and third-party intrusion (footsteps, excavations, drillings, etc.) simulated and measured at FEBUS Optics test bench in South-West France. Water and air leaks were simulated using electrovalves of several diameters (1mm, 3mm and 5mm), and landslides with a plate whose inclination was controlled by 4 cylinders. These data were acquired under controlled conditions in a small-scale model of pipeline (around 20m long) along different fiber optic cables installed along the structure.

Our method relies on several tools. A Machine Learning algorithm called Random Forest is used to pre-classify the DAS signal. Our implementation of this algorithm works in flow for a real time event identification. For DTSS signal, a simple threshold is used to detect if a strain or temperature variation occurs. Both results are then gathered and analyzed using a decisional table to return a classification result. According to the potential threat represented by its identified class, the event is considered as dangerous or not. Using this method, we obtain good results with a good classification rate (threat/non-threat) of 93%, compared to 87% if the DAS is used without the DTSS. We have noticed that the combination of both devices enables a better classification, especially for landslides hard to detect with the DAS. This combination enables to dramatically reduce the part of undetected threats from 16% to 4%.

How to cite: Huynh, C., Jestin, C., Hibert, C., Malet, J.-P., Lanticq, V., and Clément, P.: A real-time classification method for pipeline monitoring combining Distributed Acoustic Sensing and Distributed Temperature and Strain Sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4963, https://doi.org/10.5194/egusphere-egu22-4963, 2022.

EGU22-5327 | Presentations | SM2.1

HDAS (High-Fidelity Distributed Acoustic Sensing) as a monitoring tool during 2021 Cumbre Vieja eruption 

José Barrancos, Luca D'Auria, Germán Padilla, Javier Preciado-Garbayo, and Nemesio M. Pérez

La Palma is the second youngest and westernmost among Canary Island. Cumbre Vieja volcano formed in the last stage of the geological evolution of the island and had suffered eight volcanic eruptions over the previous 500 years. In 2017, two remarkable seismic swarms interrupted a seismic silence from the last eruption (Teneguía, 1971). Since then, nine additional seismic swarms have occurred at Cumbre Vieja volcano. On September 11th, 2021, seismic activity began to increase, and the depths of the earthquakes showed an upward migration. Finally, on September 19th, the eruption started after just a week of precursors.

During recent years, the seismic activity has been recorded by Red Sísmica Canaria (C7), composed of 6 seismic broadband stations, which was reinforced during the eruption by five additional broadband stations, three accelerometers and a seismic array consisting of 10 broadband stations.

Furthermore, as a result of a collaboration between INVOLCAN, ITER, CANALINK and Aragón Photonics Labs, it was possible to install, on October 19th, an HDAS (High-fidelity Distributed Acoustic Sensor). The HDAS was installed about 10 km from the eruptive vent and was connected to a submarine fibre optic cable directed toward Tenerife Island. Since then, the HDAS has been recording seismic with a temporal sampling rate of 100 Hz and a spatial sampling rate of 10m for a total length of 50 km using Raman Amplification. For more than two months, in addition to the intense volcanic tremor, the HDAS recorded thousands of earthquakes as well as regional and teleseismic events. On December 13th, 2021, after an intense paroxysmal phase with an eruptive column that reached 8 km in height, the volcanic tremor quickly decreased, and the eruption suddenly stopped. Only a weak volcano-tectonic seismicity and small amplitude long-period events were recorded in the next month.

This valuable dataset will provide a milestone for the development of techniques aimed at using DAS as a real-time volcano monitoring tool and studying the internal structure of active volcanoes.

How to cite: Barrancos, J., D'Auria, L., Padilla, G., Preciado-Garbayo, J., and Pérez, N. M.: HDAS (High-Fidelity Distributed Acoustic Sensing) as a monitoring tool during 2021 Cumbre Vieja eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5327, https://doi.org/10.5194/egusphere-egu22-5327, 2022.

EGU22-5551 | Presentations | SM2.1

A showcase pilot of seismic campaign using Distributed Acoustic Sensing solutions 

Camille Jestin, Christophe Maisons, Aurélien Chérubini, Laure Duboeuf, and Jean-Claude Puech

Distributed Acoustic Sensing (DAS) is a rapidly evolving technology that can turn a fibre optic cable into thousands of acoustic sensors. In this study, we propose to present a seismic survey conducted as a business showcase relying on a collaborative work supported by five partners: FEBUS Optics, RealTimeSeismic (RTS), Gallego Technic Geophysics (GTG), Petro LS and Well-SENSE. The project was carried out at a deep solution mining site developed for salt production, operated by KEMONE, and located nearby Montpellier (South of France).

The seismic campaign was based on two different cable deployments.

On the first hand, a Vertical Seismic Profile survey was conducted on borehole seismic measurements using two different fibre optic cables deployed in a 1800m deep vertical well. The first set of tests was performed along a Petro LS wireline cable including optical fibres. This deployment corresponds to a conventional wireline operation. The second set of data has been acquired along a FibreLine Intervention system (FLI) developed by WellSENSE. The deployment of the FLI system relies on the unspooling a bare optical fibre using a probe along a wellbore. This solution is single-use and sacrificial and can be left in the well at the end of the survey.

On another hand, a short 400m-surface 2D profile has been achieved along both a fibre optic cable and a set of STRYDE nodes deployed by GTG.

Fibre optic cables have been connected to FEBUS DAS interrogator to collect distributed acoustic measurements.  The seismic tests, performed in collaboration with GTG, have been achieved with basic “weight drops” (1T falling from 4m) for the checkshot surveys and with an "IVI Mark 4" 44,000-pound seismic vibrator for VSP shots at offset from wellhead reaching 865m. Acquired data have been analysed by RTS.

This work will describe the survey, present the results, and discuss the learnings in two ways:  the optimisation of acquisition setups and processing parameters to obtain the best exploitable results and seismic surveys perspectives and challenges using DAS technology.

How to cite: Jestin, C., Maisons, C., Chérubini, A., Duboeuf, L., and Puech, J.-C.: A showcase pilot of seismic campaign using Distributed Acoustic Sensing solutions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5551, https://doi.org/10.5194/egusphere-egu22-5551, 2022.

EGU22-5743 | Presentations | SM2.1

Making sense of urban DAS data with clustering of coherence-based array features 

Julius Grimm and Piero Poli

Seismic noise monitoring in urban areas can yield valuable information about near-surface structures and noise sources like traffic activity. Distributed Acoustic Sensing (DAS) is ideal for this task due to its dense spatial resolution and the abundance of existing fiber-optic cables below cities.
A 15 km long dark fiber below the city of Grenoble was transformed into a dense seismic antenna by connecting it to a Febus A1-R interrogator unit. The DAS system acquired data continuosly for 11 days with a sampling frequency of 250 Hz and a channel spacing of 19.2 m, resulting in a total of 782 channels. The cable runs through the entirety of the city, crossing below streets, tram tracks and a river. Various noise sources are visible on the raw strain-rate data. A local earthquake (1.3 MLv) was also recorded during the acquisition period.
To characterize the wavefield, the data is divided into smaller sub-windows and coherence matrices at different frequency bands are computed for each sub-window. Clustering is then performed directly on the covariance matrices, with the goal of identifying repeating sub-structures in the covariance matrices (e.g. localized repeating noise sources). Finding underlying patterns in the complex dataset helps us to better understand the spatio-temporal distribution of the occurring signals.

How to cite: Grimm, J. and Poli, P.: Making sense of urban DAS data with clustering of coherence-based array features, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5743, https://doi.org/10.5194/egusphere-egu22-5743, 2022.

EGU22-5952 | Presentations | SM2.1

Strombolian seismic activity characterisation using fibre-optic cable and distributed acoustic sensing 

Jean-Philippe Metaxian, Francesco Biagioli, Maurizio Ripepe, Eléonore Stutzmann, Pascal Bernard, Roberto Longo, Marie-Paule Bouin, and Corentin Caudron

Stromboli is an open-conduit volcano characterized by mild intermittent explosive activity that produces jets of gas and incandescent blocks. Explosions occur at a typical rate of 3-10 events per hour, VLP signals have dominant periods between 2 and 30 seconds. Seismic activity is also characterized by less energy short-period volcanic tremor related to the continuous out-bursting of small gas bubbles in the upper part of the magmatic column. The high rate of activity as well as the broadband frequency contents of emitted signals make Stromboli volcano an ideal site for testing new techniques of fibre-optic sensing.

In September 2020, approximately 1 km of fiber-optic cable was deployed on the Northeast flank of Stromboli volcano, together with several seismometers, to record the seismic signals radiated by the persistent Strombolian activity via both DAS and inertial-seismometers, and to compare their records.

The cable was buried manually about 30 cm deep over a relatively linear path at first and in a triangle-shaped array with 30-meters-long sides in the highest part of the deployment. The strain rate was recorded using a DAS interrogator Febus A1-R with a sampling frequency of 2000 Hz, a spatial interval of 2.4 m and a gauge length of 5m. Data were re-sampled at 200 Hz. A network of 22 nodes SmartSolo IGU-16HR 3C geophones (5 Hz) has been distributed over the fibre path. A Guralp digitizer equipped with a CMG CMG-40T 30 sec seismometer and an infrasound sensor were placed in the upper part of the path. The geolocation of the cable was obtained by performing kinematic GPS measurements with 2 Leica GR25 receivers. All equipment recorded simultaneously several hundreds of explosion quakes between September 20 and 23.

Data analysis provided the following main results:

  • DAS interrogator clearly recorded the numerous explosion-quakes which occurred during the experiment, as well as lower amplitude tremor and LP events.
  • DAS spectrum exhibits a lower resolution at long periods with a cut-off frequency of approximately 3 Hz.
  • VLP seismic events generated by Strombolian activity are identified only at a few DAS channels belonging to a specific portion of the path, which seems affected by local amplification. At these channels, they display waveforms similar to those sensed by the Güralp CMG-40T.
  • Comparison of DAS strain waveform to particle velocity recorded by co-located seismometers shows a perfect match in phase and a good agreement in amplitude.
  • Beamforming methods have been applied to nodes data located on the upper triangle and to strain rate data, both in the 3-5 Hz frequency band. Slightly different back-azimuths were obtained, values estimated via DAS point more to the southwest with respect to the crater area. Apparent velocities obtained with DAS recordings have lower values compared to those obtained with nodes.

How to cite: Metaxian, J.-P., Biagioli, F., Ripepe, M., Stutzmann, E., Bernard, P., Longo, R., Bouin, M.-P., and Caudron, C.: Strombolian seismic activity characterisation using fibre-optic cable and distributed acoustic sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5952, https://doi.org/10.5194/egusphere-egu22-5952, 2022.

EGU22-6580 | Presentations | SM2.1

Quantifying microseismic noise generation from coastal reflection of gravity waves using DAS 

Gauthier Guerin, Diane Rivet, Martijn van den Ende, Eléonore Stutzmann, Anthony Sladen, and Jean-Paul Ampuero

Secondary microseisms are the most energetic noise in continuous seismometer recordings, and they are generated by interactions between ocean waves. Coastal reflections of ocean waves leading to coastal microseismic sources are hard to estimate in various global numerical wave models, and independent quantification of these coastal sources through direct measurements can therefore greatly improve these models. Here, we exploit a 40 km long submarine optical fiber cable located offshore Toulon, France using Distributed Acoustic Sensing (DAS). We record both the amplitude and frequency of ocean gravity waves, as well as secondary microseisms caused by the interaction of gravity waves incident and reflected from the coast. By leveraging the spatially distributed nature of DAS measurements, additional fundamental information are recovered such as the velocity and azimuth of the waves. On average, 30\% of the gravity waves are reflected at the shore and lead to the generation of local secondary microseisms that manifest as Scholte waves. These local sources can give way to other sources depending on the characteristics of the swell, such as its azimuth or its strength. These sources represent the most energetic contribution to the secondary microseism recorded along the optical fiber, as well as on an onshore broadband station. Furthermore, we estimate the coastal reflection coefficient R$^2$ to be constant at around 0.07 for our 5-day time series. The use of DAS in an underwater environment provides a wealth of information on coastal reflection sources, reflection of gravity waves and new constraints for numerical models of microseismic noise.

How to cite: Guerin, G., Rivet, D., van den Ende, M., Stutzmann, E., Sladen, A., and Ampuero, J.-P.: Quantifying microseismic noise generation from coastal reflection of gravity waves using DAS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6580, https://doi.org/10.5194/egusphere-egu22-6580, 2022.

EGU22-6976 | Presentations | SM2.1

Comparison between Distributed Acoustic Sensing (DAS) and strain meter measurements at the Black Forest Observatory 

Jérôme Azzola, Nasim Karamzadeh Toularoud, Emmanuel Gaucher, Thomas Forbriger, Rudolf Widmer-Schnidrig, Felix Bögelspacher, Michael Frietsch, and Andeas Rietbrock

We present an original DAS measurement station, equipped with the Febus A1-R interrogator, which has been deployed at the Black Forest Observatory (Schiltach, Germany). The objective of this deployment is twofold. The first is to test the deployed fibre optic cables and to better characterise the recorded signals. The second is to define standards for the processing of these DAS measurements, with a view to using the equipment for passive seismic monitoring in the INSIDE project (supported by the German Federal Ministry for Economic Affairs and Energy, BMWi).

Testing sensors involving new acquisition technologies, such as instruments based on Distributed Fiber Optic Sensing (DFOS), is part of the observatory's goals, in order to assess, to maintain and to improve signal quality. Interestingly, reference geophysical instruments are also deployed on a permanent basis in this low seismic-noise environment. Our analyses thus benefit from the records of the observatory's measuring instruments, in particular a set of three strain meters recording along various azimuths. This configuration enables a unique comparison between strain meter and DAS measurements. In addition, an STS-2 seismometer (part of German Regional Seismic Network, GRSN) allows for additional comparisons.

These instruments provide a basis for a comparative analysis between the DAS records and the measurements of well-calibrated sensing devices (STS-2 sensor, strain meter array). Such a comparison is indeed essential to physically understand the measurements provided by the Febus A1-R interrogator and to characterise the coupling between the ground and the fiber, in various deployment configurations.

We present the experiment where we investigate several Fiber Optic Cable layouts, with currently our most successful setup involving loading a dedicated fiber with sandbags. We discuss different processing approaches, resulting in a considerable improvement of the fit between DAS and strain array acquisitions. The presented comparative analysis is based on the recordings of different earthquakes, including regional and teleseismic events.

How to cite: Azzola, J., Toularoud, N. K., Gaucher, E., Forbriger, T., Widmer-Schnidrig, R., Bögelspacher, F., Frietsch, M., and Rietbrock, A.: Comparison between Distributed Acoustic Sensing (DAS) and strain meter measurements at the Black Forest Observatory, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6976, https://doi.org/10.5194/egusphere-egu22-6976, 2022.

EGU22-6984 | Presentations | SM2.1

Array signal processing on distributed acoustic sensing data: directivity effects in slowness space 

Sven Peter Näsholm, Kamran Iranpour, Andreas Wuestefeld, Ben Dando, Alan Baird, and Volker Oye

Distributed Acoustic Sensing (DAS) involves the transmission of laser pulses along a fiber-optic cable. These pulses are backscattered at fiber inhomogeneities and again detected by the same interrogator unit that emits the pulses. Elastic deformation along the fiber causes phase shifts in the backscattered laser pulses which are converted to spatially averaged strain measurements, typically at regular fiber intervals.

DAS systems provide the potential to employ array processing algorithms. However, there are certain differences between DAS and conventional sensors. The current paper is focused on taking these differences into account. While seismic sensors typically record the directional particle displacement, velocity, or acceleration, the DAS axial strain is inherently proportional to the spatial gradient of the axial cable displacement. DAS is therefore insensitive to broadside displacement, e.g., broadside P-waves. In classical delay-and-sum beamforming, the array response function is the far-field response on a horizontal slowness (or wavenumber) grid. However, for geometrically non-linear DAS layouts, the angle between wavefront and cable varies, requiring the analysis of a steered response that varies with the direction of arrival. This contrasts with the traditional array response function which is given in terms of slowness difference between arrival and steering.

This paper provides a framework for DAS steered response estimation accounting also for cable directivity and gauge-length averaging – hereby demonstrating the applicability of DAS in array seismology and to assess DAS design aspects. It bridges a gap between DAS and array theory frameworks and communities, facilitating increased employment of DAS as a seismic array.

How to cite: Näsholm, S. P., Iranpour, K., Wuestefeld, A., Dando, B., Baird, A., and Oye, V.: Array signal processing on distributed acoustic sensing data: directivity effects in slowness space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6984, https://doi.org/10.5194/egusphere-egu22-6984, 2022.

EGU22-7153 | Presentations | SM2.1

MEGLIO: an experiment to record seismic waves on a commercial fiber optic cable through interferometry measures with an ultra stable laser. 

Andre Herrero, Davide Calonico, Francesco Piccolo, Francesco Carpentieri, Aladino Govoni, Lucia Margheriti, Maurizio Vassallo, Rita di Giovambattista, Salvatore Stramondo, Cecilia Clivati, Roberto Concas, Simone Donadello, Fabio Simone Priuli, Filippo Orio, and Andrea Romualdi

The experiment MEGLIO follows the seminal work of Marra et al. (2018) where the authors demonstrate the possibility to observe seismic waves on fiber optic cables over large distances. The measure was based on an interferometric technique using an ultra stable laser. In theory, this active measurement technique is compatible with a commercial operation on a fiber, i.e. the fiber does not need to be dark. In 2019, Open Fiber, the largest optic fiber infrastructure provider in Italy, has decided to test this new technology on its own commercial network on land.

A team of experts in the different fields has been gathered to achieve this goal : besides Open Fiber, Metallurgica Bresciana; INRiM, which initially developed the technique, for their expertise on laser and sensors; Bain & Company for the analysis and the processing of the data; INGV for the expertise in the seismology field and for the validation of the observations.

The first year has been dedicated to developing the sensors. In the meantime, a buried optic cable has been chosen in function of its length and the seismicity nearby. The best candidate was the fiber connecting the towns of Ascoli Piceno (Marche, Italy) and Teramo (Abruzzo, Italy) for a length of around 30 km. Although  this technique allows using cable lengths larger than 5.000 km, for this first test we have decided to keep the length short. Actually the cable is mainly buried underneath a road with medium traffic, passes across different bridges and viaducts, starts in the middle of a town and loops in the middle of another town. Thus we expected a strong anthropic noise on the data.

The measurement on the field started in mid June 2020 and the system was operational in early July. We also installed a seismic station at one end of the cable. During these first six months, we have compared the observations on the fiber with the Italian national seismic catalog and the worldwide catalog for the major events. We consider the first results a success. We have detected so far nearly all the seismic activity with magnitude larger than 2.5 for epicentral distance lesser than 50 km. Moreover, we have recorded large events in Mediterranean region and teleseisms. Finally we have recorded new and interesting noise signals. Collecting additional events will be helpful for a better characterization of the technique, its performances and for a statistical analysis.

How to cite: Herrero, A., Calonico, D., Piccolo, F., Carpentieri, F., Govoni, A., Margheriti, L., Vassallo, M., di Giovambattista, R., Stramondo, S., Clivati, C., Concas, R., Donadello, S., Priuli, F. S., Orio, F., and Romualdi, A.: MEGLIO: an experiment to record seismic waves on a commercial fiber optic cable through interferometry measures with an ultra stable laser., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7153, https://doi.org/10.5194/egusphere-egu22-7153, 2022.

EGU22-7182 | Presentations | SM2.1 | Highlight

Monitoring a submarine strike-slip fault, using a fiber optic strain cable 

Marc-Andre Gutscher, Jean-Yves Royer, David Graindorge, Shane Murphy, Frauke Klingelhoefer, Arnaud Gaillot, Chastity Aiken, Antonio Cattaneo, Giovanni Barreca, Lionel Quetel, Giorgio Riccobene, Salvatore Aurnia, Lucia Margheriti, Milena Moretti, Sebastian Krastel, Florian Petersen, Morelia Urlaub, Heidrun Kopp, Gilda Currenti, and Philippe Jousset

The goal of the ERC (European Research Council) funded project - FOCUS is to apply laser reflectometry on submarine fiber optic cables to detect deformation at the seafloor in real time using BOTDR (Brillouin Optical Time Domain Reflectometry). This technique is commonly used monitoring large-scale engineering infrastructures (e.g. - bridges, dams, pipelines, etc.) and can measure very small strains (<< 1 mm/m) at very large distances (10 - 200 km), but until now has never been used to study tectonic faults and deformation on the seafloor.

Here, we report that BOTDR measurements detected movement at the seafloor consistent with ≥1 cm dextral strike-slip on the North Alfeo fault, 25 km offshore Catania, Sicily over the past 10 months. In Oct. 2020 a dedicated 6-km long fiber-optic strain cable was connected to the INFN-LNS (Catania physics institute) cabled seafloor observatory at 2060 m depth and deployed across this submarine fault, thus providing continuous monitoring of seafloor deformation at a spatial resolution of 2 m. The laser observations indicate significant elongation (20 - 40 microstrain) at two fault crossings, with most of the movement occurring between 19 and 21 Nov. 2020. A network of 8 seafloor geodetic stations for direct path measurements was also deployed in Oct. 2020, on both sides of the fault to provide an independent measure of relative seafloor movements. These positioning data are being downloaded during ongoing oceanographic expeditions to the working area (Aug. 2021 R/V Tethys; Jan. 2022 R/V PourquoiPas) using an acoustic modem to communicate with the stations on the seafloor. An additional experiment was performed in Sept. 2021 using an ROV on the Fugro vessel Handin Tide, by weighing down unburied portions of the submarine cable with pellet bags and sandbags (~25kg each) spaced every 5m. The response was observed simultaneously by DAS (Distributed Acoustic Sensing) recordings using two DAS interrogators (a Febus and a Silixa). The strain caused by the bag deployments was observed using BOTDR and typically produced a 50 - 100 microstrain signal across the 120 meter-long segments which were weighed down. In Jan. 2022 during the FocusX2 marine expedition, 21 ocean bottom seismometers were deployed for 12-14 months, which together with 15 temporary land-stations as well as the existing network of permanent stations (both operated by INGV) will allow us to perform a regional land-sea passive seismological monitoring experiment.

How to cite: Gutscher, M.-A., Royer, J.-Y., Graindorge, D., Murphy, S., Klingelhoefer, F., Gaillot, A., Aiken, C., Cattaneo, A., Barreca, G., Quetel, L., Riccobene, G., Aurnia, S., Margheriti, L., Moretti, M., Krastel, S., Petersen, F., Urlaub, M., Kopp, H., Currenti, G., and Jousset, P.: Monitoring a submarine strike-slip fault, using a fiber optic strain cable, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7182, https://doi.org/10.5194/egusphere-egu22-7182, 2022.

EGU22-7203 | Presentations | SM2.1

Multiphase observations of a meteoroid in Iceland recorded over 40 km of telecommunications cables and a large-N network 

Ismael Vera Rodriguez, Torsten Dahm, Marius P. Isken, Toni Kraft, Oliver D. Lamb, Sin-Mei Wu, Sebastian Heimann, Pilar Sanchez-Pastor, Christopher Wollin, Alan F. Baird, Andreas Wüstefeld, Sigríður Kristjánsdóttir, Kristín Jónsdóttir, Eva P. S. Eibl, Bettina P. Goertz-Allmann, Philippe Jousset, Volker Oye, and Anne Obermann

On July 2, 2021, around 22:44 CET, a meteoroid was observed crossing the sky near Lake Thingvallavatn east of Reykjavik in Iceland. During this event, a large-N seismic network consisting of 500, 3-component geophones was monitoring local seismicity associated with the Hengill geothermal field southwest of the lake.  In addition to the large-N network, two fiber optic telecommunication cables, covering a total length of more than 40 km, were connected to distributed acoustic sensing (DAS) interrogation units. The systems were deployed under the frame of the DEEPEN collaboration project between the Eidgenössische Technische Hochschule Zürich (ETHZ), the German Research Centre for Geosciences (GFZ), NORSAR, and Iceland Geo-survey (ISOR). Both the large-N and the DAS recordings display multiple trains of infrasound arrivals from the meteoroid that coupled to the surface of the earth at the locations of the sensors. In particular, three strong phases and multiple other weaker arrivals can be identified in the DAS data.

Fitting each of the strong phases assuming point sources (i.e., fragmentations) generates travel time residuals on the order of several seconds, resulting in an unsatisfactory explanation of the observations. On the other hand, inverting the arrival times for three independent hypersonic-trajectories generating Mach cone waves reduces travel time residuals to well under 0.5 s for each arrival. However, whereas the 1st arrival is well constrained by more than 900 travel times from the large-N, DAS and additional seismic stations distributed over the Reykjanes peninsula, the 2nd and 3rd arrivals are mainly constrained by DAS observations near Lake Thingvallavatn. The less well-constrained, latter trajectories show a weak agreement with the trajectory of the first arrival. Currently, neither the multi-Mach-cone model nor the multi-fragmentation model explain all our observations satisfactorily. Thus, traditional models for interpreting meteoroid observations appear unsuitable to explain the combination of phase arrivals in the large-N network and DAS data consistently.

How to cite: Vera Rodriguez, I., Dahm, T., Isken, M. P., Kraft, T., Lamb, O. D., Wu, S.-M., Heimann, S., Sanchez-Pastor, P., Wollin, C., Baird, A. F., Wüstefeld, A., Kristjánsdóttir, S., Jónsdóttir, K., Eibl, E. P. S., Goertz-Allmann, B. P., Jousset, P., Oye, V., and Obermann, A.: Multiphase observations of a meteoroid in Iceland recorded over 40 km of telecommunications cables and a large-N network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7203, https://doi.org/10.5194/egusphere-egu22-7203, 2022.

EGU22-7311 | Presentations | SM2.1

Calibration and repositioning of an optical fibre cable from acoustic noise obtained by DAS technology 

Lucas Papotto, Benoit DeCacqueray, and Diane Rivet

DAS (Distributed Acoustic Sensing) turns fibre optic cables used for telecommunications into multi-sensor antenna arrays. This technology makes it possible to detect an acoustic signal from a natural source such as cetacean or micro-earthquakes, or a man-made source by measuring the deformation of the cable. At sea, the coupling between the optical fibre and the ground on which it rests, as well as the calibration of the cable, is a critical point when the configuration of the cable is unknown. Is the fibre buried or suspended? What is the depth of the sensor being studied? What impact do these parameters have on the signal? The answers to these questions have an impact on the quality of the results obtained, the location of sources - seismic or acoustic - and the characterisation of the amplitude of signals are examples of this. Here, a first approach to study this calibration is proposed. Acoustic noise generated by passing ships in the vicinity of a 42km long optical fibre off Toulon, south-east France, is used to obtain signals for which the position and the signal of the source are known. Then, the synthetic and theoretical signal representing the ship's passage is modelled (3D model, AIS Long/Lat coordinates and depth, propagation speed in water c₀ = 1530m/s). This simulation allows us to compare the real and synthetic signals, in order to make assumptions about the actual cable configuration. We compare the signals through beamforming, f-k diagram and time-frequency diagram in particular. The grid search approach allowed us to determine the new position or orientation of a portion of the antenna. This new position is then evaluated from the signals of different vessels.

How to cite: Papotto, L., DeCacqueray, B., and Rivet, D.: Calibration and repositioning of an optical fibre cable from acoustic noise obtained by DAS technology, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7311, https://doi.org/10.5194/egusphere-egu22-7311, 2022.

EGU22-7742 | Presentations | SM2.1

Strain evolution on a submarine cable during the 2020-2021 Etna eruption 

Shane Murphy, Pierre Garreau, Mimmo Palano, Stephan Ker, Lionel Quetel, Philippe Jousset, Giorgio Riccobene, Salvatore Aurnia, Gilda Currenti, and Marc-Andre Gutscher

On the 13th December 2020, a Strombolian eruption occurred on Mount Etna. We present a study of the temporal and spatial variation of strain measured at the underwater base of volcano during this event. 

As part of the FOCUS project, a BOTDR (Brillouin Optical Time Domain Reflectometry) interrogator has been connected to the INFN-LNS ( Istituto Nazionale di Fisica Nucleare - Laboratori Nazionali del Sud) fibre optic cable that extends from the port of Catania 25km offshore to TTS (Test Site South) in a water depth of 2km. This interrogator has been continuously recording the relative strain changes at 2m spacing along the length of the cable every 2 hrs since May 2020. 

On preliminary analysis, a change in strain is observed at the around the time of the eruption, however this variation occurs close to the shore where seasonal variations in water temperatures are in the order of 5°C. As Brillouin frequency shifts are caused by both temperature and strain variations, it is necessary to remove this effect. To do so, numerical simulations of seasonal sea temperature specific to offshore Catania have used to estimate the change in temperature along the cable. This temperature change is then converted to a Brillouin frequency shift and removed from the frequency shift recorded by the interrogator before being converted to relative strain measurements. This processing produces a strain signature that is consistent with deformation observed by nearby geodetic stations on land.

How to cite: Murphy, S., Garreau, P., Palano, M., Ker, S., Quetel, L., Jousset, P., Riccobene, G., Aurnia, S., Currenti, G., and Gutscher, M.-A.: Strain evolution on a submarine cable during the 2020-2021 Etna eruption, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7742, https://doi.org/10.5194/egusphere-egu22-7742, 2022.

EGU22-8113 | Presentations | SM2.1

Exploration of Distributed Acoustic Sensing (DAS) data-space using a trans-dimensional algorithm, for locating geothermal induced microseismicity 

Nicola Piana Agostinetti, Emanuele Bozzi, Alberto Villa, and Gilberto Saccorotti

Distributed Acoustic sensing (DAS) data have been widely recognised as the next generation of  seismic data for applied geophysics, given the ultra-high spatial resolution achieved. DAS data are recorded along a fiber optic cable at pre-defined distances (called “channels”, generally with 1-10 meters spacing). DAS data have been benchmarked to standard seismic data (e.g. geophones) for tasks related to both exploration and monitoring of georesources.

The analysis of DAS data has to face two key-issues: the amount of data available and their “directionality”. First, the huge amount of data recorded, e.g. in monitoring activities related to georesources exploitation, can not be easily handled with standard seismic workflow, given the spatial and temporal sampling (for example, manual picking of P-wave arrivals for 10 000 channels is not feasible). Moreover, standard seismic workflow have been generally developed for “sparse" network of sensors, i.e. for punctual measurements, without considering the possibility of recording the quasi-continuous seismic wavefield along a km-long cable. With the term “directionality" we mean the ability of the DAS data to record horizontal strain-rate only in the direction of the fiber optic cable. This can be seen as a measure of a single horizontal component in a standard seismometer. Obviously, standard seismic workflow have not been developed to work correctly for a network of seismometers with a unique horizontal component, oriented with variable azimuth from one seismometer to the other. More important, “directionality” can easily bias the recognition of the seismic phase arriving at the channel, which could be, based on the cable azimuth and the seismic noise level, a P-wave or an S-wave. 

We developed a novel application for exploring DAS data-space in a way that: (1) data are automatically down weighted with the distance from the event source; (2) recorded phases are associated to P- or S- waves with a probabilistic approach, without pre-defined phase identification; and (3) the presence of outliers is also statistically considered, each phase being potentially a converted/refracted wave to be discarded. Our methodology makes use of a trans-dimensional algorithm, for selecting relevant weights with distance. Thus, all inferences in the data-space are fully data-driven, without imposing additional constrains from the seismologist.

How to cite: Piana Agostinetti, N., Bozzi, E., Villa, A., and Saccorotti, G.: Exploration of Distributed Acoustic Sensing (DAS) data-space using a trans-dimensional algorithm, for locating geothermal induced microseismicity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8113, https://doi.org/10.5194/egusphere-egu22-8113, 2022.

EGU22-8294 | Presentations | SM2.1 | Highlight

Real-Time Magnitude Determination and Ground Motion Prediction using Optical Fiber Distributed Acoustic Sensing for Earthquake Early Warning 

Itzhak Lior, Diane Rivet, Anthony Sladen, Diego Mercerat, and Jean-Paul Ampuero

Distributed Acoustic Sensing (DAS) is ideally suited for the challenges of Earthquake Early Warning (EEW). These distributed measurements allow for robust discrimination between earthquakes and noise, and remote recordings at hard to reach places, such as offshore, close to the hypocenters of most of the largest earthquakes on Earth. In this study, we propose the first application of DAS for EEW. We present a framework for real-time strain-rate to ground accelerations conversion, magnitude estimation and ground shaking prediction. The conversion is applied using the local slant-stack transform, adapted for real-time applications. Since currently, DAS earthquake datasets are limited to low-to-medium magnitudes, an empirical magnitude estimation approach is not feasible. To estimate the magnitude, we derive an Omega-squared-model based theoretical description for acceleration root-mean-squares (rms), a measure that can be calculated in the time-domain. Finally, peak ground motions are predicted via ground motion prediction equation that are derived using the same theoretical model, thus constituting a self-consistent EEW scheme. The method is validated using a composite dataset of earthquakes from different tectonic settings up to a magnitude of 5.7. Being theoretical, the presented approach is readily applicable to any DAS array in any seismic region and allows for continuous updating of magnitude and ground shaking predictions with time. Applying this method to optical fibers deployed near on-land and underwater faults could be decisive in the performance of EEW systems, significantly improving earthquake warning times and allowing for better preparedness for intense shaking.

How to cite: Lior, I., Rivet, D., Sladen, A., Mercerat, D., and Ampuero, J.-P.: Real-Time Magnitude Determination and Ground Motion Prediction using Optical Fiber Distributed Acoustic Sensing for Earthquake Early Warning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8294, https://doi.org/10.5194/egusphere-egu22-8294, 2022.

EGU22-8414 | Presentations | SM2.1

Towards microseismic moment tensor inversion in boreholes with DAS 

Katinka Tuinstra, Federica Lanza, Andreas Fichtner, Andrea Zunino, Francesco Grigoli, Antonio Pio Rinaldi, and Stefan Wiemer

We present preliminary results on a moment tensor inversion workflow for Distributed Acoustic Sensing (DAS). It makes use of a fast-marching Eikonal solver and synthetically modeled data. The study specifically focuses on borehole settings for geothermal sites. Distributed Acoustic Sensing measures the wavefield with high spatial and temporal resolution. In borehole settings, individual DAS traces generally prove to be noisier than co-located geophones, whereas the densely spaced DAS shot-gathers show features that would have otherwise been missed by the commonly more sparsely distributed geophone chains. For example, the coherency in the DAS records shows the polarity reversals of the arriving wavefield in great detail, which can help constrain the moment tensor of the seismic source. The synthetic tests encompass different source types and source positions relative to the deployed fiber to assess moment tensor resolvability. Further tests include the addition of a three-component seismometer at different positions to investigate an optimal network configuration, as well as various noise conditions to mimic real data. The synthetic tests are tailored to prepare for the data from future microseismicity monitoring with DAS in the conditions of the Utah FORGE geothermal test site, Utah, USA. The proposed method aims at improving amplitude-based moment tensor inversion for DAS deployed in downhole or underground lab contexts.

How to cite: Tuinstra, K., Lanza, F., Fichtner, A., Zunino, A., Grigoli, F., Rinaldi, A. P., and Wiemer, S.: Towards microseismic moment tensor inversion in boreholes with DAS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8414, https://doi.org/10.5194/egusphere-egu22-8414, 2022.

EGU22-8664 | Presentations | SM2.1

Seismic Exploration and monitoring of geothermal reservoirs usiNg distributed fibre optic Sensing - the joint project SENSE 

CharLotte Krawczyk, Leila Ehsaniezhad, Christopher Wollin, Johannes Hart, and Martin Lipus

For a successful operation of energy or resources use in the subsurface, exploration for potential reservoir or storage horizons, monitoring of structural health and control of induced seismic unrest are essential both from a technical and a socio-economic perspective.  Furthermore, large-scale seismic surveys in densely populated areas are difficult to carry out due to the effort required to install sources and receivers and are associated with high financial and logistical costs.  Within the joint project SENSE*, a seismic exploration and monitoring approach is tested, which is based on fibre-optic sensing in urban areas.

Besides the further development of sensing devices, the monitoring of borehole operations as well as the development of processing workflows form central parts of the joint activities. In addition, the seismic wave field was recorded and the localisation of the cables was tested along existing telecommunication cables in Berlin. Further testing of measuring conditions in an urban environment was also conducted along an optic fibre separately laid out in an accessible heating tunnel.

We suggest a workflow for virtual shot gather extraction (e.g., band pass filtering, tapering, whitening, removal of poor traces before and after cross-correlation, stacking), that is finally including a coherence-based approach.  The picking of dispersion curves in the 1-7 Hz frequency range and inversion yield a shear wave velocity model for the subsurface down to a. 300 m depth.  Several velocity interfaces are evident, and a densely staggered zone appears between 220-270 m depth.  From lab measurements a distributed backscatter measurement in OTDR mode shows that high reflections and moderate loss at connectors can be achieved in a several hundred m distance.  Depending on drilling campaign progress, we will also present first results gained during the borehole experiment running until February 2022.

* The SENSE Research Group includes in addition to the authors of this abstract Andre Kloth and Sascha Liehr (DiGOS), Katerina Krebber and Masoud Zabihi (BAM), Bernd Weber (gempa), and Thomas Reinsch (IEG).

How to cite: Krawczyk, C., Ehsaniezhad, L., Wollin, C., Hart, J., and Lipus, M.: Seismic Exploration and monitoring of geothermal reservoirs usiNg distributed fibre optic Sensing - the joint project SENSE, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8664, https://doi.org/10.5194/egusphere-egu22-8664, 2022.

EGU22-8787 | Presentations | SM2.1

PSD analysis and seismic event detectability of Distributed Acoustic Sensing (DAS) mesurements from several monitoring sites 

Nasim Karamzadeh Toularoud, Jérôme Azzola, Emmanuel Gaucher, Thomas Forbriger, Rudolf Widmer-Schnidrig, Felix Bögelspacher, Michael Frietsch, and Andreas Rietbrock

High spatial and temporal resolution of distributed acoustic sensing (DAS) measurements makes them very attractive in different applications in seismology, such as seismic noise analysis (e.g. Bahavar et al 2020, Spica et al 2020) and seismic event detection (e.g. Ajo-Franklin et al 2019, Fernandez Ruiz 2020, Jousset 2020). The quantity measured by a DAS is strain or strain rate of an optic fiber cable, which is related to the spatial gradient of displacement and velocity that is usually measured by single point seismometers. The amplitude (and signal to noise ratio, SNR) and frequency resolutions of DAS recordings depend on spatial and temporal acquisition parameters, such as i.e. gauge-length (GL) and derivative time (DT), the latter being of importance only if the device records the strain rate.

In this study, our aims have been to investigate, experimentally, how to adapt the averaging parameters such as GL and DT to gain sensitivity in frequency bands of interests, and to investigate the seismic event detection capability of DAS data under specific set up. We recorded samples of DAS raw data, over a few hours at the German Black Forest Observatory (BFO) and in Sardinia, Italy.  We studied the spectral characteristics of strain and strain rate converted from DAS raw data, to analyze the sensitivity of DAS measurements to GL and DT. The power spectral densities are compared with the strain meter recordings at BFO site as a benchmark, which is recorded using the strain-meter arrays measuring horizontal strain in three different directions independently from the DAS (For details about the DAS measurement station at BFO see Azzola et al.  EGU 2022). We concluded about the lower limit of the DAS noise level that is achievable with employing different acquisition parameters. Accordingly, we applied suitable parameters for continuous strain-rate data acquisition at another experimental site in Georgia, which is related to the DAMAST (Dams and Seismicity) project.  

During the acquisition time periods at BFO and in Georgia, the visibility of local, regional and teleseismic events on the DAS data has been investigated. At both sites, a broadband seismometer is continuously operating, and can be considered as a reference to evaluate the event detection capability of the DAS recordings taking into account the monitoring set-up, i.e. cable types,  cable coupling to the ground, directional sensitivity and acquisition parameters. In addition, at BFO the DAS seismic event detection capability is evaluated comparing with the strain-meter array. Examples of detected seismic events by DAS are discussed, in terms of achievable SNR for each frequency content and comparison with the seismometers and strain-meter array.

How to cite: Karamzadeh Toularoud, N., Azzola, J., Gaucher, E., Forbriger, T., Widmer-Schnidrig, R., Bögelspacher, F., Frietsch, M., and Rietbrock, A.: PSD analysis and seismic event detectability of Distributed Acoustic Sensing (DAS) mesurements from several monitoring sites, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8787, https://doi.org/10.5194/egusphere-egu22-8787, 2022.

EGU22-10322 | Presentations | SM2.1

Strain accumulation along a 21km long optic fibre during a seismic crisis in Iceland, 2020 

Christopher Wollin, Philippe Jousset, Thomas Reinsch, Martin Lipus, and Charlotte Krawczyk

Slow slip plays an important role in accommodating plate motion along plate boundaries throughout the world. Further understanding of the interplay between aseismic and seismic slip has gained particular attention as it is crucial for the assessment of seismic risk. A wide range of instruments and acquisition techniques exist to quantify tectonic deformation which spans multiple orders of magnitude in duration as well as spatial extend. For example, seismometers acquire dense temporal data, however are sparsely deployed, leading to spatial aliasing. As opposite, remote sensing techniques have wide aperture but rather crude temporal resolution and accuracy (mm-range). In selected areas, strain is continuously measured with laser or borehole strainmeters.
In this contribution, we investigate the distribution of permanent strain along a telecommunication optic fibre on the Reykjanes Peninsula, South West Iceland. Continuous strain-rate was recorded via DAS (Distributed Acoustic Sensing) over a period of six months during the recent unrest of the Svartsengi volcano which began in January 2020. The interrogated fibre connects the town of Gridavik with the Svartsengi geothermal power plant and was patched to a second fibre leading to the western most tip of the Reykjanes Peninsula. It is approximately between 10 and 20km west of the active volcanic area which produced abundant local seismicity as well as surface uplift and subsidence in areas crossed with the optical fiber. The fibre was installed in a trench at less than one meter depth and consists of two roughly straight segments of 7 and 14km length. Whereas the longer segment trends WSW parallel to the strike of the Mid-Atlantic Ridge at this geographic height, the shorter segment trends NEN and thus almost coincides with the maximum compressive stress axis of the region.
Inspection of the spatio-temporal strain-rate records after the occurrence of local earthquakes indicates the accumulation of compressive as well as extensive strain in short fibre sections of a few dozen meters which could correlate with local geologic features like faults or dykes. This holds for events of M~2.5 and fibre segments in epicentral distances of more than 20km. Preliminary results regarding the total deformation of the fibre as response to an individual seismic event show a distinct behaviour for differently oriented fibre segments correlating with the overall stress regime, i.e. shortening in the order of some dozen nanometers in the direction of SHmax. Unfortunately, recordings of the two largest intermediate M>=4.8 events indicate saturation of the recording system or loss of ground coupling thus preventing a meaningful interpretation of their effect on permanent surface motion. 
Perspectively, our efforts aim at investigating the feasibility of distributed optical strain-rate measurements along telecommunication infrastructure to track locally accumulated strain.

How to cite: Wollin, C., Jousset, P., Reinsch, T., Lipus, M., and Krawczyk, C.: Strain accumulation along a 21km long optic fibre during a seismic crisis in Iceland, 2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10322, https://doi.org/10.5194/egusphere-egu22-10322, 2022.

EGU22-10574 | Presentations | SM2.1

Innovative high resolution optical geophysical instruments at the termination of long fibers: first results from the Les Saintes optical ocean bottom seismometer, and from the Stromboli optical strainmeter 

Pascal Bernard, Guy Plantier, Philippe Ménard, Yann Hello, Guillaume Savaton, Jean-Philippe Metaxian, Maurizio Ripepe, Marie-Paule Bouin, Frederick Boudin, Romain Feron, Sébastien Deroussi, and Roberto Moretti and the optic-OBS-strain-2022 team

In June 2022, in the frame of the PREST interreg Caraïbe project, we installed an optical OBS offshore the Les Saintes archipelago (Guadeloupe, Lesser Antilles), at the termination of a 5.5 km long optic cable buried in the sea floor and landing in Terre-de-Bas island (FIBROSAINTES campaign: Antea vessel from the FOF, plow from GEOAZUR). This innovative seismometer, developped in the last decade by ESEO, is based on Fabry-Perot (FP) interferometry, tracking at high resolution (rms 30 pm) the displacement of the mobile mass of a 10 Hz, 3 component, purely mechanical geophone (no electronics nor feed-back). This optically cabled OBS is the marine version of the optical seismometer installed at the top of La Soufrière volcano of Guadeloupe, in 2019, at the termination of a 1.5 km long fiber (HIPERSIS ANR project). Both seismometers are telemetered in real-time to the Guadeloupe Observatory (IPGP/OVSG). The optical seismometer, located at a water depth of 43 m near the edge of the immersed reef, is aimed at improving the location of the swarm-like seismicity which still persists after the Les Saintes 2004, M6.3 normal fault earthquake. The considerable advantage of such a purely optical submarine sensor over commercial, electric ones is that its robustness, due to the absence of electrical component, guarantees a very low probability of failure, and thus significantly reduces the costs of maintenance. In May 2022, an optical pressiometer and an optical hydrostatic tiltmeter designed and constructed by ENS shoud be installed offshore and connected to the long fiber, next to the optical OBS.

Based on the same FP interrogator, ESEO and IPGP recently developped a high resolution fiber strainmeter, the sensing part being a 5 m long fiber, to be buried or cemented to the ground. A prototype has been installed mid-September 2021 on the Stromboli volcano, in the frame of the MONIDAS (ANR) and LOFIGH (Labex Univearth, Univ. Paris) projects. The interrogator was located in the old volcanological observatory, downslope, and the optical sensors, at 500 m altitude, were plugged at the end of a 3 km optic cable. They consist of three fibers, 5 m long each, buried 50 cm into the ground. Their different orientation allowed to retrieve the complete local strain field. The four weeks of continuous operation clearly recorded the dynamic strain from the frequent ordinary summital explosion ( several per hour), and, most importantly, the major explosion of the 6th of October (only a few per year). The records show a clear precursory signal, starting 120s before this explosion, corresponding to a transient compression, oriented in the crater azimuth, peaking at 0.9 microstrain  10 s before the explosion.

These two successfull installations of optical instruments open promising perspectives for the seismic and strain real-time monitoring in many sites, offshore, on volcanoes, and more generally in any site, natural or industrial, presenting harsh environmental conditions, where commercial, electrical sensors are difficult and/or costly to install and to maintain, or simply cannot be operated.

How to cite: Bernard, P., Plantier, G., Ménard, P., Hello, Y., Savaton, G., Metaxian, J.-P., Ripepe, M., Bouin, M.-P., Boudin, F., Feron, R., Deroussi, S., and Moretti, R. and the optic-OBS-strain-2022 team: Innovative high resolution optical geophysical instruments at the termination of long fibers: first results from the Les Saintes optical ocean bottom seismometer, and from the Stromboli optical strainmeter, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10574, https://doi.org/10.5194/egusphere-egu22-10574, 2022.

EGU22-11311 | Presentations | SM2.1

Overcoming limitations of seismic monitoring using fibre-optic distributed acoustic sensing 

Regina Maaß, Sven Schippkus, Céline Hadziioannou, Benjamin Schwarz, Charlotte Krawczyk, and Philippe Jousset

Seismic monitoring refers to the measurement of time-lapse changes of seismic wave velocities and is a frequently used technique to detect dynamic changes in the Earth‘s crust. Its applications include a broad range of topics, such as natural hazard assessment and structural health monitoring. To obtain reliable measurements, results are usually stacked over time. Thereby, temporal resolution is lost, which makes the measurement less sensitive to short-term environmental processes. Another problem is that conventional datasets often lack spatial density and velocity changes can only be attributed to large areas. Recently, distributed acoustic sensing (DAS) has gained a lot of attention as a way to achieve high spatial resolution at low cost. DAS is based on Rayleigh-scattering of photons within an optical fibre. Because measurements can be taken every few meters along the cable, the fibre is turned into a large seismic array that provides information about the Earth’s crust at unprecedented resolution.

In our study, we explore the potential of DAS for monitoring studies. Specifically, we investigate how spatial stacking of DAS traces affects the measurements of velocity variations. We use data recorded by a 21-km-long dark fibre located on Reykjanes Pensinsula, Iceland. The cable is sampled with a channel spacing of 4 meters. We analyze the energy of the oceans microseism continuously recorded between March and September 2020. At first, we stack adjacent traces on the fibre in space. We then cross correlate the stacks to obtain approximations of the Green’s functions between different DAS-channels. By measuring changes in the coda waveform of the extracted seismograms, velocity variations can be inferred. Our analysis shows that spatial stacking improves the reliability of our measurements considerably. Because of that, less temporal stacking is required and the time resolution of our measurements can be increased. In addition, the enhancement of the data quality helps resolve velocity variations in space, allowing us to observe variations propagating along the cable over time. These velocity changes are likely linked to magmatic intrusions associated with a series of repeated uplifts on the Peninsula. Our results highlight the potential of DAS for improving the localization capabilities and accuracy of seismic monitoring studies.

How to cite: Maaß, R., Schippkus, S., Hadziioannou, C., Schwarz, B., Krawczyk, C., and Jousset, P.: Overcoming limitations of seismic monitoring using fibre-optic distributed acoustic sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11311, https://doi.org/10.5194/egusphere-egu22-11311, 2022.

EGU22-11508 | Presentations | SM2.1 | Highlight

Building a new type of seafloor observatory on submarine telecom fiber optic cables in Chile 

Diane Rivet, Sergio Barrientos, Rodrigo Sánchez-Olavarría, Jean-Paul Ampuero, Itzhak Lior, Jose-Antonio Bustamente Prado, and German-Alberto Villarroel Opazo

In most subduction zones, a great portion of seismicity is located offshore, away from permanent onland seismic networks. Chile is not the exception; since the upgraded seismic observation system began operating in 2013, 35% of the ~7000 earthquakes with M≥3 recorded yearly were located offshore. Most importantly, the epicenters of the largest earthquakes (M>7.5) from 2014 to 2016 were located offshore as well.

The Chilean national seismic network is mainly composed of coastal and inland stations, except for two stations located on oceanic islands, Rapa Nui (Easter Island) and Juan Fernandez archipelago. This station configuration makes it difficult to observe in sufficient detail the lower-magnitude seismicity at the nucleation points of large events. Moreover, the lack of seafloor stations limits the efficiency of earthquake early warning systems during offshore events. These challenges could be overcome by permanently instrumenting existing submarine telecom cables with Distributed Acoustic Sensing (DAS).

Thanks to GTD, a private telecommunications company that owns a 3500-km-long network of marine fiber optic cables with twelve landing points in Chile (Prat project), from Arica (~ 18⁰S) to Puerto Montt (~ 41⁰S), we conducted the POST (Submarine Earthquake Observation Project in Spanish) DAS experiment on the northern leg of the Concón landing site of the Prat cable. This experiment, one of the first to be conducted on a commercial undersea infrastructure in a very seismically active region, was carried out from October 28 to December 3, 2021. Based on the longitudinal strain-rate data measured along 150 km of cable with a spatial resolution of 4 meters and a temporal sampling of 125 Hz, we present preliminary results of analyses to assess the possibility of building a new type of permanent, real-time and distributed seafloor observatory for continuous monitoring of active faults and earthquake early warning systems.

How to cite: Rivet, D., Barrientos, S., Sánchez-Olavarría, R., Ampuero, J.-P., Lior, I., Bustamente Prado, J.-A., and Villarroel Opazo, G.-A.: Building a new type of seafloor observatory on submarine telecom fiber optic cables in Chile, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11508, https://doi.org/10.5194/egusphere-egu22-11508, 2022.

EGU22-11599 | Presentations | SM2.1

Comparing two fiber-optic sensing systems: Distributed Acoustic Sensing and Direct Transmission 

Daniel Bowden, Andreas Fichtner, Thomas Nikas, Adonis Bogris, Konstantinos Lentas, Christos Simos, Krystyna Smolinski, Iraklis Simos, and Nikolaos Melis

Distributed Acoustic Sensing (DAS) systems have gained popularity in recent years due to the dense spatial coverage of strain observations; with one fiber and one interrogator researchers can have access to thousands of strain or strain-rate observations over a region. DAS systems have a limited range, however, with usual experiments being on the order of 10’s of kilometers, owing to their reliance on weakly backscattered light. In contrast, systems that transmit light through a fiber and measure signals on the other end (or looped back) can traverse significantly longer distances (e.g., Marra et. al 2018, Zhan et. al 2021, Bogris et. al 2021), and have the added advantages of being potentially cheaper and potentially operating in parallel with active telecommunications purposes. The disadvantage of such transmission systems is that only a single measurement of strain along the entire distance is given.

During September - October 2021, we operated examples of both systems side-by-side using telecommunications fibers underneath North Athens, Greece, in collaboration with the OTE telecommunications provider. Several earthquakes were detected by both systems, and we compare observations from both. The DAS system is a Silixa iDAS Interrogator measuring strain-rate. The newly designed transmission system relies on interferometric use of microwave frequency dissemination; signals sent along the fiber and back in a closed loop are compared to what was sent to measure phase differences (Bogris et. al 2021). We find that both systems are successful in sensing earthquakes and agree remarkably well when DAS signals are integrated over the length of the cable to properly mimic the transmission observations.

The direct transmission system, however, may not be as intuitive to interpret as an integral of displacement ground motions along the fiber. We discuss both theoretical and data-driven examples of how the observed phases depend on the curvature of a given length of fiber, and describe how asymmetries in the fiber’s index of refraction play a role in producing observed signals. Such an understanding is crucial if one is to properly interpret the signals from such a system (e.g., especially very long trans-oceanic cables). Given a full theoretical framework, we also discuss a strategy for seismic tomography given such a system: with a very long fiber, the spatial sensitivity should evolve over time as seismic signals reach different sections.

How to cite: Bowden, D., Fichtner, A., Nikas, T., Bogris, A., Lentas, K., Simos, C., Smolinski, K., Simos, I., and Melis, N.: Comparing two fiber-optic sensing systems: Distributed Acoustic Sensing and Direct Transmission, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11599, https://doi.org/10.5194/egusphere-egu22-11599, 2022.

EGU22-11864 | Presentations | SM2.1

Distributed Acoustic Sensing in the Athens Metropolitan Area: Preliminary Results 

Krystyna T. Smolinski, Daniel C. Bowden, Konstantinos Lentas, Nikolaos S. Melis, Christos Simos, Adonis Bogris, Iraklis Simos, Thomas Nikas, and Andreas Fichtner

Once a niche technology, Distributed Acoustic Sensing (DAS) has gained increasing popularity over the last decade, due to its versatility and ability to capture extremely dense seismic datasets in a wide range of challenging environments. While DAS has been utilised in some particularly remote locations, such as on glaciers and volcanoes, it also holds a great deal of potential closer to home; beneath our cities. As DAS is able to be used with existing telecommunication fibres, urban areas contain huge potential networks of strain or strain-rate sensors, right beneath our feet. This data enables us to monitor the local environment, recording events such as earthquakes, as well as characterising and monitoring the shallow subsurface. DAS experiments using dark fibres are unintrusive and highly repeatable, meaning that this method is ideal for long-term site monitoring.

In collaboration with the OTE Group (the largest telecommunications company in Greece), we have collected urban DAS data beneath North-East Athens, utilising existing, in-situ telecommunication fibres. This large dataset contains a wide range of anthropogenic signals, as well as many seismic events, ranging from small, local events, to an internationally reported Magnitude 6.4 earthquake in Crete.

We conduct a preliminary analysis of the dataset, identifying and assessing the earthquake signals recorded. This will be compared with the event catalogue of the local, regional network in Athens, to determine our sensitivity to events of different magnitudes, and in a range of locations. We hope to gain an understanding of how DAS could be combined with the existing network for seismic monitoring and earthquake detection.

Moving forward, we aim to also apply ambient noise methods to this dataset in order to extract dispersion measurements, and ultimately invert for a shallow velocity model of the suburbs of Athens.

How to cite: Smolinski, K. T., Bowden, D. C., Lentas, K., Melis, N. S., Simos, C., Bogris, A., Simos, I., Nikas, T., and Fichtner, A.: Distributed Acoustic Sensing in the Athens Metropolitan Area: Preliminary Results, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11864, https://doi.org/10.5194/egusphere-egu22-11864, 2022.

EGU22-11869 | Presentations | SM2.1

Long range distributed acoustic sensing technology for subsea geophysical applications 

Erlend Rønnekleiv, Ole Henrik Waagaard, Jan Petter Morten, and Jan Kristoffer Brenne

Recent advances in range and performance of distributed acoustic sensing (DAS) enable new geophysical applications by measuring fiber strain in existing telecom cables and subsea power cables that incorporate optical fibers. We will  present new field data showing the usability of DAS for environmental and geophysical applications, focusing especially on seabed surface waves and the sub-Hz domain. These examples show that highly sensitive DAS technology can be a valuable tool within seismology and oceanography.

The sensitive range along the fiber for DAS was previously limited to about 50 km. We will demonstrate a newly developed system (named OptoDAS) that allows for launching several orders of more optical power into the fiber, and thereby significantly improving the range beyond 150 km.

This new interrogation approach allows for high degree of flexibility optimizing the interrogation parameters to optimize the noise floor, spatial and temporal resolution according to the application. The gauge length (spatial resolution) can be set from 2 to 40 m. For interrogation of 10 km fiber, we achieve a record low noise floor of 1.4 pε/√Hz with 10 m spatial resolution. For interrogation of fibers beyond 150 km, we achieve a noise floor below 50 pε/√Hz up to 100 km. Above 100 km, the noise is limited by the level of reflected optical power, and the noise increases by ~0.3-0.4 dB/km, corresponding to the dual path optical loss in the fiber.

A modern instrument control interface allows for automatic optimalization of interrogation parameters based on application parameters in a few minutes. The instrument computer provides a flexible platform for different applications. The high-capacity storage system can store recorded time-series of several weeks to support e.g., geophysical investigations where extensive post-processing is required. The computational capacity can also be used for real-time visualization and advanced signal processing, for example for event detection and direct reporting of estimated parameters.

The OptoDAS system can convert a submarine cable into a 100 km+ densely sampled array.  From the recordings on a telecom cable in the North Sea, we will show examples of propagating Rayleigh and Love acoustical modes bounded to the seafloor surface. These modes can be excited by acoustic sources on or above the seafloor, such as trawls and anchors. The dense spatial sampling allows for accurate estimates of the location of these sources. The system also allows for applications in seismology and earthquake monitoring. When attached to a cable with non-straight geometry, the measurements have substantial information to determine the location of seismic events. This will be demonstrated using field data from the North Sea telecom cable.

From recordings on a submarine cable between Norway and Denmark, we present the DAS response in the frequency range 0.1 mHz-10Hz across a cable span of 120 km. The response in this frequency range will be a combination of temperature changes, ocean swells and tides. We show that increasing the gauge length in post-processing allows for improving the sensitivity for detecting ultra-low frequency signals.

How to cite: Rønnekleiv, E., Waagaard, O. H., Morten, J. P., and Brenne, J. K.: Long range distributed acoustic sensing technology for subsea geophysical applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11869, https://doi.org/10.5194/egusphere-egu22-11869, 2022.

Due to the combined effect of human-driven depletion and anthropogenic climate change, groundwater storage is decreasing across the globe. This trend will potentially have an adverse impact on future human socio-economic development, by increasing the frequency and duration of both hydrological and socio-economic droughts as well as generating inter-sectoral competition for limited water resources.

Large-scale modelling studies on changes in groundwater availability can be separated into two big families. First, hydrological impact models actively consider water usage across sectors but ignore land-atmosphere interactions by design. Second, Earth System Models consider two-way interactions between climate and groundwater resources, but almost never consider the anthropogenic water resource depletion, except in some cases for irrigation.

The goal of this study is to connect the expertise of these two families by implementing domestic and industrial water usage in the Community Earth System Model version 2. Using land-atmosphere coupled simulations, we will revisit previously computed trends in future groundwater availability by simultaneously accounting for climate change and anthropogenic water resource usage.

How to cite: Taranu, I. S. and Thiery, W.: Implementing sectoral water usage in the Community Earth System Model for projecting future water resource availability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-596, https://doi.org/10.5194/egusphere-egu22-596, 2022.

EGU22-898 | Presentations | HS7.3

Salinity-inclusive water scarcity: examples from food bowl regions of the US and Australia 

Josefin Thorslund, Marc F.P. Bierkens, Anna Scaini, Edwin H. Sutanudjaja, and Michelle T.H. van Vliet

Irrigated agriculture sustains more than 40% of global food production and uses up to 90 % of the world’s water resources. Water scarcity for the irrigation water use sector is a common problem, which may be driven by both water shortages and increased salinity levels. Limited studies however considered salinity issues in water scarcity assessment. We here developed a salinity-inclusive water scarcity framework for the irrigation sector, accounting for crop-specific irrigation water demands and salinity tolerance and its relation to water availability and salinity levels of both surface and groundwater resources. We assess temporal and spatial variation of water scarcity in agricultural river basins of the Central Valley (California) and the Murray Darling Basin (Australia), which are important food bowl regions. Our results show that including salinity and crop-specific salinity tolerances leads to very different water scarcity levels, compared to water scarcity approaches based on water quantity only, particularly at local scales. Further, our results from the Central Valley region highlights that severe water scarcity can be strongly alleviated by conjunctive groundwater use, to dilute and lower salinity levels below crop specific tolerance values in many sub-basins. However, groundwater resources needed for dilution frequently exceed renewable groundwater rates in this region, posing additional risks for groundwater depletion. Taken together, through capturing these dynamics, our water scarcity framework can support local-regional water management and provide a useful tool for sustainable water use and assessing the impact of agricultural practices, such as crop choices, on water scarcity levels.

How to cite: Thorslund, J., Bierkens, M. F. P., Scaini, A., Sutanudjaja, E. H., and van Vliet, M. T. H.: Salinity-inclusive water scarcity: examples from food bowl regions of the US and Australia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-898, https://doi.org/10.5194/egusphere-egu22-898, 2022.

Quantification of the Water Losses (WL) components in Water Distribution Networks (WDNs) is a vital task towards their reduction. However, current WL estimation methods rely on semi-empirical approaches with high uncertainty levels, which usually lead to inaccurate estimates of the lost volume. Here, we compare the probabilistic Minimum Night Flow (MNF) estimation method introduced by Serafeim et al. (2021) to the Water Balance components analysis, introduced by the International Water Association (IWA). The strong point of the Serafeim et al. (2021) approach is that it uses statistical metrics to filter out noise effects in the flow timeseries used for MNF estimation, leading to more accurate estimation of the low flows during night hours. The effectiveness of the applied methods is tested via a large-scale, real world application to the 4 largest Pressure Management Areas (PMAs) of the WDN of the city of Patras, the third largest city in Greece (see Serafeim at al., 2022). Although methodologically different, the two approaches lead to very similar results, substantiating the robustness of the Serafeim at al. (2021) approach which allows for reliable confidence interval estimation of the observed Minimum Night Flows, making it particularly suited for engineering applications.

Acknowledgements

The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 1162).

References

Serafeim, A.V., Kokosalakis, G., Deidda, R., Karathanasi I. and Langousis A (2021) Probabilistic estimation of minimum night flow in water distribution networks: large-scale application to the city of Patras in western Greece, Stoch. Environ. Res. Risk. Assess., https://doi.org/10.1007/s00477-021-02042-9

Serafeim, A.V.; Kokosalakis, G.; Deidda, R.; Karathanasi, I.; Langousis, A. (2022) Probabilistic Minimum Night Flow Estimation in Water Distribution Networks and Comparison with the Water Balance Approach: Large-Scale Application to the City Center of Patras in Western Greece, Water, 14, 98, https://doi.org/10.3390/w14010098

How to cite: Langousis, A., Serafeim, A., Kokosalakis, G., Deidda, R., and Karathanasi, I.: Probabilistic water losses estimation in water distribution networks and comparison with the top down - water balance approach: A large-scale application to the city center of Patras in western Greece, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1974, https://doi.org/10.5194/egusphere-egu22-1974, 2022.

EGU22-2855 | Presentations | HS7.3

Parametric model for probabilistic estimation of water losses in water distribution networks: A large scale real world application to the city of Patras in western Greece 

Athanasios V. Serafeim, George Kokosalakis, Roberto Deidda, Irene Karathanasi, and Andreas Langousis

Abstract

Quantification of the leakage volume in pressure management areas (PMAs) is a vital task for water agencies’ financial viability. However, currently, there is no rigorous approach for their parametric modeling on the basis of networks’ specific characteristics and inlet/operating pressures. To bridge this gap, the current work focuses on the development of a probabilistic framework for minimum night flow (MNF) estimation in water distribution networks that: 1) parametrizes the MNF as a function of the network’s specific characteristics, and 2) parametrically describes water losses in individual PMAs as a function of the inlet/operating pressures. MNF estimates are obtained using the robust, non-parametric, probabilistic minimum night flow (MNF) estimation methodology developed and validated by Serafeim et al. (2021 and 2022), which allows for confidence interval estimation of the observed MNFs. The effectiveness of the developed model is tested in a large-scale real world application to the water distribution network of the city of Patras in western Greece, which serves approximately 200,000 consumers with more than 700 km of pipeline. The developed framework is validated through flow-pressure tests conducted by the Municipal Enterprise of Water Supply and Sewerage of the City of Patras to 78 PMAs of the network, indicating that the developed framework can be effectively used to improve water loss estimation and flow-pressure management in a morphologically and operationally diverse set of PMAs.

Acknowledgements

The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant” (Project Number: 1162).

 

References

Serafeim, A.V., Kokosalakis, G., Deidda, R., Karathanasi I. and Langousis A (2021) Probabilistic estimation of minimum night flow in water distribution networks: large-scale application to the city of Patras in western Greece, Stoch. Environ. Res. Risk. Assess., https://doi.org/10.1007/s00477-021-02042-9

Serafeim, A.V.; Kokosalakis, G.; Deidda, R.; Karathanasi, I.; Langousis, A. (2022) Probabilistic Minimum Night Flow Estimation in Water Distribution Networks and Comparison with the Water Balance Approach: Large-Scale Application to the City Center of Patras in Western Greece, Water, 14, 98, https://doi.org/10.3390/w14010098

How to cite: Serafeim, A. V., Kokosalakis, G., Deidda, R., Karathanasi, I., and Langousis, A.: Parametric model for probabilistic estimation of water losses in water distribution networks: A large scale real world application to the city of Patras in western Greece, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2855, https://doi.org/10.5194/egusphere-egu22-2855, 2022.

EGU22-3301 | Presentations | HS7.3

Monitoring of agricultural drought from remote sensing products and in-situ meteorological data 

Mathis Neuhauser, Thomas Tilak, Christophe Point-Dumont, and Alexandre Peltier

The extreme events increasingly present in the Pacific (El Nino / La Nina phenomena) have significant consequences on island territories. The effect of climate change and drought episodes is therefore a central concern in many Pacific islands like Vanuatu, Wallis-and-Futuna, French Polynesia, etc. The intense drought events have undeniable impacts on biodiversity, agricultural crops and water resource, as was the case in 2019 for New Caledonia. In particular, projections in New Caledonia count on a possible increase in temperatures of 3°C and a water deficit of 20% in 2100 with longer and more intense drought episodes and an even greater west coast/east coast disparity (Dutheil, 2018). To date, the monitoring and anticipation of these drought episodes is done via meteorological measurements providing information on the rainfall deficit and not on the water stress of plants. In addition, the data are only available on a few measurement points and are not continuous over the territories.

In order to meet this need, a tool for monitoring environmental and agricultural drought using satellite images and meteorological data is being developed and validated in New Caledonia: Earth Observations for Drought Monitoring (EO4DM) project. This project is carried out in collaboration with Météo-France NC as a technical partner and the local Rural Agency as end user, and aims to provide a tool to help decision-making to institutions and management assistance for farmers. This solution will provide data constituting a singularly important source of information whose valuations and contributions can be multiple: agriculture, resource management (water), security (monitoring of risks linked to floods, fires), environment, etc.

To do so, various surface indices reflecting the state of the vegetation and certain soil properties such as humidity and temperature were estimated from different satellite sensors (MODIS, Sentinel-2, Landsat-8, ASCAT) in order to address different space scales from the field to regional scale. These indices were normalized over a relatively long period, allowing access to drought indicators: VHI (Vegetation Health Index; Kogan et al., 1997), VAI (Vegetation Anomaly Index; Amri et al., 2011), MAI (Moisture Anomaly Index; Amri et al., 2012) or TAI (Temperature Anomaly Index; Le Page and Zribi, 2019). Combined with in-situ meteorological products like SPI (Standardized Precipitation Index; McKee et al., 1993) and SPEI (Standardized Precipitation Evapotranspiration Index; Vicente-Serrano et al., 2010), these indicators assess the intensity of drought episodes and estimate their severity over the entire territory.

How to cite: Neuhauser, M., Tilak, T., Point-Dumont, C., and Peltier, A.: Monitoring of agricultural drought from remote sensing products and in-situ meteorological data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3301, https://doi.org/10.5194/egusphere-egu22-3301, 2022.

EGU22-5834 | Presentations | HS7.3

Performance of regional climate models in simulating rainy seasons in West Africa 

Torsten Weber, Vincent O. Ajayi, Imoleayo E. Gbode, Daniel Abel, Katrin Ziegler, Heiko Paeth, and Seydou B. Traore

Agriculture in West Africa is highly dependent on rainfall during the rainy seasons. Therefore, modifications in rainy season characteristics due to recent and future climate change have a direct impact on crop yields and production in the region. Consequently, stakeholders and decision-makers need reliable regional climate change information on rainy seasons in order to develop appropriate adaptation measures.

Regional Climate Models (RCMs) can provide information on climate change at high temporal and spatial resolution through dynamic downscaling of climate projections generated by Earth System Models (ESMs). In order to assess the performance of RCMs in simulating rainy seasons and their characteristics such as onset and cessation, length and total sum of rainfall, a thorough evaluation of RCMs is required.

The current study evaluates the performance of three different RCMs (REMO2015, RegCM4-7 and CCLM5-0-15) in simulating rainy seasons in West Africa using gridded observational data sets. For the assessment, we will use the ERA-INTERIM driven simulations of the RCMs from the Coordinated Output for Regional Evaluations (CORE) embedded in the WCRP Coordinated Regional Climate Downscaling Experiment (CORDEX) for Africa with a spatial resolution of about 25 km.

How to cite: Weber, T., Ajayi, V. O., Gbode, I. E., Abel, D., Ziegler, K., Paeth, H., and Traore, S. B.: Performance of regional climate models in simulating rainy seasons in West Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5834, https://doi.org/10.5194/egusphere-egu22-5834, 2022.

EGU22-7460 | Presentations | HS7.3

Environmental, economic and social sustainability of Alternate Wetting and Drying rice irrigation in Northern Italy 

Olfa Gharsallah, Alice Mayer, Marco Romani, Andrea Ricciardelli, Sara Caleca, Michele Rienzner, Stefano Corsi, Giovanni Ottaiano, Giulio Gilardi, and Arianna Facchi

Italy is the Europe’s leading rice producer, with over half of total European production. The main rice area is in the north-western part of the country (Lombardy and Piedmont regions). In this area, irrigation of rice has been traditionally carried out by flooding; the introduction of alternative water-saving irrigation strategies could reduce water consumption, but their overall environmental and economic sustainability, as well as their social acceptability, should be investigated.

An experimental platform was set up in the core of the Italian rice district (Lomellina, PV) to compare different rice irrigation management options: wet seeding and traditional flooding (WFL), dry seeding and delayed flooding (DFL), wet seeding and alternated wetting and drying (AWD). Six plots of about 20 m x 80 m each were set-up, with two replicates for each irrigation option. One out of two replicates for each option was instrumented with: water inflow and outflow meters, set of piezometers, set of tensiometers and water tubes for the irrigation management in the AWD plots. Proper agronomic practices were adopted for the three management options. Periodic measurements of crop biometric parameters (LAI, crop height, crop rooting depth) were performed and rice grain yields and quality (As and Cd in the grain) were determined. Data measured in the field, together with those provided by the farmer, concerning the agronomic inputs and the economic costs incurred for the three irrigation options, were used to assess their economic and environmental sustainability through a set of quantitative indicators. Finally, through interviews with rice growers of the area, barriers to the adoption of the AWD technique were assessed and ways of overcoming them identified. In order to support water management decisions and policies, data collected at the farm level are extrapolated to the irrigation district level through a semi-distributed agro-hydrological model, used to compare the overall irrigation efficiency achieved implementing AWD when compared to WFL.

How to cite: Gharsallah, O., Mayer, A., Romani, M., Ricciardelli, A., Caleca, S., Rienzner, M., Corsi, S., Ottaiano, G., Gilardi, G., and Facchi, A.: Environmental, economic and social sustainability of Alternate Wetting and Drying rice irrigation in Northern Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7460, https://doi.org/10.5194/egusphere-egu22-7460, 2022.

EGU22-8093 | Presentations | HS7.3

Can an agro-hydrological model improve the irrigation management of maize under a center pivot? 

Arianna Facchi, Alice Mayer, Bianca Ortuani, and Alberto Crema

Plain areas of Northern Italy are characterized by a strong agricultural and zootechnical vocation. In the Lombardy region, the total utilized agricultural area (UAA) is about 700,000 ha, 72% of which is irrigated. Globally, about one half of the UAA is cropped with maize, but in some provinces this crop reaches almost the totality of the UAA. Maize is typically irrigated by border irrigation; however, in the context of the climate change and of the increased competition for the use of water in the plain, it is crucial to optimize the use of this resource.

This study is aimed at demonstrating the applicability of Precision Irrigation approaches in a large farm located in the core of the maize basin of the Lombardy plain (La Canova farm, BS, Italy; http://lacanovasrl.it/). In the farm, irrigation is provided by center pivots and linear irrigation systems. Although sprinkler irrigation can reduce the applied irrigation volumes compared to border irrigation, at present, a uniform irrigation rate is provided at fixed time intervals without accounting for spatial heterogeneity of soil or crop development.

During the agricultural season 2021, in a 15 hectares surface cropped with maize under a center pivot the irrigation was applied following a variable-rate approach. The soil variability was investigated using an Electromagnetic induction (EMI) sensor; through the application of cluster analysis techniques to the EMI survey, four types of soils were detected and characterized through a traditional soil sampling. According to soil variability and pivot geometry, four management zones (MZ) were identified: two MZs were characterized prevalently by coarse soils while the other two by medium-fine soils. In one ‘coarse’ MZ and one ‘fine’ MZ the irrigation was managed with the support of soil probes installed at two depth, and by a physically based agro-hydrological model (SWAP, https://www.swap.alterra.nl/) fed with weather forecasts at 7 days (https://www.abacofarmer.com/). A MATLAB code was developed to run the whole modelling system. Irrigation in the other two MZs was applied by the farmer according to the farm’s typical management (about 25-30 mm every four days). In the MZs managed with Variable Rate irrigation, the model was used to identify the optimal water depth to be applied at each irrigation event, depending on the soil water balance computed for the following 5 days; in doing this, a 4-day turn and a minimum irrigation depth of 18-25 mm (as a function of the time of the season) were respected, since they were constraints imposed by the farmer. Despite the constraints, compared to the reference MZs, the approach adopted led to a water saving of about 20 and 25% for the ‘coarse’ and ‘fine’ MZs, respectively, without a loss of yield. In the next step, the approach adopted will be used to estimate the water and energy saving achievable at the farm scale.

How to cite: Facchi, A., Mayer, A., Ortuani, B., and Crema, A.: Can an agro-hydrological model improve the irrigation management of maize under a center pivot?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8093, https://doi.org/10.5194/egusphere-egu22-8093, 2022.

EGU22-8392 | Presentations | HS7.3

Limnological responses to active management of the invasive aquatic fern Salvinia molesta in Las Curias Reservoir, San Juan, Puerto Rico. 

Xavier García López, Jorge Ortiz Zayas, Rodrigo Díaz, Aurelio Castro Jiménez, and Moisés Abdelrahman López

In the Anthropocene, human action and globalization are closely linked to the deterioration of natural habitats and water resources. Invasive aquatic weeds have been recognized as a major problem in watersheds worldwide due to their environmental impacts. This study focuses on the management of the Las Curias Reservoir in Cupey Puerto Rico in the Río Piedras watershed since the arrival of Salvinia molesta after Hurricane María in 2017.
Aquatic weed control consists of three methods: biological, mechanical, and chemical. Since December 2019, with the help of federal and local agencies, the University of Puerto Rico in Rio Piedras and a community-driven initiative led to the introduction of the Cyrtobagous salviniae in Las Curias Reservoir.  This insect is considered an effective biological control agent for S.  molesta.  Simultaneously, community members initiated a mechanical removal campaign using an aquatic harvester. Monthly sampling was conducted to measure physicochemical, biochemical, and biophysical variables in the reservoir in response to the reduction of S. molesta cover. In addition, monthly drone flights were conducted to create orthomosaic maps of the plant coverage over the water surface, as part of the monitoring of the ecosystem health and characterization. Probably the propagation of S. molesta occurred due to eutrophication after an increase in nutrient-rich sewage discharges from septic tanks and faulty sewage pump stations affected by power outages after Hurricane Maria. By 2019, the reservoir was completely covered with S. molesta. It is not until August 2020 that we noticed considerable changes in the reduction of plant density. Upon the reduction of S. molesta coverage, we found increases in the mean of water temperature (+3 Cِ°), dissolved oxygen (+1.4 mg/L), pH (+0.5) specific conductance (+118.3 µS/cm) and in light penetration (+255.6 
μmo/m^2/s).  The water stored in Las Curias could become an invaluable source of raw water for public supply during future droughts, especially in the densely populated San Juan Metropolitan Area, where Las Curias is located. Therefore, its restoration is socially relevant and justifiable. 

How to cite: García López, X., Ortiz Zayas, J., Díaz, R., Castro Jiménez, A., and Abdelrahman López, M.: Limnological responses to active management of the invasive aquatic fern Salvinia molesta in Las Curias Reservoir, San Juan, Puerto Rico., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8392, https://doi.org/10.5194/egusphere-egu22-8392, 2022.

EGU22-8409 | Presentations | HS7.3

Statistical methodology for PRV malfunction detection and alerting in Water Distribution Networks 

Anastasios Perdios, George Kokosalakis, Irene Karathanasi, and Andreas Langousis

As the outflow velocity from a pipe crack increases with increasing hydraulic pressure, pressure management concepts have been widely applied to reduce water losses in the delivering and distribution parts of water networks. In this context, pressure reducing valves (PRVs) have been commonly used to regulate pressures and therefore reduce water losses, in both water supply and water distribution networks, by reducing the upstream pressure to a set outlet pressure (i.e. downstream of the PRV), usually referred to as set point.

As all types of mechanical equipment, PRVs exhibit malfunctions affecting pressure regulation, which can be defined as events when the outlet pressure does not match the set point. These events can be classified in two categories: a) high frequency fluctuations around the set point, and b) prolonged systematic deviations from the set point. Since PRV malfunctions result in systematic or random deviations of the outlet pressure from the set point, their detection can be approached in a statistical context.

In this study, we develop a novel framework for detection of PRV malfunctions in water supply and water distribution networks, which uses: a) the root mean squared error (RMSE) as a proper statistical metric for monitoring the performance of a PRV by detecting individual malfunctions (i.e. malfunction occurrences) in the high-resolution pressure time series, and b) the hazard function concept to identify a proper duration of sequential events from (a) to issue alerts.

The suggested methodology is implemented using pressure data at 1-min temporal resolution from pressure management area “Diagora” of the water distribution network of the city of Patras (the third largest city in Greece), for a 3 year period from 01 January 2017 to 31 December 2019. The obtained results show that the developed statistical approach effectively detects major PRV malfunctions (as reported by the Municipal Water Supply Company and Sewerage of Patras, DEYAP), allowing it to be used for operational purposes.

Acknowledgments:

This research is co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T2EDK-4177).

How to cite: Perdios, A., Kokosalakis, G., Karathanasi, I., and Langousis, A.: Statistical methodology for PRV malfunction detection and alerting in Water Distribution Networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8409, https://doi.org/10.5194/egusphere-egu22-8409, 2022.

EGU22-8898 | Presentations | HS7.3

Small Islands – Precipitation in the Future 

Maria Meirelles, Fernanda Carvalho, Diamantino Henriques, and Patrícia Navarro

For most islands, there is very little published literature documenting the probability, frequency, severity,or consequences of climate change impacts, such as an decrease in precipitation. Some times, projections of future climate change impacts are limited by the lack of model skill in projecting the climatic variables that matter to small islands. The Azores are an archipelago formed by nine high volcanic islands, presenting a relatively small land area where precipitation is of orographic origin. Relatively projections up to the end of the 21st century, they were used for the same geographic region - the Azores region between 37 °N - 40°N and 32°W - 25°W - the results of the CMIP5 project for the RCPs (Representative Concentration Pathways) scenarios; trajectories describe four possible future climate scenarios, which depend on the amount of greenhouse gases emissions that may be emitted in the coming years. The four RCP scenarios (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5), correspond to four radiative forcing intervals for the year 2100, to pre-industrial values ​​(+2.6, +4.5, +6.0 and +8.5 W/m2, respectively). Most of the CMIP5 climate data and projections used in this work they are freely available on the Climate Ex plorer portal (https://climexp.knmi.nl/) of the KNMI (Koninklijk Nederlands Meteorologisch Instituut). Anomaly of the average annual precipitation for the Azores was calculated in the 1979-2019 period and its projections are estimated up to 2100, according to the RCP scenarios (Figure 1). In this case, the average variation calculated for the three scenarios for annual precipitation is -7.8 mm; in the case of the scenario more pessimistic (RCP 8.5), the models show for the Azores a decrease in average annual precipitation of about 9.8 mm/day until the end of the century, compared to the average of the last 30 years. According to the RCP4.5 scenario, a decrease is observed which is accentuated from the northwest to the southeast in the region under consideration, especially affecting the islands of the central and eastern groups. Of the calculations results for the average of the models an increase of the maximum number consecutive days with low rainfall (<1mm) from + 0.2 to 4.8 days / year until the year 2100. The demand for water affects basically four activities: the agriculture, energy production, industrial uses and consumption human. The projections found for the Azores of a decrease in precipitation are in line with other small island regions, such as the Caribbean and Mediterranean region. Thus, these regions become more vulnerable to social, economic and environmental impacts.

How to cite: Meirelles, M., Carvalho, F., Henriques, D., and Navarro, P.: Small Islands – Precipitation in the Future, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8898, https://doi.org/10.5194/egusphere-egu22-8898, 2022.

EGU22-10221 | Presentations | HS7.3 | Highlight

Pandemic Medical Supply Needs with a Coincident Natural Disaster and an Analysis of COVID-19 Data Availability 

Paul Churchyard, Ajay Gupta, and Joshua Lieberman

The Open Geospatial Consortium’s Disaster Pilot 2021 focused on turning earth observation and reporting data into decision ready indicators (DRI) for disaster response and management.  HSR.health as a Pilot participant  developed the recipe for, and produced a Medical Supply Needs Index that indicates what medical supplies, such as Personal Protective Equipment, are needed to respond to COVID-19 cases throughout a population. Medical Supply Needs Indices were calculated for areas within the Pilot focus regions and shared via a dashboard-style application. HSR.health and collaborators then set up an integrated demonstration showing the Medical Supply Needs Index updating in real-time as a result of data on the occurrence and impacts of multiple coincident natural disasters such as flooding, landslides, and pandemic spread. HSR.health also carried out work within the Pilot to apply and evaluate the draft Health Spatial Data Infrastructure (HSDI) model developed in the pre-Pilot OGC Health Spatial Data Infrastructure Concept Development Study. This included research into the availability of pandemic-related health related data in the US and in Peru, as well as investigation of the spatiotemporal granularity or resolution of observation data best suited to support indicators for community-level public health interventions.

How to cite: Churchyard, P., Gupta, A., and Lieberman, J.: Pandemic Medical Supply Needs with a Coincident Natural Disaster and an Analysis of COVID-19 Data Availability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10221, https://doi.org/10.5194/egusphere-egu22-10221, 2022.

Coastal cities in India houses nearly 100 million people and are evenly distributed across India’s 7516-kilometer coastline. These cities are important centers of socio-economic activities in the country and are some of the densely populated regions in the world. A number of studies recently have predicted that there is a risk of substantial portions of these cities’ areas being lost to the sea due to sea-level rise in the next few decades, since a major portion of these cities are at a near zero elevation from the mean sea level (M.S.L). Further, in the past few decades, major coastal cities in India have been repeatedly affected by recurrent extreme rainfall events and subsequent floodings. Several studies document that rapid change in the Indian monsoon, increased frequency in the formation of cyclones and the swift changes in the hydro-climatic regime in the Indian Ocean are the major contributors to the occurrence of these extreme precipitations events. While we can safely conclude that these events are likely to occur more frequently in the future, it is important to understand the factors that control and influence these events, comprehend how the cities are and will be affected, and develop feasible policy changes and mitigation action for effective governance. In this paper, we have taken the case of Chennai – an important coastal city located in the southern part of India that has been severely affected by extreme precipitation and subsequent flooding (notably the infamous 2015 Chennai floods) in the past few years, to study the influencing factors contributing to these events and the ground challenges faced by the government machinery in planning and managing these disasters effectively. Our findings indicate that there is a notable variation in the monsoon rainfall pattern in Chennai and the net annual rainfall in the city has increased significantly in the past decade (by ~15%). Further, we found that significant urban centers in the city, especially the regions that are at near zero elevation (± 5 meters above M.S.L) are more vulnerable to flooding, and the important contributing factors to the increased severity of the recent floodings include the lack of adequate stormwater drainage infrastructure and poor policy choice of converting natural surface water bodies (lakes and ponds) into towns during the past three to four decades. We also discuss the planning and execution of Chennai city’s mitigation action during the 2021 floods, analyze its success and shortcomings, and suggest sustainable and feasible policy changes and measures that can be adopted for better management of similar events in the future in other coastal cities as well.

How to cite: Mohanavelu, A. and Soundharajan, B.-S.: Increased frequency of urban floodings in coastal Indian cities caused by variation in monsoon rainfall: Influencing factors, challenges, and solutions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10483, https://doi.org/10.5194/egusphere-egu22-10483, 2022.

EGU22-11068 | Presentations | HS7.3

Optimal sowing dates for major crops in India under climate change 

Aditya Narayan Sharma, Sai Jagadeesh Gaddam, and Prasanna Venkatesh Sampath

Agriculture plays a pivotal role in supporting the socioeconomic situation of millions of farmers in India, which is increasingly coming under threat due to climate change. In particular, the future changes in rainfall patterns has the potential to directly affect the irrigation water demands, thereby impacting water consumption, agricultural productivity, and influencing food security. For instance, the optimal sowing dates for crops may change according to the altered rainfall patterns. With this motivation, we studied the impacts of shifts in sowing periods in order to identify the optimal sowing dates for a particular crop. First, we collected daily temperature and rainfall data for India at a resolution of 0.25o from different GCM models (EC-Earth 3 and EC-Earth 3 veg) under different SSP scenarios (SSP 126, SSP 245, SSP370, SSP585). Also, region-wise agricultural data such as crop acreage and sowing dates were collected for seven major crops - paddy, wheat, maize, groundnut, sugarcane, red gram, black gram, and soybean. Subsequently, we estimated the reference evapotranspiration using the modified Penman-Monteith method. The estimated reference evapotranspiration and rainfall data were incorporated into FAO’s CROPWAT model to calculate the irrigation water requirements (IWR) of the selected crops. The optimal IWR for each crop was selected by varying the sowing dates at fifteen-day intervals across the year (twenty-four dates for the year). Preliminary results indicate that there is considerable scope for water savings by shifting the sowing dates of staple crops to account for climate change impacts. These strategies may become vital for policymakers in the coming decades to reduce the stresses on water without endangering food security. Indeed, such strategies require the cooperation of various stakeholders for better implementation at multiple scales.

How to cite: Sharma, A. N., Gaddam, S. J., and Sampath, P. V.: Optimal sowing dates for major crops in India under climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11068, https://doi.org/10.5194/egusphere-egu22-11068, 2022.

EGU22-11145 | Presentations | HS7.3

Optimizing cropping patterns under the influence of climate change 

Sindhuja Reddy Pasula, Swethu Sree Gudem, Sai Jagadeesh Gaddam, and Prasanna Venkatesh Sampath

The world needs 70% more food by 2050, increasing the pressure on the available water resources. With the climate change threat approaching, the water stress will further be exacerbated that would adversely affect food security. In countries like India, with extensive cultivation of staple crops like paddy, there has been a rapid increase in the total water consumption. At the same time, cultivation of crops such as pulses and millets has not been sufficient to satisfy the nutritional requirements of India’s population. With the increased likelihood of droughts and floods due to the advent of climate change, it becomes imperative to achieve water, food, and nutritional security into the future. This study attempts to optimise cropping patterns to minimise future water requirement, while satisfying the nutritional and caloric requirements of future generations. We perform the analysis for the southern Indian state of Andhra Pradesh, where agriculture depends predominantly on irrigation. To achieve this objective of optimization, we collected bias-corrected climate datasets from three General Circulation Models (BCC-CSM2-MR, INM-CM5-0, MPI-ESM1-2 HR) that include future rainfall and temperature information from 2021 to 2050. Further, we collected crop-wise farm-level data of five major crops in the state - paddy, sugarcane, groundnut, sorghum, and red gram. The irrigation water requirement (IWR) of the selected crops was estimated using FAO’s CROPWAT model under two different scenarios - SSP 245, SSP 585. Further, we developed an optimization model to obtain the optimal cropping pattern that minimises water consumption. Future food requirements in terms of protein and calorie demands and arable land available for cultivation were used as constraints to perform this optimization. Preliminary results indicate that shifting from water-intensive crops like sugarcane to relatively more nutritious crops like red gram and sorghum has the potential to significantly reduce water consumption, while also enhancing the nutritional security of the region. Interestingly, the optimization results indicated that the southern part of the study region required more interventions in terms of crop diversification as compared to the northern part. Such insights could help decision makers to devise holistic policies, enhancing the water-food security under different climate change scenarios. Further, this research could be extended to domains such as economics, ecology, and energy to achieve overall sustainability in the agricultural sector.

How to cite: Pasula, S. R., Gudem, S. S., Gaddam, S. J., and Sampath, P. V.: Optimizing cropping patterns under the influence of climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11145, https://doi.org/10.5194/egusphere-egu22-11145, 2022.

EGU22-11578 | Presentations | HS7.3

The role of urban streams in the microplastics contamination scenario: the case study of the Mugnone Creek (Florence, Italy) 

Alessio Monnanni, Gabriele Bicocchi, Eleonora De Beni, Valentina Rimondi, Tania Martellini, David Chelazzi, Alessandra Cincinelli, Stefania Venturi, Guia Morelli, Pierfranco Lattanzi, and Pilario Costagliola

Due to their spread, abundance and potential impact on food security and human health, microplastics (MPs) are emerging global pollutants. Metropolitan areas are among the main sources of MPs (1 μm - 5 mm); indeed, about 80% of the MPs found in the oceans come from freshwaters. In particular, impervious surfaces runoff in urban areas results in the transport of large quantities of solid wastes, comprising MPs, to the superficial water bodies. Thus, the ecological state of urban streams represents a reliable indicator to evaluate the environmental impact of a city. In this study, we report data about MPs in stream sediments and waters of a minor urban stream, the Mugnone Creek (MC), which flows across the highly urbanized city of Florence (Italy) and discharges to the Arno River.

Several sites along the 17 km-long MC were chosen, including “greenfield” sites upstream of the Florence urban area, urban-impacted sites located along congested roads, and the MC outlet. The stream sediments were collected in June 2019, while stream waters were recovered via glass bottles twice a year (June and December) in 2019 and 2020, to account for seasonal variability. Stream discharge was simultaneously determined during water sampling to allow mass flow calculations of contaminants.

Water samples were filtered onto glass microfiber filters (ø 47 mm) and observed by HD digital stereomicroscope; a similar method was followed for sediments after a density separation step (NaCl saturated solution) and H2O2 digestion. Fourier Transform Infrared Spectroscopy (FT-IR) was used for identification and characterization of MPs. Microparticles classification was based on polymer type, shape and colour.

MPs concentration in sediments showed an increasing trend from the pre-urban site to the outlet. A maximum value (1.540 MPs/kg) was reached immediately after the Terzolle Creek confluence, which drains the large University Hospital District of Careggi. Fibers were the dominant shape class of polymers observed and blue/black items stand out among the colour classes. The highest concentrations of MPs in water samples were recorded during winter seasons (up to 16.000 items/m3), with a predominance of fibers and blue/black colours. Polymer classification by FTIR indicated the presence of (in order of abundance): PA (polyamide), PET (Polyethylene Terephthalate), SBR (butadiene-styrene rubber), PP (Polypropylene), blend PP+PE (PP+Polyethylene), PTFE (Polytetrafluoroethylene) and PU (Polyurethane). The black-SBR polymers likely related to tyre abrasion occurring during vehicles driving, since they were especially found on a site close to traffic-congested roads. In addition to synthetic particles, high concentrations of natural fibers (mainly cellulose) were found in waters at all sites. Up to 109 synthetic particles are estimated to be discharged daily by MC to the Arno River during the winter season, a load much higher than creeks with similar urbanization context worldwide. Mass loads of natural fibers were of the same order of magnitude of MPs in every season.

Studies are in progress to elucidate the impact on local biota and to characterize the anthropic pressure on the Arno River, aiming to improve the knowledge about the environmental status of one of the main Italian river basins.

How to cite: Monnanni, A., Bicocchi, G., De Beni, E., Rimondi, V., Martellini, T., Chelazzi, D., Cincinelli, A., Venturi, S., Morelli, G., Lattanzi, P., and Costagliola, P.: The role of urban streams in the microplastics contamination scenario: the case study of the Mugnone Creek (Florence, Italy), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11578, https://doi.org/10.5194/egusphere-egu22-11578, 2022.

Due to climate change, extreme weather conditions such as droughts may have an increasing impact on the water demand and the productivity of irrigated agriculture. For the adaptation to changing climate conditions, knowledge about adequate irrigation control strategies and information, e.g., about future climate development and soil properties, is of great importance for the optimal operation of irrigation systems. We consider climate and soil variability within one probabilistic simulation-optimization framework for irrigation scheduling based on Monte Carlo simulations to support informed decisions. The framework implements optimizers for full, deficit, and supplemental irrigation strategies. We provide the  Matlab code as the open source Deficit Irrigation Toolbox (DIT). For this analysis, we apply DIT for preliminary test simulations for a global numerical deficit irrigation experiment (GDIE) which allows for the analysis of both the impact of the selected irrigation strategy on water productivity and the value of information about (i) different scheduling methods, (ii) climate development, and (iii) soil hydraulic properties. The first results show a strong dependency on the value of information about climate and soil for sites required for increasing water productivity in different climate regions. Moreover, DIT can enable and support the site-specific transformation of low efficient rainfed and irrigated systems achieving higher water productivity and food insecurity on a local scale.

How to cite: Schütze, N. and Dietz, A.: Comparison of the value of information for the management of deficit irrigation systems in different climate regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11631, https://doi.org/10.5194/egusphere-egu22-11631, 2022.

EGU22-12798 | Presentations | HS7.3

FORESHELL Project: development of sanitary/weather-environmental predictive technological tools to enhance the efficiency and sustainability of shellfish farming. 

Barbara Tomassetti, Annalina Lombardi, Valentina Colaiuda, Federica Conti, Giuseppina Mascilongo, Fabrizio Capoccioni, Domitilla Pulcini, Gabriella Di Francesco, Ludovica Di Renzo, Chiara Profico, Carla Ippoliti, Carla Giansante, Nicola Ferri, and Federica Di Giacinto

Many of the estuaries and coastal areas in Europe are used for the cultivation and harvesting of bivalve mollusks. Mussel farming is strongly influenced by weather and environmental conditions. Several studies have shown that the sanitary conditions of shellfish are related to hydrological factors of rivers adjacent to the farming area, as rivers are the main routes of bacteriological contamination from the surface or sub-surface.

The "FORESHELL" project, funded by Costa Blu FLAG as part of the EMFF 2014-20 program of the Abruzzo Region, is carrying out a pilot initiative for the development of sanitary/weather-environmental predictive technological tools in order to improve efficiency and sustainability of the mussel farm located at the Giuliano Maritime District.

A specific sampling schedule is established before and after severe weather events to determine the E. coli
concentration in freshwater at the river mouths and in mussels/seawater in the farming site. At the same time, the hydrographic basins of the rivers close to the farm, Vibrata and Salinello, are constantly monitored trough the hydrological model (CHyM), to predict the occurrence of flow discharge peaks at mouth of the river. In addition, the satellites and the in-situ probe acquire environmental parameters such as sea water temperature, salinity, chlorophyll-a, sea currents and wave motion.

The web application for data visualization is under construction, as well as the early warning reports to the farmer. Furthermore, the growth of mussels is constantly monitored with biometric controls. The implementation of all phases of the FORESHELL project are proceeding according to the timeline in order to develop innovative tools useful for the management of mussel farming area.

How to cite: Tomassetti, B., Lombardi, A., Colaiuda, V., Conti, F., Mascilongo, G., Capoccioni, F., Pulcini, D., Di Francesco, G., Di Renzo, L., Profico, C., Ippoliti, C., Giansante, C., Ferri, N., and Di Giacinto, F.: FORESHELL Project: development of sanitary/weather-environmental predictive technological tools to enhance the efficiency and sustainability of shellfish farming., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12798, https://doi.org/10.5194/egusphere-egu22-12798, 2022.

The Intensity and frequency of extreme storms have been increasing due to possible climate change, making it challenging to manage stormwaters in highly urbanized areas. Without an adequate and appropriate stormwater system, these storms may cause significant damage and losses to live and properties. Low Impact Development (LID) is a recent but widely accepted alternative for managing the increased stormwater. However, limited research is available to understand their effectiveness and optimize the mix of LIDs and conventional stormwater systems. This study evaluates the performance of several LIDs under current and future storm conditions, identify the best performing mixes of LIDs and convention stormwater system and provide a decision-making tool for urban stormwater management. The methodologies will be tested for Renton City, which is part of the Seattle Metropolitan Area.

In order to achieve our objective, first, a statistical rainfall-runoff model will be developed to assess the current stormwater system and estimate runoff for the current and future periods. The final results indicate a significant increase in runoff due to the increased rainfall in the future (2020-2040) compared to the past (1995-2014). The Stormwater Management Model (SWMM) will then be used to simulate the rainfall-runoff under conventional and LIDs (e.g., bio-retention, rain barrels, rain gardens, infiltration trenches, and permeable pavement) stormwater system. The final results show that the performance of LIDs in reducing total runoff volume varies with the types and combinations of LIDs. A 30% to 75% reduction in runoff was achieved for the past and future 50-year and 100-year storms. A Genetic Algorithm (GA) is used to optimize the LID and conventional stormwater system considering the reduction in runoff, installation and maintenance costs. The type, size, location, and number of different LIDs will be considered as decision variables for the GA. Finally, the study aims at developing a comprehensive framework to evaluate the performance of LIDs under present and future storms and identify cost and performance effective LIDs in a given urban area. The framework introduced in this study will help local authorities and practitioners to implement appropriate climate change adaptation strategies by maximizing the benefit from LIDs and ensure sustainable stormwater management for the current and future climates.

How to cite: Abduljaleel, Y. and Demissie, Y.: Evaluation and Optimization of Low Impact Development Designs for Sustainable Stormwater Management in a Changing Climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13, https://doi.org/10.5194/egusphere-egu22-13, 2022.

EGU22-1830 | Presentations | HS7.5

Prestorm root zone soil moisture conditions critical for flood forecasting in Europe 

Christian Massari, Francesco Marra, Yves Tramblay, Wade Crow, Stefania Camici, Sara Modanesi, Luca Brocca, and Gaby Gruendemann

Recent evidences suggest that in Europe, flood frequency and precipitation frequencies are often not aligned. Beside other factors pre-storm conditions exert a significant impact on flood generation thus their knowledge is paramount for a proper flood forecasting. A number of predictors have been used in the past to understand how much precipitation is transformed into runoff (i.e., runoff coefficient, RC). Notable examples are the antecedent precipitation index (API), the prestorm river discharge and soil moisture. On top of these new products potentially available from satellite observations like surface soil moisture and total water storage anomalies (TWSA), root zone soil moisture from reanalysis and hydrological models can be used along with precipitation to predict in advance the severity of the storm runoff.Our goal here is to provide an objective description of the role played by different predictors for hydrologic forecasting in Europe. In particular, we aim at answering the following research questions:

  • How variable is runoff coefficient across the European catchments?
  • How much are surface and root zone soil moisture, river discharge, antecedent precipitation and total water storage anomalies able to explain the RC variability across European floods?
  • Under which conditions (climate period, location and flood magnitude) are the different pre-storm indices able to predict this runoff coefficient variability?

We answered these questions using long term (1980-2016) precipitation and river discharge observations from more than 100 basins covering different European regions. Results demonstrated that root zone soil moisture and TWSA are the best predictors of prestorm conditions under a variety of climatic and geographic features and thus their correct representation in land surface and hydrological models is strategic for an effective flood forecasting.

How to cite: Massari, C., Marra, F., Tramblay, Y., Crow, W., Camici, S., Modanesi, S., Brocca, L., and Gruendemann, G.: Prestorm root zone soil moisture conditions critical for flood forecasting in Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1830, https://doi.org/10.5194/egusphere-egu22-1830, 2022.

EGU22-1981 | Presentations | HS7.5 | Highlight

Globally consistent tropical cyclones impact forecast system for population displacement 

Pui Man Kam, Christopher Fairless, and David N. Bresch

Tropical cyclones (TCs) displace millions of people every year. Displaced people are subject to heightened risks to their physical and mental well-being. We present the first results of a TC impact forecast system for population displacement, aiding the decision-making process for planning early prevention and mitigation actions. For example, planning precautionary evacuations and the allocation of humanitarian aid. We work closely with the Internal Displacement Monitoring Centre (IDMC) to develop a global TC impact forecast system that predicts the number of people potentially affected or displaced.

We build the impact forecast system using a python-based, open-source, globally consistent platform called CLIMADA (CLIMate ADAptation). The platform integrates probabilistic hazard, exposure, and vulnerability information to compute the potential impacts from TC events. The first prototype of the forecast system extracts information from ECMWF ensemble TC forecast tracks, a global population layer at ~1km resolution, and vulnerability functions that relate the exposed people to the intensity of TC wind speed. We show case studies of recent TC events to reveal the potential of the displacement forecast system, the uncertainties of the forecast results

The displacement forecast system will provide richer information for decision-makers and help improve warnings. The open-source data and codes of this implementation are also transferable to other users, hazards, and impact types. 

How to cite: Kam, P. M., Fairless, C., and Bresch, D. N.: Globally consistent tropical cyclones impact forecast system for population displacement, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1981, https://doi.org/10.5194/egusphere-egu22-1981, 2022.

EGU22-1985 | Presentations | HS7.5

Extremes in South African Rainfall: Mean Characteristics and Seamless Variability Across Multiple Timescales 

Asmat Ullah, Benjamin Pohl, Julien Pergaud, Bastien Dieppois, and Mathieu Rouault

Rainfall extremes are of major and increasing importance in semi-arid countries and their variability has strong implications for water resource and climate impacts on the local societies and environment. Here, we examine extremes intraseasonal descriptors (ISDs) in austral summer rainfall (November–February) over South Africa (SA). Using daily observations from 225 rain gauges, ERA5 reanalysis and satellite estimates (TRMM-3B42), we propose a novel typology of wet extreme events based on their spatial fraction, thus differentiating large- and small-scale extremes. Long-term variability of both types of extreme rainfall events is then extensively discussed. The relationship between these two types of rainfall extremes and different modes of climate variability is further explored at multiple timescales. At low-frequency modes, rainfall extremes are assessed at interannual (IV: 2−8 years) and quasi-decadal (QDV: 8−13 years) timescales which are primarily associated with El Niño Southern Oscillation (ENSO) and Interdecadal Pacific Oscillation (IPO) respectively. At high-frequency modes, rainfall extremes are evaluated with synoptic-scale variability related to seven convective regimes of Tropical Temperate Troughs (TTTs: 3–7 days) and intraseasonal variability associated with eight phases of the Madden-Julien Oscillation (MJO: 30–60 days).

The results demonstrate that using 7% of spatial fraction simultaneously exceeding the local threshold of the 90th percentile produces remarkable results in characterizing rainfall extremes into large- and small-scale extremes. Austral summer total rainfall is found to be primarily shaped by large-scale extremes which constitute more than half of the rainfall amount under observation, and nearly half in ERA5. Observation (ERA5) shows an average of 8 ± 5 (20 ± 7) days per season associated with large-scale extremes, which are comprised in 5 ± 3 (10 ± 3) spells with an average persistence of at least 2 days. Overall, we find a strong dependence of total rainfall on the number of wet days and wet spells that are associated with large-scale extremes. We also find that large- and small-scale extremes are well-organized and spatially coherent yet extreme conditions during small-scale events are found sporadic over the region, contrasting with large-scale events for which extreme conditions are found over a larger and coherent region.

Teleconnections with global SSTs confirm that La Niña conditions favor overall wet conditions and wet extremes in SA. The frequency of large-scale extremes is consistently related to warmer SSTs in the North Atlantic while their link with warmer Indian and tropical South Atlantic Ocean found stronger without ENSO influence. At low-frequency timescale, risk ratio assessment shows that the frequency (total rainfall) of large-scale extremes is significantly modified by IV (QDV) timescale. We note strong variations in the frequency (total rainfall) of large-scale (small-scale) extremes when IV timescale lies in strong positive phase (i.e., +0.5 standard deviation). At high-frequency timescale, the synoptic-scale variability associated with TTT events, are mostly responsible for changes in large-scale extremes as nearly 75% of such events occur during early to mature TTT regimes (3−5) whereas small-scale extremes were found equiprobable during all synoptic regimes. A risk ratio assessment suggests that the probability of large-scale extremes in TTT regime 5 significantly enhance (suppress) during MJO phases 6−8 (1−2).

How to cite: Ullah, A., Pohl, B., Pergaud, J., Dieppois, B., and Rouault, M.: Extremes in South African Rainfall: Mean Characteristics and Seamless Variability Across Multiple Timescales, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1985, https://doi.org/10.5194/egusphere-egu22-1985, 2022.

EGU22-2474 | Presentations | HS7.5

Modelling flood events in Venice Lagoon with a cumulant CO lattice Boltzmann shallow water model 

Jessica Padrone, Silvia Di Francesco, and Sara Venturi

In this work a multi-relaxation time (MRT) Lattice Boltzmann model based on the use of non-conventional collision operator is used to simulate the flood event in Venice Lagoon.

Numerical methods (finite difference, finite volume and finite element methods) that solve the macroscopic equations of fluid mechanics (Navier Stokes equations), are usually employed for these aims. Most of these methods put in evidence that the application of bed slope and friction forces can lead to inaccurate solutions due to numerical errors.

In addition, the extension of these schemes to complex geometries is not straightforward and some of these approaches are very computational expensive if applied to real flows. Since the problems are posed at a large scale, it should be the aim to develop a simple and accurate representation of the source term to simulate realistic shallow water flows.

The LBM approach is a versatile method and it has been extensively applied in different fields.

Non-conventional Lattice Boltzmann models based on central moments and cumulants collision operators allows to simulate large-scale hydraulic problems such as flooding events and the use of a GIS environment allows to set the information related to topography, initial conditions (water depth and velocity values distribution), boundary conditions (position and type of solid and inlet/outlet boundaries), external force (value and distribution of roughness coefficients, obstacles position) and to make this data available for the execution of the numerical model.

In order to validate the correctness of the proposed mathematical model for Venice Lagoon, the real flood event that took place on November 12, 2019 is simulated: several field data are available for this test case; the results, in terms of water level and velocity field are compared with recorded data, verifying the accordance. Moreover, technical solutions for hydraulic risk evaluation and mitigation, taking account of the expected sea level rise, due to climate change, are proposed.

How to cite: Padrone, J., Di Francesco, S., and Venturi, S.: Modelling flood events in Venice Lagoon with a cumulant CO lattice Boltzmann shallow water model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2474, https://doi.org/10.5194/egusphere-egu22-2474, 2022.

EGU22-2578 | Presentations | HS7.5

Identification of regional landslide triggering thresholds in the Lombardy region using multivariate statistical analysis 

Nunziarita Palazzolo, David Johnny Peres, Enrico Creaco, and Antonino Cancelliere

Landslides represent a critical natural hazard in many mountain and hilly regions worldwide, provoking causalities and property damages. Landslide triggering thresholds are at the basis of early warning systems to protect livelihoods. Traditionally, landslide triggering thresholds are expressed in terms of not more than two or three precipitation variables, mostly rainfall event depth, and duration. Indeed, the availability of soil moisture information and its proxies (such as antecedent precipitation), can improve the performance of landslide triggering thresholds, thus calling for a multivariate approach.  

Given the above context, this study aims to develop regional landslide triggering thresholds by using multivariate statistical analysis to investigate the performance of multiple combinations of rainfall variables and event soil moisture data, in the identification of regional rainfall thresholds for landslide initiation. Lombardy region (northern Italy) was selected as a study area since it is one of the most susceptible Italian regions to landslide risk. The data on landslides were retrieved from the FraneIalia project that is a comprehensive spatio-temporal database of recent landslides affecting the Italian territory from 2010 onwards. For the Lombardy region, from 2010 to 2019, 592 landslides events triggered by rainfall were detected, all distributed within the mountain and hilly areas of the region.

Precipitation and soil moisture time series, instead, were derived from the ERA5-Land reanalysis dataset and the rainfall events were reconstructed using the CTRL-T code developed by IRPI-CNR, which characterizes each rainfall event by duration, mean intensity, total depth, and peak intensity. The most probable rainfall conditions associated with each landslide are, then, computed based on the distance between the rain gauge and the landslide location. Different combinations of precipitation and soil moisture variables are tested using dimensionality reduction multivariate statistical techniques. An optimization procedure is set up with the aim to maximize the True Skill Statistic (TSS) ROC index associated with parametric thresholds. Several multivariate combinations show better performances than the traditional depth-duration power-law thresholds.  

How to cite: Palazzolo, N., Peres, D. J., Creaco, E., and Cancelliere, A.: Identification of regional landslide triggering thresholds in the Lombardy region using multivariate statistical analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2578, https://doi.org/10.5194/egusphere-egu22-2578, 2022.

EGU22-3026 | Presentations | HS7.5 | Highlight

The RiskChanges tool for multi-hazard risk-informed planning at local government level 

Cees van Westen, Manzul Hazarika, Ashok Dahal, Tek Kshetri, Anish Shakya, and Syams Nashrrullah

Local governments are faced with increasing levels of risk from extreme hydro-meteorological events such as (tropical) storms,  flooding, landslides, drought, heatwaves, wildfires, etc. The frequency and interaction of these events, also in combination with other events that do not have a direct climate driver, makes that it is likely that many areas are faced with higher impacts from compounding events. Global trends such as population growth, urbanization, increased dependency on technology also contributed to larger exposure and vulnerability. In order to plan for future developments, and for reducing the increasing levels of risk, local governments require to plan ahead and evaluate the options available for reducing the risk under future scenarios. For this task Spatial Decision Support Systems are required that allow local governments to make informed decisions, considering the current and future levels of risk. RiskChanges is a Spatial Decision Support System for the analysis of current and future multi-hazard risk at a local level, in order to analyze optimal risk reduction alternatives. The system is developed by the University of Twente in collaboration with the Asian Institute of Technology, GeoInformatics Centre. RiskChanges ( http://www.riskchanges.org/ ) is an Open-Source, web-based tool, based on a series of python scripts, which are integrated into a Graphical User Interface. The tool includes several major features: multi-hazard, multiple assets, a vulnerability curve database, multi-user approach, comparison of risk, and spatial analysis. Users can upload their own datasets (in the form of hazard maps, elements-at-risk maps, administrative unit maps, and vulnerability curves). The tool contains an open-source vulnerability curve database, allowing to sharing of physical vulnerability curves among users. Multiple users can collaborate on the same project, and provide different input data. The multi-hazard feature allows performing the risk assessment for multiple natural and manmade hazard interactions. Exposure and vulnerability are combined in a loss calculation for each combination of element-at-risk and hazard. Loss maps are integrated into a risk map, where the user indicates the interaction between the hazard types. The system allows to analyze the risk of multiple asset types with different spatial characteristics.  Users can compare the risk for the current situation and future scenarios and/or planning alternatives.  

How to cite: van Westen, C., Hazarika, M., Dahal, A., Kshetri, T., Shakya, A., and Nashrrullah, S.: The RiskChanges tool for multi-hazard risk-informed planning at local government level, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3026, https://doi.org/10.5194/egusphere-egu22-3026, 2022.

EGU22-3200 | Presentations | HS7.5

Dynamic flood hazard maps based on traffic flow forecasts using mobile phone data 

Babak Razdar, Rodolfo Metulini, Maurizio Carpita, and Roberto Ranzi

Maps of flooding risk and exposure generally assume people and vehicles density constant over time, although this is not the case in the real world, as crowding is a highly dynamic process in urban areas. Monitoring and forecasting people mobility is a relevant aspect for metropolitan areas subjected to high risk of flooding. Information and communication technologies (ICT) along with big data are massively used, e.g., to support the optimization of traffic flows and the study of urban systems. In particular, mobile phone network data suits with the aim of producing dynamic information on people's movements that can be used to develop dynamic exposure to flood risk maps for areas with hydrogeological criticality, as done by Balistrocchi et al. (2020).

In this work we aim at proposing a time series modelling strategy to obtain “real time” traffic flows prediction. To do so we use mobile phone origin-destination signals on the flow of Telecom Italia Mobile (TIM) users among different census areas (ACE of ISTAT, the Italian National Statistical Institute), and for the MoSoRe Project 2020-2022 and recorded at hourly basis from September 2020 to August 2021.

An Harmonic Dynamic Regression (HDR) model (Hyndman, Athanasopoulos, 2021) as it follows:

Flow= α+Fourier.day (K_d )+Fourier.week (K_w )+ Month+ε_(ARIMA(p,d,q))                        (1)

is proposed, where multiple seasonal periods are modelled with a properly selected number of Fourier basis, month is a dummy variable to account for different levels of flows by months and the error component is structured as an ARIMA.

HDR model suits for our purposes due to the strong daily and weekly patterns in traffic flows, as also confirmed by preliminar results on the accuracy of prediction based on a cross-validation strategy.

In future developments, the model in equation 1 may be improved by adding proper features as explanatory variables to increase the prediction accuracy, such as, e.g., the presence of people in the census area of origin and in the census area of destination of the flow, or precipitation data.

People’s and vehicles’ exposure obtained from mobile phone data and processed with the above stochastic model are then combined to flooding hazard maps estimated for different storm return period in a urbanized area close to Brescia to estimate dynamic flood risk maps.      

References

Balistrocchi, M., Metulini, R, Carpita, M., Ranzi, R.: Dynamic maps of human exposure to floods based on mobile phone data. Natural Hazards and Earth System Sciences, 20: 3485{3500 (2020).

Hyndman, R. J., Athanasopoulos, G.: Forecasting: principles and practice. 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3 (2021)

How to cite: Razdar, B., Metulini, R., Carpita, M., and Ranzi, R.: Dynamic flood hazard maps based on traffic flow forecasts using mobile phone data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3200, https://doi.org/10.5194/egusphere-egu22-3200, 2022.

EGU22-4011 | Presentations | HS7.5

Long waves in the Port of Klaipėda 

Laura Nesteckytė, Loreta Klepšaitė-Rimkienė, and Kai Antero Myrberg

The entire strait is the base of the port aquatorium and a vital shipping artery from the Baltic Sea to the Curonian Lagoon as well as a complex water system connecting two water basins of different sizes and depths and nature: differing considerably in salinity and density. Although the quays are well protected from the waves of the open sea, dangerous water level fluctuations still occur in the port area, the origin of which is not yet well understood. This study aims to identify the occurrence and main characteristics of the long waves, with the period from minutes to several hours, to identify their origin and impact.

Analysis of the spectral composition of these oscillations is based on continuous pressure recordings at a frequency of 4 Hz in Klaipėda harbour during the stormy season 2016-2017 and repeated during calm and stormy seasons in 2021. Most of the oscillation energy is concentrated in two frequency bands. Significant water level changes occurred due to infragravity motions with periods of 30 s (0.03 Hz) and disturbances with the typical periods of wind waves on the Lithuanian coast with periods of 3-10 s (0.1-0.3 Hz). The highest peak in the wind wave frequency band corresponds to typical storm conditions in the Baltic Sea with periods of 5-9 s. While the typical amplitudes of the oscillations in this range were modest, hazardous changes in water level occurred at lower frequencies with amplitudes of 0.5 m. The record shows the presence of harbour oscillations with periods of 30-200 s (0.005-0.03 Hz) and seiches of the Curonian Lagoon with periods of 1200 s (0.0008 Hz).

The largest oscillations are created by a combination of wind waves and infragravity waves with periods that roughly match the natural seiche periods of Klaipėda Strait and harbour oscillations and seiches can be observed not only during the stormy season.

How to cite: Nesteckytė, L., Klepšaitė-Rimkienė, L., and Myrberg, K. A.: Long waves in the Port of Klaipėda, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4011, https://doi.org/10.5194/egusphere-egu22-4011, 2022.

EGU22-4523 | Presentations | HS7.5

High-impact weather events in Greece: Analysis of the period 2000-2020 

Katerina Papagiannaki, Vassiliki Kotroni, Konstantinos Lagouvardos, and Antonis Bezes

The subject of this presentation is the assessment of the occurrence, intensity, and impact severity of weather-related events with socio-economic implications during the period 2000-2020 in Greece. The aim is to draw critical conclusions through the distribution of events at the temporal and spatial level and in relation to their societal impact as measured by a qualitative impact-severity index. The data derived from the High Impact Weather Events (HIWE) database that has been developed by the METEO Unit at the National Observatory of Athens (NOA), is systematically updated and publicly available. The analysis includes events related to floods, lightning activity, hail, snow/frost, windstorms, and tornados having caused impacts on life (injury or death) and/or infrastructure. The presentation provides an overview of the data used and methodology applied for assessing weather-related hazards, and the results of their analysis that include the evolution of events, the most damaging phenomena, and the areas most exposed to each phenomenon. This work was conducted in the frame of CLIMPACT – National Νetwork on Climate Change and its Impacts, a flagship initiative on climate change to coordinate a Pan-Hellenic network of institutions.

How to cite: Papagiannaki, K., Kotroni, V., Lagouvardos, K., and Bezes, A.: High-impact weather events in Greece: Analysis of the period 2000-2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4523, https://doi.org/10.5194/egusphere-egu22-4523, 2022.

EGU22-4716 | Presentations | HS7.5

Vulnerability scenarios for flash floods occurred in Campanian Apennines (South Italy) 

Giovanni Forte, Melania De Falco, Nicoletta Santangelo, and Antonio Santo

Flash floods are related to short duration and high intensity rainfalls, they are common phenomena in many parts of Europe as well as Italy. These events can result in debris flow, debris flood or water flood. The main differences are in the triggering, propagation, and depositional phases and more importantly in terms of velocity, impact forces and associated damage.
In Campania Region (Southern Italy) these phenomena historically involved the catchments several times, with an increase in frequency in the last decade. They are associated to small watershed – fan systems that fall in the southern Apennines characterized by intermittent flow. The alluvial fans in the outlet zones are highly urbanized, hence the population living in the deposition areas is exposed to high risk. 
In this study, the geomorphic response to flash floods is assessed through magnitude evaluation of some flash floods recently occurred in heterogeneous geological and geomorphological settings in both coastal and inland areas. Each scenario is reconstructed through the mapping of areal extent, water heights, particle sizes and estimate of volumes and built damage aiming at vulnerability definition, a relevant topic considering the global climate changes.
In this study, an approach aimed at developing vulnerability curves is proposed. It is based on a application of a typical method widely adopted in the earthquake engineering that in this case assume as intensity parameter the water height measured in post-event surveys. 
The results are expressed as vulnerability curves at different damage scenarios that can be valuable tools for local authorities, emergency, and disaster planners since they can assist decision making analysis of protection measures for future events.

How to cite: Forte, G., De Falco, M., Santangelo, N., and Santo, A.: Vulnerability scenarios for flash floods occurred in Campanian Apennines (South Italy), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4716, https://doi.org/10.5194/egusphere-egu22-4716, 2022.

EGU22-5214 | Presentations | HS7.5

Global analysis of emergency service provision to vulnerable populations during floods of various magnitude under climate change 

Sarah Johnson, Robert Wilby, Dapeng Yu, and Tom Matthews

In a world of increasing global flood hazards, vulnerable populations (very young and elderly) are disproportionately affected by flooding due to their low self-reliance, weak political voice and insufficient inclusion into climate adaptation and emergency response plans. These individuals account for most flood casualties and often rely on emergency services due to flood induced injuries, exacerbated medical conditions, and requiring evacuative assistance. However, emergency service demand often exceeds the potential capacity whilst flooded roads and short emergency response timeframes decrease accessibility, service area, and population coverage; but how does this compare across the globe and what will the future hold?

To answer this question, a global analytical framework has been created to determine the spatial, temporal, and demographic variability of emergency service provision during floods. This is based on global fluvial and coastal flooding (at 10-year and 100-year return periods), and present and future flood conditions (present-day and 2050, under RCP 4.5 and RCP 8.5 climate scenarios). The framework includes a hotspot analysis to identify the extent and distribution of flood hazards and at-risk vulnerable populations, an accessibility analysis to identify emergency service accessibility to vulnerable populations based on restrictions of flood barriers and response-time frameworks, and a vulnerability analysis to compare the environmental injustice of emergency service provision between key demographic groups.

The highlighted geographical and temporal differences in emergency service provision globally and between regions, in addition to the framework itself, can be used by national and international organisations to inform strategic planning of emergency response operations and major investments of infrastructure, services, and facilities to maximise the benefit to the disproportionately affected vulnerable populations. This includes the production of more detailed flood hazard and evacuation maps that highlight vulnerability hotspots, the prioritisation of vulnerable population groups in emergency response plans to minimise geographic and population disparities of flood injuries and fatalities, and the allocation of emergency service hubs in regions of high vulnerability but low emergency response provision.

How to cite: Johnson, S., Wilby, R., Yu, D., and Matthews, T.: Global analysis of emergency service provision to vulnerable populations during floods of various magnitude under climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5214, https://doi.org/10.5194/egusphere-egu22-5214, 2022.

EGU22-6434 | Presentations | HS7.5

Preliminary analysis of high-resolution precipitation in Friuli Venezia Giulia region, Italy 

Elisa Arnone, Dario Treppiedi, and Leonardo Noto

The northeastern area of Italy, and specifically of Friuli Venezia Giulia region (FVG), is characterized by the heaviest precipitation annual totals in the country. Effects of both prolonged and extreme precipitation can be particularly damaging in this area, causing debris flow, flash floods, avalanches. Due to the very short times of concentration and hydrological response of the mountain watersheds of the analyzed area, extreme and short events are of particular interest. The region has a dense ground-station network which is managed by the regional Civil Protection Agency, constituted by 2 main rain-gauges networks, based on CAE and Micros-SIAP technology, respectively; this last is co-managed by the OSMER-ARPA (OSservatorio MEteorologico Regionale-Agenzia Regionale per la Protezione dell’Ambiente) FVG. The networks count a total of about 200 rain-gauges; for some stations, data at 5-minute resolution are available since the 1996 (CAE network), whereas Micros-SIAP works continuously and at high resolution since the early 2000s. Over the last two decades, the temporal resolution of stations has been progressively increased up to 1-minute step.

This work presents a comprehensive analysis of the available dataset at high temporal resolution (i.e. 30 min, 5 min and 1 min) to verify whether trends in very short rainfall duration are underway. The continuous time series of data recorded by a sample of rain-gauges by the two networks are first analyzed. A preliminary analysis aims at verifying the consistency of the dataset at the higher resolutions. Statistical trends are then assessed by comparing two methods, i.e., the classical Mann-Kendall and the quantile regression at different thresholds and durations. Differently than the traditional methods that require a subset of data (e.g., the rainfall annual maxima), the quantile regression method allows to detect changes in the tails of the rainfall distributions and to screen the whole rainfall time series.

How to cite: Arnone, E., Treppiedi, D., and Noto, L.: Preliminary analysis of high-resolution precipitation in Friuli Venezia Giulia region, Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6434, https://doi.org/10.5194/egusphere-egu22-6434, 2022.

EGU22-6884 | Presentations | HS7.5

Spatial relationship between extreme rainfall anomalies and density of the triggered landslides 

Slim Mtibaa and Haruka Tsunetaka

Precipitation extremes affect the landscape differently and often drive numerous landslides widespread with disparate densities and features. Revealing the factors that govern this spatial variability is critical for understanding landslide susceptibility and developing prediction models. To this end, examining the peculiarities of the triggering rainfall event at spatial and temporal scales emerges as a promising method. Here, we relied on radar gauge-analyzed (R/A) rainfall estimates (period > 30 years, spatial resolution ≈ 5 km) and a landslide inventory for studying the spatial relationship between rainfall anomalies and triggered landslide density. The landslide inventory counts more than 7,600 shallow landslides distributed in about 550 km2 and triggered by an extreme rainfall event that hit the Kyushu area in southern Japan in July 2017. A total of 23 R/A cells with different landslide densities were identified from the landslide inventory. A standard period of 72 h (Pstd), where the cumulative rainfall during the triggering event is maximum, was used to evaluate the spatial rainfall peculiarities at short (1 – 24 h) and long (48 – 72) timescales. Subsequently, rainfall anomalies were discussed by plotting the mean intensities computed at multiple timescales within the Pstd in the intensity duration frequency (IDF) curves developed for each R/A cell. The spatial density of triggered landslides was strongly influenced by the rainfall intensities that exceeded the 100-years return levels at disparate timescales and demonstrated anomalies. More than 65 % of the triggered landslides were located in only three R/A cells. In these cells, rainfall intensities of the triggering event exceeded the 100-years return level at the various timescales (from short to long) within the Pstd, favoring numerous landslides of different geometric features. Rainfall intensities in cells with low landslide density reached the 100-years return levels at short timescales (3 – 24 h). However, this was not necessarily achieved in all low landslide density R/A cells. These preliminary results highlighted the spatial impacts of rainfall anomalies computed at multiple timescales on landslide densities and features and motivated further analysis.

How to cite: Mtibaa, S. and Tsunetaka, H.: Spatial relationship between extreme rainfall anomalies and density of the triggered landslides, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6884, https://doi.org/10.5194/egusphere-egu22-6884, 2022.

EGU22-8096 | Presentations | HS7.5

Relationship between atmospheric rivers and landslides in western North America 

Sara M. Vallejo-Bernal, Frederik Wolf, Lisa Luna, Niklas Boers, Norbert Marwan, and Jürgen Kurths

In this study, we investigate the relationship between land-falling atmospheric rivers (ARs) and landslides in western North America. ARs are channels of enhanced water vapor flux in the atmosphere and play an essential role in the water supply for precipitation in the midlatitudes. However, they can also trigger natural hazards such as floods and landslides. Our objective is to determine if the occurrence of landslides in western North America can be attributed to ARs hitting the western coastline and causing rainfall at the locations of the landslides and to characterize the strength and persistence of the ARs that lead to landslides. To that aim, we use landslide records with daily temporal resolution along with daily rainfall estimates from the ERA5 reanalysis, for the period between 1996 and 2018. We propose and run two attribution models to relate landslides to rainfall and rainfall to ARs and subsequently verify statistically if there is a unique and significant association between the landslides and the ARs. Our results show that the majority of the landslides reported along the western coast of North America are preceded by an AR. In the coastal regions, ARs and landslides are significantly correlated. Further inland, landslides are less likely, but those that do occur are significantly correlated with very intense ARs. Understanding and revealing the impacts of ARs on landslides in western North America will lead to better forecasts and risk assessments of these natural hazards.

How to cite: Vallejo-Bernal, S. M., Wolf, F., Luna, L., Boers, N., Marwan, N., and Kurths, J.: Relationship between atmospheric rivers and landslides in western North America, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8096, https://doi.org/10.5194/egusphere-egu22-8096, 2022.

EGU22-8229 | Presentations | HS7.5 | Highlight

User-driven platform to facilitate community data access, collaboration, and knowledge sharing on Nature-Based Solutions as mitigation measures for hydro-meteorological hazards 

Laura S. Leo, Milan Kalas, Joy Ommer, Sasa Vranić, Irina Pavlova, Zahra Amirzada, and Silvana Di Sabatino

In the context of disaster risk management and climate change adaptation, Nature-based Solutions (NBS) are being increasingly recognized and promoted as viable measures against hydro-meteorological hazards, while also being able to provide a range of environmental, social, and economic benefits. Yet, the employment of NBS to mitigate the impact of hydro-meteorological phenomena remains still sporadic and uncoordinated at the global and European level.

In order to assist competent authorities, practitioners and other stakeholders in developing successful NBS interventions for hydro-meteorological risk mitigation and climate change adaptation, while also raising general public awareness and community stewardship of NBS, the EU-H2020 project OPERANDUM has recently launched a multi-dimensional, open and user-friendly web-platform called GeoIKP (Geospatial Information Knowledge Platform).

GeoIKP follows a multi-stakeholder approach demonstrated through the integration of multiple modules related to science, policy and practice. This contribution offers an overview of GeoIKP and discusses in detail some of the innovative aspects and tools of the platform. It represents the first example of NBS web-platform with advanced interface customization. Functionalities and graphical interfaces are tailored to match specific user needs and interests for six different user profiles: 1) policy bodies (from international to local level), 2) knowledge-based organizations (research institutions, labs and data providers), 3) companies or private businesses, 4) associations, interest groups and grass-roots movements, 5) citizens and 6) other affected or interested parties (e.g. media outlets).

The platform combines the latest scientific and technological knowledge on the topic gathered within OPERANDUM with advanced webGIS functionalities, analytical algorithms, and a comprehensive repository for NBS data (and metadata) management and cataloging. The highly structured and comprehensive data model adopted here enables to query the database and/or filter the results based on a multitude of individual parameters which encompass all different dimensions of NBS (e.g. geophysical, societal, environmental, etc.). This not only allows for a straightforward and automatic association to one or more thematic aspects of NBS, but also enhances standardization, discoverability and interoperability of NBS data in the context of disaster risk management and climate change adaptation.

Among its functionalities, GeoIKP offers an interactive map which enables users to visualize and combine in real time geo-referenced datasets on a variety of thematic areas (hydro-meteorological hazards and associated socio-ecological risks, land cover/use characteristics, climate, Earth and ground observations, etc.), thus providing evidence-base support for the planning and management of NBS in a given geographic area. Through the map, the user can also access a geo-catalogue of existing NBS, and thus discover how NBS have been employed worldwide for hydro-meteorological risk reduction and climate change adaptation. At the same time, the platform serves as a hub for the growing NBS community to share information, tools, data, and experiences to reduce hydro-meteorological hazards. For example, scientists and practitioners can freely contribute to GeoIKP data repository as well as to the NBS catalogue, while the “Citizen Stories” functionality gives a voice to vulnerable, affected or concerned citizens to share personal experiences of how and why they started applying NBS to their areas, and to inspire others to take action.

How to cite: Leo, L. S., Kalas, M., Ommer, J., Vranić, S., Pavlova, I., Amirzada, Z., and Di Sabatino, S.: User-driven platform to facilitate community data access, collaboration, and knowledge sharing on Nature-Based Solutions as mitigation measures for hydro-meteorological hazards, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8229, https://doi.org/10.5194/egusphere-egu22-8229, 2022.

EGU22-10067 | Presentations | HS7.5

Correlation of Meteorological and Hydrological Droughts using Observational and Modelled Data in the Guadalquivir River Basin 

Emilio Romero-Jiménez, Matilde García-Valdecasas Ojeda, Patricio Yeste, Juan José Rosa-Cánovas, María Jesús Esteban-Parra, Yolanda Castro-Díez, and Sonia R. Gámiz-Fortis

Future scenarios of climate change foresee an increase in frequency, duration, and severity of droughts, especially in arid and semiarid regions. This predictions require an intensive study of drought mechanics, starting with how past and present droughts behave, and continuing with the study of future droughts.
In this research, it has been studied how a precipitation decrease that causes a meteorological drought is related to hydrological drought, caused by a decrease in river streamflow. The area of study is located in the Guadalquivir River basin, south of the Iberian Peninsula, which serves as an example of semiarid region. Two different sources of streamflow data are used: observational data obtained from the Spanish Centre for Public Work Experimentation and Study (CEDEX), which takes into consideration regulation from reservoirs, and modelled data obtained with the Variable Infiltration Capacity (VIC) model. The use of two data sources allows for a comparison of results, serving as a validation for future projects that will rely on the use of modelled data to study the behaviour of droughts in the near future.
The numerical description and correlation of droughts is performed by means of drought indices, such as the Standardized Precipitation Evapotranspiration Index (SPEI) or the Standardized Streamflow Index (SSI), each describing one drought type, respectively meteorological and hydrological.


Keywords: Drought indices, Hydrological model, Observational data, Guadalquivir basin.


Acknowledgements
This work was funded by FEDER/Junta de Andalucía-Consejería de Economía y Conocimiento, project B-RNM-336-UGR18, by the Spanish Ministry of Economy and Competitiveness project CGL2017-89836-390 R with additional support from FEDER Funds, and by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades (project P20_00035).

How to cite: Romero-Jiménez, E., García-Valdecasas Ojeda, M., Yeste, P., Rosa-Cánovas, J. J., Esteban-Parra, M. J., Castro-Díez, Y., and Gámiz-Fortis, S. R.: Correlation of Meteorological and Hydrological Droughts using Observational and Modelled Data in the Guadalquivir River Basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10067, https://doi.org/10.5194/egusphere-egu22-10067, 2022.

EGU22-10468 | Presentations | HS7.5 | Highlight

Reconstruction of the July 2021 European floods footprint – from field measurements to hydraulic model calibration 

Jose Luis Salinas Illarena, Ludovico Nicotina, Stephan Tillmanns, Daniel Bernet, Panagiotis Rentzos, Stefano Zanardo, Yang Yang, Shuangcai Li, and Arno Hilberts

Between 13th and 16th July 2021, low-pressure system Bernd caused heavy flooding in parts of eastern Belgium, western Germany, and north-eastern France. In many of these areas, the 24 hours rainfall amounts exceeded the mean monthly precipitation (T. Junghänel et al. 2021). With at least 220 reported fatalities and insured loss estimates ranging between 10 and 13 EUR billion, it is one of the most devastating natural catastrophes in the central-European region of the last decades (GDV 2021).
Given the relevance of this event, a detailed reconstruction of the flood footprint would be of interest for both earth scientists and the insurance industry. For this purpose, a reconnaissance field trip was organised between 1st and 3rd November 2021 to affected municipalities in the German states of North Rhine-Westphalia, Rhineland-Palatinate, and the Belgian province of Liège. Remaining flood marks in buildings and other infrastructure were measured for over 200 locations, and water depths were inferred from them. In addition, information was collected on the degree of damage to buildings, as well as on the stage of reconstruction and clean-up. The focus was on areas that did not get much media attention back in July 2021, smaller ungauged streams, and, in general, any location where the flood depths and damages could not be easily inferred from other sources. The information collected during this field trip, combined with updated E-OBS precipitation data, river discharge gauge data, satellite imagery, as well as media and authorities’ reports was used to input, calibrate, and validate the different components of the RMS in-house flood model chain. In particular, the depth measurements from the reconnaissance trip were useful to calibrate the inundation model in municipalities affected by flash flooding from small to medium-sized ungauged streams, or by pluvial flooding. These point measurements allowed for a more detailed and comprehensive reconstruction of the flood depths over the entire affected area, beyond the better monitored larger river systems.

T. Junghänel, et al. (2021) Hydro-klimatologische Einordnung der Stark- und Dauerniederschläge in Teilen Deutschlands im Zusammenhang mit dem Tiefdruckgebiet „Bernd“ vom 12. bis 19. Juli 2021, DWD Geschäftsbereich Klima und Umwelt, https://www.dwd.de/DE/leistungen/besondereereignisse/niederschlag/20210721_bericht_starkniederschlaege_tief_bernd.pdf

GDV (2021) Hochwasserkatastrophe: Versicherer zahlen bereits über drei Milliarden Euro, https://www.gdv.de/de/medien/aktuell/hochwasserkatastrophe-versicherer-zahlen-bereits-ueber-drei-milliarden-euro--73798

How to cite: Salinas Illarena, J. L., Nicotina, L., Tillmanns, S., Bernet, D., Rentzos, P., Zanardo, S., Yang, Y., Li, S., and Hilberts, A.: Reconstruction of the July 2021 European floods footprint – from field measurements to hydraulic model calibration, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10468, https://doi.org/10.5194/egusphere-egu22-10468, 2022.

EGU22-10722 | Presentations | HS7.5

Storm characteristics and extreme sub-daily precipitation statistics over CONUS 

Diogo Araujo, Francesco Marra, Haider Ali, Hayley Fowler, and Efthymios Nikolopoulos

The analysis of short-duration precipitation extremes is of foremost importance as heavy precipitation is directly related to many hazards, e.g. flash floods, landslides and crop damage. Here, we adopt an extreme value framework based on the concept of ordinary events, defined as independent realizations of the process of interest. In particular, we aim at investigating the link between the characteristics of ordinary storms (e.g. seasonality, average duration, autocorrelation) and the statistics of the emerging extremes at sub-daily durations (1-24 h). We used the Global Sub-Daily Rainfall (GSDR) dataset, which provides quality controlled hourly precipitation data from rain gauges over the Contiguous United States (CONUS). 

First, we tested the hypothesis that a Weibull distribution can describe the tail of ordinary events and independently reproduce the annual maxima. Then, we quantified the portion of ordinary events, termed tail hereinafter, which share the statistical properties with annual maxima. Analysis of the storm characteristics show shorter average duration storms (< 12h) in the central portion of CONUS, between latitudes 90ºW and 105ºW. Seasonality analysis showed predominance of summer events in all central and eastern areas, with exception to a region encompassing the northwestern areas of the southern US states, which are dominated by spring events. On the western coast, winter events dominate the tail of the distribution of the ordinary events. The majority of these events happened in the afternoon (12PM to 6PM) or night (6PM to 12AM). The parameters describing our extreme value distribution revealed insightful features. The scale parameter of the Weibull distribution describing the tail followed the local climatology, with higher values over the southeast of CONUS (region characterized for high intensity precipitation), and small values over the northwest. The shape parameter indicates heavier-tailed distributions on the north and central regions of the US, as opposed to the majority of stations CONUS-wide. On average, the number of events per year is larger in the east (50 to 100 events per year) when compared to the west (0 to 50 events per year) . 

Further analyses include investigating the influence of storm properties in the parameters of our extreme value distribution. This link, if proven significant, can be used to establish predictors for extreme precipitation statistics that stem from characteristics of ordinary storm events.

How to cite: Araujo, D., Marra, F., Ali, H., Fowler, H., and Nikolopoulos, E.: Storm characteristics and extreme sub-daily precipitation statistics over CONUS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10722, https://doi.org/10.5194/egusphere-egu22-10722, 2022.

EGU22-11082 | Presentations | HS7.5

Towards a quantitative spatiotemporal assessment of probabilistic landslide risk for large-area applications: challenges and perspectives.   

Massimiliano Pittore, Stefan Steger, Mateo Moreno, Piero Campalani, Kathrin Renner, Carlos Villacis, Jesica Piñón, Eduardo Pérez, Lydia Rincón de la Rosa, Idriss Achour, and Emmanuel Noel

 The probabilistic assessment of risk due to landslides for Disaster Risk Reduction (DRR) purposes in terms of absolute and quantitative metrics (e.g., number of expected fatalities, economic damage) is still quite challenging. If, on the one side, landslide susceptibility models based on the combined statistical analysis of observed events and geomorphological predisposing factors can be efficiently implemented, they must be integrated by further hypothesis and information to capture the complexity of landslides hazard and be efficiently used for the assessment of risk. For instance, most susceptibility models are static and do not formally account for main triggering conditions (e.g., rainfall or seismic activity). Furthermore, they do not include any probabilistic information on the frequency/magnitude relationships of the related events, hence conveying relative and partial information. In this contribution, a simplified framework for probabilistic landslides risk assessment is presented and its application for multi-hazard risk assessment in Burundi is discussed. The proposed approach is based on the integration of multi-temporal susceptibility models accounting for monthly average precipitation patterns into a heterogeneous Poisson point process model. The occurrence process model is used to generate a large portfolio of events, each associated with a feature representing its magnitude whose distribution is modelled by a simple power law. These events can be combined with exposure and fragility/vulnerability information to obtain a probabilistic assessment of risk of different adverse consequences on people, assets and infrastructure.

The proposed approach has been exemplified in the context of a multi-hazard risk assessment at national scale for Burundi and has proved successful in providing spatialised absolute and relative risk estimates that could be compared and combined with risk assessments related to other hazards (e.g., earthquakes and floods) with different characteristics and return periods.

 The practical implementation was based on the available data for the targeted region, which is limited, and relies on several assumptions and hypothesis that are accompanied by a significant level of uncertainty. The results have been preliminarily assessed using the data provided by the IOM Emergency Tracking Tool (ETT) from the period 2018-2021. The results indicate that the framework is flexible and can be used to obtain actionable information on risk due to landslides at different temporal and spatial scales. Our findings further highlight the importance of addressing landslide risk from a larger, interdisciplinary perspective, fostering the systematic collection of risk-oriented data (e.g., event inventories including information on damage and loss) and the synergies among different actors involved in DRR and Climate Change Adaptation. The potential and limitations of the proposed approach for regional landslide risk and for multi-hazard risk assessment will be discussed. The described research activities have been carried out within the framework of an international project funded by the European Union, implemented by the International Organization of Migration (IOM) and coordinated by IDOM (Spain).

How to cite: Pittore, M., Steger, S., Moreno, M., Campalani, P., Renner, K., Villacis, C., Piñón, J., Pérez, E., Rincón de la Rosa, L., Achour, I., and Noel, E.: Towards a quantitative spatiotemporal assessment of probabilistic landslide risk for large-area applications: challenges and perspectives.  , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11082, https://doi.org/10.5194/egusphere-egu22-11082, 2022.

EGU22-11559 | Presentations | HS7.5 | Highlight

Evaluation of the extreme rainfall event of July 2021 in Western Germany and its impact based on the Catalogue of Radar-based Heavy Rainfall Events (CatRaRE) 

Ewelina Walawender, Katharina Lengfeld, Tanja Winterrath, and Elmar Weigl

Within a few days of July 2021, extreme heavy rainfall associated with the low-pressure weather system “Bernd” caused severe flooding in Western Germany (North Rhine-Westphalia and Rhineland-Palatinate), as well as in Luxembourg, and parts of Belgium and the Netherlands. In Germany, this devastating event resulted in at least 184 fatalities.

In our presentation, we take a closer look at this event as classified in the Catalogue of Radar-based Heavy Rainfall Events (CatRaRE), derived from 21 years of climatological radar data (RADKLIM 1km,1h) for the area of Germany.

The CatRaRE Catalogue covers both the attributes of all classified heavy rainfall events as well as their spatial extent. The dataset is published annually by the German Meteorological Service and is freely available for all interested users at: dwd.de/catrare.

We present the extent and parameters of this extreme rainfall as an event classified in the CatRaRE together with a comprehensive analysis and comparison against all heavy precipitation events lasting between 1 to 72 hours which occurred in Germany in the period from 2001 to 2020. Apart from various extremity statistics such as return period, heavy precipitation index, and weather extremity indices, additional variables are examined as predictors for a potential impact: e.g. antecedent precipitation index, population density, land cover, imperviousness degree and topography indices.

How to cite: Walawender, E., Lengfeld, K., Winterrath, T., and Weigl, E.: Evaluation of the extreme rainfall event of July 2021 in Western Germany and its impact based on the Catalogue of Radar-based Heavy Rainfall Events (CatRaRE), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11559, https://doi.org/10.5194/egusphere-egu22-11559, 2022.

EGU22-11993 | Presentations | HS7.5

Accounting for long-term climatic trends in Probable Maximum Precipitation estimation 

Jaya Bhatt and Venkata Vemavarapu Srinivas

The compounding evidence on the aberrant behavior of extreme precipitation has drawn attention of hydrometeorologists towards re-evaluating the existing hydraulic design criteria for protection of large structures (e.g., spillways of dams, nuclear power plants) in changing climate. Traditionally, design flood estimates for those structures were based on Probable Maximum Precipitation (PMP) to minimize or avert the risk of failure and consequent catastrophic damage to mankind and the environment. PMP, as defined by the World Meteorological Organization (WMO), does not account for long-term climatic trends. However, in recent decades, there has been an increase in frequency and magnitude of extreme precipitation events in different parts of the globe. This necessitates devising potential strategies to arrive at effective PMP estimates to re-assess the existing design criteria.  Against this backdrop, researchers have been actively developing new methods or modifying the existing ones to adapt to changing climate. The majority of these methods are physics-based whose application demands voluminous data on various hydrometeorological variables and computationally intensive systems to run simulations on weather models. In comparison, statistical approaches are simple and not data intensive. Among available statistical approaches, Hershfield method is widely used due to its ease of application. There is a dearth of attempts to extend it for use in climate change scenarios.

In the present study, a new variant of Hershfield method is proposed which yields reliable PMP estimates by accounting for long-term trends in precipitation data for better estimation of at-site frequency factor in the climate change scenario. The applicability of the proposed method is illustrated over India considering 119 years (1901-2019) long 0.25-degree gridded precipitation records from IMD (India Meteorological Department). The country has more than 5000 dams, and currently PMP estimates are being considered for risk analyses of several ageing dams through the aid of the World Bank, under DRIP (Dam Rehabilitation and Improvement Project). The proposed methodology is applied to arrive at PMP estimates for sites/grids in homogeneous precipitation regions delineated in the country using cluster analysis. The overall impact of increasing/decreasing trend of precipitation on the regional estimate of frequency factor and one-day PMP estimates is clearly demonstrated using the proposed and conventional Hershfield methods.

How to cite: Bhatt, J. and Srinivas, V. V.: Accounting for long-term climatic trends in Probable Maximum Precipitation estimation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11993, https://doi.org/10.5194/egusphere-egu22-11993, 2022.

EGU22-12046 | Presentations | HS7.5 | Highlight

Impact-based Forecasting: Bridging the gap between forecast and post flood impact with remote sensing 

Margherita Sarcinella, Brianna R. Pagán, Jeremy S. Pal, Arthur H. Essenfelder, Lisa Landuyt, and Jaroslav Mysiak

The economic loss associated with natural hazards has drastically increased over the past decades, reaching over $210 billion dollars worldwide in 2020. The explication of regional-scale climate change effects with the tendency to exacerbate local climate criticalities has long jeopardized disaster resilience and the coping capacity of many communities. There is a lack of a robust operational linkage between the pre-disaster and post-disaster segments when a disaster occurs. This hampers an effective emergency response often leading to delayed humanitarian intervention and unplanned evacuations. Moreover, the great amount of openly available impact information on past events is commonly discarded and the forecast potential which the data yields has yet to be fully explored. In this context, the Impact-based Forecasting (IbF) approach aims to interconnect pre-emptive planning for early action with post-disaster impacts while taking advantage of historical data. The underlying principle of IbF is that the magnitude of an event is translated to site-specific impact information. Therefore, a paradigm shift from the conventional magnitude-likelihood relationship to impact-likelihood is proposed. This research develops a method to fully exploit the potential of IbF while overcoming the typical site-specificity of emergency response through remote sensing and automation. While the IbF framework allows for a multi-hazard approach, here we present a method targeting the ex-ante impact assessment of riverine floods. The analysis consists of two main components: i) the delineation of the flood extent from Sentinel-1 SAR imagery and ii) the definition of the event impact on the population, land and built environment. The IbF impact-likelihood relationship is ultimately derived by matching the two components for a historical event series. A fully automated Google Earth Engine algorithm for flood extent mapping with a 10 m spatial resolution has been developed to detect floodwater with a single-scene classification based on an automated thresholding method. The flood magnitude is then matched with open-access geodata such as human settlements, population density, land cover and infrastructure from the OpenStreetMap catalogue to generate the impact assessment. Once trained on several site or region specific past events, it can automatically forecast the impact associated with a given event magnitude. Here we apply the technique to three case studies including the flooding associated with the Tropical Cyclone Idai, which made landfall in Mozambique in March 2019 causing over 1200 fatalities and $2 billion worth of damage. The performance of the flood mapping algorithm has been evaluated as satisfactory for the impact application and further validation at two additional sites is ongoing. Therefore, local triggers can be set to ensure a valuable temporal window to promptly plan and estimate the cost of intervention on the field. This work is a first step to providing a consistent and regionally transferable disaster preparedness tool that allows for multi-hazard impact forecasts.

How to cite: Sarcinella, M., Pagán, B. R., Pal, J. S., Essenfelder, A. H., Landuyt, L., and Mysiak, J.: Impact-based Forecasting: Bridging the gap between forecast and post flood impact with remote sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12046, https://doi.org/10.5194/egusphere-egu22-12046, 2022.

EGU22-12696 | Presentations | HS7.5

Comprehensive risk assessment of July 2021 European flooding including associated uncertainties 

Punit Bhola, Margot Doucet, Stefanie Alarcon, and Bernhard Reinhardt

In July 2021, low-pressure system “Bernd” parked itself over central Europe in July 2021 and caused significant flooding in western Germany and neighbouring countries. The devastating flooding led to more than 180 causalities in Germany and caused catastrophic losses by disrupting infrastructure.

As the flood event unfolded, we at Verisk Extreme Event Solutions, re-modelled the event using state-of-the-art flood models by simulating river flows in our hydrological and flood inundation patterns in hydraulic model from observed precipitation fields derived from NASA’s Global Precipitation Measurement (GPM). Using the remodelled hazard and our Industry Exposure Database (IED), we provided a range of insured loss estimates for the insurance and reinsurance market. We will discuss the event with respect to hazard and uncertainties associated with risks, such as demand surge, cost inflation and infrastructure damage.

How to cite: Bhola, P., Doucet, M., Alarcon, S., and Reinhardt, B.: Comprehensive risk assessment of July 2021 European flooding including associated uncertainties, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12696, https://doi.org/10.5194/egusphere-egu22-12696, 2022.

EGU22-12786 | Presentations | HS7.5

New Dam Break Risk Assessment Method in Fuzzy Framework 

Anubhav Goel and Venkata Vemavarapu Srinivas

Dams are useful for mitigation of floods, and at the same time there is a risk of dam breach or failure from floods, apart from seismic hazards and factors such as ageing of dam material. In recent decades, there is an alarming increase in dam breach events. This has drawn the attention of hydrologists to have a relook at methodologies being considered for dam risk analysis. Effective risk analysis requires accounting for both failure probability of dam and dam break consequences. There are numerous factors which effect the consequences, and there is considerable amount of uncertainty, vagueness and ambiguity among them due to lack of data and knowledge. To address this, we propose a new dam break risk assessment method in fuzzy framework. It considers fuzzy hierarchical model for risk assessment based on combination of static and variable fuzzy set theory. A hierarchical structure is devised for various factors influencing dam break consequences. Furthermore, weights are assigned to the factors using Fuzzy Analytical Hierarchy Process (FAHP). Thereafter, weighted information of different factors is comprehended to arrive at estimate of a risk index. The effectiveness of proposed method is demonstrated through case study on Hemavathi dam located in upper reaches of Cauvery River basin, India. It is a composite dam with masonary spillway and earthen flanges. The catchment area of river up to the dam site is 2904 sq. Km. Furthermore, height of dam above riverbed level is 44.5 m, and its gross storage capacity is 1047 Mm3. As per Bureau of Indian Standards (BIS) the dam is classified as large dam and therefore qualifies for Probable Maximum Flood (PMF) as design flood. Breach analysis of Hemavathi dam was performed using 1D-2D coupled HEC- RAS model to map the extent of flooding downstream of the dam using PMF (corresponding to 2-day PMP) as inflow and maintaining initial pool level in reservoir at maximum water level (MWL). For comprehensive risk assessment, life loss, economic loss, and social and environmental influence caused by dam break are considered in the model.

How to cite: Goel, A. and Srinivas, V. V.: New Dam Break Risk Assessment Method in Fuzzy Framework, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12786, https://doi.org/10.5194/egusphere-egu22-12786, 2022.

EGU22-986 | Presentations | HS3.6

Quantifying solute transport numerical dispersion in integrated surface-subsurface hydrological modeling 

Beatrice Gatto, Claudio Paniconi, Paolo Salandin, and Matteo Camporese

Numerical dispersion is a well-known problem that affects solute transport in groundwater simulations and can lead to wrong results, in terms of plume path overestimation and overprediction of contaminant dispersion. Numerical dispersion is generally introduced through stabilization techniques aimed at preventing oscillations, with the side effect of increasing mass spreading. Even though this issue has long been investigated in subsurface hydrology, little is known about its possible impacts on integrated surface–subsurface hydrological models (ISSHMs). In this study, we analyze numerical dispersion in the CATchment HYdrology (CATHY) model. In CATHY, a robust and computationally efficient time-splitting technique is implemented for the solution of the subsurface transport equation, whereby the advective part is solved on elements with an explicit finite volume scheme and the dispersive part is solved on nodes with an implicit finite element scheme. Taken alone, the advection and dispersion solvers provide accurate results. However, when coupled, the continuous transfer of concentration from elements to nodes, and vice versa, gives rise to a particular form of numerical dispersion. We assess the nature and impact of this artificial spreading through two sets of synthetic experiments. In the first set, the subsurface transport of a nonreactive tracer in two soil column test cases is simulated and compared with known analytical solutions. Different input dispersion coefficients and mesh discretizations are tested, in order to quantify the numerical error and define a criterion for its containment. In the second set of experiments, fully coupled surface–subsurface processes are simulated using two idealized hillslopes, one concave and one convex, and we examine how the additional subsurface dispersion affects the representation of pre-event water contribution to the streamflow hydrograph. Overall, we show that the numerical dispersion in CATHY that is caused by the transfer of information between elements and nodes can be kept under control if the grid Péclet number is less than 1. It is also suggested that the test cases used in this study can be useful benchmarks for integrated surface–subsurface hydrological models, for which thus far only flow benchmarks have been proposed.

How to cite: Gatto, B., Paniconi, C., Salandin, P., and Camporese, M.: Quantifying solute transport numerical dispersion in integrated surface-subsurface hydrological modeling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-986, https://doi.org/10.5194/egusphere-egu22-986, 2022.

EGU22-1210 | Presentations | HS3.6

An alternative strategy for combining likelihood values in Bayesian calibration to improve model predictions 

Michelle Viswanathan, Tobias K. D. Weber, and Anneli Guthke

Conveying uncertainty in model predictions is essential, especially when these predictions are used for decision-making. Models are not only expected to achieve the best possible fit to available calibration data but to also capture future observations within realistic uncertainty intervals. Model calibration using Bayesian inference facilitates the tuning of model parameters based on existing observations, while accounting for uncertainties. The model is tested against observed data through the likelihood function which defines the probability of the data being generated by the given model and its parameters. Inference of most plausible parameter values is influenced by the method used to combine likelihood values from different observation data sets. In the classical method of combining likelihood values, referred to here as the AND calibration strategy, it is inherently assumed that the given model is true (error-free), and that observations in different data sets are similarly informative for the inference problem. However, practically every model applied to real-world case studies suffers from model-structural errors that are typically dynamic, i.e., they vary over time. A requirement for the imperfect model to fit all data sets simultaneously will inevitably lead to an underestimation of uncertainty due to a collapse of the resulting posterior parameter distributions. Additionally, biased 'compromise solutions' to the parameter estimation problem result in large prediction errors that impair subsequent conclusions. 
    
We present an alternative AND/OR calibration strategy which provides a formal framework to relax posterior predictive intervals and minimize posterior collapse by incorporating knowledge about similarities and differences between data sets. As a case study, we applied this approach to calibrate a plant phenology model (SPASS) to observations of the silage maize crop grown at five sites in southwestern Germany between 2010 and 2016. We compared model predictions of phenology on using the classical AND calibration strategy with those from two scenarios (OR and ANDOR) in the AND/OR strategy of combining likelihoods from the different data sets. The OR scenario represents an extreme contrast to the AND strategy as all data sets are assumed to be distinct, and the model is allowed to find individual good fits to each period adjusting to the individual type and strength of model error. The ANDOR scenario acts as an intermediate solution between the two extremes by accounting for known similarities and differences between data sets, and hence grouping them according to anticipated type and strength of model error. 
    
We found that the OR scenario led to lower precision but higher accuracy of prediction results as compared to the classical AND calibration. The ANDOR scenario led to higher accuracy as compared to the AND strategy and higher precision as compared to the OR scenario. Our proposed approach has the potential to improve the prediction capability of dynamic models in general, by considering the effect of model error when calibrating to different data sets.

How to cite: Viswanathan, M., Weber, T. K. D., and Guthke, A.: An alternative strategy for combining likelihood values in Bayesian calibration to improve model predictions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1210, https://doi.org/10.5194/egusphere-egu22-1210, 2022.

EGU22-1459 | Presentations | HS3.6

Modelling decisions: a quantification of their influence on model results 

Janneke Remmers, Ryan Teuling, and Lieke Melsen

Scientific hydrological modellers make multiple decisions during the modelling process, e.g. related to the calibration period and temporal resolution. These decisions affect the model results. Modelling decisions can refer to several steps in the modelling process. In this study, modelling decisions refer to the decisions made during the whole modelling process, beyond the definition of the model structure. This study is based on an analysis of interviews with scientific hydrological modellers, thus taking actual practices into account. Six modelling decisions were identified from the interviews, which are mainly motivated by personal and team experience (calibration method, calibration period, parameters to calibrate, pre-processing of input data, spin-up period, and temporal resolution). Different options for these six decisions, as encountered in the interviews, were implemented and evaluated in a controlled modelling environment, in our case the modular modelling framework Raven, to quantify their impact on model output. The variation in the results is analysed using three hydrological signatures to determine which decisions affect the results and how they affect the results. Each model output is a hypothesis of the reality; it is an interpretation of the real system underpinned by scientific reasoning and/or expert knowledge. Currently, there is a lack of knowledge and understanding about which modelling decisions are taken and why they are taken. Consequently, the influence of modelling decisions is unknown. Quantifying this influence, which was done in this study, can raise awareness among scientists. This study pinpoints what aspects are important to consider in studying modelling decisions, and can be an incentive to clarify and improve modelling procedures.

How to cite: Remmers, J., Teuling, R., and Melsen, L.: Modelling decisions: a quantification of their influence on model results, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1459, https://doi.org/10.5194/egusphere-egu22-1459, 2022.

EGU22-1639 | Presentations | HS3.6

Rigorous Exploration of Complex Environmental Models to Advance Scientific Understanding 

Robert Reinecke, Francesca Pianosi, and Thorsten Wagener

Environmental models are central for advancing science by increasingly serving as a digital twin of the earth and its components. They allow us to conduct experiments to test hypotheses and understand dominant processes that are infeasible to do in the real world. To foster our knowledge, we build increasingly complex models hoping that they become more complete and realistic images of the real world. However, we believe that our scientific progress is slowed down as methods for the rigorous exploration of these models, in the face of unavoidable data- and epistemic-uncertainties, do not evolve in a similar manner.

Based on an extensive literature review, we show that even though methods for such rigorous exploration of model responses, e.g., global sensitivity analysis methods, are well established, there is an upper boundary to which level of model complexity they are applied today. Still, we claim that the potential for their utilization in a wider context is significant.

We argue here that a key issue to consider in this context is the framing of the sensitivity analysis problem. We show, using published examples, how problem framing defines the outcome of a sensitivity analysis in the context of scientific advancement. Without appropriate framing, sensitivity analysis of complex models reduces to a diagnostic analysis of the model, with only limited transferability of the conclusions to the real-world system.

How to cite: Reinecke, R., Pianosi, F., and Wagener, T.: Rigorous Exploration of Complex Environmental Models to Advance Scientific Understanding, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1639, https://doi.org/10.5194/egusphere-egu22-1639, 2022.

We propose a method to analyse, classify and compare dynamical systems of arbitrary dimension by the two key features uncertainty and complexity. It starts by subdividing the system’s time-trajectory into a number of time slices. For all values in a time slice, the Shannon information entropy is calculated, measuring within-slice variability. System uncertainty is then expressed by the mean entropy of all time slices. We define system complexity as “uncertainty about uncertainty”, and express it by the entropy of the entropies of all time slices. Calculating and plotting uncertainty u and complexity c for many different numbers of time slices yields the c-u-curve. Systems can be analysed, compared and classified by the c-u-curve in terms of i) its overall shape, ii) mean and maximum uncertainty, iii) mean and maximum complexity, and iv) its characteristic time scale expressed by the width of the time slice for which maximum complexity occurs. We demonstrate the method at the example of both synthetic and real-world time series (constant, random noise, Lorenz attractor, precipitation and streamflow) and show that conclusions drawn from the c-u-curve are in accordance with expectations. The method is based on unit-free probabilities and therefore permits application to and comparison of arbitrary data. It naturally expands from single- to multivariate systems, and from deterministic to probabilistic value representations, allowing e.g. application to ensemble model predictions. 

How to cite: Ehret, U. and Dey, P.: c-u-curve: A method to analyze, classify and compare dynamical systems by uncertainty and complexity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1742, https://doi.org/10.5194/egusphere-egu22-1742, 2022.

EGU22-1870 | Presentations | HS3.6

Inference of (geostatistical) hyperparameters with the correlated pseudo-marginal method 

Lea Friedli, Niklas Linde, David Ginsbourger, Alejandro Fernandez Visentini, and Arnaud Doucet

We consider non-linear Bayesian inversion problems to infer the (geostatistical) hyperparameters of a random field describing (hydro)geological or geophysical properties by inversion of hydrogeological or geophysical data. This problem is of particular importance in the non-ergodic setting as no analytical upscaling relationships exist linking the data (resulting from a specific field realization) to the hyperparameters specifying the spatial distribution of the underlying random field (e.g., mean, standard deviation, and integral scales). Jointly inferring the hyperparameters and the "true" realization of the field (typically involving many thousands of unknowns) brings important computational challenges, such that in practice, simplifying model assumptions (such as homogeneity or ergodicity) are made. To prevent the errors resulting from such simplified assumptions while circumventing the burden of high-dimensional full inversions, we use a pseudo-marginal Metropolis-Hastings algorithm that treats the random field as a latent variable. In this random effect model, the intractable likelihood of observing the hyperparameters given the data is estimated by Monte Carlo averaging over realizations of the random field. To increase the efficiency of the method, low-variance approximations of the likelihood ratio are ensured by correlating the samples used in the proposed and current steps of the Markov chain and by using importance sampling. We assess the performance of this correlated pseudo-marginal method to the problem of inferring the hyperparameters of fracture aperture fields using borehole ground-penetrating radar (GPR) reflection data. We demonstrate that the correlated pseudo-marginal method bypasses the computational challenges of a very high-dimensional target space while avoiding the strong bias and too low uncertainty ranges obtained when employing simplified model assumptions. These advantages also apply when using the posterior of the hyperparameters describing the aperture field to predict its effective hydraulic transmissivity.

How to cite: Friedli, L., Linde, N., Ginsbourger, D., Fernandez Visentini, A., and Doucet, A.: Inference of (geostatistical) hyperparameters with the correlated pseudo-marginal method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1870, https://doi.org/10.5194/egusphere-egu22-1870, 2022.

This study proposes a new approach for quantitively assessing the importance of precipitation features in space and time to predict streamflow discharge (and, hence, sensitivity). For this, we combine well-performing deep-learning (DL) models with interpretability tools.

The DL models are composed of convolutional neural networks (CNNs) and long-short term memory (LSTM) networks. Their input is precipitation data distributed over the watershed and taken back in time (other inputs, meteorological and watershed properties, can also be included). Its output is streamflow discharge at a present or future time. Interpretability tools allow learning about the modeled system. We used the Integrated Gradients method that provides a level of importance (IG value) for each space-time precipitation feature for a given streamflow prediction. We applied the models and interpretability tools to several watersheds in the US and India.

To understand the importance of precipitation features for flood generation, we compared spatial and temporal patterns of IG for high flows vs. low and medium flows. Our results so far indicate some similar patterns for the two categories of flows, but others are distinctly different. For example, common IG mods exist at short times before the discharge, but mods are substantially different when considered further back in time. Similarly, some spatial cores of high IG appear in both flow categories, but other watershed cores are featured only for high flows. These IG time and space pattern differences are presumably associated with slow and fast flow paths and threshold-runoff mechanisms.

There are several advantages to the proposed approach: 1) recent studies have shown DL models to outperform standard process-based hydrological models, 2) given data availability and quality, DL models are much easier to train and validate, compared to process-based hydrological models, and therefore many watersheds can be included in the analysis, 3) DL models do not explicitly represent hydrological processes, and thus sensitivities derived in this approach are assured to represent patterns arise from the data. The main disadvantage of the proposed approach is its limitation to gauged watersheds only; however, large data sets are publicly available to exploit sensitivities of gauged streamflow.

It should be stressed out that learning about hydrological sensitivities with DL models is proposed here as a complementary approach to analyzing process-based hydrological models. Even though DL is considered black-box models, together with interpretability tools, they can highlight hard or impossible sensitivities to resolve with standard models.

How to cite: Morin, E., Rojas, R., and Wiesel, A.: Quantifying space-time patterns of precipitation importance for flood generation via interpretability of deep-learning models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1907, https://doi.org/10.5194/egusphere-egu22-1907, 2022.

EGU22-2220 | Presentations | HS3.6

Inversion of Hydraulic Tomography Data from the Grimsel Test Site with a Discrete Fracture Network Model 

Lisa Maria Ringel, Mohammadreza Jalali, and Peter Bayer

This study aims at the stochastic characterization of fractured rocks with a low-permeability matrix based on transient data from hydraulic tomography experiments. In such rocks, fractures function as main flowpaths. Therefore, adequate insight about distribution and properties of fractures is essential for many applications such as groundwater remediation, constructing nuclear waste repositories or developing enhanced geothermal systems. At the Grimsel test site in Switzerland, multiple hydraulic tests have been conducted to investigate the hydraulic properties and structure of the fracture network between two shear zones. We present results from combined stochastic inversion of these tests to infer the fracture network of the studied crystalline rock formation.

Data from geological mapping at Grimsel and the hydraulic tomography experiments that were undertaken as part of in-situ stimulation and circulation experiments provide the prior knowledge for the model inversion. This information is used for the setting-up of a site-specific conceptual model, to define the boundary and initial conditions of the groundwater flow model, and for the configuration of the inversion problem. The pressure signals we apply for the inversion stem from cross-borehole constant rate injection tests recorded at different depths, whereby the different intervals are isolated by packer systems.

In the forward model, the fractures are represented explicitly as three-dimensional (3D) discrete fracture network (DFN). The geometric and hydraulic properties of the DFN are described by the Bayesian equation. The properties are inferred by sampling iteratively from the posterior density function according to the reversible jump Markov chain Monte Carlo sampling strategy. The goal of this inversion is providing DFN realizations that minimize the error between the simulated and observed pressure signals and that meet the prior information. During the course of the inversion, the number of fractures is iteratively adjusted by adding or deleting a fracture. Furthermore, the parameters of the DFN are adapted by moving a fracture and by changing the fracture length or hydraulic properties. Thereby, the algorithm switches between updates that change the number of parameters and updates that keep the number of parameters but adjust their value. The inversion results reveal the main structural and hydraulic characteristics of the DFN, the preferential flowpaths, and the uncertainty of the estimated model parameters.

How to cite: Ringel, L. M., Jalali, M., and Bayer, P.: Inversion of Hydraulic Tomography Data from the Grimsel Test Site with a Discrete Fracture Network Model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2220, https://doi.org/10.5194/egusphere-egu22-2220, 2022.

EGU22-2388 | Presentations | HS3.6

Estimation of simulation parameters for steady and transient 3D flow modeling at watershed scale 

Gillien Latour, Pierre Horgue, François Renard, Romain Guibert, and Gérald Debenest
Unsaturated water flows at watershed scale or Darcy-scale are generally described by the Richardson-Richards equation. This equation is highly non-linear and simulation domains are limited by computational costs. The porousMultiphaseFoam toolbox is a Finite Volume tool capable of modeling multiphase flows in porous media, including the solving of the Richardson-Richards equation. As it has been developed using the OpenFOAM environment, the software is natively fully parallelized and can be used on super computers. By using experimental data from real site with geographical informations and piezometrics values, an iterative algorithm is set up to solve an inverse problem in order to evaluate an adequate permeability field. This procedure is initially implemented using simplified aquifer model with a 2D saturated modeling approach. A similar procedure using a full 3D model of the actual site is performed (handling both saturated and unsaturated area). The results are compared between the two approaches (2D and 3D) for steady simulations and new post-processing tools are also introduced to spatialize the error between the two models and define the areas for which the behaviour of the models is different. In a second part, an optimization of the Van Genuchten parameters is performed to reproduce transient experimental data. The 3D numerical results at the watershed scale are also compared to the reference simulations using a 1D unsaturated + 2D satured modeling approach.

How to cite: Latour, G., Horgue, P., Renard, F., Guibert, R., and Debenest, G.: Estimation of simulation parameters for steady and transient 3D flow modeling at watershed scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2388, https://doi.org/10.5194/egusphere-egu22-2388, 2022.

EGU22-2782 | Presentations | HS3.6

Global Sensitivity Analysis of an integrated parallel hydrologic model: ParFlow-CLM 

Wei Qu, Heye Bogena, Christoph Schüth, Harry Vereecken, and Stephan Schulz

An integrated parallel hydrologic model (ParFlow-CLM) was constructed to predict water and energy transport between subsurface, land surface, and atmosphere for a synthetic study using basic physical properties of the Stettbach headwater catchment, Germany. Based on this model, a global sensitivity analysis was performed using the Latin-Hypercube (LH) sampling strategy followed by the One-factor-At-a-Time (OAT) method to identify the most influential and interactive parameters affecting the main hydrologic processes. In addition, the sensitivity analysis was also carried out for assumptions of different slopes and meteorological conditions to show the transferability of the results to regions with other topographies and climates. Our results show that the simulated energy fluxes, i.e. latent heat flux, sensible heat flux and soil heat flux, are more sensitive to the parameters of wilting point, leaf area index, and stem area index, especially for steep slope and subarctic climate conditions. The simulated water fluxes, i.e. evaporation, transpiration, infiltration, and runoff, are most sensitive to soil porosity, van-Genuchen parameter n, wilting point, and leaf area index. The subsurface water storage and groundwater storage were most sensitive to soil porosity, while the surface water storage is most sensitive to the Manning’s n parameter. For the different slope and climate conditions, the rank order of in input parameter sensitivity was consistent, but the magnitude of parameter sensitivity was very different. The strongest deviation in parameter sensitivity occurred for sensible heat flux under different slope conditions and for transpiration under different climate conditions. This study provides an efficient method of the identification of the most important input parameters of the model and how the variation in the output of a numerical model can be attributed to variations of its input factors. The results help to better understand process representation of the model and reduce the computational cost of running high numbers of simulations. 

How to cite: Qu, W., Bogena, H., Schüth, C., Vereecken, H., and Schulz, S.: Global Sensitivity Analysis of an integrated parallel hydrologic model: ParFlow-CLM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2782, https://doi.org/10.5194/egusphere-egu22-2782, 2022.

EGU22-3691 | Presentations | HS3.6

Hydrogeological inference by adaptive sequential Monte Carlo with geostatistical resampling model proposals 

Macarena Amaya, Niklas Linde, and Eric Laloy

For strongly non-linear inverse problems, Markov chain Monte Carlo (MCMC) methods may fail to properly explore the posterior probability density function (PDF). Particle methods are very well suited for parallelization and offer an alternative approach whereby the posterior PDF is approximated using the states and weights of a population of evolving particles. In addition, it provides reliable estimates of the evidence (marginal likelihood) that is needed for Bayesian model selection at essentially no cost. We consider adaptive sequential Monte Carlo (ASMC), which is an extension of annealed importance sampling (AIS). In these methods, importance sampling is performed over a sequence of intermediate distributions, known as power posteriors, linking the prior to the posterior PDF. The main advantages of ASMC with respect to AIS are that it adaptively tunes the tempering between neighboring distributions and it performs resampling of particles when the variance of the particle weights becomes too large. We consider a challenging synthetic groundwater transport inverse problem with a categorical channelized 2D hydraulic conductivity field designed such that the posterior facies distribution includes two distinct modes with equal probability. The model proposals are obtained by iteratively re-simulating a fraction of the current model using conditional multi-point statistics (MPS) simulations. We focus here on the ability of ASMC to explore the posterior PDF and compare it with previously published results obtained with parallel tempering (PT), a state-of-the-art MCMC inversion approach that runs multiple interacting chains targeting different power posteriors. For a similar computational budget involving 24 particles for ASMC and 24 chains for PT, the ASMC implementation outperforms the results obtained by PT: the models fit the data better and the reference likelihood value is contained in the ASMC sampled likelihood range, while this is not the case for PT range. Moreover, we show that ASMC recovers both reference modes, while none of them is recovered by PT. However, with 24 particles there is one of the modes that has a higher weight than the other while the approximation is improved when moving to a larger number of particles. As a future development, we suggest that including fast surrogate modeling (e.g., polynomial chaos expansion) within ASMC for the MCMC steps used to evolve the particles in-between importance sampling steps would strongly reduce the computational cost while still ensuring results of similar quality as the importance sampling steps could still be performed using the regular more costly forward solver.

How to cite: Amaya, M., Linde, N., and Laloy, E.: Hydrogeological inference by adaptive sequential Monte Carlo with geostatistical resampling model proposals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3691, https://doi.org/10.5194/egusphere-egu22-3691, 2022.

EGU22-3782 | Presentations | HS3.6

Uncertainty assessment and data-worth evaluation for estimating soil hydraulic parameters and recharge fluxes from lysimeter data 

Marleen Schübl, Christine Stumpp, and Giuseppe Brunetti

Transient measurements from lysimeters are frequently coupled with Richards-based solvers to inversely estimate soil hydraulic parameters (SHPs) and numerically describe vadose zone water fluxes, such as recharge. To reduce model predictive uncertainty, the lysimeter experiment should be designed to maximize the information content of observations. However, in practice, this is generally done by relying on the a priori expertise of the scientist/user, without exploiting the advantages of model-based experimental design. Thus, the main aim of this study is to demonstrate how model-based experimental design can be used to maximize the information content of observations in multiple scenarios encompassing different soil textural compositions and climatic conditions. The hydrological model HYDRUS is coupled with a Nested Sampling estimator to calculate the parameters’ posterior distributions and the Kullback-Leibler divergences. Results indicate that the combination of seepage flow, soil water content, and soil matric potential measurements generally leads to highly informative designs, especially for fine textured soils, while results from coarse soils are generally affected by higher uncertainty. Furthermore, soil matric potential proves to be more informative than soil water content measurements. Additionally, the propagation of parameter uncertainties in a contrasting (dry) climate scenario strongly increased prediction uncertainties for sandy soil, not only in terms of the cumulative amount and magnitude of the peak, but also in the temporal variability of the seepage flow. 

How to cite: Schübl, M., Stumpp, C., and Brunetti, G.: Uncertainty assessment and data-worth evaluation for estimating soil hydraulic parameters and recharge fluxes from lysimeter data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3782, https://doi.org/10.5194/egusphere-egu22-3782, 2022.

EGU22-6882 | Presentations | HS3.6 | Highlight

A review of conceptual model uncertainty in groundwater research 

Okke Batelaan, Trine Enemark, Luk Peeters, and Dirk Mallants

For more than a century, the strong advice in geology has been to rely on multiple working hypotheses. However, in groundwater research, as supported by modelling, often a stepwise approach with respect to complexity is promoted and preferred by many. Defining a hypothesis, let alone multiple hypotheses, and testing these via groundwater models is rarely applied. The so-called ‘conceptual model’ is generally considered the starting point of our beloved modelling method. A conceptual model summarises our current knowledge about a groundwater system, describing the hydrogeology and the dominating processes. Conceptual model development should involve formulating hypotheses and leading to choices in the modelling that steer the model predictions. As many conceptual models can explain the available data, multiple hypotheses allow assessing the conceptual or structural uncertainty.

This presentation aims to review some of the key ideas of 125 years of research on (not) handling conceptual hydrogeological uncertainty, identify current approaches, unify scattered insights, and develop a systematic methodology of hydrogeological conceptual model development and testing. We advocate for a systematic model development approach based on mutually exclusive, collectively exhaustive range of hypotheses, although this is not fully achievable. We provide examples of this approach and the consequential model testing. It is argued that following this scientific recipe of refuting alternative models; we will increase the learnings of our research, reduce the risk of conceptual surprises and improve the robustness of the groundwater assessments. We conclude that acknowledging and explicitly accounting for conceptual uncertainty goes a long way in producing more reproducible groundwater research. Hypothesis testing is essential to increase system understanding by analyzing and refuting alternative conceptual models. It also provides more confidence in groundwater model predictions leading to improved groundwater management, which is more important than ever.

How to cite: Batelaan, O., Enemark, T., Peeters, L., and Mallants, D.: A review of conceptual model uncertainty in groundwater research, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6882, https://doi.org/10.5194/egusphere-egu22-6882, 2022.

EGU22-7774 | Presentations | HS3.6

Efficient inversion with complex geostatistical priors using normalizing flows and variational inference 

Shiran Levy, Eric Laloy, and Niklas Linde

We propose an approach for solving geophysical inverse problems which significantly reduces computational costs as compared to Markov chain Monte Carlo (MCMC) methods while providing enhanced uncertainty quantification as compared to efficient gradient-based deterministic methods. The proposed approach relies on variational inference (VI), which seeks to approximate the unnormalized posterior distribution parametrically for a given family of distributions by solving an optimization problem. Although prone to bias if the family of distributions is too limited, VI provides a computationally-efficient approach that scales well to high-dimensional problems. To enhance the expressiveness of the parameterized posterior in the context of geophysical inverse problems, we use a combination of VI and inverse autoregressive flows (IAF), a type of normalizing flows that has been shown to be efficient for machine learning tasks. The IAF consists of invertible neural transport maps transforming an initial density of random variables into a target density, in which the mapping of each instance is conditioned on previous ones. In the combined VI-IAF routine, the approximate distribution is parameterized by the IAF, therefore, the potential expressiveness of the unnormalized posterior is determined by the architecture of the network. The parameters of the IAF are learned by minimizing the Kullback-Leibler divergence between the approximated posterior, which is obtained from samples drawn from a standard normal distribution that are pushed forward through the IAF, and the target posterior distribution. We test this approach on problems in which complex geostatistical priors are described by latent variables within a deep generative model (DGM) of the adversarial type. Previous results have concluded that inversion based on gradient-based optimization techniques perform poorly in this setting because of the high nonlinearity of the generator. Preliminary results involving linear physics suggest that the VI-IAF routine can recover the true model and provides high-quality uncertainty quantification at a low computational cost. As a next step, we will consider cases where the forward model is nonlinear and include comparison against standard MCMC sampling. As most of the inverse problem nonlinearity arises from the DGM generator, we do not expect significant differences in the quality of the approximations with respect to the linear physics case.

How to cite: Levy, S., Laloy, E., and Linde, N.: Efficient inversion with complex geostatistical priors using normalizing flows and variational inference, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7774, https://doi.org/10.5194/egusphere-egu22-7774, 2022.

EGU22-8583 | Presentations | HS3.6

Quantifying transport ability of hindcast and forecast ocean models 

Makrina Agaoglou, Guillermo García-Sánchez, Amaia Marcano Larrinaga, Gabriel Mouttapa, and Ana M. Mancho

In the last years, there has been much interest in uncertainty quantification involving trajectories in ocean data sets. As more and more oceanic data become available the assessing quality of ocean models to address transport problems like oil spills, chemical or plastic transportation becomes of vital importance. In our work we are using two types of ocean models: the hindcast and the forecast in a specific domain in the North Atlantic, where drifter trajectory data were available. The hindcast approach requires running ocean (or atmospheric) models for a past period the duration of which is usually for several decades. On the other hand forecast approach is to predict future stages. Both ocean products are provided by CMEMS. Hindcast data includes extra observational data that was time-delayed and therefore to the original forecast run. This means that in principle, hindcast data are more accurate than archived forecast data. In this work, we focus on the comparison of the transport capacity between hindcast and forecast products in the Gulf stream and the Atlantic Ocean, based on the dynamical structures of the dynamical systems describing the underlying transport problem, in the spirit of [1]. In this work, we go a step forwards, by quantifying the transport performance of each model against observed drifters using tools developed in [2].

Acknowledgments

MA acknowledges support from the grant CEX2019-000904-S and IJC2019-040168-I funded by: MCIN/AEI/ 10.13039/501100011033, AMM and GGS acknowledge support from CSIC PIE grant Ref. 202250E001.

References

[1] C. Mendoza, A. M. Mancho, and S. Wiggins, Lagrangian descriptors and the assessment of the predictive capacity of oceanic data sets, Nonlin. Processes Geophys., 21, 677–689, 2014, doi:10.5194/npg-21-677-2014

[2] G.García-Sánchez, A.M.Mancho, and S.Wiggins, A bridge between invariant dynamical structures and uncertainty quantification, Commun Nonlinear Sci Numer Simulat 104, 106016, 2022, doi:10.1016/j.cnsns.2021.106016 

How to cite: Agaoglou, M., García-Sánchez, G., Marcano Larrinaga, A., Mouttapa, G., and Mancho, A. M.: Quantifying transport ability of hindcast and forecast ocean models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8583, https://doi.org/10.5194/egusphere-egu22-8583, 2022.

Conceptual models are indispensable tools for hydrology. In order to use them for making probabilistic predictions, they need to be equipped with an adequate error model, which, for ease of inference, is traditionally formulated as an additive error on the output (discharge). However, the main sources of uncertainty in hydrological modelling are typically not to be found on the output, but on the input (rain) and in the model structure. Therefore, more reliable error models and probabilistic predictions can be obtained by incorporating those uncertainties directly where they arise, that is, into the model. This, however, leads us to stochastic models, which render traditional inference algorithms such as the Metropolis algorithm infeasible due to their expensive likelihood functions. However, thanks to recent advancements in algorithms and computing power, full-fledged Bayesian inference with stochastic models is no longer off-limit for hydrological applications. We demonstrate this with a case study from urban hydrology, for which we employ a highly efficient Hamiltonian Monte Carlo inference algorithm with a time-scale separation.

How to cite: Ulzega, S. and Albert, C.: Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8729, https://doi.org/10.5194/egusphere-egu22-8729, 2022.

In this work we introduce hydroMOPSO, a novel multi-objective R package that combines two search mechanisms to maintain diversity of the population and accelerate its convergence towards the Pareto-optimal set: Particle Swarm Optimisation (PSO) and genetic operations. hydroMOPSO is model-independent, which allows to interface any model code with the calibration engine, including models available in R (e.g., TUWmodel, airGR, topmodel), but also any other complex models that can be run from the system console (e.g. SWAT+, Raven, WEAP). In addition, hydroMOPSO is platform-independent, which allows it to run on GNU/Linux, Mac OSX and Windows systems, among others.

Considering the long execution time of some real-world models, we used three benchmark functions to search for a configuration that allows to reach the Pareto-optimal front with a low number of model evaluations, analysing different combinations of: i) the swarm size in PSO, ii) the maximum number of particles in the external archive, and iii) the maximum number of genetic operations in the external archive. In addition, the previous configuration was then evaluated against other state-of-the-art multi-objective optimisation algorithms (MMOPSO, NSGA-II, NSGA-III). Finally, hydroMOPSO was used to calibrate a GR4J-CemaNeige hydrological model implemented in the Raven modelling framework (http://raven.uwaterloo.ca), using two goodness-of-fit functions: i) the modified Kling-Gupta efficiency (KGE') and ii) the Nash-Sutcliffe efficiency with inverted flows (iNSE).

Our results showed that the configuration selected for hydroMOPSO makes it very competitive or even superior against MMOPSO, NSGA-II and NSGA- III in terms of the number of function evaluations required to achieve stabilisation in the Pareto front, and also showed some advantages of using a compromise solution instead of a single-objective one for the estimation of hydrological model parameters.

How to cite: Marinao-Rivas, R. and Zambrano-Bigiarini, M.: hydroMOPSO: A versatile Particle Swarm Optimization R package for multi-objective calibration of environmental and hydrological models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9902, https://doi.org/10.5194/egusphere-egu22-9902, 2022.

EGU22-10431 | Presentations | HS3.6

Consistency and variability of spatial and temporal patterns of parameter dominance on four simulated hydrological variables in mHM in a large basin study 

Björn Guse, Stefan Lüdtke, Oldrich Rakovec, Stephan Thober, Thorsten Wagener, and Luis Samaniego

Model parameters are implemented in hydrological models to represent hydrological processes as accurate as possible under different catchment conditions. In the case of the mesoscale Hydrological Model (mHM), its parameters are estimated via transfer functions and scaling rules using the Multiscale Parameter Regionalization (MPR) approach [1]. Hereby, one consistent parameter set is selected for the entire model domain. To understand the impact of model parameters on simulated variables under different hydrological conditions, the spatio-temporal variability of parameter dominance and its relationship to the corresponding processes needs to be investigated.

In this study, mHM is applied to more than hundred German basins including the headwater areas in neighboring countries. To analyze the relevance of model parameters, a temporally resolved parameter sensitivity analysis using the FAST algorithm [2] is applied to derive dominant model parameters for each day. The temporal scale was further aggregated to monthly and seasonal averaged sensitivities. In analyzing a large number of basins, not only the temporal but also the spatial variability in the parameter relevance could be assessed. Four hydrological variables were used as target variable for the sensitivity analysis, i.e. runoff, actual evapotranspiration, soil moisture and groundwater recharge.

The analysis of the temporal parameter sensitivity shows that the dominant parameters vary in space and time and in using different target variables. Soil material parameters are most dominant on runoff and recharge. A switch in parameter dominance between different seasons was detected for an infiltration and an evapotranspiration parameter that are dominant on soil moisture in winter and summer, respectively. The opposite seasonal dominance pattern of these two parameters was identified on actual evapotranspiration. Further, each parameter shows high sensitivities to either high or low values of one or more hydrological variable(s). The parameter estimation approach leads to spatial consistent patterns of parameter dominances. Spatial differences and similarities in parameter sensitivities could be explained by catchment variability.

The results improve the understanding of how model parameter controls the simulated processes in mHM. This information could be useful for more efficient parameter identification, model calibration and improved MPR transfer functions.

 

References

[1] Samaniego et al. (2010, WRR), https://doi.org/10.1029/2008WR007327

[2] Reusser et al. (2011, WRR), https://doi.org/10.1029/2010WR009947

How to cite: Guse, B., Lüdtke, S., Rakovec, O., Thober, S., Wagener, T., and Samaniego, L.: Consistency and variability of spatial and temporal patterns of parameter dominance on four simulated hydrological variables in mHM in a large basin study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10431, https://doi.org/10.5194/egusphere-egu22-10431, 2022.

EGU22-10654 | Presentations | HS3.6 | Highlight

Uncertainty assessment with Bluecat: Recognising randomness as a fundamental component of physics 

Alberto Montanari and Demetris Koutsoyiannis

We present a new method for simulating and predicting hydrologic variables and in particular river flows, which is rooted in the probability theory and conceived in order to provide a reliable quantification of its uncertainty for operational applications. In fact, recent practical experience during extreme events has shown that simulation and prediction uncertainty is essential information for decision makers and the public. A reliable and transparent uncertainty assessment has also been shown to be essential to gain public and institutional trust in real science. Our approach, that we term with the acronym "Bluecat", assumes that randomness is a fundamental component of physics and results from a theoretical and numerical development. Bluecat is conceived to make a transparent and intuitive use of uncertain observations which in turn mirror the observed reality. Therefore, Bluecat makes use of a rigorous theory while at the same time proofing the concept that environmental resources should be managed by making the best use of empirical evidence and experience and recognising randomness as an intrinsic property of hydrological systems. We provide an open and user friendly software to apply the method to the simulation and prediction of river flows and test Bluecat's reliability for operational applications.

How to cite: Montanari, A. and Koutsoyiannis, D.: Uncertainty assessment with Bluecat: Recognising randomness as a fundamental component of physics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10654, https://doi.org/10.5194/egusphere-egu22-10654, 2022.

EGU22-11794 | Presentations | HS3.6

Effect of regional heterogeneities on inversion stability and estimated hydraulic properties field 

Hervé Jourde, Mohammed Aliouache, Pierre Fischer, Xiaoguang Wang, and Gerard Massonnat

Hydraulic tomography showed great potential on estimating the spatial distribution of heterogeneous aquifer properties in the last decade.  Though this method is highly performant on synthetic studies, the transition from an application to synthetic models to real field applications is often associated to numerical instabilities. Inversion techniques can also suffer from ill-posedness and non-uniqueness of the estimates since several solutions might correctly mimic the observed hydraulic data. In this work, we investigate the origin of the instabilities observed when trying to perform HT using real field drawdown data. We firstly identify the cause of these instabilities. We then use different approaches, where one is proposed, in order to regain inverse model stability, which also allows to estimate different hydraulic property fields at local and regional scales. Results show that ill-posed models can lead into inversion instability while different approaches that limit these instabilities may lead into different estimates. The study also shows that the late time hydraulic responses are strongly linked to the boundary conditions and thus to the regional heterogeneity. Accordingly, the use on these late-time data in inversion might require a larger dimension of the inverted domain, so that it is recommended to position the boundary conditions of the forward model far away from the wells. Also, the use of the proposed technique might provide a performant tool to obtain a satisfying fitting of observation, but also to assess both the site scale heterogeneity and the surrounding variabilities.

How to cite: Jourde, H., Aliouache, M., Fischer, P., Wang, X., and Massonnat, G.: Effect of regional heterogeneities on inversion stability and estimated hydraulic properties field, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11794, https://doi.org/10.5194/egusphere-egu22-11794, 2022.

EGU22-11844 | Presentations | HS3.6

Benchmarking Automatically Identified Model Structures with a Large Model Ensemble 

Diana Spieler, Kan Lei, and Niels Schütze

Recent studies have introduced methods to simultaneously calibrate model structure choices and parameter values to identify an appropriate (conceptual) model structure for a given catchment. This can be done through mixed-integer optimization to identify the graph structure that links dominant flow processes (Spieler et al., 2020) or, likewise, by continuous optimization of weights when blending multiple flux equations to describe flow processes within a model (Chlumsky et al., 2021). Here, we use the combination of the mixed-integer optimization algorithm DDS and the modular modelling framework RAVEN and refer to it as Automatic Model Structure Identification (AMSI) framework.

This study validates the AMSI framework by comparing the performance of the identified AMSI model structures to two different benchmark ensembles. The first ensemble consists of the best model structures from the brute force calibration of all possible structures included in the AMSI model space (7488+). The second ensemble consists of 35+ MARRMoT structures representing a structurally more divers set of models than currently implemented in the AMSI framework. These structures stem from the MARRMoT Toolbox introduced by Knoben et al. (2019) providing established conceptual model structures based on hydrologic literature.

We analyze if the model structure(s) AMSI identifies are identical to the best performing structures of the brute force calibration and comparable in their performance to the MARRMoT ensemble. We can conclude that model structures identified with the AMSI framework can compete with the structurally more divers MARRMoT ensemble. In fact, we were surprised to see how well we do with a simple two storage structure over the 12 tested MOPEX catchments (Duan et al.,2006). We aim to discuss several emerging questions, such as the selection of a robust model structure, Equifinality in model structures, and the role of structural complexity.

 

Spieler et al. (2020). https://doi.org/10.1029/2019WR027009

Chlumsky et al. (2021). https://doi.org/10.1029/2020WR029229

Knoben et al. (2019). https://doi.org/10.5194/gmd-12-2463-2019

Duan et al. (2006). https://doi.org/10.1016/j.jhydrol.2005.07.031

How to cite: Spieler, D., Lei, K., and Schütze, N.: Benchmarking Automatically Identified Model Structures with a Large Model Ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11844, https://doi.org/10.5194/egusphere-egu22-11844, 2022.

Pearson’s correlation is usually used as a criterion for the presence or absence of a relationship between time series, but it is not always indicative for nonlinear systems like climate. Therefore, we implement one of the methods of nonlinear dynamics to detect connections in the Sun-climate system. Here we estimate the causal relationship between Total Solar Irradiance (TSI) and Ocean climate indices over the past few decades using the method of conditional dispersions (Cenys et al., 1991). We use a conceptual ocean-atmosphere model (Jin, 1997) with TSI added as a forcing to calibrate the method. We show that the method provides expected results for connection between TSI and the model temperature. Premixing of Gaussian noise to model data leads to decrease of detectable causality with increase of noise amplitude, and the similar effect occurs in empirical data. Moreover, in the case of the empirical data, we show that the method can be used to independently estimate uncertainties of Ocean climate indices.

How to cite: Skakun, A. and Volobuev, D.: Ocean climate indices and Total Solar Irradiance: causality over the past few decades and revision of indices uncertainties, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12691, https://doi.org/10.5194/egusphere-egu22-12691, 2022.

EGU22-2716 | Presentations | ERE4.4

Self-awareness for robust miner robot autonomy 

Esther Aguado, Ricardo Sanz, and Claudio Rossi

Robustness and resilience are crucial requirements for robots operating in unstructured and hazardous environments, such as the systems developed within the ROBOMINERS project. The miner robot shall handle, at least to some extent, the disturbances it may suffer; especially given the reduced possibility of human intervention in deep, small, and difficult-to-access deposits. In ROBOMINERS, we use self-awareness mechanisms to enhance robot miner autonomy. This capability enables the robot to be aware of the state of all its components (both hardware and software) and to what extent they are complying with their functions. Moreover, self-aware systems can reason about the run-time state and detect the causes of system failures. Depending on the specific characteristics of the affected robot, failure management mechanisms can be implemented at different levels. Robots can be designed to change their physical or software configuration, change the functions of some of their components, or adapt their behaviour to match mission needs. Our approach uses the knowledge of the systems engineer through machine-readable metamodels to provide the robot with information about the mission, the environment, and itself. These formal models allow the system to reason about its run-time situation. The ROBOMINERS resilience-augmenting solution is based on deep modeling of the functional architecture of the autonomous robot in combination with runtime reasoning. The reflective reasoning of the robot allows for both self-diagnosis and reconfiguration during mining operations. One of the main advantages of this knowledge-centric approach is the explicit definition, allocation, and linkage of system requirements, design decisions, system realization, and run-time information. This approach can transparently use robot structural and functional redundancy to ensure mission satisfaction, even in the presence of faults. Moreover, the use of several meta-models and ontologies allows the segmentation of information into different domains and levels of abstraction. These independent assets can then be re-targeted and adapted to a variety of systems, sub-systems, and contexts to improve asset reuse.

How to cite: Aguado, E., Sanz, R., and Rossi, C.: Self-awareness for robust miner robot autonomy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2716, https://doi.org/10.5194/egusphere-egu22-2716, 2022.

EGU22-2740 | Presentations | ERE4.4

Modular collaborative resilient robots for mining operations 

Virgilio Gomez and Miguel Hernando

In the framework of the ROBOMINERS project, we are developing a set of modular collaborative robots that can perform mining operations. The purpose of this work is to face the challenge of taking modular robots out of the academic context and to provide robotic miners with the needed resilience which will be based on four pillars: redundancy, physical reconfiguration, adaptive behavior, and system reconfiguration. To do so, we are working on a scaled prototype based on a highly configurable modular robot that allows the connection between several autonomous robots (modules) and functional submodules (e.g., sensors, mining tools, locomotion devices) where resilience, energy sharing, self-reconfigurability, modularity, and self-awareness capabilities will be tested both in simulation and real-world scenarios. For each robot module, a lightweight and compact main structure is composed of three compartments and three docking ports for each of the robot legs. In each of these compartments most of the electronic components that allow the proper functioning of the robot are located, while in the legs a 4 degrees of freedom closed chain parallel mechanism powered by multi-turn servomotors is responsible of moving the interchangeable end effectors (screws, continuous tracks, legs) designed with a common coupling interface. In addition, an innovative soft telescopic robot arm (Patent pending) is placed at the front of the robot module and allows the coupling of another robot, sensing or actuation module. In parallel to the robot prototype development, a digital twin is being developed in order to test and improve different configurations, localization, mine mapping, and control algorithms techniques before deploying them in the robot.

How to cite: Gomez, V. and Hernando, M.: Modular collaborative resilient robots for mining operations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2740, https://doi.org/10.5194/egusphere-egu22-2740, 2022.

EGU22-5726 | Presentations | ERE4.4

Dynamic modelling of a screw actuator for improved locomotion control on various terrains 

Walid Remmas, Roza Gkliva, and Asko Ristolainen

Different types of terrains can be encountered in mining environments, varying from hard rock bottom to mud, including gravel and sand. In our research we are investigating the usage of Archimedean screw actuators for locomotion in mining environments, as they are mechanically robust and can work on various substrates. The limitations on using screw locomotion in autonomous robotics include its inherent property of slippage that varies depending on the type of terrain. Moreover, the dynamic model of an Archimedean screw depends on variables such as shear stress or sinkage, which are difficult to measure with the onboard sensors. To accurately model and later control such robots, we focus on the dynamic modelling of the screw-ground interaction based on real experiments. In this work, we approximate the dynamics of an Archimedean screw to those of different tire models available in the literature. The proposed models are used to; (1) Simulate the ground-screw interaction with several types of grounds. (2) Estimate the robot pose based on odometry. (3) Design adaptive controllers able to control the robot in grounds with varying properties. We validate the proposed dynamic models based on experimental force measurements, and we evaluate the accuracy of the derived odometry models based on visually measured ground truth data.

How to cite: Remmas, W., Gkliva, R., and Ristolainen, A.: Dynamic modelling of a screw actuator for improved locomotion control on various terrains, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5726, https://doi.org/10.5194/egusphere-egu22-5726, 2022.

EGU22-7427 | Presentations | ERE4.4

UNEXUP, towards the exploration of underwater environments with a robotic solution 

Márcio Pinto, Norbert Zajzon, Luís Lopes, Balazs Bodo, Stephen Henley, José Almeida, Jussi Aaltonen, and Gorazd Žibret

UNEXUP is a project co-funded by EIT RawMaterials that started in January 2020 and will be concluded in December 2022. The main objective is to develop, test, and commercialize a novel robot-based technology to survey flooded mines and other underwater structures. The robots are equipped with geoscientific and navigation instruments that allow the collection of valuable data from sites that cannot be assessed without human risks or high investments for dewatering, for example.

This technology was initially developed during the H2020 UNEXMIN project – UNEXUP predecessor, during which three (UX-1) robots were built and tested in five different underwater sites in Europe with increasingly challenging conditions. From the lessons learned on these pilot tests, the engineers collected crucial points for improvement – in close connection with the feedback and requirements from potential users of technology.

In UNEXUP the objective is to build two new robots, with improved software and hardware compared to the previous generation, and to launch them to the market as a commercial service. The first robot, UX-1Neo, was developed in 2020; while UX-2 will be ready in 2022.

UX-1Neo is the upscaled version of the UX-1, equipped with improved navigational and geoscientific instruments and sensors. The upscaling robot has performed four field missions in 2021 – ranging from flooded mines, a water well, and an underwater cave.

The field missions proved the added value that the technology can provide to the mining community and other sectors involving underwater structures. UX-1Neo is a modular vehicle, ca. 90 kg, with swappable batteries, autonomy of approximately 9 hours, and depth capacity of 500 m. An IMU and DVL support the navigation of the robot, to measure the position and depth during the missions. The multibeam (1) and scanning sonars (2) allow the robot to map close, mid, and long-range cavities, and to detect and avoid obstacles in the environment. In addition, the robot is equipped with six SLSs (Structured Light Systems) for more detailed mapping when visibility and turbidity allow. Six cameras – natural light – allow the visualization of the environment and identification of rock types and geological structures. The motion control is supported by eight thrusters, and a mechanical pendulum, for pitch position lock.

The geoscientific instrumentation in UX-1Neo includes a hyperspectral unit, water sampler unit, water chemistry unit (pH, oxygen concentration, EC, temperature, pressure), sub-bottom profiler and a fluxgate magnetometer. This payload allows geoscientists to collect and interpret spatial and geoscientific data from underwater sites.

UX-2 is being developed with increased modularity and depth range compared to UX-1Neo, and some instruments and sensors in UX-1Neo were designed to be compatible with UX-2. It will have higher Technology Readiness Level; and a rock sampling unit supported by a robotic arm. Therefore, the UX-2 will be able to perform in even more challenging environments – broadening the applications of the commercial service – and extending its reliability to perform.

How to cite: Pinto, M., Zajzon, N., Lopes, L., Bodo, B., Henley, S., Almeida, J., Aaltonen, J., and Žibret, G.: UNEXUP, towards the exploration of underwater environments with a robotic solution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7427, https://doi.org/10.5194/egusphere-egu22-7427, 2022.

EGU22-10299 | Presentations | ERE4.4

Overcoming the challenges of 3D modeling in harsh, confined, underwater environments: A case study 

Zorana Milosevic, Richard Zoltan Papp, and Hilco van Moerkerk

It is estimated that there are more than 8000 abandoned, flooded mines in Europe, many of which lack any information on their status and layout. Accurate and detailed 3D modeling plays a key role in fully understanding these complex environments and determining their remaining hidden potential. However, acquiring the needed data is a challenging task since these environments are extremely hazardous for traditional methods such as human diving. Additionally, human divers can reach only a limited depth range, much smaller than that of a standard mine. Therefore, underwater vehicles appear as a natural alternative for overcoming the disadvantages of direct human exploration. The UX1-Neo is a semi-autonomous underwater robotic system built precisely for this use. This small spherical robot with a 0.7m diameter has a 500m depth rating and various sensors for surveying the environment, such as multibeam and scanning sonars, structured light projectors, and multispectral cameras. 

 

The unfavorable properties of the water medium, such as light scattering and attenuation, pose additional difficulties for data acquisition in these complex environments. Furthermore, mine tunnels are a GPS-denied environment, which makes the modeling system rely entirely on the robot's inertial navigation system, which is prone to error due to the dead-reckoning drift. Conventional methods for correcting this drift, such as SLAM, face additional challenges in these repetitive environments (shafts and tunnels) due to their highly symmetric structures and lack of distinctive features. Additionally, during the exploration of a salt mine, Solotvyno (Ukraine), we faced a new challenge, a refraction of the sonar data due to the salty water, which required further processing in order to create an accurate 3D map of the mine. 

 

Rapid developments in the field of underwater photogrammetry are producing impressive results; however, they still have difficulties with the environments with low light, which causes blurring of details, low image contrast, and in general, lack of features needed for image matching. Also, underwater images are prone to contain an excessive amount of blue light, making the features even less visible. Moreover, photogrammetry technology struggles with repetitive environments due to the same reasons as SLAM.

 

In this work, we demonstrate the challenges faced during our exploration of the Solotvyno salt mine with the UX1-Neo robot and how we overcame them in order to produce a detailed 3D model. In particular, we illustrate that sensor and data redundancy is vital during operations and post-processing. Each UX1-Neo sensor contributes to a complete, coherent picture of the environment. However, using many sensors produces an enormous amount of data that require further filtering: hundreds of millions of points are reduced to a few million using both automated and manual methods. Images also require processing due to the aforementioned reasons: using CLAHE contrast enhancement together with white balancing algorithms, we produce suitable images for photogrammetry. Additionally, data gathered from multiple missions need to be combined for a complete model: we show the importance of robot orientation initialization and external surveying of the robot's launch location to correctly align scans of different missions.

How to cite: Milosevic, Z., Papp, R. Z., and van Moerkerk, H.: Overcoming the challenges of 3D modeling in harsh, confined, underwater environments: A case study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10299, https://doi.org/10.5194/egusphere-egu22-10299, 2022.

In Europe there are a lot of abandoned mines that could be reopened with the use of innovative techniques; this is one of the aims of the ROBOMINERS project.

The use of the mining robots will especially be relevant for mineral deposits that are small or difficult to access.

Knowledge of type and dimensions of this mines is fundamental aiming to design and plan on-site tests of this robot.

In this article are explained the selection criteria of some mines in Italy among all the abandoned mines available at national level that could be investigated with robot miner.

In Italy there are about 3000 abandoned mining sites. Among these, eleven sites distributed throughout the national territory were selected.

Starting from a national public database containing all the abandoned mining sites and using an ad hoc KPI-matrix, some pilot sites were selected that met the required features.

The selection was carried out, according to the objectives of the project, preferring mining sites in urban areas, located at great depths or considered not economically relevant by traditional mining.

Among these, preference was given to metal-bearing ore deposits that could be better excavated with robot.

In order to characterize the selected sites, the following data have been collected for every site:

  • Geographic informations;
  • Historic time range of exploration ;
  • Deposit type;
  • Commodities available;
  • Main host rock.

Data collection was performed starting from the national database and subsequently integrating the informations with further data from bibliographic sources.

Data collection for the selected mines is of primary importance because the type of deposits can affect the correct functioning of the robot.

In order to design robot tools correctly is therefore essential to know in advance the geographic and geological features of the mine in order to carry out on-site tests.

How to cite: Tucci, E. and Ruggeri, R.: Robotics for raw material: the importance of data collection in the design of the appropriate equipments for exploring abandoned mines, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10484, https://doi.org/10.5194/egusphere-egu22-10484, 2022.

EGU22-11184 | Presentations | ERE4.4

Robotic mining: a new approach to geological modelling 

Hilco van Moerkerk and Paulina Dobrowolska

Within the EU funded Horizon 2020 project ROBOMINERS (www.robominers.eu) we were challenged to consider how autonomous robotic mining could be integrated with geological modelling. How would an autonomous robotic miner know where and whether an orebody is worth extracting? The orebody and all related geological aspects would need to be modelled in a comprehensible, self-containing format the robot can use directly.  To this end we envisioned a robot that would know where the orebody is, its important characteristics, and would have the ability to interpret the orebody in real-time and update its geological knowledge of the orebody as it excavates.

The modelled orebody could only be approximate at the robot scale (estimated at 1m maximum diameter!) as detailed information would be lacking. This led to re-evaluating existing geological modelling practices to see how they would fit within the robotic mining concept.

In our work we developed a novel approach to traditional geological modelling by combining three essential elements:

  • Replacement of blocks in block modelling with tetrahedra
  • Functional modelling framework to create model descriptors
  • Machine Learning

A tetrahedron is the most basic 3D element, similar to a triangle being the most basic 2D element to represent objects. Tetrahedra can be made to accurately reflect a boundary and are therefore always either inside or outside of that boundary. They are commonly used in Finite Element Methods (FEM) and have found their way into geophysics, geomechanics and flow modelling, but until now, not into geological modelling. Another major advantage is that a tetrahedral grid can be constructed at multi-resolution scales. However, it also means geological features need to be described in a way that allows them to be represented at those scales (e.g. mine scale versus robot scale).

One method to deal with these scale issues is to use a functional representation: representing geological features with (mathematical) functions. With functions, a value from that function at ANY point in 3D space can be retrieved to see if  that point is either inside or outside of a unit. Functions have been used under the Implicit Modelling (IM) banner. However, the functions can be also seen as classifiers between regions. Machine Learning’s (ML) core functionality is to provide is a mechanism for classifying data and estimate values or labels to unknown points. In our work, were therefore integrated high performance ML algorithms into IM.

With these three key elements we developed a system that can represent a complete 3D geological model in a consistent and ordered way by describing it rather than actually creating it. The model description can then create an optimized FEM model at any resolution when needed, even though the descriptor does not change. The ultimate aim is that the descriptors and functions will be used directly by the robot to optimize its path planning, without needing large data transfers.

How to cite: van Moerkerk, H. and Dobrowolska, P.: Robotic mining: a new approach to geological modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11184, https://doi.org/10.5194/egusphere-egu22-11184, 2022.

EGU22-11395 | Presentations | ERE4.4 | Highlight

Robot-miners for a new mining future 

Luís Lopes, Claudio Rossi, Balazs Bodo, Giorgia Stasi, Christian Burlet, Stephen Henley, Vítor Correia, Tobias Pinkse, Alicja Kot-Niewiadomska, Jussi Aaltonen, Michael Berner, Nelson Cristo, Éva Hartai, Gorazd Žibret, Janos Horvath, and Asko Ristolainen

A multi-disciplinary team – the ROBOMINERS consortium – is creating a robot-miner for the future exploitation of difficult to access deposits. The approach builds on using robotics-related capacities for the mining sector. In particular, the ROBOMINERS vision foresees the use of a modular and reconfigurable robot in a mining setting where activities are nearly invisible. Mining will be more socio-environmentally viable, thus contributing to a more safe and sustainable supply of mineral raw materials fostered by the EU Raw Materials policies. When compared to current mining methods, the ROBOMINERS approach aims at: no presence of people in the mine, less mining waste produced and mining infrastructure needed, less investment, the possibility to explore currently uneconomic resources and development of new underground small-sized mines.

In the past two years, work focused on studying and designing enabling technologies, robot components and capabilities. The next steps will include integration of different software and hardware components leading to the development of the first robotic prototype (December 2022). Critical aspects of previous studies included 1) biological inspiration, 2) perception and localisation tools, 3) robot's behaviour, navigation and control, 4) actuation methods, 5) modularity, 6) autonomy and resilience, and 7) the selective mining ability, including development of ore perception and specialized production tools. Knowledge and technology transfer from these sub-fields to the robot-miner concept were possible thanks to collaborative work developed by the different mining and robotics teams in the laboratory and online, even during the COVID-19 times.

At the same time, the vision of a new mining robotic "ecosystem" is being developed: 1) computer models and simulations, 2) data management and visualisation systems, 3) rock mechanical and geotechnical characterisation, 4) analysing ground/rock support methods, bulk transportation methods, backfilling types and mining methods, and 5) sketching  upstream and downstream mining industry analogues for the ROBOMINERS concept.

Merging of robotics and geoscientific know-how for the purpose of creating test environments (simulated and real), construction of scale models (actual and virtual), iterative development and testing key robotic functions, together with the creation of a pool of deposits that could become viable targets for future extraction, and economical studies, back up the implementation capacity of the technology.

Thanks to the integration of the previous mentioned aspects, the mining machine will be able to perform autonomous selective ore extraction. The prototype will be tested at targeted areas representatives, including abandoned and/or operating mines, small but high-grade mineral deposits, unexplored/explored non-economic occurrences and ultra-depth, not easily accessible environments. Possible current candidates for testing purposes include mines in Estonia, Slovenia or Belgium. The trials are scheduled for 2023 and will provide a first case for the operability of this new mining machine and concept.

ROBOMINERS aims at delivering a proof of concept for the feasibility of this technology line at Technology Readiness Level 4, being validated in the lab and in the test mine locations. With future-proof improvements to the technology (deriving from roadmapping) it could enable the EU to access mineral raw materials from domestic sources that are otherwise inaccessible or uneconomic.

How to cite: Lopes, L., Rossi, C., Bodo, B., Stasi, G., Burlet, C., Henley, S., Correia, V., Pinkse, T., Kot-Niewiadomska, A., Aaltonen, J., Berner, M., Cristo, N., Hartai, É., Žibret, G., Horvath, J., and Ristolainen, A.: Robot-miners for a new mining future, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11395, https://doi.org/10.5194/egusphere-egu22-11395, 2022.

EGU22-12566 | Presentations | ERE4.4

The ROBOMINERS mineralogical sensors: spectrometer prototypes for autonomous in-stream, in-slurry geochemical diagnostics. 

Christian Burlet, Giorgia Stasi, Tobias Pinkse, Laura Piho, and Asko Ristolainen

ROBOMINERS (Bio-Inspired, Modular and Reconfigurable Robot Miners, Grant Agreement No. 820971, http://www.robominers.eu) is an European project funded by the European Commission's Horizon 2020 Framework Programme. The project brings roboticists and geoscientists together to explore new mining and sensing technologies and demonstrate a small robot-miner prototype designed to exploit unconventional and uneconomical mineral deposits (technology readiness level 4 to 5). This approach could change the current mining paradigms dictated by larger existing machines, while reducing mining waste and environmental footprint (Lopez and al. 2020).

One of the key function of ROBOMINERS is the “selective real-time mining”, in other words the ability for the miner to choose an optimal progression path while mining in a particular orebody geometry (inspired by the petroleum industry geo-steering technique). This will be done by a continuous monitoring of the surrounding rock properties (hardness, abrasivity, electrochemistry, thermal conductivity, 3D electrical/induced polarization tomography), and by a “digestive mineralogy” unit, performing on-board mineralogical/geochemical diagnostics of the extracted material.

After an extensive review and tests on existing sensing techniques, the consortium selected a few sensing methods, based on the considered environment (underground gallery drilling, mud/slurry-filled environment with very limited to no visibility) and the opportunity to test proven techniques as well as original methods that can be distributed on and in the miner body.

Mineralogical sensor prototypes on ROBOMINERS are articulated on 3 techniques : multi/hyperspectral reflectance, UV fluorescence and Laser-induced breakdown spectrometry (LIBS). The first two techniques are well established and easily integratable on a robotic platform. ROBOMINERS will demonstrate how miniatirization/distribution of these sensors on and in the robot can yield fast diagnostics from the excavated material. LIBS is a very interesting atomic emission technique for real-time monitoring of slurries with fast multi-element detection and low detection limits, even on light elements. It has been already used as a competitive approach to monitor slurries using flow cells in mining (Khajehzadeh et al., 2017) and inside molten metals in metallurgy applications (Moreau et al., 2018). LIBS typically achieve fast and sensitive analysis in a few micro- to milli- seconds. While true quantitative measurements remain a challenge outside a controlled lab environment, qualitative and semi-quantitative measurements are possible and is very relevant for ROBOMINERS selective mining application.

The work presented here deals with the conceptualization of the spectrometer suite. Tested slurry analogs include mixtures of lead-zinc sulfides, copper cobalt oxides, phosphorites and oil shales. Once an fixed instrumental setup is selected, the next development steps include retrofitting for testing in an industrial scale slurry circulation system at the K-UTEC facilities (Sondershausen, Germany) and, after validation of all components, integration on the ROBOMINERS prototype for the field demonstrations planned in 2023.

References.

Lopes, B. Bodo, C. Rossi, S. Henley, G. Žibret, A. Kot-Niewiadomska, V. Correia, Advances in Geosciences, Volume 54, 2020, 99–108

Khajehzadeh, O. Haavisto, L. Koresaar, , Minerals Engineering, Volume 113, 2017, pp 83-94

Moreau, A. Hamel, P. Bouchard, and M. Sabsabi, , CIM Journal, Volume 9, No. 2, 2018

How to cite: Burlet, C., Stasi, G., Pinkse, T., Piho, L., and Ristolainen, A.: The ROBOMINERS mineralogical sensors: spectrometer prototypes for autonomous in-stream, in-slurry geochemical diagnostics., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12566, https://doi.org/10.5194/egusphere-egu22-12566, 2022.

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