NP – Nonlinear Processes in Geosciences

NP2.1 – Nonlinear Multiscale and Stochastic Dynamics of the Earth System

Recently, there has been much interest in issuing subseasonal to seasonal (S2S) forecasts, although their skill is often debated. In addition to large systematic errors, ensemble systems are often overconfident, i.e. have incorrect information about the uncertainty of a particular forecast. Stochastic parameterization schemes are used routinely to remedy the problem of overconfidence, but also have the potential to reduce systematic model errors. 

Here, we study the impact of adding a stochastic parameterization scheme in coupled simulations with the climate model CESM.  Physical processes associated with S2S-predictability, like the Madden-Julian  Oscillation (MJO) and Northern Hemispheric blocking are analyzed. In the simulations with a stochastic parameterization scheme, the northward propagation of the MJO is captured better, leading to an improved MJO lifecycle. The impact on other atmospheric fields like precipitation and winds will be discussed. 

How to cite: Berner, J.: Benefits of stochastic parameterizations in subseasonal to seasonal (S2S) forecasts , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16321,, 2021.

EGU21-14142 | vPICO presentations | NP2.1

Data-driven stochastic model discovery of organized clouds dynamics

Mickael D. Chekroun, Tom Dror, Orit Altaratz, and Ilan Koren

The discovery of dynamical equations governing time-evolving observations issued from a complex dynamical system requires a statistical formulation, since information concerning neglected variables or unobserved degrees of freedom is necessarily incomplete. At the same time, an equation that is closed within a small number of observables is often obtained only by approximations. Thus, the relevance of approximations must be understood before any attempt to derive a closed set of equations. This is where closure formalisms are of usefulness and the corresponding mathematical structures serve as a guide for knowing what to approximate. Many such formalisms are available from turbulence theory, quantum field theories, to statistical physics. 

Observables of interest often include response functions, spectra of fluctuations, or low-order moments, etc. These quantities correspond to moments of the full probability density function (PDF),  the mother of all system's statistics but itself beyond the reach of standard closure theories, except in special cases. Yet, to have, for a given choice of observables,  a (good) class of closure models able to produce out-of-sample reliable occurrences, is of prime importance. When derived on a firm basis, such closure models may indeed allow for analyzing in greater details certain features of a given phenomenon for which available data are limited, by e.g. drawing a large ensemble of statistical emulations of this phenomenon, from the closure model.  

This is the goal that will be pursued here for a special but common class of clouds, namely continental shallow cumulus (Cu) that can be found from low to mid/high latitudes, across a wide range of scales, and that play a growing role in the Earth's radiative budget. These clouds typically organize through a variety of patterns such as cloud streets, clusters, or mesoscale arcs. Based on observables suitably extracted from high-resolution satellite observations, it will be shown that the efficient learning of hidden, stochastic, variables along with their interaction laws with the observed variables is key for the derivation of relevant stochastic data-driven models. To do so, our approach will rely on the Mori-Zwanzig closure theory to guide the search of the constitutive elements, on one hand, while their learning will exploit recent advances in data-driven stochastic modeling techniques, on the other. 

As a byproduct, dynamical equations involving a few variables are learned from high-resolution satellite observations of continental shallow Cu. These equations will be shown to take the form of differential equations that include lagged effects, and are driven by a spatially correlated white noise. It will be finally shown that the combined effects of these terms allow to generate easily statistical ensembles of shallow Cu that exhibit a wide range of spatio-temporal variability while displaying consistency with the shallow Cu's organizational and multiscale features, from observations. Based on such large ensembles, new physical insights are attainable and their interpretation will be discussed.  This work is supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program [Grant Agreement No. 810370].

How to cite: Chekroun, M. D., Dror, T., Altaratz, O., and Koren, I.: Data-driven stochastic model discovery of organized clouds dynamics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14142,, 2021.

Stochastic subgrid-scale parametrizations aim to incorporate effects of unresolved processes in an effective model by sampling from a distribution usually described in terms of resolved modes. This is an active research area in climate, weather and ocean science where processes evolved in a wide range of spatial and temporal scales. In this study, we evaluate the performance of conditional generative adversarial network (GAN) in parametrizing subgrid-scale effects in a finite-difference discretization of stochastically forced Burgers equation. We define resolved modes as local spatial averages and deviations from these averages are the unresolved degrees of freedom. We train Wesserstein GAN (WGAN) conditioned on the resolved variables to learn the distribution of subgrid flux tendencies for resolved modes and, thus, represent the effect of unresolved scales. Resulting WGAN is then used in an effective model to reproduce the statistical features of resolved modes. We demonstrate that various stationary statistical quantities such as spectrum, moments, autocorrelation, etc. are well approximated by this effective model.

How to cite: Timofeyev, I. and Alcala, J.: Subgrid-scale parametrization of unresolved scales in forced Burgers equation using Generative Adversarial Networks (GAN), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6910,, 2021.

EGU21-14346 | vPICO presentations | NP2.1

Understanding intrinsic ocean variability by suppressing regional stochastic variability

Mao-Lin Shen, Noel Keenlyside, and Ping-Gin Chiu

Intrinsic ocean variability is essential for climate prediction because it is less sensitive to stochastic process, but it is very difficult to be identified due to internal climate variability. Here we use regional interactive ensemble applied on ocean-atmosphere interface (RIE-OA) to suppress atmosphere stochastic variability and to reveal intrinsic variability as well as to understand climate dynamic across multiple timescales. Five atmosphere general circulation models (AGCM) are coupled to an ocean general circulation model (OGCM) over the North Atlantic basin (20oN to Denmark Strait and Greenland-Scotland ridge). The OGCM interacts with fluxes from a selected AGCM globally except over the North Atlantic basin where the OGCM interacts with the ensemble averaged fluxes from the five AGCMs. The five AGCMs, on the other hand, feel the same ocean states. Hence, the atmosphere stochastic variability impacting the ocean is one-fifth weaker than stand-alone configuration (control case). This leads to reduction of the local climate variability, such as Atlantic Multidecadal Variability, but should not reduce intrinsic variability. Comparing control cases and RIE-OA case, we found the intrinsic ocean variability, a narrow-banded low-frequency (about 8 to 20 years) signal over the North Atlantic Subtropical Gyre, is not influenced by the weakened stochastic variability. More details will be discussed.

How to cite: Shen, M.-L., Keenlyside, N., and Chiu, P.-G.: Understanding intrinsic ocean variability by suppressing regional stochastic variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14346,, 2021.

EGU21-10194 | vPICO presentations | NP2.1

A comparison of data-driven approaches to build low-dimensional ocean models

Niraj Agarwal, Dmitri Kondrashov, Peter Dueben, Eugene Ryzhov, and Pavel Berloff

I will present a comprehensive inter-comparison of linear regression, stochastic and deep-learning-based models for reduced-order statistical modelling of the simplified ocean circulation. The reference dataset is provided by the top 150 empirical orthogonal functions (EOFs) and principal components (PCs) of an idealized, eddy-resolving, double-gyre ocean model. Our goal is to have a systematic and comprehensive assessment of the skills, costs and complexities of all the models considered.

The model based on linear regression is considered as a baseline. Additionally, we investigate stochastic models (linear regression plus additive-noise and a multi-level approach), deep-learning models (a feed-forward Artificial Neural Network (ANN), a Long Short Term Memory (LSTM)), and deep-learning augmented linear regression models (also called hybrid models). We also explored stochastically improved deep learning methods by adding spatially correlated white noise in the deep learning models to account for the residuals and left out variance in the discarded PCs. The assessment metrics considered are climatology, variance, RMSE, instantaneous correlation coefficients, frequency map, prediction horizon, and computational costs for training and predictions.  

Until now, we found that the hybrid LSTM models perform the best, followed by the multi-level linear stochastic model and multiplicative white noise model. Additionally, hybrid models found to perform better when augmented by spatially correlated white noise.  This suggests that an amalgam of physics, memory effects, and stochasticity provides the best strategy for low-order representation of oceanic process. However, LSTM was also found to be most expensive to train and forecast amongst all. Skills of simple stochastic models are similar to those of the linear regression model but superior to those of the pure deep learning models, as evidenced by relatively better frequency maps, infinite prediction horizon, and low running cost.

Overall, our analysis promotes multi-level stochastic methods, with memory effects, and stochastic hybrid methods for low-dimensional ocean models as a more practical option when compared to pure deep-learning solutions as they are more accurate, stable, and low-cost. Furthermore, this is an ongoing research project and more updated results will be discussed at the time of presentation.

How to cite: Agarwal, N., Kondrashov, D., Dueben, P., Ryzhov, E., and Berloff, P.: A comparison of data-driven approaches to build low-dimensional ocean models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10194,, 2021.

EGU21-7722 | vPICO presentations | NP2.1

Coexistence of equatorial waves & turbulence in moist shallow water simulations 

Josef Schröttle, Dl Suhas, Nili Harnik, and Jai Sukhatme

Moist equatorial waves, responsible for a large fraction of synoptic and intraseasonal tropical variability, are visible in satellite observations of cloud top temperature and outgoing longwave radiation, with familiar dispersion relations appearing in space time spectra once a background red-like spectrum is removed (Kiladis et al., 2009).

Studies have suggested that the large-scale planetary waves in the equatorial region can be excited by smaller scale gravity waves (Yang & Ingersoll, 2013), baroclinic waves from the extratropics (Wedi & Smolarkiewicz, 2010), or localized synoptic scale heating (Gill 1980, 1982). In this study we examine the possibility that a continuous forcing of anomalies at the mesoscale can, through a turbulent upscale cascade, excites these waves, thus explaining both: the peaks along dispersion relations and the background red spectrum.

Our underlying assumption is that within the tropics (excluding wave forcing from the extratropics), a prerequisite to form coherent heating of ≈1000 km zonal length on an aqua planet is self-aggregation. Over the last two decades, self-aggregation has been studied over a wide range of scenarios up to the atmospheric mesoscale. In this study we examine the aggregation from the mesoscale up to the planetary scale, by applying mesoscale stochastic forcing in idealized spherical shallow water model. In particular, we examine the dependence of the large scale spectra on the field being stochastically forced, and on the existence of moisture.

We find that indeed, continuous stochastic forcing at the mesoscale can excite two-dimensional turbulence and linear tropical wave modes. When vorticity or moisture are forced in the simulations at wavenumber 100, a classical -5/3 slope of the eddy kinetic energy spectrum forms in an upscale energy cascade up to the planetary scale. Furthermore, equatorial waves emerge and Wheeler-Kiladis plots reveal a rich temporal and spatial structure of Rossby, Kelvin, Yanai, and Inertial Gravity waves. On the other hand, stochastic forcing of the divergence, or height fields only leads to a turbulent field when applied at planetary scales. Some explanations for this strong dependence on the type of forcing, and the role of moisture, will be discussed. 

How to cite: Schröttle, J., Suhas, D., Harnik, N., and Sukhatme, J.: Coexistence of equatorial waves & turbulence in moist shallow water simulations , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7722,, 2021.

EGU21-135 | vPICO presentations | NP2.1

On the multiscale fractal features of a low-order coupled ocean-atmosphere model in comparison with reanalysis data

Tommaso Alberti, Reik Donner, and Stéphane Vannitsem

Atmosphere and ocean dynamics display many complex features and are characterized by a wide variety of processes and couplings across different timescales. Here we use Multivariate Empirical Mode Decomposition (MEMD; Rehman and Mandic, 2010) to investigate the multivariate and multiscale properties of a low-order model of the ocean-atmosphere coupled dynamics (Vannitsem, 2017). The MEMD allows us to decompose the original data into a series of oscillating patterns with time-dependent amplitude and phase by exploiting the local features of data and without any a priori assumptions on the decomposition basis. Moreover, each oscillating pattern, usually named Multivariate Intrinsic Mode Function (MIMF), can be used as a source of local (in terms of scale) fluctuations and information. This information allows us to derive multiscale measures when looking at the behavior of the generalized fractal dimensions at different scales (Hentschel and Procaccia, 1983) that can be seen as a sort of multivariate and multiscale generalized fractal dimensions (Alberti et al., 2020). With these two approaches, we demonstrate that the coupled ocean-atmosphere dynamics presents a rich variety of common features, although with a different nature of the fractal properties between the ocean and the atmosphere at different timescales. The MEMD results allow us to capture the main dynamics of the phase-space trajectory that can be used for reconstructing the skeleton of the phase-space dynamics, while the evaluation of the fractal dimensions at different timescales characterize the intrinsic complexity of oscillating patterns that can be related to the attractor properties. Our results support the interpretation of the coupled ocean-atmosphere dynamics as well as the investigation of general deterministic-chaotic dissipative dynamical systems in terms of invariant manifolds, bifurcations, as well as (strange) attractors in their phase-space, whose geometric and topological properties are a reflection of the dynamical regimes of the system at different scales. We compare the results obtained for the low-order dynamical model with those derived from the reanalysis data and demonstrate that a similar scale-dependent behavior is found, thus also confirming the suitability of the proposed system to model the ocean-atmosphere dynamics at different timescales and to describe topological and geometrical features of its phase-space.



Alberti, T., Consolini, G., Ditlevsen, P. D., Donner, R. V., Quattrociocchi, V. (2020). Multiscale measures of phase-space trajectories. Chaos 30, 123116.

Alberti, T., Donner, R. V., and Vannitsem, S. (2021). Multiscale fractal dimension analysis of a reduced order model of coupled ocean-atmosphere dynamics. Earth Syst. Dynam. Discuss. [preprint],, in review.

Hentschel, H. G. E., Procaccia, I. (1983). The infinite number of generalized dimensions of fractals and strange attractors. Physica D 8, 435–444.

Rehman, N., Mandic, D. P. (2010). Multivariate empirical mode decomposition. Proceedings of the Royal Society A, 466, 1291–1302.

Vannitsem S., Predictability of large-scale atmospheric motions: Lyapunov exponents and error dynamics, Chaos, 27, 032101, 2017. 

How to cite: Alberti, T., Donner, R., and Vannitsem, S.: On the multiscale fractal features of a low-order coupled ocean-atmosphere model in comparison with reanalysis data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-135,, 2021.

EGU21-238 | vPICO presentations | NP2.1

Classification of time series of temperature variations from climatically homogeneous regions using Hurst Space Analysis

Suzana Blesic, Darko Sarvan, Milica Tosic, and Marko Borovinic

We used the Hurst Space Analysis (HSA), a technique that we recently developed to cluster or differentiate records from an arbitrary complex system based on the presence and influence of cycles in their statistical functions, to classify climatic data from climatically homogeneous regions according to their long-term persistent (LTP) character. For our analysis we selected four types of HadCRUT4 cells of temperature records over regions homogeneous in both climate and topography, which are sufficiently populated with ground observational stations. These cells bound: Pannonian and West Siberian plains, Rocky Mountains and Himalayas mountainous regions, Arctic and sub-Arctic climates of Island and Alaska, and Gobi and Sahara deserts.

It was shown for LTP records across different complex systems that their statistical functions are rarely, as in theory, and due to their power-law dynamics, ideal linear functions on log-log graphs of time scale dependence. Instead, they frequently exhibit existence of transient crossovers in behavior, signs of trends that arise as effects of periodic or aperiodic cycles. HSA was developed so to use methods of scaling analysis – the time dependent Detrended Moving Average (tdDMA) algorithm and Wavelet Transform spectral analysis (WTS) – to analyse these cycles in data. In HSA we defined a space of p-vectors hts (that we dubbed the Hurst space) that represent record ts in any dataset, which are populated by tdDMA scaling exponents α calculated on subsets of time scale windows of time series ts that bound cyclic peaks in their WTS. In order to be able to quantify any such time series ts with a single number, we projected their relative unit vectors sts = (hts – m) / (∑i=1n (hits - mi)2)1/2  (with mi = 1/n ∑ts=1n hits) onto a unit vector e of an assigned preferred direction in the Hurst space. The definition of the ’preferred’ direction depends on the characteristic behavior one wants to investigate with HSA - projection of unit vectors sts of any record  with a ’preferred’ behavior onto the unit vector e is then always positive.

By using HSA we were able to cluster records from our selected climatically homogeneous regions according to the 'preferred' characteristic that those do not 'belong to the ocean'. We further extended HSA constructed from our dataset to group teleconnection indices that may influence their climate dynamics. In this way our results suggested that there probably exists a necessity to examine cycles in climate records as important elements of natural variability.

How to cite: Blesic, S., Sarvan, D., Tosic, M., and Borovinic, M.: Classification of time series of temperature variations from climatically homogeneous regions using Hurst Space Analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-238,, 2021.

EGU21-3628 | vPICO presentations | NP2.1

On memory and non-memory parts of surface air temperatures over China: can they be simulated by decadal hindcast experiments in CMIP5?

Feilin Xiong, Naiming Yuan, Xiaoyan Ma, Zhenghui Lu, and Jinhui Gao

It has been well recognized that, for most climatic records, their current states are influenced by both past conditions and current dynamical excitations. However, how to properly use this idea to improve the climate predictive skills, is still an open question. In this study, we evaluated the decadal hindcast experiments of 11 models (participating in phase 5 of the Coupled Model Intercomparison Project, CMIP5) in simulating the effects of past conditions (memory part, M(t)) and the current dynamical excitations (non-memory part, ε(t)). Poor skills in simulating the memory part of surface air temperatures (SAT) are found in all the considered models. Over most regions of China, the CMIP5 models significantly overestimated the long-term memory (LTM) of SAT. While in the southwest, the LTM was significantly underestimated. After removing the biased memory part from the simulations using fractional integral statistical model (FISM), the remaining non-memory part, however, was found reasonably simulated in the multi-model means. On annual scale, there were high correlations between the simulated and the observed ε(t) over most regions of the country, and for most cases they had the same sign. These findings indicated that the current errors of dynamical models may be partly due to the unrealistic simulations of the impacts from the past. To improve predictive skills, a new strategy was thus suggested. As FISM is capable of extracting M(t) quantitatively, by combining FISM with dynamical models (which may produce reasonable estimations of ε(t)), improved climate predictions with the effects of past conditions properly considered may become possible.

How to cite: Xiong, F., Yuan, N., Ma, X., Lu, Z., and Gao, J.: On memory and non-memory parts of surface air temperatures over China: can they be simulated by decadal hindcast experiments in CMIP5?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3628,, 2021.

EGU21-3866 | vPICO presentations | NP2.1

Identifying the sources of seasonal potential predictability using Fractional Integral Statistical Model with a Variance Decomposition Method

Da Nian, Naiming Yuan, Kairan Ying, Ge Liu, Zuntao Fu, Yanjun Qi, and Christian L. E. Franzke

It is well recognized that climate predictability has three origins: (i)climate memory (inertia of the climate system) that accumulated from the historical conditions, (ii) responses to external forcings, and (iii) dynamical interactions of multiple processes in the climate system. However, how to systematically identify these predictable sources is still an open question. Here, we combine a recently developed Fractional Integral Statistical Model (FISM) with a Variance Decomposition Method (VDM), to systematically estimate the potential sources of predictability. With FISM, one can extract the memory component from the considered variable. For the residual parts, VDM can then be applied to extract the slow varying covariance matrix, which contains signals related to external forcings and dynamical interactions of multiple processes in climate. To show the improvement of our methodology, we have tested it on realistic data, using monthly temperature observations over China during 1960-2017.  It is found that the climate memory component contributes a large portion of the seasonal predictability in the temperature records. Our results offer the potential for more skillful seasonal predictions compared with the results obtained using FISM or VDM alone.

How to cite: Nian, D., Yuan, N., Ying, K., Liu, G., Fu, Z., Qi, Y., and Franzke, C. L. E.: Identifying the sources of seasonal potential predictability using Fractional Integral Statistical Model with a Variance Decomposition Method, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3866,, 2021.

The soil temperature (ST) is closely related to the surface air temperature (AT), but their coupling may be affected by other factors. In this study, by using linear analysis and nonlinear causality analysis—convergent cross mapping (CCM) and its time-lagged version (time-lagged CCM), significant effects of the AT on the underlying ST were found, and the time taken to propagate downward to 320 cm can be up to 10 months. Besides the AT, the ST is also affected by memory effects—namely, its prior thermal conditions. At deeper depth (i.e., 320 cm), the effects of the AT from a particular season may be exceeded by the soil memory effects from the last season. At shallower layers (i.e., < 80 cm), the effects of the AT may be blocked by the snow cover, resulting in a poorly synchronous correlation between the AT and the ST. In northeastern China, this snow cover blockage mainly occurs in winter and then vanishes in the subsequent spring. Due to the thermal insulation effect of the snow cover, the winter ST at layers above 80 cm in northeastern China were found to continue to increase even during the recent global warming hiatus period. These findings may be instructive for better understanding ST variations, as well as land−atmosphere interactions.

How to cite: Zhang, H., Yuan, N., Ma, Z., and Huang, Y.: Understanding the Soil Temperature Variability at Different Depths: Effects of Surface Air Temperature, Snow Cover, and the Soil Memory, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3943,, 2021.

EGU21-187 | vPICO presentations | NP2.1

Multivariate Estimations of Equilibrium Climate Sensitivity from Short Transient Warming Simulations

Robbin Bastiaansen, Henk Dijkstra, and Anna von der Heydt

One of the most used metrics to gauge the effects of climate change is the equilibrium climate sensitivity, defined as the long-term (equilibrium) temperature increase resulting from instantaneous doubling of atmospheric CO2. Since global climate models cannot be fully equilibrated in practice, extrapolation techniques are used to estimate the equilibrium state from transient warming simulations. Because of the abundance of climate feedbacks – spanning a wide range of temporal scales – it is hard to extract long-term behaviour from short-time series; predominantly used techniques are only capable of detecting the single most dominant eigenmode, thus hampering their ability to give accurate long-term estimates. Here, we present an extension to those methods by incorporating data from multiple observables in a multi-component linear regression model. This way, not only the dominant but also the next-dominant eigenmodes of the climate system are captured, leading to better long-term estimates from short, non-equilibrated time series.

How to cite: Bastiaansen, R., Dijkstra, H., and von der Heydt, A.: Multivariate Estimations of Equilibrium Climate Sensitivity from Short Transient Warming Simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-187,, 2021.

EGU21-2221 | vPICO presentations | NP2.1

Predicting Climate Change through Response Operators in a Coupled General Circulation Model

Valerio Lembo, Valerio Lucarini, and Francesco Ragone

How to cite: Lembo, V., Lucarini, V., and Ragone, F.: Predicting Climate Change through Response Operators in a Coupled General Circulation Model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2221,, 2021.

EGU21-16323 | vPICO presentations | NP2.1

Forecasting rare stratospheric transitions using short simulations

Justin Finkel, Robert J. Webber, Edwin P. Gerber, Dorian S. Abbot, and Jonathan Weare

Nonlinear atmospheric dynamics produce rare events that are hard to predict and attribute due to many interacting degrees of freedom. A sudden stratospheric warming is a spectacular example in which the winter polar vortex in the stratosphere rapidly breaks down, inducing a shift in midlatitude tropospheric weather patterns that persist for up to 2-3 months.  In principle, lengthy numerical simulations can be used to predict and understand these rare transitions.  For complex models, however, the cost of the direct numerical simulation approach is often prohibitive.  We describe an alternative approach which in principle only requires relatively short duration computer simulations of the system.  Applying this methodology to a classical idealized stratospheric model with stochastic forcing, we compute optimal forecasts of sudden warming events and quantify the limits of predictability.  Statistical analysis relates these optimal forecasts to a small number of easy-to-interpret physical variables.Remarkably, we are able to estimate these quantities using a data set of simulations much shorter than the return time of the warming event.

How to cite: Finkel, J., Webber, R. J., Gerber, E. P., Abbot, D. S., and Weare, J.: Forecasting rare stratospheric transitions using short simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16323,, 2021.

In this work, we consider turbulence closures of LES (Large Eddy Simulation) type for classical decaying 2D turbulence in a priori and a posteriori experiments using explicit filtering approach. According to Germano 1986 decomposition, full subfilter stress for Gaussian filter is decomposed into three Galilean-invariant parts: Leonard, Cross and Reynolds stresses. By analysing spectral transfer of energy and enstrophy, we show that Leonard stress redistributes resolved energy toward large scales and dissipates substantial part of enstrophy, while Cross stress provides an additional enstrophy dissipation at subfilter scales and Reynolds stress predominantly injects energy into middle scales (i.e., Kinetic Energy Backscatter). Substantial part of enstrophy dissipation is located on subfilter scales, and it should be accounted for by choosing base filter wide enough compared to mesh step of LES model. Otherwise, significant fraction of enstrophy dissipation will correspond to subgrid scale stress, which is less universal and harder to approximate. As a result of a priori analysis, we propose LES closure consisting of three parts: SSM (Scale Similarity Model), which is equivalent to Leonard stress, biharmonic Smagorinsky damping as a Cross stress counterpart and ADM (Approximate Deconvolution Model) approximation for Reynolds stress ("backscatter"). The proposed model have two free parameters: Smagorinsky constant and amplitude of the backscatter. These parameters are estimated in a posteriori experiments utilizing dynamic approach and energy-enstrophy balance equation, correspondingly. The proposed model have the following distinctive features: it reproduces energy and enstrophy transfer spectra in accordance to the individual components of the subfilter forces, "reproduces" base filter and reproduces energy growth in accordance to the filtered DNS (Direct Numerical Simulation) solution.

The work was supported by the Russian Foundation for Basic Research (projects 19-35-90023, 18-05-60184) and Moscow Center for Fundamental and Applied Mathematics (agreement with the Ministry of Education and Science of the Russian Federation No. 075-15-2019-1624).

How to cite: Perezhogin, P. and Glazunov, A.: A priori and a posteriori analysis in Large eddy simulation of the two-dimensional decaying turbulence using Explicit filtering approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2382,, 2021.

EGU21-9332 | vPICO presentations | NP2.1

Using reflection seismic method to estimate the fluid dynamics parameters related to wakes

Wenhao Fan, Haibin Song, Kun Zhang, Yi Gong, Shun Yang, and Di Wu

In this study, when using reflection seismic data to study the wakes of the Batan Islands, a method for estimating the fluid dynamics parameters such as the relative vorticity (relative Rossby number) and the relative potential vorticity is proposed. Although the relative Rossby number estimation method proposed in this study cannot guarantee absolute accuracy in the calculation value, this method is more accurate in describing the positive and negative vorticity distribution for the wakes, and the resolution of the positive and negative vorticity distribution described by this method is higher than the result of the reanalysis data. For the wakes developed in the Batan Islands, the reflection events in the wake development area have the larger inclination than the reflection events in the western Pacific water distribution area. It is also found that the negative vorticity wakes are mainly distributed on the west side of the island/ridge, and the positive vorticity wakes are mainly distributed on the east side of the island/ridge. This is consistent with the understanding of previous wakes simulations. The strong vorticity values in the study area are mainly distributed at depths above 300m, and the maximum impact depth of wakes can reach 600m. At the downstream position of the wake on the survey line 7, it can be seen that the bottom boundary layer has separated, and there is the negative vorticity wakes on the west side intruding into the positive vorticity wakes on the east side , which is presumed to be caused by the disturbance of the small anticyclone existing near the Batan Islands. For the survey line 7, the negative potential vorticity is mainly distributed on the west side of the island/ridge, and the influence range can reach the sea depth of 600m. In the negative potential vorticity region, there is strong energy dissipation and vertical shear. In this study, we don’t find the existence of submesoscale coherent vortices on the survey line 7, but find the reflection structure similar to filaments on the seismic section. Combined with the analysis of the balanced Richardson number angle of survey line 7, we speculate that the wake in the negative potential vorticity distribution area has the characteristics of symmetrical instability, and the symmetrical instability may destroy the process of filaments forming submesoscale coherent vortices.

How to cite: Fan, W., Song, H., Zhang, K., Gong, Y., Yang, S., and Wu, D.: Using reflection seismic method to estimate the fluid dynamics parameters related to wakes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9332,, 2021.

Apart from its known impact to variations in the Earth’s length-of-day (LOD) variations, the role of long-period tidal forcing cycles in geophysical behaviours has remained elusive. To explore this further, tidal forcing is considered as a causative mechanisms to the following cyclic processes: El Niño Southern Oscillation (ENSO), Quasi-Biennial Oscillation (QBO), and the Chandler wobble. Annualized impulse reponse formulations and nonlinear solutions to Navier-Stokes-based Laplace's Tidal Equations  are required to make the connection to the observed patterns as the underlying periods are not strictly commensurate in relation to harmonics of the tidal cycles.  If equatorial climate phenomena such as QBO and ENSO can be explained as deterministic processes then the behavior that may be predictable. This paper suggests that QBO, ENSO, and the Chandler wobble may share a common origin of lunar and solar tidal forcing, but with differences arising due to global symmetry considerations. Through analytical approximations of nonlinear fluid dynamics and detailed time-series analysis, matching quantitative models of these behaviors can be shown.

How to cite: Pukite, P.: Nonlinear long-period tidal forcing with application to ENSO, QBO, and Chandler wobble, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10515,, 2021.

EGU21-13712 | vPICO presentations | NP2.1

The role of small wetlands for resilient ecological networks in a wetlandscape 

Bin Kim and Jeryang Park

Wetlands, affected by the hydro-climatic condition and human activities, are key elements in providing valuable ecosystem services for ecology, environment, and human. Wetlands can exist in various states (e.g., area, volume, depth, etc.) driven by both natural and human forcing, and are often distributed in a wetlandscape. In these specific landscapes, wetlands (node) and dispersal path (link) of inhabiting species organize ecological networks. Here, we generated the three ecological networks with three dispersal models (threshold distance, exponential kernel, and heavy-tailed dispersal model) and analyzed network characteristics (degree, efficiency and clustering coefficient) associated with the seasonal change of hydro-climatic condition on wetland hydrology. To identify the role of small wetlands, we analyzed two different scenarios in which the sum of wetland areas are similar but their area distributions are distinct. In the first scenario, most of the small wetlands are hydrologically disappeared while the second scenario maintains the small wetlands with a shrunk area of large wetlands. When the area of large wetlands was reduced, a slight decrease in the values of network metrics was observed due to an increase in distances between wetlands. On the other hand, when a number of small wetlands were hydrologically disappeared, all the metric values were significantly decreased compared to the network in which all wetlands were hydrologically maintained. Especially, when the disappeared wetlands were not recovered even after rainfall, possibly due to long-term dehydration of supporting soil, the network characteristics also did not recover even if the total area of wetlands were recovered. However, when the dried small wetlands were hydrologically recovered, the network characteristics also recovered rapidly. Based on our observation, we confirmed that the small wetlands, despite their extremely low areal portion in the entire wetlandscape, play a key role in maintaining the ecological network resilience. Our findings can be used for a decision-making process for wetland conservation and restoration by reflecting the functional importance of small wetlands with physical characteristics requirements such as wetland areas.

How to cite: Kim, B. and Park, J.: The role of small wetlands for resilient ecological networks in a wetlandscape , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13712,, 2021.

NP2.2 – Extremes in geophysical sciences: drivers, methods and impacts quantification

Investigating the synchrony and interdependency of heavy rainfall occurrences across and between different tropical regions offers a new perspective on the underlying physical mechanisms. In this context, studies utilizing functional network representations have recently contributed to significant advances in the understanding and prediction of extreme weather events. Here, we systematically contrast previous results on spatiotemporal extreme precipitation patterns in three key monsoon regions (India, South America and East Asia) based on the concept of event synchronization (ES) with corresponding patterns obtained using the closely related event coincidence analysis (ECA) approach. Our findings demonstrate that an additional window size parameter of ECA not involved in ES allows for a more detailed analysis of the formation and propagation processes associated with heavy precipitation events. While the resulting network connectivity patterns based on both approaches closely resemble each other for the case of the South American monsoon system and the Indian summer monsoon, there exist subtle differences that carry climatologically relevant information. We further exploit the advanced potentials provided by ECA for studying in greater detail the spatial organization of East Asian summer monsoon (EASM) related heavy precipitation across the relevant season in a time-dependent fashion. Our results show that the formation of the Baiu front as a main feature of the EASM is accompanied by a double-band of synchronous heavy rainfall with two spatially dislocated centers north and south of the front. Although these bands are closely related to low- and high-level winds which are commonly assumed to be independent of each other, it is rather their mutual interconnectivity that changes during the different phases of the EASM season in a characteristic way. The thus obtained insights could provide relevant information for improving existing forecasting strategies for monsoon onset and strength.



Odenweller, R.V. Donner: Disentangling synchrony from serial dependency in paired-event time series. Physical Review E, 101(5), 052213 (2020)

Wolf, J. Bauer, N. Boers, R.V. Donner: Event synchrony measures for functional climate network analysis: A case study on South American rainfall dynamics. Chaos, 30(3), 033102 (2020)

Wolf, U. Öztürk, K. Cheung, R.V. Donner: Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season. Earth System Dynamics Discussions. doi:10.5194/esd-2020-69 (2020)

Wolf, R.V. Donner: Spatial organization of connectivity in functional climate networks describing event synchrony of heavy precipitation. European Physical Journal Special Topics (in review)

How to cite: Donner, R. and Wolf, F.: Event synchrony based complex network analysis of heavy precipitation in different monsoon regions revealing dynamical patterns of extreme event formation and propagation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13164,, 2021.

EGU21-3984 | vPICO presentations | NP2.2 | Highlight

A complex network analysis of extreme cyclones in the Arctic

Dörthe Handorf, Ozan Sahin, Annette Rinke, and Jürgen Kurths

Under the rapid and amplified warming of the Arctic, changes in the occurrence of Arctic weather and climate extremes are evident which have substantial cryospheric and biophysical impacts like floods, droughts, coastal erosion or wildfires. Furthermore, these changes in weather and climate extremes have the potential to further amplify Arctic warming. 
Here we study extreme cyclone events in the Arctic, which often occur during winter and are associated with extreme warming events that are caused by cyclone-related heat and moisture transport into the Arctic. In that way Arctic extreme cyclones have the potential to retard sea-ice growth in autumn and winter or to initiate an earlier melt-season onset. 
To get a better understanding of these extreme cyclones and their occurrences in the Arctic, it is important to reveal the related atmospheric teleconnection patterns and understand their underlying mechanisms. In this study, the methodology of complex networks is used to identify teleconnections associated with extreme cyclones events (ECE) over Spitzbergen. We have chosen Spitzbergen, representative for the Arctic North Atlantic region which is a hot spot of Arctic climate change showing also significant recent changes in the occurrence of extreme cyclone events. 
Complex climate networks have been successfully applied in the analysis of climate teleconnections during the last decade. To analyze time series of unevenly distributed extreme events, event synchronization (ES) networks are appropriate. Using this framework, we analyze the spatial patterns of significant synchronization between extreme cyclone events over the Spitzbergen area and extreme events in sea-level pressure (SLP) in the rest of the Northern hemisphere for the extended winter season from November to March. Based on the SLP fields from the newest atmospheric reanalysis ERA5, we constructed the ES networks over the time period 1979-2019.
The spatial features of the complex network topology like Eigenvector centrality, betweenness centrality and network divergence are determined and their general relation to storm tracks, jet streams and waveguides position is discussed. Link bundles in the maps of statistically significant links of ECEs over Spitzbergen with the rest of the Northern Hemisphere have revealed two classes of teleconnections: Class 1 comprises links from various regions of the Northern hemisphere to Spitzbergen, class 2 comprises links from Spitzbergen to various regions of the Northern hemisphere. For each class three specific teleconnections have been determined. By means of composite analysis, the corresponding atmospheric conditions are characterized.
As representative of class 1, the teleconnection between extreme events in SLP over the subtropical West Pacific and delayed ECEs at Spitzbergen is investigated. The corresponding lead-lag analysis of atmospheric fields of SLP, geopotential height fields and meridional wind fields suggests that the class 1 teleconnections are caused by tropical forcing of poleward emanating Rossby wave trains. As representative of class 2, the teleconnection between ECEs at Spitzbergen and delayed extreme events in SLP over Northwest Russia is analyzed. The corresponding lead-lag analysis of atmospheric fields of SLP and geopotential height fields from the troposphere to the stratosphere suggests that the class 2 teleconnections are caused by troposphere-stratosphere coupling processes.

How to cite: Handorf, D., Sahin, O., Rinke, A., and Kurths, J.: A complex network analysis of extreme cyclones in the Arctic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3984,, 2021.

EGU21-9869 | vPICO presentations | NP2.2

Quantification of the relation between dynamical properties of meteorological variables and their predictability

Meriem Krouma, Pascal Yiou, Davide Faranda, Soulivanh Thao, and Céline Déandréis

Local properties of chaotic systems can be summarized by dynamical indicators, that describe the recurrences of all states in phase space. Faranda et al. (2017) defined such indicators with the local dimension (d, approximating the local number of degrees of freedom of the system) and the inverse of persistence (θ, approximating the time it takes to leave a local state). It has been conjectured that such indicators give access to the local predictability of systems. The aim of this study is to evaluate how the predictability of climate variables such as temperature and precipitation is related to dynamical properties of the atmospheric flow.

The predictability of a chaotic system can be evaluated through ensembles of simulations, with probability scores (e.g. Continuous Rank Probability Score, CRPS). In this work, we consider ensembles of climate forecasts with a stochastic weather generator (SWG) based on analogs of atmospheric circulation (Yiou and Déandréis, 2019). We are interested in relating predictability scores of European temperatures and precipitation, obtained with this SWG, and the local dynamical properties of the synoptic atmospheric circulation, obtained from the NCEP reanalysis. We show experimentally that the CRPS of local climate variables can be predicted from large-scale (d, \ θ) values of geopotential height fields, for time leads of 5 to 10 days. A practical application is that the predictability of local variables (in Europe) can be anticipated from large-scale dynamical quantities, which can help to dimension the size of ensemble forecasts.


Faranda, D., Messori, G., Yiou, P., 2017. Dynamical proxies of North Atlantic predictability and extremes. Sci. Rep. 7, 41278.

Caby, T. Extreme Value Theory for dynamical systems, with applications in climate and neuroscience. Mathematics [math]. Université de Toulon Sud; Universita dell’Insubria, 2019.

Yiou, P., Déandréis, C., 2019. Stochastic ensemble climate forecast with an analogue model. Geosci. Model Dev. 12, 723–734.



This project 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: Krouma, M., Yiou, P., Faranda, D., Thao, S., and Déandréis, C.: Quantification of the relation between dynamical properties of meteorological variables and their predictability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9869,, 2021.

EGU21-15642 | vPICO presentations | NP2.2

Drivers of midlatitude extreme heat waves revealed by analogues and machine learning

George Miloshevich, Dario Lucente, Corentin Herbert, and Freddy Bouchet

One of the big challenges today is to appropriately describe heat waves, which are relevant due to their impact on human society. Common characteristics in mid-latitudes involve meanders of the westerly flow and concomitant large anticyclonic anomalies of the geopotential field. These anomalies form the so-called teleconnection patterns, and thus it is natural to ask how robust such structures are in various models and how much data we require to make statistically significant inferences. In addition, it is natural to ask what are the precursor phenomena that would improve forecasting capabilities of the heat waves. In particular, what kind of long term effect does the soil moisture have and how it compares to the respective quantitative contribution to the predictability of the teleconnection patterns.


In order to answer these questions we perform various types of regression on a climate model. We construct the composite maps of the geopotential height at 500 hPa and estimate return times of heatwaves of different severity. Of particular interest to us is a committor function, which is essentially a probability a heat wave occurs given the current state of the system. Committor functions can be efficiently computed using the analogue method, which involves learning a Markov chain that produces synthetic trajectories from the real trajectories. Alternatively they can be estimated using machine learning approach. Finally we compare the composite maps in real dynamics to the ones generated by the Markov chain and observe how well the rare events are sampled, for instance to allow extending the return time plots.

How to cite: Miloshevich, G., Lucente, D., Herbert, C., and Bouchet, F.: Drivers of midlatitude extreme heat waves revealed by analogues and machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15642,, 2021.

EGU21-2592 | vPICO presentations | NP2.2

Hurricane dynamics and rapid intensification via dynamical systems indicators

Davide Faranda, Gabriele Messori, Pascal Yiou, Soulivanh Thao, Flavio Pons, and Berengere Dubrulle

Although the lifecycle of hurricanes is well understood, it is a struggle to represent their dynamics in numerical models, under both present and future climates. We consider the atmospheric circulation as a chaotic dynamical system, and show that the formation of a hurricane corresponds to a reduction of the phase space of the atmospheric dynamics to a low-dimensional state. This behavior is typical of Bose-Einstein condensates. These are states of the matter where all particles have the same dynamical properties. For hurricanes, this corresponds to a "rotational mode" around the eye of the cyclone, with all air parcels effectively behaving as spins oriented in a single direction. This finding paves the way for new parametrisations when simulating hurricanes in numerical climate models.

How to cite: Faranda, D., Messori, G., Yiou, P., Thao, S., Pons, F., and Dubrulle, B.: Hurricane dynamics and rapid intensification via dynamical systems indicators, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2592,, 2021.

EGU21-8342 | vPICO presentations | NP2.2

Instantaneous multiscale properties of solar wind dynamical regimes and their attractors

Theophile Caby, Tommaso Alberti, Davide Faranda, Reik V. Donner, Giuseppe Consolini, Sandro Vaienti, and Vincenzo Carbone

The solar wind is characterized by a multiscale dynamics showing features of chaos, turbulence, intermittency, and recurring large-scale patterns, pointing towards the existence of an underlying attractor. However, magnetic field and plasma parameters usually show different scaling regimes with different physical and dynamical properties. Here by using a recent and novel approach developed in the framework of dynamical systems  we investigate the multiscale instantaneous properties of solar wind magnetic field phase space by means of the evaluation of instantaneous dimension and stability. We show the existence of a break in the average attractor dimension occurring at the observed scaling break between the inertial and the dissipative regimes. We further show that sometimes the dynamics is higher dimensional (d>3) suggesting that the phase space is larger than that described by the system variables and invoking for an external forcing mechanism, together with the existence of at least one unstable fixed point that cannot be definitely associated with noise. Instantaneous properties of the attractor therefore provide an efficient way of evaluating dynamical properties and building up improved cascade models.

How to cite: Caby, T., Alberti, T., Faranda, D., Donner, R. V., Consolini, G., Vaienti, S., and Carbone, V.: Instantaneous multiscale properties of solar wind dynamical regimes and their attractors, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8342,, 2021.

EGU21-14291 | vPICO presentations | NP2.2 | Highlight

Explaining recent trends in extreme precipitation in the Southwestern Alps by changes in atmospheric influences 

Juliette Blanchet, Antoine Blanc, and Jean-Dominique Creutin

We analyze recent trends in extreme precipitation in the Southwestern Alps and link these trends to changes in the atmospheric influences triggering extremes. We consider a high-resolution precipitation dataset of 1x1 km2 for the period 1958-2017. A robust method of trend estimation is considered, based on nonstationary extreme value distribution and a homogeneous neighborhood approach. The results show contrasting extreme precipitation trends depending on the season. Excluding autumn, the significant trends are mostly negative in the Mediterranean area, while the French Alps show more contrasted trends, in particular in winter with significant increasing extremes in the Western and Southern French Alps and decreasing extremes in the Northern French Alps and Swiss Valais. In autumn, most of Southern France shows significant increasing trends, with up to 100% increase in the 20-year return level between 1958 and 2017, while the Northern French Alps show decreasing extremes.
By comparing these trends to changes in the occurrence of the dominant weather patterns triggering the extremes, we show that part of the significant changes in extremes can be explained by changes in the dominant influences, particularly in the Mediterranean influenced region. We also show that part of the trends in extremes are explained by a shift in the seasonality of maxima. 

How to cite: Blanchet, J., Blanc, A., and Creutin, J.-D.: Explaining recent trends in extreme precipitation in the Southwestern Alps by changes in atmospheric influences , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14291,, 2021.

Traditional extreme value analysis based on the generalised extreme value (GEV) or the generalised Pareto distribution (GPD) suffers from two drawbacks: (i) Both methods are wasteful of data as only block maxima or exceedances over a high threshold are used and the bulk of the data is disregarded, resulting in a large uncertainty in the tail inference. (ii) In the peak-over-threshold approach the choice of the threshold is often difficult in practice as there are no really objective underlying criteria.
Here, two approaches based on maximum likelihood estimation are introduced which simultaneously model the whole distribution range and thus constrain the tail inference by information from the bulk data. Firstly, the bulk matching method models the bulk of the distribution with a flexible exponential family model and the tail with a GPD. The two distributions are linked together at the threshold with appropriate matching conditions. The threshold can be estimated in an outer loop also based on the likelihood function. Secondly, in the extended generalised Pareto distribution (EGPD) model for non-negative variables the whole distribution is modelled with a GPD overlaid with a transition probability density which is again represented by an exponential family. Appropriate conditions ensure that the model is in accordance with extreme value theory both for the lower and upper tail of the distribution. The methods are successfully exemplified on simulated data as well as wind speed and precipitation data.

How to cite: Kwasniok, F.: Robust extreme value analysis by semiparametric modelling of the entire distribution range, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15271,, 2021.

EGU21-3288 | vPICO presentations | NP2.2

         Towards Analysing multivariate weather/climate extremes

Abdel Hannachi and Nickolay Trendafilov

Extreme analysis, via e.g., GEV, was developed to deal with univariate time series, and is very difficult to extend beyond that dimension. Here we explore a different method, the archetypal analysis, which focuses on multivariate extremes. The method seeks to approximate the convex hull in high-dimensional state space, by identifying corners representing "pure" types, i.e. archetypes. The method, encompasses, in particular, the virtues of EOFs and clustering. The method is presented with a new manifold-based optimization algorithm, and applied to a number of atmospheric fields, including SST and SLP gridded data. The application to SST, in particular, reveals important features related to SST extremes. The strengths and weaknesses of the method and possible future perspectives will be discussed.

How to cite: Hannachi, A. and Trendafilov, N.:          Towards Analysing multivariate weather/climate extremes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3288,, 2021.

EGU21-12186 | vPICO presentations | NP2.2

What is the added value of using downscaled CMIP5 data for  the study of climate extremes under  climate change?

Dimitri Defrance, Thomas Noël, and Harilaos Loukos

In the beginning of this century, impacts studies due to climate change were carried out directly with the outputs of the general circulation models of the Atmosphere and the Ocean (AOGCM). However, these models had very low resolutions in the order of several degrees and the climate of some areas, such as monsoon regions, was poorly reproduced. These two disadvantages make it difficult to study the evolution of extremes. Recently, more impact studies are using outputs from multiple AOGCM models that are downscaled and unbiased. The ISIMIP consortium ( participates in the dissemination of this practice by proposing several AOGCM models with a resolution of 0.5° X 0.5°.

In our study, a high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed  (Noël et al. accepted). This dataset is obtained by using the quantile – quantile method Cumulative Distribution Function transform (CDFt) (Vrac et al. 2012, 2016,, developed over  10 years to bias correct or downscale climate model output, and ERA5 land data as a reference . T

We propose in this communication to present the climate variability by the end of the century in terms of extreme climate indicators such as heat waves or heavy rainfall at the local/grid point level (e.g. city level). Particular attention will be paid to the magnitude of the changes as well as the associated uncertainty.



Vrac, M., Drobinski, P., Merlo, A., Herrmann, M., Lavaysse, C., Li, L., & Somot, S. (2012). Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment.Nat. Hazards Earth Syst. Sci., 12, 2769–2784.

Vrac, M., Noël, T., & Vautard, R. (2016). Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter. Journal of Geophysical Research: Atmospheres, 121(10), 5237-5258.

Noël, T., Loukos, H., Defrance, D., Vrac, M., & Levavasseur, G. (2020). High-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with ERA5 reanalyses for climate change impact assessments. Data in Brief (accepted,

How to cite: Defrance, D., Noël, T., and Loukos, H.: What is the added value of using downscaled CMIP5 data for  the study of climate extremes under  climate change?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12186,, 2021.

EGU21-9722 | vPICO presentations | NP2.2

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

Thordis Thorarinsdottir, Jana Sillmann, Marion Haugen, Nadine Gissibl, and Marit Sandstad

Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.

Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.

To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.

The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.

How to cite: Thorarinsdottir, T., Sillmann, J., Haugen, M., Gissibl, N., and Sandstad, M.: Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9722,, 2021.

Previous detection and attribution analyses suggest that human-induced increases in greenhouse gases have contributed to observed changes in extreme precipitation. However, all previous detection and attribution studies of observed changes in extreme precipitation i) use station data that have been heavily processed via gridding, transformation, and spatial and temporal averaging or other dimension reduction approaches, as well as using climate models to estimate the responses to external forcing, ii) also use models to estimate the unforced natural variability of extreme precipitation. Both aspects reduce user confidence in detection and attribution results.

We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing and use climate models only to obtain estimates of the space-time pattern of extreme precipitation response to external forcing. We use records of the annual maximum one day (Rx1day) or five consecutive days (Rx5day) precipitation accumulations from 5,081 land-based stations spanning the period 1950-2014 with at least 45 years of coverage, including at least 3 years in the period 2010-2014. Expected responses to external forcings are estimated from ALL and NAT forcings simulations from large ensemble simulations performed with CanESM2 and from a multi-model ensemble that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Changes at these stations are evaluated by fitting non-stationary Generalized Extreme Value (GEV) distributions at individual stations to the logarithms of observed Rx1day and Rx5day values. Non-stationarity in the GEV distribution is permitted by using location parameters that are allowed to be linearly dependent on climate model-simulated responses in ln(Rx1day) and ln(Rx5day) to different forcings. We perform the detection and attribution analysis across different spatial scales, including the global, continental, and regional scales.

The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (western Northern Hemisphere, western Eurasia, and eastern Eurasia), and many smaller IPCC regions, including C. North-America, E. Asia, E.C. Asia, E. Europe, E. North-America, N. Europe, and W. Siberia for Rx1day, and C. North-America, E. Europe, E. North-America, N. Europe, Russian-Arctic, and W. Siberia for Rx5day. Consistency between our study and previous studies substantially increases confidence in detection and attribution findings concerning extreme precipitation. The attributed effects of anthropogenic forcing on extreme precipitation include substantially decreased waiting times between extreme annual maximum events in regions where anthropogenic influence has been detected and intensification of extreme precipitation that, at a global scale is consistent with the Clausius-Clapeyron rate of about 7% per 1°C of warming.  

How to cite: Sun, Q., Zwiers, F., Zhang, X., and Yan, J.: Quantifying the human influence on the intensity of extreme 1- and 5-day precipitation amounts at global, continental, and regional scales, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8471,, 2021.

EGU21-15761 | vPICO presentations | NP2.2 | Highlight

Aligning compound extreme events as defined from climate science and sectoral impact perspectives

Radley Horton, Victoria Keener, Kai Kornhuber, Corey Lesk, and John Walsh

This talk will contrast how U.S. decision makers’ impacts-focused perspective on compound extreme events differs from climate science-based perspectives. Examples from around the U.S. will be provided, with an emphasis on cascading impacts that have spanned multiple regions and sectors. The talk will also propose a path forward for synthesizing ‘top-down’ and ‘bottom-up’ approaches to compound extremes, to facilitate adaptation. Time-permitting, preliminary findings from an analysis of sequential humid heat and extreme precipitation over the U.S. may be shown, as a guiding example. The work described reflects a collaboration of scientists funded by NOAA’s Regional Integrated Sciences and Assessments (RISA) program, charged with co-generating ‘useable science’ by working closely with stakeholders.   

How to cite: Horton, R., Keener, V., Kornhuber, K., Lesk, C., and Walsh, J.: Aligning compound extreme events as defined from climate science and sectoral impact perspectives, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15761,, 2021.

Spatioteporal variability of precipitation extremes is increasingly the focus of attention in both the climate and hydrology communites, especailly in the context of global climate change. Indicated by the Clausius-Clapeyron equation under the constant relative humudity assumption, it is expected, from the thermodynamic perspective, that extreme precipitation would increase as globe warms. However, when it comes to the regional response of precipitation to global warming, the resutls could be highly uncertain due to the influences of dynamic factors such as large-scale circlation patterns and local effects. Here, we investigate trends in a set of extreme precipitation indices (EPIs) over the Yangtze River Basin (YRB) during the period of 1960-2019. Also, we explore the possible associations between spatiotemporal variability of the EPIs and global warming, ENSO, and local effects. Our resutls show marked rising trends in frequency and intensity of Yangtze precipitation extremes. Global warming tends to enhance the frequency and intensity of preciptation extremes over the YRB. The La Niña phase of ENSO could lead to an increase of precipitation extremes in the current year, but a decrease of precipitation extremes in the coming year. Local warming mainly exerts a reducing effect on precipitation extremes, which is likely associated with the significant decrease of relative humidity in the YRB. Our findings highlight the need for a systematic approach to investigate changes in precipitation extremes over the YRB.

How to cite: Li, X.: Changes in Yangtze Precipitation Extremes and the association with thermodynamic, dynamic, and local drivers  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-772,, 2021.

EGU21-9411 | vPICO presentations | NP2.2

Solar forcing on the Northern Hemisphere weather and climate extremes during summer

Norel Rimbu, Monica Ionita, and Gerrit Lohmann

The effects of solar irradiance forcing on weather and climate extremes have received relatively less attention compared to the solar-induced changes in the mean climate. In this respect, here we investigate the possible impact of solar irradiance forcing on the Northern Hemisphere extreme weather and climate variability during summer, from a potential vorticity (PV) perspective. The generation of severe weather events in the extra-tropical regions is often related to intrusions of high PV originating from the polar lower stratosphere. Various two-dimensional PV indices, similar to those characterizing surface temperature and precipitation extremes, are defined to measure the frequency of upper level PV intrusion events. Based on long-term reanalysis data, we show that upper level high PV intrusions over Asia (Europe) are more (less) frequent during high relatively to low solar irradiance summers. Consistent with this PV pattern more (less) frequent surface extreme precipitation events are recorded during high relative to low solar irradiance summers in Asia (Europe). Patterns in the frequency of extreme temperatures are largely opposite to the corresponding extreme precipitation. Furthermore, extreme climate anomaly patterns associated with high solar irradiance forcing are similar to the corresponding patterns associated with strong monsoon circulation over Asia during summer. A preliminary analysis reveals the dominant role of upper level solar related PV anomalies in generation of extreme precipitation in the Asian monsoon region during high solar irradiance summers. A persistent blocking like circulation in the Caspian Sea region during low solar irradiance summers is associated more frequent high PV intrusions and extreme precipitation over Europe. The stability of the solar related extreme precipitation and temperature patterns in the last millennium perspective is also discussed based on proxy data as well as model simulations.


How to cite: Rimbu, N., Ionita, M., and Lohmann, G.: Solar forcing on the Northern Hemisphere weather and climate extremes during summer, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9411,, 2021.

EGU21-9895 | vPICO presentations | NP2.2

Underestimation of the projected 21st century increase in drought duration, according to emergent constraint: consistent result in CMIP5 and CMIP6 models

Irina Y. Petrova, Diego G. Miralles, Florent Brient, and Markus Donat

Droughts are defined as one of the most devastating natural disasters of modern times and a key challenge faced under climate change. The complexity of interacting physical processes that underlie the shortage of rainfall in climate models hampers accurate representation of present-day droughts, and leads to differences in their responses to increased greenhouse gas (GHG) concentrations in the future. As a result, the confidence in drought projections is currently defined as ‘medium to low' by the Intergovernmental Panel on Climate Change (IPCC), and reducing this uncertainty remains one of the main goals in coming years, with significant benefits for human and natural systems. 

In this study we explore a relationship between biases in simulated present-day values of longest annual drought (LAD) and future projections of LAD in an ensemble of CMIP5 and CMIP6 models. We find that present-day model bias explains almost 95 % of the future uncertainty in LAD by the end of the 21st century, attributed to the well-known precipitation simulation errors: “drier” models with longer annual droughts at present tend to predict larger LAD values worldwide in the future, as well as a stronger response to GHG forcing in LAD, which is significant in more than 40 % of the global land area.

Substituting observational LAD estimates from satellite data into this model-revealed “present–future relationlarship” suggests that the 21st century global mean increase in duration of annual meteorological droughts could be significantly larger than predicted by the CMIP5 and CMIP6 model ensembles. This emergent constraint reduces global mean uncertainty range in future LAD estimates from 45–100 to 75–90 days, a level more typical of the prediction range of “drier” models. The findings reveal world regions where climate change may cause stronger meteorological drought aggravation than expected, and emphasise the importance of reducing model errors, which are presently largely owed to rain biases, to increase confidence in future predictions.

How to cite: Y. Petrova, I., G. Miralles, D., Brient, F., and Donat, M.: Underestimation of the projected 21st century increase in drought duration, according to emergent constraint: consistent result in CMIP5 and CMIP6 models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9895,, 2021.

EGU21-15827 | vPICO presentations | NP2.2

A new database for Intense Mediterranean Cyclones 

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

To better assess the future risks associated with Intense Mediterranean Cyclones (IMC) a better understanding of their features, variability, frequency and intensity is required, including a robust detection method. The application of different detection algorithms provides results that are remarkably similar in some aspects but may be very different in others even using the same data. Thus, the selection of a particular method can significantly affect the results. For these reasons it is necessary to use different approaches and datasets to study the sensitivity and robustness of the detection approach. Those approaches often use minima in sea-level pressure (SLP) or extrema in relative vorticity or both to first identify the eye of the cyclone. SLP reflects the atmospheric mass distribution, and is representative of synoptic-scale atmospheric processes. On the other hand, the relative vorticity displays higher variability and is representative of the atmospheric circulation, being able to detect several local extrema (more than one centre), it can reduce uncertainties in the cyclone detection and tracking.

Therefore, within the framework of the EFIMERA project and to detect and track IMC we use a combination of different methods based on previous studies found in the literature. This new list of detected IMC events, together with the observed and well documented ones, are used here to create a new IMC database to be used for the study of their impacts and risk associated.

How to cite: Alvarez-Castro, M. C., Gallego, D., Ribera, P., Peña-Ortiz, C., and Faranda, D.: A new database for Intense Mediterranean Cyclones , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15827,, 2021.

EGU21-452 | vPICO presentations | NP2.2

A new framework for identifying and investigating seasonal climate extremes

Matthias Röthlisberger, Mauro Hermann, Christoph Frei, Flavio Lehner, Erich M. Fischer, Reto Knutti, and Heini Wernli

Previous studies have recognized the societal relevance of climatic extremes on the seasonal timescale and examined physical processes leading to individual high-impact extreme seasons (e.g., extremely wet or warm seasons). However, these findings have not yet been generalizing beyond individual case studies since at any specific location only very few seasonal events of such rarity occurred in the observational record. In this concept paper, a pragmatic approach to pool seasonal extremes across space is developed and applied to investigate hot summers and cold winters in ERA-Interim and the Community Earth System Model Large Ensemble (CESM-LENS). We identify spatial extreme season objects as contiguous regions of extreme seasonal mean temperatures based on statistical modeling. Regional pooling of extreme season objects in CESM-LENS then yields considerable samples of analogues to even the most extreme ERA-Interim events, which allow for climatological analyses of their statistical and physical characteristics.

This approach offers numerous opportunities for analyzing large samples of extreme seasons across regions, and several such analyses are illustrated exemplarily. We perform a climate model evaluation with regard to extreme season size and intensity measures and estimate how often an extreme winter like the cold North American 2013/14 winter is expected anywhere in mid-latitude regions. Moreover, we present a large set of simulated spatial analogues to this event, which allows to study commonalities and differences of their underlying physical processes. Finally, substantial but spatially varying climatological differences in the size of extreme summer and extreme winter objects are identified.

How to cite: Röthlisberger, M., Hermann, M., Frei, C., Lehner, F., Fischer, E. M., Knutti, R., and Wernli, H.: A new framework for identifying and investigating seasonal climate extremes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-452,, 2021.

NP3.1 – Scaling, multifractals and nonlinear dynamics in the atmosphere, ocean, hydrosphere and solid earth

EGU21-692 | vPICO presentations | NP3.1

Validity domains and parametrizations for white-noise and multiscale models in turbulence and wave-turbulence interactions

valentin resseguier, Erwan Hascoet, Bertrand Chapron, and Baylor Fox-Kemper

Geophysical fluid dynamics systems generally involve a wide range of spatio-temporal scales. Numerical representation can only simulate some of the scales. The others, at the unresolved scales of motion, must be parameterized for each type of phenomenon (wave, eddy, current), in terms of expected effects on the resolved scales. Most developments then assume that the fluid transport velocity has a time-uncorrelated noisy component with zero mean and stationary statistics. These approximations generally simplify theoretical descriptions, numerical simulations, data comparisons or more recently model error quantifications for data assimilation.

In the present work, we will discuss the applicability of such approximations through two examples: a surface oceanic current dynamics and swell refractions by surface currents.

When the time-decorrelation assumption is valid, we propose simple and tuning-free parametric models to represent the spatial correlations of the white-in-time small-scale velocity to help simulate the geophysical system of interest. These parametric models relies on turbulence space self-similarity and their statistical properties (e.g. spectral slope) can be easily estimated from observations of larger scale fluid velocities.

When the white-in-time approximation is not valid, we extend the previous parametric models to follow self-similarity properties in both time and space.

Numerical simulations will illustrate these theoretical developments along the presentation.

How to cite: resseguier, V., Hascoet, E., Chapron, B., and Fox-Kemper, B.: Validity domains and parametrizations for white-noise and multiscale models in turbulence and wave-turbulence interactions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-692,, 2021.

Vertical motions are fundamental to atmospheric dynamics and our understanding of phenomena such as moist convection. A long-standing problem in atmospheric sciences is to understand the mesoscale energy spectra. Several numerical studies show that the vertical velocity spectrum has a homogeneous energy distribution across the mesoscales with a flat spectrum. Compared to the energy spectra of horizontal motion, the mechanisms that govern the spectrum of vertical velocity are less well known. In the troposphere, most of the horizontal mesoscale energy comes from divergent motions. At large scales O(100 km), vertical velocity relates, to a good approximation, to the vertically averaged divergence of horizontal motions by continuity in the incompressible limit. Recent measurements from NARVAL-2 (Next Generation Remote Sensing for Validation Studies) campaign conducted in the tropical Atlantic, unveiled that mesoscale horizontal mass divergence profiles possess a rich vertical structure and high spatio-temporal variability. Although the premise of a radiatively-balanced circulation holds on the long-term average, instantaneous deviations from this equilibrium occur in the form of wave-like oscillations. Numerical studies show that our state-of-the-art models can reproduce the observed variability in mesoscale divergence. We ask the following question in support of the previous arguments: What controls the spectrum of coherent mesoscale vertical motion? We aim to elucidate the mechanisms determining the homogeneous energy distribution across horizontal scales of vertical velocity spectra. This study designs numerical experiments, which include mechanisms-denial simulations employing the Icosahedral Nonhydrostatic (ICON) model. We conducted numerical simulations on a limited-area domain located in the western tropical Atlantic (4°S – 18°N, 64°W – 42°W). This domain has a horizontal resolution of 1.25 km and a lid at 30 km—the analysis period spans 48 hours. The experiments include the following: (i) a control run using DWD NWP physics configuration (ii) a dry atmosphere with all moist processes excluded along with the latent heat surface fluxes (iii) clouds invisible to radiation and, (iv) effects of saturation adjustment on temperature neglected while maintaining surface heat fluxes. Preliminary results show that the divergence profiles horizontally averaged over 200 km present a clear dominance of vertical wavelengths of 3 – 6 km. We found autocorrelation time-scales of around 4 – 6 hours increasing with altitude outside convective areas and consistent among all simulations. All experiments show a systematic decrease of about 50% in the temporal autocorrelation inside convective areas; therefore, moist convective processes modulate divergence's temporal variability. Moreover, we found that local moist processes contribute the most considerable fraction to the energy spectrum at scales < 200 km. The spectral response to moist processes is broad and extends into the free troposphere. The spectral response of surface fluxes instead is confined to the subcloud layer.

How to cite: Morfa-Avalos, Y.: Why is the tropical sky white? Numerical investigations to elucidate the shape of mesoscale vertical velocity spectra., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13215,, 2021.

EGU21-1717 | vPICO presentations | NP3.1

Scaling Feature of the Ocean Surface Wind Field

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

The ocean surface wind plays a crucial role in the air-sea exchanges of momentum, heat, and mass, consequently is vital to the controlling of weather and climate. Due to the extremely large range of scales of the motion of the wind field, e.g., flow structures from millimeters to thousands of kilometers, the multiscale dynamics are known to be relevant. In this work, with the help of a Wiener-Khinchine theorem-based Fourier power spectrum estimator, the scaling features of the wind field provided by several satellites, i.e., QuikSCAT, Metop-A, -B, and -C, Haiyang-2B, and China France Oceanography SATellite (CFOSAT), is examined. Power-law scaling behavior is evident in the ranges of 100 to 3000 km with a scaling exponent β varying from 5/3 to 3. The global distributions and seasonal variations of the scaling exponent β have also been considered. The results show that due to the energetic convective activities in the low-latitude zones, the scaling exponents β in these regions are closer to the value of 5/3. As for the mid-latitudes, the values of β are close to 2 and independent of the variation of longitude. Concerning the seasonal variations, for most regions, the scaling exponents measured in winter are larger than those in summer. Furthermore, the seasonal variations of β in low-latitudes are stronger than those in the mid-latitudes. Our preliminary results indicate that all satellites provide a consistent scaling feature of the ocean surface wind field.

How to cite: Gao, Y., Schmitt, F. G., Hu, J., and Huang, Y.: Scaling Feature of the Ocean Surface Wind Field, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1717,, 2021.

EGU21-2588 | vPICO presentations | NP3.1

Predator-prey plankton dynamics in turbulent wakes behind islands

Alice Jaccod, Stefano Berti, Enrico Calzavarini, and Sergio Chibbaro

Plankton constitutes the productive base of marine ecosystems and plays an important role in the global carbon dioxide cycle through the process of photosynthesis. The impact of ocean hydrodynamic conditions on the biological activity of plankton species has been a subject attracting the interest of researchers during several decades. In the present study, we perform a well-resolved direct numerical simulation of a turbulent flow around an island, coupled to a predator–prey model of planktonic population dynamics, with the aim of investigating the conditions under which an algal bloom is observed.  The impact on the plankton dynamics of the turbulent regime as well as of the island shape is studied, through the investigation of spectra of velocity and plankton population density. Moreover, we focus on the correlation between the flow structures and the plankton patchiness, particularly by analyzing the effect of the sub-grid scale dynamics. The main outcome is that the response and the spatial distribution of plankton depend crucially on the relation between the time scale associated to the flow and the time related to biological growth, while they are fairly independent on the geometrical details of the obstacle. 

How to cite: Jaccod, A., Berti, S., Calzavarini, E., and Chibbaro, S.: Predator-prey plankton dynamics in turbulent wakes behind islands, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2588,, 2021.

EGU21-4860 | vPICO presentations | NP3.1

Copepods in turbulence: laboratory velocity and acceleration studies using high speed cameras

Clotilde Le Quiniou, François Schmitt, Yongxiang Huang, Enrico Calzavarini, and Sami Souissi

Planktonic copepods are tiny crustaceans, with a typical size of the order of mm, living in suspension in marine or freshwaters during their entire life cycle. They have swimming and jumping abilities and are known to be well adapted to their turbulent environment. Turbulence is known to increase their contact rate and feeding flux. However too intense turbulence is believed to have a negative effect so that a qualitative bell-shape is classically invoked to represent the contact rate of copepods versus turbulence intensity. In this framework, the objective of this work is to quantify the influence of ambient turbulence on copepod’s behavior, using trajectory analysis.

In this work, the motions of copepods were filmed using an infrared high-speed camera (1000 fps) in a turbulent environment, in the dark to avoid phototropism. The custom-made experimental set-up has been built-up in order to obtain in a central zone an isotropic and homogeneous turbulence representative of the natural environment. The flow was characterized with different tracer sizes at different turbulence intensities.

Copepods are filmed and the trajectories are extracted using signal processing routines. The instantaneous velocity, tangential and centripetal accelerations, and the local curvature are extracted for each trajectory. Their pdfs are computed, as well as different statistical moments: these indicators are studied at varying the turbulence intensity level (Reynolds number). Particles of different sizes (100 and 600 microns of mean diameters) and dead copepods are compared to living copepods statistics. This strategy allows to precisely characterize the copepods behavioral activity in relation with ambient turbulence. Ecological interpretations are drawn from the experimental results.

How to cite: Le Quiniou, C., Schmitt, F., Huang, Y., Calzavarini, E., and Souissi, S.: Copepods in turbulence: laboratory velocity and acceleration studies using high speed cameras, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4860,, 2021.

EGU21-16284 | vPICO presentations | NP3.1

Multi-scale variability of surface currents in the Gulf of Tonkin derived from HF radar observations

Manh Cuong Tran, Alexei Sentchev, and Kim Cuong Nguyen

The surface circulation in the Gulf of Tonkin (GoT) was analyzed using 2.5 year-long dataset from the High-Frequency radar (from April 2014 to October 2016). High temporal resolution of the measurements and large coverage from HFR dataset enable us to characterize the variability of surface circulation in the GoT in a wide range of scales: from tidal to annual scale. A number of techniques of data including rotary spectral analysis (RSA), principal component analysis (PCA), harmonic tidal analysis, coherent analysis, etc. were used to identify the dominant modes of variability. The tidal motions, accounting for approximately 62% of the total variability, revealed the dominance of diurnal components (K1 and O1) with 4 times larger magnitude than that of semi-diurnal constituent (M2). At seasonal scale, the monsoon wind plays an important role in driving the surface circulation in the GoT. This was supported by a tight correlation (0.7) between the wind stress and current velocities and by a large contribution (more than 50%) of the Ekman-driven component to the total variability of currents in the offshore area. Along the shore, large seasonal variability of circulation was highlighted. During the year, the seaward extension of the coastal current is primarily controlled by the cross-shore wind stress while the flow intensity is modulated by the Red River discharge.

How to cite: Tran, M. C., Sentchev, A., and Nguyen, K. C.: Multi-scale variability of surface currents in the Gulf of Tonkin derived from HF radar observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16284,, 2021.

EGU21-8821 | vPICO presentations | NP3.1

Power-law thermal stratification in lake Geneva and its seasonal evolution

Vinicius Beltram Tergolina, Yueting Jiang, François Schmitt, Stefano Berti, Enrico Calzavarini, and Orlane Anneville

Lake Geneva is one of the largest bodies of water in western Europe and the largest one in the Alps region. Besides its obvious touristic importance it supplies drinking water for a large portion of Switzerland and work for hundreds of commercial fishermen. It has been under constant monitoring since the 1970's, for the impact of human activities on its water quality and biodiversity. The lake is known to be a warm monomictic lake, thermally stratified through most of the year with the exception of winter, when small thermal vertical gradients permit mixing from top to bottom. In lake Geneva, thermal stratification is one of the main environmental drivers of phytoplankton communities which are widely used as bioindicators for freshwater ecosystems. Studies on thermal stratification are thus essential to better predict phytoplankton seasonality and the development of harmful species blooms. In this work we examine more than 20 years of surveillance data from the INRAE (National Research Institute for Agriculture, Food and Environment) regarding temperature vertical profiles and meteorological data. We review both the climatology and the temperature stratification history of the lake and refine the temperature depth profiles obtaining the yearly progressions of the mixed layer depths. We then discuss the fitting of the depth profiles through the use of power-law and exponential functions, finding that in 66% of the cases the power-law better describes the experimental data, and we report the probability density function of the related statistics throughout the seasons.  Finally, we discuss the implications of our results for the modelling of the lake turbulent regime and phytoplankton seasonality.

How to cite: Beltram Tergolina, V., Jiang, Y., Schmitt, F., Berti, S., Calzavarini, E., and Anneville, O.: Power-law thermal stratification in lake Geneva and its seasonal evolution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8821,, 2021.

High-frequency sampling at fixed positions in oceanography are installed all over the World. These provide time series of different oceanographic parameters over large range of scales and can help obtain informations on the complex coupling existing between physical, biogeochemical and biological parameters.
Here we explore the lead-lag information existing between two quantities: this is done by extracting the dissymmetry in the cross-correlation, corresponding to the statistical lead or lag of one series with respect to the other one (it is not necessarily a causality information). This analysis is done for all available parameters, two by two, giving way to generate a network of lead-lag influences.
As example this new approach is applied to the MAREL buoy system installed in Boulogne-sur-mer (France) operated by Ifremer ( It is a moored buoy equipped with physico-chemical and biological measuring devices working in continuous and autonomous conditions with measurement every 20 minutes. We consider here the measurements at high frequency of air temperature, sea temperature, salinity, dissolved oxygen, fluorescence and turbidity for all year from 2005 to 2013. The new method is applied to the whole data set and also to data every year, in order to see a time evolution of the lead-lag network of relations between all studies parameters.

How to cite: Schmitt, F.: Lead-lag statistical analysis of simultaneous high frequency oceanographic parameters in moored coastal systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5600,, 2021.

NP3.2 – Climate Variability Across Scales and Climate States

EGU21-14893 | vPICO presentations | NP3.2

Is the impact of climate oscillations changing over the Greenland Ice Sheet?

Tiago Silva, Jakob Abermann, Sonika Shahi, Wolfgang Schöner, and Brice Nöel

Greenland Block Index (GBI) and North Atlantic Oscillation (NAO) are climate indices widely used for climatological studies especially over the Greenland Ice Sheet (GrIS). Particularly in summer, they are highly and negatively correlated; both have a strong relationship to near surface processes around the GrIS; their magnitude creates non-linear feedbacks and influences the low troposphere, shaping spatial accumulation and ablation patterns.

NAO is a measure of the surface pressure difference over the North Atlantic, providing insight of intensity and location of the jet stream. GBI denotes the general circulation over Greenland at the 500-hPa level and depending on its signal promotes heat and moist advection towards inland.

Based on the 1959-2019 period, the extreme summer melt of 2019 recorded the highest mean summer GBI while the extreme summer melt of 2012 recorded the lowest mean summer NAO. Their impact, however, goes beyond the melting season since the inter-seasonal phase change of these two indices may enhance/ postpone early melt/late refreezing and vice-versa.

Supported by 62 years of high-resolution regional climate model output (RACMO2.3p2), this work uses a statistical approach to analyze inter-seasonal variability of climate oscillations and their impact on the surface energy budget components over the GrIS. Also, teleconnection changes in a changing climate are hypothesized.

How to cite: Silva, T., Abermann, J., Shahi, S., Schöner, W., and Nöel, B.: Is the impact of climate oscillations changing over the Greenland Ice Sheet?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14893,, 2021.

EGU21-9865 | vPICO presentations | NP3.2

Climate field reconstruction of North Atlantic sea surface temperatures

Tine Nilsen and Stefanie Talento

Pseudo-proxy experiments test the skill and sensitivity of two extended climate field reconstruction (CFR) methodologies in reconstructing northern North Atlantic Summer sea surface temperatures (SSTs). The Summer target data originate from one millennium-long simulation of the CESM LME (Otto-Bliesner et al. 2016) .  The experiments test the reconstruction skill systematically for input data mimicking SST marine proxies and instrumental observations, including characteristics such as sparse distribution in space, varying signal-to noise ratio and age uncertainties.

The Bayesian hierarchical model BARCAST assumes implicitly that the target variable is described as an AR(1) process in time (Tingley & Huybers 2010), while the proxy surrogate reconstruction (PSR) method makes no such assumption (Graham et al. 2007). The PSR selects climate analogues from the simulated instrumental period in our study.

Results show that both methodologies generate skillful reconstructions for perfectly dated input data, and the PSR is superior when realistic noise levels are chosen for the input data. When the input is perturbed with age-uncertainties, the methodologies are unable to generate acceptable skillful reconstructions. Facilitating in form of data clustering is tested for both methodologies in the attempt of improving reconstruction skill. This proves successful for the PSR methodology, with the best skill obtained using n=3 clusters over the reconstruction region.

Additionally, and addressed as a topic for discussion, we detect weak temporal persistence in the input data and the BARCAST reconstructions. The lack of SST persistence is found to be partly due to the input data sampling frequency: Summer means (June, July, August) averaged for every year. Analyses show that the simulated SST data exhibit weaker memory from one Summer to the next, compared to year-to-year variability based on annual means. Similar results are also found for instrumental observations. This finding stands in contrast to results of previous studies on terrestrial reconstruction, where climate reconstructions and individual proxy records exhibit strong persistence properties, also targeting the Summer season (Werner et al. 2018, Nilsen et al. 2018),.



Graham, N.E. et al. (2007), Clim. Change, 83, 241-285, doi: 10.1007/s10584-007-9239-2

Otto-Bliesner, B.L. et al (2016), Bull. Amer. Meteor. Soc., 97, 735-754, doi: 10.1175/BAMS-D-14-00233.1

 Nilsen, T. et al. (2018), Clim. Past, 14, 947-967, doi: 10.5194/cp-14-947-2018

Tingley, M. P. and P. Huybers (2010a), J. Clim., 23, 2759–2781, doi: 10.1175/2009JCLI3015.1

Werner, J. P. et al. (2018), Clim. Past, 14, 527-557, doi: 10.5194/cp-14-527-2018

How to cite: Nilsen, T. and Talento, S.: Climate field reconstruction of North Atlantic sea surface temperatures, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9865,, 2021.

EGU21-15298 | vPICO presentations | NP3.2

Assessing the stability of the AMOC during past warm climates

Megan Murphy O' Connor, Christophe Colin, and Audrey Morley

There is emergent evidence that abrupt shifts of the Atlantic Meridional Overturning Circulation (AMOC) have occurred during interglacial periods, with recent observations and model simulations showing that we may have over-estimated its stability during warm climates. In this study, we present a multi-proxy reconstruction of deep-water characteristics from the Rockall Trough in the Eastern North Atlantic to assess the variability of Nordic seas and Labrador Sea deep-water formation during past interglacial periods MIS 1, 5, 11, and 19. To test the warm climate stability hypothesis and to constrain the variability of deep-water formation for past warm climates, we performed geochemical analysis on planktic (Nd isotopes) and benthic foraminifera (δ18O and δ13C) along with sedimentological analysis. This approach allows us to reconstruct paleocurrent flow strength, as well as the origin and contribution of different water masses to one of the deep-water components of the AMOC in the Rockall Trough. We found that deep-water properties varied considerably during each of our chosen periods. For example during the Holocene εNd variability is smaller (1.8 per mil) when compared to variability during MIS 19 (3.3 per mil), an interglacial that experienced very similar orbital boundary conditions. Our results confirm that deep-water variability in the eastern North Atlantic basin was more variable in previous interglacial periods when compared to our current Holocene and provide new insight into the relative contribution of Nordic Seas Deep Water and Labrador Sea Water in the Rockall trough.

How to cite: Murphy O' Connor, M., Colin, C., and Morley, A.: Assessing the stability of the AMOC during past warm climates, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15298,, 2021.

EGU21-633 | vPICO presentations | NP3.2

Stability of the Atlantic overturning circulation under intermediate (MIS3) and full glacial (LGM) conditions and its relationship with Dansgaard-Oeschger climate variability

Xiao Zhang

EGU21-13778 | vPICO presentations | NP3.2 | Highlight

Characterising Dansgaard-Oeschger cycles: from MIS3 to today

Heather Andres and Lev Tarasov

One of the main contributors to palaeoclimate variability on millennial timescales is understood to be Dansgaard-Oeschger (D-O) cycles. Our awareness of these phenomena arises primarily from quasi-periodic, abrupt transitions of large magnitude detected in δ18O records from Greenland ice cores (e.g. Dansgaard et al, 1982; Johnsen et al, 1992), although there is evidence of similar variability in other archives and regions. D-O cycles have plenty to capture the imagination:

  • the strength and rapidity of climate changes over Greenland,

  • their regularity throughout MIS3 (~60 to 30 thousand years before present) and occurrence during the last deglaciation contrasting with their relative absence during the Last Glacial Maximum and Holocene,

  • their opposed characteristics in Greenland and Antarctica,

  • and that different models require different boundary conditions to reproduce this phenomena, if they can reproduce it at all.


This talk characterises D-Olike cycles in two different models: Planet Simulator (PlaSim, an Earth System Model with simplified atmospheric physics, thermodynamic sea ice, and simplified ocean dynamics), and COSMOS (a CMIP3-era ESM). We identify four phases to D-O cycles and commonalities and differences in their representations in these models. Finally, we examine which phases of this type of variability continue to contribute to climate variability today and what that looks like.

How to cite: Andres, H. and Tarasov, L.: Characterising Dansgaard-Oeschger cycles: from MIS3 to today, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13778,, 2021.

EGU21-15935 | vPICO presentations | NP3.2

·         Pattern and time-scale dependencies of temperature-precipitation correlations in the Northern Hemisphere extra-tropics 

Ulrike Herzschuh, Thomas Böhmer, Xianyong Cao, Raphael Herbert, Anne Dallmeyer, Richard Telford, and Stefan Kruse

Future precipitation levels under a warming climate remain uncertain because current climate models have largely failed to reproduce observed variations in temperature-precipitation correlations. Our analyses of Holocene proxy-based temperature-precipitation correlations from 1647 Northern Hemisphere extratropical pollen records reveal a significant latitudinal dependence, temporal variations between the early, middle, and late Holocene, and differences between short and long timescales. These proxy-based variations are largely consistent with patterns obtained from transient climate simulations for the Holocene. Temperature-precipitation correlations increase from short to long time-scales. While high latitudes and subtropical monsoon areas show mainly stable positive correlations throughout the Holocene, the mid-latitude pattern is temporally and spatially more variable. In particular, we identified a reversal to negative temperature-precipitation correlations in the eastern North American and European mid-latitudes during the mid-Holocene that mainly related to slowed down westerlies and a switch to moisture-limited convection under a warm climate. We conclude that the effect of climate change on land areas is more complex than the commonly assumed “wetter climate in a warmer world”. Future predictions need to consider that warming related precipitation change is time-scale dependent.

How to cite: Herzschuh, U., Böhmer, T., Cao, X., Herbert, R., Dallmeyer, A., Telford, R., and Kruse, S.: ·         Pattern and time-scale dependencies of temperature-precipitation correlations in the Northern Hemisphere extra-tropics , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15935,, 2021.

EGU21-13495 | vPICO presentations | NP3.2

Separating Climate Variability and Non-Climate Noise in Proxy Records - A case study on replicate marine sediment records

Hanna Dyck, Thomas Laepple, Andrew Dolman, Jeroen Groeneveld, and Mahyar Mohtadi

To describe earth’s former and predict the expected future climate in a general way we need to understand at least two basic characteristics of the distribution of earth’s temperature, its mean state and its temporal and spatial variance of temperature. There is some confidence in the projection of the mean state but the characteristics and changes of climate variability, especially on multi-decadal and longer time-scales are less known.

To characterize climate variability on these time scales, the instrumental record is too short. Climate proxies such as oxygen isotopes from foraminifera retrieved from marine sediments provide long records but do not exclusively carry information about the climate signal of interest. The decomposition of proxy time series into climate and non-climate components is challenging and depends on the adequate representation of the major involved biological and physical processes influencing the record. But even with a reasonable representation of the combined processes as fluctuations in proxy seasonality, bioturbation and errors in the age model, a proxy record still appears as the combination of these effects.

As a proxy record is only a single representation of this sum of effects we work on replicate measurements as a tool to characterize and separate the variability components. We therefore analysed oxygen isotopes and Mg/Ca in replicated measurements from the same sample, in replicated samples from the same sediment layer and in nearby sediment cores spanning the Holocene.  
If we compare two records the relation of them will determine the commonness of the underlaying processes. As records for example come from the same core or from cores of nearby located sites, they share the same climate signal. In the case they are from the same core they also share the errors in the age model and the time uncertainty introduced by bioturbation. Combining different types of replicates allows us the analyse the effect of different combinations of shared and independent errors.

The first two cores that we work on come from about 10 km apart located sites in the Indonesian Sea. GeoB 10054-4 was drilled in a water depths of 1076 meters, at longitude of 112°40.10’E and latitude 8°40.90’S and its average sedimentation rate was estimated as 20 cm/kyears. GeoB 100537 was drilled in a water depths of 1372 meters, at longitude 112°52.30’E and at latitude 8°40.56’S and its average sedimentation rate is estimated as 45cm/kyears.

In the presentations we will show first results of the analysis of intra core and inter core variability.

How to cite: Dyck, H., Laepple, T., Dolman, A., Groeneveld, J., and Mohtadi, M.: Separating Climate Variability and Non-Climate Noise in Proxy Records - A case study on replicate marine sediment records, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13495,, 2021.

EGU21-13795 | vPICO presentations | NP3.2

Reduced eastern tropical Atlantic sea surface temperature variability at the end of the Green Sahara

Gerald Rustic, Francesco SR Pausata, and Peter DeMenocal

Mid-Holocene proxy evidence records profound climatic changes, including alteration of the West African Monsoon system and the end of the ‘Green Sahara’ period. Model simulations have related changes in the West African Monsoon system, which controls present-day seasonal hydroclimate over much of the African continent north of the equator, to alterations of the tropical Walker circulation. Here we investigate the change in tropical sea surface temperature variability in the eastern tropical Atlantic, where ocean-atmosphere coupling is robust. Through analysis of the distribution of oxygen isotopes from the tests of individual specimens of the surface-dwelling foraminifer Globigerinoides ruber, we find that SST variability is significantly decreased at the end of the Green Sahara period ~3.5-5kya. During the period of reduced variability we also observe changes in the background state of the tropical Atlantic as characterized by the east-west SST gradient, linking variability to background conditions. We compare our record to co-eval records of tropical Pacific variability that describe changes to the El Niño Southern Oscillation, as well as to records of hydroclimate change in Southeast Asia, and find similarities in these records, suggesting a common origin of these climate signals. Taken together, this evidence points toward an alteration of the tropical Walker circulation which may, in part, be related to changes in vegetation and dust loading occurring during the drying of the Sahara at mid-Holocene.

How to cite: Rustic, G., Pausata, F. S., and DeMenocal, P.: Reduced eastern tropical Atlantic sea surface temperature variability at the end of the Green Sahara, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13795,, 2021.

EGU21-8419 | vPICO presentations | NP3.2

Linking Equatorial African precipitation to Kelvin wave processes in the CP4-Africa convection-permitting regional climate simulation

Godwin Ayesiga, Christopher Holloway, Charles Williams, Gui-Ying Yang, Rachel Stratton, and Malcolm Roberts

Synoptic timescale forecasts over Equatorial Africa are important for averting weather-and climate-related disasters and the resulting agricultural losses. Observational studies have shown that rainfall anomalies often propagate eastward across Equatorial Africa, and that there is a linkage between synoptic-scale eastward-propagating precipitation and Convectively Coupled Kelvin Waves (CCKWs) over this region. We explore the mechanisms in which CCKWs modulate the propagation of precipitation from West to East over Equatorial Africa. We examine the first Africa-wide climate simulation from a convection permitting model (CP4A) along with its global driving-model simulation (G25) and evaluate both against observations (TRMM) and ERA-Interim (ERA-I), with a focus on precipitation and Kelvin wave activity.

Lagged composites show that both simulations capture the eastward propagating precipitation signal seen in observational studies, though G25 has a weaker signal. Composite analysis on high-amplitude Kelvin waves further shows that both simulations capture the connection between the eastward propagating precipitation anomalies and Kelvin waves. In comparison to TRMM, however, the precipitation signal is weaker in G25, while CP4A is more realistic. As the Kelvin wave activity is also well represented in both simulations when compared to ERA-I, the weak precipitation signal in G25 may be partly associated with the weak coupling between the precipitation and Kelvin waves. We show that CCKWs modulate the eastward propagation of convection and precipitation across Equatorial Africa through at least two related physical processes. Firstly, an enhancement of the low-level westerlies leads to increased low-level convergence; secondly, westerly moisture flux anomalies amplify lower-to-mid-tropospheric specific humidity. Results show that both CP4A and G25 generally simulate the key horizontal features of CCKWs, with anomalous low-level westerlies in phase with positive precipitation anomalies. However, both models show a weakness in capturing the vertical profile of anomalous specific humidity, and the zonal-vertical circulation is too weak in G25 and incoherent in CP4A compared to ERA-I.

In both ERA-I and the simulations, Kelvin wave-induced convergence and the westward tilt with height of anomalous zonal winds and specific humidity tends to weaken to the east of the East African highlands. It appears that these highlands impede the coherent eastward propagation of the wave-precipitation coupled structure.

How to cite: Ayesiga, G., Holloway, C., Williams, C., Yang, G.-Y., Stratton, R., and Roberts, M.: Linking Equatorial African precipitation to Kelvin wave processes in the CP4-Africa convection-permitting regional climate simulation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8419,, 2021.

EGU21-13792 | vPICO presentations | NP3.2

The Relationship between the North Atlantic Oscillation and High-Wind Observations Across the Eastern United States 

Sylvia Stinnett, Joshua Durkee, Joshua Gilliland, Victoria Murley, Alan Black, and Gregory Goodrich

The North Atlantic Oscillation (NAO) is a high-frequency oscillation that has known influences on the climatology of weather patterns across the eastern United States. This study explores the relationship between the daily North Atlantic Oscillation index with observed high-wind events from 391 first-order weather stations across the eastern U.S. from 1973-2015. These events were determined following typical National Weather Service high-wind criteria: sustained winds of at least 18 m•s-1 for at least 1 hour or a wind gust of at least 26 m•s-1 for any duration. Since research literature shows high-wind events are often connected to parent mid-latitude cyclone tracks, and since the NAO has been shown to influence these storm tracks, it is hypothesized that changes in NAO phases are connected to spatial shifts and frequencies in high-wind observations. Initial results show a preferred southwesterly direction during each NAO phase. Variance in high-wind directions appears to increase (decrease) during negative (positive) NAO phases. Further, the greatest spatial difference in the mean center of high-wind observations was between positive and negative NAO phases. Overall, these preliminary findings indicate changes in high-wind observations may be linked to NAO phases.

How to cite: Stinnett, S., Durkee, J., Gilliland, J., Murley, V., Black, A., and Goodrich, G.: The Relationship between the North Atlantic Oscillation and High-Wind Observations Across the Eastern United States , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13792,, 2021.

EGU21-1939 | vPICO presentations | NP3.2

Timescale-dependent stability of surface air temperature and the forced temperature response

Beatrice Ellerhoff and Kira Rehfeld

Earth's climate can be understood as a dynamical system that changes due to external forcing and internal couplings. It can be characterized from the evolution of essential climate variables, such as surface air temperature. Yet, the mechanisms, amplitudes, and spatiotemporal patterns of global and local temperature fluctuations around its mean, called temperature variability, are insufficiently understood. Discrepancies exist between temperature variability from model and paleoclimate data at the temporal scale of years to centuries and at the local scale, both of which are important socio-economic scales for long-term planning.
Here, we clarify whether global and local temperature signals from the last millennia show a stationary variance on these timescales and thus behave in a stable manner or not. Therefore, we contrast power spectral densities and their scaling behaviors using simulated, observed, and reconstructed temperatures on periods between 10 and 200 years. Despite careful consideration of possible spectral biases, we find that local temperatures from paleoclimate data tend to show unstable behavior, while simulated temperatures almost exclusively show stable behavior. Conversely, the global mean temperature tends to be stable. We explain this by introducing the gain as a powerful tool to analyze the forced temperature response, based on a novel estimate of the joint power spectrum of radiative forcing.
Our analysis identifies main deficiencies in the properties of temperature variability and offers new insights into the linkage between raditative forcing and temperature response, relevant to the understanding of Earth’s dynamics and the assessment of climate risks.

How to cite: Ellerhoff, B. and Rehfeld, K.: Timescale-dependent stability of surface air temperature and the forced temperature response, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1939,, 2021.

The highly successful Budyko-Sellers energy balance models are based on the classical continuum mechanics heat equation in two spatial dimensions. When extended to the third dimension using the correct conductive-radiative surface boundary conditions, we show that surface temperature anomalies obey the (nonclassical) Half-order energy balance equation (HEBE, with exponent H = ½) implying heat is stored in the subsurface with long memory. 


Empirically, we find that both internal variability and the forced response to external variability are compatible with H ≈ 0.4.  Although already close to the HEBE and classical continuum mechanics, we argue that an even more realistic “effective media” macroweather model is a generalization: the fractional heat equation (FHE) for long-time (e.g. monthly scale anomalies).  This model retains standard diffusive and advective heat transport but generalize the (temporal) storage term.  A consequence of the FHE is that the surface temperature obeys the Fractional EBE (FEBE), generalizing the HEBE to 0< H ≤1.  We show how the resulting FEBE can be been used for monthly and seasonal forecasts as well as for multidecadal climate projections.  We argue that it can also be used for understanding and modelling past climates.

How to cite: Lovejoy, S.: Budyko-Sellers 2.0: the classical and fractional heat equations, and the fractional energy balance equation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7877,, 2021.

EGU21-572 | vPICO presentations | NP3.2

The weak volcanic response in climate models related to low-frequency Pacific variability

Giorgio Graffino and Jonathan Gregory

Volcanic eruptions are among the most important naturally occurring cause of climate variability. Their effect can outlive the residence time of the volcanic aerosol in the stratosphere, due to the intervention of the ocean as heat reservoir. Coupled models exhibit deficiencies and uncertainties in their response to volcanic forcing as well as multiannual variability. We have investigated a possible link by analysing experiments included in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP), along with several ad-hoc model simulations, in comparison with observational reanalyses and reconstructions. We introduce a novel technique to analyse the delayed response of sea surface temperature (SST) and mean sea level pressure (MSLP) in the Pacific Ocean to large volcanic eruptions, complemented with with an empirical orthogonal function analysis. Our study shows that coupled models are not able to reproduce the observed SST response to volcanic forcing, which has the shape of the cold phase of the Interdecadal Pacific Oscillation (IPO), and that their MSLP response is too weak. On the other hand, the observed MSLP response is reproduced by atmosphere-only simulations forced with realistic 20th-century SST.

How to cite: Graffino, G. and Gregory, J.: The weak volcanic response in climate models related to low-frequency Pacific variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-572,, 2021.

EGU21-9580 | vPICO presentations | NP3.2 | Highlight

Incorporating missing volcanic impacts into future climate impact assessments

Stephen Outten, Ingo Bethke, and Peter Thorne

Future climate projections for the 21st century generally do not include the effects of volcanic eruptions. While some attempt has been made to account for the integrated effect of multiple eruptions by incorporating a small continuous volcanic forcing, a recent study ( has already shown that this approach is insufficient to resolve the increased climate variance caused by individual eruptions, especially on decadal timescales. Increased climate variance exerts stresses on ecosystems and society, thus resolving the impacts of plausible future volcanic eruptions is of importance for certain adaptation and mitigation decisions.

While previous work has used a modelling approach to address this problem, in this talk we demonstrate a computationally inexpensive method to incorporate the effects of plausible volcanic eruptions into future climate projections. This method uses stochastic volcanic emulators based on 2,500 years of past volcanic activity and the characterization of the response of the climate system to individual eruptions. We will demonstrate not only this methodology, but also describe the requirements and potential for its application to the wider future projections of CMIP6.

How to cite: Outten, S., Bethke, I., and Thorne, P.: Incorporating missing volcanic impacts into future climate impact assessments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9580,, 2021.

EGU21-3000 | vPICO presentations | NP3.2

The impact of solar variability on climatic parameters

Chris G. Tzanis, Charilaos Benetatos, and Kostas Philippopoulos

Natural climate variability is partially attributed to the solar radiative forcing. The scope of this work is to increase the scientific understanding of the relative role of solar variations on the terrestrial climate. The applied methodology examines initially the variation of multiple climatic parameters (temperature, zonal wind, relative and specific humidity, sensible and latent surface heat flux, cloud cover, precipitation) in response to the 11-year solar cycle. An additional goal is to estimate the response of the climate system’s parameters to the solar forcing in multiple forecasting horizons and to evaluate the behavior of the climate system in shorter time scales. The adopted methodology includes the development of linear regression models which calculate the dependency of the climatic parameters to solar variations for each grid point of the global dataset on a monthly time scale. The solar indicator used in this study is the 10.7-cm solar radio flux (F10.7) provided by NOAA, while the climate data are extracted from the NCEP/NCAR Reanalysis 1 project with a spatial resolution of 2.5o X 2.5o for 67 years. Regarding the climate system’s response forecasting, an Artificial Neural Network has been trained for modeling and forecasting the solar indicator time series for a few time steps in advance and the effect on climatic parameters is estimated using the established regression equations. The results exhibit that the variation of the climatic parameters can be partially attributed to the 11-year solar cycle. Statistically significant areas with relatively high solar cycle signal were found in multiple pressure levels and geographical regions. Furthermore, the results indicate that the identification of a clear solar signal in the climatic data is a difficult task due to the climate system’s complexity; advanced non-linear methods could be applied in order to obtain a more accurate understanding of this research field.

How to cite: Tzanis, C. G., Benetatos, C., and Philippopoulos, K.: The impact of solar variability on climatic parameters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3000,, 2021.

What is the spatial scale of climate fluctuations, and how does this scale depend on the timescale under consideration? To answer this question, the spatio-temporal correlation structure of global surface temperature fields is characterized, for the period 1850-present, by estimating frequency spectra of the effective spatial degrees of freedom (ESDOF). These ESDOF spectra serve as a simple summarizing metric of the frequency-dependent spatial auto-correlation function. ESDOF spectra are estimated from: (a) the HadCRUT global gridded temperature anomaly dataset, based exclusively on instrumental measurements, and including detailed error variance estimates; (b) the NOAA 20th Century Reanalysis; and (c) a large ensemble of CMIP historical climate model simulations. When comparing (i) error corrected ESDOF spectra from the instrumental data to (ii) those obtained from the reanalysis and the model simulations, with HadCRUT data gaps imposed, results are found to be highly consistent among the three data sources. When the analysis is applied to the entire globe, the ESDOF spectra exhibit an almost uniform power-law frequency scaling with about 100 ESDOFs at monthly timescales and only about 2 ESDOFs at multidecadal timescales. Second-order differences in this scaling behaviour are found when the analysis is restricted to various spatial subdomains of the globe, namely, the tropics, extra-tropics, land areas, and ocean areas. A few implications of the diagnosed ESDOF reduction towards the longer timescales are briefly discussed.

How to cite: Kunz, T. and Laepple, T.: Power-law frequency scaling of surface temperature spatial degrees of freedom – estimated from instrumental data, reanalysis and climate model simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13589,, 2021.

NP3.3 – Scaling, Multifractals from Urban to Climate scales, from Theories to Big Data Analysis and Simulations

EGU21-9608 | vPICO presentations | NP3.3 | Highlight

An open source Matlab package for stochastic data analysis in the context of Fokker-Plank Equation and Integral Fluctuation Theorem

André Fuchs, Swapnil Kharche, Matthias Wächter, and Joachim Peinke

We present a user-friendly open-source Matlab package for stochastic data analysis. This package enables to perform a standard analysis of given timeseries like scaling analysis of structure functions and energy spectral density, estimation of correlation functions or investigation of the PDF’s of increments including Castaing fits. Also, this package can be used to extract the stochastic equations describing scale-dependent processes, such as the cascade process in turbulent flows, through Fokker-Planck equations and concepts of non-equilibrium stochastic thermodynamics. This stochastic treatment of scale-dependent processes has the potential for a new way to link to fluctuation theorems of non-equilibrium stochastic thermodynamics and extreme events (small scale intermittency, structures of rogue waves). 

The development of this user-friendly package greatly enhances the practicability and availability of this method, which allows a comprehensive statistical description in terms of the complexity of time series. It can also be used by researchers outside of the field of turbulence for the analysis of data with turbulent like complexity, including ocean gravity waves, stock prices and inertial particles in direct numerical simulations. Support is available: FPE IFT, where questions can be posted and generally receive quick responses from the authors.

This package was developed by the research group Turbulence, Wind energy and Stochastics (TWiSt) at the Carl von Ossietzky University of Oldenburg. We acknowledge funding by Volkswagen Foundation. 

How to cite: Fuchs, A., Kharche, S., Wächter, M., and Peinke, J.: An open source Matlab package for stochastic data analysis in the context of Fokker-Plank Equation and Integral Fluctuation Theorem, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9608,, 2021.

EGU21-7572 | vPICO presentations | NP3.3 | Highlight

Nonequilibrium scaling vs. the temporal evolution of turbulence

Marta Waclawczyk, Jan Wójtowicz, and Szymon Malinowski

Despite many airborne measurements and research campaigns our understanding of turbulence in free atmosphere is still far from sufficient. Part of the problem is the limited amount of measurement data, another part is measurement errors and last, but not least element is inadequate or not satisfactory data analysis. This presentation addresses some aspects of this last issue. The simplest way to characterize turbulence is to define/measure characteristic velocity U and length L (or time T) scales of turbulent  eddies. Two quantities necessary to estimate them are the turbulence kinetic energy K and the turbulence kinetic energy dissipation rate. A universal scaling relation between dissipation rate, turbulence kinetic energy and the turbulence length scale follows from the classical picture of the equilibrium Richardson-Kolmogorov cascade. There, the energy is transported between scales in a downward cascade until it is dissipated into heat by the smallest eddies. This universal scaling is a basis of many turbulence models, also in the context of atmospheric applications. However, a number of recent papers suggest that a universal, although different from the classical, scaling could also be observed in unsteady turbulent flows, which ate typical in the free atmosphere. In this study we investigate the nonequilibrium scaling relation between the integral length scale of turbulence and dissipation rate using velocity signals from various airborne measurements of atmospheric cloud turbulence, including that in and around convective clouds. A research aircraft measures 1D intersection of turbulent velocity field as a time series collected along the flight tajectory. Hence,  information on  temporal behavior (decay or development) of turbulence is not directly available. In this study we show how this important information can be recovered based on the observed scaling relations.

How to cite: Waclawczyk, M., Wójtowicz, J., and Malinowski, S.: Nonequilibrium scaling vs. the temporal evolution of turbulence, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7572,, 2021.

EGU21-10920 | vPICO presentations | NP3.3

Multifractals, free turbulence decay laws and the Scaling Gyroscopes Cascade model

Xin Li, Daniel Schertzer, Yelva Roustan, and Ioulia Tchiguirinskaia

Turbulence being a dissipative system decays when being "free", i.e. without any force. The law of this decay has been intriguing for quite a while. Assuming that for vanishing viscosity, the whole spectrum is self-similar, as well as stationary for low wave numbers/large eddies  (E(k,t) ≈CS kS, k → 0) , it was shown [1] that the total energy of turbulence has a power-law decay: E(t) = ∫ E(k,t) dk ≈ t-a(s): a(s) =2(s+1)/(s+3) . This was particularly thought to be relevant for s=4, C4 being proportional to the Loitsianski integral, assumed to be time-invariant [2]. However, it was shown with the help of the eddy-damped quasi-normal Markovian (EDQNM) [3] that there is an energy backscatter term transferring energy from energy-containing eddies by nonlocal triads interactions to large eddies, which behaves like TNL≈ k4 and therefore prevents the invariance of the Loitsianski integral. This implies that the theoretical exponent a(s) = 2(s+1)/(s+3)  is only valid for s<4 and that a(s) =a(4)=-(10-2γ)/7 for s≥ 4 with C4(t) ≈ t γ, γ>0. The turbulence decay is therefore slower than previously expected for s ≥ 4 due to the backscatter term that progressively stores energy in large eddies. 
EDQNM provides the estimate γ ≈ 0.16. However, a strong limitation of EDQNM and similar models (e.g. Direct Interaction Approximation, Test Field Model) is that these models are not able to represent intermittency, which is a fundamental phenomenon of turbulence [4] and this could bring into questions the previous results. We, therefore, investigate this question with the Scaling Gyroscopes Cascade (SGC) model [5], which is based on nonlocal interactions and display multifractal intermittency [6]. We first theoretically argue that SGC confirms the existence of the backscatter term, but the turbulence decay is no longer smooth but occurs by puffs and we provide numerical evidence of this.

Keywords: Loitsianski integral; intermittency; infrared spectrum; SGC model; energy decay

[1]M. Lesieur and D. Schertzer, ‘‘Amortissement auto-similaire d’une turbulence a‘ grand nombre de Reynolds,’’ J. Mec. 17, 609 1978 .

[2]Davidson, P. A. (2000). Was Loitsyansky correct? A review of the arguments. Journal of Turbulence, 1(1), 006-006.

[3]Frisch, U., Lesieur, M.,Schertzer, D. (1980). Comments on the quasi- normal Markovian approximation for fully-developed turbulence. Jour- nal of Fluid Mechanics, 97(1), 181-192.

[4]Morf, R. H., Orszag, S. A., Frisch, U. (1980). Spontaneous singularity in three-dimensional inviscid, incompressible ow. Physical Review Letters, 44(9), 572.

[5]Chigirinskaya, Y., Schertzer, D.,  Lovejoy, S. (1997). Scaling gyroscopes cascade: universal multifractal features of 2-D and 3-D turbulence. Fractals and Chaos in Chemical Engineering. World Scientific, Singapore, 371-384.

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

How to cite: Li, X., Schertzer, D., Roustan, Y., and Tchiguirinskaia, I.: Multifractals, free turbulence decay laws and the Scaling Gyroscopes Cascade model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10920,, 2021.

EGU21-11242 | vPICO presentations | NP3.3

Multifractal analysis of extinction coefficient and its consequences in characterizing atmospheric visibility

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

Extinction coefficient (σe) is a measure of light attenuation in the atmosphere, due to absorption and scattering properties of constituent gases and aerosols. In meteorological context, σe is used to understand transparency of the atmosphere, by estimating visibility or meteorological observable range (MOR). An accurate representation of visibility is required for safe functioning of various domains such as transport sectors, free optic communication, etc., and for understanding regional variations in air quality and climate. As the measurement of visibility is subjective and dependent on the instrument and range of measurement, here we attempt to characterize the same using extinction coefficient. σe was investigated under the framework of universal multifractals (UM), which is widely used to analyze and characterize geophysical fields that exhibit extreme variability over measurement scales.

For this study, σe was extracted from forward scattering visibility data by disdrometer (Campbell Scientific PWS100) located in the Paris area (France), operated by Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech (HM&Co, ENPC). As governing nonlinear equations of the atmosphere such as Navier-Stokes possess scale invariance, it was assumed here that the behavior of light attenuating particles should inherit similar scaling properties and hence be treated as multifractal fields. σe extracted from MOR measured at Paris-Charles de Gaulle airport was also subjected to multifractal analysis during the same time period for comparison. With direct analysis and simulations, it was found that σe exhibits multifratcal properties but are influenced by upper limit of visibility range in the instrument used for measurement. From the study, we suggest usage of extinction coefficient (σe) for characterizing atmospheric visibility as the former is a more physically relevant quantity which is objectively measured by instruments and directly related to particles in the atmosphere; while emphasizing the need to consider biases from instrumental range.

How to cite: Jose, J., Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Multifractal analysis of extinction coefficient and its consequences in characterizing atmospheric visibility, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11242,, 2021.

EGU21-10799 | vPICO presentations | NP3.3

Interactions between rainfall and wind turbulence in a Universal Multifractal framework

Ángel García Gago, Daniel Schertzer, and Auguste Gires

Rainfall and wind are both known to exhibit extreme variability over a wide range of spatio-temporal scales which makes such fields complex to characterize, simulate and even measure. In this paper, we present a database that will enable to characterize the Interactions between rainfall and wind turbulence. Preliminary analysis using the framework of Universal Multifractals, commonly used to analyse and simulate these fields will also be presented.

The data collected during a high resolution measurement campaign on a meteorological mast will be used. More precisely the wind, temperature, pressure, humidity and rainfall fields are collected using 3D sonic anemometers (manufactured by Thies), mini meteorological stations (manufactured by Thies), and disdrometers (Parsivel2, manufactured by OTT). The latter gives access to the size and velocity of drops falling through its sampling area. The temporal resolution is of 100 Hz for the 3D sonic anemometers, 1 Hz for the meteorological stations and 30 s for the disdrometers. The devices are installed at two heights (approx. 45 m and 80 m), which enables to assess effects of altitude.

Initial results will be presented, notably with regards to the scaling features of the various fields, their characteristic parameters, and their correlation across scales.

Authors acknowledge the RW-Turb project (supported by the French National Research Agency - ANR-19-CE05-0022), for financial support. This project aims to quantify the impact of atmospheric turbulence and rainfall on wind power production.

How to cite: García Gago, Á., Schertzer, D., and Gires, A.: Interactions between rainfall and wind turbulence in a Universal Multifractal framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10799,, 2021.

EGU21-13705 | vPICO presentations | NP3.3 | Highlight

Development of space-time rainfall intensity duration frequency curves based on a multifractal approach

David Serrano, Mahesh Lal Maskey, Adrian Rizo, and Victor Peñaranda

Changing climate signals and urban populations' growth requires proper hydrologic risk analysis to create and operate water resource infrastructures in a sustainable way. Although modernized computational facilities are becoming popular to understand different complex systems, the scientific community is still behind in proper analysis of extreme rainfall events as they are erratic in time and space. To fill the existing knowledge gap, it becomes obvious to incorporate spatiotemporal rainfall variability in designing rainfall Intensity Duration Frequency (IDF) curves.  Many statistical approaches have been suggested to describe the space-time structure of rainfall; nevertheless, none of them is enough to represent, for all observational scales, the geometrical structure observed in either rainfall time series or rainfall-derived spatial fields. This research presents a more holistic approach to derive the IDF curves without losing information and (or) statistical assumptions.  This study uses such a promising notion to understand the rainfall field's space-time geometrical structure via codimension functions. The results show us the space-time structure of rainfall exhibits a dynamical scaling, and it suggests the idea of a double multifractal spectrum for representing time and space. Based on the idea of a double multifractal spectrum, IDF curves can be shifted to Intensity – Area – Frequency – Duration (IADF) curves to get a better approach for engineering and scientific purposes. Furthermore, this research suggests that changes of parameters for this approach could reflect climate-change signals and would be useful to generate non-stationary IADF curves and improve engineering design practices.

How to cite: Serrano, D., Maskey, M. L., Rizo, A., and Peñaranda, V.: Development of space-time rainfall intensity duration frequency curves based on a multifractal approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13705,, 2021.

EGU21-6605 | vPICO presentations | NP3.3

Simulating reference rainfall scenarios for hydrological applications using a multifractal approach

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


Hydrological applications such as flood design usually deal with and are driven by region-specific reference rainfall regulations, generally expressed as Intensity-Duration-Frequency (IDF) values. The meteorological module of hydro-meteorological models used in such applications should therefore be capable of simulating these reference rainfall scenarios. The multifractal cascade framework, since it incorporates physically realistic properties of rainfall processes such as non-homogeneity (intermittency), scale invariance, and extremal statistics, seems to be an appropriate choice for this purpose. Here we suggest a rather simple discrete-in-scale multifractal cascade based approach. Hourly rainfall time-series datasets (with lengths ranging from around 28 to 35 years) over six cities (Paris, Marseille, Strasbourg, Nantes, Lyon, and Lille) in France that are characterized by different climates and a six-minute rainfall time series dataset (with a length of around 15  years) over Paris were analyzed via spectral analysis and Trace Moment analysis to understand the scaling range over which the universal multifractal theory can be considered valid. Then the Double Trace Moment analysis was performed to estimate the universal multifractal parameters α,C1 that are required by the multifractal cascade model for simulating rainfall. A renormalization technique that estimates suitable renormalization constants based on the IDF values of reference rainfall is used to simulate the reference rainfall scenarios. Although only purely temporal simulations are considered here, this approach could possibly be generalized to higher spatial dimensions as well.


Multifractals, Non-linear geophysical systems, Cascade dynamics, Scaling, Hydrology, Stochastic rainfall simulations.

How to cite: Ramanathan, A., Versini, P.-A., Schertzer, D., Tchiguirinskaia, I., Perrin, R., and Sindt, L.: Simulating reference rainfall scenarios for hydrological applications using a multifractal approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6605,, 2021.

EGU21-13856 | vPICO presentations | NP3.3

Fractal analysis of rain gauges’ inhomogeneous distribution and the associated hydrological impacts: the case study of Muriaé River basin

Priscila Celebrini de O. Campos, Igor Paz, Maria Esther Soares Marques, Ioulia Tchiguirinskaia, and Daniel Schertzer

The urban population growth requires an improvement in the resilient behavior of these areas to extreme weather events, especially heavy rainfall. In this context, well-developed urban planning should address the problems of infrastructure, sanitation, and installation of communities, primarily related to insufficiently gauged locations. The main objectives of this study were to analyze the impacts of in-situ rain gauges’ distribution associated with the elaboration of a spatial diagnosis of the occurrence of floods in the municipality of Itaperuna, Rio de Janeiro – Brazil. The methodology consisted of the spatial analysis of rain gauges’ distribution with the help of the fractal dimension concept and investigation of flood susceptibility maps prepared by the municipality based on transitory factors (which consider precipitation in the modeling) and on permanent factors (natural flood susceptibility). Both maps were validated by the cross-tabulation method, crossing each predictive map with the recorded data of flood spots measured during a major rainfall event. The results pointed that the fractal analysis of the rain gauges’ distribution presented a scaling break behavior with a low fractal dimension at the small-scale range, mostly concerned in (semi-)urban catchments, highlighting the incapacity of the local instrumentation to capture the spatial rainfall variability. Thereafter, the cross-tabulation validation method indicated that the flood susceptibility map based on transitory factors presented an unsatisfactory probability of detection of floods when compared to the map based on permanent factors. These results allowed us to take into account the hydrological uncertainties concerning the insufficient gauge network and the impacts of the sparse distribution on the choice and elaboration of flood susceptibility maps that use rainfall data as input. Finally, we performed a spatial analysis to estimate the population and habitations that can be affected by floods using the flood susceptibility map based on permanent factors.

How to cite: Campos, P. C. D. O., Paz, I., Marques, M. E. S., Tchiguirinskaia, I., and Schertzer, D.: Fractal analysis of rain gauges’ inhomogeneous distribution and the associated hydrological impacts: the case study of Muriaé River basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13856,, 2021.

EGU21-16526 | vPICO presentations | NP3.3

Precipitation nowcasting based on multifractal advection and deformation 

Victoria Santos-Duarte-Ramos

The X-band radars deliver precipitation estimates with high accuracy and space resolution (up to 100 m in space and 1 min in time). With their increasing deployment around large cities, there is an appealing need for short-term nowcasting of rainfall at high resolutions for urban applications.

Nowcasting means forecasting with lead times of up to six hours. Classical precipitation nowcasting methods include methods of image processing to identify precipitation cells and extrapolate their motion. Due to the strong nonlinearity of the precipitation processes, such methods face a number of limitations, e.g., cell identification lacks physics and can be quite ad-hoc or even fail because of their fast deformation. On the contrary, this presentation aims to demonstrate how the fast multiscale deformation of the rainfall cells could be used to improve precipitation nowcasting, with the help of new radar data and products.

Spectral and multifractal analyses of radar data enable a comparison of the structure and the morphology of both the precipitation and vector fields through space time scales. This provides a unique framework to nowcast both fields over scales relevant to urban decision-making. Overall, this presentation contributes to the development of new, reliable, operational tools to use in their full extent the high-resolution X-band data.

How to cite: Santos-Duarte-Ramos, V.: Precipitation nowcasting based on multifractal advection and deformation , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16526,, 2021.