Presentation type:
CR – Cryospheric Sciences

EGU23-4976 | Orals | MAL18 | Julia and Johannes Weertman Medal Lecture

From the tongue of the Mer de Glace to the world’s glaciers : 20 years of progress in measuring glacier mass changes from space 

Etienne Berthier, Joaquin Belart, Alejandro Blazquez, Fanny Brun, Cesar Deschamps-Berger, Ines Dussaillant, Thomas Flament, and Romain Hugonnet

In 2004, we painstakingly measured the thinning of a single glacier tongue (the Mer de Glace, Mont-Blanc) from pairs of SPOT (CNES) satellite optical stereo-images. It then took us nearly 20 years before we managed to up-scale such observations to the global scale. In this presentation, I will illustrate the advances (in terms of data availability and processing) and all the collaborative work that led to a spatially-resolved and almost complete estimation of mass changes for the more than 200,000 glaciers on Earth. These new data paint a global picture of accelerating glacier mass loss since 2000 and pave the way toward improved projections of future glacier mass and sea level contribution.

How to cite: Berthier, E., Belart, J., Blazquez, A., Brun, F., Deschamps-Berger, C., Dussaillant, I., Flament, T., and Hugonnet, R.: From the tongue of the Mer de Glace to the world’s glaciers : 20 years of progress in measuring glacier mass changes from space, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4976, https://doi.org/10.5194/egusphere-egu23-4976, 2023.

EGU23-9607 | Orals | MAL18 | Arne Richter Award for Outstanding Early Career Scientists Lecture

Changing glaciers in a changing climate through changing modelling approaches 

Harry Zekollari

Glaciers outside the ice sheets are key contributors to sea-level rise and act as essential fresh-water resources in various regions around the world. Glaciers are also important sources of natural hazards, directly impact biodiversity, and have a significant touristic value. Given these crucial societal and environmental roles, having reliable projections on the evolution of these precious ice bodies under changing climatic conditions is of paramount importance.

 

In this presentation, I will highlight how modelling glacier changes goes hand in hand with rapidly increasing remotely sensed glacier observations and derived products at the global scale (e.g., glacier outlines, surface elevation, ice thickness, surface velocities, and elevation changes). These new observations, combined with ever-increasing computational capacities and novel numerical tools have recently allowed us to transition from modelling a few individual glaciers to now modelling the evolution of glaciers at regional- to global scales while accounting for ice-dynamical processes. Whereas these recent advances are encouraging, I will also highlight the challenges that we are still facing and that we will need to tackle in the coming years to provide more trustworthy glacier evolution projections.

How to cite: Zekollari, H.: Changing glaciers in a changing climate through changing modelling approaches, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9607, https://doi.org/10.5194/egusphere-egu23-9607, 2023.

CR1 – The State of the Cryosphere: Past, Present, Future

EGU23-676 | PICO | CR1.1

Partitioning the contribution of surface mass balance and ice discharge in Antarctic glacier mass variations (2003-2020) 

Byeong-Hoon Kim, Ki-Weon Seo, Choon-Ki Lee, Jae-Seung Kim, and Won Sang Lee

We partitioned Antarctic ice mass changes (2003-2020) into the contributions of surface mass balance (SMB) and ice discharge over 27 drainage basins, based on the combined estimates of satellite gravimetry and altimetry observations and a numerical SMB model. Our analysis indicates that the ice discharge has played a dominating role in ongoing ice mass losses and accelerations, especially in the glaciers near Amundsen and Bellingshausen Sea in West Antarctica. In particular, the mass losses in the Thwaites and Pine Island Glaciers have been mostly controlled by ice discharge, while the contribution of SMB has been minor. On the other hand, SMB contributed large portions of ice mass imbalance in East Antarctica, such as glaciers near the Dronning Maud Land and Wilkes Land. An inaccurate GIA model is a potential source of uncertainty in our estimates.

How to cite: Kim, B.-H., Seo, K.-W., Lee, C.-K., Kim, J.-S., and Lee, W. S.: Partitioning the contribution of surface mass balance and ice discharge in Antarctic glacier mass variations (2003-2020), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-676, https://doi.org/10.5194/egusphere-egu23-676, 2023.

EGU23-893 | ECS | PICO | CR1.1

Future evolution of glaciers in the Caucasus: focus on debris-cover evolution. 

Taisiya Postnikova, Oleg Rybak, Harry Zekollari, Matthias Huss, and Afanasy Gubanov

Debris-cover representation is rarely included in regional or global glacier models although it plays a key role in the regulation of melt processes. Debris cover that is more than a few centimeters thick reduces melt by insulating glacier ice. However, mass loss and retreat of debris-covered glaciers are not necessarily slower than those of clear ice. Debris-covered glaciers are widespread in the Northern Caucasus. It is important to reliably quantify their evolution because the contribution of glacial runoff to total discharge is significant in the region.

This study assesses the influence of debris cover on the evolution of glaciers in the basins of the Terek and Kuban rivers in the Northern Caucasus in the 21st century and quantifies its effects on glacier mass balance, ice velocity, ice thinning, changes in glacier area, volume, and position of the glacier fronts. We use the GloGEMflow glacier model and introduce a new debris cover dynamic module. The mass balance is calibrated separately for the explicitly modelled debris cover and for clean-ice glaciers (debris cover is implicit in the degree-day factor calibration). The model is calibrated using newly mapped debris cover outlines and ice thickness data from Rounce et al. (2021). The debris evolution is simulated with a steady deposit model adapted from Verhaegen et al. (2020) and Anderson & Anderson (2016), where debris input onto the glacier surface is generated from a fixed point on the flow line.

We compare spatio-temporal changes in glacier geometry including the evolution of debris cover for the explicit and implicit debris-cover formulation for five SSP scenarios from CMIP6. The debris-cover evolution patterns differ significantly between the Terek and the Kuban basins. In the Kuban basin, glaciers located generally at lower elevations, retreat rapidly and lose ice at the debris-covered glacier tongues. On the contrary, the supraglacial debris of the Terek basin glaciers may, under certain climate scenarios, expand and play an increasingly-important role in glacier evolution with time. However, under the high-end warming scenario SSP5-8.5, the ice loss by 2100 overwhelms the debris-cover effects in both regions.

The maximum difference in glacier length, area and volume depending on the explicit or implicit mode of debris-cover modeling occurs before 2100, but by the end of the century it is eliminated due to the retreat of debris-bearing parts of the glaciers or due to the elevation-stabilization effect. In general, explicitly accounting for debris cover in the projections only has a minor effect on the overall projected regional mass loss, but improves the representation of processes on the intra-glacier scale.

This study was carried out under Governmental Order to Water Problems Institute, Russian Academy of Sciences, subject no. FMWZ- 2022-0001, and was funded by the RSF grant number 22-17-00133.

How to cite: Postnikova, T., Rybak, O., Zekollari, H., Huss, M., and Gubanov, A.: Future evolution of glaciers in the Caucasus: focus on debris-cover evolution., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-893, https://doi.org/10.5194/egusphere-egu23-893, 2023.

EGU23-2326 | PICO | CR1.1 | Highlight

Insights into and findings from global datasets on glacier distribution and changes 

Michael Zemp, Ann Windnagel, Ethan Welty, Bruce Raup, Frank Paul, Samuel Nussbaumer, Fabien Maussion, Martin Hoelzle, Regine Hock, Isabelle Gärtner-Roer, and Etienne Berthier

Glacier monitoring has been internationally coordinated since the late 19th century. For the last 25 years (i.e., 1998-2023), the compilation and dissemination of global glacier datasets has been coordinated by the Global Terrestrial Network for Glaciers (GTN-G, https://gtn-g.org). Authorized under the Global Climate Observing System (GCOS) and supported by an international Advisory Board, GTN-G is jointly run by the science officers from the World Glacier Monitoring Service (WGMS, https://wgms.ch), the US National Snow and Ice Data Center (NSIDC, https://nsidc.org), and the Global Land Ice Measurements from Space initiative (GLIMS, https://glims.org), in collaboration with related working groups of the International Association of Cryospheric Sciences (IACS, https://cryosphericsciences.org/).

We present an updated overview of the various observational glacier datasets (https://www.gtn-g.ch/data_catalogue/), including world regions for regional glacier assessments (GTN-G Glacier Regions), glacier attributes (WGI: World Glacier Inventory), centerlines, and outlines (GLIMS, RGI: Randolph Glacier Inventory), ice velocities (ITS_LIVE), ice thickness (GlaThiDa: Glacier Thickness Database), glacier photographs (GPC: Glacier Photograph Collection), and glacier maps (GMC: Glacier Map Collection). For each dataset, we provide insights into the increased amount and richness of available data. We also demonstrate the value of these datasets by presenting selected findings from our own analyses as well as from user applications.

 

How to cite: Zemp, M., Windnagel, A., Welty, E., Raup, B., Paul, F., Nussbaumer, S., Maussion, F., Hoelzle, M., Hock, R., Gärtner-Roer, I., and Berthier, E.: Insights into and findings from global datasets on glacier distribution and changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2326, https://doi.org/10.5194/egusphere-egu23-2326, 2023.

EGU23-3329 | ECS | PICO | CR1.1

Regional modeling of peripheral glaciers in Greenland: Implications for mass balance, freshwater runoff, and sea level rise  

Muhammad Shafeeque, Jan-Hendrik Malles, Anouk Vlug, Ben Marzeion, Marco Möller, and Julia Eis

Peripheral glaciers (PGs, i.e., glaciers that are dynamically decoupled from the ice sheet) play a significant role in the mass balance and freshwater runoff of Greenland's land ice. Their evolution has implications for the sea level, global climate and ocean circulation, as well as for coastal communities and ecosystems. In order to accurately model the contribution of PGs to Greenland's overall mass balance and freshwater supply, it is necessary to consider their characteristics that set them apart from the main ice sheet. In this study, we used the Open Global Glacier Model (OGGM) to conduct large-scale regional modeling of approximately 19,000 PGs and project future mass losses, freshwater runoff, and sea level contributions under different climate scenarios. Our results show that PGs are likely to experience 29 % to 52 % of mass loss compared to their 2020 levels by the end of the 21st century, resulting in 10 mm to 19 mm of sea level rise under SSP126 and SSP585, respectively. Under the high emission scenario, PGs are expected to contribute 184 Gt yr-1 of liquid freshwater and 3 Gt yr-1 of calved solid ice to the ocean during 2020-2100, affecting ocean density, circulation, and mixing. Peakwater is projected for the 2080s under SSP585, after which annual freshwater contributions are expected to decline due to reduced glacier area. Regional mass and freshwater balance differences were found to be influenced by local climate, ocean-ice interaction, proportion of marine-terminating glaciers, and initial glacier volume. The central-west, southeast, and central-east subregions will experience the largest mass losses (79 %, 69 %, and 63 % of mass in 2020), with peakwater occurring earlier than for all PGs considered together under SSP585. The northeast subregion is expected to contribute 35 % of total liquid freshwater, 73 % of solid ice calving, and 37 % of sea level rise, despite lower mass loss (wrt 2020) due to regional conditions (i.e., climate, glacier geometries, and surrounding ocean). Submarine melting is likely to impact the mass balance of marine-terminating PGs, but further research is needed to explore this effect at a regional scale. This study highlights the importance of considering the distinct behavior of PGs in modeling Greenland's freshwater balance and understanding the factors that influence their mass changes.

 

How to cite: Shafeeque, M., Malles, J.-H., Vlug, A., Marzeion, B., Möller, M., and Eis, J.: Regional modeling of peripheral glaciers in Greenland: Implications for mass balance, freshwater runoff, and sea level rise , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3329, https://doi.org/10.5194/egusphere-egu23-3329, 2023.

EGU23-3720 | PICO | CR1.1

Future changes in runoffs in the headwaters of the Tarim River 

Gonghuan Fang and Yaning Chen

The Tarim River Basin, situated in the Eurasia hinterland, serves as the heart of China’s Silk Road Economic Belt. It covers an area of 1.02 million km2 and is surrounded by the Tienshan Mountains to the north, the Kunlun Mountains to the south and the Pamir to the west. The runoff is recharged by glacier melt, snow melt, and rainfall. There are large amount of glaciers distributed in the high mountains of Tienshan and the Kunlun Mountains. In recent decades, the glaciers in the Tienshan Mountains are retreating at a faster rate, while glacier in the Kunlun Mountains show less retreat or even advancing trend (i.e., Karakorum anomaly). These changes in glaciers will definitely alter the future runoff.

This study extended the hydrological model SWAT-Glacier by incorporating a degree-day based glacier melt and accumulation module. The hydrological processes of the headwaters of the Tarim River were simulated using SWAT-Glacier model. The model was calibrated and validated using multiple variables, including glacier mass balance, snow cover area, snow water equivalent, daily streamflow and the balance between snowfall, snow melt and sublimation. The hydrological model was forced by the bias-corrected climate from 6 regional climate models in CORDEX. Results indicated that the runoffs of the headwaters originated from the south Tienshan Mountains (i.e., Kaidu River, Aksu River) demonstrated a slight increase or even decrease trend. For the Kaidu River, there will be a slight decrease in runoff under SSP585, as the contribution of glacier melt water is less than 10%. For the Kumarak River, the runoff showed slightly increase and the glacier melt runoff will reach peak point before 2050s. For the rivers originated from the north Kunlun and Karakorum Mountains, the runoff will increase dramatically.

This study provide a basin-scale runoff changes under multiple constraints in the endoreic Tarim River Basin. However, the glacier accumulation and ablation suffers from great uncertainty as the precipitation observation is rare in the high mountains. More efforts should be taken to utilize more state-of-the-art technology in revealing the meteorological and hydrological processes in these alpine catchments.

How to cite: Fang, G. and Chen, Y.: Future changes in runoffs in the headwaters of the Tarim River, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3720, https://doi.org/10.5194/egusphere-egu23-3720, 2023.

EGU23-5944 | ECS | PICO | CR1.1

GlaMBIE – An intercomparison exercise of regional and global glacier mass changes 

Livia Jakob, Michael Zemp, Noel Gourmelen, Ines Dussaillant, Samuel Urs Nussbaumer, Regine Hock, Etienne Berthier, Bert Wouters, Alex S. Gardner, Geir Moholdt, Fanny Brun, and Matthias H. Braun

Retreating and thinning glaciers are icons of climate change and impact the local hazard situation, regional runoff as well as global sea level. For past reports of the Intergovernmental Panel on Climate Change (IPCC), regional glacier change assessments were challenged by the small number and heterogeneous spatio-temporal distribution of in situ measurement series and uncertain representativeness for the respective mountain range as well as by spatial and temporal limitations and technical challenges of geodetic methods. Towards IPCC SROCC and AR6, there have been considerable improvements with respect to available geodetic datasets. Geodetic volume change assessments for entire mountain ranges have become possible thanks to recently available and comparably accurate digital elevation models (e.g., from ASTER or TanDEM-X). At the same time, new spaceborne altimetry (CryoSat-2, IceSat-2) and gravimetry (GRACE-FO) missions are in orbit and about to release data products to the science community. This opens new opportunities for regional evaluations of results from different methods as well as for truly global assessments of glacier mass changes and related contributions to sea-level rise. At the same time, the glacier research and monitoring community is facing new challenges related to the spread of different results as well as new questions with regard to best practises for data processing chains and for related uncertainty assessments.In this presentation, we introduce the Glacier Mass Balance Intercomparison Exercise (GlaMBIE) project of the European Space Agency, which is building on existing activities and the network of the International Association of Cryospheric Sciences (IACS) working group on Regional Assessments of Glacier Mass Change (RAGMAC) to tackle these challenges in a community effort. We will present our approach to develop a common framework for regional-scale glacier mass-change estimates towards a new data-driven consensus estimate of regional and global mass changes from glaciological, DEM-differencing, altimetric, and gravimetric methods.

How to cite: Jakob, L., Zemp, M., Gourmelen, N., Dussaillant, I., Nussbaumer, S. U., Hock, R., Berthier, E., Wouters, B., Gardner, A. S., Moholdt, G., Brun, F., and Braun, M. H.: GlaMBIE – An intercomparison exercise of regional and global glacier mass changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5944, https://doi.org/10.5194/egusphere-egu23-5944, 2023.

EGU23-9828 | PICO | CR1.1

Uncertainty assessment of modeling the impact of debris cover on global glacier mass change: challenges and solutions 

Seyedhamidreza Mojtabavi, David Rounce, Fabien Maussion, and Ben Marzeion

It is essential to test the potential impact of processes missing in the global-scale models that are used to project on glacier mass balance in the glacier models, such as the modification of the mass balance through debris-cover. In this study, we evaluate the impact of a parameterization of debris cover on glacier mass change projections using the Open Global Glacier Model (OGGM). The assessment of uncertainties about the potential impact of debris cover on global scale modeling is complicated by the scarcity of suitable data on debris-covered glaciers for validation (e.g., mass balance measurements for individual elevation bins on debris-covered glaciers). To calibrate and validate the mass balance module, we rely on glacier-wide geodetic volume changes. Debris cover can enhance ice melting if less than a few centimeters thick, or decrease ice melting through insulation of the underlying ice by a thick layer of debris. Ice cliffs, supraglacial ponds and streams associated with debris cover may increase the absorption of heat and increase ice melting. In OGGM, the effects of debris cover are parameterized by a simple modification of the mass balance module, through introducing an elevation-dependent temperature sensitivity parameter (“degree-day factor”) and including a debris-related melt correction factor. While debris cover plays only a minor role on glacier mass change on the global scale, this becomes important on regional and individual glacier scales. Our results show the effect of debris cover could improve model performance on the mass balance gradient, not the overall mass balance. To validate our results on the mass balance gradient, we rely on the geodetic mass balance for each elevation band.

How to cite: Mojtabavi, S., Rounce, D., Maussion, F., and Marzeion, B.: Uncertainty assessment of modeling the impact of debris cover on global glacier mass change: challenges and solutions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9828, https://doi.org/10.5194/egusphere-egu23-9828, 2023.

EGU23-10429 | ECS | PICO | CR1.1 | Highlight

Reconstruction of Glacier Mass Balance with Surface Energy Balance Modeling across Southwestern Canada 

Christina Draeger and Valentina Radic

Current state-of-the-art glacier models for regional and global scales mostly rely on empirical models, such as temperature-index models, which require glacier-specific calibration with in-situ mass balance measurements. In the absence of these measurements, the models suffer from large uncertainties in their projections of glacier mass changes, especially at local scales. One way to address this issue is to transition from the empirical models toward more physics-based models, such as surface energy balance (SEB) models of glacier melt. In this study, we evaluate the performance of a glacier evolution model based on a SEB model with minimal calibration for nearly 15,000 glaciers in Southwestern Canada for the period of 1979–2021. The SEB model is forced with ERA5 reanalysis data with minimal bias corrections or statistical downscaling. The empirical models for accumulation and albedo are, however, calibrated to maximize the match between simulated and observed glaciological mass balance availabe for about 20 glaciers in this region. The simulated regional mass balance and area change are then evaluated against the geodetic mass balance as observed for all glaciers in the region over the last two decades. This study contributes to a better understanding of the applicability of SEB models with minimal calibration in regional glaciation modeling in order to narrow uncertainties in glacier melt projections.

How to cite: Draeger, C. and Radic, V.: Reconstruction of Glacier Mass Balance with Surface Energy Balance Modeling across Southwestern Canada, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10429, https://doi.org/10.5194/egusphere-egu23-10429, 2023.

EGU23-11983 | ECS | PICO | CR1.1

Glacier projections sensitivity to temperature-index model and climate downscaling parameter calibration choices 

Lilian Schuster, David Rounce, and Fabien Maussion

A recent large-scale glacier model intercomparison revealed a strong influence of model design choice on glacier projections. Here we examine the influence of various temperature-index mass-balance models and calibration options. With the Open Global Glacier Model (OGGM) framework, we compare the performance and projections of model options such as the use of surface-type dependent degree-day factors as well as varying temporal climate resolution (daily, monthly) and downscaling strategies (temperature lapse rates, temperature and precipitation correction). We focus on 88 glaciers with long term observations of mass-balance profiles and seasonal mass-balance, allowing us to assess the added value of using multiple mass-balance statistics in the calibration process. We find that using interannual mass-balance variability to calibrate otherwise fixed parameters generally leads to an improved representation of the mass-balance gradient, which in turn is a crucial explanatory variable for future glacier evolution. Therefore, we also find a strong influence of the calibrated temperature lapse rates on future glacier volume. Using different degree-day factors for snow, firn, and ice leads to nonlinear sensitivities, where future glacier loss depends on how the accumulation area changes compared to the calibration period. Our study illustrates the strong impact of temperature-index model choice on projected glacier volume and runoff. But it cannot clearly demonstrate the added value of additional model complexity due to the lack of independent observations.

How to cite: Schuster, L., Rounce, D., and Maussion, F.: Glacier projections sensitivity to temperature-index model and climate downscaling parameter calibration choices, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11983, https://doi.org/10.5194/egusphere-egu23-11983, 2023.

EGU23-12836 | PICO | CR1.1

Examining the impact of climatic and non-climatic attributes on glacier mass budget and surging in Alaknanda Basin, India 

Atanu Bhattacharya, Kriti Mukherjee, Owen King, and Tobias Bolch

 Glacier meltwater is a significant component of the regional runoff In High Mountain Asia (HMA),. However, the majority of the HMA's glaciers are rapidly losing their mass, putting the long-term viability of meltwater as a component of river flow at risk. It is, hence, crucial to comprehend the long-term glacier response to climate change at the regional scale as well as the impact of non-climatic characteristics like morpho-topographic factors on ice loss. We estimate changes for 445 glaciers in the upper Alaknanda basin and neighboring transboundary glaciers using multi-temporal optical satellite images from 1973 to2020. Our measurements indicate a mean annual area change of −1.14 ± 0.07 m a–1 and a geodetic glacier mass balance of −0.34 ± 0.08 m w.e.a–1 for the whole period. Before 2000 (1973-2000), the mean regional glacier mass loss rate was -0.30 ± 0.07 m w.e.a-1, which increased to -0.43 ± 0.06 m w.e.a-1 during 2000-2020. The mass loss increased further (-0.68 ± 0.09 m w.e.a-1) in the recent period (2015-2020) and we observed heterogeneous mass loss both in spatial and temporal scales. Our analysis revealed that the current significant glacier imbalance is probably a result of the rising temperature trend as revealed from the ERA5 Land reanalysis data. An extended ablation season due to the strong seasonal temperature increase has further accelerated glacial mass loss. Steep and higher elevation glaciers were less affected by negative mass budget. This can be explained beside the lower average temperatures at higher elevation by a rapid transfer of snow and ice that helped them to readjust their geometry compared to glaciers at lower elevation, having more gentle slopes and lower dynamics. Such low elevation glaciers are unlikely to recover in coming decades if the current trend of warming continues. We also identified a surging glacier draining onto the Tibetan Plateau that advanced rapidly by around 800 m within three months in Sep-Dec 2019. The advance is still ongoing, though at a much-reduced rate. Our temporally detailed measurements of glacier change provide an in-depth view of glacier evolution in the Alaknanda Basin and will improve the estimation of meltwater run-off component of the hydrological cycle. 

How to cite: Bhattacharya, A., Mukherjee, K., King, O., and Bolch, T.: Examining the impact of climatic and non-climatic attributes on glacier mass budget and surging in Alaknanda Basin, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12836, https://doi.org/10.5194/egusphere-egu23-12836, 2023.

EGU23-13056 | ECS | PICO | CR1.1 | Highlight

Future glacier and runoff evolution in the Tien Shan mountains 

Lander Van Tricht, Harry Zekollari, Daniel Farinotti, Matthias Huss, Loris Compagno, and Philippe Huybrechts

Glaciers and ice caps in the Tien Shan mountains play a crucial role in terms of water supply for irrigation, industry and drinking. The retreat of these ice masses can consequently have a major impact on downstream densely populated dry lowland areas. Therefore, it is crucial to understand how ice masses in the Tien Shan are reacting to climate change and how they will evolve in the future. In this study, we model the future evolution of all glaciers and ice caps in the Tien Shan mountains under CMIP6 SSP climate scenarios using the large-scale GloGEMflow model. The model is calibrated to match glacier-specific geodetic mass balances while accounting for debris cover under recent climatic conditions (downscaled climate reanalysis, ERA5). In our modelling framework, we rely on a total of six independent ice thickness datasets, of which the effect on the modelled future glacier evolution is analysed in detail. Our results reveal an exceptionally pronounced retreat of most of the ice masses under all climate scenarios (vs. other regions), which can be related to the specific climate regime. Since most of the precipitation on Tien Shan glaciers occurs in spring and early summer, temperature increases not only increase melt (as is the case in most regions around the world), but additionally strongly influence the precipitation type (solid vs. liquid). In all scenarios, the total runoff for the major river catchments in the Tien Shan is projected to drastically reduce by the end of the 21st century. Further, peak water is modelled to be reached before the middle of the century and the annual runoff peak is anticipated to shift from early summer towards late spring.

How to cite: Van Tricht, L., Zekollari, H., Farinotti, D., Huss, M., Compagno, L., and Huybrechts, P.: Future glacier and runoff evolution in the Tien Shan mountains, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13056, https://doi.org/10.5194/egusphere-egu23-13056, 2023.

EGU23-13665 | PICO | CR1.1 | Highlight

Bedmap3: new data and gridded products of Antarctic ice thickness, surface and bed topography 

Alice Fremand, Hamish Pritchard, Peter Fretwell, and Julien Bodart

We present Bedmap3, the first comprehensive and openly available compilation of Antarctic Ice Sheet survey datasets, plus the latest gridded mapping products of ice thickness and the surface and bed topography of the whole Antarctic continent and continental shelf. For 60 years, scientists have strived to understand the past, present and future of the ice sheet, a goal that has become ever more urgent as ice loss accelerates. Key to this research has been the mapping of the bed-topography, surface slope and ice-thickness parameters that are crucial for modelling ice flow, and hence for predicting future ice loss and ensuing sea level rise. Supported by the Scientific Committee on Antarctic Research (SCAR) and data contributions from the international survey community, the Bedmap3 Action Group has now produced substantially updated gridded maps of these parameters and, for the first time, has standardized and made available all underlying geophysical survey data points from all Antarctic ice-thickness survey campaigns since the 1950s. Here, we describe the standardization that makes these and future datasets accessible under the ‘Findable, Accessible, Interoperable and Reusable’ (FAIR) data principles, allowing scientists to re-use these data freely for their own analysis. We also describe the results of our new mapping, and how this changes our view of Antarctica’s hidden landscapes and its potential to dominate future sea level rise.

How to cite: Fremand, A., Pritchard, H., Fretwell, P., and Bodart, J.: Bedmap3: new data and gridded products of Antarctic ice thickness, surface and bed topography, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13665, https://doi.org/10.5194/egusphere-egu23-13665, 2023.

EGU23-1062 | ECS | Orals | CR1.2

Automated ice ablation readings reveal the significance of summer heat waves for glacier melt 

Aaron Cremona, Matthias Huss, Johannes Marian Landmann, Joël Borner, and Daniel Farinotti

Summer heat waves have a substantial impact on glacier melt as emphasized by the extreme summer of 2022 that caused unprecedented mass losses to the Swiss glaciers. Despite the dramatic impact on glaciers, the summer of 2022 offered a unique opportunity to analyze the implications that such extraordinary events have on glacier melt and related runoff release.

This study presents a novel approach based on computer-vision techniques for automatically determining daily mass balance variations at the local scale. The approach is based on the automated recognition of color-taped ablation stakes from camera images acquired at six sites on three Alpine glaciers in the period 2019-2022. The validation of the method revealed an uncertainty of the automated readings of ±0.81 cm d-1. By comparing the automatically retrieved mass balances at the six sites with the average mass balance of the last decade derived from seasonal in situ observations, we detect extreme melt events in the summer seasons of 2019-2022.

The in-depth analysis of summer 2022 allows us to assess the impact that the summer heat waves have on glacier melt. With our approach we detect 23 days with extreme melt over the summer, emphasizing the strong correspondence between heat waves and extreme melt events. The Swiss-wide glacier mass loss during the 25 days of heat waves in 2022 is estimated as 1.27 ± 0.10 Gt, corresponding to 35% of the overall glacier mass loss in the summer of 2022. As compared to the 2010-2020 average glacier mass change, days with extreme melt in 2022 correspond to 56% of the mass change during the summer period, thus demonstrating the significance of heat waves for seasonal melt.

How to cite: Cremona, A., Huss, M., Landmann, J. M., Borner, J., and Farinotti, D.: Automated ice ablation readings reveal the significance of summer heat waves for glacier melt, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1062, https://doi.org/10.5194/egusphere-egu23-1062, 2023.

EGU23-2555 | ECS | Posters on site | CR1.2

The Changes of Hailuogou Glacier in the Southeastern Tibetan Plateau and the Impacts on Glacier Dynamics from the Mechanical Ablation 

Shuyang Xu, Ping Fu, Duncan Quincey, Meili Feng, Stuart Marsh, Qiao Liu, and Tian Jia

Glaciers in the Tibetan Plateau are melting at an unprecedented rate in the context of global warming. Hailuogou (HLG) Glacier, a rapidly receding temperate land-terminating glacier in the southeastern Tibetan Plateau, has been observed to lose mass partly through ice frontal mechanical ablation (i.e., ice collapse).

In this study, we present analysis from Uncrewed Aerial Vehicles (UAV) surveys conducted over nine field campaigns to the HLG Glacier, providing evidence of glacier change and frontal ice collapse between 2017 and 2021. Structure from Motion with Multi-View Stereo was applied to produce multi-temporal Digital Surface Models (DEMs) and orthophoto mosaics, from which geomorphological maps and DEMs of Difference were derived to quantify the changes of the glacier snout and the ice loss from frontal ice collapse. Based on that, a linear correlation of Area-Volume for frontal ice collapse was subsequently built. Planet images were used to identify additional ice collapse events (i.e., 2017 to 2021) and to extract time-sequenced glacier extents. ASTER-derived DEMs generated by NASA Ames Stereo Pipeline (ASP) were then differenced to calculate the ice volume changes in the period. Combined with frontal ice collapse events identified from Planet, the contribution of that to the glacier mass balance can be estimated from the established Area-Volume correlation.

These analyses reveal that at the margins of the glacier terminus retreated 132.1 m over the period of analysis, and that in the area specifically affected by collapsing (i.e., the glacier collapsed terminus), it retreated 236.4 m. Overall the volume lost in the terminal area was of the order of 184.61 ± 10.32 x 104 m3, within which the volume change due to observed collapsing events comprises approximately 28%. We show that ice volume changes at the terminus due to a single ice collapse event may exceed the interannual level of volume change, and the daily volume of ice loss due to ice calving exceeds the seasonal and interannual level by a factor of ~ 2.5 and 4. The contribution to the mass balance change of the entire glacier that is attributed to frontal ice collapse is limited (i.e., ranges from 0.48% to 1.12% from 2017 to 2021). However, the mechanical ablation (e.g., frontal ice collapse and subglacial/englacial conduit’s roof collapse) has probably changed the way of losing ice mass to some extent.

Our results suggest that the evolution of the HLG Glacier terminus is dominantly controlled by the frontal ice collapse. The projection of the recession rate of the HLG Glacier may well be underestimated if based on surface mass balance alone, as the frontal ice collapsing might be more frequent and larger under the context of warming. If the future evolution of glaciers such as HLG Glacier is to be robustly predicted, the contribution of mechanical ablation should be accounted for by numerical models.

How to cite: Xu, S., Fu, P., Quincey, D., Feng, M., Marsh, S., Liu, Q., and Jia, T.: The Changes of Hailuogou Glacier in the Southeastern Tibetan Plateau and the Impacts on Glacier Dynamics from the Mechanical Ablation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2555, https://doi.org/10.5194/egusphere-egu23-2555, 2023.

EGU23-2824 | ECS | Orals | CR1.2

Calibrating Surface Mass Balance Models at the Monte Sarmiento Massif, Tierra del Fuego 

Franziska Temme, David Farías-Barahona, Thorsten Seehaus, Ricardo Jaña, Jorge Arigony-Neto, Inti Gonzalez, Anselm Arndt, Tobias Sauter, Christoph Schneider, and Johannes J. Fürst

Similar to the Patagonian Icefields, the Cordillera Darwin Icefield in Tierra del Fuego experienced important ice loss during the last decades. The difficult accessibility and the harsh weather conditions in that area result in scarce in-situ observations of climatic conditions and glacier mass balances. Under these challenging conditions, this study investigates calibration strategies of surface mass balance models in the Monte Sarmiento Massif, western Cordillera Darwin, with the goal to achieve realistic simulations of the regional surface mass balance in the period 2000-2022.

We apply three calibration strategies ranging from a local single-glacier calibration to a regional calibration with and without the inclusion of a snowdrift parametrization. Furthermore, we apply four models of different complexity ranging from an empirical degree-day model to a fully-fledged surface energy balance model. This way, we examine the model transferability in space, the benefit of including regional mass change observations as calibration constraint and the advantage of increasing the model complexity regarding included processes. In-situ measurements comprise ablation stakes, ice thickness surveys and weather station records at Schiaparelli Glacier as well as elevation changes and flow velocity from satellite data for the entire study site. Performance of simulated surface mass balance is validated against geodetic mass changes and stake observations of surface melting.

Results show that transferring mass balance models in space is a challenge, and common practices can produce distinctly biased estimates. The use of remotely sensed regional observations can significantly improve model performance. Increasing the complexity level of the model does not result in a clear improvement in our case where all four models perform similarly. Including the process of snowdrift, however, significantly increases the agreement with geodetic mass balances. This highlights the important role of snowdrift for the surface mass balance in the Cordillera Darwin, where strong and consistent westerly winds prevail.

How to cite: Temme, F., Farías-Barahona, D., Seehaus, T., Jaña, R., Arigony-Neto, J., Gonzalez, I., Arndt, A., Sauter, T., Schneider, C., and Fürst, J. J.: Calibrating Surface Mass Balance Models at the Monte Sarmiento Massif, Tierra del Fuego, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2824, https://doi.org/10.5194/egusphere-egu23-2824, 2023.

EGU23-6319 | ECS | Posters on site | CR1.2

Glacier mass balance and its climatic and non-climatic drivers in the Ladakh region during 2000-2021 from remote sensing data 

Arindan Mandal, Bramha Dutt Vishwakarma, Thupstan Angchuk, Mohd Farooq Azam, Purushottam Kumar Garg, and Mohd Soheb

The Ladakh region in the western Himalaya relies directly on snow-glacier-fed first order streams for domestic and agricultural needs of the population. Despite the significant contribution of glacier meltwater towards community livelihood and its vulnerability in a warmer climate, glaciers in the Ladakh region have not been studied comprehensively. Previous studies, mostly at a poor spatial (~90 m) and temporal (10-15 years) scales, focused only on geodetic glacier mass balance estimation, hence their controlling climatic drivers remain unknown. In this study, we estimate the geodetic mass balance of the glaciers of the Ladakh region, using multiple digital elevation models of 30 m resolution acquired between 2000 (Shuttle Radar Topography Mission; SRTM) and 2021 (Advanced Spaceborne Thermal Emission and Reflection Radiometer; ASTER). Due to the large aerial coverage, we divided the whole Ladakh region into two sub-regions namely the eastern and western Ladakh. The primary climatic drivers of glacier mass balances were examined using the long-term ERA5-Land reanalysis data, complemented by available in-situ meteorological data. The role of non-climatic (morphological) variables on glacier mass balances was also investigated in detail. The results reveal a negative glacier mass balance over the Ladakh region during the last two decades, with significant spatial variability. Glaciers in western Ladakh lost higher mass (-0.35 ± 0.07 to -0.37 ± 0.07 m w.e. a-1) compared to eastern Ladakh (-0.21 ± 0.07 to -0.33 ± 0.05 m w.e. a-1). Although the widespread mass loss in Ladakh is primarily caused by warming, the variations in spatial mass loss are primarily caused by the morphological settings of the glaciers. The eastern Ladakh glaciers are located at higher elevations and small sized, whereas western Ladakh glaciers are large sized and their tongues are situated at lower elevations (low-elevation-hypsometry), therefore, the impact of temperature is much higher in them, leading to higher mass loss. The non-climatic factors (morphological control) exhibit a dominant role than climatic factors in governing the glacier mass balances, particularly in the EL. The comparison between ASTER-based and the Ice, Cloud and land Elevation Satellite (ICESat)-2 laser altimetry-based mass balances shows a good agreement, reaffirming the robustness of regional mass balance estimates. Overall, glaciers of the Ladakh region are losing mass and the western Ladakh glaciers are potentially more susceptible to warming climate compared to the eastern Ladakh.

How to cite: Mandal, A., Vishwakarma, B. D., Angchuk, T., Azam, M. F., Garg, P. K., and Soheb, M.: Glacier mass balance and its climatic and non-climatic drivers in the Ladakh region during 2000-2021 from remote sensing data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6319, https://doi.org/10.5194/egusphere-egu23-6319, 2023.

Kersten Glacier, a slope glacier on the southern flank of Kilimanjaro, has been observed to shrink for many decades. Quantitatively, the glacier’s mass balance has been studied by Mölg et al. (2009) with a distributed physically based mass balance model. In this study, the research question is revisited using the open-source COupled Snowpack and Ice surface energy and mass balance model in PYthon (COSIPY; Sauter et al., 2020). A spatially distributed simulation of the surface energy and mass balance of Kersten Glacier is performed for February 2005 to January 2008. The model is driven by hourly observations from an automated weather station in the top region of the glacier at 5873 m MSL. In this contribution, we present findings such as the different components of the mass and energy balance, their temporal variation and elevational characteristics, and compare them to the results obtained from the 2009 analysis. This should allow a first assessment of COSIPY’s skill as a future tool for simulating snow and ice variability in equatorial latitudes.

How to cite: Schramm, M. and Mölg, T.: Comparative study of the surface energy and mass balance of Kersten Glacier on Mt. Kilimanjaro: COSIPY versus previous modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6623, https://doi.org/10.5194/egusphere-egu23-6623, 2023.

EGU23-7645 | ECS | Posters on site | CR1.2

Development of supraglacial meltwater streams and their influence on the morphology of debris-covered glacier surfaces. 

Boris Ouvry, Marin Kneib, Ross S. Purves, and Andreas Vieli

Debris-covered glacier tongues are widespread in high-relief mountains and are characterised by highly undulated surfaces with supraglacial ponds and circular cliffs in hummocky topography. These features are known to strongly enhance mass loss on debris-covered surfaces and have been widely mapped, but their formation mechanisms and underlying controls have not been studied in detail.

Here we aim to investigate the role of supraglacial streams on the morphological development of debris-covered glacier surfaces and related supraglacial features such as ice cliffs based on high-resolution DEMs and orthophotos (Pleiades and UAV) from two debris-covered glaciers of contrasting spatial scales: the Satopanth Glacier located in the Indian Himalaya and the Zmuttgletscher in the European Alps. We systematically analyse the morphological development of the debris-covered surface along the glacier from the onset zone of debris cover and supraglacial channels down to the hummocky and sunken tongue surfaces. We perform this using a semi-automated approach that includes meltwater flow routing, the extraction of surface roughness, profiles and extents of supraglacial channel-influenced valleys, as well as the mapping of ice cliffs.

Based on this analysis, we find a clear and coherent succession of morphological developments along both glaciers that seems initiated through erosion from supraglacial streams. On the initially smooth debris-covered surface, locally incised and meandering channels initiate ice cliffs that progressively backwaste, creating a downstream-widening supraglacial valley with an undulated surface. This ‘mobility area’ is advected downstream even beyond the moulins where the supraglacial channels drain to the bed. Further downstream, neighbouring ‘mobility area’ valleys laterally merge and create the quasi-chaotic highly undulated surfaces typically observed on tongues of debris-covered glaciers. We integrate these interpretations into a conceptual model that links the downstream morphological development of debris-covered surfaces and explains the genesis of related features such as ice cliffs.

How to cite: Ouvry, B., Kneib, M., Purves, R. S., and Vieli, A.: Development of supraglacial meltwater streams and their influence on the morphology of debris-covered glacier surfaces., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7645, https://doi.org/10.5194/egusphere-egu23-7645, 2023.

EGU23-7890 | Posters on site | CR1.2

Improvements in the estimation of glacier surface mass balance taking into account albedo decay parameters 

Javier Calleja, Rubén Muñiz, Francisco Navarro, Jaime Otero, and Susana Fernández

The search for satellite-derived proxies of the surface mass balance (SMB) of glaciers is of crucial importance for an updated estimation of the SMB worldwide. The minimum mean  glacier albedo (αmin, calculated as the minimum mean albedo over the whole glacier) attained along a season has proved to be a good proxy. In this work we demonstrate that SMB estimations can be improved by adding albedo decay parameters as predicting variables. The SMB of Hurd glacier (Livingston Island, Antarctica) has been continuously monitored since 2001, with available values of annual, summer and winter SMB. MODIS MOD10A1 daily snow albedo product with a spatial resolution of 500 m over the glacier was downloaded using the Google Earth Engine Application Programming Interface. Data from 2000-2001 to 2020-2021 season are considered in this work. MOD10A1 data were filtered using a maximum filter followed by a first-order Butterworth filter. Each season extends from September to March of two consecutive years. The seasonal albedo was fitted to an exponential decay α=αm+Aexp(-βt), and parameters αm, A and β as well as the albedo decay duration (D) were calculated for all pixels. Mean values of αm, β, A and D were calculated over the whole glacier. Monthly and seasonal mean αmin were also estimated from MOD10A1 data. Simple linear regressions show that the minimum albedo in the period January-February explains the summer SMB, while the minimum albedo in the period December-January explains the annual SMB. Multiple linear regression models including snow albedo decay parameters improve the quality of the models, increasing the value of the coefficient of determination and decreasing the root mean square difference between measured and predicted SMB. These results show that the SMB is determined not only by αmin but also by how fast and for how long the albedo decay takes place, and by the difference between the minimum albedo and the surface albedo at the beginning of the season. On the other hand, most of the snow albedo decay takes place in the period from late September to December. Previous investigations of mass balance over the Hurd Peninsula have established that the snow melt in Hurd Peninsula takes place mostly from December to March. The fact that snow melting lags behind albedo decay can be explained if we consider that some surface snow metamorphic processes occur prior to melting and that melting continues after surface snow has attained its maximum degree of metamorphism.

How to cite: Calleja, J., Muñiz, R., Navarro, F., Otero, J., and Fernández, S.: Improvements in the estimation of glacier surface mass balance taking into account albedo decay parameters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7890, https://doi.org/10.5194/egusphere-egu23-7890, 2023.

EGU23-9820 | Orals | CR1.2

A nine-year, airborne laser scanning archive of glacier change, western Canada. 

Brian Menounos, Derek Heathfield, Steve Beffort, Nick Viner, Santiago Gonzalez Arriola, and Rob White

Western Canada contains 72% of the glaciers within the Randolph Glacier Inventory (RGI) region 2 (Western Canada and USA), and these glaciers constitute 95% of the region’s total ice cover. Recent studies exploiting stereoscopic imagery from NASA’s Terra satellite (ASTER) have reduced biases in the number, type and distribution of glaciers used to assess regional glacier mass change. The elevation uncertainty of ASTER digital terrain models, in addition to infrequent sampling, confounds its use to detect trends in elevation change at seasonal scales or for small glaciers (< 1km2). Since summer 2014, the Hakai-UNBC Airborne Coastal Observatory (ACO) routinely acquires laser altimetric data at the end of the accumulation (late-April to early May) and ablation (early to late September) over many of western Canada’s glaciers and icefields using an aircraft equipped with an 1064-nm laser scanner and dedicated positional hardware. Post-processed uncertainties of repeated, co-registered elevational data over stable terrain yield uncertainties that are typically below ±0.3 m (±1s). Since 2020, our bi-annual acquisitions sample over 800 glaciers (about 2,000 km2), which constitutes about 15% of the total areal extent of ice in RGI-02. While the area-altitude distribution of ACO sampled glaciers largely accord with those of RGI-02, our sampling program captures fewer glaciers that exist at highest elevations, and the average glacier size surveyed by us is about three times larger than the average glacier size within RGI-02 (0.77 km2). Our archive reveals important aspects of glacier elevation change that cannot be obtained from existing publicly available sources of digital terrain data such as the magnitude of seasonal to inter-annual changes during the accumulation and ablation seasons, short-term horizontal transfers of mass, changes in volume and extent of transient late-lying snow, and the effects of short-term meteorological events (e.g. heat waves or forest fires) on regional melt events for a given year. We plan to release this archive to the public so it can be used to validate in-situ mass balance measurement programs, improve melt models and provide insight into physical factors that drive glacier change.

How to cite: Menounos, B., Heathfield, D., Beffort, S., Viner, N., Gonzalez Arriola, S., and White, R.: A nine-year, airborne laser scanning archive of glacier change, western Canada., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9820, https://doi.org/10.5194/egusphere-egu23-9820, 2023.

EGU23-10964 | ECS | Orals | CR1.2

Dynamics of the Panchi Nala glacier, western Himalaya: trends and controlling factors. 

Mohit Prajapati, Purushottam Kumar Garg, Aparna Shukla, and Supratim Guha

Information on glacier velocity is imperative to understand glacier mass, ice volume, topography, surge events of the glacier and response to climate change. Therefore, inter-annual surface ice velocity (SIV) of the Panchi Nala glacier has been calculated in the current study between the first two decades of the twenty-first century. To do so, the SIV has been computed by the feature tracking technique using the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) method applied on the multi-temporal Landsat (TM and OLI) and sentinel -2 MSI images acquired between 2000 and 2021. The results of the study show that the mean velocity of the debris-covered tongue of the Panchi Nala Glacier is ∼10.60 ± 5.56 m/y during the study period. Additionally, the highest average glacier velocity is 13.77 ± 4.64 m/y, whereas the lowest is 8.92 ± 2.78 m/y, respectively, observed in 2005 and 2015. Also, the 95% confidence interval of the mean annual velocity lies between 9.76 and 11.43 m/y during the entire study period. The annual heterogeneity is linked with the variation of summer precipitation. Statistically, a 100 mm increment of summer precipitation can reduce the velocity around 1.3 m/y. The main reason behind this is the Panchi Nala glacier is located in high-elevation where the climate is much colder and during the summer precipitation, the lower temperatures cause the precipitation to take the form of snow, which freezes and accumulates on the glacier. This reduces the process of basal sliding. Further, detailed investigations with additional parameters need to be carried out to elucidate the comprehensive causes for inter-annual fluctuations in surface velocity. In this perspective, future research maybe directed towards higher temporal and spatial scale remote sensing-based investigations and validation of glacier surface velocity using field measurements, to better understand the glacier dynamics.

Keywords: Glacier surface ice velocity; debris cover; climate change; western Himalayas.

How to cite: Prajapati, M., Garg, P. K., Shukla, A., and Guha, S.: Dynamics of the Panchi Nala glacier, western Himalaya: trends and controlling factors., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10964, https://doi.org/10.5194/egusphere-egu23-10964, 2023.

EGU23-10966 | Posters on site | CR1.2

Frontal collapse of San Quintín glacier (Northern Patagonia Icefield), the last piedmont glacier lobe in the Andes 

Michał Pętlicki, Andrés Rivera, Johnatan Oberreuter, José Andrés Uribe, Johannes Reinthaler, and Francisca Bown

Glacier fronts are retreating across the globe in response to climate warming, revealing valleys, fiords, and proglacial lakes. The piedmont lobe of San Quintín, the largest glacier of the Northern Patagonia Icefield, in southern Chile, has recently entered a catastrophic phase of frontal retreat, where its terminus is rapidly disintegrating into large tabular icebergs calving into a new proglacial lake. We present results of a unique airborne GPR survey of the terminus of this large Patagonian glacier (763 km2 in 2017), complemented with an analysis of ice flow velocity, satellite imagery, and ice elevation change to show that the ongoing retreat is caused by recent detachment of a floating terminus from the glacier bed and may shortly lead to the disappearance of the last existing piedmont lobe in Patagonia. Finally, we discuss how the observations of San Quintín’s ongoing collapse may give insights into processes governing frontal retreat of fast-flowing temperate glaciers and the quasi-stability of the floating termini.

How to cite: Pętlicki, M., Rivera, A., Oberreuter, J., Uribe, J. A., Reinthaler, J., and Bown, F.: Frontal collapse of San Quintín glacier (Northern Patagonia Icefield), the last piedmont glacier lobe in the Andes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10966, https://doi.org/10.5194/egusphere-egu23-10966, 2023.

Glaciers distinct from the ice sheets melt quickly around the world. Rounce et al., 2023, recently projected (28±9) % glacier mass will be lost in 2100 compared to 2015 in the low-emission scenario SSP1-2.6. In our study, we focus on the observation of recent glacier mass change on Novaya Zemlya, Russian Arctic, using the SARIn altimeter CryoSat-2. Taking advantage of CryoSat-2's spatiotemporal resolution and a novel processor optimized for mountain glaciers, we show a prompt response to the surface temperatures modeled by the regional atmosphere model MAR. Prominent warm and high-melt years were 2013, 2016, and 2020, while 2014 featured lower surface temperature and melt than average. We discuss the potential drivers for those extreme years and furthermore exploit surface mass balance estimates to analyze the relative contribution of ice dynamics and surface processes to the observed mass loss acceleration, and regional variations therein.

How to cite: Haacker, J. and Wouters, B.: Prompt response of glaciers on Novaya Zemlya to recent warm summers shown by CryoSat swath data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11912, https://doi.org/10.5194/egusphere-egu23-11912, 2023.

EGU23-12248 | ECS | Orals | CR1.2

Meteorological Feedbacks on a Decaying Alpine Glacier 

Thomas Shaw, Pascal Buri, Michael McCarthy, Evan Miles, Álvaro Ayala, and Francesca Pellicciotti

A developed boundary layer can decouple a glacier's response to the ambient meteorological conditions, though glacier retreat can limit this boundary layer development and increase a glacier’s sensitivity to climate change. We explore six years of distributed meteorological data on a small Swiss glacier in the period 2001-2022 to highlight its changing response to local conditions. We find an increased sensitivity (ratio) of on-glacier to off-glacier temperature changes as the glacier has retreated and its debris-cover area expanded. The glacier lost ~60% of area since 1994, coinciding with notable frontal retreat post 2005 and an observed switch from down-glacier to up-glacier winds in the upper ablation zone from 2001-2022. Increased sensitivity to external temperature changes is thus driven by a combination of increased up-glacier winds and the larger extent of ice exposed to warm air at a retreating, debris-covered glacier terminus. Calculated sensible heat fluxes on the glacier are therefore increasingly determined by the conditions occurring outside the boundary layer of the glacier, highlighting the expected negative feedback of smaller Alpine glaciers as the climate continues to warm and experience an increased frequency of extreme summers.

How to cite: Shaw, T., Buri, P., McCarthy, M., Miles, E., Ayala, Á., and Pellicciotti, F.: Meteorological Feedbacks on a Decaying Alpine Glacier, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12248, https://doi.org/10.5194/egusphere-egu23-12248, 2023.

EGU23-12667 | Orals | CR1.2

The role of the ocean circulation in melting the glaciers in Iceland 

Steingrímur Jónsson

Iceland enjoys a much warmer climate than the average for its latitude. A major reason for this is the warm ocean currents in the Atlantic south of Iceland. There is a large heat flux from the ocean to the atmosphere and the air temperature therefore depends to a high degree on the ocean temperature. During the last roughly two decades, glaciers in Iceland have generally been retreating as well as having a negative mass balance due to a warmer climate, whereas during three decades prior to that, most of the glaciers in Iceland were advancing. The air temperature in Iceland south of the largest Icelandic glacier, Vatnajökull, showed a rise in temperature of about 1°C from 1995 to the early 2000’s and since then it has mostly remained at this high level. Often this warmer climate is attributed entirely to global warming. However, the temperature in the warm and saline Atlantic water south of Iceland also increased by about 1°C during the same period. This rise in ocean temperature was accompanied by an increase in salinity which indicates that the temperature rise was mostly due to a change in the ocean circulation, resulting in advection of warmer and saltier water to the area. In the period from 1995 to the early 2000’s the ocean heat flux with the Atlantic water across the Greenland-Scotland ridge increased by 21 TW, partly through Denmark Strait towards the continental shelf north of Iceland. The increased heat flux was attributed to a rising temperature as well as increased flow of Atlantic water. Only about 0.5% of this heat flux increase is needed to explain the recent melting of Icelandic glaciers. With a relatively sudden 1°C rise in temperature the glaciers will take decades to reach equilibrium with this new temperature and if the temperature does not decrease, the glaciers will continue to lose mass. There are records of advancing and retreating Icelandic glaciers from 1930 and they show a good correspondence with the Atlantic Multidecadal Oscillation (AMO), that reflects temperature variations in the North Atlantic Ocean.

How to cite: Jónsson, S.: The role of the ocean circulation in melting the glaciers in Iceland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12667, https://doi.org/10.5194/egusphere-egu23-12667, 2023.

EGU23-12767 | Orals | CR1.2

Modelling the future of Nevado Coropuna (Peru), the world’s largest tropical ice cap. 

Ramón Pellitero, Martí Bonshoms, Jeremy C. Ely, and Giovanni Liguori

With around 46 km2, Nevado Coropuna (NC, 15°32’S, 72°39’W; 6377 m) is the largest tropical icecap in the world. NC is situated on a stratovolcano structure with six peaks over 6000 meters, in the arid border of the Andean plateau, Southern Peru. NC is a vital source of freshwater for the communities within the Majes valley and the vast irrigation plans located in the same valley and on the arid coastal strip. Our MOTICE project will model the evolution of NC until 2100 CE in response to climate change.

We present initial results on the modelling of NC, which will be used to tune the glaciological parameters for the projections under different RCP scenarios. Mass balance was modelled using the COupled Snowpack and Ice surface energy and MAss balance model in Python (COSIPY). This was forced with climate data for the 1950-2020 period from the ERA5-Land reanalysis, which provided surface pressure, cloud cover, incoming shortwave and longwave radiation, wind speed, 2-meter air temperature and relative humidity fields. This was combined with the RAIN4PE gridded product outputs for daily precipitation during the 1981-2015 period. The climate dataset was downscaled and validated with observed temperature and relative humidity from a weather station located on the Cavalca glacier (5800 m above sea level), at the northern part of NC. Glacier mass balance results were validated with measured mass balance in the same glacier for the 2014-2019 period. The mass-balance outputs from COSIPY were used for glacial flow modelling, using the Parallel Ice Sheet Model (PISM).

Subglacial topography was modelled using the Volume and Topography Automation (VOLTA) tool in a DEM that had been previously corrected with 70 differential GPR points measured “in situ”. The subglacial topography was also tuned and validated against in-situ GPR measurements in four glaciers of NC. Both GPR and GPS measurements were conducted during the 2022 fieldwork campaign, in which large areas of debris-covered ice were also located, mapped and measured. However, debris covered ice has not been considered in this initial model run.

Our preliminary results were compared to the actual 1955-2020 glacier surface evolution, which was retrieved from aerial photography and topographic maps for the initial stage in 1955 and from satellite images from 1975 onwards. This work highlighted the difficulty of modelling tropical glaciers, especially accounting for processes important to tropical ice, such as sublimation, and short-lived meteorological events.

How to cite: Pellitero, R., Bonshoms, M., Ely, J. C., and Liguori, G.: Modelling the future of Nevado Coropuna (Peru), the world’s largest tropical ice cap., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12767, https://doi.org/10.5194/egusphere-egu23-12767, 2023.

EGU23-12975 | ECS | Posters on site | CR1.2

Sensitivity of Folgefonna ice cap to anthropogenic climate change 

Rebekka Frøystad and Andreas Born

Glaciers in Norway are retreating, following the global trend caused by climate change. Future mass loss is projected to increase and cause the majority of Norwegian glaciers to disappear by the end of the century. This alters runoff and downstream hydrology, thus affecting available water resources for local communities. Quantifying this future change is essential to aid societal climate adaptation.

In this work, our aim is to assess both the rate of change and how trustworthy these estimates are given uncertainty in future climate and simulation tools. We organize the study around the Folgefonna ice cap in Western Norway, a prime example of a maritime ice cap. For the people living in its vicinity, the ice cap is important as it acts as a reservoir for both hydropower generation and drinking water. Despite this, little is known about how it will change in the future.

Using the model BESSI (The Bergen Snow Simulator), we simulate the surface mass balance of Folgefonna at a high spatial resolution. Recent developments of BESSI have made it a suitable option for small-scale glacier studies. These model alterations are presented here as well as results of past and future surface mass balance for the ice cap. We quantify how sensitive Folgefonna is to climate change and discuss limitations to the tools available for future glacier projections.

How to cite: Frøystad, R. and Born, A.: Sensitivity of Folgefonna ice cap to anthropogenic climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12975, https://doi.org/10.5194/egusphere-egu23-12975, 2023.

EGU23-13874 | ECS | Posters on site | CR1.2

Energy and mass balance of Mera glacier (Everest region, Central Himalaya) and its sensitivity to climate 

Arbindra Khadka, Patrick Wagnon, and Fanny Brun

Recent glacier mass changes are very heterogeneous in High Mountain Asia, owing to climatic variability and the mass balance sensitivity to climate, which may differ from one region to another. Mera glacier in the Everest region is one of the longest field-based monitored and well-studied glaciers of the Central Himalaya. In this study, we examine the sensitivity of Mera glacier mass balance to climate variables using the COupled Snowpack and Ice surface energy and mass balance model in PYthon (COSIPY), using 4 years (2016-2020) of in-situ meteorological data recorded at different elevations in the ablation and accumulation zones of the glacier. This shows that the net short-wave radiation is the main energy input at the surface, and in turn albedo is a key parameter controlling the glacier mass balance. As a result, at 5360 m asl, in the ablation zone, surface melt accounts for 90% of mass loss whereas sublimation and subsurface melt account for less than 10%. This analysis is performed at point scale at 5360 and 5770 m asl, in the ablation and accumulation zones respectively, as well as in a distributed way. We produce and analyze 88 distinct climatic scenarios, varying from dry and warm to wet and cold conditions. Dry conditions, primary during the pre-monsoon and secondary during the monsoon, strongly decrease the glacier mass balance, revealing that the annual amount and the seasonal distribution of snowfalls primary drives the glacier-wide mass balance of Mera Glacier.   

How to cite: Khadka, A., Wagnon, P., and Brun, F.: Energy and mass balance of Mera glacier (Everest region, Central Himalaya) and its sensitivity to climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13874, https://doi.org/10.5194/egusphere-egu23-13874, 2023.

EGU23-14348 | Posters on site | CR1.2

Modelling the future evolution of an alpine debris-covered glacier 

Martin Rückamp, Mathieu Morlighem, and Christoph Mayer

Debris-covered glaciers can react differently to external forcings than clean-surface glaciers. Depending on its thickness, a supraglacial debris layer impacts the glacier mass balance by either enhancing the surface melt or protecting the underlying ice. Based on previous works focusing on simple flowline geometries, we extended the model setup to three-dimensional complex geometries. The framework is implemented using the Ice-sheet and Sea-level System Model (ISSM) and applied to a typical alpine glacier geometry. Ice dynamics are solved on high-resolution with full-Stokes and coupled to the surface debris transport equation. The employed surface mass balance (SMB) model is capable of describing the melt rate for all debris thicknesses by including turbulent fluxes within the upper debris cover. This SMB formulation resolves the enhanced melt rates for a thin debris cover as well as the decreasing melt rates for thickening debris. To test the sensitivity of future projections of alpine glaciers on the debris layer, simulations are forced with high-resolution regional climate model (RCM) data from the EURO-CORDEX ensemble (RCP2.6 and RCP8.5).

How to cite: Rückamp, M., Morlighem, M., and Mayer, C.: Modelling the future evolution of an alpine debris-covered glacier, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14348, https://doi.org/10.5194/egusphere-egu23-14348, 2023.

EGU23-16195 | ECS | Posters on site | CR1.2

Rates of High Mountain Asian Glacier Ice Loss from ICESat-2 Observations 

Javed Hassan, William Colgan, and Shfaqat Abbas Khan

The importance of glaciers in High Mountain Asia (HMA) is significant in sustaining mountain hydrology and the runoff of several river systems that originate from these glaciers. The availability of numerous remote sensing products provides an opportunity to assess the current regional glacier mass balance comprehensively. We updated and presented recent glacier mass loss from the HMA and regional variability using Ice, Cloud, and Land Elevation Satellite (ICESat-2) data from October 2018 to December 2021. The HMA experienced accelerated mass loss in recent years of -62.62 ± 11.81 Gt a-1, (-17.11 ± 2.89 m w.e a-1). All 22 regions of HMA lost mass during the study period, regional mass loss range between -0.28 ± 0.47 m w.e. a-1 in the Western Kunlun Shan and -1.71 ± 0.22 m w.e. a-1 in the Hengduan Shan. The highest mass loss occurred at Hengduan Shan, East Tibetan Mountains, and Nyainqentanglha. Glaciers within the altitude range up to 4000 m a.s.l experienced the most negative mass balance. In recent years glacier mass loss increased by more than two-fold compared to previous studies even in the regions where glaciers were previously in balance or less negative mass balance such as Western Kunlun Shan (-0.28 ± 0.47 m w.e. a-1), Eastern Pamir (-0.47 ± 0.374 m w.e. a-1), and Karakoram (-0.61 ± 0.40 m w.e. a-1). The complex regional pattern of variable glacial mass balance and recent mass loss can be attributed to heterogeneous climate change signals and changes in meteorological conditions over the regions.

How to cite: Hassan, J., Colgan, W., and Abbas Khan, S.: Rates of High Mountain Asian Glacier Ice Loss from ICESat-2 Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16195, https://doi.org/10.5194/egusphere-egu23-16195, 2023.

CR2 – Instrumental and paleo-archive observations, analyses and data methodologies in the cryospheric sciences

EGU23-225 | ECS | Orals | CR2.1

Sub-seasonal snowline dynamics of glaciers in Central Asia from multi-sensor satellite observations, 2000-2021 

Dilara Kim, Mattia Callegari, Tobias Ullmann, and Martina Barandun

Glaciers are an important contributor to the freshwater supply in the Central Asian region. Their response to climate change has profound consequences for the land-use applications, and is thus essential to understand. The collapse of the Soviet Union has interrupted the vast majority of the conducted glacier mass balance observations, which began to re-establish in 2010. The existing data gap, limited spatial resolution of glaciological measurements, and the high heterogeneity of the region limits the use of in-situ data. Mass balance models rely on observation-based calibration and validation data, such as transient snowlines (TSLs), a transition between snow and ice-covered surfaces on a glacier at a given point in time. At the end of the ablation season TSL approximates the equilibrium line. From TSL we can calculate the snow-covered area fraction (SCAF), the area on the glacier surface that is snow covered in relation to the total glacier area. The TSL and SCAF can be mapped from satellite imagery due to the distinctive spectral and structural signature of snow over time. Our approach presented in this contribution is based on the MODIS time-series, harnessing the advantage of long and close-to-daily observations records for the period before high-resolution satellites became available. To resolve the issue of MODIS coarse spatial resolution, we retrieved SCAF from multispectral Sentinel-2 and cloud-independent Sentinel-1 SAR imagery using established workflow. The automatic classification and calculation of SCAF is performed using the cloud computing service of the Google Earth Engine, which makes the entire approach easily applicable on a large number of remote glaciers worldwide. We validated the results independently with Landsat data over selected glaciers in Central Asia. From the  SCAF time-series we analysed changes in various parameters indicative for the atmospheric conditions and its changes  (amongst others the length of ablation period, the minimum SCAF, and the seasonal SCAF changes ) as well as their 20-year trends. Our results provide a unique time series of temporally and spatially high-resolved SCAF estimates giving observation-based information on the heterogeneity of the region’s climatic setting as well as its changes on subseasonal scale. 

How to cite: Kim, D., Callegari, M., Ullmann, T., and Barandun, M.: Sub-seasonal snowline dynamics of glaciers in Central Asia from multi-sensor satellite observations, 2000-2021, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-225, https://doi.org/10.5194/egusphere-egu23-225, 2023.

EGU23-629 | ECS | Posters on site | CR2.1

Estimation of paleo-extent and volume of glaciers in the Baspa basin, India 

Nidhiya Jose, Anil Kulkarni, Satheesh Sk, and Sajeev Krishnan

Understanding water resources' long-term availability under varying climatic conditions is vital for water security and adaptation strategies. Due to increased temperature and changes in precipitation patterns, the Himalayan glaciers undergo rapid mass loss. The extent of glaciation has varied considerably since the global Last Glacier Maxima (LGM), ~18-24 ka. Moraine studies provide a better understanding of glaciation’s paleo-extent and timing. This work investigates the extent of glaciation and volume in the Baspa basin during the global LGM. We reconstruct the extent and volume using different remote sensing techniques, including the laminar flow method (HIGHTIM), Topo to Raster tool, Volume – Area scaling, and polygon area method. These methods are applied on 61 glaciers in the basin, considering glacial geomorphology and topographic parameters of the terrain. The glacier boundary and moraines were delineated using Landsat 8 satellite image and high resolution google earth images. The current spatial distribution of ice thickness and volume was estimated using the HIGHTHIM model. Further, the ice thickness was extrapolated to the moraine area using the Topo to Raster tool of ArcGIS, with the help of Cartosat DEM to estimate paleo glacier volume. On smaller glaciers (area<1km2), the current and paleo volume was estimated using the volume-area scaling equation. The glaciated area covers about 161.3 ± 8 km2 and the moraine area is calculated as 49.3 ± 0.46 km2. The estimate suggests ice volume at LGM is 18.71 km3 and the current volume is 8.51 km3, i.e., about a 52% loss in ice volume from LGM. We propose estimating the mass loss in the current decades, which will help understand the acceleration of mass loss under global warming conditions.

How to cite: Jose, N., Kulkarni, A., Sk, S., and Krishnan, S.: Estimation of paleo-extent and volume of glaciers in the Baspa basin, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-629, https://doi.org/10.5194/egusphere-egu23-629, 2023.

EGU23-898 | ECS | Posters on site | CR2.1

Simulation of water-induced seismic waveforms in glaciers through hydrodynamic modelling 

Jared Magyar, Anya M. Reading, Ross J. Turner, and Sue Cook

This work aims to contribute to progress in the detection of hidden or transient hydrological events. Passive seismic methods offer high temporal resolution and the ability to monitor seismic sources hidden from direct view, making it an ideal candidate to complement other in-situ and satellite methods in these cases. The dynamics of a glacier can be greatly affected by its hydrological system, whether this be through water mediated ice fracturing, or the influence the water has on friction at the ice-bed interface. Effective detection of moving meltwater is therefore of great interest for anticipating future glacier changes and sensitivities.

To effectively infer any hidden process from the observed seismic waveforms, we require a physically rigorous modelling framework. Our work therefore combines hydrodynamic models depicting meltwater flow with seismic wave propagation methods to produce synthetic seismograms. The hydrodynamic model of choice is smoothed particle hydrodynamics (SPH). This is a full, three-dimensional computational fluid dynamics method, meaning we can make minimal assumptions on the exact seismogenic mechanism prior to simulation. SPH has the capacity to capture a broad range of signal-generating processes that may prove to be of interest for modelling meltwater flow, such as fluid-solid impact events, free-surface behaviour (e.g., wave breaks), and some forms of turbulence. Beyond the modelling of complex flow, SPH also allows a simple implementation of arbitrarily shaped solid boundaries and the computation of force of the water on these boundaries; a necessary output for waveform simulation.

We propose a correspondence between different types of meltwater flow and the attributes of the waveforms they produce, as a step towards better detection and characterisation of hidden and short-lived events. Across a diverse set of model geometries and flow types, we anticipate the collection of synthetically generated signals will be useful for categorising archived and real-time signals according to a mechanistic process using unsupervised machine learning methods in ongoing work.

How to cite: Magyar, J., Reading, A. M., Turner, R. J., and Cook, S.: Simulation of water-induced seismic waveforms in glaciers through hydrodynamic modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-898, https://doi.org/10.5194/egusphere-egu23-898, 2023.

EGU23-913 | ECS | Posters on site | CR2.1

Analytical framework to model seismic signals from fluid particle collisions in hydrodynamic simulations of glacier melt water 

Ross J. Turner, Jared Magyar, Sue Cook, and Anya M. Reading

We present an analytic framework to model seismic body waves due to supraglacial, englacial or subglacial flows in solid ice based on a smoothed particle hydrodynamic (SPH) simulation. Consisting of two parts, i) hydrodynamic modelling and ii) seismic wave propagation, the flexible framework allows for a pre-existing fluid simulation to be supplied to generate synthetic seismic signals. The field of glacier-related seismology has seen rapid development in recent years, with an expanded availability of passive seismic datasets that contain records of seismic disturbances generated by glacier processes. Some of these processes, such as basal slip and crevasse propagation, have mechanisms with plate tectonic deformation counterparts, however, many glacier signals are generated by moving melt water. This contribution aims to inform the interpretation of such signals.

Our approach tracks the trajectories of fluid particles near the water-ice interface, as recorded in standard simulation outputs, to create a catalogue describing the energetics of each collision. We illustrate the capability of this framework using four end-member cases of water flow along surface channels with different geometries. Seismic signals are simulated at a variety of locations around the channel based on the impulse of the database of simulated collisions. We consider the change in character of the seismic waveforms by modelling frequency-dependent attenuation and weak dispersion in the glacial ice, in addition to the standard geometric spreading. The acceleration time series produced in this work are invariant to the temporal and spatial resolution of the hydrodynamic simulation, provided more than some minimum resolution is used. These time series may be converted to velocity or displacement for comparison with observed seismic signals.

Investigating the seismic waves generated for our four channel geometries, we find distinct waveform envelope shapes with different first and later amplitude peaks matching initial and subsequent collisions of the melt water surge with the supraglacial channel walls. The change in waveform character with distance is also captured such that the character attributes due to the process and the those due to the propagation effects may be understood. The flexibility inherent in the model framework will allow for the generation of the seismic signals from simulations of a variety of different water flow geometries including simple 3D channels into and through a glacier. We make the code available as an open source resource for the polar geophysics community with the aim of adding to the toolbox of available approaches to inform the potential future seismic monitoring of melt water movement and related glacier processes.

How to cite: Turner, R. J., Magyar, J., Cook, S., and Reading, A. M.: Analytical framework to model seismic signals from fluid particle collisions in hydrodynamic simulations of glacier melt water, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-913, https://doi.org/10.5194/egusphere-egu23-913, 2023.

EGU23-3941 | Posters on site | CR2.1

On-site albedo data and their relationship to the long-term evolution of surface morphology in glaciers of Hurd Peninsula, South Shetland Islands, Antarctica 

Susana del Carmen Fernandez Menendez, Javier Fernandez Calleja, Ruben Muñiz Sanchez, Jaime Otero Garcia, and Francisco Navarro Valero

Snow and ice albedo play a crucial role in the mass losses from the NW Antarctic Peninsula and the South Shetlands Islands since absorption of solar radiation is the largest energy source for surface melt in the cryosphere. Snow albedo exhibits a large variation at different time scales (hours, days, seasonal, long-term trend). It has been established that the thickness of the snow/ice layer that affects the albedo varies from 20 cm to 50 cm.   The surface geomorphology of this layer exerts a strong control in the snow albedo evolution over a day because they affect the amount of solar energy received, and the exposure to prevailing winds. In order to discover patterns of variation between snow albedo and surface geomorphology over Hurd Peninsula glaciers at metric scales, we used four DEMs (1957,2000, 2013 and 2019) of 1 m of spatial resolution. In the four DEMs topographical variables (altitude, slope, plan and profile curvature, aspect) and indexes (diurnal differential heating, wind exposition, roughness index) were calculated using QGis_Gdal_SAGA tools.  Because of the high spatiotemporal variability of the snow cover, the albedo data obtained from fixed stations provide a partial picture of the actual field behaviour.  In order to obtain spatially distributed albedo measurements over Hurd Peninsula glaciers, we designed a portable albedometer. The device consists of two pyranometers, one facing the sky and another facing the ground at 1,20m above the ground, working together with a GNSS. The dataloggers of each pyranometer were set up to take a measurement every 5 seconds. Using this equipment, in January of 2018 and 2019 we surveyed the glaciers of Hurd Peninsula along the same tracks but under different weather conditions (fog, clear sky, clouds and clearings). The population of albedo data obtained with this method was about 800 measured points per track per day.  Using QGis we obtained the values of topographical variables and indexes for all the albedo points. Lineal correlations between albedo and topographical variables and indexes were explored. The R2 was especially high in the tracks performed during open sky days. There are not significant correlations between DTMs variables and albedo data in tracks performed in foggy days. Moreover, we built a Linear Regression Model (forward stepwise) of open_sky day’s albedo with Adjusted R²= 0.86 and Std. Error of estimate: 0.00428 with diurnal differential heating_2019, altitudes (1957, 2000, 2013), slope_2019 and convexity_2019 as predictive variables. To estimate the surface albedo all across Hurd Peninsula extending linear models using QGis. Also, we calculated the DTM of 1957-2019 altitude changes in meters (Minimum=-32.694, Maximum=53.188, Mean=7.959, StdDev=10.526), which shows strong correlation with albedo of all open sky tracks.  We interpreted these results in relation to the density, structure and state of metamorphism of the snow cover that could represent the layer of snow that affects the albedo in Hurd Peninsula glaciers. The preliminary results seem to indicate that the surface melting intra-annual variability of the Hurd Peninsula glaciers, registered in the glacier surfaces geomorphology, exerts a strong influence on the current albedo.

How to cite: Fernandez Menendez, S. C., Fernandez Calleja, J., Muñiz Sanchez, R., Otero Garcia, J., and Navarro Valero, F.: On-site albedo data and their relationship to the long-term evolution of surface morphology in glaciers of Hurd Peninsula, South Shetland Islands, Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3941, https://doi.org/10.5194/egusphere-egu23-3941, 2023.

EGU23-4768 | Orals | CR2.1

Toward improvement of satellite-derived thermal resistance for supra-glacial debris 

Hiroto Nagai, Takayuki Nuimura, Masayuki Takigawa, Lavkush Patel, Sourav Laha, Bhanu Pratap, Keiko Konya, Paramanand Sharma, Koji Fujita, Yota Sato, and Akiko Sakai

Surface melting of alpine glaciers is spatially and temporally heterogeneous and is strongly influenced by climatic and non-climatic variables. Especially supra-glacial debris causes significant uncertainty on the ice melting rate with its physical property and thickness. A thick debris layer decrease ice melting rate, whereas a thin layer increase it with its low albedo. Therefore, a scientific method for spatial quantification of debris influences on the melting rate should be established to assess future projection of glacier shrinkage corresponding to the climate change.

Estimating  thermal resistance (TR), which quantifies how hard the ground heat flux (G) travels to ice-debris interface, with remote-sensing data has been attempted in several studies. However, uncertainties caused by the linear temperature gradient have not been resolved, as well as non-negligible underestimation of TR values. Therefore, this study aims to assess TR calculations on multi-spatial and multi-temporal conditions in detail, and discusses the current state of TR estimation and the potential for further improvement.

A study site is defined in Satopanth glacier [30.77°N; 79.40°E], a debris-covered glacier located in Garhwal region, India. Sampling domains of 12 circles with 100-m radius are put with 1-km intervals through the flow line of supra-glacial debris. In addition, four circles of the same size were placed outside the glacier in the lower reaches.

To calculate TR, first, broadband albedo and surface temperature (Ts) are calculated from all available Landsat-8 data in an orbit path [Path 145; Row 39] acquired from 2013 to the present (N= 81). These have a revisit cycle of 16 days. Cloud-covered pixels and frozen pixels (Ts < 0°C) are removed. Second, downward shortwave/longwave radiations and sensible/latent heat flux are collected from a 1-hour resolution product of ERA5. Combining these inputs, considering surface energy budget, G is calculated as a residue of energy flux, and then TR is calculated as Ts (°C) divided by G.

Our result shows a positive correlation that higher Ts leads higher values of G and TR. This trend have no significant difference between debris-covered surface and off-glacier terrains. It suggests that, in most sample areas with relatively thick debris, G does not reach the ice-debris interface. High-gradient inclinations of G versus Ts increase is identified in a lower part of Ts range (0-5°C). It may be caused by heat absorption because of ice mass under relatively thin debris layer, but such inclination is not reflected in TR’s gradient. In the lower Ts surfaces (0-5°C), slightly lower R is estimated for thinner-debris domains. For the thinner debris layer TR might reflects debris thickness. These features in other multiple glaciers will be shown and compared in the presentation.

Our result suggested that maximum debris thickness of G transfer (DTmax) may be defined. Smaller than the DTmax, G transfer may be estimated, whereas larger than the DTmax, G might be zero. In such domain, spatial distributions of ice cliff and supra-glacial pond are more dominant for melting projection. Further assessments might derive a perspective of multiple models for TR estimation according to debris thickness.

How to cite: Nagai, H., Nuimura, T., Takigawa, M., Patel, L., Laha, S., Pratap, B., Konya, K., Sharma, P., Fujita, K., Sato, Y., and Sakai, A.: Toward improvement of satellite-derived thermal resistance for supra-glacial debris, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4768, https://doi.org/10.5194/egusphere-egu23-4768, 2023.

EGU23-6296 | ECS | Posters on site | CR2.1

Minor pulsations of Abramov glacier (Kyrgyzstan) observed with multi-sensor optical remote sensing 

Enrico Mattea, Horst Machguth, Etienne Berthier, and Martin Hoelzle

In situ monitoring of glacier mass balance – through a network of reference sites – is essential to improve process understanding and detect changes, as well as provide calibration and validation for local and large-scale modeling and remote sensing studies. Still, the interpretation and representativeness of measured mass balances can be affected by unsteady glacier flow dynamics, from minor pulsations to extreme surges. Such events can alter mass balance gradients and apparent trends; englacial water storage associated with glacier surges can lead to biases in high‑resolution geodetic assessments, and in situ measurements can be disrupted by surface changes during rapid ice movement. At the same time, mass balance and its perturbations can exert multiple influences on glacier dynamics, from an indirect control on surge frequency to the direct triggering of instabilities and propagating waves. So far, a very small number of glaciers worldwide has seen combined long-term observations of mass balance and unsteady ice flow, limiting understanding of their interaction.

Here, we investigate the flow dynamics of the Abramov glacier (Pamir-Alay, Kyrgyzstan), whose measured mass balance series is one of the longest in Central Asia. We use multi-sensor optical remote sensing, including the recently released SPOT World Heritage archive as well as ASTER, IRS-1C/D and RapidEye data, to augment the Landsat record over the past 25 years. Through a dense series of digital elevation models and orthoimages, we quantify a front advance over 2000‑2005 at sub‑seasonal resolution. While the event was not observed in situ, negative mass balances in the preceding decades support an interpretation in terms of unstable ice dynamics. Moreover, asynchronous front advances on several neighboring glaciers reveal a high prevalence of unsteady ice flow in the region. We compare the Abramov advance to a previous surge of 1972/73, which was well documented with extensive in situ measurements but was virtually unknown outside Soviet glaciology. Both events do not fit well within the traditional surge models of hydrological or thermal switch, similar to previous observations in the Pamir-Karakoram.

At present, the west branch of the Abramov glacier is undergoing a slow, regular and widespread inter‑annual speed-up. In the light of continued surface thinning and front retreat, the current evolution likely represents the build-up phase to the next episode of dynamic instability.

How to cite: Mattea, E., Machguth, H., Berthier, E., and Hoelzle, M.: Minor pulsations of Abramov glacier (Kyrgyzstan) observed with multi-sensor optical remote sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6296, https://doi.org/10.5194/egusphere-egu23-6296, 2023.

EGU23-8711 | Posters on site | CR2.1

dhdt: a Python library to transform shifting shadows to glacier elevation change 

Bas Altena and Francesco Nattino

The free and open data policy of the Landsat legacy and the Sentinel-2 satellite constellation have enhanced our knowledge considerably. This enrichment is due to an increase in spatial spread; from glacier change at targeted glaciers, to a coverage at regional or global scale. Procedures for snow cover mapping and glacier outline product generation from such multi-spectral data are well matured. Though more potential is present for glacier specific information in such data sources.

Recently several studies presented the capabilities of elevation change extraction via shadow cast detection. Though these are at the proof of concept stage, as many steps are based on heuristics and manual adjustments/measurements.

Here we present a fully automatic pipeline which is based on an open source library, which we developed. The emphasis of this code base, which we named dhdt, is focused on large scale processing. Hence, all steps are fully automatic and structured so distributed processing is possible. It generates spatial temporal elevation changes, over small mountain glaciers. We hope, this will advance the glaciological community forward, giving a new instrument in its toolbox.

How to cite: Altena, B. and Nattino, F.: dhdt: a Python library to transform shifting shadows to glacier elevation change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8711, https://doi.org/10.5194/egusphere-egu23-8711, 2023.

EGU23-9215 | ECS | Orals | CR2.1

Observing glacier elevation changes from spaceborne optical and radar sensors 

Livia Piermattei, Fanny Brun, Christian Sommer, Matthias H. Braun, and Michael Zemp

Quantifying glacier elevation and volume changes is critical to understanding the response of glaciers to climate change and related impacts, such as regional runoff and global sea-level rise. Spaceborne remote sensing techniques enable the quantification of spatially distributed glacier elevation changes at regional and global scales using multi-temporal digital elevation models (DEMs). A growing number of spaceborne studies exist to assess glacier elevation changes but they show widespread differences often beyond the error bars. Here, we present the results of a community-based inter-comparison experiment using spaceborne optical (ASTER) and radar (TanDEM-X) sensors to assess elevation changes for selected individual glaciers and regional glacier samples. Using a predefined set of DEMs, participating groups provided their own estimates using various processing strategies.  

For the selected individual glaciers, the results were validated using airborne data. The validation shows that the median of the spaceborne ensemble is biased by a few decimetres per year with a standard deviation of about half a meter per year. An interesting finding is that no sensor and no processing strategy perform significantly better for all experiment sites. At the regional scale, we find that the co-registration of DEMs is the most relevant processing step for an accurate assessment of elevation change. Other corrections such as gap filling, filtering, and radar penetration have less impact in general but can be essential for individual cases. Temporal corrections (i.e. seasonal and annual) can have a great impact; however, they are not yet well resolved by the remote sensing community.

Our study confirms that the currently available spaceborne geodetic assessments result in relatively widespread glacier elevation changes. Therefore, we recommend an ensemble approach of observations from multiple observational sources. Furthermore, there is a need to establish best practices for related uncertainty estimates.

How to cite: Piermattei, L., Brun, F., Sommer, C., Braun, M. H., and Zemp, M.: Observing glacier elevation changes from spaceborne optical and radar sensors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9215, https://doi.org/10.5194/egusphere-egu23-9215, 2023.

EGU23-11141 | ECS | Posters on site | CR2.1

Quantification of glacier flow velocities using time-lapse photogrammetry in conjunction with high-resolution digital elevation models 

Franziska Mayrhofer, Bernhard Grasemann, and Martin Schöpfer

Time-lapse photogrammetry in conjunction with high-resolution digital elevation models is used to quantify the surficial velocity field and the ablation of the Pasterze, a rapidly retreating alpine valley glacier in Austria. Three automatic time-lapse cameras were installed along the orographic left valley flank, c. 150 m above the glacier’s surface, to monitor retreat and ablation from July to September 2020. Although time-lapse photogrammetry offers spatial and temporal high-resolution data, the various processing steps to calculate the glacier’s velocity field are challenging. Digital image correlation of the time-lapse series photos is achieved using a published Python library (How et al. (2020) PyTrx: a Python-based monoscopic terrestrial photogrammetry toolset for glaciology. Frontiers in Earth Science 8:21, doi:10.3389/feart.2020.00021), which was adapted for the Pasterze data set. Typical time intervals for the digital image correlation were two to ten days. Factors that hamper computing flow velocities from time-lapse series photos alone are low contrast of the glacier’s surface and ablation rates exceeding the horizontal flow velocity. The latter problem is solved with the aid of two high-resolution digital elevation models (DEMs), which were calculated using 790 drone images from flight missions on July 13 and September 15 2020. The photogrammetric (Structure from Motion) software Agisoft Metashape (v. 1.8.4) is used to calculate the two DEMs from dense point clouds with a resolution of 5 cm/pixel. Linear interpolation of the glacier’s elevation between the July and September DEM was used to provide an approximate surface for a given date within the monitoring period. With this additional processing step, we can project tracked pixels from the digital image correlation process on an adapted DEM, which reflects the absolute height on a certain date best. Our workflow illustrates that time-lapse series photos taken obliquely to the glacier’s surface can be used to compute the surficial velocity field with the aid of digital elevation models that yield the glacier’s surface at the beginning and at the end of the monitoring period. In fact, the glacier’s velocity field computed in that fashion is consistent with direct measurements from a sparse network of stakes and mapped structures.

How to cite: Mayrhofer, F., Grasemann, B., and Schöpfer, M.: Quantification of glacier flow velocities using time-lapse photogrammetry in conjunction with high-resolution digital elevation models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11141, https://doi.org/10.5194/egusphere-egu23-11141, 2023.

EGU23-11201 | ECS | Posters virtual | CR2.1

Proposed algorithm for the identification of glacier cover from Sentinel-2A images 

Juan de Dios Fernandez, Brandon Fajardo, Yadira Curo, Mayra Mejia, Gladis Celmi, Danny Robles, and Alberto Castañeda

In Peru, there are 20 glacial mountain ranges that in almost 60 years have lost 54% of their glacial coverage. The accelerated glacial recession due to climate change raises the need to know the levels of retraction of the glacier surface efficiently and reliably. During the preparation of the last inventory by the National Institute for Research in Glaciers and Mountain Ecosystems (INAIGEM), it was identified that carrying out the inventory faces challenges, such as differentiating between temporary snow and glacier ice, especially in areas with complex meteorological characteristics. like the Cordillera Blanca (Peru). Due to the Peruvian Andes’ geographical and climatic complexity, satellite images usually show cloudiness and temporary snow, even in the dry season (April to November). In this way, the objective is to obtain an ideal image with annual glacier coverage with minimal snow reflecting the current glacier surface. For this, the script "Normalized Differentiated Snow Index - minimum NDSI" was developed, which analyzed the Sentinel-2A image catalog of the year 2020 and delimited the glaciers of the Huascarán and Huandoy systems in the Cordillera Blanca.

The proposed methodology aims to evaluate the techniques for generating glacier cover, which allows the proposed objective to get the minimum glacier area. Three glacier cover generation techniques were evaluated: mosaic, medium, and minimum. For the mosaic and average reductions, the algorithm applied a cloud filter to the Sentinel-2A image set and calculated the NDSI for the month that was lowest in the historical average from Landsat 5, 7, and 8 images (1990-2020), applying a threshold of 0.4 and exporting the results with mean and mosaic reduction, respectively. While the minimum NDSI was calculated annually (2020) from Sentinel-2A images, with a cloud filter to which the reduction by minimums is applied, within the same area of interest, applying the threshold of 0.4 and exporting the results in raster format. Finally, the three results were evaluated in terms of the percentage of overestimation concerning glacier coverage in 2016.

The results reveal that the Huascarán and Huandoy glacier systems present a lower NDSI value during August and October, with standard deviations of 0.12 and 0.14, respectively. The glacier cover generated by the minimum NDSI was compared in the percentage of overestimation (m2) concerning 2016 with the average and mosaic NDSI, finding as a result that the minimum glacier cover, for the year 2020, evidences a lower percentage of temporal snow in the Huascarán system between 157.12% and 76.84% less than the filtering methods: average and mosaic. Likewise, in the Huandoy system, there is evidence of a lower percentage of temporary snow between 205.91% and 191.65% less than the average and mosaic methods. Finally, it is necessary to indicate that the developed algorithm has improved the obtaining of glacier coverage from the inventory developed by INAIGEM and reduces the overestimation of glacier coverage due to temporary snow.

How to cite: Fernandez, J. D. D., Fajardo, B., Curo, Y., Mejia, M., Celmi, G., Robles, D., and Castañeda, A.: Proposed algorithm for the identification of glacier cover from Sentinel-2A images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11201, https://doi.org/10.5194/egusphere-egu23-11201, 2023.

EGU23-12612 | Orals | CR2.1

Annual to seasonal glacier mass balance in High Mountain Asia derived from Pléiades stereo images: examples from the Pamir and the Tibetan Plateau 

Tobias Bolch, Daniel Falaschi, Atanu Bhattacharya, Lei Huang, and Owen King

Glaciers are crucial sources of freshwater in particular for the arid lowlands surrounding High Mountain Asia. In order to better constrain glacio-hydrological models, annual, or even better, seasonal information about glacier mass changes is highly beneficial. In this study, we test the suitability of very high-resolution Pleiades DEMs to measure glacier-wide mass balance at annual and seasonal scales in two regions of High Mountain Asia (Muztagh Ata in Eastern Pamir and parts of Western Nyainqêntanglha, South-central Tibetan Plateau), where recent estimates have shown contrasting glacier behaviour. We find that the average annual mass balance in Muztagh Ata between 2020 and 2022 was -0.11 ±0.21 m w.e. a-1, suggesting the continuation of a recent phase of slight mass loss following a prolonged period of balanced mass budgets previously observed. The mean annual mass balance in Western Nyainqêntanglha for the same period was highly negative (-0.60 ±0.15 m w.e. a-1 on average), suggesting increased mass loss rates. The 2022 winter (+0.21 ±0.24 m w.e.) and summer (-0.31 ±0.15 m w.e.) mass budgets in Muztag Ata and Western Nyainqêntanglha (-0.04 ±0.27 m w.e. [winter]; -0.66 ±0.07 m w.e. [summer]) suggest winter and summer accumulation-type regimes, respectively. We support our findings by implementing a Sentinel-1–based Glacier Index to identify the firn and wet snow areas on glaciers and characterize accumulation type and demonstrate the potential of very high-resolution Pleiades data to monitor mass balance at short time scales and to improve our understanding of glacier accumulation regimes across High Mountain Asia.

How to cite: Bolch, T., Falaschi, D., Bhattacharya, A., Huang, L., and King, O.: Annual to seasonal glacier mass balance in High Mountain Asia derived from Pléiades stereo images: examples from the Pamir and the Tibetan Plateau, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12612, https://doi.org/10.5194/egusphere-egu23-12612, 2023.

EGU23-12724 | Orals | CR2.1

Glacier extents in Peru and Bolivia are overestimated in RGIv6 by 27% 

Frank Paul and Philipp Rastner

Glaciers in the tropical Andes of Peru and Bolivia are important but rapidly declining water resources. Precise knowledge of their extent is thus mandatory for calculation of their volume, area changes and mass balance. Due to wrongly mapped seasonal snow, the glacier outlines currently available from the widely used RGIv6 are often too large, resulting in errors for change assessment and volume estimation. Apart from snow cover, also frequent cloud cover and shadows cast by the steep terrain make glacier mapping in this region challenging.

For this study, we have mapped all glaciers in Peru and Bolivia using cloud-free Landsat TM scenes from 1998 and Sentinel-2 scenes from 2020. In both years seasonal snow off glaciers was largely absent. Glacier extents were mapped with a standard band ratio (red/SWIR) and a scene specific threshold value. Wrongly classified lakes and missing debris cover were manually corrected, the latter also by using the very high-resolution satellite images available in the ESRI Basemap. The Copernicus DEM GLO-30 was used to derive new drainage divides and topographic information for each glacier.

In total, we mapped 3586 glaciers larger than 0.01 km2 covering an area of 1747 km2 in 1998. This is 419 km2 or 20% less than the 2166 km2 in RGIv6. As glacier outlines in RGIv6 are from 2000 to 2009 and most area change studies found continuous and strong area decrease over this period, a ‘back-calculation’ of all glacier areas to the year 1998 with an annual shrinkage rate of 1% gives a mean area overestimation of 27% for 1998. This value varies regionally and is smaller in the Cordillera Blanca and much larger (>50%) in other regions. The related modelled glacier volumes for these regions are thus also overestimated. From 1998 to 2020 glaciers have lost 23% of their area (-1% per year).

How to cite: Paul, F. and Rastner, P.: Glacier extents in Peru and Bolivia are overestimated in RGIv6 by 27%, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12724, https://doi.org/10.5194/egusphere-egu23-12724, 2023.

EGU23-13794 | ECS | Orals | CR2.1

Glacier change monitoring using optical satellite imagery: the case of Forni Glacier 

Lorenza Ranaldi, Valeria Belloni, Davide Fugazza, and Mattia Crespi

Glaciers are one of the most important indicators of climate change. Monitoring their evolution is, therefore, crucial for safeguarding the Earth’s ecosystem. In this study, exploiting photogrammetric optical satellite processing techniques, we used image pairs from Ikonos-2 and Plèiades-HR satellites to generate Digital Surface Models (DSMs) of Forni Glacier (Ortles–Cevedale group, Italy) and compute its morphological variations between 2009 and 2016. In addition, we used DSMs generated from Unmanned Aerial Vehicle (UAV) acquisitions collected during summer campaigns from 2014 to 2021 for comparison with very high-resolution DSMs on the terminal portion of the glacier including its tongue, which is also the area more affected by morphological changes. To evaluate the glacier height loss, DSMs co-registration was applied to remove DSM biases due to inconsistencies in the georeferencing of the different satellite image pairs. For this purpose, we used the 2016 UAV DSM as a reference, and we co-registered all the optical DSMs to the 2016 UAV DSM using the Nuth and Kaab algorithm. The DSM height differences after co-registration highlighted a final accuracy of one meter. Since optical satellite data have the advantage of providing information on very large areas, we analysed glacier change not only on small areas of Forni Glacier tongue but also on larger regions including parts of the entire glacial apparatus to depict the evolution of the glacier at different altitudes. Results from optical DSMs were consistent with the average annual variation of the glacier suggested by UAV DSMs analysis, confirming an average 5.00 m/y loss on the Forni tongue during 2014-2016.  Furthermore, based on both UAV and optical data, melting trends have highlighted how climate change is causing an acceleration in the melting process, with values averaging 3.3 m/y in the period 2009-2013, 3.8 m/y in 2009-2016 and 4.7 m/y in 2009-2021. With reference to the optical data only, we observed that the intensity of melting varied at different altitudes, with 10 m of maximum variation above 3000 m, and 30 m between 2600-3000 m during 2009-2016. Our results suggested that despite the limitations related to weather conditions (e.g. cloud coverage) and time revisit, high-resolution optical satellite imagery can certainly be used to estimate relevant morphological variations of glaciers in the order of meter/years, offering the opportunity of monitoring large-scale areas.


 

 

 

How to cite: Ranaldi, L., Belloni, V., Fugazza, D., and Crespi, M.: Glacier change monitoring using optical satellite imagery: the case of Forni Glacier, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13794, https://doi.org/10.5194/egusphere-egu23-13794, 2023.

EGU23-13863 | ECS | Orals | CR2.1

Climatic control on seasonal variations of moutain glacier surface velocity 

Ugo Nanni, Dirk Scherler, Francois Ayoub, Romain Millan, Frederic Herman, and Jean-Philippe Avouac

Glacier displacement can in principle be measured at the large-scale by cross-correlation of satellite images. At weekly to monthly scales, the expected displacement is often of the same order as the noise for the commonly used satellite images, complicating the retrieval of accurate glacier velocity. Assessments of velocity changes on short time scales and over complex areas such as mountain ranges are therefore still lacking, but are essential to better understand how glacier dynamics are driven by internal and external factors. In this study, we take advantage of the wide availability and redundancy of satellite imagery over the Western Pamir to retrieve glacier velocity changes over 10 days for 7 years for a wide range of glacier geometry and dynamics. Our results reveal strong seasonal trends. In spring/summer, we observe velocity increases of up to 300% compared to a slow winter period. These accelerations clearly migrate upglacier throughout the melt-season, which we link to changes in subglacial hydrology efficiency. In autumn, we observe glacier accelerations that have rarely been observed before. These episodes are primarily confined to the upper ablation zone with a clear downglacier migration. We suggest that they result from glacier instabilities caused by sudden subglacial pressurization in response to (1) supraglacial pond drainage and/or (2) gradual closure of the hydrological system. Our 10-day resolved measurements allow us to characterize the short-term response of glacier to changing meteorological and climatic conditions.

How to cite: Nanni, U., Scherler, D., Ayoub, F., Millan, R., Herman, F., and Avouac, J.-P.: Climatic control on seasonal variations of moutain glacier surface velocity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13863, https://doi.org/10.5194/egusphere-egu23-13863, 2023.

EGU23-13870 | Orals | CR2.1

Recent advances in monitoring surface mass balance of the Greenland ice sheet 

Andreas P. Ahlstrøm, Robert S. Fausto, Jason E. Box, Nanna B. Karlsson, Penelope R. How, Patrick J. Wright, Baptiste Vandecrux, Anja Rutishauser, Kenneth D. Mankoff, William T. Colgan, Michele Citterio, Alexandra Messerli, Anne M. Solgaard, Signe H. Larsen, Niels J. Korsgaard, Kristian K. Kjeldsen, Rasmus B. Nielsen, Derek Houtz, and Signe B. Andersen and the GEUS GlacioLab Team

With temperatures in the Arctic rising rapidly at a rate of 3-4 times the global mean, monitoring the state of the Greenland ice sheet has never been more relevant.

In-situ observations from the Arctic, in particular from the Greenland ice sheet, are scarce due to the cost and difficulty of maintaining instrumentation in the harsh and remote environment. Yet, the fate of the ice sheet concerns 100s of millions of people living in coastal zones worldwide. To gain understanding of the ice sheet processes leading to sea level rise and increase our ability to capture those in climate models, there is an urgent need to collect in-situ observations from the ice sheet surface. Similarly, ground-truthing observations are necessary for validation and calibration of satellite-derived estimates of ice sheet change.

The Geological Survey of Denmark and Greenland (GEUS), along with partner institutions Asiaq and DTU Space, currently operates a combined network of 40 automatic weather stations (AWS) on ice in Greenland, mainly through the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-Net).

GEUS has implemented a new pipeline providing near-real-time hourly weather observations from the PROMICE and GC-Net stations to its users and the World Meteorological Organization (WMO) for use in numerical weather prediction. The satellite-transmitted AWS data is processed and submitted to the WMO via the Danish Meteorological Institute with a latency of 7 minutes after observations are recorded.

Here, we present the recent advances in our AWS instrumentation, data processing and database solution to invite discussion on how we can best meet the community needs for in-situ observations from the ice sheet.

How to cite: Ahlstrøm, A. P., Fausto, R. S., Box, J. E., Karlsson, N. B., How, P. R., Wright, P. J., Vandecrux, B., Rutishauser, A., Mankoff, K. D., Colgan, W. T., Citterio, M., Messerli, A., Solgaard, A. M., Larsen, S. H., Korsgaard, N. J., Kjeldsen, K. K., Nielsen, R. B., Houtz, D., and Andersen, S. B. and the GEUS GlacioLab Team: Recent advances in monitoring surface mass balance of the Greenland ice sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13870, https://doi.org/10.5194/egusphere-egu23-13870, 2023.

EGU23-14011 | Orals | CR2.1 | Highlight

Remote sensing analysis of Marmolada (Italy) and Juuku pass (Kyrgyzstan) glacier collapses 

Simon Gascoin and Etienne Berthier

Summer 2022, in less than a week, two glaciers collapsed. First in Italy on July 3 (Marmolada) then in Kyrgyzstan on July 9 (Juuku pass). The collapse of the Marmolada glacier caused eleven fatalities. In both cases, we immediately requested the tasking of Pléiades satellites to estimate the collapse volumes by photogrammetry. In both cases, the images were acquired three days after the event, and less than one day later we had the first estimates. We found that the Marmolada glacier lost 65,000 ± 10,000 m3. The Jukuu pass glacier lost a volume almost twenty times greater (1,145,000 m3), which remains however much lower than the volumes involved during the collapse of the Aru glaciers on the Tibetan plateau in 2016 (68 and 83 million m3) or the Kolka Glacier in 2002 (130 million m3). From the Marmolada elevation model and orthoimages we could also estimate that the rupture was approximately 80 m wide and 25 m deep.  In the case of the Juuku glacier, the tongue of the glacier collapsed entirely over a width of almost 300 m and the maximum elevation drop reached 50 m.

How to cite: Gascoin, S. and Berthier, E.: Remote sensing analysis of Marmolada (Italy) and Juuku pass (Kyrgyzstan) glacier collapses, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14011, https://doi.org/10.5194/egusphere-egu23-14011, 2023.

EGU23-15647 | ECS | Posters on site | CR2.1

Glaciological monitoring at A. P. Olsen Ice Cap in NE Greenland 

Signe Hillerup Larsen, Anja Rutishauser, Daniel Binder, Niels Korsgaard, Bernhard Hynek, and Michele Citterio

In situ glaciological monitoring of A. P. Olsen Ice Cap in NE Greenland has been ongoing since 2008. The monitoring effort is part of the Greenland Ecosystem Monitoring (GEM) programme at Zackenberg research station. The monitoring includes: Three automatic weather and ablation stations along a transect, manual stake observations upon every visit in April, ground based radar profiles of last year snow accumulation and geodetic mass balance from satellite based observations. Here we present an overview of all glaciological monitoring data, and use the dataset to estimate the mass balance of A. P. Olsen over the past 13 years.

How to cite: Hillerup Larsen, S., Rutishauser, A., Binder, D., Korsgaard, N., Hynek, B., and Citterio, M.: Glaciological monitoring at A. P. Olsen Ice Cap in NE Greenland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15647, https://doi.org/10.5194/egusphere-egu23-15647, 2023.

EGU23-15661 | ECS | Orals | CR2.1

Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland 

Jacqueline Bannwart, Livia Piermattei, Inés Dussaillant, Lukas Krieger, Dana Floricioiu, Etienne Berthier, Claudia Roeoesli, Horst Machguth, and Michael Zemp

Digital elevation models (DEMs) from the spaceborne interferometric radar mission TanDEM-X hold a large potential for glacier elevation change assessments and monitoring. However, a bias is potentially introduced through the penetration of the X-band signal into snow and firn that can be substantial. The magnitude of this bias has been analysed in some glaciarized regions of the world; still, the knowledge about X-band penetration of TanDEM-X in the European Alps is limited.

In this study, we investigated the unique situation of almost synchronous acquisition of TanDEM-X and Pléiades DEMs over the Grosser Aletschgletscher, complemented with in-situ observations (ground penetrating radar, snow cores, snow pits), all within a four-day period in late winter 2021. The comparison of the TanDEM-X and Pléiades DEM revealed an elevation bias due to radar penetration of up to 8 m above 3400 m. Further, the concurrent in-situ measurements reveal that the signal is not obstructed by the last summer horizon but reaches into perennial firn.

Our study improves our understanding about the magnitude of X-band penetration of TanDEM-X in the Alps and the underlying process with a relevance for glaciology, snow science, remote sensing and the wider geoscience community.

How to cite: Bannwart, J., Piermattei, L., Dussaillant, I., Krieger, L., Floricioiu, D., Berthier, E., Roeoesli, C., Machguth, H., and Zemp, M.: Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15661, https://doi.org/10.5194/egusphere-egu23-15661, 2023.

EGU23-15715 | ECS | Posters on site | CR2.1

Glacier changes in Iceland since the Little Ice Age maximum - glacier outlines, terminus measurements and photographic evidence 

Hrafnhildur Hannesdóttir, Oddur Sigurðsson, Snævarr Guðmundsson, Joaquín M.C. Belart, Ragnar H. Þrastarson, Finnur Pálsson, Eyjólfur Magnússon, and Tómas Jóhannesson

A national glacier outline inventory for several different epochs since the end of the Little Ice Age (LIA) in Iceland has been created with input from several research groups and institutions, and has been submitted to the GLIMS (Global Land Ice Measurements from Space, nsidc.org/glims) database, where it is openly available. The glacier outlines have been revised and updated for consistency and the most representative outline chosen. The maximum glacier extent during the LIA was not reached simultaneously in Iceland, but many glaciers started retreating from their outermost LIA moraines around 1890. The total area of glaciers in Iceland in 2021 was ~10,300 km2. The total glacier area has decreased by ~2300 km2 since the end of the 19th century and by ~830 km2 since ca. 2000. During the first two decades of the 21st century, the decrease rate has on average been ~40 km2 a–1. In this period, some tens of small glaciers have disappeared entirely. Temporal glacier inventories are important for climate change studies, for calibration of glacier models, for studies of glacier surges and glacier dynamics, and they are essential for better understanding of the state of glaciers. Although surges, volcanic eruptions and jökulhlaups influence the position of some glacier termini, glacier variations have been rather synchronous in Iceland, largely following climatic variations since the end of the 19th century.  The glacier outlines are also available on a new glacier web portal (www.islenskirjoklar.is), together with measurements of frontal positions and mass balance and numerous photographs of glaciers at different times. The photographic glacier archive will be updated through systematic photographic surveys, including rephotography of historical photos, based on a collaboration of the Iceland Glaciological Society and Extreme Ice Survey. This website, which is intended for scientists, students and lay people alike, is a joint effort of institutions involved in glacier research in Iceland and the Iceland Glaciological Society. It will serve as a powerful tool for outreach on glacier and climate change in Iceland. The glacier inventory is planned to be updated every other year in the future as part of regular monitoring of glacier changes in Iceland. Furthermore, the larger ice caps will be divided into ice-flow basins along the ice divides of individual outlet glaciers determined from ice-surface DEMs, which will allow for more detailed analysis of area variations with time.

How to cite: Hannesdóttir, H., Sigurðsson, O., Guðmundsson, S., Belart, J. M. C., Þrastarson, R. H., Pálsson, F., Magnússon, E., and Jóhannesson, T.: Glacier changes in Iceland since the Little Ice Age maximum - glacier outlines, terminus measurements and photographic evidence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15715, https://doi.org/10.5194/egusphere-egu23-15715, 2023.

EGU23-16174 | Orals | CR2.1

An annual mass balance estimate for each of the world’s glaciers based on observations. 

Ines Dussaillant, Romain Hugonnet, Matthias Huss, Etienne Berthier, and Michael Zemp

The geodetic method has become a popular tool to measure glacier elevation changes over large glacierized regions with high accuracy for multiannual/decadal time periods. In contrast, the glaciological method provides annually to seasonally resolved information on glacier mass balance, but only for a small sample of the world’s glaciers (less than 1%). Various methods have been proposed to bridge the gap regarding the spatio-temporal coverage of glacier change observations and provide annually resolved glacier mass balances using the geodetic sample as calibration. Thanks to a new globally near-complete (96% of the world’s glaciers) dataset of geodetic mass balance between 2000 and 2020, a global-scale assessment of annual mass changes at glacier-specific level has now become feasible. Inspired by previous methodological frameworks, we developed a new approach to combine the glacier outlines from the globally complete Randolph Glacier Inventory with the mass balance and elevation change observations from the Fluctuation of Glaciers database of the World Glacier Monitoring Service (WGMS). Our results provide a global assessment of annual glacier mass change and related uncertainties for every individual glacier since 1976. The glacier-specific time series can then be integrated into an annually-resolved gridded global glacier change product at any user-requested spatial resolution, useful for comparison with, for example, gravity-based products, calibration or validation of glacier mass balance models operating at a global scale and to improve assessments of glacier contribution to regional hydrology and global sea-level rise. These developments additionally open a new door of opportunity to keep on pushing the frontiers of glacier change observations towards future assessments of global glacier mass changes at increased temporal resolutions.

How to cite: Dussaillant, I., Hugonnet, R., Huss, M., Berthier, E., and Zemp, M.: An annual mass balance estimate for each of the world’s glaciers based on observations., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16174, https://doi.org/10.5194/egusphere-egu23-16174, 2023.

EGU23-16473 | Posters on site | CR2.1

Monitoring the flow of the Greenland Ice Sheet: The PROMICE ice velocity product and recent updates 

Anne Solgaard, Anders Kusk, Signe Hillerup Larsen, Kenneth Mankoff, and Robert Fausto

We present the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) ice velocity product, which is a time series of ice velocity mosaics derived using offset tracking on Sentinel-1 SAR data.  The time series starting in January 2016 is continuously updated with a new mosaic every 12 days and is posted at 500 m grid resolution. Within PROMICE, the ice velocity product is used directly as input to estimate the solid ice discharge from the Greenland Ice Sheet as well as to study ice dynamic processes on seasonal and multi-annual time scales. Recently, we have made changes to the processing chain due to spurious cases of slow down detected in a few glaciers in Southeast Greenland. In this contribution, we discuss how this was resolved as well as other recent improvements to the product.

How to cite: Solgaard, A., Kusk, A., Hillerup Larsen, S., Mankoff, K., and Fausto, R.: Monitoring the flow of the Greenland Ice Sheet: The PROMICE ice velocity product and recent updates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16473, https://doi.org/10.5194/egusphere-egu23-16473, 2023.

EGU23-16573 | Posters on site | CR2.1

Exploring seismicity and ice cover of Livingston Island - research projects 

Liliya Dimitrova, Gergana Georgieva, and Vasil Gourev

Livingston Island is one of the eleven islands of the South Shetland Archipelago which is separated from the Antarctic Peninsula by Bransfield Strait and from South America by the Drake Passage. The South Shetland Islands, where the Bulgarian Antarctic Base is located, is characterized by complex geodynamics, including: subduction zones, zones of splitting of the old crust and arising of a new crust, numerous volcanoes. The most of the territory of the Island is coated by permanent snow cover and glaciers. In recent decades, a number of scientific institutions have been working on projects related to the study of various aspects of the seismic regime and the structure of the Еarth's crust in the region of the South Shetland Islands and Antarctica in general, as well as the state and dynamics of the ice sheet. However, this region of the Earth remains still unexplored. Harsh climatic conditions are a serious obstacle for conducting long-term research. Bulgarian scientists from Sofia University "Sv. Kliment Ohridski" and the National Institute of Geophysics, Geodesy and Geography of the Bulgarian Academy of Sciences have been studying the seismicity of the Livingston Island region and the behavior of the glaciers near the Bulgarian Antarctic Base within the framework of three scientific projects since 2014. The projects are funded by the Bulgarian Scientific Fund and National Center for Polar Studies.

One of the aim of the projects is to study the activity of the glaciers during different seasons by combining seismic and GNSS measurements. Seismic registration was carried out by the seismic station LIVV installed previously as temporary station and later developed as a year-round one. GNSS measurements were carried out at certain points on the surface of the glacier Balkan. The resulting estimates of the speed of glacier movement at these points were interpreted in conjunction with the seismic data, thus making an initial attempt to determine the nature of the recorded icequakes. The relationship between seismic activity in the glacier and the change in the temperature of the environment during the astral summer and the astral winter was studied.

The study of the seismicity and ground structure of Livingston Island and the surrounding area have been carried out using the data recorded by the Broadband seismic equipment of the LIVV station, applying a software code developed for this purpose.

Investigating seasonal variations in seismic noise is another goal of the projects. The recorded seismic noise provided information on the condition and behavior of the seismic equipment throughout the recording period, as well as on the sources of seismic noise and their influence on the recording capabilities of the station.

How to cite: Dimitrova, L., Georgieva, G., and Gourev, V.: Exploring seismicity and ice cover of Livingston Island - research projects, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16573, https://doi.org/10.5194/egusphere-egu23-16573, 2023.

EGU23-16855 | Posters on site | CR2.1 | Highlight

Accelerating ice loss from peripheral glaciers in North Greenland 

Shfaqat Abbas Khan, William Colgan, Thomas Neumann, Michiel van den Broeke, Kelly Brunt, Brice Noël, Jonathan Bamber, Javed Hassan, and Anders Bjørk

The Arctic is warming more rapidly than the rest of the world. This warming has had an especially profound impact on Greenland’s ice cover. Only 4% of Greenland’s ice cover are small peripheral glaciers that are distinct from the ice sheet proper. Despite comprising this relatively small area, these small peripheral gIaciers are responsible for 11% of the ice loss associated with Greenland’s recent sea-level rise contribution. Using the satellite laser platforms ICESat and ICESat-2, we estimate that ice loss from these Greenland glaciers increased from 27±6 Gt/yr (2003–2009) to 42±6 Gt/yr (2018–2021). We find that the largest acceleration in ice loss is in North Greenland, where we observe ice loss to increase by a factor of four between 2003 and 2021. In some areas, it appears that recent increases in snowfall at high altitudes have partially counteracted recent increases in melt at low altitudes. While many recent Greenland ice loss assessments have focused on only the ice sheet, the recent sharp increase in ice loss from small peripheral glaciers highlights the importance of accurately monitoring Greenland’s small peripheral glaciers. These small peripheral glaciers appear poised to play an outsized role in Greenland ice loss for decades to come.

How to cite: Khan, S. A., Colgan, W., Neumann, T., van den Broeke, M., Brunt, K., Noël, B., Bamber, J., Hassan, J., and Bjørk, A.: Accelerating ice loss from peripheral glaciers in North Greenland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16855, https://doi.org/10.5194/egusphere-egu23-16855, 2023.

EGU23-40 | PICO | CR2.2

Investigating firn and ice anisotropy around the EastGRIP Camp, North East Greenland Ice Stream, from ambient noise surface waves 

Emma Pearce, Dimitri Zigone, Charlie Schoonman, Steven Franke, Olaf Eisen, and Joachim Rimpot

We use cross-correlations of ambient seismic noise data between pairs of 9 broadband three component seismometers to investigate variations in velocity structure and anisotropy in the vicinity of the EastGRIP camp along and across flow of the Northeast Greenland Ice Stream (NEGIS).

From the 9-component correlation tensors associated with all station pairs we derive dispersion curves of Rayleigh and Love wave group velocities between station pairs at frequencies from 1 to 25 Hz. The distributions of the Rayleigh and Love group velocities exhibit anisotropy variations for the along and across flow component. To better assess those variations, we invert the dispersions curves to shear wave velocities in the horizontal (Vsh) and vertical (Vsv) direction for the top 300 m of the NEGIS using a Markov Chain Monte Carlo approach.

The reconstructed 1-D shear velocity model revels radial anisotropy in the NEGIS. Along and across flow vertical shear wave velocities (Vsv) identify comparable velocity profiles for all depths. However, horizontal shear wave velocities (Vsh) are faster by approximately 250 m/s in the along flow direction below a depth of 100 m, i.e. below the firn-ice transition.

This type of anisotropy seems to arise from the alignment of a crystallographic preferred orientation, due to deformation associated with shear zones. The role of anisotropy as e.g. created by air bubbles in the firn and ice matrix, is yet unclear.

Faster Vsh velocities in the along flow direction support that the NEGIS has crystal orientation alignment normal to the plane of shear compression (i.e. ice crystals orientated across flow) within the upper 300 m of the ice stream and are in alignment with the results from other methods. We demonstrate that simple, short duration (2-3 weeks), passive seismic deployment and environmental noise-based analysis can be used to determine the anisotropy of the upper part of ice masses.

How to cite: Pearce, E., Zigone, D., Schoonman, C., Franke, S., Eisen, O., and Rimpot, J.: Investigating firn and ice anisotropy around the EastGRIP Camp, North East Greenland Ice Stream, from ambient noise surface waves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-40, https://doi.org/10.5194/egusphere-egu23-40, 2023.

EGU23-92 | ECS | PICO | CR2.2

Improving identification of glacier bed materials using converted-wave seismics 

Ronan Agnew, Adam Booth, Alex Brisbourne, Roger Clark, and Andy Smith

When modelling ice sheet and glacier dynamics, a consideration of basal conditions is essential. Bed topography, hydrology and materials provide important controls on ice flow; however, the materials underlying large sections of the polar ice sheets are unknown. Seismic amplitude-versus-offset (AVO) analysis provides a means of inferring glacier bed properties, namely acoustic impedance and Poisson's ratio, by measuring bed reflectivity as a function of incidence angle.

However, existing methods of applying AVO to glaciology only consider the compressional-wave component of the wavefield and solutions suffer from non-uniqueness. This can be addressed using multi-component seismic datasets, in which a strong converted-wave arrival (downgoing compressional-wave energy converted to shear-wave energy upon reflection at the glacier bed) is often present. We present a method of jointly inverting compressional (PP) and converted-wave (PS) seismic data to improve constraint of glacier bed properties.

Using synthetic data, we demonstrate that for typical survey geometries, joint inversion of PP- and PS-wave AVO data delivers better-constrained bed acoustic impedance and Poisson’s ratio estimates compared with PP-only inversion. Furthermore, joint inversion can produce comparably constrained results to PP inversion when using input data with a smaller range of incidence angles/offsets (0-30 degree incidence for joint inversion, versus 0-60 degrees for PP- only). This could simplify future field acquisitions on very thick ice, where obtaining data at large incidence angles is difficult.

Joint AVO inversion therefore has the potential to improve identification of glacier bed materials and simplify field acquisitions of glacial AVO data. We also present preliminary results from Korff Ice Rise, West Antarctica, where better constraints on bed conditions can help improve our knowledge of ice sheet history in the Weddell Sea sector. Routine measurements of this kind will help constrain ice-sheet model inputs and reduce uncertainty in predictions of sea-level rise.

How to cite: Agnew, R., Booth, A., Brisbourne, A., Clark, R., and Smith, A.: Improving identification of glacier bed materials using converted-wave seismics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-92, https://doi.org/10.5194/egusphere-egu23-92, 2023.

EGU23-93 | ECS | PICO | CR2.2

Efficient neural network-based detection of seismicity in fibre optic data from Store Glacier, West Greenland 

Andrew Pretorius, Adam Booth, Emma Smith, Andy Nowacki, Sjoerd de Ridder, Poul Christoffersen, and Bryn Hubbard

Seismic surveys are widely used to characterise the properties of glaciers, their basal material and conditions, and ice dynamics. The emerging technology of Distributed Acoustic Sensing (DAS) uses fibre optic cables as seismic sensors, allowing observations to be made at higher spatial resolution than possible using traditional geophone deployments. Passive DAS surveys generate large data volumes from which the rate of occurrence and failure mechanism of ice quakes can be constrained, but such large datasets are computationally expensive and time consuming to analyse. Machine learning tools can provide an effective means of automatically identifying seismic events within the data set, avoiding a bottleneck in the data analysis process.

Here, we present a novel approach to machine learning for a borehole-deployed DAS system on Store Glacier, West Greenland. Data were acquired in July 2019, as part of the RESPONDER project, using a Silixa iDAS interrogator and a BRUsens fibre optic cable installed in a 1043 m-deep borehole. The data set includes controlled-source vertical seismic profiles (VSPs) and a 3-day passive record of cryoseismicity.  To identify seismic events in this record, we used a convolutional neural network (CNN). A CNN is a deep learning algorithm and a powerful classification tool, widely applied to the analysis of images and time series data, i.e. to recognise seismic phases for long-range earthquake detection.

For the Store Glacier data set, a CNN was trained on hand-labelled, uniformly-sized time-windows of data, focusing initially on the high-signal-to-noise-ratio seismic arrivals in the VSPs. The trained CNN achieved an accuracy of 90% in recognising seismic energy in new windows. However, the computational time taken for training proved impractical. Training a CNN instead to identify events in the frequency-wavenumber (f-k) domain both reduced the size of each data sample by a factor of 340, yet still provided accurate classification. This decrease in input data volume yields a dramatic decrease in the time required for detection. The CNN required only 1.2 s, with an additional 5.6 s to implement the f-k transform, to process 30 s of data, compared with 129 s to process the same data in the time domain. This suggests that f-k approaches have potential for real-time DAS applications.

Continuing analysis will assess the temporal distribution of passively recorded seismicity over the 3 days of data. Beyond this current phase of work, estimated source locations and focal mechanisms of detected events could be used to provide information on basal conditions, internal deformation and crevasse formation. These new seismic observations will help further constrain the ice dynamics and hydrological properties of Store Glacier that have been observed in previous studies of the area.

The efficiency of training a CNN for event identification in the f-k domain allows detailed insight to be made into the origins and style of glacier seismicity, facilitating further development to passive DAS instrumentation and its applications.

How to cite: Pretorius, A., Booth, A., Smith, E., Nowacki, A., de Ridder, S., Christoffersen, P., and Hubbard, B.: Efficient neural network-based detection of seismicity in fibre optic data from Store Glacier, West Greenland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-93, https://doi.org/10.5194/egusphere-egu23-93, 2023.

EGU23-952 | PICO | CR2.2

Variability of surface density at Dotson Ice Shelf, West Antarctica 

Clare Eayrs, Lucas Beem, Choon-Ki Lee, Won Sang Lee, Jiwoong Chung, Christopher Pierce, Jamey Stutz, and David Holland

The ice mass balance of Antarctica has been steadily and strongly decreasing over recent decades, with major ramifications for global sea levels. Satellite remote sensing offers global, daily coverage of ice mass changes, which is essential for understanding land ice changes and their effects on global climate. However, we need to correct for processes including firn densification, glacial isostatic adjustment, elastic compensation of the Earth’s surface, ocean tides, and inverse barometer effect. Of these corrections, understanding the changes to the firn layer constitutes one of the largest uncertainties in making estimates of the surface mass balance from space. Furthermore, the development of firn models that aid our understanding of firn densification processes is hampered by a lack of observations.

Radar sounder reflections contain information about the roughness and permittivity of the reflecting interface, allowing us to map the spatial variability of the ice surface characteristics. In 2022, a helicopter-mounted ice-penetrating radar system developed by the University of Texas Institute for Geophysics collected high-quality radar observations over the Dotson Ice Shelf, West Antarctica. These surveys obtained clearly defined surface and bed reflections. We derived near-surface density along these survey flight lines using the radar statistical reconnaissance method developed by Grima, 2014. We calibrated our estimates with contemporary observations, including ground penetrating radar, a shallow ice core, an Autonomous phase-sensitive Radio Echo-sounder (ApRES), and radar soundings of well-defined surfaces from a calibration flight.

How to cite: Eayrs, C., Beem, L., Lee, C.-K., Lee, W. S., Chung, J., Pierce, C., Stutz, J., and Holland, D.: Variability of surface density at Dotson Ice Shelf, West Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-952, https://doi.org/10.5194/egusphere-egu23-952, 2023.

EGU23-1137 | ECS | PICO | CR2.2

Hydraulic behaviour of a mountain permafrost subsoil revealed by an infiltration experiment and ERT time-lapse measurements 

Mirko Pavoni, Jacopo Boaga, Alberto Carrera, Giulia Zuecco, Luca Carturan, and Matteo Zumiani

Although rock glaciers represent a common periglacial landform in the alpine environment, and have a significant contribution to the hydrological regime of the related areas, their hydrodynamic is relatively less defined if compared to moraines, talus, and hillslope deposits. So far, the hydraulic behaviour of frozen layers that may be found inside rock glaciers has been investigated only with geochemical analysis of their spring water. These previous studies observed that the frozen layer acts as an aquiclude (or aquitard) and separates a supra-permafrost flow component, originating from snow-ice melting and rainwater, and a deeper aquifer at the bottom of the rock glacier systems.

In this work we verified, for the first time with a geophysical monitoring method, the low-permeability hydraulic behaviour associated to the frozen layer of mountain permafrost subsoils. In the inactive rock glacier of Sadole Valley (Southern Alps, Trento Province, Italy) we performed an infiltration experiment combined with 2D electrical resistivity tomography (ERT) measurements in time-lapse configuration. Considering the same ERT transect, a time zero dataset (t0) has been collected before the water injection, subsequently about 800 liters of salt water have been spilled (approximately in a point) on the surface of the rock glacier in the middle of the electrodes array, and 10 ERT datasets have been collected periodically in the following 24 hours. To highlight the variations of electrical resistivity in the frozen subsoil, related to the injected salt water flow, only the inverted resistivity model derived from t0 dataset has been represented in terms of absolute resistivities, while the other time steps results have been evaluated in terms of percentage changes of resistivity with respect to the t0 initial model.

Our results clearly agree with the assumption that a frozen layer acts as an aquiclude (or aquitard) in a mountain permafrost aquifer, since during the infiltration experiment the injected salt water was not able to infiltrate into the underlying permafrost layer. The positive outcome of this test, fronting impervious environment and logistic constraints, opens up interesting future scenarios regarding the application of this geophysical monitoring method for the hydraulic characterization of rock glaciers. The experiment, used in this work to evaluate the permeability of the frozen layer, could be adapted in future to evaluate (in a quantitative way) the hydraulic conductivity of the active layer in rock glacier aquifers.

How to cite: Pavoni, M., Boaga, J., Carrera, A., Zuecco, G., Carturan, L., and Zumiani, M.: Hydraulic behaviour of a mountain permafrost subsoil revealed by an infiltration experiment and ERT time-lapse measurements, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1137, https://doi.org/10.5194/egusphere-egu23-1137, 2023.

EGU23-2250 | ECS | PICO | CR2.2

Ice slab thickening drives surface runoff expansion from the Greenland Ice Sheet’s percolation zone - and vice versa 

Nicolas Jullien, Andrew Tedstone, and Horst Machguth

On the Greenland Ice Sheet, the firn layer holds the potential to trap and refreeze surface meltwater within its pore space. Acting as a buffer, it prevents meltwater from leaving the ice sheet. However, several meter-thick ice slabs have developed in the firn during the last two decades, reducing subsurface permeability and inhibiting vertical meltwater percolation. Ice slabs are located above the long-term equilibrium line along the west, north and northeast coasts of the ice sheet. Through time, ice slabs have thickened while new ones have developed at higher elevations. Concomitantly, the area of the ice sheet drained by surface rivers has increased by 29% from 1985 to 2020. Nowadays, 5-10% of surface losses through meltwater runoff originates from these newly drained areas, which correspond strongly with where ice slabs are located.

Here, we demonstrate that the highest elevation which is drained by surface rivers – termed the maximum visible runoff limit – is controlled by the ice content in the subsurface firn. Using ice slab thickness derived from the accumulation radar and annual maximum visible limit retrievals from Landsat imagery from 2002 to 2018, we show that a sub-surface ice content threshold triggers the shift from a ‘firn deep percolation regime’ to a ‘firn runoff regime’. Although ice slabs act as an aquitard, vertical meltwater percolation can still take place where visible meltwater ponds at the surface. We show that once the firn runoff regime is underway, ice slabs are thicker in locations with active surface hydrology compared to locations where no meltwater is visible at the surface. Spatial heterogeneity in ice slab thickness is therefore predominantly controlled by surface hydrology features.

How to cite: Jullien, N., Tedstone, A., and Machguth, H.: Ice slab thickening drives surface runoff expansion from the Greenland Ice Sheet’s percolation zone - and vice versa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2250, https://doi.org/10.5194/egusphere-egu23-2250, 2023.

EGU23-2856 | ECS | PICO | CR2.2

New Representation of Synthetic Aperture Radar Images for Enhanced Ice-Sounding Interpretation 

Álvaro Arenas-Pingarrón, Hugh F.J. Corr, Paul V. Brennan, Carl Robinson, Tom A. Jordan, Alex Brisbourne, and Carlos Martín

The processing of Synthetic Aperture Radar (SAR) images is based on the coherent integration of Doppler frequencies. The associated Doppler spectrum is generated from the variation of the relative location between the radar and the scatterer. In geometries where the moving radar-platform follows a straight trajectory at constant velocity, the Doppler frequency depends on the angle of elevation from the radar to the scatterer, according to the electromagnetic (EM) propagation. In ice-sounding by airborne SAR, the EM path depends on the air-ice interface and the firn ice properties. For any of the scatterers under test, after integrating the received radar echoes from the multiple radar locations into a single pixel, the resulting amplitude image forgets which is the backscattering angle from each of the radar locations. However, this information is still within the Doppler spectrum of the image. We decompose the Doppler spectrum of the SAR image into three non-overlapping sub-bands; assign to each sub-band one of the primary colours red, green or blue, forming three sub-images; and finally merge the sub-images into a single one. Rather than a single full-beamwidth averaged amplitude value, the new composition now includes angular backscattering information, coded by one of the primary colours. Blue colour is assigned to scattering received from forwards, when the scatterer is ahead of the radar (positive Doppler frequencies); green approximately from the vertical (near zero-Doppler geometries); and red to scattering received from backwards (negative Doppler). Thus, heterogeneous scattering will be represented by one or two colours, whereas homogeneous scattering will be grey, with all the primary colours uniformly weighted. Features like internal layering, crevasses, SAR focussing quality and discrimination of multiple reflections from surface and bottom, can now be better interpreted. We present and discuss the results from the British Antarctic Survey (BAS) airborne radar PASIN2 for deep-ice sounding, in Recovery and Rutford ice streams, respectively in East and West Antarctica during seasons 2016/17 and 2019/20.

How to cite: Arenas-Pingarrón, Á., Corr, H. F. J., Brennan, P. V., Robinson, C., Jordan, T. A., Brisbourne, A., and Martín, C.: New Representation of Synthetic Aperture Radar Images for Enhanced Ice-Sounding Interpretation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2856, https://doi.org/10.5194/egusphere-egu23-2856, 2023.

In many regions of the Northern Hemisphere, permafrost is thawing due to climate change. In steep terrain, this permafrost degradation can affect slope stability. In one of Iceland's eastern fjords, Seyðisfjörður, nine major landslide cycles have occurred in the last century, originating from the lower parts (< 500 m a.s.l.) of the Strandartindur slopes, with the largest landslide event ever recorded in Iceland occurring in December 2020. Its triggering mechanism is being intensively studied and its development is being monitored. In addition to these instabilities, slow movements are also observed in the upper part (> 500 - 1010 m a.s.l.) of these slopes. In these upper areas, it is not known whether permafrost is present in the subsurface or what is causing it to creep downward. To further investigate the stability of these slopes, it is important to know and map the distribution and condition of possible permafrost layers. Therefore, electrical resistivity tomography (ERT) and ground penetrating radar (GPR) measurements were performed to study the presence and distribution of permafrost in the mountain, Strandartindur, above Seyðisfjörður. A combination of measurements is used as ERT responds primarily to the electrical resistivity of the subsurface, but this can depend strongly on other factors such as porosity, water content, etc., and GPR can help map the presence of different interfaces in the soil determined by their different physical properties, such as relative electrical permittivity, but also conductivity, which is the reciprocal of resistivity. Combining the two methods allows us to get a clearer picture of the subsurface. As a benchmark for ERT measurements in the field, a laboratory setup was performed with soil and rock samples at different temperatures and water saturations to study the behavior of frozen and non-frozen conditions in our geologic environment. With all of these measurements, we aim to answer the questions of whether permafrost is present in the selected area, what the distribution of permafrost is, whether we can use laboratory ERT to establish reference resistivity values, and if these methods are appropriate for this area.

How to cite: von der Esch, A., Piispa, E. J., and Sæmundsson, Þ.: Electrical Resistivity Tomography and Ground-Penetrating Radar Measurements for Permafrost Detection on a Mountain Slope at Strandartindur, Seyðisfjörður - East Iceland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4135, https://doi.org/10.5194/egusphere-egu23-4135, 2023.

EGU23-4895 | ECS | PICO | CR2.2

Detecting permafrost freeze-thaw front propagation using time-laps ERT observations in a large column experiment 

Jelte de Bruin, Victor Bense, and Martine van der Ploeg

Cold regions are increasingly subjected to higher air temperatures, causing warming of permafrost and a deepening of the active layer. This activates hydrogeological groundwater flow and new groundwater pathways to emerge. Monitoring of the active layer depth occurs mainly with the use of temperature observations, but a more flexible and non-invasive method to study transient subsurface processes is with the use of Electrical Resistivity Tomography (ERT) observations. 

Automated time-laps ERT arrays are used to monitor the frozen ground evolution during various seasons, observing resistivity variations during freezing and thawing. Similarly, the leaching of meltwater into the ground under freezing/thawing conditions can be observed. Not only geophysical changes such as fluctuations in water content and water table, but also temperature variations affect the electrical resistivity field. In order to track the development of permafrost active-layer freeze-thaw fronts using ERT observations, it is thus essential that the effect of temperature on the resistivity is clearly defined at realistic scales representing field conditions. Our aim is to determine fluid resistivity at various stages during freezing and thawing and validate current temperature–resistivity relations for partly frozen soils.

This study used a soil column (0.4 m diameter, 1 m heigh) equipped with 96 stainless steel electrodes placed at 8 horizontal rings of 12 electrodes each at various heights around the circumference of the column alongside with temperature sensors. The column was fully insulated on the sides and top except for the bottom, creating a 1D heat transfer system. The soil column was filled with quartzite sand with a D50 of 350 (μm) and organic matter content of 5 (wgt %). The experimental setup was placed within a climate chamber where the column was frozen to -4 °C and thawed to 3 °C over a 3-month period. During the freezing and thawing phase, a full 3D resistivity image was collected using the ERT at a weekly interval. Initial results show that the setup is capable of simulating permafrost freezing and thawing dynamics and ongoing work focuses on the relation between the temperature and time lapse ERT resistivity observations.

How to cite: de Bruin, J., Bense, V., and van der Ploeg, M.: Detecting permafrost freeze-thaw front propagation using time-laps ERT observations in a large column experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4895, https://doi.org/10.5194/egusphere-egu23-4895, 2023.

EGU23-5545 | ECS | PICO | CR2.2

High resolution maps of the sub-ice platelet layer in Atka Bay from electromagnetic induction sounding 

Mara Neudert, Stefanie Arndt, Markus Schulze, Stefan Hendricks, and Christian Haas

We present maps of the sub-ice platelet layer (SIPL) thickness and ice volume fraction beneath the land-fast sea ice in Atka Bay adjacent to the Ekström Ice Shelf (southeastern Weddell Sea, Antarctica). The widespread SIPL beneath Antarctic fast ice is indicative of basal melt of nearby ice shelves, contributes to the sea ice mass balance and provides a unique ecological habitat. Where plumes of supercooled Ice Shelf Water (ISW) rise to the surface rapid formation of platelet ice can lead to the presence of a semi-consolidated SIPL beneath consolidated fast ice.

Here we present data from extensive electromagnetic (EM) induction surveying with the multi-frequency EM sounder GEM-2 between May and December, 2022. It includes monthly survey data along a fixed transect line across Atka Bay between May and October, as well as comprehensive mapping across the entire bay in November and December. The GEM-2 surveys were supplemented by drill hole thickness measurements, ice coring and CTD profiles. A new data processing and inversion scheme was successfully applied to over 1000 km of EM profiles with a horizontal resolution of one meter. We obtained layer thicknesses of the consolidated ice plus snow layer, the SIPL, and the respective layer conductivities. The latter were used to derive SIPL ice volume fraction and an indicator for flooding at the snow-ice interface. The robustness of the method was validated by drill hole transects and CTD profiles.

Our results support conclusions about the spatial variability of the ocean heat flux linked to outflow of ISW from beneath the ice shelf cavity. Temporally, we found that the end of SIPL growth and the onset of its thinning in summer can be linked to the disappearance of supercooled water in the upper water column.

How to cite: Neudert, M., Arndt, S., Schulze, M., Hendricks, S., and Haas, C.: High resolution maps of the sub-ice platelet layer in Atka Bay from electromagnetic induction sounding, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5545, https://doi.org/10.5194/egusphere-egu23-5545, 2023.

EGU23-5851 | PICO | CR2.2

Measuring snow and avalanche properties using acoustic and seismic distributed fiber optic sensing 

Alexander Prokop, Nicola P. Agostinetti, and Bernhard Graseman

Since 2012 we monitor avalanche activity using distributed acoustic and seismic fiber optic sensing at our avalanche test area at Lech am Arlberg, Austria. The method is based on an optical time domain reflectometer system that detects seismic vibrations and acoustic signals on a fiber optic cable that can have a length of up to 30 km in 80 cm resolution. While in the first years we focused on successfully developing an operational avalanche detection system that is able to tell in real time reliably when an avalanche was triggered and what the size of the avalanche is, we now present our investigations of the seismic signals to measure snow properties such as snow depth and avalanche properties such as flow behavior. Our test in winter 2022 recorded by blasting triggered avalanches and during data post processing we extracted seismic guided waves. We discuss methods for extracting information from guided waves for measuring snow depth, which was verified against spatial snow depth measurements from terrestrial laser scanning. Analyzing the seismic signals of avalanches with run-out distances ranging from a few metres to approximately 250 m allows us to differentiate between wet and snow avalanches, which is discussed in the context of avalanche dynamics.

How to cite: Prokop, A., Agostinetti, N. P., and Graseman, B.: Measuring snow and avalanche properties using acoustic and seismic distributed fiber optic sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5851, https://doi.org/10.5194/egusphere-egu23-5851, 2023.

EGU23-6935 | PICO | CR2.2

A new view of a 1970s radar dataset from Greenland 

Nanna Bjørnholt Karlsson, Dustin Schroeder, Louise S. Sørensen, Winnie Chu, Thomas Teisberg, Angelo Tarzano, Niels Skou, and Jørgen Dall

The short observational record is one of the main obstacles to improving the present understanding of the future of the Polar ice sheets. While the quantity and quality of observations presently are increasing observations from before the 1990s are scarce. Here, we present the first results from a newly digitized ice-penetrating radar dataset acquired over the Greenland Ice Sheet in the 1970s. The data consist of more than 170,000 km of radar flight lines. While the ice thickness information from the data has been digitized by previous studies, the data itself (notably the z-scopes) were until recently only available as 35-mm films, microfiche copies of the films, and enlarged positives: Formats that are not usable for digital analysis.

In 2019, the film rolls were scanned by a digital scanner and subsequently, a large effort has been directed at carrying out quality control of the data with the view of making them publicly available.  Here we present the first results from this digitization. The overall data quality is good, and we are able to retrieve valuable information on layer stratigraphy and ice-flow dynamics.

How to cite: Karlsson, N. B., Schroeder, D., Sørensen, L. S., Chu, W., Teisberg, T., Tarzano, A., Skou, N., and Dall, J.: A new view of a 1970s radar dataset from Greenland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6935, https://doi.org/10.5194/egusphere-egu23-6935, 2023.

EGU23-7198 | PICO | CR2.2

Climatic imprint in the mechanical properties of ice sheets and its effect on ice flow: Observations fromSouth Pole and EPICA Dome C ice cores 

Carlos Martin, Robert Mulvaney, Howard Conway, Michelle Koutnik, C. Max Stevens, Hugh Corr, Catherine Ritz, Keith Nicholls, Reinhard Drews, and M. Reza Ershadi

The climatic conditions over ice sheets at the time of snow deposition and compaction imprint distinctive crystallographic properties to the resulting ice. As it gets buried, its macroscopic structure evolves due to vertical compression but retains traces of the climatic imprint that generate distinctive mechanical, thermal and optical properties. Because climate alternates between glacial periods, that are colder and dustier, and interglacial periods, the ice sheets are composed from layers with alternating mechanical properties. Here we compare ice core dust content, crystal orientation fabrics and englacial vertical strain-rates, measured with a phase-sensitive radar (ApRES), at the South Pole and EPICA Dome C ice cores. In agreement with previous observations, we show that ice deposited during glacial periods develops stronger crystal orientation fabrics. In addition, we show that ice deposited during glacial periods is harder in vertical compression and horizontal extension, up to about three times, but softer in shear. These variations in mechanical properties are ignored in ice-flow models but they could be critical for the interpretation of ice core records. Also, we show that the changes in crystal orientation fabrics due to transitions from interglacial to glacial conditions can be detected by radar. This information can be used to constrain age-depth at future ice-core locations.

How to cite: Martin, C., Mulvaney, R., Conway, H., Koutnik, M., Stevens, C. M., Corr, H., Ritz, C., Nicholls, K., Drews, R., and Ershadi, M. R.: Climatic imprint in the mechanical properties of ice sheets and its effect on ice flow: Observations fromSouth Pole and EPICA Dome C ice cores, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7198, https://doi.org/10.5194/egusphere-egu23-7198, 2023.

EGU23-7695 | ECS | PICO | CR2.2

Spatial variation of ice crystal fabric and implications of anisotropic flow in the Northeast Greenland Ice Stream 

Tamara Gerber and Olaf Eisen and the NEGIS community

Anisotropic crystal fabrics in ice sheets develop as a consequence of deformation and hence record information of past ice flow. Simultaneously, the fabric affects the present-day bulk mechanical properties of glacier ice because the susceptibility of ice crystals to deformation is highly anisotropic. This is particularly relevant in dynamic areas such as fast-flowing glaciers and ice streams, where the formation of strong fabrics might play a critical role in facilitating ice flow. Anisotropy is ignored in most state-of-the-art ice sheet models, and while its importance has long been recognized, accounting for fabric evolution and its impact on the ice viscosity has only recently become feasible. Both the application of such models to ice streams and their verification through in-situ observations are still rare. We present an extensive dataset of fabric anisotropy derived from ground-based and air-borne radar data, covering approximately 24,000 km2 of the Northeast Greenland Ice Stream onset region. Our methods yield the horizontal anisotropy and are based on travel time anisotropy as well as birefringence-induced power modulation of radar signals. These methods complement each other and show good agreement. We compare the in-situ observations with the results obtained from a fabric-evolution model employed along flow line bundles in the ice stream onset to discuss the fabric in light of past flow history and its significance for the current flow mechanics of the ice stream.

 

How to cite: Gerber, T. and Eisen, O. and the NEGIS community: Spatial variation of ice crystal fabric and implications of anisotropic flow in the Northeast Greenland Ice Stream, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7695, https://doi.org/10.5194/egusphere-egu23-7695, 2023.

EGU23-8197 | ECS | PICO | CR2.2

Ice rise evolution derived from radar investigations at a promontory triple junction, Dronning Maud Land, East Antarctica 

M. Reza Ershadi, Reinhard Drews, Veronica Tsibulskaya, Sainan Sun, Clara Henry, Falk Oraschewski, Inka Koch, Carlos Martin, Jean-Louis Tison, Sarah Wauthy, Paul Bons, Olaf Eisen, and Frank Pattyn

Promontory ice rises are locally grounded features adjacent to ice shelves that are still connected to the ice sheet. Ice rises are an archive for the atmospheric and ice dynamic history of the respective outflow regions where the presence, absence, or migration of Raymond arches in radar stratigraphy represents a memory of the ice-rise evolution. However, ice rises and their inferred dynamic history are not yet used to constrain large-scale ice flow model spin-ups because matching the arch amplitudes includes many unknown parameters, e.g., those pertaining to ice rheology. In particular, anisotropic ice flow models predict gradients in ice fabric anisotropy on either side of an ice divide. However, this has thus far not been validated with observations.

 

The ground-based phase-sensitive Radio Echo Sounder (pRES) has previously been used to infer ice fabric types for various flow regimes using the co-polarized polarimetric coherence phase as a metric to extract information from the birefringent radar backscatter. Here, we apply this technique using quad-polarimetric radar data along a 5 km transect across a ridge near the triple junction of Hammarryggen Ice Rise at the Princess Ragnhild Coast. A comparison with ice core data collected at the dome shows that the magnitude of ice fabric anisotropy can reliably be reconstructed from the quad-polarimetric data. We use the combined dataset also to infer the spatial variation of ice fabric orientations in the vicinity of the triple junction. The observations are integrated with airborne radar profiles and strain rates based on the shallow ice approximation. We then discuss whether estimated anisotropy from radar polarimetry on ice rises, in general, can be another observational constraint to better ice rises as an archive of ice dynamics.

How to cite: Ershadi, M. R., Drews, R., Tsibulskaya, V., Sun, S., Henry, C., Oraschewski, F., Koch, I., Martin, C., Tison, J.-L., Wauthy, S., Bons, P., Eisen, O., and Pattyn, F.: Ice rise evolution derived from radar investigations at a promontory triple junction, Dronning Maud Land, East Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8197, https://doi.org/10.5194/egusphere-egu23-8197, 2023.

EGU23-9273 | ECS | PICO | CR2.2

Layer-optimized SAR processing with a mobile pRES to illuminate the internal layering of an alpine glacier 

Falk M. Oraschewski, Inka Koch, Mohammadreza Ershadi, Jonathan Hawkins, Olaf Eisen, and Reinhard Drews

The internal, isochronous layering of glaciers is shaped by accumulation and ice deformation. Information about these processes can be inferred from observing the layers using radar sounding. The reflectivity of the layers depends on density (permittivity) and acidity (conductivity) contrasts which tend to decrease with depth. At places like alpine glaciers where logistic limitations often only allow the deployment of lightweight and power-constrained ground-penetrating radar systems, it can therefore be challenging to illuminate the deeper radio-stratigraphy.

The phase-sensitive Radio Echo Sounder (pRES) is a lightweight frequency modulated continuous wave radar which allows the use of coherent Synthetic Aperture Radar (SAR) processing techniques to improve the signal-to-noise ratio of internal reflection horizons. Using a mobile pRES we collected a radar profile on an alpine glacier (Colle Gnifetti, Italy/Switzerland). Here, we demonstrate how to apply layer-optimized SAR techniques to make deep internal layers visible, which could not be seen by a conventional pulsed radar. We evaluate the requirements on spatial resolution and positioning accuracy during data acquisition, necessary for applying layer-optimized SAR processing, as they constrain the feasibility of the method. We further discuss implications on how density and acidity contribute to decreasing dielectric contrasts.

How to cite: Oraschewski, F. M., Koch, I., Ershadi, M., Hawkins, J., Eisen, O., and Drews, R.: Layer-optimized SAR processing with a mobile pRES to illuminate the internal layering of an alpine glacier, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9273, https://doi.org/10.5194/egusphere-egu23-9273, 2023.

EGU23-9619 | ECS | PICO | CR2.2

High-density 3D and 4D GPR data acquisitions over alpine glaciers using a newly developed drone-based system. 

Bastien Ruols, Ludovic Baron, and James Irving

We have developed a drone-based GPR system at the University of Lausanne that allows for the safe and efficient acquisition of large, high-density, 3D and 4D datasets over alpine glaciers. The system is able to record approximately 4 line-km of high-quality GPR data per set of drone batteries in less than 30 minutes of operation which, combined with multiple sets of batteries and/or the possibility of charging at the field site, means that 3D datasets over a large area can be acquired with unprecedented efficiency. The latter performance is possible thanks to (i) a custom-made real-time-sampling GPR controller that has been specifically designed for glaciers studies, (ii) minimization of the total payload weight using custom-built antennas and carbon-fiber components, and (iii) development of an optimized survey methodology. Further, because the drone is equipped with real-time kinematic GPS positioning, survey paths can be repeated with great precision, which opens new opportunities in term of 4D data acquisitions.

In the summer of 2022, we acquired both 3D and 4D data over two Swiss glaciers. On the Otemma glacier, we surveyed a grid of 462 profiles representing a total length of 112 line-km of data in only four days. After 3D binning, the trace spacing intervals in the in-line and crossline directions were respectively 0.4 m and 1 m, making this arguably the largest 3D GPR dataset of such density ever recorded over ice. The interface between the ice and the bedrock, visible on all profiles, extends to 1000 ns which translates into a depth of approximately 80 m. In addition, internal englacial and subglacial 3D structures are clearly detectable.

In parallel, we visited the Rhône glacier on a monthly basis between June and September 2022. A collapse feature, identified by the presence of large circular crevasses, had formed and was evolving close to the snout of the glacier. This represented a great opportunity to test the 4D acquisition capabilities of our system. We collected four high-density 3D datasets on the same survey grid. The repeatability of the trajectories was excellent as the paths differ only by a few centimeters between occurrences. Clear variations in the internal structure of the glacier are visible which will be investigated in the upcoming months.

How to cite: Ruols, B., Baron, L., and Irving, J.: High-density 3D and 4D GPR data acquisitions over alpine glaciers using a newly developed drone-based system., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9619, https://doi.org/10.5194/egusphere-egu23-9619, 2023.

The Whillans Ice Stream (WIS) is a major outlet of the West Antarctic Ice Sheet. Significantly, the downstream portion of the WIS is presently decelerating, possibly stagnating by the end of this century. Additionally, this downstream region of WIS is unique in that it moves primarily by stick-slip motion. However, both the rate of deceleration as well as the percent of motion accommodated by stick-slip motion is spatially variable. Such spatial variability is potentially linked to associated variability in basal conditions. Active source seismic measurement are capable of providing high-resolution insights into basal conditions, however, they are time-consuming to collect, limiting the spatial extent over which they can be acquired. In this presentation, we will use passive seismic measurements collected at over 50 seismic stations to map sediment thickness and ice-bed conditions across the region. This will be done using the receiver function method which images the depth and physical properties of sediments by modeling the arrival times and amplitudes of seismic waves that interact with subglacial sedimentary structures. We will first map conditions at the ice-bed interface by using relatively high-frequency waveforms (> 2 Hz) as they are sensitive to the physical properties of the shallow (< 20m ) subglacial sediments layers. Across the entirety of the study region, we find that this uppermost layer of sediments is characterized by relatively high porosity sediments.  Second, we will utilize lower frequencies (< 2 Hz) to map the depth basement, finding that the entire region is underlain 100’s of meters of sediments (Gustafson et al., Science, 2022). We will use our maps of sediment properties and thickness to investigate potential mechanisms for the observed variability in deceleration and stick-slip behavior of the WIS.  

How to cite: Winberry, J. P.: Basal Conditions and Sedimentary Structure of the Whillans Ice Stream., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9621, https://doi.org/10.5194/egusphere-egu23-9621, 2023.

Englacial temperature and water content play critical roles in glacier dynamics, both within ice sheets and mountain glaciers. As radio wave attenuation is sensitive to both of these properties, radio-echo sounding (RES) serves as a useful tool for mapping out their distributions within glaciers. Ground-based bistatic surveys, in which multi-offset measurements are taken, provide a large diversity in bed incidence angles and travel-path lengths. Provided the anomaly of interest is sufficiently sampled, these measurements can be exploited to perform attenuation tomography, thereby recovering the distribution of englacial radio wave attenuation from which englacial temperature can be estimated. Extensive RES surveys have been carried out over Antarctica using airborne radar; however, due to the monostatic geometry, methods for estimation of englacial radio wave attenuation and basal roughness have relied primarily on nadir returns. These estimates are often derived from 2D spatial correlation of basal return power and ice thickness or by employing layer-tracking methods. These techniques are limited in that the former uses echoes from a large spatial footprint, preventing the detection of small-scale anomalies, while the latter assumes a known, spatially invariant reflectivity for tracked layers. However, by considering returns from off-nadir in airborne surveys, techniques from multi-offset surface surveys can be modified and extended to perform airborne attenuation tomography. While not reaching the range of path diversity achievable in surface-based surveys due to limitations imposed by total internal reflection at the ice-air interface, airborne off-nadir returns contain valuable information about subglacial and englacial conditions that is often ignored. Thus, we propose a method for estimating englacial attenuation and basal roughness using the drop in power from the peak to tail of hyperbolic scattering events in unfocussed radargrams associated with the rough bed surface. The travel-paths of the bed returns across a given hyperbolic event vary in both length and bed incidence angle. Thus, the drop in return power across a hyperbolic event gives insight into both the integrated attenuation along a travel-path, as well as the scattering function at the bed. Specular reflections from internal layers with varying dips similarly provide diversity in travel-path lengths, allowing the derivation of a relationship between path length and return power without the complications brought about by diffuse scattering at rough surfaces. Using the diverse path lengths and angles through the ice, a tomographic inversion to map the spatial distribution englacial attenuation anomalies can be implemented. This technique is applied to synthetic data, as well as data collected using the British Antarctic Survey’s Polarimetric-radar Airborne Science Instrument (PASIN), specifically to lines collected over the Eastern Shear Margin of Thwaites Glacier. This location was chosen as constraining bed conditions and identifying expected englacial thermal anomalies are critical to understanding the history and modelling the future of Thwaites.

How to cite: May, D., Schroeder, D., and Young, T. J.: Radar Attenuation Tomography for Mapping Englacial Temperature Distributions Using Off-Nadir Airborne Radio-Echo Sounding, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9833, https://doi.org/10.5194/egusphere-egu23-9833, 2023.

EGU23-10127 | ECS | PICO | CR2.2

Monitoring lake ice with acoustic sensors 

Christoph Wetter, Cédric Schmelzbach, and Simon C. Stähler

Monitoring of the thickness and elastic parameters of floating ice on lakes and the sea is of interest in understanding the climate change impact on Alpine and Arctic environments, assessing ice safety for recreational and engineering purposes, studying ice shelves as well as exploring possibilities for the future exploration of the icy crusts of ocean worlds in our solar system. A multitude of geophysical methods exist today to monitor sea and lake ice thickness as well as elastic parameters. Mostly, seismic and radar measurements are used. Both methods have in common that they come with significant logistical effort and expensive equipment. In this study, we present a novel low cost approach using acoustic sensors for ice monitoring.

We explored the possibility of using microphones deployed on frozen lakes in the Swiss Alps to monitor the lake ice-thickness using acoustic signals originating from frequently occurring ice quakes. Data were obtained during a three-month-long field campaign at Lake St. Moritz in Switzerland in winter 2021/2022. Three microphone stations were placed on the lake in addition to five conventional seismometers. These seismometers were used to compare the acoustic signals with the seismic ice quake recordings. Additionally, also active-source experiments were conducted using hammer strokes as source, which were used to constrain elastic parameters of the ice.

The acoustic recordings of ice quakes allowed us to exploit the unique characteristics of so-called air-coupled waves to determine time-dependent ice thickness curves of Lake St. Moritz for winter 2021/2022 using acoustic data only. Furthermore, the acoustic data allowed us to gain new insights into the ice/air coupling of seismic waves in ice. 

How to cite: Wetter, C., Schmelzbach, C., and Stähler, S. C.: Monitoring lake ice with acoustic sensors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10127, https://doi.org/10.5194/egusphere-egu23-10127, 2023.

EGU23-11787 | ECS | PICO | CR2.2

Validating manual measurements of snow water equivalent against a reference standard 

Alexander Radlherr and Michael Winkler

The snowpack is a key component in several fields like climatology, hydrology, or natural hazards research and mitigation, not least in mountainous regions. One of the most considerable snowpack features is the snow water equivalent (SWE), representing the mass of water stored in the snowpack and – in another perspective – the weight straining objects the snow is settling on (snow load). In comparison to snow depth, measuring SWE is rather complex and prone to errors. Consecutive observations of SWE do not have a long tradition in many regions.

Despite various recent developments in measuring SWE by means of remote sensing or other noninvasive methods, e.g. with scales, GNSS reflectometry, signal attenuation and time delay techniques, cosmic-ray neutron sensing, etc. the standard measuring technique still are snow tubes or gauging cylinders, often in combination with digging pits. Tubing-technique is commonly used as reference for the validation of named modern methods, although studies addressing its accuracy, precision and repeatability are very rare.

This contribution provides results from comparing different types of SWE measurement tubes with reference standard oberservation. Several field tests were executed at different sites in the Austrian Alps covering a great variety of snow conditions (e.g. dry and wet), snow depths and SWEs. For the reference observation 3x4 m rectangular fields were dug snow-free and the respective snow masses have been weighted stepwise using ca. 50-liter-buckets. Due to the large total mass of snow of typically around two tons per rectangular, relative uncertainties are extremely small and the results highly accurate. Additionally, different snow tubes were compared to each other. The cylinder or tube designs vary a lot: from meters long metal coring tubes of typical inner diameters of ca. 4-7 cm (without the need of pits) or PVC cylinders with typical lengths of 0.5 to 1.5 m and diameters ranging from about 5-20 cm to small aluminum tubes holding a maximum of 0.5 liter of snow.  

Many statistical measures like variance and bias vary quite a lot primarily depending on the equipment used, but also on the different snow conditions. A synopsis on the suitability of the various methods depending on the questioning or objective of the observation is provided.

How to cite: Radlherr, A. and Winkler, M.: Validating manual measurements of snow water equivalent against a reference standard, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11787, https://doi.org/10.5194/egusphere-egu23-11787, 2023.

EGU23-13127 | PICO | CR2.2

Snow depth sensitivity to mean temperature and elevation in the European Alps 

Matthew Switanek, Wolfgang Schöner, and Gernot Resch

Many of the gauged snow depth measurements in the European Alps began in the late 19th and early 20th centuries. We leverage this reasonably long period of record to investigate the historical sensitivity of snow depths as a function of precipitation, mean temperature, and elevation. By controlling for changes in precipitation, we can isolate the influence that different temperature changes have on snow depths at varying elevation bands. This simple, yet effective, approach to defining our historical sensitivity can provide a robust observational framework to evaluate the impact that a range of different future warming scenarios would have on snow depths across the Alps. As a result, adaptation and mitigation measures can be put in place for a variety of end users, such as ski tourism and water resource management. Furthermore, this provides an observational reference by which to evaluate the performance of climate model simulations.

How to cite: Switanek, M., Schöner, W., and Resch, G.: Snow depth sensitivity to mean temperature and elevation in the European Alps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13127, https://doi.org/10.5194/egusphere-egu23-13127, 2023.

EGU23-14390 | ECS | PICO | CR2.2

Long-time permafrost evolution in alpine bedrock: quantifying climate change effects with geoelectrical monitoring 

Riccardo Scandroglio, Maike Offer, and Michael Krautblatter

While climate change driven increase in air temperature has been correctly modeled in recent decades, the extent of its consequences is still uncertain. In high alpine environments, especially in steep rock walls, permafrost degradation reduces slope stability with critical consequences for people and infrastructures: to properly assess the risk, the rate of these changes must be monitored. In the last decades, electrical resistivity tomography (ERT) has been used in more than hundred studies to detect permafrost, but there are only limited long-term monitoring cases that mostly do not provide quantitative information. 

Here we compare ERT measurements from two alpine landforms with different altitude and lithology: Steintälli ridge (3160m asl, CH) and Mt. Zugspitze rock wall (2750 m asl, DE/AT). Standard procedures and permanently installed electrodes allow the collection of a unique dataset of consistent measurements since 2006. Supporting information like resistivity-temperature calibration from former studies, rock surface and borehole temperatures as well as active seismic refraction measurements enable an advanced quantitative interpretation of the results. 

Permafrost at both sites is close to disappearing and in both cases resistivity changes are evident and in good agreement with air temperature increase, although with different magnitudes according to the landform. The yearly 3D measurements of the Steintälli ridge show a sudden and conspicuous degradation (~40% of the volume in 15 years), while the monthly 2D monitoring of the north face of Mt. Zugspitze shows slow constant decrease in summer (~15% of the surface in 15 years) and a strong variation in winter in correlation with snow-height. 

For the first time we provide a quantification of alpine permafrost degradation rates in different landforms over 15 years. These datasets help to better understand the different characteristics of the thermal responses to the climate change induced stress on alpine permafrost environments.

How to cite: Scandroglio, R., Offer, M., and Krautblatter, M.: Long-time permafrost evolution in alpine bedrock: quantifying climate change effects with geoelectrical monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14390, https://doi.org/10.5194/egusphere-egu23-14390, 2023.

EGU23-16308 | ECS | PICO | CR2.2

Thwaites Glacier Eastern Shear Margin: Insights from two broadband seismic arrays 

Emma C. Smith, Marianne Karplus, Jake Walter, Nori Nakata, Adam D. Booth, Lucia Gonzalez, Andrew Pretorius, Ronan Agnew, Stephen Veitch, Eliza J. Dawson, Daniel May, Paul Summers, Tun Jan Young, Poul Christoffersen, and Slawek Tulaczyk

The stability of Thwaites Glacier, the second largest marine ice stream in West Antarctica, is a major source of uncertainty in future predictions of global sea level rise. Critical to understanding the stability of Thwaites Glacier, is understanding the dynamics of the shear margins, which provide important lateral resistance that counters basal weakening associated with ice flow acceleration and forcing at the grounding line. The eastern shear margin is of interest, as it is poorly topographically constrained, meaning it could migrate rapidly, causing further ice flow acceleration and drawing a larger volume of ice into the fast-flowing ice stream. 

We present initial insights from a 2-year-long seismic record, from two broadband seismic arrays each with 7 stations, deployed across the eastern shear margin of Thwaites Glacier. We have applied a variety of processing methods to these data to detect and locate icequakes from different origins and analyse them in the context of shear-margin dynamics. Preliminary results suggest there is basal seismicity concentrated near the ice-bed interface on the slow-moving side of the margin, as opposed to within the ice-stream itself. Some of the identified seismic events appear to exhibit clear shear-wave splitting, suggesting a strong anisotropy in the ice, which would be consistent with polarization observed in recently published radar studies from the field site. Further analysis of the split shear-waves will allow us to better constrain the region's ice-fabric, infer past shear-margin location, and assess the future stability of this ice rheology.  

With such a large quantity of data, manual event identification is unpractical, and hence we are employing machine-learning approaches to identify and locate icequakes of interest in these data. Our results and forthcoming results from upcoming active-seismic field seasons have important implications for better understanding the stability of glacier and ice stream shear margins. 

How to cite: Smith, E. C., Karplus, M., Walter, J., Nakata, N., Booth, A. D., Gonzalez, L., Pretorius, A., Agnew, R., Veitch, S., Dawson, E. J., May, D., Summers, P., Young, T. J., Christoffersen, P., and Tulaczyk, S.: Thwaites Glacier Eastern Shear Margin: Insights from two broadband seismic arrays, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16308, https://doi.org/10.5194/egusphere-egu23-16308, 2023.

EGU23-16342 | PICO | CR2.2

Intercomparison of quantification methods for snow microstructure during the SnowAPP experiment 

Anna Kontu, Leena Leppänen, Roberta Pirazzini, Henna-Reetta Hannula, Juha Lemmetyinen, Petri Räisänen, Amy McFarlane, Pedro Espin Lopez, Kati Anttila, Aleksi Rimali, Hanne Suokanerva, Jianwei Yang, Teruo Aoki, Masashi Niwano, Ghislain Picard, Ines Ollivier, Laurent Arnaud, Margaret Matzl, Ioanna Merkouriad, and Martin Schneebeli

Snow microstructure defines the physical, mechanical and electromagnetic properties of snow. Accurate information of snow structure is needed by many applications, including avalanche forecasting (Hirashima et al., 2008) and numerical weather prediction (de Rosnay et al., 2014). The interaction of electromagnetic waves with snow properties can be applied in satellite remote sensing to retrieve, for example, global information of snow mass (Pulliainen et al., 2020). Objective in-situ observations of snow microstructure are needed to validate and develop both physical models and satellite snow retrieval algorithms. Conventional measurements of snow grain size are unsatisfactory in this regard, as the parameter is difficult to measure objectively, and even its definition is ambiguous (Mätzler, 2002). Hence, recent efforts have focused on developing forward models of microwave interactions and snow specific surface area (SSA), which can be objectively measured in field and laboratory conditions using various methods. A recently proposed approach links SSA to microwave scattering properties through another physically defined parameter (Picard et al., 2022).

In the SnowAPP project, three field campaigns were carried out at the Finnish Meteorological Institute Arctic Research Centre in Sodankylä, with the goal of collecting data on snow microstructural properties and establishing the relation of microstructure to both optical reflectance and microwave emission and scattering from snow.  During the spring 2019 campaign, six different methods were used for measuring SSA; and several methods were used for measuring snow density, another important factor affecting especially the extinction of microwave energy. Furthermore, multi-frequency radiometry and a wide-band, high resolution spectrometer were used to measure microwave emission and reflectance. In this study, we compare objectively the SSA and density values obtained by the different methods in a round-robin exercise. The relation of measured snow microstructures to measured spectral properties of snow are discussed.

SnowAPP was funded by the Academy of Finland, with contributions from WSL Institute for Snow and Avalanche Research SLF, Centre Tecnològic de Telecomunicacions de Catalunya, Beijing Normal University, National Institute for Polar Research, Meteorological Research Institute (Japan), and Université Grenoble Alpes.

 

de Rosnay, P., Balsamo, G., Albergel, C., Muñoz-Sabater, J., & Isaksen, L. (2014). Initialisation of land surface variables for numerical weather prediction. Surveys in Geophysics, 35(3), 607–621. https://doi.org/10.1007/s10712-012-9207-x

Hirashima, H., Nishimura, K., Yamaguchi, S., Sato, A., & Lehning, M. (2008). Avalanche forecasting in a heavy snowfall area using the snowpack model. Cold Regions Science and Technology, 51(2–3), 191–203. https://doi.org/10.1016/j.coldregions.2007.05.013

Mätzler, C., 2002. Relation between grain-size and correlation length of snow. J. Glaciol., (48)162: 461-466.

Picard, G., Löwe, H., Domine, F., Arnaud, L., Larue, F., Favier, V., & Meur, E. le. (2022). The Microwave Snow Grain Size: A New Concept to Predict Satellite Observations Over Snow-Covered Regions. https://doi.org/10.1029/2021AV000630

Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., & Norberg, J. (2020). Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature, 581(7808), 294–298. https://doi.org/10.1038/s41586-020-2258-0

 

How to cite: Kontu, A., Leppänen, L., Pirazzini, R., Hannula, H.-R., Lemmetyinen, J., Räisänen, P., McFarlane, A., Espin Lopez, P., Anttila, K., Rimali, A., Suokanerva, H., Yang, J., Aoki, T., Niwano, M., Picard, G., Ollivier, I., Arnaud, L., Matzl, M., Merkouriad, I., and Schneebeli, M.: Intercomparison of quantification methods for snow microstructure during the SnowAPP experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16342, https://doi.org/10.5194/egusphere-egu23-16342, 2023.

EGU23-16522 | ECS | PICO | CR2.2

Resolving ice content heterogeneity within permafrost peatlands using high-frequency induced polarisation. 

Madhuri Gopaldas Sugand, Andreas Hördt, and Andrew Binley

Permafrost peatlands are highly vulnerable ecosystems in a warming climate; their thaw greatly impacts carbon storage capacity and endangers existing landscape morphology. Due to their remoteness and, in some cases, protected status, it is difficult to characterise and monitor the subsurface using invasive methods. Geophysical investigations are useful in such cases allowing relatively rapid and extensive subsurface mapping. We focus here on the emerging high-frequency induced polarisation (HFIP) method, which can be effective in permafrost hydrology research as the geoelectrical properties of frozen water display a characteristic frequency-dependence between ranges of 100 Hz and 100 kHz.

HFIP field measurements were conducted using the Chameleon-II equipment (Radic Research) on two peat permafrost sites located in Abisko, Northern Sweden: Storflaket mire and Heliport mire. The sites have been subject to routine permafrost monitoring since 1978 and are known to have an upper peat layer underlain by a silt-rich subsoil. We present the results of 2D surveys measuring frequencies ranging from 1 Hz to 57 kHz, which capture a high-frequency phase shift peak. Field data are inverted for each measured frequency separately with ResIPy, using an appropriate data error quantification model. The spectral data analysis captures heterogeneity within the subsurface, i.e., layered medium, permafrost mire boundary and ice-rich versus ice-poor regions. Identification of spectrally distinct regions allows the application of an appropriate relaxation model. For this study, we apply a two-component mixture model for ice-content estimation. Our results extend the existing knowledge at this site by quantifying ice content in a 2D plane, thus improving the foundation for further modelling studies.

How to cite: Sugand, M. G., Hördt, A., and Binley, A.: Resolving ice content heterogeneity within permafrost peatlands using high-frequency induced polarisation., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16522, https://doi.org/10.5194/egusphere-egu23-16522, 2023.

EGU23-787 | ECS | PICO | CR2.3

Mapping stagnant ice and age in the Dome Fuji region, Antarctica, by combining radar internal layer stratigraphy and flow modeling 

Zhuo Wang, Olaf Eisen, Ailsa Chung, Daniel Steinhage, Frédéric Parrenin, and Johannes Freitag

The Dome Fuji (DF) region in Antarctica is a potential site for holding an ice record older than one million years. Here, we combine the internal airborne radar stratigraphy with a 1-D inverse model to reconstruct the age field of ice in the DF region. As part of the Beyond EPICA - Oldest Ice reconnaissance (OIR), the region around DF was surveyed with a total of 19000 km of radar lines in the 2016/17 Antarctic summer. Internal stratigraphy in this region has now been traced. Through these tracked radar isochrones, we transfer the age-depth scale from DF ice core to the adjacent 500 km2 region. A 1-D inverse model has been applied at each point of the survey to extend the age estimates to deeper regions of the ice sheet where no direct or continuous link of internal stratigraphy to the ice cores is possible, and to construct basal thermal state and accumulation rates. Through the reliability index of each model, we can evaluate the reliability of the 1-D assumption. Mapped age of basal ice and age density imply there might exist promising sites with ice older than 1.5 million years in the DF region. Moreover, the deduced basal state, i.e., melting rates and stagnant ice provide constraints for finding old-ice sites with a cold base. The accumulation rate ranges from 0.014 to 0.038 m a-1 (in ice equivalent) in the DF region, which is also an important criterion for potential old ice.

How to cite: Wang, Z., Eisen, O., Chung, A., Steinhage, D., Parrenin, F., and Freitag, J.: Mapping stagnant ice and age in the Dome Fuji region, Antarctica, by combining radar internal layer stratigraphy and flow modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-787, https://doi.org/10.5194/egusphere-egu23-787, 2023.

EGU23-792 | ECS | PICO | CR2.3

What to watch out for when assimilating ice-cores as regional SMB proxies? 

Marie G. P. Cavitte, Hugues Goosse, Kenichi Matsuoka, Sarah Wauthy, Rahul Dey, Vikram Goel, Jean-Louis Tison, Brice Van Liefferinge, and Thamban Meloth

Ice cores remain the highest resolution proxy for measuring past surface mass balance (SMB) that can be used for model-data comparison. However, there is a clear difference in the spatial resolution of the ice cores, with a surface sample on the order of cm2, and the spatial resolution of models, with at best a surface footprint on the order of a few km2. Comparing ice core SMB records and model SMB outputs directly is therefore not a one-to-one comparison. In addition, it is well known that ice cores, as point measurements, sample very local SMB conditions which can be affected by local wind redistribution of the SMB at the surface.

We set out to answer the question: how representative are ice-cores of regional SMB? For this, we use several ground-penetrating radar (GPR) surveys in East Antarctica, which have co-located ice core drill sites. Most of our sites share a relatively similar climatology, as they are all coastal ice promontories/rises along the Dronning Maud Land coast, with the exception of the Dome Fuji survey on the high plateau in the interior of the continent.

We will show that the comparison of the SMB signals of the GPR and the ice core records allows us to estimate the spatial footprint of the ice cores, and that this spatial footprint varies widely from site to site. We will provide a summary of the spatial and temporal characteristics for each location.

How to cite: Cavitte, M. G. P., Goosse, H., Matsuoka, K., Wauthy, S., Dey, R., Goel, V., Tison, J.-L., Van Liefferinge, B., and Meloth, T.: What to watch out for when assimilating ice-cores as regional SMB proxies?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-792, https://doi.org/10.5194/egusphere-egu23-792, 2023.

EGU23-2178 | ECS | PICO | CR2.3

Radar-derived ice fabric anisotropy and implications on flow enhancement along the Thwaites Glacier Eastern Shear Margin 

Tun Jan Young, Carlos Martin, Thomas Jordan, Ole Zeising, Olaf Eisen, Poul Christoffersen, David Lilien, and Nicholas Rathmann

Glaciers and ice streams account for the majority of ice mass discharge to the ocean from the Antarctic Ice Sheet, and are bounded by intense bands of shear that separate fast-flowing from slow or stagnant ice, called shear margins. The anisotropy of glacier ice (i.e. a preferred crystal orientation) stemming from high rates of shear at these margins can greatly facilitate fast streaming ice flow, however it is still poorly understood due to a lack of in-situ measurements. If anisotropy is incorporated into numerical ice sheet models at all, it is usually as a simple scalar enhancement factor that represents the "flow law" that governs the model's rheology. Ground-based and airborne radar observations along two transects fully crossing the Eastern Shear Margin of Thwaites Glacier reveal rapid development of highly anisotropic fabric tightly concentrated around a lateral maximum in surface shear strain. These measurements of fabric strength at the centre of the shear margin are indicative of a horizontal pole configuration, which potentially represents ice that is “softened” to shearing in some directions and hardened in others. The resulting flow enhancement revealed by our results suggest that the viscosity of ice is highly variable and regime-dependent, and supports the importance of considering anisotropic flow laws to model the rheology of ice sheets.

How to cite: Young, T. J., Martin, C., Jordan, T., Zeising, O., Eisen, O., Christoffersen, P., Lilien, D., and Rathmann, N.: Radar-derived ice fabric anisotropy and implications on flow enhancement along the Thwaites Glacier Eastern Shear Margin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2178, https://doi.org/10.5194/egusphere-egu23-2178, 2023.

Permafrost, the frozen layer beneath a freezing and thawing active layer, is an impermeable frozen soil that persists for multiple years. The gradual thawing of permafrost and thickening of the active layer allows a glimpse into the evolution of the hydraulic processes that shape the periglacial landscape. One question in understanding the governing mechanics within the rapidly evolving periglacial landscape is how water retains within or segregates through the active layer to eventually feed rivers. 

In this exploratory study, we analyze data from multiple periglacial hydraulic catchments over time and characterize their hydraulic response rate to stressors. We test whether deconvolution and demixing of noisy time series can isolate precipitation from thawing permafrost signals in river discharge. We use the Ensemble Rainfall-Runoff (ERRA) script, which is effective in inferring nonstationary and nonlinear responses to precipitation using Runoff Response Distribution (RRD), to further test temperature signatures. Using this tool, we measure the RRD for the same catchments both over the years and over the summer months. We hypothesize that an increase in active layer thickness over years and over summer months will delay the RRD due to an increase in water storage.

By analyzing the parameters that change the RRD of periglacial systems with time, soil moisture content, average seasonal and yearly temperatures, and precipitation, we can begin a systematic understanding of how the active layer modulates hydraulic responses and how the responses may be different from other hydraulic systems.

How to cite: Culha, C. and Kirchner, J.: Characterizing melt water properties in the periglacial active layer through seasonal and yearly variations in catchment hydrology., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4291, https://doi.org/10.5194/egusphere-egu23-4291, 2023.

EGU23-5504 | PICO | CR2.3

Sensitivity of the mass conservation method to the regularisation scheme 

Fabien Gillet-Chaulet, Eliot Jager, and Mathieu Morlighem

While being one of the most important variables for predicting the future of the ice sheets, observations of ice thickness are only available along flight tracks, separated by a few to a few tens of kilometres. For many applications, these observations need to be interpolated on grids at a much higher resolution than the actual average spacing between tracks.

The mass conservation method is an inverse method that combines the sparse ice thickness data with high resolution surface velocity observations to obtain a high-resolution map of ice thickness that conserves mass and minimizes the departure from observations.  As with any inverse method, the problem is ill-posed and requires some regularisation. The classical approach is to use a Tikhonov regularisation that penalizes the spatial derivatives of the ice thickness and therefore favours smooth solutions with implicit spatial correlation structures. In a Bayesian framework, regularization can be seen as an implicit assumption for the prior probability distribution of the inverted parameter. Other popular geostatistical interpolation algorithms, such as kriging, usually require to parameterize the spatial correlation of the interpolated field using standard correlation functions (e.g., gaussian, exponential, Matèrn).

Here we replace the Tikhonov regularisation term in the mass conservation method  by a term that penalises the departure from a prior, where the error statistics are parametrized with the same standard correlation functions. This makes the regularisation independent from grid spacing and regularisation weights do not need to be adjusted. We present and discuss the sensitivity of the mass conservation method to the regularisation scheme using a suite of synthetic and “true” bed from deglaciated areas and show that prescribing the correct regularisation always provides the most accurate solution.

How to cite: Gillet-Chaulet, F., Jager, E., and Morlighem, M.: Sensitivity of the mass conservation method to the regularisation scheme, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5504, https://doi.org/10.5194/egusphere-egu23-5504, 2023.

EGU23-6770 | PICO | CR2.3

Greenland ice-stream dynamics: short-lived and agile? 

Olaf Eisen, Steven Franke, Paul D. Bons, Julien Westhoff, Ilka Weikusat, Tobias Binder, Kyra Streng, Daniel Steinhage, Veit Helm, John D. Paden, Graeme Eagles, and Daniela Jansen

Reliable knowledge of ice discharge dynamics for the Greenland ice sheet via its ice streams is essential if we are to understand its stability under future climate scenarios as well as their dynamics in the past, especially when using numerical models for diagnosis and prediction. Currently active ice streams in Greenland have been well mapped using remote-sensing data while past ice-stream paths in what are now deglaciated regions can be reconstructed from the landforms they left behind. However, little is known about possible former and now defunct ice streams in areas still covered by ice. Here we use radio-echo sounding data to decipher the regional ice-flow history of the northeastern Greenland ice sheet on the basis of its internal stratigraphy. By creating a three-dimensional reconstruction of time-equivalent horizons, we map folds deep below the surface that we then attribute to the deformation caused by now-extinct ice streams. We propose that locally this ancient ice-!ow regime was much more focused and reached much farther inland than today’s and was deactivated when the main drainage system was reconfigured and relocated southwards. The insight that major ice streams in Greenland might start, shift or abruptly disappear will affect our approaches to understanding and modelling the past or future response of Earth’s ice sheets to global warming. Such behaviour has to be sufficiently reproduced by numerical models operating on the mid- to longer-term timescales to be considered adequate physical representations of the naturally occuring dynamic behaviour of ice streams.

How to cite: Eisen, O., Franke, S., Bons, P. D., Westhoff, J., Weikusat, I., Binder, T., Streng, K., Steinhage, D., Helm, V., Paden, J. D., Eagles, G., and Jansen, D.: Greenland ice-stream dynamics: short-lived and agile?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6770, https://doi.org/10.5194/egusphere-egu23-6770, 2023.

EGU23-6900 | ECS | PICO | CR2.3

Determining Basal Mass Balance of Ice Shelves Using Simulation-Based Inference 

Guy Moss, Vjeran Višnjević, Cornelius Schröder, Jakob Macke, and Reinhard Drews

The ice shelves buttressing the Antarctic ice sheet determine its stability. Over half of all mass loss in Antarctica occurs due to ice melting at the water-ice boundary at the base of ice shelves. Different contemporary methods of estimating the spatial distribution of the melting rates do not produce consistent results, and provide no information about decadal to centennial timescales. We explore a new method to infer the spatial distribution of the basal mass balance (BMB) using the internal stratigraphy which may contain additional information not present in other sources such as ice thickness and surface velocities alone. The method estimates the Bayesian posterior distribution of the BMB,  and provides us with a principled measure of uncertainty in our estimates. 

 

Our inference procedure is based on simulation-based inference (SBI) [1], a novel machine learning inference method. SBI utilizes artificial neural networks to approximate probability distributions which characterize those parameters that yield data-compatible simulations, without the need of an explicit likelihood function. We demonstrate the validity of our method on a synthetic ice shelf example, and then apply it to Ekström ice shelf, East Antarctica, where we have radar measurements of the internal stratigraphy. The inference procedure relies on a simulator of the dynamics of the ice shelves. For this we use the Shallow Shelf Approximation (SSA) implemented in the Python library Icepack [2], and a time-discretized layer tracing scheme [3].  These detailed simulations, along with available stratigraphic data and the SBI methodology, allows us to compute a spatially-varying posterior distribution of the melting rate. This distribution corroborates existing estimates and extends upon them by quantifying the uncertainty in our inference. This uncertainty should be incorporated in future forecasting of ice shelf dynamics and stability analysis.

 

[1] Lueckmann et al.: Benchmarking simulation-based inference (2020).

[2] Shapero et al.: icepack: a new glacier flow modeling package in Python, version 1.0. (2021).

[3] Born: Tracer transport in an isochronal ice-sheet model (2017).



How to cite: Moss, G., Višnjević, V., Schröder, C., Macke, J., and Drews, R.: Determining Basal Mass Balance of Ice Shelves Using Simulation-Based Inference, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6900, https://doi.org/10.5194/egusphere-egu23-6900, 2023.

EGU23-8775 | PICO | CR2.3

Advancements in RUC Snow Model for Implementation in the Regional Application of the Unified Forecasting System (UFS) 

Tatiana Smirnova, Anton Kliewer, Siwei He, and Stan Benjamin

RUC land surface model (LSM) was designed for short-range weather predictions with an emphasis on severe weather. The model has been operational at NCEP since 1998. Currently it is utilized in the operational WRF-based Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) regional models. Being available to the world WRF community, RUC LSM is also used as a land-surface component in operational weather prediction models in Austria, New Zealand and Switzerland.

At present time, RUC LSM is being tested in the regional application of the UFS-based Rapid Refresh FV3 Standalone (RRFS) model to replace operational RAP and HRRR at NCEP.

RUC LSM has improved and matured over the years. The unique feature of this land-surface model is continuous evolution of soil/snow states within moderately coupled land data assimilation (MCLDA). To avoid possible drifts, this feature requires high skill from RUC LSM as well as accurate atmospheric forcing. Continuous snow cycling includes the following snow state variables: snow cover fraction, snow depth, snow water equivalent and snow temperature. To avoid possible inaccuracies in the location of cycled snow on the ground, snow depth is corrected daily using 4-km IMS snow cover information. Work is also underway to further improve RUC snow model for better surface predictions over snow-covered areas. RUC snow model uses “mosaic” approach to account for subgrid variability of snow cover. Within this approach, snow-covered and snow-free portions of the grid cells are treated separately in the solution of energy and moisture budgets. Thus, snow cover fraction becomes a critical parameter, and modifications to its computation have been developed and tested in the RRFS retrospective experiments. Results from these validation experiments will be presented at the meeting.

How to cite: Smirnova, T., Kliewer, A., He, S., and Benjamin, S.: Advancements in RUC Snow Model for Implementation in the Regional Application of the Unified Forecasting System (UFS), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8775, https://doi.org/10.5194/egusphere-egu23-8775, 2023.

EGU23-9080 | ECS | PICO | CR2.3

Historical snow and ice temperature compilation documents the recent warming of the Greenland ice sheet 

Baptiste Vandecrux, Robert S. Fausto, Jason E. Box, Federico Covi, Regine Hock, Asa Rennermalm, Achim Heilig, Jakob Abermann, Dirk Van As, Anja Løkkegaard, Xavier Fettweis, Paul C. J. P. Smeets, Peter Kuipers Munneke, Michiel Van Den Broeke, Max Brils, Peter L. Langen, Ruth Mottram, and Andreas P. Ahlstrøm

The Greenland ice sheet mass loss is one of the main sources of contemporary sea-level rise. The mass loss is primarily caused by surface melt and the resulting runoff. During the melt season, the ice sheet’s surface receives energy from sunlight absorption and sensible heating, which subsequently heats the subsurface snow and ice. The energy from the previous melt season can also enhance melting in the following summer as less heating is required to bring the snow and ice to the melting point. Subsurface temperatures are therefore both a result and a driver of the timing and magnitude of surface melt on the ice sheet. We present a dataset of more than 3900 measurements of ice, snow and firn temperature at 10 m depth across the Greenland ice sheet spanning the years from 1912 to 2022. We construct an artificial neural network (ANN) model that takes as input the ERA5 reanalysis monthly near-surface air temperature and snowfall for the 1954-2022 period and train it on our compilation of observed 10-meter temperature. We use our dataset and the ANN to evaluate three broadly used regional climate models (RACMO, MAR and HIRHAM). Our ANN model provides an unprecedented and observation-based description of the recent warming of the ice sheet’s near-surface and our evaluation of the three climate models highlights future development for the models. Overall, these findings improve our understanding of the ice sheet’s response to recent atmospheric warming and will help reduce uncertainties of ice sheet surface mass balance estimates.

How to cite: Vandecrux, B., Fausto, R. S., Box, J. E., Covi, F., Hock, R., Rennermalm, A., Heilig, A., Abermann, J., Van As, D., Løkkegaard, A., Fettweis, X., Smeets, P. C. J. P., Kuipers Munneke, P., Van Den Broeke, M., Brils, M., Langen, P. L., Mottram, R., and Ahlstrøm, A. P.: Historical snow and ice temperature compilation documents the recent warming of the Greenland ice sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9080, https://doi.org/10.5194/egusphere-egu23-9080, 2023.

EGU23-12495 | ECS | PICO | CR2.3

Radar forward modelling as a precursor for statistical inference 

Leah Sophie Muhle, Guy Moss, A. Clara J. Henry, and Reinhard Drews

Projections of the future development of the Antarctic Ice Sheet still exhibit a large degree of uncertainty due to difficulties in constraining parameters of ice-flow models such as basal boundary conditions. Deriving better estimates of these parameters from radargrams could greatly improve model accuracy, but integration of inferred radar attributes into ice-flow models is not yet widespread.

Here, we develop a radar forward modeling framework that is geared to train a machine learning workflow (likely simulation-based inference) to extract radar attributes such as the internal stratigraphy and basal boundary conditions (e.g., frozen vs. wet) from radar data. The workflow starts with ice-dynamic forward models predicting physically sound stratigraphies and internal/basal temperatures for synthetic flow settings using shallow ice, shallow shelf and higher order ice-flow models. This is then used as input to the radar simulator (here gprMax), which calculates the radar image produced by such a stratigraphy. To do so, we match the synthetic permittivities of the modeled stratigraphy with statistical properties known from ice-core logging data and prescribe temperature dependent attenuation via an Arrhenius relation. gprMax is optimized for acceleration using GPUs which can be efficiently employed when solving the FDTD discretized Maxwell equations. Currently, 200 m wide and 500 m deep sections can be simulated on a single NVIDIA GeForce RTX 2070 Super graphics card within 390 minutes. The runtime can be substantially improved in a HPC environment. In order to obtain radar simulations comparable with observations, we also add system specific noise and contributions from volume scattering with variable surface roughness. Here, we focus on 50 MHz pulse radar for which we have many observational counterparts. However, the workflow is designed to encompass multiple ice-dynamic settings and different radar frequencies.

The application of physical forward models will result in physically meaningful radargrams which are indistinguishable from observations. This provides a tool to create datasets for training machine learning workflows for inference without the limitations of hand-labeled data.

How to cite: Muhle, L. S., Moss, G., Henry, A. C. J., and Drews, R.: Radar forward modelling as a precursor for statistical inference, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12495, https://doi.org/10.5194/egusphere-egu23-12495, 2023.

EGU23-12553 | ECS | PICO | CR2.3

At the bottom of ice streams: unraveling the physics of sliding onset through a glacier-scale field experiment 

Elisa Mantelli, Reinhard Drews, Olaf Eisen, Daniel Farinotti, Martin Luethi, Laurent Mingo, Dustin Schroeder, and Andreas Vieli

Fast ice stream flow at speeds of hundreds to thousands of meters per year is sustained by sliding at the ice sheet base, whereas slow flow outside of ice streams is characterized by limited-to-no basal sliding. In this sense, the transition from no sliding to significant sliding exerts a key control on ice stream flow. The detailed physical processes that enable the onset of basal sliding are somewhat debated, but laboratory experiments, recent theoretical work, and a handful of direct observations support the notion of sliding initiating below the melting point as a result of regelation and premelting. 

In this contribution we describe a recently funded glacier-scale field experiment that has been designed to advance the understanding of sliding onset physics by testing the hypothesis that sliding starts below the melting point. The experiment will take place at the Grenzgletscher (Swiss Alps), which is known to have a cold-based accumulation region and a temperate-based ablation region. Our work will involve extensive surface geophysics (radio echo sounding, terrestrial radar interferometry, radar thermal tomography) aimed at identifying the sliding onset region. This work will guide the site selection for a subsequent borehole study of englacial deformation that is meant illuminate the relation between sliding velocity and basal temperature. The borehole work will allow us to test systematically the hypothesis that sliding starts below the melting point through an extended region of temperature-dependent sliding, and possibly to advance the formulation of temperature-dependent friction laws that are used to describe the onset of sliding in ice flow models.

The focus of this contribution will be specifically on the experimental design - how it is informed by existing theory and observations, and how it will support theoretical and ice flow modeling advances, at the glacier scale and beyond.

How to cite: Mantelli, E., Drews, R., Eisen, O., Farinotti, D., Luethi, M., Mingo, L., Schroeder, D., and Vieli, A.: At the bottom of ice streams: unraveling the physics of sliding onset through a glacier-scale field experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12553, https://doi.org/10.5194/egusphere-egu23-12553, 2023.

EGU23-14292 | ECS | PICO | CR2.3

Bias correction of climate models using observations over Antarctica. 

Jeremy Carter, Erick Chacón Montalván, and Amber Leeson

Regional Climate Models (RCM) are the primary source of climate data available for impact studies over Antarctica. These climate-models experience significant, large-scale biases over Antarctica for variables such as snowfall, surface temperature and melt. Correcting for these biases is desirable for impact models being driven by meteorological data that aim to produce optimal estimates of for example surface run-off and ice discharge. Typical approaches to bias correction often neglect the handling of uncertainties in parameter estimates and don’t account for the different supports of climate-model and observed data. Here a fully Bayesian approach using latent Gaussian processes is proposed for bias correction, where parameter uncertainties are propagated through the model. Advantages of this approach are demonstrated by bias-correcting RCM output for near-surface air temperature over Antarctica.

How to cite: Carter, J., Chacón Montalván, E., and Leeson, A.: Bias correction of climate models using observations over Antarctica., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14292, https://doi.org/10.5194/egusphere-egu23-14292, 2023.

EGU23-15374 | ECS | PICO | CR2.3

Reconstructing accumulation rates of the Greenland ice sheet using dated radiostratigraphy 

Philipp Immanuel Voigt and Andreas Born

The stability of the Greenland ice sheet is poorly constrained even for benchmark periods such as the mid-Holocene or Eemian. Since ice stratigraphy holds a record of both surface mass balance (SMB) and ice dynamics, dated radiostratigraphy offer a potential route to improved reconstructions. Here we explicitly simulate isochrones and employ inverse methods to optimize the solution. The Englacial Layer Simulation Architecture (ELSA) coupled with a thermomechanical ice sheet model computes the isochrones or ice layers, which enable the direct comparison with the radiostratigraphy data. The accumulation rates force ELSA, and are adjusted until the model reproduces the observations within their uncertainties. We deploy the Ensemble Kalman Smoother for the data assimilation. This results in not only the reconstruction of the SMB; an optimized simulation of the ice sheet is obtained by solving the corresponding forward problem. Hence, the contribution to sea level change by Greenland over the same period can also be constrained.

Here we present our initial approach and preliminary results of SMB reconstruction. Future plans and expansions of the work are also presented, involving the study of several model parameters such as basal traction.

How to cite: Voigt, P. I. and Born, A.: Reconstructing accumulation rates of the Greenland ice sheet using dated radiostratigraphy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15374, https://doi.org/10.5194/egusphere-egu23-15374, 2023.

EGU23-88 | ECS | Orals | CR2.4 | Highlight

A new blue ice area map of Antarctica 

Veronica Tollenaar, Harry Zekollari, Devis Tuia, Marc Rußwurm, Benjamin Kellenberger, Stef Lhermitte, and Frank Pattyn

Whereas most of the continent of Antarctica is covered by snow, in some areas, blue-colored ice emerges to the surface. In these blue ice areas (BIAs), mass is removed at the surface through ablative processes. This mass removal exposes deeper layers of ice that are normally located closer to the underlying bedrock. As a result, we can find old ice at the surface of BIAs, as well as the material contained within the ice, such as meteorites and terrestrial rocks. BIAs are unique locations for sampling old ice for palaeoclimatic purposes and collecting meteorites (about ⅔ of all meteorites ever retrieved on Earth come from Antarctica BIAs). Hence, a high-quality BIA map is essential for meteorite searches, the quest for the oldest ice, and surface mass balance modeling.

Prior efforts to map BIAs across the Antarctic continent using remote sensing are single-sensor based, introducing biases related to temporary snow coverage of the exposed ice, and sensor-dependent conditions such as solar illumination angles, anisotropic reflectance, or cloud coverage. To overcome these challenges, we opt for using multi-sensor observations in a deep learning framework to create a new BIA map. The observations we use are (i) radar backscatter, (ii) surface morphology, (iii) elevation, and (iv) multi-spectral reflectance. The deep learning algorithm consists of the well-established convolutional neural network U-Net, which allows for an efficient training process and inclusion of spatial context. The algorithm outputs a pixel-level prediction of blue ice presence. Moreover, by training multiple, randomly initialized models and rotating and flipping data, we obtain multiple predictions for each pixel. Thanks to this data augmentation at test time, we estimate the variation in the predictions, which we then use as an indication of uncertainty. 

We use an existing dataset of BIA outlines as reference for training the model. It is known that these existing labels are noisy due to i) large uncertainties related to the use of a single sensor, and ii) biases as a result of applying a threshold that is based on local observations over the entire continent. However, convolutional neural networks, combined with regularization methods like weight decay and batch normalization, can learn from underlying ‘clean’ patterns of noisy labels during initial epochs of training (i.e., at the start of the training process). Here, we demonstrate this noise-eliminating property by assessing the algorithm's performance on noisy pixels that are used for training, where we see that over 80% of these noisy instances are attributed correctly. Furthermore, we optimize the performance of the neural network based on a reduced set of "noise-free", hand-labeled validation data. Last,  we test the performance of our model on hand-labeled test data, therefore having a realistic estimate of the model performance on precise, so far unused data. These tests indicate that it is possible for the neural net to learn how to map blue ice from the noisy data, leading to an improved map of BIAs in Antarctica.

How to cite: Tollenaar, V., Zekollari, H., Tuia, D., Rußwurm, M., Kellenberger, B., Lhermitte, S., and Pattyn, F.: A new blue ice area map of Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-88, https://doi.org/10.5194/egusphere-egu23-88, 2023.

EGU23-180 | ECS | Orals | CR2.4

A Random Forest approach to quality-chacking automatic snow-depth sensor measurements 

Giulia Blandini, Francesco Avanzi, Simone Gabellani, Denise Ponziani, Hervè Stevenin, Sara Ratto, and Luca Ferraris

Advanced environmental technologies have made available an increasing amount of data from remote sensing satellites, and more sophisticated ground data. Their assimilation into dynamic models is progressively becoming the most frequent, and conceivably the most successful, solution to estimate snow water resources. Models reliability is therefore bounded to data quality, which is often low in mountain, high-elevation, and unattended settings. To add new value to snow-depth sensor measurements, we developed a machine-learning algorithm to automatize the QA/QC procedure of near-surface snow depth observations collected through ground stations data. Starting from a consolidated manual classification, based on the expert knowledge of hydrologists in Valle D'Aosta, a Random Forest classifier was developed to discriminate snow cover from grass or bare ground data and detect random errors (e.g., spikes). The model was trained and tested on Valle d’Aosta data and then validated on 3 years of data from 30 stations on the Italian territory. The F1 score was used as scoring metric, being it most suited to describe the performances of a model in case of a multiclass imbalanced classification problem. The model proved to be robust and reliable in the classification of snow cover and grass/bare ground discrimination (F1 values above 90%), yet less reliable in random error detection, mostly due to the dataset imbalance. No clear correlation with single year meteorology was found in the training domain, and the promising results from the generalization to a larger domain corroborates the model robustness and reliability.This machine learning application of data quality assessment provides more reliable snow ground data, enhancing the quality of snow models.

How to cite: Blandini, G., Avanzi, F., Gabellani, S., Ponziani, D., Stevenin, H., Ratto, S., and Ferraris, L.: A Random Forest approach to quality-chacking automatic snow-depth sensor measurements, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-180, https://doi.org/10.5194/egusphere-egu23-180, 2023.

EGU23-452 | Posters virtual | CR2.4

Polar Ice Coverage Classified by Various Machine Learning Algorithms 

Octavian Dumitru, Gottfried Schwarz, Chandrabali Karmakar, and Mihai Datcu

The European Copernicus Sentinel-1 SAR mission offers a unique chance to compare and analyse long time series of freely accessible SAR images with frequent coverage in the northern polar areas. In our case, during the ExtremeEarth project (H2020 grant agreement No 825258), we concentrated on a two-year analysis of multi-season ice cover categories around Belgica Bank in Greenland where we can easily use typical examples of SAR image targets ranging from snow-covered ice to melting ice surfaces as well as open sea scenes with ships and icebergs.

Our primary goal was to search for most powerful ice type classification algorithms exploiting the well-known characteristics of the Sentinel-1 satellites for SAR imaging in polar areas, both taken from ascending and descending orbit branches with C-band transmission and an incidence angle of about 39°, a resulting ground sampling distance of 10 m or more, HH or HV polarization, and recorded in wide-swath or high-resolution modes as provided and distributed routinely by ESA´s level-1 processing system as amplitude or complex-valued data.

In order to be compatible with established international ice type standards we used the Canadian MANICE semantic labelling system providing up to 10 different polar ice and polar target types.

Our algorithms are based on a patch-based classification approach, where we assigned the most probable primary label for each given square image patch with a size of 256×256 pixels. This prevented us from creating many noise-related single-pixel categories.

Within the ExtremeEarth project, were generated semantic classification maps, topic representations, change maps, or physical scattering representations. A library of algorithms was created, among these algorithms we mention the following ones: classification based on Gabor filtering and SVMs, classification based on compression rates, variational auto-encoders for SAR feature learning, topic representations based on LDA, physical scattering representations based on LDA and CNNs, etc.

When the attempted image content classification based on current machine learning approaches, it turned out that we had to consider several important parameters such as typical applications, main semantic goals to be reached, applied processing algorithms, common types of data, available datasets and already predefined categories to be used, pixel-based versus patch-based data processing, single- and multi-labelling of image patches, confidence calculations and annotations, as well as attainable runtimes, implementation effort and risk - all depending on the target area characteristics. When it came to time series of target area images, we also had to consider the chances offered by short and long data sequences.

It turned out that this large number of aspects can be grouped together depending on the applied human expert supervision approach for semantic classification, namely unsupervised, self-supervised, semi-supervised, and supervised algorithms together with their individual training and testing strategies. In future, we want provide some justifications for next-generation remote sensing applications that require (near) real-time capabilities.

How to cite: Dumitru, O., Schwarz, G., Karmakar, C., and Datcu, M.: Polar Ice Coverage Classified by Various Machine Learning Algorithms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-452, https://doi.org/10.5194/egusphere-egu23-452, 2023.

EGU23-2096 | ECS | Orals | CR2.4

The Evolution of the Snow Facies on the Greenland Ice Sheet Observed by the Last Decade of TanDEM-X Interferometric SAR Data 

Alexandre Becker Campos, Paola Rizzoli, Carolina Gonzalez, José-Luis Bueso-Bello, and Matthias Braun

Climate change and the resulting accelerating melt on the Greenland and Antarctic ice sheets are causing dramatic and irreversible changes at a global scale, significantly contributing to sea-level rise. In this scenario, monitoring the evolution of diagenetic snow facies can provide valuable insights to better comprehend climate-related variables and trends. Previous studies of the Greenland ice sheet led to the definition of four main snow facies, depending on the amount of snow melt and on the properties of the snow pack itself: the inner dry snow zone, where melt does not occur; the percolation zone, where a limited amount of melt per year occurs, leading to the generation of larger snow grains and the formation of small ice structures; the wet snow zone, where a substantial part of the snow melt drains off during summer and is characterized by the presence of multiple ice layers; and the outer ablation zone, where the previous year accumulation completely melts during summer, resulting in a surface of bare ice and surface moraine. By exploiting X-band TanDEM-X interferometric synthetic aperture radar (InSAR) acquisitions, previous works explored the idea of classifying different snow facies of the Greenland ice sheet utilizing an unsupervised machine learning clustering approach. The analysis was performed using data acquired in winter 2010/2011 only, under the assumption of stable climatic conditions and similar acquisition geometries. In this paper, we further investigate the evolution of the snow facies of Greenland throughout the last decade of TanDEM-X observations, proposing unsupervised machine learning strategies for snow facies characterization by using InSAR features such as backscatter, volume decorrelation, the incidence angle and height of ambiguity. We use TanDEM-X data acquired during the winter of 2010/2011, 2015/2016, 2016/2017, 2020/2021, and 2021/2022, where full or partial coverage of the Greenland ice sheet is available. The challenges and caveats of such approaches for different image acquisition geometries will be presented. Finally, the potential of TanDEM-X for investigating large-scale interannual changes in the dry snow zone over Greenland will be investigated as well.  

How to cite: Becker Campos, A., Rizzoli, P., Gonzalez, C., Bueso-Bello, J.-L., and Braun, M.: The Evolution of the Snow Facies on the Greenland Ice Sheet Observed by the Last Decade of TanDEM-X Interferometric SAR Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2096, https://doi.org/10.5194/egusphere-egu23-2096, 2023.

EGU23-5069 | ECS | Orals | CR2.4

Comparability of Deep Learning Techniques for Calving Front Segmentation in SAR Imagery 

Nora Gourmelon, Thorsten Seehaus, Matthias Braun, Andreas Maier, and Vincent Christlein

Monitoring glacier change processes leads to a better understanding of how glaciers respond to various external forcings. For sub-annual remote sensing, Synthetic Aperture Radar (SAR) imagery is indispensable, as polar night and cloud cover limit the temporal continuity of optical imagery. Especially with the launch of the Sentinel-1 mission, the availability of suitable SAR imagery has increased substantially. This high amount of data leads to another challenge: Manual inspection is no longer feasible. Therefore, in recent years, many studies applied deep learning to automate the segmentation of calving fronts in satellite imagery. To ensure comparability between different deep learning models, they must be trained and tested on the same data with a predefined test set and evaluated with the same metrics. A dataset intended to be used for this purpose is called a benchmark dataset and needs to provide the ready-to-use satellite imagery and the corresponding calving front labels. Gourmelon et al. [1] provide such a dataset for SAR imagery of calving fronts called CaFFe (https://doi.pangaea.de/10.1594/PANGAEA.940950). CaFFe includes multi-mission data (ERS-1/2, RADARSAT 1, Envisat, ALOS, Sentinel-1A/B, TerraSAR-X, and TanDEM-X), providing a spatial resolution between 6 and 20 meters and covering the period from 1996 to 2020. It contains images of seven glaciers from Antarctica to Greenland and Alaska. For each of the 681 images contained in the benchmark, two labels are provided: One displaying the calving front versus background and the other showing different zones (ocean, rock outcrops, glacier area, and no information available). CaFFe is split into a train set and a predefined out-of-sample test set, which comprises all images from two of the seven glaciers. A split of the train set into training and validation is not specified, as different approaches like cross-validation shall be possible. The benchmark covers a wide variety of different conditions to capture the variability of SAR calving front images. For example, images with open oceans and images with ice-melange-covered oceans are included in the dataset. Especially including images featuring ice-melange is of great importance, as deep learning models have shown difficulties in accurately segmenting calving fronts under this condition. Including images with ice-melange in the train set helps models to learn accurate predictions even under these circumstances.  Adding such images to the test set ensures that evaluated models are able to cope with ice melange. The test set of CaFFe is specifically designed to be challenging, such that the generalizability of models to different conditions and spatial transferability even to other continents can be verified. Gourmelon et al. provide baselines (one for each of the available labels) complementing the benchmark dataset. Current collaborative work aims to evaluate recently published deep learning techniques for calving front extraction on CaFFe and compare it with the baselines.

[1] N. Gourmelon, T. Seehaus, M. Braun, A. Maier, and V. Christlein: "Calving Fronts and Where to Find Them: A Benchmark Dataset and Methodology for Automatic Glacier Calving Front Extraction from SAR Imagery," Earth System Science Data, vol. 14, no. 9, pp. 4287-4313, 2022, doi: 10.5194/essd-14-4287-2022.

How to cite: Gourmelon, N., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: Comparability of Deep Learning Techniques for Calving Front Segmentation in SAR Imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5069, https://doi.org/10.5194/egusphere-egu23-5069, 2023.

EGU23-5137 | ECS | Posters on site | CR2.4

Functional Inversion of Glacier Rheology from Ice Velocities using ODINN.jl 

Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Fernando Pérez, and Bert Wouters
Inversion methods play an important role in glacier models, both to calibrate and estimate parameters of interest (e.g. Glen's coefficients). However, inversions are usually made for each glacier individually, without using any global information, i.e. without deriving general laws governing the spatiotemporal variability of those parameters. The reason behind this limitation is twofold: the statistical challenge of making constrained inferences with multiple glaciers, and the computational limitation of processing massive glacier datasets. Machine learning powered with differential programming is a tool that can address both limitations.

 

We introduce a statistical framework for functional inversion of physical processes governing global-scale glacier changes. We apply this framework to invert a prescribed function describing the spatial variability of Glen’s coefficient (A). Instead of estimating a single parameter per glacier, we learn the parameters of a regressor (i.e. a neural network) that encodes information related to each glacier (i.e. long-term air temperature) to the parameter of interest. The inversion is done by embedding a neural network inside the Shallow Ice Approximation PDE - resulting in a Universal Differential Equation - with the goal of minimizing the error on the simulated ice surface velocities. We previously had shown that this hybrid model training is possible thanks to the use of differential programming, enabling differentiation of a PDE, a numerical solver and a neural network simultaneously. In this work we upscale this approach to include larger datasets and with the goal of learning real empirical laws from observations.

This framework is built inside ODINN.jl, an open-source package in the Julia programming language for global glacier evolution modelling using Universal Differential Equations. ODINN exploits the latest generation of ice surface velocities and geodetic mass balance remote sensing products, as well as many preprocessing tools from the Open Global Glacier Model (OGGM).

How to cite: Bolibar, J., Sapienza, F., Maussion, F., Lguensat, R., Pérez, F., and Wouters, B.: Functional Inversion of Glacier Rheology from Ice Velocities using ODINN.jl, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5137, https://doi.org/10.5194/egusphere-egu23-5137, 2023.

EGU23-5656 | ECS | Orals | CR2.4

Towards pan-Arctic glacier calving front variability with deep learning 

Tian Li, Konrad Heidler, Lichao Mou, Adam Igneczi, Xiaoxiang Zhu, and Jonathan Bamber

The Arctic has been warming four times faster than the global mean over the last forty years. In response, glaciers across the Arctic have been retreating and losing mass at accelerated rates in recent years, including Greenland, Alaska, Canadian Arctic, Iceland, Svalbard and Russian Arctic. To predict their evolution with confidence, it is important to understand the mechanisms driving mass loss across the Arctic, especially the interconnected relationships between glacier retreat, ice dynamics, and mass imbalance. Over the past several decades, satellite remote sensing has been used to image glaciers over large spatial scales and at high temporal resolution. The volume of data produced, however, has challenged the traditional manual-based approaches to quantify glacier calving dynamics at a sub-annual scale across the whole Arctic. To address this limitation, we use a fully automated deep learning approach to generate a new calving front dataset for pan-Arctic glaciers at a high temporal resolution, by harmonizing multiple satellite missions that are available from the 1970s onwards, including optical missions such as Landsat, ASTER and Sentinel-2, and various SAR missions such as ERS-1/2, Envisat, RADARSAT-1, TerraSAR-X and Sentinel-1. We first present a new training dataset for the Arctic glaciers. We then present a new deep-learning framework for mapping the pan-Arctic glacier calving fronts. We show the interannual and seasonal variability of glacier termini positions by applying this method at scale and investigate the responses of Arctic glaciers to climate change.

How to cite: Li, T., Heidler, K., Mou, L., Igneczi, A., Zhu, X., and Bamber, J.: Towards pan-Arctic glacier calving front variability with deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5656, https://doi.org/10.5194/egusphere-egu23-5656, 2023.

EGU23-6495 | ECS | Orals | CR2.4

Probabilistic detection and tracking of icebergs in the Amundsen Sea embayment 

Ben Evans, Scott Hosking, Andrew Fleming, and Alan Lowe

Accurate estimates of iceberg populations, disintegration rates and iceberg movements are essential to understand ice sheet contributions to global sea level change and freshwater and heat balances. Knowledge and prediction of iceberg distributions is also important for the safety and efficiency of shipping operations in polar seas. The dynamics, persistence, fragmentation rates, melt rates and dispersal of icebergs are, however, poorly understood due to a lack of automated approaches for monitoring them. Better monitoring of icebergs would help parameterise the locations and quantities of freshwater and nutrient inputs within hydrographic and ecological models respectively and help mitigate collision hazards for navigation.

Here we present a combination of Bayesian approaches to the identification of icebergs in synthetic aperture radar imagery and their subsequent tracking across multiple years. For detection we use a Dirichlet Process Mixture Model, while for tracking we adapt Bayesian Tracker, a probabilistic multi-object tracking algorithm originally developed for cell microscopy applications. We are able to reconstruct iceberg paths and lineages, which we validate against synthetic data and manual annotations. We demonstrate that icebergs across the size distribution can be tracked successfully from their point of calving in dense fields of objects, through dispersal and fragmentation, to distal locations.

How to cite: Evans, B., Hosking, S., Fleming, A., and Lowe, A.: Probabilistic detection and tracking of icebergs in the Amundsen Sea embayment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6495, https://doi.org/10.5194/egusphere-egu23-6495, 2023.

EGU23-6858 | ECS | Posters on site | CR2.4

Temporal and spatial stationnarity of the snow regime assessed through deep-learning auto-encodding of SAR-image stacks 

Flora Weissgerber and Mathias Montginoux

Snow cover can be measured from multi-spectral optical images through the Normalized Difference Snow Index (NDSI). However, cloud cover affects the acquisition frequency. Radar snow detection offers independence from weather conditions. Despite existing method to detect snow melt [1] or measure snow depth [2], no existing method offers the possibility to detect any type of snow using only SAR images. To assess the different evolutions of the SAR signal during a winter season, we use a deep learning auto-encoding approach for the 2018-2019 winter over the Guil Basin in the French Hautes-Alpes using SLC Sentinel-1 images for three relative orbits: D66 (Descending), A88 (Ascending) and D139 (Descending). The images were geocoded using the French IGN DEM (BDALTI). On top of displaying the most representative temporal SAR signal profils on this area, this study help us to assess the spatial stationnarity of the SAR signal.

All the temporal profils were auto-encoded in three embedding following the framework detailed in [3]. The network was trained five times for each orbit. The chosen embeddings were the ones exhibiting the smaller correlation, leading to absolute value correlation between 0.10 and 0.20. The correlation between these embedding and the geographical features (latitude, longitude, altitude and incidence angle) is also below 0.30. 

Then these embbedings were used to group the pixels in six clusters using a kmeans framework. The mean temporal profile was estimated for each cluster, as well as the histogram of the elevation distribution. Behaviours appear consistently for the three orbits. One cluster correspond to shadow or dark areas pixels, with a constitent low backscattering over the year and a spread elevation histogram. Another cluster correspond to pixels in high altitude areas which exhibit an increase in backscattering between October and March that we attribute to snow fall. The third  cluster includes also high altitude pixels with a short drop of backscattering in October and May, certainly related to snow melt. The spatial pattern of the clusters for the A88 orbit shows a east-west shift in the class repartition while for the D66 and D139 the class repartition is more impacted by the altitude and follow the southward mountain arc. In further work, a  train/validation/test dataset with no dataset shift will be design using the stationnarity of this cluster, as well as a second test set introducing a geographical dataset shift that can take in account both the ascending/descending differences and the topographic and climatic variation of snow cover.  

[1] T. Nagler, et al.  "Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR". Remote Sens. 2016. https://doi.org/10.3390/rs8040348 
[2] H. Lievens, et al. "Snow depth variability in the Northern Hemisphere mountains observed from space". Nat Commun 2019. https://doi.org/10.1038/s41467-019-12566-y
[3] T. Di Martino et al. "Beets or Cotton? Blind Extraction of Fine Agricultural Classes Using a Convolutional Autoencoder Applied to Temporal SAR Signatures".IEEE TGRS 2022, 10.1109/TGRS.2021.3100637.

Acknoledgment: This work is part of the AI4GEO project. The authors would like to thank Thomas Di Martino for his precious advices.

How to cite: Weissgerber, F. and Montginoux, M.: Temporal and spatial stationnarity of the snow regime assessed through deep-learning auto-encodding of SAR-image stacks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6858, https://doi.org/10.5194/egusphere-egu23-6858, 2023.

EGU23-6961 | Orals | CR2.4

Improving and evaluating the IPCC land ice emulator 

Tamsin Edwards, Jonathan Rougier, and Fiona Turner

The emulator used for projections of the land ice contribution to sea level by 2100 in the Intergovernmental Panel on Climate Change Sixth Assessment Report (Edwards et al., 2021) used a novel approach for incorporating structural uncertainty from the underlying computer models. This statistical (Gaussian Process) emulator represented entire multi-model ensembles at once -- for the Greenland and Antarctic ice sheets, and the world’s glaciers, under the model intercomparison projects ISMIP6 and GlacierMIP, respectively -- by using a noise (nugget) term to allow for multiple estimates of sea level contribution for a given set of model input values, analogous to kriging of spatial data.

However, the emulator was rather simple in other respects: in particular, the sea level projection for each year from 2015-2100, and from each region of land ice (splitting Antarctica into 3 regions, and the glaciers into 19), were modelled independently, so temporal correlations emerged only upon smoothing the projections. The emulator was also not formally evaluated with observations, because the underlying simulations were only driven with meaningful climate forcings from 2015. These limitations have presented difficulties for users, who often need continuous time series projections and prefer, of course, these to be assessed with observations.

Here the IPCC land ice emulator is improved for interpretation and use by decision-makers by estimating spatio-temporal correlations directly from the underlying simulations (Rougier, 2008; Rougier et al., 2009), to produce meaningful trajectories of sea level contribution from each land ice source. The extent to which the land ice emulator can be evaluated with data, now and in future, is also discussed.

 

References:

Edwards et al. (2021) Projected land ice contributions to twenty-first-century sea level rise, Nature, 593, 74–82.

Rougier, J. (2008), Efficient Emulators for Multivariate Deterministic Functions, Journal of Computational and Graphical Statistics, 17(4):827–843.

Rougier, J.C. et al. (2009), Expert Knowledge and Multivariate Emulation: The Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIE-GCM), Technometrics, 51(4), 414-424.

How to cite: Edwards, T., Rougier, J., and Turner, F.: Improving and evaluating the IPCC land ice emulator, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6961, https://doi.org/10.5194/egusphere-egu23-6961, 2023.

EGU23-7240 | ECS | Orals | CR2.4

Snow cover estimation by deep-learning segmentic segmentation of radar images based on optical image references 

Mathias Montginoux, Flora Weissgerber, Céline Monteil, and Alexandre Girard

In order to improve the forecasting of hydraulic production, EDF uses optical satellite images to evaluate the snow cover [1]. These images are acquired daily by the MODIS instrument of the Terra satellite and provide a snow product through the Normalized Difference Snow Index (NDSI). However, part of the information on the snow cover is lost due to clouds. To complete those gaps, radar satellite images can be interesting because it does not depend on weather conditions.

Dry snow and wet snow have different SAR signature. Wet snow can be detected since its backscatter decreases [2]. Dry snow detection is more challenging. It may be performed with a polarimetric approach [3], and the snow depth (SD) can be estimated using optical images as auxiliary inputs [4]. In this work, wet snow was detected and SD was estimated over the Guil basin in the Alps (420 km²) for the years 2018 and 2019 on three relative orbits of Sentinel-1: the D66 (descending, 87 images), A88 (ascending, 119 images), and D139 (descending, 90 images). The results show an accumulation of snow in autumn on the SD and a peak of snowmelt in spring on the detection of wet snow.

Then we propose to detect the snow from SAR images using a convolutional neural network trained with optical images from MODIS as labels. For the dataset, a smaller area is chosen around Abriès (of approximately 59km²) and we select 36 images for each of the three orbits to study the winter 2018-2019. A binary semantic segmentation is computed from two SAR inputs: Rwet from [2], and Rdry a polarimetric ratio inspired from [3]. The trained model, called SESAR U-net, gives a snow detection with an overall accuracy of 80% for our test set. This low accuracy result can be explained by the fact that MODIS images have a resolution 25 to 100 times coarser than the SAR images, which hinder both the training and the evaluation of the model. Further works will consider the uncertainty of the MODIS label in the loss computation to improve the training.

[1] M. Le Lay et al., “Use of snow data in a hydrological distributed model: different approaches for improving model realism,” in EGU General Assembly Conference Abstracts, EGU General Assembly Conference Abstracts, p. 14545, Apr. 2018.
[2] T. Nagler et al., “Advancements for snowmelt monitoring by means of sentinel-1 sar,” Remote Sensing, vol. 8, 04 2016.
[3] A. Reppucci et al., “Estimation of snow-pack characteristics by means of polarimetric SAR data,” in Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV (C. M. U. Neale and A. Maltese, eds.), vol. 8531, p. 85310Z, International Society for Optics and Photonics, SPIE, 2012.
[4] H. Lievens et al., “Snow depth variability in the Northern Hemisphere mountains observed from space,” Nature Communications, vol. 10, p. 4629, Dec. 2019.

How to cite: Montginoux, M., Weissgerber, F., Monteil, C., and Girard, A.: Snow cover estimation by deep-learning segmentic segmentation of radar images based on optical image references, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7240, https://doi.org/10.5194/egusphere-egu23-7240, 2023.

EGU23-8339 | ECS | Posters on site | CR2.4

Improving ice thickness estimation of glaciers using deep learning methods : a case study in the Swiss Alps 

Lorenzo Lopez Uroz, Yajing Yan, Alexandre Benoit, Antoine Rabatel, Amaury Dehecq, and Sophie Giffard-Roisin

Accurate estimation of ice thickness is essential for understanding and predicting the behaviors of glaciers, since the ice thickness provides valuable information about the glacier state and helps anticipate the evolution of whole glaciers systems. This latter knowledge is particularly important given the fact that glaciers act as natural reservoirs in the global water cycle. They are also indicators of the current and past state of global and local climate, as the properties and dynamics of glaciers reveal insights into the environmental conditions they have experienced.

Current methods for estimating ice thickness face issues : field measurements such as the Ground Penetrating Range method can be costly and may not provide dense, continuous, renewable coverage. On the other hand, methods based on physical modeling can be computationally intensive and are dependent on assumptions about model parameters that may be unreliable in a poorly understood context. Results may thus be sensitive to the choice of prior information and prone to bias when working with limited or noisy data.

Deep learning methods provide a promising solution for ice thickness prediction. One key advantage is their ability to handle large, multi-dimensional datasets and to learn directly from raw data without prior knowledge. Additionally, deep learning models are able to exploit non-linear relationships between datasets. Using such models also allows simultaneous training of other tasks, such as terrain classification to identify the presence of glaciers when it is not provided or outdated. In this study, we propose the use of such an approach relying on convolutional models based on VGGNet, ResNet and U-Net.

Our goal is to obtain an accurate estimation of the glacier thickness distribution. We propose the use of neural networks in order to 1) be free from statistical/physical assumptions, 2) leverage deep relationships between observed data and physical parameters to be estimated, 3) overcome inaccuracies in collected data, and 4) accurately represent complex patterns such as non-linear thickness variations within the glacier. Additionally, it is important that these models should not be prone to common issues of deep learning such as overfitting and lack of explainability.

We conduct our study on Alpine glaciers in Switzerland. The input data for our neural network models includes: 1) average ice velocity fields calculated from correlation of Sentinel-2 images with a resolution of 50 metres, and 2) altitudes and slopes derived from the Swiss digital elevation model with a resolution of 10 metres. To verify the accuracy of the predicted ice thickness values, we use ground truth data obtained from GPR surveys conducted in profile form, from 2012 to 2021.

In addition to estimating the ice thickness, we also perform direct classification of glaciers vs. non-glacier areas. Results demonstrate the feasibility of quickly training a neural network model with limited training data and producing stable, high-quality ice thickness estimates for different glaciers in the study region.

How to cite: Lopez Uroz, L., Yan, Y., Benoit, A., Rabatel, A., Dehecq, A., and Giffard-Roisin, S.: Improving ice thickness estimation of glaciers using deep learning methods : a case study in the Swiss Alps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8339, https://doi.org/10.5194/egusphere-egu23-8339, 2023.

ESA has developed a series of seven TEPs on different subjects to provide insight into how our oceans, atmosphere, land and ice operate and interact as part of an interconnected earth system by exploiting the unprecedented flow of high-quality global data on the state of our planet, combined with long-term EO archives, in-situ networks and models. The Polar Thematic Exploitation Platform (Polar TEP) was developed to address the particular needs of the polar community.

Polar TEP provides a complete working environment where users can access algorithms and data remotely to obtain computing resources and tools that they might not otherwise have and avoid the need to download and manage large volumes of data. This new approach removes the need to transfer large Earth Observation data sets around the world, while increasing the analytical power available to researchers and operational service providers. Polar TEP provides new ways to exploit EO and other large datasets for research scientists, industry, operational service providers, regional authorities, and policy analysts. Polar TEP provides:

  • Data Discovery - Polar TEP makes satellite and other polar data easily accessible for browsing or analysis within the cloud or within the user’s own environment. The infrastructure takes care of the complexity of handling satellite imagery archives and makes the data available via web services. Users can instantly access petabytes of Sentinel, Landsat, and other Earth observation imagery, both historic and the latest acquisitions.
  • Interactive Development Environment - Polar TEP offers a managed JupyterLab instance with curated base images. The platform provides different flavors of computational resources and a network file system for persistent data storage. Headless notebook execution is supported.
  • Machine Learning - Polar TEP has implemented the MLflow platform to support machine learning activities. MLflow manages all stages of the machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry.
  • Execution Environment - Docker containers are used to provide processors with a separate custom environment having minimal execution overhead. The computing resources used by the execution environment are scaled to the current demand.
  • Application Hosting Environment - Users can host their own applications on a VM within the Polar TEP environment.
  • Story Telling - Polar TEP provides tools to communicate analysis results to other researchers or the public.

Polar TEP is an integral part of the wider polar data ecosystem, contributing to data interoperability and fostering the use of information about the polar regions to support environmental protection, safety, and sustainable economic development.

This presentation will illustrate how the power of Polar TEP to process massive amounts of data is being applied to topics such as machine learning for operational sea ice charting, daily calculations of Greenland ice sheet albedo, and providing information to support traditional ways of life in the Arctic.

How to cite: Arthurs, D.: Polar TEP – A Platform for Polar Big Data Analytics and Machine Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8706, https://doi.org/10.5194/egusphere-egu23-8706, 2023.

EGU23-9122 | ECS | Orals | CR2.4

Predicting Glacier Terminus Retreat Using Machine Learning 

Kevin Shionalyn, Ginny Catania, Daniel Trugman, Denis Felikson, and Leigh Stearns

While a majority of mass loss from the Greenland Ice Shelf is attributed to glacial terminus retreat via calving, the superimposed force factors of the ice-ocean interface create a challenge for physically modeling terminus change. Here we use time series of environmental and glacial data, input as features into a machine learning regression model, to forecast terminus retreat for marine-terminating glaciers in Greenland. We then identify the critical features that most impact a glacier’s likelihood of retreat using feature importance analysis. We further analyze the heterogeneous outcomes for individual glaciers to classify them by their terminus change profile.  By better understanding the parameters impacting glacial retreat, we inform physical models to reduce uncertainty in mass change projections.

How to cite: Shionalyn, K., Catania, G., Trugman, D., Felikson, D., and Stearns, L.: Predicting Glacier Terminus Retreat Using Machine Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9122, https://doi.org/10.5194/egusphere-egu23-9122, 2023.

EGU23-9532 | ECS | Orals | CR2.4

Tracing Extended Internal Stratigraphy in Ice Sheets using Computer Vision Approaches 

Hameed Moqadam, Daniel Steinhage, Olaf Eisen, and Adalbert Wilhelm

Polar ice sheets Greenland and Antarctica are integral parts of the climate system. Understanding their history, dynamics and past accumulation rates determines projections of sea level change. Ice englacial stratigraphy is used to assign ages, taken from ice cores, to radar reflections and subsequently connect these known layers over large areas. One of the main methods to investigate these characteristics is radar reflections. Ground-penetrating radar (GPR) has been used as the primary technique to detect internal ice architecture.

Mapping the internal reflection horizons in order to study and investigate the features, accumulation rates, and ice streams is an important step, which is conventionally done through a semi-automatic process. Such methods are prone to shortcomings in terms of continuity and layer geometry. Moreover, it is highly time-consuming to map an entire profile, the abundance of unmapped radar profiles especially from antarctic ice sheet is an evidence for this. Thus, there is the need for more comprehensive and efficient methods.

The use of machine learning to perform this task automatically will make a significant difference for internal layer detection in terms of efficiency and accuracy. Such machine and deep learning methods would be a suitable fit for radar surveys with different properties, such as center frequencies, making them appropriate for both ice, firn and snow data. In this project, apart from classical computer vision methods and image processing, deep learning methods are used to map the internal reflection horizons (IRH). Convolutional Neural Networks (CNN) are a powerful tool to learn features and track the IRHs continuously.

In this talk, the implemented classical computer vision methods are enumerated, and the machine learning methods that have been used (the specific pre-processing methods unique to this project, labeling method, architecture and hyperparameters) are explained. The results from some more promising architectures such as U-net are presented and compared to the results from image processing methods. The main challenges in this project are lack of complete training data, unknown number of IRHs in a profile, and abundance of features in a single radargram. These challenges are shown and possible solutions are presented.

How to cite: Moqadam, H., Steinhage, D., Eisen, O., and Wilhelm, A.: Tracing Extended Internal Stratigraphy in Ice Sheets using Computer Vision Approaches, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9532, https://doi.org/10.5194/egusphere-egu23-9532, 2023.

EGU23-9812 | ECS | Orals | CR2.4 | Highlight

Assessment of ice mélange impacts on tidewater glacier dynamics using high resolution ICEYE imagery 

William D. Harcourt, Leigh A. Stearns, Michael G. Shahin, and Siddharth Shankar

There is growing evidence that ice mélange, the granular mixture of sea ice and icebergs at the termini of tidewater glaciers, impacts ice sheet discharge through physical buttressing forces and alterations to fjord circulation via iceberg melting. However, ice mélange is a highly dynamic, fragmented and mobile phenomenon which varies over a range of timescales (e.g. hours, days, weeks) and hence is difficult to monitor using traditional ground-based and spaceborne sensors. In this contribution, we utilise high spatio-temporal satellite imagery acquired from the ICEYE small satellite constellation to assess correlations between ice mélange characteristics and tidewater glacier dynamics. ICEYE is a growing constellation of 20+ small satellites each equipped with an X-band Synthetic Aperture Radar (SAR) and capable of mapping the entire globe at least once a day with fine spatial resolution (1-3 m). We utilised the ICEYE SAR imagery to study the perennial mélange matrix at the terminus of Helheim Glacier in southeast Greenland. ICEYE SAR imagery was acquired during summer and winter to assess how seasonal ice mélange conditions impact tidewater glacier dynamics. Sentinel-1 SAR imagery and ground-based TLS 3D data from two autonomous terrestrial laser scanners (ATLAS) were used to validate remote sensing analysis and provide additional data sources for interpretation of the glaciological processes. We will report on the following: (1) a spatial texture analysis (e.g. Grey Level Co-occurrence Matrix (GLCM), Gabor Transforms) of ice mélange at the terminus of Helheim Glacier using high resolution ICEYE SAR imagery; (2) results of hierarchical and random forest classifiers to map icebergs, sea ice and open water within the ice mélange matrix; (3) quantification of glacier and mélange flow variability at daily to weekly timescales; and (4) the development of observational models correlating ice mélange texture, iceberg distributions, mélange/glacier flow rates, and tidewater glacier stability. Our case study at Helheim Glacier aims to demonstrate a new approach to rapidly monitor ice mélange conditions and tidewater glacier stability using high resolution SAR imagery. In particular, this study pushes forward our Earth Observation capabilities and will help us better understand the complex processes operating at the ice-ocean interface which is critical for improved predictions of how the Greenland Ice Sheet will evolve under a warming climate.

How to cite: Harcourt, W. D., Stearns, L. A., Shahin, M. G., and Shankar, S.: Assessment of ice mélange impacts on tidewater glacier dynamics using high resolution ICEYE imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9812, https://doi.org/10.5194/egusphere-egu23-9812, 2023.

EGU23-9816 | ECS | Orals | CR2.4

Classifying sea ice in high-resolution SAR imagery using deep learning 

Andrew McDonald, Joshua Dimasaka, Meghan Plumridge, Jay Torry, Andrés Camilo Zúñiga González, Louisa van Zeeland, Martin Rogers, and Scott Hosking

Sea ice plays a vital role in Earth’s human-climate system. It regulates the Earth’s overall energy balance by seasonally increasing surface albedo and reflecting solar radiation; it governs thermodynamic exchanges between the ocean and atmosphere and thereby impacts mid-latitude weather patterns; it buttresses key continental ice shelves in Greenland and Antarctica; it provides an ecosystem in which land, marine, and airborne species thrive; it enables the livelihoods of indigenous populations across the Arctic; it poses a major obstacle to global shipping logistics; and it serves as a key indicator of climate change given the sensitivity of the polar regions to anthropogenically-induced warming. Regular and automated monitoring of sea ice concentration and type may therefore prove valuable to a broad and diverse set of parties. Conventional approaches in sea ice monitoring involve the use of remotely sensed microwave radiometer data with low resolution of 6-25 km and high instrumental sensitivities to environmental factors such as atmospheric water vapour, near-surface brightness temperature, and wind-induced surface roughening. Dual-polarity synthetic aperture radar (SAR) imagery offers a higher resolution alternative, which can also distinguish between sea ice and open water year-round independent of weather conditions. However, manual interpretation of such imagery is time-consuming. In this work, we develop a deep learning system to automatically generate high-resolution maps of sea ice concentration and type using 40m-resolution SAR imagery obtained from the Sentinel-1 mission between 2017 and 2021. Focusing on the East Weddell Sea, a region where compacted sea ice is renowned for inhibiting ship navigation and an active area of iceberg calving, we train the system against reference sea ice charts produced through manual interpretation by experts. We identify strengths and weaknesses of the system and discuss implications for future research at the intersection of machine learning and polar science.

How to cite: McDonald, A., Dimasaka, J., Plumridge, M., Torry, J., Zúñiga González, A. C., van Zeeland, L., Rogers, M., and Hosking, S.: Classifying sea ice in high-resolution SAR imagery using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9816, https://doi.org/10.5194/egusphere-egu23-9816, 2023.

EGU23-10088 | ECS | Orals | CR2.4

Removing Atmospheric Noise from Interferograms in Mountainous Regions with a Deep Convolutional Neural Network 

George Brencher, Scott Henderson, and David Shean

Atmospheric errors in interferometric synthetic aperture radar (InSAR)-derived estimates of surface deformation often obscure real signals, especially in mountainous terrain. By taking advantage of the differing spatial characteristics of periglacial landforms and atmospheric noise, we trained a deep convolutional neural network (CNN) to remove atmospheric noise from individual interferograms. Unlike existing corrections, which rely on coarse climate reanalysis or radiometer data, this computer vision correction is applied at the spatial and temporal resolution of the interferogram. We processed Sentinel 1 interferograms of the Colorado Rocky Mountains using the Alaska Satellite Facility's Hybrid Pluggable Processing Pipeline (ASF HyP3) and used the Miami INsar Time-series software in PYthon (MintPy) package to generate low-noise line-of-sight (LOS) velocity maps containing primarily rock glacier and hillslope motion. These maps were combined with noisy short temporal-baseline interferograms to contrive a training dataset. Model performance was assessed using the structural similarity index measure (SSIM) and compared to that of other widely used corrections. We find that our CNN significantly outperforms standard corrections and that previously hidden intraseasonal kinematic behavior is revealed in Colorado rock glaciers. We suggest that insights from external validation against GNSS data and sensitivity analysis could be used to further improve model performance and assess model scalability and transferability. 

How to cite: Brencher, G., Henderson, S., and Shean, D.: Removing Atmospheric Noise from Interferograms in Mountainous Regions with a Deep Convolutional Neural Network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10088, https://doi.org/10.5194/egusphere-egu23-10088, 2023.

EGU23-10377 | ECS | Orals | CR2.4

Predicting Greenland Ice Albedo Using A Physically-Based Convolutional Long Short-Term Memory Network 

Raf Antwerpen, Marco Tedesco, Pierre Gentine, Patrick Alexander, and Willem Jan van de Berg

Global mean sea level rise has been accelerating significantly over the past decades, a substantial part of which is attributed to increased surface melting from the Greenland ice sheet (GrIS). Climate models project the GrIS to contribute 9-18 cm to global mean sea level rise by 2100 for the Shared Socioeconomic Pathway SSP5-8.5. The significant uncertainty in this projection prevents accurate mitigation of the effects of sea level rise. The uncertainty stems from a not-comprehensive understanding of the physical processes controlling surface melting. In particular, we lack understanding of ice albedo evolution/variability, a crucial factor in surface melt processes. Ice albedo is a complex and highly variable property of the ice surface that is not well represented in climate model projections, leading to imprecise predictions of sea level rise. The high complexity and number of drivers and feedbacks responsible for ice albedo variability prevent us from building a comprehensive predictive ice albedo model that accurately incorporates all these processes.


From this point of view, we adopt a machine learning-based approach to predict ice albedo variability on the GrIS. We use daily regional climate model output of atmospheric, radiative, and glaciological variables from the Modèle Atmosphérique Régional (MAR) as input data and daily broadband albedo data from the Moderate Resolution Imaging Spectroradiometer (MODIS) as output data. From these data, we construct a Convolutional Long Short-Term Memory (CNN-LSTM) network that models daily ice albedo variability on 6.5 km spatial resolution. A CNN is a neural network that works particularly well for extracting patterns from spatial data. An LSTM is a special kind of recurrent neural network (RNN) that is well-suited for finding patterns and trends in temporal data on a much longer time scale than classic RNNs. Preliminary results show a significant improvement of the correlation between observed and simulated bare ice albedo, with the CNN-LSTM outperforming MAR. Besides the predictive ability of this physically-based machine learning ice albedo model and its suitability for implementation in climate models, it also allows us to gain understanding of what variables drive ice albedo variability on the GrIS, now and in the future.

How to cite: Antwerpen, R., Tedesco, M., Gentine, P., Alexander, P., and van de Berg, W. J.: Predicting Greenland Ice Albedo Using A Physically-Based Convolutional Long Short-Term Memory Network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10377, https://doi.org/10.5194/egusphere-egu23-10377, 2023.

EGU23-10430 | ECS | Orals | CR2.4

Self-organising maps and surface melt on East Antarctic ice shelves 

Dominic Saunderson, Andrew Mackintosh, Felicity McCormack, Richard Selwyn Jones, and Ghislain Picard

Surface melt occurs on most ice shelves in Antarctica each summer, with potential impacts on their strength and stability and thus on the ice sheet's contribution to global sea level rise. However, many questions remain regarding the spatiotemporal variability of surface melt and the processes driving it, particularly in East Antarctica where few in situ observations exist. Previous work in this field has largely relied on remote sensing observations to monitor the occurrence and extent of surface melt, often using metrics such as the onset and freeze-up dates of melt each summer, the number of melt days, or the cumulative melting area. Whilst such metrics are often necessary to handle the sheer volume of data produced by satellite observations, much of the information contained within the datasets is lost, hindering attempts to build a more complete picture of melt variability at different spatial and temporal scales, and thus of disentangling the different processes driving melt.

To help address this problem, we use the machine learning approach of a Self-Organising Map (SOM) and nearly two decades (2002/03–2020/21) of daily observations from the AMSR-E and AMSR-2 passive microwave sensors, gridded at a spatial resolution of 12.5 km. Here, we present results focused on the Shackleton Ice Shelf in East Antarctica, but our code, implemented in the R programming language, is openly available and can be applied to any Antarctic ice shelf, or adapted for use with other melt datasets.

Our results show that the daily distribution of surface melt on the Shackleton Ice Shelf can be described by nine representative spatial patterns of melt. These patterns demonstrate the potential for heterogeneous melt behaviour across the shelf, and thus provide insight into the influence of surface topography, katabatic winds, and surface albedo in driving surface melt. A sensitivity analysis of the SOM algorithm shows that the same general spatial patterns are returned repeatedly regardless of the parameter values used, strengthening confidence in our results and interpretation, and demonstrating the suitability of our approach. We further examine the temporal variability of the nine melt patterns, both within and across melt seasons, finding that there are no significant trends in any of the patterns. Instead, our analysis identifies a number of summers with unusual melt behaviour and also reveals correlations with shelf-wide, summer-averaged surface air temperatures, highlighting that both local and large-scale controls are important for driving surface melt in Antarctica.

How to cite: Saunderson, D., Mackintosh, A., McCormack, F., Jones, R. S., and Picard, G.: Self-organising maps and surface melt on East Antarctic ice shelves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10430, https://doi.org/10.5194/egusphere-egu23-10430, 2023.

EGU23-10867 | ECS | Posters on site | CR2.4 | Highlight

Using interactive object segmentation to derive avalanche outlines from webcam imagery 

Elisabeth Hafner, Lucien Oberson, Theodora Kontogianni, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler

Safety related applications like avalanche warning or risk management depend on timely information on avalanche occurrences. Today, this is gathered in a non-systematic way by observers in the field, even though remote sensing already proved capable of providing spatially continuous information on avalanche occurrences over large regions. Satellite imagery has the big advantage of large coverage, however the information is available only on selected dates. Depending on the application, a better temporal resolution is necessary. Webcams are ubiquitous and capture numerous avalanche prone slopes several times a day. The cameras mounted in a stable position may even be georeferenced to allow for an exact transfer of the location from the image to a map. To complement the knowledge about avalanche occurrences with more precise release time information, we propose making use of this webcam imagery for avalanche mapping.

For humans, avalanches are relatively easy to identify in imagery, but the manual mapping of their outlines is cumbersome and time intensive. To counter this, we propose automating the process with deep learning. Relying on interactive object segmentation we want to extract the avalanche outlines from those images in a time efficient manner with feedback from human experts (in the form of few corrective clicks on an image). We test existing models, searching for the best fit for avalanche outline segmentation. By adapting the best model where necessary we are aiming for outlines of good quality with a low number of clicks. For imagery we rely on current and archive data from our 14 webcams covering the Dischma valley near Davos, Switzerland with imagery available every 30 minutes during the day. Since the images are georeferenced, we may import identified avalanches directly into designated databases and therefore make them available for the relevant stakeholders.

On a more long-term perspective, the resulting avalanche outlines will enlarge the webcam training, test and validation dataset and consequently help to fully automate the avalanche outline identification from webcam imagery with object segmentation.

How to cite: Hafner, E., Oberson, L., Kontogianni, T., Daudt, R. C., Wegner, J. D., Schindler, K., and Bühler, Y.: Using interactive object segmentation to derive avalanche outlines from webcam imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10867, https://doi.org/10.5194/egusphere-egu23-10867, 2023.

EGU23-11891 | ECS | Orals | CR2.4

Seismological Monitoring of Calving Events in Greenland: A Machine Learning Approach 

Emilie Pirot, Clément Hibert, and Anne Mangeney

Greenland and other polar regions are highly sensitive to global warming. The impact of climate change on Greenland's glaciers can be seen through an increase in calving events. To better understand this impact, it is important to quantify and document calving activity. However, direct observations are difficult to perform repeatedly over long periods of time due to the hostile climatic conditions, the lack of human witnesses and of the possibility to install in-situ sensors in these remote areas.

With the installation of a regional seismological network in Greenland in the 2000’ ,and the densification of the one in north-eastern Canada, seismic signals caused by large volume calving events, known as glacial earthquakes, were recorded at distances of hundreds of km from the source. These signals have a wide range of frequencies, making it hard to distinguish them from tectonic events, anthropogenic noise, and other natural noise. Using two catalogs of known events - one of 444 glacial earthquakes that occurred between 1993 and 2013, and one for 400 earthquakes that occurred during the same time period selected from USGC - we trained and tested a detection algorithm based on the STA/LTA method to extract event signals from continuous data. We then trained a supervised machine learning algorithm (Random Forest) to automatically classify these signals into two different classes : glacial earthquakes and earthquakes, with a probability of belonging to each class.

With a workflow designed to limit the false alarm rate based on the probability scores of each events, we finally analyzed over 800 days of data from the Greenland regional seismic network and identified almost 1500 new glacial earthquake events using the trained machine learning model. Our detection methods makes it possible to detect four times more ice-quake than the original catalogue.

How to cite: Pirot, E., Hibert, C., and Mangeney, A.: Seismological Monitoring of Calving Events in Greenland: A Machine Learning Approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11891, https://doi.org/10.5194/egusphere-egu23-11891, 2023.

EGU23-11954 | ECS | Posters on site | CR2.4

Waveform retracking based on a Convolutional Neural Network applied to Cryosat-2 altimeter data 

Alireza Dehghanpour, Veit Helm, Angelika Humburt, Ronny Hänsch, and Martin Horwath

The Antarctic Ice Sheet is an important indicator of climate change and a major contributor to sea level rise. Hence, precise, long-term observations of surface elevation change are required to assess changes and their contribution to sea level rise. Satellite altimetry has been used by various missions to measure surface elevation change since 1992. It has been shown that, next to the surface slope and complex topography, one of the most challenging issues is the spatial and temporal variability of radar pulse penetration into the snowpack, especially over the vast East Antarctic plateau. This results in an inaccurate measurement of the true surface elevation and consequently affects surface elevation change (SEC) estimates.

To increase the accuracy and correct the SEC, we developed a deep convolutional neural network (CNN) architecture. The CNN was trained by a simulated waveform data set containing more than 3.6 million waveforms, considering different surface slopes, topography, and attenuation. The CNN follows standard architectural design choices. The successfully trained network is finally applied as a CNN-retracker to the full time series of CryoSat-2 low resolution mode (LRM) waveforms over the Antarctic ice sheet. We will show the CNN retrieved SEC and compare it to estimates of conventional retrackers like OCOG or ICE2. Our preliminary results show reduced uncertainty and a strongly reduced time variable radar penetration, making backscatter or leading edge corrections typically applied in SEC processing obsolete. This technique provides new opportunities to utilize convolutional neural networks in altimetry, waveform retracking, and processing altimetry data, which can be applied to historical, recent, and future altimetry missions.

How to cite: Dehghanpour, A., Helm, V., Humburt, A., Hänsch, R., and Horwath, M.: Waveform retracking based on a Convolutional Neural Network applied to Cryosat-2 altimeter data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11954, https://doi.org/10.5194/egusphere-egu23-11954, 2023.

EGU23-12361 | ECS | Orals | CR2.4

The use of multivariate Gaussian process emulation in making projections of land ice contributions to sea level rise 

Fiona Turner, Tamsin Edwards, and Jonathan Rougier

Better understanding changes in the cryosphere is key to predicting future global sea level rise, as is being done in the PROTECT project (https://protect-slr.eu). There are large uncertainties around how these changes will present over the next few centuries, with the Antarctic ice sheet being the component with the most varied predictions of potential mass change; statistical methods are required in order to quantify this uncertainty and estimate more robust projections.

We present here results from a multivariate Gaussian process emulator (Rougier, 2008; Rougier et al., 2009) of an ensemble of ice sheet and glacier models. We build projec- tions of contributions to global sea level rise over several centuries from the Antarctic and Greenland ice sheets, and the world’s glaciers, emulating them individually in order to better understand the biases and internal variability each model contains. Our use of an outer-product emulator allows us to model multi-variate output, resulting in projections over several centuries rather than a single year at a time. We predict changes for differ- ent Shared Socioeconomic Pathways (SSPs) to show how different emissions scenarios will affect land ice contributions to sea level rise, and demonstrate the differing sensitivity to parameters and forcings of the ensemble of models used.

References

Rougier, J. (2008). Efficient emulators for multivariate deterministic functions. Journal of Computational and Graphical Statistics, 17(4):827–843.

Rougier, J., Guillas, S., Maute, A., and Richmond, A. D. (2009). Expert knowledge and multivariate emulation: The thermosphere–ionosphere electrodynamics general circula- tion model (tie-gcm). Technometrics, 51(4):414–424.

How to cite: Turner, F., Edwards, T., and Rougier, J.: The use of multivariate Gaussian process emulation in making projections of land ice contributions to sea level rise, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12361, https://doi.org/10.5194/egusphere-egu23-12361, 2023.

EGU23-12774 | Posters on site | CR2.4

Next steps to a modular machine learning-based data pipeline for automated snow avalanche detection in the Austrian Alps 

Kathrin Lisa Kapper, Thomas Goelles, Stefan Muckenhuber, Andreas Trügler, Jakob Abermann, Birgit Schlager, Christoph Gaisberger, Jakob Grahn, Eirik Malnes, Alexander Prokop, and Wolfgang Schöner

Snow avalanches pose a significant danger to the population and infrastructure in the Austrian Alps. Although rigorous prevention and mitigation mechanisms are in place in Austria, accidents cannot be prevented, and victims are mourned every year. A comprehensive mapping of avalanches would be desirable to support the work of local avalanche commissions to improve future avalanche predictions. In recent years, mapping of avalanches from satellite images has been proven to be a promising and fast approach to monitor the avalanche activity. The Copernicus Sentinel-1 mission provides weather independent synthetic aperture radar data, free of charge since 2014, that has been shown to be suitable for avalanche mapping in a test region in Norway. Several recent approaches of avalanche detection make use of deep learning-based algorithms to improve the detection rate compared to conventional segmentation algorithms.

          Building upon the success of these deep learning-based approaches, we are setting up a modular data pipeline to map previous avalanche cycles in Sentinel-1 imagery in the Austrian Alps. As segmentation algorithm we make use of a common U-Net approach as a baseline and compare it to mapping results from an additional algorithm that has originally been applied to an autonomous driving problem. As a first test case, the extensive labelled training dataset of around 25 000 avalanche outlines from Switzerland will be used to train the U-Net; further test cases will include the training dataset of around 3 000 avalanches in Norway and around 800 avalanches in Greenland. To obtain training data of avalanches in Austria we tested an approach by manually mapping avalanches from Sentinel-2 satellite imagery and aerial photos.

          In a new approach, we will introduce high-resolution weather data, e.g., weather station data, to the learning-based algorithm to improve the detection performance. The avalanches detected with the algorithm will be quantitatively evaluated against held-out test sets and ground-truth data where available. Detection results in Austria will additionally be validated with in situ measurements from the MOLISENS lidar system and the RIEGL VZ-6000 laser scanner. Moreover, we will assess the possibilities of learning-based approaches in the context of avalanche forecasting.

How to cite: Kapper, K. L., Goelles, T., Muckenhuber, S., Trügler, A., Abermann, J., Schlager, B., Gaisberger, C., Grahn, J., Malnes, E., Prokop, A., and Schöner, W.: Next steps to a modular machine learning-based data pipeline for automated snow avalanche detection in the Austrian Alps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12774, https://doi.org/10.5194/egusphere-egu23-12774, 2023.

EGU23-12810 | ECS | Posters on site | CR2.4

Deep learning for surrogate modelling of neXtSIM 

Charlotte Durand, Tobias Finn, Alban Farchi, Marc Bocquet, and Einar Olason

A novel generation of sea-ice models with Elasto-Brittle rheologies can represent the drift and deformation of sea-ice with an unprecedented resolution and accuracy. To speed-up these computationally heavy simulations and to facilitate subgrid-scale parameterizations, we investigate supervised deep learning techniques for surrogate modelling of large-scale, Arctic-wide, neXtSIM Lagrangian simulations. We tailor convolutional neural networks to emulate the sea-ice thickness for 12 hours in advance. In our most successful approach, the U-Net learns to make beneficially use of information from multiple temporal and spatial scales, an important feature of the neural network for sea-ice prediction. Consequently, cycling the neural network performs in average 36% better than persistence on a daily timescale and up to 43 % on a monthly timescale. These promising results therefore demonstrate a way towards surrogate modelling of Arctic-wide simulations. 

How to cite: Durand, C., Finn, T., Farchi, A., Bocquet, M., and Olason, E.: Deep learning for surrogate modelling of neXtSIM, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12810, https://doi.org/10.5194/egusphere-egu23-12810, 2023.

EGU23-12927 | ECS | Orals | CR2.4

The Added Value of Remote Sensing Data in Downscaling Regional Climate Models 

Sophie de Roda Husman, Zhongyang Hu, Peter Kuipers Munneke, Maurice van Tiggelen, Stef Lhermitte, and Bert Wouters

Small-scale, subgrid processes on the ice sheets, such as localized surface melt, remain unnoticed by current coarse-resolution Regional Climate Models (RCMs), leading to uncertainties in climate reanalyses and projections. Deep learning allows us to enhance the spatial resolution of RCMs but requires sophisticated model development. Earlier studies have shown that rudimental techniques, such as single-image super-resolution, have failed to capture Antarctic surface melt patterns accurately, because the spatial transferability of these models is low. In this study, we add remote sensing data to a super-resolution model: daily observations of surface albedo from MODIS are used to guide the downscaling of low-resolution surface melt (RACMO2, 27 km) to a high-resolution version (RACMO2, 5.5 km) for a 20-year period, between 2001-2019. We extend a conventional SRResNet and add the MODIS data in different configurations (i.e., spatial-channel communication, content communication, and empirical-physical activation). The models are trained over the Antarctic Peninsula, for which RACMO2 simulations are available at 5.5 km resolution (Van Wessem et al., 2016). We verify the performance of the models with three independent datasets to inspect (1) the overall performance (using QuickSCAT); (2) spatial patterns (using Sentinel-1); and (3) temporal patterns (using automatic weather stations). Our work shows the potential of adding remote sensing data to deep learning-based downscaling models, leading to improved spatial transferability compared to single-image downscaling models.

How to cite: de Roda Husman, S., Hu, Z., Kuipers Munneke, P., van Tiggelen, M., Lhermitte, S., and Wouters, B.: The Added Value of Remote Sensing Data in Downscaling Regional Climate Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12927, https://doi.org/10.5194/egusphere-egu23-12927, 2023.

EGU23-13038 | ECS | Orals | CR2.4 | Highlight

The AutoICE Competition: Automatically Mapping Sea Ice in the Arctic 

Andreas Stokholm, Jørgen Buus-Hinkler, Tore Wulf, Anton Korosov, Roberto Saldo, David Arthurs, Rune Solberg, Nicolas Longépé, and Matilde Kreiner

The AutoICE Competition, launched on ESA’s AI4EO platform, brings together AI and Earth Observation practitioners to address the challenge of “automated sea ice mapping” from Sentinel-1 SAR data. Traversing the polar waters safely and efficiently requires up-to-date maps of the constantly moving and changing sea ice conditions showing the current sea ice extent, local concentration, and auxiliary descriptions of the ice conditions. For several decades, sea ice charts have been manually produced by visually inspecting and analysing satellite imagery.

The objective of the AutoICE challenge is to advance the state-of-the-art for automatic sea ice parameter retrieval from SAR data to derive more robust and accurate sea ice maps. The challenge design and evaluation criteria have been created with input from machine learning experts and members of the International Ice Charting Working Group (IICWG). In this competition, participants are tasked to build machine learning models using the available state-of-the-art challenge dataset and to submit their model results for each of the three sea ice parameters: sea ice concentration, stage of development and floe size. The dataset made available in this challenge contains Sentinel-1 active microwave (SAR) data and corresponding Microwave Radiometer (MWR) data from the AMSR2 satellite sensor to enable challenge participants to exploit the advantages of both instruments and to create data fusion models. Label data in the challenge datasets are ice charts produced by both the Greenland ice service at the Danish Meteorological Institute (DMI) and the Canadian Ice Service (CIS). The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. Two versions of the challenge dataset are available, a raw dataset and a ready-to-train dataset. The datasets each consist of the same 513 training and 20 test (without label data) scenes, however, the ready-to-train version has been further prepared for model training. In addition, a number of tools are made available to help the participants get started quickly, including access to machine learning computing resources on the ESA Polar Thematic Exploitation Platform (Polar TEP). The competition was initiated on the 23rd of  November 2022 and is expected to conclude on the 17th of April 2023.

Here, we present the overall challenge, the underlying objective, the available state-of-the-art dataset and resources, the progress of the challenge and its results, as well as a sneak peek of our upcoming ASID-v3 dataset. 

How to cite: Stokholm, A., Buus-Hinkler, J., Wulf, T., Korosov, A., Saldo, R., Arthurs, D., Solberg, R., Longépé, N., and Kreiner, M.: The AutoICE Competition: Automatically Mapping Sea Ice in the Arctic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13038, https://doi.org/10.5194/egusphere-egu23-13038, 2023.

EGU23-14207 | ECS | Orals | CR2.4

Mapping Antarctic Crevasses at High Spatiotemporal Resolution with Deep Learning applied to Synthetic Apertur Radar Data 

Trystan Surawy-Stepney, Anna E. Hogg, Stephen L. Cornford, and David Hogg

Understanding how the presence of fractured ice alters the dynamics, hydrology and energy balance of glaciers and ice shelves is important in determining the future evolution of the Antarctic Ice Sheet (AIS). However, these processes are not all well understood, and large-scale quantitative observations of fractures are sparse. Fortunately, the large amount of satellite data covering Antarctica gives us the opportunity to change this.

The Sentinel-1 satellite cluster, from the European Space Agency's Copernicus programme, has acquired synthetic aperture radar (SAR) data over the AIS with a repeat period of 6-12 days for the last 8 years. A broad range of crevasse types are visible in this imagery: rifts, surface crevasses and some basal crevasses on ice shelves, and fine surface crevasses on grounded ice streams - even those bridged by snow or pixel-scale in width.

In this study, we use machine learning to automatically map crevasses in this imagery; producing monthly composite maps over the AIS at 50m resolution. We developed algorithms to partition crevasses into those on grounded and floating ice, and extract these features in parallel using a mixture of convolutional neural networks, trained in a weakly supervised way, and more traditional computer vision techniques.

Having developed parallelisable routines for the large-scale batch processing of SAR data, we have processed every Sentinel-1 acquisition over the Antarctic Ice Sheet. The resulting dense timeseries of fracture maps allows us to assess the evolution of crevasses during the Sentinel-1 acquisition period. By measuring the density of fractures we develop a method to quantify structural change on ice shelves, and investigate those of the Amundsen Sea Embayment. We show an increase in crevassing in buttressing regions of the Pine Island and Thwaites ice shelves over the last 8 years, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses.

Finally, we develop methods demonstrating how our fracture data can be assimilated into numerical modelling experiments aiming to quantify the impact of ice shelf fracture on glacier dynamics.

How to cite: Surawy-Stepney, T., Hogg, A. E., Cornford, S. L., and Hogg, D.: Mapping Antarctic Crevasses at High Spatiotemporal Resolution with Deep Learning applied to Synthetic Apertur Radar Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14207, https://doi.org/10.5194/egusphere-egu23-14207, 2023.

EGU23-14210 | ECS | Posters virtual | CR2.4

Detection of Arctic rivers streamflow drivers through automatic feature selection 

Mattia Zeno, Matteo Sangiorgio, and Andrea Castelletti

Runoff from Arctic rivers has a direct influence on the sea ice dynamics in the Arctic Ocean, producing significant effects from local to global scales. Despite their key role, the knowledge of the processes that influence the Arctic rivers streamflow is still limited, and their behavior is not fully understood.

In the literature, these analyses are usually performed adopting classical statistical methods and simple linear models, which are probably unable to fully capture underlying nonlinearities and redundancy of candidate drivers.

In this study, we use automatic feature selection techniques to detect the main drivers of the five major Arctic Rivers’ runoff (Ob, Yenisei, and Lena in Asia, Mackenzie and Yukon in North America). Daily time series of temperature and precipitation recorded by several stations spread across the Arctic region, and the average snow cover of each basin are used as candidate input variables.

The feature selection analysis is carried out with two algorithms: Wrapper for Quasi Equally Informative Subset Selection (W-QEISS) and Iterative Input Section (IIS). W-QEISS adopts neural predictive models to select alternative sets of drivers providing similar in terms of accuracy, but with different relevance, redundancy, and cardinality. Conversely, IIS directly produces a ranking of the input variables relying on tree-based models and combining computational efficiency and scalability to high input dimensionality.

The two algorithms achieve noticeably consistent results, with minor differences that can be explained by numerical factors typical of machine learning. Results also show that autoregressive terms have a crucial role in all the hydrological basins, while the importance of the other drivers is different for each river.

This preliminary research opens the floor for further analysis to broaden the knowledge of Arctic hydro-meteorological dynamics.

How to cite: Zeno, M., Sangiorgio, M., and Castelletti, A.: Detection of Arctic rivers streamflow drivers through automatic feature selection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14210, https://doi.org/10.5194/egusphere-egu23-14210, 2023.

EGU23-14307 | ECS | Orals | CR2.4

Delineating giant Antarctic icebergs with Deep Learning 

Anne Braakmann-Folgmann, Andrew Shepherd, David Hogg, and Ella Redmond

Icebergs account for half of all ice loss from Antarctica. Their melting affects the surrounding ocean properties through the intrusion of cold, fresh meltwater and the release of terrigenious nutrients. This in turn influences the local ocean circulation, sea ice formation and biological production. To locate and quantify the fresh water flux from Antarctic icebergs, we need to track them and monitor changes in their area and thickness. While the locations of large icebergs are tracked operationally by manual inspection, delineation of iceberg extent requires detailed analysis – either also manually or through automated segmentation of high resolution satellite imagery.

In this study, we apply three machine learning techniques to 191 Sentinel-1 images between 2014 and 2020 and assess their skill to segment seven giant Antarctic icebergs between 54 and 1052 km2 in size. Most previous studies to detect icebergs have focused on smaller bergs. In contrast, we aim to segment selected giant icebergs with the goal to automate the calculation of their changing area, volume, and freshwater input. Two of our techniques are standard segmentation techniques (k-means and Otsu thresholding) and the third one is a deep neural network (U-net). It is the first study to apply a deep learning algorithm to iceberg detection.

We analyse the strengths and weaknesses of the different machine learning approaches across a range of challenging environmental conditions: These include scenes where the iceberg is surrounded by deformed sea ice, when other big bergs are present and when berg fragments are close to the main iceberg. We also cover cases when the iceberg drifts close to the coast and summer images with surface thawing conditions, which invert the backscatter contrast between iceberg and ocean.

How to cite: Braakmann-Folgmann, A., Shepherd, A., Hogg, D., and Redmond, E.: Delineating giant Antarctic icebergs with Deep Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14307, https://doi.org/10.5194/egusphere-egu23-14307, 2023.

EGU23-17128 | ECS | Orals | CR2.4

Ghub: A new community-driven data-model resource for ice-sheet scientists 

Sophie Goliber, Jason Briner, Sophie Nowicki, Beata Csatho, Renette Jones-Ivey, William Lipscomb, Abani Patra, Kristin Poinar, Justin Quinn, Anton Schenk, and Katherine Thayer-Calder

The urgency in reducing uncertainties of near-term sea level rise relies on improved modeling of ice sheet response to climate change. Predicting future ice sheet change requires a tremendous effort across a range of disciplines in ice sheet science, including expertise in observational data, paleoglaciology, numerical ice sheet modeling, and the widespread use of emerging methodologies for learning from the data. However, significant knowledge and disciplinary barriers make collaboration between data and model groups the exception rather than the norm. We seek to improve the efficiency in collaboration among traditionally disparate approaches to this problem. We present Ghub, a community-building scientific and educational cyberinfrastructure framework that includes models and data processing tools, online simulation, and collaboration support, available for use at theghub.org. Ghub enables collaboration between ice sheet scientific communities and acts as a host for the open-source tools that are becoming more common in the field of ice sheet science. We provide an overview of the Ghub framework, with examples of tools, tutorials, and educational content that are ready to use, and visions for extending these and other upcoming developments. These tools target a wide range of audiences, ranging from ice sheet modeling community efforts such as the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) to more specialized process-orientated investigations. We also outline the process for scientists to host their data and tools on the platform.

How to cite: Goliber, S., Briner, J., Nowicki, S., Csatho, B., Jones-Ivey, R., Lipscomb, W., Patra, A., Poinar, K., Quinn, J., Schenk, A., and Thayer-Calder, K.: Ghub: A new community-driven data-model resource for ice-sheet scientists, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17128, https://doi.org/10.5194/egusphere-egu23-17128, 2023.

EGU23-17318 | Orals | CR2.4

Discrimination of sea ice leads and floes using Deep Learning applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) imaging spectrometer 

Weibin Chen, Michel Tsamados, Rosie Willatt, So Takao, Connor Nelson, Isobel Lawrence, Sanggyun Lee, David Brockley, Jack Landy, Claude De Rijke-Thomas, Dorsa Shirazi, Julienne Stroeve, and Alistair Francis

The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution radar altimetry data over the polar regions up to 81 degrees North. The combination of synthetic aperture radar (SAR) mode altimetry from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer, results in the creation of the first satellite platform that offers coincident optical imagery and SAR radar altimetry. We utilise these datasets to validate existing surface classification algorithms, in addition to investigating novel applications of deep learning to classify sea-ice from leads. This is important for estimating sea-ice thickness and to predict future changes in the Arctic and Antarctic regions. In particular, we propose the use of Vision Transformers (ViT) for this task and demonstrate their effectiveness, with accuracy reaching above 92%. We compare our automated results with human classification using the software IRIS. 

How to cite: Chen, W., Tsamados, M., Willatt, R., Takao, S., Nelson, C., Lawrence, I., Lee, S., Brockley, D., Landy, J., De Rijke-Thomas, C., Shirazi, D., Stroeve, J., and Francis, A.: Discrimination of sea ice leads and floes using Deep Learning applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) imaging spectrometer, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17318, https://doi.org/10.5194/egusphere-egu23-17318, 2023.

EGU23-17323 | Orals | CR2.4

Fast interpolation of satellite altimetry data with probabilistic machine learning and GPU 

Ronald MacEachern, Michel Tsamados, William Gregory, Isobel Lawrence, and So Takao

Recent work has demonstrated how Gaussian Process Regression (GPR) can be used to interpolate Pan-Arctic radar freeboard of sea ice as measured by satellites. Sea ice freeboard is crucial to measuring sea ice thickness, and thus sea ice volume, which can play an important role in climate models. Similarly sea surface heights from altimetry are essential to determine the geostrophic currents from space. Using GPR can be computationally burdensome for modest dataset sizes and prohibitive for large datasets. To avoid having to deal with a large dataset the raw satellite observations were binned (averaged) onto a regularly spaced grid. We look at how these calculations can be reduced in terms of run time by utilising a Graphical Processing Unit (GPU), a dedicated GPR python package and by making practical adjustments to the methodology. We find by adopting these changes the overall run time of a single day’s interpolation can be greatly reduced by a factor of over 60, making it practical to run such calculations on an environment with a GPU. We then extend the method to use raw satellite observation data (no binning), which greatly increases the number of training points, requiring the use a sparse method for GPR. We conclude with recommendations for further work on this subject as it has the potential for widespread use in remote sensing applications.

How to cite: MacEachern, R., Tsamados, M., Gregory, W., Lawrence, I., and Takao, S.: Fast interpolation of satellite altimetry data with probabilistic machine learning and GPU, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17323, https://doi.org/10.5194/egusphere-egu23-17323, 2023.

EGU23-17372 | ECS | Posters on site | CR2.4

Analysis of ice shelf front dynamics in Pine Island Bay (Antarctica) based on long-term SAR time series and deep learning 

Luisa Wagner, Celia Baumhoer, Andreas Dietz, Claudia Kuenzer, and Tobias Ullmann

Ice shelves, the floating extensions of glaciers and ice sheets, create a safety band around Antarctica. They control the flow of ice that drains into the ocean by buttressing the upstream grounded ice. Loss of ice shelf stability and integrity results in reduced buttressing and leads to increased discharge contributing to global sea level rise. Therefore, it is important to monitor ice shelf dynamics to accurately estimate future sea level rise.

So far, the potential of SAR data has not yet been full exhausted as data of early SAR satellites has only been used to a very limited extent for calving front monitoring. To fill this research gap, we made use of the entire ERS and Envisat archive within West Antarctic Pine Island Bay, a region that requires particular attention due to drastic ongoing changes. A 20-year time series (1992-2011) of ice shelf front dynamics was derived based on a deep neural network architecture that combines segmentation and edge detection. By testing different data preparation, training and post-processing configurations we identified the best performing model for ERS and Envisat data. This includes transfer learning based on a model originally trained on Sentinel-1 data and post-processing with filtering and temporal compositing to remove artefacts from geolocation errors and limited data availability.

The resulting product of yearly, half-year and monthly ice shelf front positions reveals individual dynamic patterns for all five investigated ice shelves. The most considerable fluctuations were found for Pine Island Ice Shelf in terms of frequency of calving events (multiple cycles of calving and re-advance) and Thwaites ice tongue in terms of size of break-up (80 km retreat in early 2002). Despite different change rates and magnitudes, most ice shelves show similar signs of destabilisation. This manifests through retreating front positions and changing ice shelf geometries. Signs of weakening appear in the form of fracturing, disintegration events and loss of connection to lateral confinements.

How to cite: Wagner, L., Baumhoer, C., Dietz, A., Kuenzer, C., and Ullmann, T.: Analysis of ice shelf front dynamics in Pine Island Bay (Antarctica) based on long-term SAR time series and deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17372, https://doi.org/10.5194/egusphere-egu23-17372, 2023.

EGU23-582 | ECS | Posters on site | ITS1.13/AS5.2

Modeling the Variability of Terrestrial Carbon Fluxes using Transformers 

Swarnalee Mazumder and Ayush Prasad

The terrestrial carbon cycle is one of the largest sources of uncertainty in climate projections. The terrestrial carbon sink which removes a quarter of anthropogenic CO2 emissions; is highly variable in time and space depending on climate. Previous studies have found that data-driven models such as random forest, artificial neural networks and long short-term memory networks can be used to accurately model Net Ecosystem Exchange (NEE) and Gross Primary Productivity (GPP) accurately, which are two important metrics to quantify the direction and magnitude of CO2 transfer between the land surface and the atmosphere. Recently, a new class of machine learning models called transformers have gained widespread attention in natural language processing tasks due to their ability to learn from large volumes of sequential data. In this work, we use Transformers to model NEE and GPP from 1996-2022 at 39 Flux stations in the ICOS Europe network using ERA5 reanalysis data. We can compare our results with traditional machine learning approaches to evaluate the generalisability and predictive performance of transformers for carbon flux modelling.

How to cite: Mazumder, S. and Prasad, A.: Modeling the Variability of Terrestrial Carbon Fluxes using Transformers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-582, https://doi.org/10.5194/egusphere-egu23-582, 2023.

EGU23-1825 | ECS | Orals | ITS1.13/AS5.2

Spatial representation learning for ensemble weather simulations using invariant variational autoencoders 

Jieyu Chen, Kevin Höhlein, and Sebastian Lerch

Weather forecasts today are typically issued in the form of ensemble simulations based on multiple runs of numerical weather prediction models with different perturbations in the initial states and the model physics. In light of the continuously increasing spatial resolutions of operational weather models, this results in large, high-dimensional datasets that nonetheless contain relevant spatial and temporal structure, as well as information about the predictive uncertainty. We propose invariant variational autoencoder (iVAE) models based on convolutional neural network architectures to learn low-dimensional representations of the spatial forecast fields. We specifically aim to account for the ensemble character of the input data and discuss methodological questions about the optimal design of suitable dimensionality reduction methods in this setting. Thereby, our iVAE models extend previous work where low-dimensional representations of single, deterministic forecast fields were learned and utilized for incorporating spatial information into localized ensemble post-processing methods based on neural networks [1], which were able to improve upon model utilizing location-specific inputs only [2]. By additionally incorporating the ensemble dimension and learning representation for probability distributions of spatial fields, we aim to enable a more flexible modeling of relevant predictive information contained in the full forecast ensemble. Additional potential applications include data compression and the generation of forecast ensembles of arbitrary size.

We illustrate our methodological developments based on a 10-year dataset of gridded ensemble forecasts from the European Centre for Medium-Range Weather Forecasts of several meteorological variables over Europe. Specifically, we investigate alternative model architectures and highlight the importance of tailoring the loss function to the specific problem at hand.

References:

[1] Lerch, S. & Polsterer, K.L. (2022). Convolutional autoencoders for spatially-informed ensemble post-processing. ICLR 2022 AI for Earth and Space Science Workshop, https://arxiv.org/abs/2204.05102.

[2] Rasp, S. & Lerch, S. (2018). Neural networks for post-processing ensemble weather forecasts. Monthly Weather Review, 146, 3885-3900.

How to cite: Chen, J., Höhlein, K., and Lerch, S.: Spatial representation learning for ensemble weather simulations using invariant variational autoencoders, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1825, https://doi.org/10.5194/egusphere-egu23-1825, 2023.

EGU23-3117 | Orals | ITS1.13/AS5.2

AtmoRep: Large Scale Representation Learning for Atmospheric Data 

Christian Lessig, Ilaria Luise, and Martin Schultz

The AtmoRep project asks if one can train one neural network that represents and describes all atmospheric dynamics. AtmoRep’s ambition is hence to demonstrate that the concept of large-scale representation learning, whose principle feasibility and potential was established by large language models such as GPT-3, is also applicable to scientific data and in particular to atmospheric dynamics. The project is enabled by the large amounts of atmospheric observations that have been made in the past as well as advances on neural network architectures and self-supervised learning that allow for effective training on petabytes of data. Eventually, we aim to train on all of the ERA5 reanalysis and, furthermore, fine tune on observational data such as satellite measurements to move beyond the limits of reanalyses.

We will present the theoretical formulation of AtmoRep as an approximate representation for the atmosphere as a stochastic dynamical system. We will also detail our transformer-based network architecture and the training protocol for self-supervised learning so that unlabelled data such as reanalyses, simulation outputs and observations can be employed for training and re-fining the network. Results will be presented for the performance of AtmoRep for downscaling, precipitation forecasting, the prediction of tropical convection initialization, and for model correction. Furthermore, we also demonstrate that AtmoRep has substantial zero-short skill, i.e., it is capable to perform well on tasks it was not trained for. Zero- and few-shot performance (or in context learning) is one of the hallmarks of large-scale representation learning and to our knowledge has never been demonstrated in the geosciences.

How to cite: Lessig, C., Luise, I., and Schultz, M.: AtmoRep: Large Scale Representation Learning for Atmospheric Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3117, https://doi.org/10.5194/egusphere-egu23-3117, 2023.

Numerical Earth system models (ESMs) are our primary tool for projecting future climate scenarios. Their simulation output is used by impact models that assess the effect of anthropogenic global warming, e.g., on flood events, vegetation changes or crop yields. Precipitation, an atmospheric variable with arguably one of the largest socio-economic impacts, involves various processes on a wide range of spatial-temporal scales. However, these cannot be completely resolved in ESMs due to the limited discretization of the numerical model. 
This can lead to biases in the ESM output that need to be corrected in a post-processing step prior to feeding ESM output into impact models, which are calibrated with observations [1]. While established post-processing methods successfully improve the modelled temporal statistics for each grid cell individually, unrealistic spatial features that require a larger spatial context are not addressed.
Here, we apply a cycle-consistent generative adversarial network (CycleGAN) [2] that is physically constrained to the precipitation output from Coupled Model Intercomparison Project phase 6 (CMIP6)  ESMs to correct both temporal distributions and spatial patterns. The CycleGAN can be naturally trained on daily ESM and reanalysis fields that are unpaired due to the deviating trajectories of the ESM and observation-based ground truth. 
We evaluate our method against a state-of-the-art bias adjustment framework (ISIMIP3BASD) [3] and find that it outperforms it in correcting spatial patterns and achieves comparable results on temporal distributions. We further discuss the representation of extreme events and suitable metrics for quantifying the realisticness of unpaired precipitation fields.

 [1] Cannon, A.J., et al. "Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?." Journal of Climate 28.17 (2015): 6938-6959.

[2] Zhu, J.-Y., et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." Proceedings of the IEEE international conference on computer vision. 2017.

[3] Lange, S. "Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0)." Geoscientific Model Development 12.7 (2019): 3055-3070.

How to cite: Hess, P., Lange, S., and Boers, N.: Improving global CMIP6 Earth system model precipitation output with generative adversarial networks for unpaired image-to-image translation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3128, https://doi.org/10.5194/egusphere-egu23-3128, 2023.

EGU23-3256 | Orals | ITS1.13/AS5.2

Emulating radiative transfer in a numerical weather prediction model 

Matthew Chantry, Peter Ukkonen, Robin Hogan, and Peter Dueben

Machine learning, and particularly neural networks, have been touted as a valuable accelerator for physical processes. By training on data generated from an existing algorithm a network may theoretically learn a more efficient representation and accelerate the computations via emulation. For many parameterized physical processes in weather and climate models this being actively pursued. Here, we examine the value of this approach for radiative transfer within the IFS, an operational numerical weather prediction model where both accuracy and speed are vital. By designing custom, physics-informed, neural networks we achieve outstanding offline accuracy for both longwave and shortwave processes. In coupled testing we find minimal changes to forecast scores at near operational resolutions. We carry out coupled inference on GPUs to maximise the speed benefits from the emulator approach.

How to cite: Chantry, M., Ukkonen, P., Hogan, R., and Dueben, P.: Emulating radiative transfer in a numerical weather prediction model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3256, https://doi.org/10.5194/egusphere-egu23-3256, 2023.

EGU23-3321 | ECS | Orals | ITS1.13/AS5.2

Using machine learning to improve dynamical predictions in a coupled model 

Zikang He, Julien Brajard, Yiguo Wang, Xidong Wang, and Zheqi Shen

Dynamical models used in climate prediction often have systematic errors that can bias the predictions. In this study, we utilized machine learning to address this issue. Machine learning was applied to learn the error corrected by data assimilation and thus build a data-driven model to emulate the dynamical model error. A hybrid model was constructed by combining the dynamical and data-driven models. We tested the hybrid model using synthetic observations generated by a simplified high-resolution coupled ocean-atmosphere model (MAOOAM, De Cruz et al., 2016) and compared its performance to that of a low-resolution version of the same model used as a standalone dynamical model.

To evaluate the forecast skill of the hybrid model, we produced ensemble predictions based on initial conditions determined through data assimilation. The results show that the hybrid model significantly improves the forecast skill for both atmospheric and oceanic variables compared to the dynamical model alone. To explore what affects short-term forecast skills and long-term forecast skills, we built two other hybrid models by correcting errors either only atmospheric or only oceanic variables. For short-term atmospheric forecasts, the results show that correcting only oceanic errors has no effect on atmosphere variables forecasts but correcting only atmospheric variables shows similar forecast skill to correcting both atmospheric and oceanic errors. For the long-term forecast of oceanic variables, correcting the oceanic error can improve the forecast skill, but correcting both atmospheric and oceanic errors can obtain the best forecast skill. The results indicate that for the long-term forecast of oceanic variables, bias correction of both oceanic and atmospheric components can have a significant effect.

How to cite: He, Z., Brajard, J., Wang, Y., Wang, X., and Shen, Z.: Using machine learning to improve dynamical predictions in a coupled model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3321, https://doi.org/10.5194/egusphere-egu23-3321, 2023.

EGU23-3340 | ECS | Orals | ITS1.13/AS5.2

An iterative data-driven emulator of an ocean general circulation model 

Rachel Furner, Peter Haynes, Dan(i) Jones, Dave Munday, Brooks Paige, and Emily Shuckburgh

Data-driven models are becoming increasingly competent at tasks fundamental to weather and climate prediction. Relative to machine learning (ML) based atmospheric models, which have shown promise in short-term forecasting, ML-based ocean forecasting remains somewhat unexplored. In this work, we present a data-driven emulator of an ocean GCM and show that performance over a single predictive step is skilful across all variables under consideration. Iterating such data-driven models poses additional challenges, with many models suffering from over-smoothing of fields or instabilities in the predictions. We compare a variety of methods for iterating our data-driven emulator and assess them by looking at how well they agree with the underlying GCM in the very short term and how realistic the fields remain for longer-term forecasts. Due to the chaotic nature of the system being forecast, we would not expect any model to agree with the GCM accurately over long time periods, but instead we expect fields to continue to exhibit physically realistic behaviour at ever increasing lead times. Specifically, we expect well-represented fields to remain stable whilst also maintaining the presence and sharpness of features seen in both reality and in GCM predictions, with reduced emphasis on accurately representing the location and timing of these features. This nuanced and temporally changing definition of what constitutes a ‘good’ forecast at increasing lead times generates questions over both (1) how one defines suitable metrics for assessing data-driven models, and perhaps more importantly, (2) identifying the most promising loss functions to use to optimise these models.

How to cite: Furner, R., Haynes, P., Jones, D., Munday, D., Paige, B., and Shuckburgh, E.: An iterative data-driven emulator of an ocean general circulation model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3340, https://doi.org/10.5194/egusphere-egu23-3340, 2023.

EGU23-4337 | Orals | ITS1.13/AS5.2 | Highlight

Towards a new surrogate model for predicting short-term NOx-O3 effects from aviation using Gaussian processes 

Pratik Rao, Richard Dwight, Deepali Singh, Jin Maruhashi, Irene Dedoussi, Volker Grewe, and Christine Frömming

While efforts have been made to curb CO2 emissions from aviation, the more uncertain non-CO2 effects that contribute about two-thirds to the warming in terms of radiative forcing (RF), still require attention. The most important non-CO2 effects include persistent line-shaped contrails, contrail-induced cirrus clouds and nitrogen oxide (NOx) emissions that alter the ozone (O3) and methane (CH4) concentrations, both of which are greenhouse gases, and the emission of water vapour (H2O). The climate impact of these non-CO2 effects depends on emission location and prevailing weather situation; thus, it can potentially be reduced by advantageous re-routing of flights using Climate Change Functions (CCFs), which are a measure for the climate effect of a locally confined aviation emission. CCFs are calculated using a modelling chain starting from the instantaneous RF (iRF) measured at the tropopause that results from aviation emissions. However, the iRF is a product of computationally intensive chemistry-climate model (EMAC) simulations and is currently restricted to a limited number of days and only to the North Atlantic Flight Corridor. This makes it impossible to run EMAC on an operational basis for global flight planning. A step in this direction lead to a surrogate model called algorithmic Climate Change Functions (aCCFs), derived by regressing CCFs (training data) against 2 or 3 local atmospheric variables at the time of emission (features) with simple regression techniques and are applicable only in parts of the Northern hemisphere. It was found that in the specific case of O3 aCCFs, which provide a reasonable first estimate for the short-term impact of aviation NOx on O3 warming using temperature and geopotential as features, can be vastly improved [1]. There is aleatoric uncertainty in the full-order model (EMAC), stemming from unknown sources (missing features) and randomness in the known features, which can introduce heteroscedasticity in the data. Deterministic surrogates (e.g. aCCFs) only predict point estimates of the conditional average, thereby providing an incomplete picture of the stochastic response. Thus, the goal of this research is to build a new surrogate model for iRF, which is achieved by :

1. Expanding the geographical coverage of iRF (training data) by running EMAC simulations in more regions (North & South America, Eurasia, Africa and Australasia) at multiple cruise flight altitudes,

2. Following an objective approach to selecting atmospheric variables (feature selection) and considering the importance of local as well as non-local effects,

3. Regressing the iRF against selected atmospheric variables using supervised machine learning techniques such as homoscedastic and heteroscedastic Gaussian process regression.

We present a new surrogate model that predicts iRF of aviation NOx-O3 effects on a regular basis with confidence levels, which not only improves our scientific understanding of NOx-O3 effects, but also increases the potential of global climate-optimised flight planning.

References

[1] Rao, P.; et al. Case Study for Testing the Validity of NOx-Ozone Algorithmic Climate Change Functions for Optimising Flight Trajectories. Aerospace 20229, 231. https://doi.org/10.3390/aerospace9050231

How to cite: Rao, P., Dwight, R., Singh, D., Maruhashi, J., Dedoussi, I., Grewe, V., and Frömming, C.: Towards a new surrogate model for predicting short-term NOx-O3 effects from aviation using Gaussian processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4337, https://doi.org/10.5194/egusphere-egu23-4337, 2023.

Time transfer functions describe the change of state variables over time in geoscientific numerical simulation models. The identification of these functions is an essential but challenging step in model building. While traditional methods rely on qualitative understanding or first order principles, the availability of large spatio-temporal data sets from direct measurements or extremely detailed physical-based system modelling has enabled the use of machine learning methods to discover the time transfer function directly from data. In this study we explore the feasibility of this data driven approach for numerical simulation of the co-evolution of soil, hydrology, vegetation, and grazing on landscape scale, at geological timescales. From empirical observation and hyper resolution (1 m, 1 week) modelling (Karssenberg et al, 2017) it has been shown that a hillslope system shows complex behaviour with two stable states, respectively high biomass on deep soils (healthy state) and low biomass on thin soils (degraded or desertic state). A catastrophic shift from healthy to degraded state occurs under changes of external forcing (climate, grazing pressure), with a transient between states that is rapid or slow depending on system characteristics. To identify and use the time transfer functions of this system at hillslope scale we follow four procedural steps. First, an extremely large data set of hillslope average soil and vegetation state is generated by a mechanistic hyper resolution (1 m, 1 week) system model, forcing it with different variations in grazing pressure over time. Secondly, a machine learning model predicting the rate of change in soil and vegetation as function of soil, vegetation, and grazing pressure, is trained on this data set. In the third step, we explore the ability of this trained machine learning model to predict the rate of system change (soil and vegetation) on untrained data. Finally, in the fourth step, we use the trained machine learning model as time transfer function in a forward numerical simulation of a hillslope to determine whether it is capable of representing the known complex behaviour of the system. Our findings are that the approach is in principle feasible. We compared the use of a deep neural network and a random forest. Both can achieve great fitting precision, although the latter performs much faster and requires less training data. Even though the machine learning based time transfer function shows differences in the rates of change in system state from those calculated using expert knowledge in Karssenberg et al. (2017), forward simulation appeared to be possible with system behaviour generally in line with that observed in the data from the hyper resolution model. Our findings indicate that discovery of time transfer functions from data is possible. Next steps need to involve the use observational data (e.g., from remote sensing) to test the approach using data from real-world systems.

 

Karssenberg, D., Bierkens, M.F.P., Rietkerk, M., Catastrophic Shifts in Semiarid Vegetation-Soil Systems May Unfold Rapidly or Slowly. The American Naturalist 2017. Vol. 190, pp. E145–E155.

How to cite: Pomarol Moya, O. and Karssenberg, D.: Machine learning for data driven discovery of time transfer functions in numerical modelling: simulating catastrophic shifts in vegetation-soil systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4454, https://doi.org/10.5194/egusphere-egu23-4454, 2023.

EGU23-4695 | Posters on site | ITS1.13/AS5.2

Development of PBL Parameterization Emulator using Neural Networks 

Jiyeon Jang, Tae-Jin Oh, Sojung An, Wooyeon Park, Inchae Na, and Junghan Kim

Physical parameterization is one of the major components of Numerical Weather Prediction system. In Korean Integrated Model (KIM), physical parameterizations account for about 30 % of the total computation time. There are many studies of developing neural network based emulators to replace and accelerate physics based parameterization. In this study, we develop a planetary boundary layer(PBL) emulator which is based on Shin-Hong (Hong et al., 2006, 2010; Shin and Hong, 2013, 2015) scheme that computes the parameterized effects of vertical turbulent eddy diffusion of momentum, water vapor, and sensible heat fluxes. We compare the emulator performance with Multi-Layer Perceptron (MLP) based architectures: simple MLP, MLP application version, and MLP-mixer(Tolstikhin et al., 2021). MLP application version divides data into several vertical groups for better approximation of each vertical group layers. MLP-mixer is MLP based architecture that performs well in computer vision without using convolution and self-attention. We evaluate the resulting MLP based emulator performance. MLP application version and MLP-mixer showed significant performance improvement over simple MLP.

How to cite: Jang, J., Oh, T.-J., An, S., Park, W., Na, I., and Kim, J.: Development of PBL Parameterization Emulator using Neural Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4695, https://doi.org/10.5194/egusphere-egu23-4695, 2023.

EGU23-4817 | ECS | Posters on site | ITS1.13/AS5.2

Algorithmic optimisation of key parameters of OpenIFS 

Lauri Tuppi, Madeleine Ekblom, Pirkka Ollinaho, and Heikki Järvinen

Numerical weather prediction models contain parameters that are inherently uncertain and cannot be determined exactly. Traditionally, the parameter tuning has been done manually, which can be an extremely labourious task. Tuning the entire model usually requires adjusting a relatively large amount of parameters. In case of manual tuning, the need to balance a number of requirements at the same time can lead the tuning process being a maze of subjective choices. It is, therefore, desirable to have reliable objective approaches for estimation of optimal values and uncertainties of these parameters. In this presentation we present how to optimise 20 key physical parameters having a strong impact on forecast quality. These parameters belong to the Stochastically Perturbed Parameters Scheme in the atmospheric model Open Integrated Forecasting System.

The results show that simultaneous optimisation of O(20) parameters is possible with O(100) algorithm steps using an ensemble of O(20) members, and that the optimised parameters lead to substantial enhancement of predictive skill. The enhanced predictive skill can be attributed to reduced biases in low-level winds and upper-tropospheric humidity in the optimised model. We find that the optimisation process is dependent on the starting values of the parameters that are optimised (starting from better suited values results in a better model). The results also show that the applicability of the tuned parameter values across different model resolutions is somewhat questionable since the model biases seem to be resolution-specific. Moreover, our optimisation algorithm tends to treat the parameter covariances poorly limiting its ability to converge to the global optimum.

How to cite: Tuppi, L., Ekblom, M., Ollinaho, P., and Järvinen, H.: Algorithmic optimisation of key parameters of OpenIFS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4817, https://doi.org/10.5194/egusphere-egu23-4817, 2023.

EGU23-5003 | ECS | Posters on site | ITS1.13/AS5.2

Towards machine-learning calibration of cloud parameters in the kilometre-resolution ICON atmosphere model 

Hannah Marie Eichholz, Jan Kretzschmar, Duncan Watson-Parris, Josefine Umlauft, and Johannes Quaas

In the preparation of the global kilometre-resolution coupled ICON climate model, it is necessary to calibrate cloud microphysical parameters. Here we explore the avenue towards optimally calibrating such parameters using machine learning. The emulator developed by Watson-Parris et al. (2021) is employed in combination with a perturbed-parameter ensemble of limited-area atmosphere-only ICON simulations for the North Atlantic ocean. In a first step, the autoconversion scaling parameter is calibrated, using satellite-retrieved top-of-atmosphere and bottom-of-atmosphere radiation fluxes. For this purpose, limited area simulations of the north atlantic are performed with ICON. In which different cloud microphysical parameters are changed, in order to evaluate possible influences on the output of radiation fluxes.

How to cite: Eichholz, H. M., Kretzschmar, J., Watson-Parris, D., Umlauft, J., and Quaas, J.: Towards machine-learning calibration of cloud parameters in the kilometre-resolution ICON atmosphere model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5003, https://doi.org/10.5194/egusphere-egu23-5003, 2023.

EGU23-5149 | ECS | Posters on site | ITS1.13/AS5.2

Machine Learning Parameterization for Super-droplet Cloud Microphysics Scheme 

Shivani Sharma and David Greenberg

Machine learning approaches have been widely used for improving the representation of subgrid scale parameterizations in Earth System Models. In our study we target the Cloud Microphysics parameterization, in particular the two-moment bulk scheme of the ICON (Icosahedral Non-hydrostatic) Model. 

 

Cloud microphysics parameterization schemes suffer from an accuracy/speed tradeoff. The simplest schemes, often heavy with assumptions (such as the bulk moment schemes) are most common in operational weather prediction models. Conversely, the more complex schemes with fewer assumptions –e.g. Lagrangian schemes such as the super-droplet method (SDM)– are computationally expensive and used only within research and development. SDM allows easy representation of complex scenarios with multiple hydrometeors and can also be used for simulating cloud-aerosol interactions. To bridge this gap and to make the use of more complex microphysical schemes feasible within operational models, we use a data-driven approach. 

 

Here we train a neural network to mimic the behavior of SDM simulations in a warm-rain scenario in a dimensionless control volume. The network behaves like a dynamical system that converts cloud droplets to rain droplets–represented as bulk moments–with only the current system state as the input. We use a multi-step training loss to stabilize the network over long integration periods, especially in cases with extremely low cloud water to start with. We find that the network is stable across various initial conditions and in many cases, emulates the SDM simulations better than the traditional bulk moment schemes. Our network also performs better than any previous ML-based attempts to learn from SDM. This opens the possibility of using the trained network as a proxy for imitating the computationally expensive SDM within operational weather prediction models with minimum computational overhead. 

How to cite: Sharma, S. and Greenberg, D.: Machine Learning Parameterization for Super-droplet Cloud Microphysics Scheme, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5149, https://doi.org/10.5194/egusphere-egu23-5149, 2023.

EGU23-5523 | ECS | Orals | ITS1.13/AS5.2

Using weak constrained neural networks to improve simulations in the gray zone 

Yvonne Ruckstuhl, Raphael Kriegmair, Stephan Rasp, and George Craig

Machine learning represents a potential method to cope with the gray zone problem of representing motions in dynamical systems on scales comparable to the model resolution. Here we explore the possibility of using a neural network to directly learn the error caused by unresolved scales. We use a modified shallow water model which includes highly nonlinear processes mimicking atmospheric convection. To create the training dataset, we run the model in a high- and a low-resolution setup and compare the difference after one low-resolution time step, starting from the same initial conditions, thereby obtaining an exact target. The neural network is able to learn a large portion of the difference when evaluated on single time step predictions on a validation dataset. When coupled to the low-resolution model, we find large forecast improvements up to 1 d on average. After this, the accumulated error due to the mass conservation violation of the neural network starts to dominate and deteriorates the forecast. This deterioration can effectively be delayed by adding a penalty term to the loss function used to train the ANN to conserve mass in a weak sense. This study reinforces the need to include physical constraints in neural network parameterizations.

How to cite: Ruckstuhl, Y., Kriegmair, R., Rasp, S., and Craig, G.: Using weak constrained neural networks to improve simulations in the gray zone, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5523, https://doi.org/10.5194/egusphere-egu23-5523, 2023.

EGU23-5766 | ECS | Orals | ITS1.13/AS5.2

Best Practices for Fortran-Python Bridges to Integrate Neural Networks in Earth System Models 

Caroline Arnold, Shivani Sharma, Tobias Weigel, and David Greenberg

In recent years, machine learning (ML) based parameterizations have become increasingly common in Earth System Models (ESM). Sub-grid scale physical processes that would be computationally too expensive, e.g., atmospheric chemistry and cloud microphysics, can be emulated by ML algorithms such as neural networks.

Neural networks are trained first on simulations of the sub-grid scale process that is to be emulated. They are then used in so-called inference mode to make predictions during the ESM run, replacing the original parameterization. Training usually requires GPUs, while inference may be done on CPU architectures.

At first, neural networks are evaluated offline, i.e., independently of the ESM on appropriate datasets. However, their performance can ultimately only be evaluated in an online setting, where the ML algorithm is coupled to the ESM, including nonlinear interactions.

We want to shorten the time spent in neural network development and offline testing and move quickly to online evaluation of ML components in our ESM of choice, ICON (Icosahedral Nonhydrostatic Weather and Climate Model). Since ICON is written in Fortran, and modern ML algorithms are developed in the Python ecosystem, this requires efficient bridges between the two programming languages. The Fortran-Python bridge must be flexible to allow for iterative development of the neural network. Changes to the ESM codebase should be as few as possible, and the runtime overhead should not limit development.

In our contribution we explore three strategies to call the neural network inference from within Fortran using (i) embedded Python code compiled in a dynamic library, (ii) pipes, and (iii) MPI using the ICON coupler YAC. We provide quantitative benchmarks for the proposed Fortran-Python bridges and assess their overall suitability in a qualitative way to derive best practices. The Fortran-Python bridge enables scientists and developers to evaluate ML components in an online setting, and can be extended to other parameterizations and ESMs.

How to cite: Arnold, C., Sharma, S., Weigel, T., and Greenberg, D.: Best Practices for Fortran-Python Bridges to Integrate Neural Networks in Earth System Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5766, https://doi.org/10.5194/egusphere-egu23-5766, 2023.

EGU23-6287 | Orals | ITS1.13/AS5.2

Approximation and Optimization of Atmospheric Simulations in High Spatio-Temporal Resolution with Neural Networks 

Elnaz Azmi, Jörg Meyer, Marcus Strobl, Michael Weimer, and Achim Streit

Accurate forecasts of the atmosphere demand large-scale simulations with high spatio-temporal resolution. Atmospheric chemistry modeling, for example, usually requires solving a system of hundreds of coupled ordinary partial differential equations. Due to the computational complexity, large high performance computing resources are required, which is a challenge as the spatio-temporal resolution increases. Machine learning methods and specially deep learning can offer an approximation of the simulations with some factor of speed-up while using less compute resources. The goal of this study is to investigate the feasibility, opportunities but also challenges and pitfalls of replacing the compute-intensive chemistry of a state-of-the-art atmospheric chemistry model with a trained neural network model to forecast the concentration of trace gases at each grid cell and to reduce the computational complexity of the simulation. In this work, we introduce a neural network model (ICONET) to forecast trace gas concentrations without executing the traditional compute-intensive atmospheric simulations. ICONET is equipped with a multifeature Long Short Term Memory (LSTM) model to forecast atmospheric chemicals iteratively in time. We generated the training and test dataset, our ground truth for ICONET, by execution of an atmospheric chemistry simulation in ICON-ART. Applying the ICONET trained model to forecast a test dataset results in a good fit of the forecast values compared to our ground truth dataset. We discuss appropriate metrics to evaluate the quality of models and present the quality of the ICONET forecasts with RMSE and KGE metrics. The variety in the nature of trace gases limits the model's learning and forecast skills according to the variable. In addition to the quality of the ICONET forecasts, we described the computational efficiency of ICONET as its run time speed-up in comparison to the run time of the ICON-ART simulation. The ICONET forecast showed a speed-up factor of 3.1 over the run time of the atmospheric chemistry simulation of ICON-ART, which is a significant achievement, especially when considering the importance of ensemble simulations.

How to cite: Azmi, E., Meyer, J., Strobl, M., Weimer, M., and Streit, A.: Approximation and Optimization of Atmospheric Simulations in High Spatio-Temporal Resolution with Neural Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6287, https://doi.org/10.5194/egusphere-egu23-6287, 2023.

EGU23-6836 | ECS | Posters on site | ITS1.13/AS5.2

Parameterising melt at the base of Antarctic ice shelves with a feedforward neural network 

Clara Burgard, Nicolas C. Jourdain, Pierre Mathiot, and Robin Smith

One of the largest sources of uncertainty when projecting the Antarctic contribution to sea-level rise is the ocean-induced melt at the base of Antarctic ice shelves. This is because resolving the ocean circulation and the ice-ocean interactions occurring in the cavity below the ice shelves is computationally expensive.

Instead, for large ensembles and long-term projections of the ice-sheet evolution, ice-sheet models currently rely on parameterisations to link the ocean temperature and salinity in front of ice shelves to the melt at their base. However, current physics-based parameterisations struggle to accurately simulate basal melt patterns.

As an alternative approach, we explore the potential use of a deep feedforward neural network as a basal melt parameterisation. To do so, we train a neural network to emulate basal melt rates simulated by highly-resolved circum-Antarctic ocean simulations. We explore the influence of different input variables and show that the neural network struggles to generalise to ice-shelf geometries unseen during training, while it generalises better on timesteps unseen during training. We also test the parameterisation on separate coupled ocean-ice simulations to assess the neural network’s performance on independent data.  

How to cite: Burgard, C., Jourdain, N. C., Mathiot, P., and Smith, R.: Parameterising melt at the base of Antarctic ice shelves with a feedforward neural network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6836, https://doi.org/10.5194/egusphere-egu23-6836, 2023.

EGU23-7281 | ECS | Posters on site | ITS1.13/AS5.2

Neural network surrogate models for multiple scattering: Application to OMPS LP simulations 

Michael Himes, Natalya Kramarova, Tong Zhu, Jungbin Mok, Matthew Bandel, Zachary Fasnacht, and Robert Loughman

Retrieving ozone from limb measurements necessitates the modeling of scattered light through the atmosphere.  However, accurately modeling multiple scattering (MS) during retrieval requires excessive computational resources; consequently, operational retrieval models employ approximations in lieu of the full MS calculation.  Here we consider an alternative MS approximation method, where we use radiative transfer (RT) simulations to train neural network models to predict the MS radiances.  We present our findings regarding the best-performing network hyperparameters, normalization schemes, and input/output data structures.  Using RT calculations based on measurements by the Ozone Mapping and Profiling Suite's Limb Profiler (OMPS/LP), we compare the accuracy of these neural-network models with both the full MS calculation as well as the current MS approximation methods utilized during OMPS/LP retrievals.

How to cite: Himes, M., Kramarova, N., Zhu, T., Mok, J., Bandel, M., Fasnacht, Z., and Loughman, R.: Neural network surrogate models for multiple scattering: Application to OMPS LP simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7281, https://doi.org/10.5194/egusphere-egu23-7281, 2023.

EGU23-7368 | ECS | Posters on site | ITS1.13/AS5.2

Comparison of Methods for Learning Differential Equations from Data 

Christof Schötz

Some results from the DEEB (Differential Equation Estimation Benchmark) are presented. In DEEB, we compare different machine learning approaches and statistical methods for estimating nonlinear dynamics from data. Such methods constitute an important building block for purely data-driven earth system models as well as hybrid models which combine physical knowledge with past observations.

Specifically, we examine approaches for solving the following problem: Given time-state-observations of a deterministic ordinary differential equation (ODE) with measurement noise in the state, predict the future evolution of the system. Of particular interest are systems with chaotic behavior - like Lorenz 63 - and nonparametric settings, in which the functional form of the ODE is completely unknown (in particular, not restricted to a polynomial of low order). To create a fair comparison of methods, a benchmark database was created which includes datasets of simulated observations from different dynamical systems with different complexity and varying noise levels. The list of methods we compare includes: echo state networks, Gaussian processes, Neural ODEs, SINDy, thin plate splines, and more.

Although some methods consistently perform better than others throughout different datasets, there seems to be no silver bullet.

How to cite: Schötz, C.: Comparison of Methods for Learning Differential Equations from Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7368, https://doi.org/10.5194/egusphere-egu23-7368, 2023.

EGU23-7391 | ECS | Posters on site | ITS1.13/AS5.2

Learning fluid dynamical statistics using stochastic neural networks 

Martin Brolly
Many practical problems in fluid dynamics demand an empirical approach, where statistics estimated from data inform understanding and modelling. In this context data-driven probabilistic modelling offers an elegant alternative to ad hoc estimation procedures. Probabilistic models are useful as emulators, but also offer an attractive means of estimating particular statistics of interest. In this paradigm one can rely on proper scoring rules for model comparison and validation, and invoke Bayesian statistics to obtain rigorous uncertainty quantification. Stochastic neural networks provide a particularly rich class of probabilistic models, which, when paired with modern optimisation algorithms and GPUs, can be remarkably efficient. We demonstrate this approach by learning the single particle transition density of ocean surface drifters from decades of Global Drifter Program observations using a Bayesian mixture density network. From this we derive maps of various displacement statistics and corresponding uncertainty maps. Our model also offers a means of simulating drifter trajectories as a discrete-time Markov process, which could be used to study the transport of plankton or plastic in the upper ocean.

How to cite: Brolly, M.: Learning fluid dynamical statistics using stochastic neural networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7391, https://doi.org/10.5194/egusphere-egu23-7391, 2023.

EGU23-7492 | Posters on site | ITS1.13/AS5.2

Machine Learning and Microseism as a Tool for Sea Wave Monitoring 

Flavio Cannavo', Vittorio Minio, Susanna Saitta, Salvatore Alparone, Alfio Marco Borzì, Andrea Cannata, Giuseppe Ciraolo, Danilo Contrafatto, Sebastiano D’Amico, Giuseppe Di Grazia, and Graziano Larocca

Monitoring the state of the sea is a fundamental task for economic activities in the coastal zone, such as transport, tourism and infrastructure design. In recent years, regular wave height monitoring for marine risk assessment and mitigation has become unavoidable as global warming impacts in more intense and frequent swells.
In particular, the Mediterranean Sea has been considered as one of the most responsive regions to global warming, which may promote the intensification of hazardous natural phenomena as strong winds, heavy precipitation and high sea waves. Because of the high density population along the Mediterranean coastlines, heavy swells could have major socio-economic consequences. To reduce the impacts of such scenarios, the development of more advanced monitoring systems of the sea state becomes necessary.
In the last decade, it has been demonstrated how seismometers can be used to measure sea conditions by exploiting the characteristics of a part of the seismic signal called microseism. Microseism is the continuous seismic signal recorded in the frequency band of 0.05 and 0.4 Hz that is likely generated by interactions of sea waves together and with seafloor or shorelines.
In this work, in the framework of i-WaveNET INTERREG project, we performed a regression analysis to develop a model capable of predicting the sea state in the Sicily Channel (Italy) using microseism, acquired by onshore instruments installed in Sicily and Malta. Considering the complexity of the relationship between spatial sea wave height data and seismic data measured at individual stations, we used supervised machine learning (ML) techniques to develop the prediction model. As input data we used the hourly Root Mean Squared (RMS) amplitude of the seismic signal recorded by 14 broadband stations, along the three components, and in different frequency bands, during 2018 - 2021. These stations, belonging to the permanent seismic networks managed by the National Institute of Geophysics and Volcanology INGV and the Department of Geosciences of the University of Malta, consist of three-component broadband seismometers that record at a sampling frequency of 100 Hz.
As for the target, the significant sea wave height data from Copernicus Marine Environment Monitoring Service (CMEMS) for the same period were used. Such data is the hindcast product of the Mediterranean Sea Waves forecasting system, with hourly temporal resolution and 1/24° spatial resolution. After a feature selection step, we compared three different kinds of ML algorithms for regression: K-Nearest-Neighbors (KNN), Random Forest (RF) and Light Gradient Boosting (LGB). The hyperparameters were tuned by using a grid-search algorithm, and the best models were selected by cross-validation.  Different metrics, such as MAE, R2 and RMSE, were considered to evaluate the generalization capabilities of the models and special attention was paid to evaluate the predictive ability of the models for extreme wave height values.
Results show model predictive capabilities good enough to develop a sea monitoring system to complement the systems currently in use.

How to cite: Cannavo', F., Minio, V., Saitta, S., Alparone, S., Borzì, A. M., Cannata, A., Ciraolo, G., Contrafatto, D., D’Amico, S., Di Grazia, G., and Larocca, G.: Machine Learning and Microseism as a Tool for Sea Wave Monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7492, https://doi.org/10.5194/egusphere-egu23-7492, 2023.

EGU23-7561 | ECS | Posters on site | ITS1.13/AS5.2

Deep Learning guided statistical downscaling of climate projections for use in hydrological impact modeling in Danish peatlands 

Thea Quistgaard, Peter L. Langen, Tanja Denager, Raphael Schneider, and Simon Stisen

A course of action to combat the emission of greenhouse gasses (GHG) in a Danish context is to re-wet previously drained peatlands and thereby return them to their natural hydrological state acting as GHG sinks. GHG emissions from peatlands are known to be closely coupled to the hydrological dynamics through the groundwater table depth (WTD). To understand the effect of a changing and variable climate on the spatio-temporal dynamics of hydrological processes and the associated uncertainties, we aim to produce a high-resolution local-scale climate projection ensemble from the global-scale CMIP6 projections.

With focus on hydrological impacts, uncertainties and possible extreme endmembers, this study aims to span the full ensemble of local-scale climate projections in the Danish geographical area corresponding to the CMIP6-ensemble of Global Climate Models (GCMs). Deep learning founded statistical downscaling methods are applied bridge the gap from GCMs to local-scale climate change and variability, which in turn will be used in field-scale hydrological modeling. The approach is developed to specifically accommodate the resolutions, event types and conditions relevant for assessing the impacts on peatland GHG emissions through their relationship with WTD dynamics by applying stacked conditional generative adversarial networks (CGANs) to best downscale precipitation, temperature, and evaporation. In the future, the approach is anticipated to be extended to directly assess the impacts of climate change and ensemble uncertainty on peatland hydrology variability and extremes.

How to cite: Quistgaard, T., Langen, P. L., Denager, T., Schneider, R., and Stisen, S.: Deep Learning guided statistical downscaling of climate projections for use in hydrological impact modeling in Danish peatlands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7561, https://doi.org/10.5194/egusphere-egu23-7561, 2023.

EGU23-8288 | Orals | ITS1.13/AS5.2

Learning operational altimetry mapping from ocean models 

Quentin Febvre, Ronan Fablet, Julien Le Sommer, Clément Ubelmann, and Simon Benaïchouche

In oceanography, altimetry products are used to measure the height of the ocean surface, and ocean modeling is used to understand and predict the behavior of the ocean. There are two main types of gridded altimetry products: operational sea level products, such as DUACS, which are used for forecasting and reconstruction, and ocean model reanalyses, such as Glorys 12, which are used to forecast seasonal trends and assess physical characteristics. However, advances in ocean modeling do not always directly benefit operational forecast or reconstruction products.

In this study, we investigate the potential for deep learning methods, which have been successfully applied in simulated setups, to leverage ocean modeling efforts for improving operational altimetry products. Specifically, we ask under what conditions the knowledge learned from ocean simulations can be applied to real-world operational altimetry mapping. We consider the impact of simulation grid resolution, observation data reanalysis, and physical processes modeled on the performance of a deep learning model.

Our results show that the deep learning model outperforms current operational methods on a regional domain around the Gulfstream, with a 50km improvement in resolved scale. This improvement has the potential to enhance the accuracy of operational altimetry products, which are used for a range of important applications, such as climate monitoring and understanding mesoscale ocean dynamics.

How to cite: Febvre, Q., Fablet, R., Le Sommer, J., Ubelmann, C., and Benaïchouche, S.: Learning operational altimetry mapping from ocean models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8288, https://doi.org/10.5194/egusphere-egu23-8288, 2023.

EGU23-9285 | ECS | Orals | ITS1.13/AS5.2

Stabilized Neural Differential Equations for Hybrid Modeling with Conservation Laws 

Alistair White and Niklas Boers

Neural Differential Equations (NDEs) provide a powerful framework for hybrid modeling. Unfortunately, the flexibility of the neural network component of the model comes at the expense of potentially violating known physical invariants, such as conservation laws, during inference. This shortcoming is especially critical for applications requiring long simulations, such as climate modeling, where significant deviations from the physical invariants can develop over time. It is hoped that enforcing physical invariants will help address two of the main barriers to adoption for hybrid models in climate modeling: (1) long-term numerical stability, and (2) generalization to out-of-sample conditions unseen during training, such as climate change scenarios. We introduce Stabilized Neural Differential Equations, which augment an NDE model with compensating terms that ensure physical invariants remain approximately satisfied during numerical simulations. We apply Stabilized NDEs to the double pendulum and Hénon–Heiles systems, both of which are conservative, chaotic dynamical systems possessing a time-independent Hamiltonian. We evaluate Stabilized NDEs using both short-term and long-term prediction tasks, analogous to weather and climate prediction, respectively. Stabilized NDEs perform at least as well as unstabilized models at the “weather prediction” task, that is, predicting the exact near-term state of the system given initial conditions. On the other hand, Stabilized NDEs significantly outperform unstabilized models at the “climate prediction” task, that is, predicting long-term statistical properties of the system. In particular, Stabilized NDEs conserve energy during long simulations and consequently reproduce the long-term dynamics of the target system with far higher accuracy than non-energy conserving models. Stabilized NDEs also remain numerically stable for significantly longer than unstabilized models. As well as providing a new and lightweight method for combining physical invariants with NDEs, our results highlight the relevance of enforcing conservation laws for the long-term numerical stability and physical accuracy of hybrid models.

How to cite: White, A. and Boers, N.: Stabilized Neural Differential Equations for Hybrid Modeling with Conservation Laws, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9285, https://doi.org/10.5194/egusphere-egu23-9285, 2023.

EGU23-10135 | ECS | Orals | ITS1.13/AS5.2

Exploring physics-informed machine learning for accelerated simulation of permafrost processes 

Brian Groenke, Moritz Langer, Guillermo Gallego, and Julia Boike

Permafrost, i.e. ground material that remains perennially frozen, plays a key role in Arctic ecosystems. Monitoring the response of permafrost to rapid climate change remains difficult due to the sparse availability of long-term, high quality measurements of the subsurface. Numerical models are therefore an indispensable tool for understanding the evolution of Arctic permafrost. However, large scale simulation of the hydrothermal processes affecting permafrost is challenging due to the highly nonlinear effects of phase change in porous media. The resulting computational cost of such simulations is especially prohibitive for sensitivity analysis and parameter estimation tasks where a large number of simulations may be necessary for robust inference of quantities such as temperature, water fluxes, and soil properties. In this work, we explore the applicability of recently developed physics-informed machine learning (PIML) methods for accelerating numerical models of permafrost hydrothermal dynamics. We present a preliminary assessment of two possible applications of PIML in this context: (1) linearization of the nonlinear PDE system according to Koopman operator theory in order to reduce the computational burden of large scale simulations, and (2) efficient parameterization of the surface energy balance and snow dynamics on the subsurface hydrothermal regime. By combining the predictive power of machine learning with the underlying conservation laws, PIML can potentially enable researchers and practitioners interested in permafrost to explore complex process interactions at larger spatiotemporal scales.

How to cite: Groenke, B., Langer, M., Gallego, G., and Boike, J.: Exploring physics-informed machine learning for accelerated simulation of permafrost processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10135, https://doi.org/10.5194/egusphere-egu23-10135, 2023.

EGU23-10256 | ECS | Posters on site | ITS1.13/AS5.2

Foehn Wind Analysis using Unsupervised Deep Anomaly Detection 

Tobias Milz, Marte Hofsteenge, Marwan Katurji, and Varvara Vetrova

Foehn winds are accelerated, warm and dry winds that can have significant environmental impacts as they descend into the lee of a mountain range. For example, in the McMurdo Dry Valleys in Antarctica, foehn events can cause ice and glacial melt and destabilise ice shelves, which if lost, resulting in a rise in sea level. Consequently, there is a strong interest in a deeper understanding of foehn winds and their meteorological signatures. Most current automatic detection methods rely on rule-based methodologies that require static thresholds of meteorological parameters. However, the patterns of foehn winds are hard to define and differ between alpine valleys around the world. Consequently, data-driven solutions might help create more accurate detection and prediction methodologies. 

State-of-the-art machine learning approaches to this problem have shown promising results but follow a supervised learning paradigm. As such, these approaches require accurate labels, which for the most part, are being created by imprecise static rule-based algorithms. Consequently, the resulting machine-learning models are trained to recognise the same static definitions of the foehn wind signatures. 

In this paper, we introduce and compare the first unsupervised machine-learning approaches for detecting foehn wind events. We focus on data from the Mc Murdo Dry Valleys as an example, however, due to the unsupervised nature of these approaches, our solutions can recognise a more dynamic definition of foehn wind events and are therefore, independent of the location. The first approach is based on multivariate time-series clustering, while the second utilises a deep autoencoder-based anomaly detection method to identify foehn wind events. Our best model achieves an f1-score of 88%, matching or surpassing previous machine-learning methods while providing a more flexible and inclusive definition of foehn events. 

How to cite: Milz, T., Hofsteenge, M., Katurji, M., and Vetrova, V.: Foehn Wind Analysis using Unsupervised Deep Anomaly Detection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10256, https://doi.org/10.5194/egusphere-egu23-10256, 2023.

EGU23-10351 | ECS | Orals | ITS1.13/AS5.2

Deep learning of systematic sea ice model errors from data assimilation increments 

William Gregory, Mitchell Bushuk, Alistair Adcroft, and Yongfei Zhang

Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short-term corrections, or analysis increments, closely mirror the systematic bias patterns of the dynamical model. In this work, we show that Convolutional Neural Networks (CNNs) can be used to learn a mapping from model state variables to analysis increments, thus promoting the feasibility of a data-driven model parameterization which predicts state-dependent model errors. We showcase this problem using an ice-ocean data assimilation system within the fully coupled Seamless system for Prediction and EArth system Research (SPEAR) model at the Geophysical Fluid Dynamics Laboratory (GFDL), which assimilates satellite observations of sea ice concentration. The CNN then takes inputs of data assimilation forecast states and tendencies, and makes predictions of the corresponding sea ice concentration increments. Specifically, the inputs are sea ice concentration, sea-surface temperature, ice velocities, ice thickness, net shortwave radiation, ice-surface skin temperature, and sea-surface salinity. We show that the CNN is able to make skilful predictions of the increments, particularly between December and February in both the Arctic and Antarctic, with average daily spatial pattern correlations of 0.72 and 0.79, respectively. Initial investigation of implementation of the CNN into the fully coupled SPEAR model shows that the CNN can reduce biases in retrospective seasonal sea ice forecasts by emulating a data assimilation system, further suggesting that systematic sea ice biases could be reduced in a free-running climate simulation.

How to cite: Gregory, W., Bushuk, M., Adcroft, A., and Zhang, Y.: Deep learning of systematic sea ice model errors from data assimilation increments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10351, https://doi.org/10.5194/egusphere-egu23-10351, 2023.

Current numerical weather prediction models contain significant systematic errors, due in part to indeterminate ground forcing (GF). This study considers an optimal virtual GF (GFo) derived by training observed and simulated datasets of 10-m wind speeds (WS10) for summer and winter. The GFo is added to an offline surface multilayer model (SMM) to revise predictions of WS10 in China by the Weather Research and Forecasting model (WRF). This revision is a data-based optimization under physical constraints. It reduces WS10 errors and offers wide applicability. The resulting model outperforms two purely physical forecasts (the original WRF forecast and the SMM with physical GF parameterized using urban, vegetation, and subgrid topography) and two purely data-based revisions (i.e., multilinear regression and multilayer perceptron). Compared with original WRF forecasting, using the GFo scheme reduces the Root Mean Square Error (RMSE) in WS10 across China by 25% in summer and 32% in winter. The frontal area index of GFo indicates that it includes both the effects of indeterminate GF and other possible complex physical processes associated with WS10.

How to cite: Feng, J.: Mitigate forecast error in surface wind speed using an offline single-column model with optimal ground forcing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10394, https://doi.org/10.5194/egusphere-egu23-10394, 2023.

EGU23-10726 | Posters virtual | ITS1.13/AS5.2

A hybrid VMD-WT-InceptionTime model for multi-horizon short-term air temperature forecasting in Alaska 

Jaakko Putkonen, M. Aymane Ahajjam, Timothy Pasch, and Robert Chance

The lack of ground level observation stations outside of settlements makes monitoring and forecasting local weather and permafrost challenging in the Arctic. Such predictive pieces of information are essential to help prepare for potentially hazardous weather conditions, especially during winter. In this study, we aim at enhancing predictive analytics in Alaska of permafrost and temperature by using a hybrid forecasting technique. In particular, we propose VMD-WT-InceptionTime model for short-term air temperature forecasting.

This proposed technique incorporates data preprocessing techniques and deep learning to enhance the accuracy of the next seven days air temperature forecasts. Initially, the Spearman correlation coefficient is utilized to examine the relationship between different inputs and the forecast target temperature. Following this, Variational Mode Decomposition (VMD) is used to decompose the most output-correlated input variables (i.e., temperature and relative humidity) to extract intrinsic and non-stationary time-frequency features from the original sequences. The Wavelet Transform (WT) is then employed to further extract intrinsic multi-resolution patterns from these decomposed input variables. Finally, a deep InceptionTime model is used for multi-step air temperature forecasting using these processed sequences. This forecasting technique was developed using an open dataset holding 20+ years of data from three locations in Alaska: North Slope, Alaska, Arctic National Wildlife Refuge, Alaska, and Diomede Island region, Bering Strait. Model performance has been rigorously evaluated of metrics including RMSE, MAPE and error.

Results highlight the effectiveness of the proposed hybrid model in providing more accurate short-term forecasts than several baselines (GBDT, SVR, ExtraTrees, RF, ARIMA, LSTM, GRU, and Transformer). More specifically, this technique reported RMSE and MAPE average increase rates amounting to 11.21% and 16.13% in North Slope, 30.01% and 34.97% in Arctic National Wildlife Refuge, and 16.39%, 23.46% in Diomede Island region. In addition, the proposed technique produces forecasts over all seven horizons with a maximum error of <1.5K, a minimum error of >-1.2K, and an average error lower than 0.18K for North Slope. For Arctic National Wildlife Refuge, a maximum error of <1K, a minimum error of >-0.9K, and an average of < 0.1K. While a maximum error of <0.9K, a minimum error of >-0.8K, and an average of <0.13K, for Diomede Island region. However, the worst performances achieved were errors of around 6K in the third horizon (i.e., 3rd day) for North Slope and the Arctic National Wildlife Refuge and the last horizon (i.e., 7th day) for the Diomede Islands region. Most of the worst performances of the proposed technique in all three locations can be attributed to having to produce forecasts of higher variations and wider temperature ranges than their averages.

Overall, this research highlights the potential of the decomposition techniques and deep learning to: 1) reveal and effectively learn the underlying cyclicity of air temperatures at varying resolutions that allows for accurate predictions without any knowledge of the governing physics, 2) produce accurate multi-step temperature forecasts in Arctic climates.

How to cite: Putkonen, J., Ahajjam, M. A., Pasch, T., and Chance, R.: A hybrid VMD-WT-InceptionTime model for multi-horizon short-term air temperature forecasting in Alaska, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10726, https://doi.org/10.5194/egusphere-egu23-10726, 2023.

EGU23-10810 | ECS | Orals | ITS1.13/AS5.2

Oceanfourcast: Emulating Ocean Models with Transformers for Adjoint-based Data Assimilation 

Suyash Bire, Björn Lütjens, Dava Newman, and Chris Hill

Adjoints have become a staple of the oceanic and atmospheric numerical modeling community over the past couple of decades as they are useful for tuning of dynamical models, sensitivity analyses, and data assimilation. One such application is generation of reanalysis datasets, which provide an optimal record of our past weather, climate, and ocean. For example, the state-of-the-art ocean-ice renanalysis dataset, ECCO, is created by optimally combining a numerical ocean model with heterogeneous observations through a technique called data assimilation. Data assimilation in ECCO minimizes the distance between model and observations by calculating adjoints, i.e., gradients of the loss w.r.t. simulation forcing fields (wind and surface heat fluxes). The forcing fields are iteratively updated and the model is rerun until the loss is minimized to ensure that the numerical model does not drastically deviate from the observations. Calculating adjoints, however, either requires  disproportionately high computational resources  or rewriting the dynamical model code to be autodifferentiable. 

Therefore, we ask if deep learning-based emulators can provide fast and accurate adjoints. Ocean data is smooth, high-dimensional, and has complex spatiotemporal correlations. Therefore, as an initial foray into ocean emulators, we leverage a combination of neural operators and transformers. Specifically, we have adapted the FourCastNet architecture, which has successfully emulated ERA5 weather data in seconds rather than hours, to emulate an idealized ocean simulation.

We generated a ground-truth dataset by simulating a double-gyre, an idealized representation of the North Atlantic Ocean, using MITgcm, a state-of-the-art dynamical model. The model was forced by zonal wind at the surface and relaxation to a meridional profile of temperature — warm/cold temperatures at low/high latitudes. This simulation produced turbulent western boundary currents embedded in the large-scale gyre circulation. We performed 4 additional simulations by modifying the magnitude of SST relaxation and wind forcing to introduce diversity in the dataset. From these simulations, we used 4 state variables (meridional and zonal surface velocities, pressure, and temperature) as well as the forcing fields (zonal wind velocity and relaxation SST profile) sampled in 10-day steps. The dataset was split into training, validation, and test datasets such that validation and test datasets were unseen during training. These datasets provide an ideal testbed for evaluating and comparing the performance of data-driven ocean emulators.

We used this data to train and evaluate Oceanfourcast. Our initial results in the following figure show that our model, Oceanfourcast, can successfully predict the streamfunction and pressure for a lead time of 1 month. 

We are currently working on generating adjoints from Oceanfourcast.  We expect the adjoint calculation to require significantly less compute time than that from a full-scale dynamical model like MITgcm.  Our work shows a promising path towards deep-learning augmented data assimilation and uncertainty quantification.

How to cite: Bire, S., Lütjens, B., Newman, D., and Hill, C.: Oceanfourcast: Emulating Ocean Models with Transformers for Adjoint-based Data Assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10810, https://doi.org/10.5194/egusphere-egu23-10810, 2023.

EGU23-10904 | ECS | Posters on site | ITS1.13/AS5.2

On the choice of turbulence eddy fluxes to learn from in data-driven methods 

Feier Yan, Julian Mak, and Yan Wang

Recent works have demonstrated the viability of employing data-driven / machine learning 
methods for the purposes of learning more about ocean turbulence, with applications to turbulence parameterisations in ocean general circulation models. Focusing on mesoscale geostrophic turbulence in the ocean context, works thus far have mostly focused on the choice of algorithms and testing of trained up models. Here we focus instead on the choice of eddy flux data to learn from. We argue that, for mesoscale geostrophic turbulence, it might be beneficial from a theoretical as well as practical point of view to learn from eddy fluxes with dynamically inert rotational fluxes removed (ideally in a gauge invariant fashion), instead of the divergence of the eddy fluxes as has been considered thus far. Outlooks for physically constrained and interpretable machine learning will be given in light of the results. 

How to cite: Yan, F., Mak, J., and Wang, Y.: On the choice of turbulence eddy fluxes to learn from in data-driven methods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10904, https://doi.org/10.5194/egusphere-egu23-10904, 2023.

EGU23-10959 | Orals | ITS1.13/AS5.2

Deep learning parameterization of small-scale vertical velocity variability for atmospheric models 

Donifan Barahona, Katherine Breen, and Heike Kalesse-Los

Small-scale fluctuations in vertical wind velocity, unresolved by climate and weather forecast models play a particularly important role in determining vapor and tracer fluxes, turbulence and cloud formation. Fluctuations in vertical wind velocity are challenging to represent since they depend on orography, large scale circulation features, convection and wind shear. Parameterizations developed using data retrieved at specific locations typically lack generalization and may introduce error when applied on a wide range of different conditions. Retrievals of vertical wind velocity are also difficult and subject to large uncertainty. This work develops a new data-driven, neural network representation of subgrid scale variability in vertical wind velocity. Using a novel deep learning technique, the new parameterization merges data from high-resolution global cloud resolving model simulations with high frequency Radar and Lidar retrievals.  Our method aims to reproduce observed statistics rather than fitting individual measurements. Hence it is resilient to experimental uncertainty and robust to generalization. The neural network parameterization can be driven by weather forecast and reanalysis products to make real time estimations. It is shown that the new parameterization generalizes well outside of the training data and reproduces much better the statistics of vertical wind velocity than purely data-driven models.

How to cite: Barahona, D., Breen, K., and Kalesse-Los, H.: Deep learning parameterization of small-scale vertical velocity variability for atmospheric models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10959, https://doi.org/10.5194/egusphere-egu23-10959, 2023.

EGU23-11293 | ECS | Posters on site | ITS1.13/AS5.2

National scale agricultural development dynamics under socio-political drivers in Saudi Arabia since 1990 

Ting Li, Oliver López Valencia, Kasper Johansen, and Matthew McCabe

Driven in large part by policy initiatives designed to increase food security and realized via the construction of thousands of center-pivot irrigation fields since the 1970s, agriculture development in Saudi Arabia has undergone tremendous changes. However, little is known about the accurate number, acreage, and the changing dynamics of the fields. To bridge the knowledge gap between the political drivers and in-field response, we leveraged a hybrid machine learning framework by implementing Density-Based Spatial Clustering of Applications with Noise, Convolutional Neural Networks, and Spectral Clustering in a stepwise manner to delineate the center-pivot fields on a national scale in Saudi Arabia using historical Landsat imagery since 1990. The framework achieved producer's and user's accuracies larger than  83.7% and 90.2%, respectively, when assessed against 28,000 manually delineated fields collected from different regions and periods. We explored multi-decadal dynamics of the agricultural development in Saudi Arabia by quantifying the number, acreage, and size distribution of center-pivot fields, along with the first and last detection year of the fields since 1990. The agricultural development in Saudi Arabia experienced four stages, including an initialization stage before 1990, a contraction stage from 1990 to 2010, an expansion stage from 2010 to 2016, and an ongoing contraction stage since 2016. Most of the fields predated 1990, representing over 8,800 km2 in that year, as a result of the policy initiatives to stimulate wheat production, promoting Saudi Arabia as the sixth largest exporter of wheat in the 1980s. A decreasing trend was observed from 1990 to 2010, with an average of 8,011 km2 of fields detected during those two decades, which was a response to the policy initiative implemented to phase-out wheat after 1990. As a consequence of planting fodder crops to promote the dairy industry, the number and extent of fields increased rapidly from 2010 to 2015 and reached its peak in 2016, with 33,961 fields representing 9,400 km2. Agricultural extent has seen a continuous decline since 2016 to a level lower than 1990 values in 2020. This decline has been related to sustainable policy initiatives implemented for the Saudi Vision 2030. There is some evidence of an uptick in 2021 — also observed in an ongoing analysis for 2022 — which might be in response to global influences, such as the COVID-19 pandemic and the more recent conflict in the Ukraine, which has disrupted the international supply of agricultural products. The results provide a historical account of agricultural activity throughout the Kingdom and provide a basis for informed decision-making on sustainable irrigation and agricultural practices, helping to better protect and manage the nation's threatened groundwater resources, and providing insights into the resilience and elasticity of the Saudi Arabian food system to global perturbations.

How to cite: Li, T., López Valencia, O., Johansen, K., and McCabe, M.: National scale agricultural development dynamics under socio-political drivers in Saudi Arabia since 1990, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11293, https://doi.org/10.5194/egusphere-egu23-11293, 2023.

EGU23-11687 | ECS | Orals | ITS1.13/AS5.2

Objectively Determining the Number of Similar Hydrographic Clusters with Unsupervised Machine Learning 

Carola Trahms, Yannick Wölker, and Arne Biastoch

Determining the number of existing water masses and defining their boundaries is subject to ongoing discussion in physical oceanography. Traditionally, water masses are defined manually by experts setting constraints based on experience and previous knowledge about the hydrographic properties describing them. In recent years, clustering, an unsupervised machine learning approach, has been introduced as a tool to determine clusters, i.e., volumes, with similar hydrographic properties without explicitly defining their hydrographic constraints. However, the exact number of clusters to be looked for is set manually by an expert up until now. 

We propose a method that determines a fitting number of clusters for hydrographic clusters in a data driven way. In a first step, the method averages the data in different-sized slices along the time or depth axis as the structure of the hydrographic space changes strongly either in time or depth. Then the method applies clustering algorithms on the averaged data and calculates off-the-shelf evaluation scores (Davies-Bouldin, Calinski-Harabasz, Silhouette Coefficient) for several predefined numbers of clusters. In the last step, the optimal number of clusters is determined by analyzing the cluster evaluation scores across different numbers of clusters for optima or relevant changes in trend. 

For validation we applied this method to the output for the subpolar North Atlantic between 1993 and 1997 of the high-resolution Atlantic Ocean model VIKING20X, in direct exchange with domain experts to discuss the resulting clusters. Due to the change from strong to weak deep convection in these years, the hydrographic properties vary strongly in the time and depth dimension, providing a specific challenge to our methodology. 

Our findings suggest that it is possible to identify an optimal number of clusters using the off-the-shelf cluster evaluation scores that catch the underlying structure of the hydrographic space. The optimal number of clusters identified by our data-driven method agrees with the optimal number of clusters found by expert interviews. These findings contribute to aiding and objectifying water mass definitions across multiple expert decisions, and demonstrate the benefit of introducing data science methods to analyses in physical oceanography.

How to cite: Trahms, C., Wölker, Y., and Biastoch, A.: Objectively Determining the Number of Similar Hydrographic Clusters with Unsupervised Machine Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11687, https://doi.org/10.5194/egusphere-egu23-11687, 2023.

EGU23-11906 | ECS | Orals | ITS1.13/AS5.2

Untapping the potential of geostationary EO data to understand drought impacts with XAI 

Basil Kraft, Gregory Duveiller, Markus Reichstein, and Martin Jung

Ecosystems are affected by extreme climate conditions such as droughts worldwide but we still lack understanding of the involved dynamics. Which factors render an ecosystem more resilient, and on which temporal scales do weather patterns affect vegetation state and physiology? Traditional approaches to tackle such questions involve assumption-based land surface modeling or inversions. Machine learning (ML) methods can provide a complementary perspective on how ecosystems respond to climate in a more data-driven and assumption-free manner. However, ML depends heavily on data, and commonly used observations of vegetation at best contain one observation per day, but most products are provided at 16-daily to monthly temporal resolution. This masks important processes at sub-monthly time scales. In addition, ML models are inherently difficult to interpret, which still limits their applicability for process understanding.

In the present study, we combine modern deep learning models in the time domain with observations from the geostationary Meteosat Second Generation (MSG) satellite, centered over Africa. We model fractional vegetation cover (representing vegetation state) and land surface temperature (as a proxy for water stress) from MSG as a function of meteorology and static geofactors. MSG collects observations at sub-daily frequency, rendering it into an excellent tool to study short- to mid-term land surface processes. Furthermore, we use methods from explainable ML for post-hoc model interpretation to identify meteorological drivers of vegetation dynamics and their interaction with key geofactors.

From the analysis, we expect to gather novel insights into ecosystem response to droughts with high temporal fidelity. Drought response of vegetation can be highly diverse and complex especially in arid to semi-arid regions prevalent in Africa. Also, we assess the potential of explainable machine learning to discover new linkages and knowledge and discuss potential pitfalls of the approach. Explainable machine learning, combined with potent deep learning approaches and modern Earth observation products offers the opportunity to complement assumption-based modeling to predict and understand ecosystem response to extreme climate.

How to cite: Kraft, B., Duveiller, G., Reichstein, M., and Jung, M.: Untapping the potential of geostationary EO data to understand drought impacts with XAI, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11906, https://doi.org/10.5194/egusphere-egu23-11906, 2023.

EGU23-11958 | ECS | Posters on site | ITS1.13/AS5.2

Modelling Soil Temperature and Soil Moisture in Space, Depth, and Time with Machine Learning Techniques 

Maiken Baumberger, Linda Adorf, Bettina Haas, Nele Meyer, and Hanna Meyer

Soil temperature and soil moisture variations have large effects on ecological processes in the soil. To investigate and understand these processes, high-resolution data of soil temperature and soil moisture are required. Here, we present an approach to generate data of soil temperature and soil moisture continuously in space, depth, and time for a 400 km² study area in the Fichtel Mountains (Germany). As reference data, measurements with 1 m long soil probes were taken. To cover many different locations, the available 15 soil probes were shifted regularly in the course of one year. With this approach, around 250 different locations in forest sites, on meadows and on agricultural fields were captured under a variety of meteorological conditions. These measurements are combined with readily available meteorological data, satellite data and soil maps in a machine learning approach to learn the complex relations between these variables. We aim for a model which can predict the soil temperature and soil moisture continuously for our study area in the Fichtel Mountains, with a spatial resolution of 10 m x 10 m, down to 1 m depth with segments of 10 cm each and in an hourly resolution in time. Here, we present the results of our pilot study where we focus on the temperature and moisture change within the depth down to 1 m at one single location. To take temporal lags into account, we construct a Long Short-Term Memory network based on meteorological data as predictors to make temperature and moisture predictions in time and depth. The results indicate a high ability of the model to reproduce the time series of the single location and highlight the potential of the approach for the space-time-depth mapping of soil temperature and soil moisture.

How to cite: Baumberger, M., Adorf, L., Haas, B., Meyer, N., and Meyer, H.: Modelling Soil Temperature and Soil Moisture in Space, Depth, and Time with Machine Learning Techniques, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11958, https://doi.org/10.5194/egusphere-egu23-11958, 2023.

EGU23-12218 | Posters on site | ITS1.13/AS5.2

Bias correction of aircraft temperature observations in the Korean Integrated Model based on a deep learning approach 

Hui-nae Kwon, Hyeon-ju Jeon, Jeon-ho Kang, In-hyuk Kwon, and Seon Ki Park

The aircraft-based observation is one of the important anchor data used in the numerical weather prediction (NWP) models. Nevertheless, the bias has been noted in the temperature observation through several previous studies. As the performance on the hybrid four-dimensional ensemble variational (hybrid-4DEnVar) data assimilation (DA) system of the Korean Integrated Model (KIM) ⸺ the operational model in the Korea Meteorological Administration (KMA) ⸺ has been advanced, the need for the aircraft temperature bias correction (BC) has been confirmed. Accordingly, as a preliminary study on the BC, the static BC method based on the linear regression was applied to the KIM Package for Observation Processing (KPOP) system. However, the results showed there were limitations of a spatial discontinuity and a dependency on the calculation period of BC coefficients.

In this study, we tried to develop the machine learning-based bias estimation model to overcome these limitations. The MultiLayer Perceptron (MLP) based learning was performed to consider the vertical, spatial and temporal characteristics of each observation by flight IDs and phases, and at the same time to consider the correlation among observation variables. As a result of removing the predicted bias from the bias estimation model, the mean of the background innovation (O-B) decreases from 0.2217 K to 0.0136 K in a given test period. Afterwards, in order to verify the analysis field impact for BC, the bias estimation model will be grafted onto the KPOP system and then several DA cycle experiments will be conducted in the KIM.

How to cite: Kwon, H., Jeon, H., Kang, J., Kwon, I., and Park, S. K.: Bias correction of aircraft temperature observations in the Korean Integrated Model based on a deep learning approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12218, https://doi.org/10.5194/egusphere-egu23-12218, 2023.

EGU23-12355 | ECS | Orals | ITS1.13/AS5.2

Comparison of NWP Models Used in Training Surrogate Wave Models 

Ajit Pillai, Ian Ashton, Jiaxin Chen, and Edward Steele

Machine learning is increasingly being applied to ocean wave modelling. Surrogate modelling has the potential to reduce or bypass the large computational requirements, creating a low computational-cost model that offers a high level of accuracy. One approach integrates in-situ measurements and historical model runs to achieve the spatial coverage of the model and the accuracy of the in-situ measurements. Once operational, such a system requires very little computational power, meaning that it could be deployed to a mobile phone, operational vessel, or autonomous vessel to give continuous data. As such, it makes a significant change to the availability of met-ocean data with potential to revolutionise data provision and use in marine and coastal settings.

This presentation explores the impact that an underlying physics-based model can have in such a machine learning driven framework; comparing training the system on a bespoke regional SWAN wave model developed for wave energy developments in the South West of the UK against training using the larger North-West European Shelf long term hindcast wave model run by the UK Met Office. The presentation discusses the differences in the underlying NWP models, and the impacts that these have on the surrogate wave models’ accuracy in both nowcasting and forecasting wave conditions at areas of interest for renewable energy developments. The results identify the importance in having a high quality, validated, NWP model for training such a system and the way in which the machine learning methods can propagate and exaggerate the underlying model uncertainties.

How to cite: Pillai, A., Ashton, I., Chen, J., and Steele, E.: Comparison of NWP Models Used in Training Surrogate Wave Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12355, https://doi.org/10.5194/egusphere-egu23-12355, 2023.

EGU23-12403 | ECS | Orals | ITS1.13/AS5.2

PseudoSpectralNet: A hybrid neural differential equation for atmosphere models 

Maximilian Gelbrecht and Niklas Boers

When predicting complex systems such as parts of the Earth system, one typically relies on differential equations which often can be incomplete, missing unknown influences or include errors through their discretization. To remedy those effects, we present PseudoSpectralNet (PSN): a hybrid model that incorporates both a knowledge-based part of an atmosphere model and a data-driven part, an artificial neural network (ANN). PSN is a neural differential equation (NDE): it defines the right-hand side of a differential equation, combining a physical model with ANNs and is able to train its parameters inside this NDE. Similar to the approach of many atmosphere models, part of the model is computed in the spherical harmonics domain, and other parts in the grid domain. The model consists of ANN layers in each domain, information about derivatives, and parameters such as the orography. We demonstrate the capabilities of PSN on the well-studied Marshall Molteni Quasigeostrophic Model.

How to cite: Gelbrecht, M. and Boers, N.: PseudoSpectralNet: A hybrid neural differential equation for atmosphere models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12403, https://doi.org/10.5194/egusphere-egu23-12403, 2023.

EGU23-12458 | ECS | Posters on site | ITS1.13/AS5.2

Training Deep Data Assimilation Networks on Sparse and Noisy Observations 

Vadim Zinchenko and David Greenberg

Data Assimilation (DA) is a challenging and expensive computational problem targetting hidden variables in high-dimensional spaces. 4DVar methods are widely used in weather forecasting to fit simulations to sparse observations by optimization over numerical model input. The complexity of this inverse problem and the sequential nature of common 4DVar approaches lead to long computation times with limited opportunity for parallelization. Here we propose using machine learning (ML) algorithms to replace the entire 4DVar optimization problem with a single forward pass through a neural network that maps from noisy and incomplete observations at multiple time points to a complete system state estimate at a single time point. We train the neural network using a loss function derived from the weak-constraint 4DVar objective, including terms incorporating errors in both model and data. In contrast to standard 4DVar approaches, our method amortizes the computational investment of training to avoid solving optimization problems for each assimilation window, and its non-sequential nature allows for easy parallelization along the time axis for both training and inference. In contrast to most previous ML-based data assimilation methods, our approach does not require access to complete, noise-free simulations for supervised learning or gradient-free approximations such as Ensemble Kalman filtering. To demonstrate the potential of our approach, we show a proof-of-concept on the chaotic Lorenz'96 system, using a novel "1.5D Unet" architecture combining 1D and 2D convolutions.

How to cite: Zinchenko, V. and Greenberg, D.: Training Deep Data Assimilation Networks on Sparse and Noisy Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12458, https://doi.org/10.5194/egusphere-egu23-12458, 2023.

EGU23-12566 | Posters on site | ITS1.13/AS5.2

Comparison of PM2.5 concentrations prediction model performance using Artificial Intelligence 

Kyung-Hui Wang, Chae-Yeon Lee, Ju-Yong Lee, Min-Woo Jung, Dong-Geon Kim, Seung-Hee Han, Dae-Ryun Choi, and Hui-young Yun

Since PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 µm) directly threatens public health, in order to take appropriate measures(prevention) in advance, the Korea Ministry of Environment(MOE) has been implementing PM10 forecast nationwide since February 2014. PM2.5 forecasts have been implemented nationwide since January 2015. The currently implemented PM forecast by the MOE subdivides the country into 19 regions, and forecasts the level of PM in 4 stages of “Good”, “Moderate”, “Unhealthy”, and “Very unhealthy”.

Currently PM air quality forecasting system operated by the MOE is based on a numerical forecast model along with a weather and emission model. Numerical forecasting model has fundamental limitations such as the uncertainty of input data such as emissions and meteorological data, and the numerical model itself. Recently, many studies on predicting PM using artificial intelligence such as DNN, RNN, LSTM, and CNN have been conducted to overcome the limitations of numerical models.

In this study, in order to improve the prediction performance of the numerical model, past observational data (air quality and meteorological data) and numerical forecasting model data (chemical transport model) are used as input data. The machine learning model consists of DNN and Seq2Seq, and predicts 3 days (D+0, D+1, D+2) using 6-hour and 1-hour average input data, respectively. The PM2.5 concentrations predicted by the machine learning model and the numerical model were compared with the PM2.5 measurements.

The machine learning models were trained for input data from 2015 to 2020, and their PM forecasting performance was tested for 2021. Compared to the numerical model, the machine learning model tended to increase ACC and be similar or lower to FAR and POD.

Time series trend was showed machine learning PM forecasting trend is more similar to PM measurements compared with numerical model. Especially, machine learning forecasting model can appropriately predict PM low and high concentrations that numerical model is used to overestimate.

Machine learning forecasting model with DNN and Seq2Seq can found improvement of PM forecasting performance compared with numerical forecasting model. However, the machine learning model has limitations that the model can not consider external inflow effects.

In order to overcome the drawback, the models should be updated and added some other machine learning module such as CNN with spatial features of PM concentrations.

 

Acknowledgements

This study was supported in part by the ‘Experts Training Graduate Program for Particulate Matter Management’ from the Ministry of Environment, Korea and by a grant from the National Institute of Environmental Research (NIER), funded by the Ministry of Environment (ME) of the Republic of Korea (NIER-2022-04-02-068).

 

How to cite: Wang, K.-H., Lee, C.-Y., Lee, J.-Y., Jung, M.-W., Kim, D.-G., Han, S.-H., Choi, D.-R., and Yun, H.: Comparison of PM2.5 concentrations prediction model performance using Artificial Intelligence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12566, https://doi.org/10.5194/egusphere-egu23-12566, 2023.

EGU23-13013 | ECS | Posters on site | ITS1.13/AS5.2

Using cGAN for cloud classification from RGB pictures 

Markus Rosenberger, Manfred Dorninger, and Martin Weißmann

Clouds of all kinds play a large role in many atmospheric processes including, e.g. radiation and moisture transport, and their type allows an insight into the dynamics going on in the atmosphere. Hence, the observation of clouds from Earth's surface has always been important to analyse the current weather and its evolution during the day. However, cloud observations by human observers are labour-intensive and hence also costy. In addition to this, cloud classifications done by human observers are always subjective to some extent. Finding an efficient method for automated observations would solve both problems. Although clouds have already been operationally observed using satellites for decades, observations from the surface shed a light on a different set of characteristics. Moreover, the WMO also defined their cloud classification standards according to visual cloud properties when observations are done at the Earth’s surface. Thus, in this work a utilization of machine learning methods to classify clouds from RGB pictures taken at the surface is proposed. Explicitly, a conditional Generative Adversarial Network (cGAN) is trained to discriminate between 30 different categories, 10 for each cloud level - low, medium and high; Besides showing robust results in different image classification problems, an additional advantage of using a GAN instead of a classical convolutional neural network is that its output can also artificially enhance the size of the training data set. This is especially useful if the number of available pictures is unevenly distributed among the different classes. Additional background observations like cloud cover and cloud base height can also be used to further improve the performance of the cGAN. Together with a cloud camera, a properly trained cGAN can observe and classify clouds with a high temporal resolution of the order of seconds, which can be used, e.g. for model verification or to efficiently monitor the current status of the weather as well as its short-time evolution. First results will also be presented.

How to cite: Rosenberger, M., Dorninger, M., and Weißmann, M.: Using cGAN for cloud classification from RGB pictures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13013, https://doi.org/10.5194/egusphere-egu23-13013, 2023.

EGU23-13143 | ECS | Posters on site | ITS1.13/AS5.2

Comparison of LSTM, GraphNN, and IrradPhyDNet based Approaches for High-resolution Solar Irradiance Nowcasting 

Petrina Papazek, Irene Schicker, and Pascal Gfähler

With fast parallel computing hardware, particularly GPUs, becoming more accessible in the geosciences the now efficiently running deep learning techniques are ready to handle larger amounts of recorded observation and satellite derived data and are able to learn complex structures across time-series. Thus, a suitable deep learning setup is able to generate highly-resolved weather forecasts in real-time and on demand. Forecasts of irradiance and radiation can be challenging in machine learning as they embrace a high degree of diurnal and seasonal variation.

Continuously extended PV/solar power production grows into one of our most important fossil-fuel free energy sources. Unlike the just recently emerging PV power observations, solar irradiance offers long time-series from automized weather station networks. Being directly linked to PV outputs, forecasting highly resolved solar irradiance from nowcasting to short-range plays a crucial role in decision support and managing PV.

In this study, we investigate the suitability of several deep learning techniques adopted and developed to a set of heterogeneous data sources on selected locations. We compare the forecast results to traditional – however computationally expensive - numerical weather prediction models (NWP) and rapid update cycle models. Relevant input features include 3D-fields from NWP models (e.g.: AROME), satellite data and products (e.g.: CAMS), radiation time series from remote sensing, and observation time time-series (site observations and close sites). The amount of time-series data can be extended by a synthetic data generator, a part of our deep learning framework. Our main models investigated includes a sequence-to-sequence LSTM (long-short-term-memory) model using a climatological background model or NWP for post-processing, a Graph NN model, and an analogs based deep learning method. Furthermore, a novel neural network model based on two other ideas, the IrradianceNet and the PhyDNet, was developed. IrradPhyDNet combines the skills of IrradianceNet and PhyDNet and showed improved performance in comparison to the original models.

Results obtained by the developed methods yield, in general, high forecast-skills. For selected case studies of extreme events (e.g. Saharan dust) all novel methods could outperform the traditional methods.  Different combinations of inputs and processing-steps are part of the analysis.

How to cite: Papazek, P., Schicker, I., and Gfähler, P.: Comparison of LSTM, GraphNN, and IrradPhyDNet based Approaches for High-resolution Solar Irradiance Nowcasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13143, https://doi.org/10.5194/egusphere-egu23-13143, 2023.

EGU23-13322 | ECS | Posters on site | ITS1.13/AS5.2

Nodal Ambient Noise Tomography and automatic picking of dispersion curves with convolutional neural network: case study at Vulcano-Lipari, Italy 

Douglas Stumpp, Elliot Amir Jiwani-Brown, Célia Barat, Matteo Lupi, Francisco Muñoz, Thomas Planes, and Geneviève Savard

The ambient noise tomography (ANT) method is widely adopted to reconstruct shear-wave velocity anomalies and to generate high-resolution images of the crust and upper-mantle. A critical step in this process is the extraction of surface-wave dispersion curves from cross-correlation functions of continuous ambient noise recordings, which is traditionally performed manually on the dispersion spectrograms through human-machine interfaces. Picking of dispersion curves is sometimes prone to bias due to human interpretation. Furthermore, it is a laborious and time-consuming task that needs to be resolved in an automatized manner, especially when dealing with dense seismic network of nodal geophones where the large amount of generated data severely hinders manual picking approaches. In the last decade, several studies successfully employed machine learning methods in Earth Sciences and across many seismological applications. Early studies have shown versatile and reliable solutions by treating dispersion curve extraction as a visual recognition problem. 

We review and adapt a specific machine learning approach, deep convolutional neural networks, for use on dispersion spectrograms generated with the usual frequency-time analysis (FTAN) processing on ambient noise cross-correlations. To train and calibrate the algorithm we use several available datasets acquired from previous experiments across different geological settings. The main dataset consists of records acquired with a dense local geophone network (150 short period stations sampling at 250 Hz) deployed for one month in October 2021. The dataset has been acquired during the volcanic unrest of the Vulcano-Lipari complex, Italy. The network also accounts for additional 17 permanent broadband stations (sampling at 100 Hz) maintained by the National Institute of Geophysics and Volcanology (INGV) in Italy. We evaluate the performance of the dispersion curves extraction algorithm. The automatically-picked dispersion curves will be used to construct a shear-wave velocity model of the Vulcano-Lipari magmatic plumbing system and the surrounding area of the Aeolian archipelago.

 

How to cite: Stumpp, D., Amir Jiwani-Brown, E., Barat, C., Lupi, M., Muñoz, F., Planes, T., and Savard, G.: Nodal Ambient Noise Tomography and automatic picking of dispersion curves with convolutional neural network: case study at Vulcano-Lipari, Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13322, https://doi.org/10.5194/egusphere-egu23-13322, 2023.

EGU23-13367 | ECS | Posters on site | ITS1.13/AS5.2

Framework for creating daily semantic segmentation maps of classified eddies using SLA along-track altimetry data 

Eike Bolmer, Adili Abulaitijiang, Luciana Fenoglio-Marc, Jürgen Kusche, and Ribana Roscher

Mesoscale eddies are gyrating currents in the ocean and have horizontal scales from 10 km up to 100 km and above. They transport water mass, heat, and nutrients and therefore are of interest among others to marine biologists, oceanographers, and geodesists. Usually, gridded sea level anomaly maps, processed from several radar altimetry missions, are used to detect eddies. However, operational processors create multi-mission (processing level 4) SLA grid maps with an effective spatiotemporal resolution far lower than their grid spacing and temporal resolution. 

This drawback leads to erroneous eddy detection. We, therefore, investigate if the higher-resolution along-track data could be used instead to solve the problem of classifying the SLA observations into cyclonic, anticyclonic, or no eddies in a more accurate way than using processed SLA grid map products. With our framework, we aim to infer a daily two-dimensional segmentation map of classified eddies. Due to repeat cycles between 10 and 35 days and cross-track spacing of a few 10 km to a few 100 km, ocean eddies are clearly visible in altimeter observations but are typically covered only by a few ground tracks where the spatiotemporal context within the input data is highly variable each day. However conventional convolutional neural networks (CNNs) rely on data without varying gaps or jumps in time and space in order to use the intrinsic spatial or temporal context of the observations. Therefore, this is a challenge that needs to be addressed with a deep neural network that on the one hand utilizes the spatiotemporal context information within the modality of along-track data and on the other hand is able to output a two-dimensional segmentation map from data of varying sparsity. Our approach with our architecture Teddy is to use a transformer module to encode and process the spatiotemporal information along with the ground track's sea level anomaly data that produces a sparse feature map. This will then be fed into a sparsity invariant convolutional neural network in order to infer a two-dimensional segmentation map of classified eddies. Reference data that is used to train Teddy is produced by an open-source geometry-based approach (py-eddy-tracker [1]). 

The focus of this presentation is on how we implemented this approach in order to derive two-dimensional segmentation maps of classified eddies with our deep neural network architecture Teddy from along-track altimetry. We show results and limitations for the classification of eddies using only along-track SLA data from the multi-mission level 3 product of the Copernicus Marine Environment Monitoring Service (CMEMS) within the 2017 - 2019 period for the Gulf Stream region. We find that using our methodology, we can create two-dimensional maps of classified eddies from along-track data without using preprocessed SLA grid maps.

[1] Evan Mason, Ananda Pascual, and James C. McWilliams, “A new sea surface height–based code for oceanic mesoscale eddy tracking,” Journal of Atmospheric and Oceanic Technology, vol. 31, no. 5, pp. 1181–1188, 2014.

How to cite: Bolmer, E., Abulaitijiang, A., Fenoglio-Marc, L., Kusche, J., and Roscher, R.: Framework for creating daily semantic segmentation maps of classified eddies using SLA along-track altimetry data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13367, https://doi.org/10.5194/egusphere-egu23-13367, 2023.

EGU23-13771 | Orals | ITS1.13/AS5.2

Machine Learning Emulation of 3D Shortwave Radiative Transfer for Shallow Cumulus Cloud Fields 

Jui-Yuan Christine Chiu, Chen-Kuang Kevin Yang, Jake J. Gristey, Graham Feingold, and William I. Gustafson

Clouds play an important role in determining the Earth’s radiation budget. Despite their complex and three-dimensional (3D) structures, their interactions with radiation in models are often simplified to one-dimensional (1D), considering the time required to compute radiative transfer. Such a simplification ignores cloud Inhomogeneity and horizontal photon transport in radiative processes, which may be an acceptable approximation for low-resolution models, but can lead to significant errors and impact cloud evolution predictions in high-resolution simulations. Since model developments and operations are heading toward a higher resolution that is more susceptible to radiation errors, a fast and accurate 3D radiative transfer scheme becomes important and necessary. To address the need, we develop a machine-learning-based 3D radiative transfer emulator to provide surface radiation, shortwave fluxes at all layers, and heating rate profiles. The emulators are trained for highly heterogeneous shallow cumulus under different solar positions. We will discuss the performance of the emulators in accuracy and efficiency and discuss their potential applications.

How to cite: Chiu, J.-Y. C., Yang, C.-K. K., Gristey, J. J., Feingold, G., and Gustafson, W. I.: Machine Learning Emulation of 3D Shortwave Radiative Transfer for Shallow Cumulus Cloud Fields, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13771, https://doi.org/10.5194/egusphere-egu23-13771, 2023.

EGU23-14051 | ECS | Posters on site | ITS1.13/AS5.2

Multi-modal data assimilation of sea surface currents from AIS data streams and satellite altimetry using 4DVARNet 

Simon Benaïchouche, Clément Le Goff, Brahim Boussidi, François Rousseau, and Ronan Fablet

Over the last decades, space oceanography missions, particularly altimeter missions, have greatly advanced our ability to observe sea surface dynamics. However, they still struggle to resolve spatial scales below ~ 100 km. On a global scale, sea surface current are derived from sea surface height by a geostrophical assumption. While future altimeter missions should improve the observation of sea surface height, the observation of sea surface current using altimetry techniques would remains indirect. In the other hands, recent works have considered the use of AIS (automated identification system) as a new mean to reconstruct sea surface current : AIS data streams provide an indirect observational models of total currents including ageostrophic phenomenas. In this work we consider the use of the supervised learning framework 4DVARNet, a supervised data driven approach that allow us to perform multi-modal experiments : We focus on an Observing System Simulation Experiment (OSSE) in a region of the Gulf-Stream and we show that the joint use of AIS and sea surface height (SSH) measurement could improve the reconstruction of sea surface current with respect to product derived solely from AIS or SSH observations in terms of physical and time scale resolved. 

How to cite: Benaïchouche, S., Le Goff, C., Boussidi, B., Rousseau, F., and Fablet, R.: Multi-modal data assimilation of sea surface currents from AIS data streams and satellite altimetry using 4DVARNet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14051, https://doi.org/10.5194/egusphere-egu23-14051, 2023.

EGU23-15183 | ECS | Orals | ITS1.13/AS5.2

Deep learning approximations of a CFD model for operational wind and turbulence forecasting 

Margrethe Kvale Loe and John Bjørnar Bremnes

The Norwegian Meteorological Institute has for many years applied a CFD model to downscale operational NWP forecasts to 100-200m spatial resolution for wind and turbulence forecasting for about 20 Norwegian airports. Due to high computational costs, however, the CFD model can only be run twice per day, each time producing a 12-hour forecast. An approximate approach requiring far less compute resources using deep learning has therefore been developed. In this, the relation between relevant NWP forecast variables at grids of 2.5 km spatial resolution and wind and turbulence from the CFD model has been approximated using neural networks with basic convolutional and dense layers. The deep learning models have been trained on approximately two year of the data separately for each airport. The results show that the models are to a large extent able to capture the characteristics of their corresponding CDF simulations, and the method is in due time intended to fully replace the current operational solution. 

How to cite: Loe, M. K. and Bremnes, J. B.: Deep learning approximations of a CFD model for operational wind and turbulence forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15183, https://doi.org/10.5194/egusphere-egu23-15183, 2023.

EGU23-15684 | ECS | Posters on site | ITS1.13/AS5.2

Semi-supervised feature-based learning for prediction of Mass Accumulation Rate of sediments 

Naveenkumar Parameswaran, Everardo Gonzalez, Ewa Bur­wicz-Ga­ler­ne, David Greenberg, Klaus Wallmann, and Malte Braack

Mass accumulation rates of sediments[g/cm2/yr] or sedimentation rates[cm/yr] on the seafloor are important to understand various benthic properties, like the rate of carbon sequestration in the seafloor and seafloor geomechanical stability. Several machine learning models, such as random forests, and k-Nearest Neighbours have been proposed for the prediction of geospatial data in marine geosciences, but face significant challenges such as the limited amount of labels for training purposes, skewed data distribution, a large number of features etc. Previous model predictions show deviation in the global sediment budget, a parameter used to determine a model's predicitve validity, revealing the lack of accurate representation of sedimentation rate by the state of the art models. 

Here we present a semi-supervised deep learning methodology to improve the prediction of sedimentation rates, making use of around 9x106  unlabelled data points. The semi-supervised neural network implementation has two parts: an unsupervised pretraining using an encoder-decoder network. The encoder with the optimized weights from the unsupervised training is then taken out and fitted with layers that lead to the target dimension. This network is then fine-tuned with 2782 labelled data points, which are observed sedimentation rates from peer-reviewed sources. The fine-tuned model then predicts the rate and quantity of sediment accumulating on the ocean floor, globally.

The developed semi-supervised neural network provide better predictions than supervised models trained only on labelled data. The predictions from the semi-supervised neural network are compared with that of the supervised neural network with and without dimensionality reduction(using Principle Component Analysis).

How to cite: Parameswaran, N., Gonzalez, E., Bur­wicz-Ga­ler­ne, E., Greenberg, D., Wallmann, K., and Braack, M.: Semi-supervised feature-based learning for prediction of Mass Accumulation Rate of sediments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15684, https://doi.org/10.5194/egusphere-egu23-15684, 2023.

EGU23-15756 | ECS | Posters on site | ITS1.13/AS5.2

Physiography improvements in numerical weather prediction digital twin engines 

Thomas Rieutord, Geoffrey Bessardon, and Emily Gleeson

The next generation of numerical weather prediction model (so-called digital twin engines) will reach hectometric scale, for which the existing physiography databases are insufficient. Our work leverages machine learning and open-access data to produce a more accurate and higher resolution physiography database. One component to improve is the land cover map. The reference data gathers multiple high-resolution thematic maps thanks to an agreement-based decision tree. The input data are taken from the Sentinel-2 satellite. Then, the land cover map generation is made with image segmentation. This work implements and compares several algorithms of different families to study their suitability to the land cover classification problem. The sensitivity to the data quality will also be studied. Compared to existing work, this work is innovative in the reference map construction (both leveraging existing maps and fit for end-user purpose) and the diversity of algorithms to produce our land cover map comparison.

How to cite: Rieutord, T., Bessardon, G., and Gleeson, E.: Physiography improvements in numerical weather prediction digital twin engines, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15756, https://doi.org/10.5194/egusphere-egu23-15756, 2023.

EGU23-15892 | ECS | Posters on site | ITS1.13/AS5.2

Towards emulated Lagrangian particle dispersion model footprints for satellite observations 

Elena Fillola, Raul Santos-Rodriguez, and Matt Rigby

Lagrangian particle dispersion models (LPDMs) have been used extensively to calculate source-receptor relationships (“footprints”) for use in greenhouse gas (GHG) flux inversions. However, because a backward-running model simulation is required for each data point, LPDMs do not scale well to very large datasets, which makes them unsuitable for use in GHG inversions using high-resolution satellite instruments such as TROPOMI. In this work, we demonstrate how Machine Learning (ML) can be used to accelerate footprint production, by first presenting a proof-of-concept emulator for ground-based site observations, and then discussing work in progress to create an emulator suitable to satellite observations. In Fillola et al (2023), we presented a ML emulator for NAME, the Met Office’s LPDM, which outputs footprints for a small region around an observation point using purely meteorological variables as inputs. The footprint magnitude at each grid cell in the domain is modelled independently using gradient-boosted regression trees. The model is evaluated for seven sites, producing a footprint in 10ms, compared to around 10 minutes for the 3D simulator, and achieving R2 values between 0.6 and 0.8 for CH4 concentrations simulated at the sites when compared to the timeseries generated by NAME. Following on from this work, we demonstrate how this same emulator can be applied to satellite data to reproduce footprints immediately around any measurement point in the domain, evaluating this application with data for Brazil and North Africa and obtaining R2 values of around 0.5 for simulated CH4 concentrations. Furthermore, we propose new emulator architectures for LPDMs applied to satellite observations. These new architectures should tackle some of the weaknesses in the existing approach, for example, by propagating information more flexibly in space and time, potentially improving accuracy of the derived footprints and extending the prediction capabilities to bigger domains.

How to cite: Fillola, E., Santos-Rodriguez, R., and Rigby, M.: Towards emulated Lagrangian particle dispersion model footprints for satellite observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15892, https://doi.org/10.5194/egusphere-egu23-15892, 2023.

EGU23-15994 | ECS | Posters on site | ITS1.13/AS5.2

Uncertainty quantification in variational data assimilation with deep learning 

Nicolas Lafon, Philippe Naveau, and Ronan Fablet

The spatio-temporal reconstruction of a dynamical process from some observationaldata is at the core of a wide range of applications in geosciences. This is particularly true for weather forecasting, operational oceanography and climate studies. However, the re35 construction of a given dynamic and the prediction of future states must take into ac36 count the uncertainties that affect the system. Thus, the available observational measurements are only provided with a limited accuracy. Besides, the encoded physical equa38 tions that model the evolution of the system do not capture the full complexity of the real system. Finally, the numerical approximation generates a non-negligible error. For these reasons, it seems relevant to calculate a probability distribution of the state system rather than the most probable state. Using recent advances in machine learning techniques for inverse problems, we propose an algorithm that jointly learns a parametric distribution of the state, the dynamics governing the evolution of the parameters, and a solver. Experiments conducted on synthetic reference datasets, as well as on datasets describing environmental systems, validate our approach.

How to cite: Lafon, N., Naveau, P., and Fablet, R.: Uncertainty quantification in variational data assimilation with deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15994, https://doi.org/10.5194/egusphere-egu23-15994, 2023.

EGU23-16287 | ECS | Posters on site | ITS1.13/AS5.2

A machine learning emulator for forest carbon stocks and fluxes 

Carolina Natel de Moura, David Martin Belda, Peter Antoni, and Almut Arneth

Forests are a significant carbon sink of the total carbon dioxide (CO2) emitted by humans. Climate change is expected to impact forest systems, and their role in the terrestrial carbon cycle in several ways – for example, the fertilization effect of increased atmospheric CO2, and the lengthening of the growing season in northern temperate and boreal areas may increase forest productivity, while more frequent extreme climate events such as storms and windthrows or drought spells, as well as wildfires might reduce disturbances return period, hence increasing forest land loss and reduction of the carbon stored in the vegetation and soils. In addition, forest management in response to an increased demand for wood products and fuel can affect the carbon storage in ecosystems and wood products. State-of-the-art Dynamic Global Vegetation Models (DGVMs) simulate the forest responses to environmental and human processes, however running these models globally for many climate and management scenarios becomes challenging due to computational restraints. Integration of process-based models and machine learning methods through emulation allows us to speed up computationally expensive simulations. In this work, we explore the use of machine learning to surrogate the LPJ-GUESS DGVM. This emulator is spatially-aware to represent forests across the globe in a flexible spatial resolution, and consider past climate and forest management practices to account for legacy effects. The training data for the emulator is derived from dedicated runs of the DGVM sampled across four dimensions relevant to forest carbon and yield: atmospheric CO2 concentration, air Temperature, Precipitation, and forest Management (CTPM). The emulator can capture relevant forest responses to climate and management in a lightweight form, and will support the development of the coupled socio-economic/ecologic model of the land system, namely LandSyMM (landsymm.earth). Other relevant scientific applications include the analysis of optimal forestry protocols under climate change, and the forest potential in climate change mitigation.

 

How to cite: Natel de Moura, C., Belda, D. M., Antoni, P., and Arneth, A.: A machine learning emulator for forest carbon stocks and fluxes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16287, https://doi.org/10.5194/egusphere-egu23-16287, 2023.

EGU23-16597 | Posters on site | ITS1.13/AS5.2 | Highlight

Global Decadal Sea Surface Height Forecast with Conformal Prediction 

Nils Lehmann, Jonathan Bamber, and Xiaoxiang Zhu

One of the many ways in which anthropogenic climate change impacts our planet is
rising sea levels. The rate of sea level rise (SLR) across the oceans is,
however, not uniform in space or time and is influenced by a complex interplay
of ocean dynamics, heat uptake, and surface forcing. As a consequence,
short-term (years to a decade) regional SLR patterns are difficult to model
using conventional deterministic approaches. For example, the latest climate
model projections (called CMIP6) show some agreement in the globally integrated
rate of SLR but poor agreement when it comes to spatially-resolved
patterns. However, such forecasts are valuable for adaptation planning in
coastal areas and for protecting low lying assets.
Rather than a deterministic modeling approach, here we explore the possibility
of exploiting the high quality satellite altimeter derived record of sea surface
height variations, which cover the global oceans outside of ice-infested waters
over a period of 30 years. Alongside this rich and unique satellite record,
several data-driven models have shown tremendous potential for various
applications in Earth System science. We explore several data-driven deep
learning approaches for sea surface height forecasts over multi-annual to
decadal time frames. A limitation of some machine learning approaches is the
lack of any kind of uncertainty quantification, which is problematic for
applications where actionable evidence is sought. As a consequence, we equip
our models with a rigorous measure of uncertainty, namely conformal prediction which
is a model and dataset agnostic method that provides calibrated predictive
uncertainty with proven coverage guarantees. Based on a 30-year satellite
altimetry record and auxiliary climate forcing data from reanalysis such as
ERA5, we demonstrate that our methodology is a viable and attractive alternative
for decadal sea surface height forecasts.

How to cite: Lehmann, N., Bamber, J., and Zhu, X.: Global Decadal Sea Surface Height Forecast with Conformal Prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16597, https://doi.org/10.5194/egusphere-egu23-16597, 2023.

EGU23-16936 | ECS | Orals | ITS1.13/AS5.2

Analysis of marine heat waves using machine learning 

Said Ouala, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, and Ronan Fablet

Sea surface temperature (SST) is a critical parameter in the global climate system and plays a vital role in many marine processes, including ocean circulation, evaporation, and the exchange of heat and moisture between the ocean and atmosphere. As such, understanding the variability of SST is important for a range of applications, including weather and climate prediction, ocean circulation modeling, and marine resource management.

The dynamics of SST is the compound of multiple degrees of freedom that interact across a continuum of Spatio-temporal scales. A first-order approximation of such a system was initially introduced by Hasselmann. In his pioneering work, Hasselmann (1976) discussed the interest in using a two-scale stochastic model to represent the interactions between slow and fast variables of the global ocean, climate, and atmosphere system. In this paper, we examine the potential of machine learning techniques to derive relevant dynamical models of Sea Surface Temperature Anomaly (SSTA) data in the Mediterranean Sea. We focus on the seasonal modulation of the SSTA and aim to understand the factors that influence the temporal variability of SSTA extremes. Our analysis shows that the variability of the SSTA can indeed well be decomposed into slow and fast components. The dynamics of the slow variables are associated with the seasonal cycle, while the dynamics of the fast variables are linked to the SSTA response to rapid underlying processes such as the local wind variability. Based on these observations, we approximate the probability density function of the SSTA data using a stochastic differential equation parameterized by a neural network. In this model, the drift function represents the seasonal cycle and the diffusion function represents the envelope of the fast SSTA response.

 

How to cite: Ouala, S., Chapron, B., Collard, F., Gaultier, L., and Fablet, R.: Analysis of marine heat waves using machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16936, https://doi.org/10.5194/egusphere-egu23-16936, 2023.

EGU23-32 | ECS | PICO | HS6.6

Machine Learning and LiDAR Snowheight Maps from UAVs Reveal Clusters of Snow Variability in a Sub-Alpine Forest. 

Joschka Geissler, Lars Rathmann, and Markus Weiler

Snow plays a crucial role in the hydrological cycle as it serves as an intermediate storage of winter precipitation and renews groundwater reserves. It is therefore of central importance for, among others, our drinking water supply and agriculture. Snow interacts with its environment in many ways, is constantly changing with time, and thus has a highly heterogeneous spatial and temporal distribution. Therefore, modelling snow variability is difficult, especially when additional components such as forests add complexity. To increase our understanding of the spatiotemporal variability of snow as well as to validate snow models, we need reliable validation data. For these purposes, airborne LiDAR surveys or time series derived from snow sensors on the point scale are commonly used. However, these are disadvantageously limited to one point either in space or in time. In this study, we profited from current advances in LiDAR and drone technology, as well as machine learning, to bridge this gap. We present a new dataset on snow variability in forests for the Alptal, a sub-alpine, forested valley in the pre-alps, Switzerland. The core dataset consists of a dense sensor network, repeated UAV-based LiDAR flights and manual snow height and density measurements. Using modern machine learning algorithms, we determine four clusters of similar spatiotemporal behaviour regarding their snowheight. These clusters are characterized and further used to derive daily snow depth and snow water equivalent maps. By using the latter, we obtain spatially continuous key hydrological variables. The results suggest that snow occurs in clusters that reoccur in space. These clusters underline the relation between canopy cover and spatial snow accumulation patterns and (the much more complex) spatial ablation patterns. The presented dataset and derived products are the first to our knowledge that provide daily, high-resolution snow height and hydrologic variables based on field data. The results of this study can therefore improve our understanding of the spatiotemporal variability of snow in forested environments. Moreover, they are ideally suited for the validation of modern snow models.

How to cite: Geissler, J., Rathmann, L., and Weiler, M.: Machine Learning and LiDAR Snowheight Maps from UAVs Reveal Clusters of Snow Variability in a Sub-Alpine Forest., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-32, https://doi.org/10.5194/egusphere-egu23-32, 2023.

Seasonal snow in the northern regions plays an important role providing water resources for both consumption and hydropower generation. Moreover, the snow depth in the northern Finland during winter exceeds 1 m, impacting local agriculture, vegetation, tourism and recreational activities. The objective of this study is to estimate snow depth using an empirical methodology applied to synthetic aperture radar (SAR) images and compare with in situ measurements collected by automatic weather stations (AWS) and snow courses in northern Finland. Snow depth estimates with high spatiotemporal resolution can improve our understand of seasonal snow mass in complex access areas. Here, we use an adapted version of the empirical methodology developed by Lievens et al. (2019) to estimate snow depth using Sentinel-1 constellation (C-band). The algorithm utilizes changes in the cross-polarized backscatter measurements of SAR images repeatedly acquired on the same orbit to avoid geometry distortions. We use SNAP toolbox, combined with the Copernicus digital elevation model (DEM), posted at 30 meters, in the pre-processing stage.  The snow retrievals between 2019 and 2022 are compared to three automatic weather stations and four snow courses measurements collected over the same period. The ongoing Sentinel-1 snow depth retrievals during the winter 2021/2022 demonstrate a correlation of 0.76, when compared to in situ measurements, supporting the potential ability to derive snow changes in regions where in situ measurements of snow are currently lacking. Despite the good agreement between the empirical algorithm and the collected datasets on land, further investigation is still necessary to better understand the backscatter response over frozen lake areas. Thanks to the effort of international space agencies, we have available currently, and in a near future, global coverage at high resolution SAR imagery and, combined with installed automatic weather stations, opens the possibility of a wide spatial monitoring of snow variations.

How to cite: Lemos, A. and Riihelä, A.: Snow Depth derived from Sentinel-1 compared to in-situ observations in northern Finland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-998, https://doi.org/10.5194/egusphere-egu23-998, 2023.

EGU23-1920 | ECS | PICO | HS6.6 | Highlight

Monitoring the Spatiotemporal Dynamics of Arctic Winter Snow/Ice with MoonlightRemote Sensing 

Di Liu and Qingling Zhang

The Arctic region has been experiencing significant climate change, with the loss of snow and ice accelerating at an alarming rate. Accurate monitoring of the spatiotemporal dynamics of snow and ice is essential for understanding and predicting the impacts of climate change on Arctic ecosystems and their feedback on global climate. In this paper, we use the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite to monitor the spatiotemporal dynamics of snow and ice in polar regions. The VIIRS/Day/Night Band (DNB) is a unique instrument that can provide high-resolution imagery of the Earth's surface at night, with a spatial resolution of 750 m and a sensitivity of 0.01 nW/cm2/sr. This enables the detection of faint moonlight and artificial light and allows for mapping snow and ice in polar winter when no sunlight is available for months.  Our aims demonstrate the potential of moonlight remote sensing for continuous monitoring of snow/ice in the Arctic region and analyse the importance of continuous monitoring and research on the impacts of climate change on the Arctic ecosystem and the potential for Arctic seaway.

How to cite: Liu, D. and Zhang, Q.: Monitoring the Spatiotemporal Dynamics of Arctic Winter Snow/Ice with MoonlightRemote Sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1920, https://doi.org/10.5194/egusphere-egu23-1920, 2023.

EGU23-3304 | ECS | PICO | HS6.6

Retrieval of snow layer and melt pond properties based on airborne hyperspectral imagery 

Sophie Rosenburg, Charlotte Lange, Evelyn Jäkel, Michael Schäfer, and Manfred Wendisch

The melting snow layer, as a composition of ice, liquid water, and air, supplies meltwater in the runoff phase inducing the melt pond formation. These melting processes of Arctic sea ice alter the surface reflection properties and thereby affect the energy budget. Such sea ice surface reflection properties were surveyed by airborne hyperspectral imagery within the framework of an Arctic field campaign performed in May/June 2017. A retrieval approach based on different absorption indices of pure ice and liquid water in the near infrared spectral range is applied to the campaign data retrieving the spatial distribution of snow layer liquid water fraction and effective radius of snow grains. For the same sceneries the melt pond depth was retrieved based on an existing approach that isolates the dependence of a melt pond reflectance spectrum on the pond depth by eliminating the reflection contribution of the pond ice bottom. The presented retrieval methods show the potential of airborne hyperspectral imagery to map the transition phase of the Arctic sea ice surface examining the snow layer composition and melt pond bathymetry.

How to cite: Rosenburg, S., Lange, C., Jäkel, E., Schäfer, M., and Wendisch, M.: Retrieval of snow layer and melt pond properties based on airborne hyperspectral imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3304, https://doi.org/10.5194/egusphere-egu23-3304, 2023.

EGU23-4196 | PICO | HS6.6

Snow cover monitoring in the Arctic (Svalbard) with RADARSAT Constellation Mission (RCM). Comparison with in-situ measurements and TerraSAR-X data 

Jean-Pierre Dedieu, Joep Van Noort, Benoit Montpetit, Manon Levistre, Simon Vauclare, Anna Wendleder, Julia Boike, Eric Bernard, Jean-Charles Gallet, and Hans-Werner Jacobi

Arctic snow cover dynamics exhibit modification in terms of extent and duration due to recent changes in climate, i.e. increasing temperatures and changing precipitation patterns, i.e. winter rain-on-snow events (WROS). Remote sensing methods based on active radar images (SAR) have demonstrated a significant advantage for snow monitoring, (i) capturing physical and dielectric properties, (ii) overcoming the weakness of optical images limited by cloud cover and polar night.

The aim of this study is dedicated to the analysis of the spatial and temporal variability of snow cover in the Ny-Ålesund area on the BrØgger peninsula, Svalbard (N 78°55’ / E 11° 55’). In-situ snow measurements from two automated weather stations (Ny-Ålesund, and Bayelva), regular snowpits around the village and in spring on the Austre Loéven, were compared with the spaceborne dataset.

The RADARSAT Constellation Mission (RCM) is comprised of three satellites into closely coordinated orbits operating in C-band (5.4 GHz, 5.5 cm). The high temporal (4-day repeat cycle) and spatial resolution of the sensors in Quad-Pol mode (9-m) or Compact-Pol mode (5-m) provide a helpful performance for detecting the spatial variability of snow properties. RCM data are also compared to images of the TerraSAR-X satellite (DLR, Germany) operated in X-band (9.6 GHz, 3.1 cm) at 5-m spatial resolution. Both RCM QP mode and TSX data were acquired with medium incidence angles (33° to 39°) providing better snow penetration for volume backscattering. The RCM CP data were only available under low (23°) and high (53°) beam angles.

The following two snow properties were analyzed:

WROS detection: the focus was set on the 16-17 March 2022 event (+ 5.5 °C, 62 mm). RCM data at cross-polarization VH or HV can clearly detect the impact of rain on snow, indicating an intensity drop of -10 dB, even on the glacier at high elevation.

Snow depth retrieval: the study covers spring 2021 (March-June) and the complete winter season 2021-2022 (November-June).

- Concerning QP mode, better correlation between snow depth and SAR backscattering is observed by the cross-pol VH component, retrieving more volume backscattering information than co-pol configuration or total backscattering power (Span). We observe also that descending orbit images (06 :30 AM) provide a better correlation with snow depth than ascending orbit (15 :30 PM) data.

- Concerning CP mode and Span (RH+RV), the low incidence images (23°) do not match the snow depth observations due to main surface backscattering, contrariwise the high incidence images correlate better with in-situ observations. The analyses of the Stokes vector elements showed a satisfying correlation for the g3 element and the Relative Phase polarimetric decomposition.

Finally, a comparison of Span temporal values between RCM at C-band and TSX at X-band indicates similar time profiles, but clearly lower values of -5 to -10 dB at the C-band.

How to cite: Dedieu, J.-P., Van Noort, J., Montpetit, B., Levistre, M., Vauclare, S., Wendleder, A., Boike, J., Bernard, E., Gallet, J.-C., and Jacobi, H.-W.: Snow cover monitoring in the Arctic (Svalbard) with RADARSAT Constellation Mission (RCM). Comparison with in-situ measurements and TerraSAR-X data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4196, https://doi.org/10.5194/egusphere-egu23-4196, 2023.

EGU23-6176 | ECS | PICO | HS6.6

Dynamics in mountain SNOW water resources by MODEs of climate variability assessed from satellite observations 

Jonas-Frederik Jans, Ezra Beernaert, Hans Lievens, and Niko Verhoest

Satellite information concerning the snow water equivalent (SWE) stored in the world’s mountain ranges is still lacking. This observation gap hinders the accurate estimation of total seasonal water storage in snow. Therefore, the SNOW-MODE project aims to address this gap by improving and developing two satellite retrieval methods to estimate SWE. Firstly, a recently developed empirical change detection algorithm for SWE retrieval from Sentinel-1 (S1) backscatter observations will be thoroughly analyzed and, if possible, improved. Secondly, a snow (Bic-DMRT), soil (Oh) and vegetation (WCM) radiative transfer model (RTM) will be coupled and inverted to estimate SWE using S1 radar backscatter observations and auxiliary snow, soil and vegetation properties. This method will be applied at the point- and grid-scale. The point-scale approach will make use of detailed in-situ measurements and novel tower-mounted radar measurements for RTM development and validation of the retrievals, whereas the grid-scale approach will utilize data generated from a land surface and a snow model. The inclusion of the grid-scale approach allows to investigate whether spatial patterns in SWE can be accurately represented by the S1 retrievals.

Subsequently, both S1 retrieval methods (i.e., change detection and RTM) will be compared over several mountain regions in the Northern Hemisphere (High-Mountain Asia and European and western United States mountains) to assess their uncertainties, validity conditions and main strengths as well as shortcomings. Furthermore, a physics-based snow model (e.g., SnowClim) will also be utilized to simulate snow depth and SWE on a daily basis. To improve the simulation results, the meteorological forcings will be downscaled to a resolution of 500 meter. Further improvements will be aspired by assimilating the mountainous snow depth retrievals (either from the RTM or change detection method) into the snow model. Finally, the generated SWE dataset will be related to modes of climate variability and will be translated into basin-scale water resources availability for society. 

How to cite: Jans, J.-F., Beernaert, E., Lievens, H., and Verhoest, N.: Dynamics in mountain SNOW water resources by MODEs of climate variability assessed from satellite observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6176, https://doi.org/10.5194/egusphere-egu23-6176, 2023.

EGU23-7050 | ECS | PICO | HS6.6 | Highlight

SWE retrieval in the European Alps based on Sentinel-1 snow depth observations and modeled snow density 

Lucas Boeykens, Hans Lievens, Ezra Beernaert, Jonas-Frederik Jans, and Niko Verhoest

Seasonal snow is an essential source of water, especially in mountainous regions. However, accurate satellite observations of the snow water equivalent (SWE), i.e., snow depth multiplied by the snow density, are still lacking. Therefore, new and robust remote sensing techniques are urgently needed. This study presents a novel method for SWE retrieval in mountainous regions at sub-weekly temporal and 500-m spatial resolution, based on snow depth observations from the ESA and Copernicus Sentinel-1 (S1) satellite mission and model simulations of snow density. The snow depth observations rely on a change detection algorithm which translates the temporal changes in the S1 radar backscatter measurements into the accumulation or ablation of snow. The snow density estimates are obtained from different modeling approaches, including empirical methods (e.g., based on the day of the year, the snow depth, snow climate class, etc.) and a physics-based mass and energy balance model. The performance of the different snow density modeling approaches is here compared, both with respect to their ability to accurately simulate in situ measurements of snow density, as well as their ability to accurately simulate in situ measurements of SWE after combination with the S1 snow depth observations. The performance is evaluated over the European Alps, using a large dataset of in situ time series measurements for the period 2015-2022. The results show that the physics-based snow density modeling approach outperforms the empirical approaches, yielding high spatio-temporal correlation between S1 SWE retrievals and in situ measurements. Therefore, the study demonstrates the capability of the Sentinel-1 satellite mission, in combination with a physics-based snow model, to accurately represent the spatio-temporal distribution of SWE in mountainous regions, which can benefit a large range of applications, including hydropower generation, water management, flood forecasting, and numerical weather prediction.

How to cite: Boeykens, L., Lievens, H., Beernaert, E., Jans, J.-F., and Verhoest, N.: SWE retrieval in the European Alps based on Sentinel-1 snow depth observations and modeled snow density, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7050, https://doi.org/10.5194/egusphere-egu23-7050, 2023.

EGU23-7343 | ECS | PICO | HS6.6

Using synthetic snow cover maps to determine the degree-day snowmelt factor of a distributed hydrological model 

Pau Wiersma, Fatemeh Zakeri, and Grégoire Mariéthoz

Snowmelt can vary largely across time and space, especially in complex terrain. However, hydrological models often represent snowmelt using a single static degree-day factor that relates the melt runoff with air temperature. Seasonally or spatially varying degree-day factors have been shown to better capture the snowmelt heterogeneity, but still rely on simplified parameterizations. One interesting solution proposed in the literature is to use MODIS satellite imagery to capture the true snowmelt heterogeneity, and use it to inform hydrological models on the temporal and spatial evolution of the degree-day factor on a near-daily basis. However, the limited spatial resolution of MODIS makes this process difficult to apply in complex mountainous terrain. Meanwhile, Landsat or Sentinel 2 satellite imagery could be an interesting alternative as they have a much higher spatial resolution but fall short in terms of temporal resolution. In this study, we overcome both these obstacles with a synthetically generated daily snow cover time series based on Landsat resampling. We use the daily synthetic snow cover maps to derive the snow cover depletion in each coarse resolution hydrological model grid cell, which in turn defines the degree-day factor for each cell using a transfer function. To capture the inherent uncertainty of this methodology, we run an ensemble of models using different meteorological forcings and different stochastic realizations of the synthetic snow cover maps. The resulting degree-day factors are evaluated through the skill of the modeled streamflow and snow water equivalent, using different transfer functions in several snow-influenced catchments in Switzerland. 

How to cite: Wiersma, P., Zakeri, F., and Mariéthoz, G.: Using synthetic snow cover maps to determine the degree-day snowmelt factor of a distributed hydrological model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7343, https://doi.org/10.5194/egusphere-egu23-7343, 2023.

EGU23-7537 | ECS | PICO | HS6.6

A new opportunity to measure snow depth from space: evaluation of retrievals from ICESat-2 using airborne laser-scanning data 

César Deschamps-Berger, Simon Gascoin, David Shean, Hannah Besso, Ambroise Guiot, and Juan Ignacio López Moreno

The unprecedented precision of the altimetry satellite ICESat-2 and the increasing availability of high-resolution elevation datasets open new opportunities to measure snow depth in the mountains, a critical variable for ecosystems and water resources monitoring. We retrieved snow depth over the upper Tuolumne basin (California, USA) for three years by differencing ICESat-2 ATL06 snow-on elevations and various snow-off elevation sources, including ATL06 and external digital elevation models. The snow presence of each ATL06 segment (i.e. point measurements regularly spaced every 20 m) can be determined from the number of photons returned by the ground surface. Snow depth derived from ATL06 data only (snow-on and snow-off) provided a poor temporal and spatial coverage, limiting its utility. However, using airborne lidar or satellite photogrammetry elevation models as snow-off elevation source yielded an accuracy of ~0.2 m (bias), a precision of ~0.5 m for low slopes and ~1.2 m for steeper areas, compared to eight reference airborne lidar snow depth maps. The snow depth derived from ICESat-2 ATL06 will help address the challenge of measuring the snow depth in unmonitored mountainous areas.

How to cite: Deschamps-Berger, C., Gascoin, S., Shean, D., Besso, H., Guiot, A., and López Moreno, J. I.: A new opportunity to measure snow depth from space: evaluation of retrievals from ICESat-2 using airborne laser-scanning data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7537, https://doi.org/10.5194/egusphere-egu23-7537, 2023.

EGU23-8018 | ECS | PICO | HS6.6

Sensitivity of Sentinel-1 observations to snow properties, comparing radar backscatter, polarimetric decomposition parameters, and interferometric phase changes 

Morgane De Breuck, Hans Lievens, Jonas-Frederik Jans, Ezra Beernaert, and Niko Verhoest

Remote sensing can offer important information on snow properties at the global scale. However, the sensitivity of satellite radar measurements at C-band (5.4 GHz) from the ESA and Copernicus Sentinel-1 (S1) mission to snow properties still requires further investigation. This study provides the first results of a detailed sensitivity analysis, carried out over the European Alps at 1-km spatial resolution. It includes three processing types of radar measurements: radar backscatter observations in vertical-vertical (VV) and vertical-horizontal (VH) polarizations, polarimetric decomposition parameters (e.g., H-Alpha dual polarization decomposition), and interferometric phase change and coherence between successive S1 acquisitions from the same relative orbit. The sensitivity of the different radar measurements is investigated with respect to snow properties (snow depth, snow water equivalent, wet-dry snow state), soil properties (surface soil moisture, soil temperature), and vegetation properties (LAI), and furthermore stratified by snow climatology, land cover, and elevation. Preliminary results suggest that, in regions with significant snowfall and limited vegetation, the VH backscatter correlates strongest with snow depth and SWE, whereas the VV backscatter is more strongly correlated with soil properties. The Alpha polarimetric decomposition parameter increases with snow accumulation, indicating increased contributions of volume scattering and multiple scattering. The often low interferometric coherence is confounding the interpretation of interferometric phase changes in mountainous regions. In conclusion, the first results of this sensitivity study indicate the usefulness of S1 radar backscatter and polarimetric decomposition parameters for snow retrieval algorithm development.

How to cite: De Breuck, M., Lievens, H., Jans, J.-F., Beernaert, E., and Verhoest, N.: Sensitivity of Sentinel-1 observations to snow properties, comparing radar backscatter, polarimetric decomposition parameters, and interferometric phase changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8018, https://doi.org/10.5194/egusphere-egu23-8018, 2023.

EGU23-8142 | PICO | HS6.6

Monitoring the impact of rain-on-snow events across the Arctic with satellite data 

Annett Bartsch, Helena Bergstedt, Xaver Muri, Kimmo Rautiainen, Leena Leppänen, Kyle Joly, Aleksandr Sokolov, Pavel Orekhov, Dorothee Ehrich, and Eeva Marietta Soininen

Rain-on-Snow (ROS) events change snow pack properties and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. Active as well as passive microwave sensors have been used in the past to document ROS on regional scale. Either wet snow during a ROS event or the formation of crust afterwards are identified in most cases. The fusion of both approaches is promising for circumpolar monitoring.

C-band radar is of special interest due to good data availability including a range of nominal spatial resolution (10 m–12.5 km). Previous studies indicated that radar backscatter is suitable to identify snow structure change. As an example L-band passive microwave observations from SMOS and C-band backscatter from Metop ASCAT have been jointly analysed and compared to snowpit observations in Scandinavia and Northwestern Siberia.

A circumpolar dataset of potential ROS has been created. The gridded information has been eventually aggregated for events. Larger mid-winter events have been eventually extracted for 2012-2021. They occur mostly in the NE part of northern Eurasia (mostly November) and across Alaska (mostly December). The spatiotemporal patters of these events and the magnitude of snow structure change will be presented and discussed.

How to cite: Bartsch, A., Bergstedt, H., Muri, X., Rautiainen, K., Leppänen, L., Joly, K., Sokolov, A., Orekhov, P., Ehrich, D., and Soininen, E. M.: Monitoring the impact of rain-on-snow events across the Arctic with satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8142, https://doi.org/10.5194/egusphere-egu23-8142, 2023.

EGU23-8530 | PICO | HS6.6 | Highlight

Advancing science readiness for a new snow mass radar mission 

Chris Derksen, Benoit Montpetit, Vincent Vionnet, Vincent Fortin, Juha Lemmetyinen, Richard Kelly, and Aaron Thompson

Environment and Climate Change Canada (ECCC) and the Canadian Space Agency (CSA) continue to advance a new satellite Ku-band radar mission focused on providing moderate resolution (500 m) information on seasonal snow mass. Like many regions of the northern hemisphere, estimates of the amount of water stored as seasonal snow are highly uncertain across Canada. To address this gap, a technical concept capable of providing dual-polarization (VV/VH), moderate resolution (500 m), wide swath (~250 km), and high duty cycle (~25% SAR-on time) Ku-band radar measurements at two frequencies (13.5; 17.25 GHz) is under development. Parallel to engineering studies to address the technical readiness, a range of activities are in progress to advance scientific readiness. In this presentation, we will review how recent progress within the mission science team and across the snow community has provided a sound science foundation for the mission, and identify risks to meeting the required level of readiness within the required timeline for full mission implementation. Key areas include:

  • Implementation of computationally efficient SWE retrieval techniques, including parameterized forward model simulations for prediction of snow volume scattering, physical snow modeling to provide initial estimates of snow microstructure, and consideration of background characteristics;
  • Incorporation of land surface model SWE estimates to infill gaps with no radar-derived SWE information due to dense forest, wet snow, and swath gaps;
  • Direct assimilation of Ku-band backscatter into environmental prediction systems (analogous to how SMOS and SMAP data have improved soil moisture analysis through radiance-based assimilation);
  • Segmentation of wet from dry snow;
  • Continued advancement of the understanding of the physics of Ku-band backscatter response to variations snow through new experimental tower and airborne measurements.

Ku-band radar is a viable approach for a terrestrial snow mass mission because these measurements are sensitive to SWE through the volume scattering properties of dry snow and can discriminate the wet versus dry state of snow cover. To justify investment in such a mission, however, the scientific pieces must be in place. Balanced and honest assessments of the state of scientific readiness, the likelihood for emerging capabilities, and the level of engagement across the snow community are essential to ensure a healthy mission development process.

How to cite: Derksen, C., Montpetit, B., Vionnet, V., Fortin, V., Lemmetyinen, J., Kelly, R., and Thompson, A.: Advancing science readiness for a new snow mass radar mission, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8530, https://doi.org/10.5194/egusphere-egu23-8530, 2023.

In order to more quantitatively compare the differences between radar reflectivity and snowfall intensity against ground-observed snow depth, snow depth ground observation data and weather radar observation data were analyzed. For radar observation data, cumulative reflectivity and precipitation intensity derived from reflectivity, differential reflectivity, and specific differential phase (Quantitative Precipitation Estimation) were compared. As a result of the analysis, it was found that the precipitation intensity was similar to the variation according to the snow depth and time compared to the radar reflectivity. However, although the initial accumulation tendency of snow fall was very well matched due to the characteristics of snow cover, which is sensitive to temperature and has accumulation and melting characteristics, the melting tendency from daytime showed a difference. Therefore, it is judged that more accurate snow depth can be estimated only when precipitation intensity estimation method according to temperature is derived and used in addition to methods such as accumulation of reflectivity.

 

Acknowledgement

This research was supported by a grant(2022-MOIS61-003) of Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).

 

How to cite: Kang, N., Hwang, S., and Yoon, J.: Comparison of correlations between radar reflectivity and radar precipitation intensity for snow depth, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10436, https://doi.org/10.5194/egusphere-egu23-10436, 2023.

EGU23-11618 | ECS | PICO | HS6.6

Validation of high resolution remotely sensed and modeled snow cover with webcam imagery 

Andreas Kollert, Andreas Mayr, Martin Rutzinger, and Stefan Dullinger
Recently, snow cover has gained a lot of interest as an important driver of plant species distribution in arctic and alpine environments, especially on small spatial scales . However, variation of snow cover at this scale is hardly resolved by open satellite data. Hence, linking remotely sensed snow cover and critical patterns and processes in vegetation can be challenging due to a mismatch in spatial resolution.
We present a study based on a high alpine network of three webcams for the validation of snow cover products covering an entire year. Satellite based snow cover products (Landsat, Sentinel-2, downscaled MODIS products) are benchmarked on webcam-derived snow cover. While optical satellite remote sensing is a valuable tool for characterizing snow cover dynamics at the scale of tens of meters, cloud cover causes considerable data gaps. As a temporally and spatially more continuous estimate, we additionally produce meter-scale snow cover using the openAmundsen model, and we compare this to the webcam derived snow cover as well. For all datasets, ecologically relevant indicators like snow cover duration and the number of snow-free days are aggregated and validated both for the entire year and on a sub-seasonal scale.

How to cite: Kollert, A., Mayr, A., Rutzinger, M., and Dullinger, S.: Validation of high resolution remotely sensed and modeled snow cover with webcam imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11618, https://doi.org/10.5194/egusphere-egu23-11618, 2023.

EGU23-11702 | ECS | PICO | HS6.6

Seasonal evaluation of morphological indexes in quantifying snow cover patterns in the Zugspitze area 

Lucia Ferrarin, Franziska Koch, Karsten Schulz, and Daniele Bocchiola

The spatiotemporal distribution of snow cover affects several processes at different scales, such as the Earth’s energy balance, the hydrological cycle and ecosystems functions, with important implications on many aspects of human life. Topography, meteorological conditions in general and wind in particular affect the evolution of seasonal snow cover patterns during snow accumulation and ablation. With the help of remote sensing techniques, such as Sentinel-2 imagery, it is feasible to study snow cover patterns also in complex terrain. Satellite based morphological analysis of snow cover patterns may provide i) information on snow cover and its connection to morphology and alpine topography ii) a valuable complement to ground-based data and snow-hydrological simulations. In this study, we evaluate the effectiveness of two types of geometric indexes, i) MN, Minkowsky numbers (representing area, perimeter and Euler characteristic), and ii) CL, Average chord length, in quantitatively describing the morphology of Sentinel-2 derived snow cover patterns within the high-alpine area of Zugspitze at the boarder of Germany and Austria for a five-year period. MN and CL have been used previously in different fields, e.g. soil sciences, but to the authors’ knowledge, these measures have never been applied in the field of snow cover pattern monitoring before. We present the seasonal evolution of MN and CL, as well as their correlation to topographic features (e.g., aspect, slope, curvature) and meteorological and snow variables. The individual indexes show distinct differences during snow accumulation and ablation and a clear annual periodicity. MN and CL can effectively quantify some aspects of the dynamic of snow cover patterns, although further analysis are necessary to conclude if such morphologic pattern descriptors can substantively improve the accuracy of the understanding and the modelling of snow-related processes.

How to cite: Ferrarin, L., Koch, F., Schulz, K., and Bocchiola, D.: Seasonal evaluation of morphological indexes in quantifying snow cover patterns in the Zugspitze area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11702, https://doi.org/10.5194/egusphere-egu23-11702, 2023.

EGU23-12126 | ECS | PICO | HS6.6

Evaluation of LDAPS Snow information by MERRA-2 and ASOS over the South Korea 

Hyunho Jeon, Jaehwan Jeong, Yangwon Lee, and Minha Choi

In the past decade, heavy snow has recorded the third-highest disaster damage in Korea after typhoons and heavy rain. In addition, snowfall is one of the important factors in the water cycle, and it directly affects hydrological factors such as evapotranspiration and soil moisture. Due to the topographical features of Korea, snowfall occurs heterogeneously, so it has limitations to use only in-situ data for snow monitoring. Although grid data such as remote sensing and model simulated data has been suggested as an alternative to this, it is also difficult to use only grid data due to the characteristics of snow that influence spectral behavior depending on grain size, age, etc. In this study, snow depth data was evaluated using model simulated data and ground observation data over the South Korea. For data, Local Data Assimilation and Prediction System [LDAPS] (provided with 3 hours of temporal resolution and 1.5 km of spatial resolution), Modern-Era Retrospective analysis for Research and Applications, version 2 [MERRA-2] (provided with 1 hour of temporal resolution and 0.5° × 0.623° of spatial resolution) and Automated Synoptic Observing System [ASOS] (provided with 1 hour of temporal resolution) were used. The applicability of each data was evaluated with topographic data, and long-term trend of snow depth was analyzed. This study can help to predict snow information, with the combination of various reanalysis data and model simulated forecast dataset.

 

Keywords: Snow Depth, LDAPS, MERRA-2, ASOS

 

Acknowledgment

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF-2021R1A6A3A01087645).

How to cite: Jeon, H., Jeong, J., Lee, Y., and Choi, M.: Evaluation of LDAPS Snow information by MERRA-2 and ASOS over the South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12126, https://doi.org/10.5194/egusphere-egu23-12126, 2023.

EGU23-12702 | PICO | HS6.6

Wet Snow Mapping in the Karakoram using SAR and Topographic Data 

Shiyi Li, Lanqing Huang, Philipp Bernhard, and Irena Hajnsek

Wet snow is a critical component of the cryosphere, and its spatial and temporal distribution has important implications for water resources, natural hazards, and the regional climate. However, mapping wet snow in alpine regions such as the Karakoram is challenging due to complex topography, harsh weather conditions, and limited in-situ observations.

Previous studies have shown that synthetic aperture radar (SAR) can effectively detect wet snow surfaces using the backscattering ratio between the current and reference images (e.g. the average of summer acquisitions). However, its regional application on a large-scale and complex terrain is hampered, as the ratio value is easily affected by the land cover, local topography, surface roughness, and snow wetness.

In this study, we present a new approach for mapping wet snow in the Karakoram using a combination of SAR data and topographic information. The SAR data used in the analysis were obtained from Sentinel-1, and the topographic data included a digital elevation model (DEM), slope angle, and slope aspect ratio. We first used a Gaussian Mixture Model to classify the ratio image of Sentinel-1 into wet snow (WS) and non-wet snow (NWS) classes, then transformed the two classes into a logistic function to characterize the probability of WS based on the backscattering ratio. Secondly, we categorized the image based on the topography and calculated the likelihood of WS for each topographic bin using the WS probability. The joint WS likelihood map was finally obtained by multiplying the WS probability on the backscattering ratio with the WS likelihood on topography, and a binary WS map was generated by setting a threshold on the joint likelihood map.

The proposed method was validated using snow maps generated from Sentinel-2 images. Compared with the traditional method of using only the SAR backscattering ratio, our method significantly reduced false negative detections and preserved the high true positive rate, indicating an improvement of robustness and accuracy by combining SAR and topographic data for regional wet snow mapping.

This study demonstrates a practical method of merging SAR backscattering features and topographic information for robust regional wet snow mapping in complex mountain ranges. It also provides new insights into the incorporation of different datasets using a probabilistic framework. With the proposed method, the operational monitoring of wet snow distribution in the Karakoram using SAR becomes feasible and reliable.

How to cite: Li, S., Huang, L., Bernhard, P., and Hajnsek, I.: Wet Snow Mapping in the Karakoram using SAR and Topographic Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12702, https://doi.org/10.5194/egusphere-egu23-12702, 2023.

EGU23-15295 | ECS | PICO | HS6.6 | Highlight

Analysis of ALOS2 L-band repeat-pass InSAR for the retrieval of Snow Water Equivalent over boreal forest. 

Jorge Jorge Ruiz, Juha Lemmetyinen, Ioanna Merkouriadi, Juval Cohen, Anna Kontu, Jouni Pulliainen, and Jaan Praks

The mass of seasonal snow is a challenging parameter to measure from space. This is a significant observational gap as information on snow mass would be required by diverse applications such as flood prevention, and water resource management. Snow Water Equivalent (SWE) describes the amount of liquid water that is stored in the snowpack. A promising technique to measure changes in SWE over time is repeat-pass Interferometric SAR (InSAR), since it provides high spatial resolution and reasonable temporal resolution. The retrieval technique relies on the phase difference induced by the increase in propagation path due to snow accumulation since snow has a higher permittivity than air [1]. The retrieval has been demonstrated using a wide range of sensors [1-3]. In a recent work [4], the usability of L-, S-, C-, and X- frequency bands (1-10 GHz) was analysed in the context of coherence conservation and SWE retrieval. L-band emerged as a solid candidate, as this band appeared more resilient against temporal decorrelation in snow while enabling retrieval of large amounts of SWE.

The Copernicus Radar Observation System for Europe in L-band (ROSE-L), estimated to be launched in 2028, is one of the six Copernicus high-priority Sentinel Expansion missions selected for implementation. The mission will consist of two satellites with a 180 degrees orbit phasing, allowing a temporal baseline of 6 days. We present an analysis of L-band ALOS2 imagery over Sodankylä, in northern Finland, applied for SWE retrieval using the InSAR method. The landscape is dominated by coniferous forest, presenting a challenge for large-scale retrieval of SWE. Due to ALOS2 revisit time of 14 days, it is prone to suffer from temporal decorrelation. We analysed the coherence conservation considering environmental events, land cover, canopy cover and topography. We introduce SnowModel [5], a high-resolution, spatially distributed physical snow evolution model, for comparison to InSAR SWE retrievals. SnowModel simulations were used to calibrate the interferometric phase, allowing a comparison between the two and demonstrating in which areas and under which conditions the retrieval works.

 

[1] T. Guneriussen, K. A. Hogda, H. Johnsen and I. Lauknes, "InSAR for estimation of changes in snow water equivalent of dry snow," in IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 10, pp. 2101-2108, Oct. 2001, doi: 10.1109/36.957273.

[2] T. Nagler et al., "Airborne Experiment on Insar Snow Mass Retrieval in Alpine Environment," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 4549-4552, doi: 10.1109/IGARSS46834.2022.9883809.

[3] S. Leinss, A. Wiesmann, J. Lemmetyinen and I. Hajnsek, "Snow Water Equivalent of Dry Snow Measured by Differential Interferometry," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 8, pp. 3773-3790, Aug. 2015.

[4] J. J. Ruiz et al., "Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 4306715, doi: 10.1109/TGRS.2022.3223760.

[5] Liston, Glen E.; Elder, Kelly. 2006. A distributed snow-evolution modeling system (SnowModel). Journal of Hydrometeorology. 7(6): 1259-1276

 

How to cite: Jorge Ruiz, J., Lemmetyinen, J., Merkouriadi, I., Cohen, J., Kontu, A., Pulliainen, J., and Praks, J.: Analysis of ALOS2 L-band repeat-pass InSAR for the retrieval of Snow Water Equivalent over boreal forest., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15295, https://doi.org/10.5194/egusphere-egu23-15295, 2023.

EGU23-15479 | PICO | HS6.6

Retrieval of snow grain size and albedo using EnMAP spaceborne observations 

Alexander Kokhanovsky, Maximillian Brell, Sabine Chabrillat, Saskia Förster, and Karl Segl

Cryosphere is an integral part of the terrestrial ecosystem with important linkages and feedbacks generated through its influence on moisture fluxes, hydrology, and climate change due to temporal changes in snow/ice extent, impurity load and albedo. Therefore, snow and ice properties including ice and snow albedo and extent are monitored using ground and satellite instrumentation. The measurements are performed at various temporal and spatial scales using passive and active remote sensing instrumentation in a broad spectral range. The high spatial resolution is highly relevant for studies of cryosphere due to the rapid horizontal variability of snow properties and impurity load (dust outbreaks, algae blooms, structures on the snow surface/sastrugi). The instruments with low spatial resolution are not capable to resolve fine scales of snow variability. The accurate information on specific snow features (e.g., spatial distribution of algae blooms) can be hardly detected using measurements performed on the scale 0.3-1.0km.  The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission (Guanter et al., 2015), which provides information on evolution of aquatic and terrestrial ecosystems including cryosphere on the spatial scale of 30m, is capable to resolve the fine scale variability of snow properties. The measurements of EnMAP can be also used to assess the sub-pixel snow variability for coarse spatial resolution satellite missions and assess the accuracy of satellite products derived on the coarse spatial grid (e.g., snow fraction). This paper is aimed at the adaptation of the previously proposed snow remote sensing technique (Kokhanovsky et al., 2023) to EnMAP measurements. The retrievals are based on the asymptotic radiative transfer theory valid for weakly absorbing multiply light scattering turbid media (Kokhanovsky, 2021). The local optical properties of snow are calculated using the geometrical optics approximation, which is a valid technique for snow due to large size of ice grains as compared to the wavelength of the incident solar light in the spectral range under study. In particular, the spectral snow albedo and snow grain size are retrieved using EnMAP measurements performed by the SWIR EnMAP detector in the spectral range 900 - 1283nm. The snow specific surface area (SSA) and broadband albedo (BBA) are also derived using EnMAP measurements. The example of retrievals over Concordia station in Antarctica is given. It has been found that the effective ice grain diameter is around 0.23mm, SSA=28m*m/kg, and BBA=0.81, which is similar to the values of snow parameters measured at this location at the same season using both ground and satellite instrumentation.

References

Guanter, L., H. Kaufmann, K. Segl, et al., 2015: The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation. Remote Sensing 7: 8830-8857.

Kokhanovsky, A., 2021: Snow Optics, Cham: Springer.

Kokhanovsky, A., B. Vandecrux, A. Wehrlé, O. Danne, C. Brockmann, and J. E. Box, 2023: Improved Retrieval of Snow and Ice Properties Using Spaceborne OLCI/S-3 Spectral Reflectance Measurements: Updated Atmospheric Correction and Snow Impurity Load Estimation. Remote Sensing 15: 1-25.

How to cite: Kokhanovsky, A., Brell, M., Chabrillat, S., Förster, S., and Segl, K.: Retrieval of snow grain size and albedo using EnMAP spaceborne observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15479, https://doi.org/10.5194/egusphere-egu23-15479, 2023.

EGU23-15926 | ECS | PICO | HS6.6 | Highlight

Liquid water content in a seasonal snowpack: a comparison between satellite products and model simulations 

Greta Cazzaniga, Ali Nadir Arslan, and Carlo De Michele

The spatial and temporal quantification of the liquid water content (LWC) of the snowpack in alpine regions provides information on the short-term availability of water, which could eventually lead to wet snow avalanches or river floods. The monitoring and forecasting of snow wetness are hence of paramount importance in many fields, from operational avalanche forecasting to hydropower production and flood prediction, when combined with hydrological models. 
Remote sensing is an essential tool for snow monitoring as it offers observations of the snowpack's physical properties. For instance, Sentinel-1 satellites provide C-band synthetic aperture radar (SAR) data at high temporal and spatial resolutions and are capable to detect the presence of wet snow.  On the other side, many snow models were built in literature to simulate snowpack mass dynamics in space and time (see e.g., Crocus and HyS model) and can provide predictions of variables of interest in snow hydrology, such as the LWC. 
In the present work, we aim at identifying and quantifying the differences between satellite products and model snow estimates. In particular, the comparison is led among (1) Sentinel-1-based wet-snow products, (2) HSAF products, coming from the processing of data from Earth observation satellites and revealing the wet or dry status of the snow mantle, and (3) simulations of the liquid water content from HyS model, a temperature-index model, leveraged in both its one-layer and two-layer version. The case study is the Mallero basin, a middle-size alpine basin, whose flow regime is strongly influenced by snow melting and glacier ablation in the spring and summer seasons. 
The comparison returns a good agreement between Sentinel-1 products and HyS simulations. The short period of mismatches between the two outputs is analyzed to identify the physical processes that the model is not able to reproduce. On the other side HSAF products have a coarser resolution if compared to Sentinel-1 products and for this, they can just provide a qualitative overview of the snow mantle status, over a middle-size basin. Moreover, such products are also limited by the effect of cloud covering that makes it impossible to have information on the snow wetness when it is present.

How to cite: Cazzaniga, G., Arslan, A. N., and De Michele, C.: Liquid water content in a seasonal snowpack: a comparison between satellite products and model simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15926, https://doi.org/10.5194/egusphere-egu23-15926, 2023.

EGU23-16000 | PICO | HS6.6 | Highlight

Role of snow for changes hydrological regimes in the Lena river basin 

David Gustafsson, Jude Musuuza, Katharina Klemeth, Denica Bozhinova, Andrea Popp, Liudmila Lebedeva, and Tetsuya Hiyama

The study investigates the role of snow for the climate change impacts in hydrological regimes across the Lena river basin in Yakutia, Eastern Siberia using a hydrological model constrained by in-situ and satellite-based snow and river discharge observations. The river runoff observations in large and medium sized rivers show an increase over recent decades that can be associated with increasing air temperature and precipitation, as well as changes in snow, glaciers, and permafrost. We assessed the relation between changes in snow and streamflow using the satellite-based ESA CCI snow data and the hydrological model HYPE. The streamflow trend analysis showed a general pattern of increasing monthly mean and minimum stream flows from October to April, but more frequent in larger river basins, and especially if the last 20 years are included in the trend analysis. This can be explained by the increasing autumn precipitation, but the absence of change in annual maximum flow and streamflow in June also suggests relation to changes in the snow. The snow data shows a pattern of decreasing maximum snow water equivalent in the western part of the study area, and a corresponding decreasing trend of number of days with snow cover. These results are in line with the trends in observed streamflow; a short snow cover period (and increasing amount of autumn and winter rainfall, not shown here) as well as a lower maximum snow water equivalent could contribute both to the increasing winter runoff, and the absence of increasing streamflow in early summer. 

This work was conducted as part of the HYPE-ERAS project funded by FORMAS (project DNR: 2019-02332), RFBR (project No. 20-55-71005), and JST (Grant No. JPMJBF2003) through the Belmont Forum Collaborative Research Action: Resilience in the Rapidly Changing Arctic.

How to cite: Gustafsson, D., Musuuza, J., Klemeth, K., Bozhinova, D., Popp, A., Lebedeva, L., and Hiyama, T.: Role of snow for changes hydrological regimes in the Lena river basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16000, https://doi.org/10.5194/egusphere-egu23-16000, 2023.

EGU23-16822 | PICO | HS6.6

An integrated remote-sensing approach for prairie snowpack 

Eric A. Sproles, Ross T. Palomaki, Madison Woodley, and Samual E. Tuttle

In a low-relief, agricultural landscape we integrate detailed measurements from plane-based L-band SAR (UAVSAR), drone-based LiDAR and photogrammetry, cosmic ray neutron sensor (CRNS), and field assessments to disentangle and quantify how topography, wind, and vegetation influence the spatial distribution snow cover and water equivalent. Seasonal snow in prairie and temperate grasslands environments helps sustain agriculture, socio-environmental systems, and aquifers while also exacerbating flooding in wetter years. Because these expansive landscapes cover roughly 10% of the earth’s surface, quantifying snow and snow water equivalent (SWE) is critical to better resolve water and energy budgets from local to global scales. Present day, remotely-sensed observations and conventional automated ground-based observations (e.g. SWE scales) of seasonal snow in these biomes contain considerable uncertainty. Optical imagery can detect the presence/absence of snowpack, but lacks the capacity to provide estimates of SWE. Synthetic Aperture Radar (SAR) provides a potential path forward to quantify SWE in grassland and agricultural environments, but current measurements are poorly constrained, especially in prairie environments. The Central Agricultural Research Center (CARC) in central Montana, USA (47ºN, 110ºW) served as field site for NASA’s SnowEx 2021 Mission and was distinct from other campaign locations due to its prairie landscape, controlled agricultural vegetation patterns, and ephemeral snow cover. The CRNS measures an integrated snow signal over several hectares, allowing for continuous estimations of SWE that are less influenced than smaller scale observations by the significant spatial heterogeneity of prairie snow. Initial results show that CRNS effectively quantifies an integrated SWE signal at the study site (R2 ≥ 0.90).  Interferometric UAVSAR products and drone flights provide complementary high resolution snow information for narrow time periods that effectively identify snow presence across areas with different crop types (wheat, barley, peas) and stubble heights (0-0.6 m) . The limited number of UAVSAR flights in 2021 preclude a full season or multi-year analysis. However our integrated sensing approach and analysis provides a framework to reduce uncertainty in future efforts, and better constrain measurements from the upcoming L-band NISAR mission that is expected to be launched in January 2024.   

How to cite: Sproles, E. A., Palomaki, R. T., Woodley, M., and Tuttle, S. E.: An integrated remote-sensing approach for prairie snowpack, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16822, https://doi.org/10.5194/egusphere-egu23-16822, 2023.

EGU23-17234 | ECS | PICO | HS6.6

Tower based C-band measurements of an alpine snowpack 

Isis Brangers, Hans-Peter Marshall, Grabielle J.M. De Lannoy, and Hans Lievens

Measuring snow from space is still a significant challenge in hydrology. Work by Lievens et al. (2019) for the first time showed the potential of the Sentinel-1 C-band radar mission to measure snow depth from space. However, the physical interactions between snow grains and the comparatively long C-band waves are not sufficiently understood. To improve this understanding, a tower based C-band radar experiment was set up in Idaho’s Rocky mountains starting from January 2020. The ultra-wideband radar system recorded the reflections in the time-domain, allowing to study the return throughout the different layers of the snowpack at a fine resolution. Reference data of the stratigraphy and snow properties were collected during ~weekly site visits. Our results indicate that some volume scattering is present at C-band for dry snow, and that the backscatter return increases substantially after melt-freeze cycles and with the appearance of ice features within the snowpack.

How to cite: Brangers, I., Marshall, H.-P., De Lannoy, G. J. M., and Lievens, H.: Tower based C-band measurements of an alpine snowpack, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17234, https://doi.org/10.5194/egusphere-egu23-17234, 2023.

EGU23-675 | ECS | Posters on site | GM2.2

Can we monitor shallow groundwater using ambient seismic noise? 

Antonia Kiel, René Steinmann, Eric Larose, and Céline Hadziioannou

Nowadays, the majority of detailed information about groundwater is acquired by wells that provide limited insight in time and especially space. Therefore, it would be interesting to monitor groundwater by continuously measuring seismic velocity changes in the subsurface. The shallow soil is affected by environmental influences like temperature, rainfall or drought, which in turn changes the seismic velocity in the subsurface.

In this study, we use three-component seismometers, which are placed next to an in-situ measurement station of soil conditions (moisture and temperature at different depths) and a meteorological station in the city of Hamburg, Germany. We investigate the sensitivity of high-frequency (> 1 Hz) seismic waves with an anthropogenic origin to ground moisture changes in the uppermost layers of soil. To monitor velocity changes, Passive Image Interferometry is applied. Using the three-component data, we are able to retrieve Rayleigh and Love waves. Relative velocity changes are retrieved using the stretching method. A comparison of seasonal seismic velocity changes and environmental changes shows a positive correlation between velocity and temperature, as well as a negative correlation between velocity and groundwater content. Freezing events are exceptions, as they cause relative velocity increases twice as high as seasonal changes.

The aim of this work is to eliminate temperature effects to work towards inferring water content directly from seismic velocity changes. To eliminate the contribution of temperature, its relation to seismic velocity changes and water content is quantified using regression. Since the relative velocity change is influenced by both temperature and water content, a time period of stable water content is used to quantify the relation between velocity change and temperature. As a result, the residual relative velocity change reproduces the residual water content.

How to cite: Kiel, A., Steinmann, R., Larose, E., and Hadziioannou, C.: Can we monitor shallow groundwater using ambient seismic noise?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-675, https://doi.org/10.5194/egusphere-egu23-675, 2023.

EGU23-714 | ECS | Orals | GM2.2

Seismic imaging of the submarine Kolumbo Volcanic Chain reveals its volcano-tectonic evolution and link to Santorini 

Jonas Preine, Christian Hübscher, Jens Karstens, Gareth Crutchley, and Paraskevi Nomikou

Located in the southern Aegean Sea, the Christiana-Santorini-Kolumbo volcanic field is one of the most hazardous volcanic regions in the world and lies in an active continental rift zone. Northeast of Santorini lies the Kolumbo Volcanic Chain (KVC), which comprises more than 20 submarine volcanic cones, with the Kolumbo volcano representing the most prominent edifice of this chain. However, due to their inaccessibility, little is known about the spatio-temporal evolution and tectonic control of these submarine volcanoes and their link to the volcanic plumbing system of Santorini. We will present multichannel reflection seismic data that allow us to image the internal architecture of the KVC and study its link to Santorini. Using a seismostratigraphic framework, we are able to show the KVC evolved during two episodes, which initiated at approx. 1 Ma with the formation of mainly effusive volcanic edifices along a NE-SW trending zone. Most of the cones of the second episode represent submarine pumice cones that were formed by submarine explosive eruptions between 0.7 and 0.3 Ma and partly developed on top of volcanic edifices from the first episode. Our data show that two prominent normal faults underlie the KVC, indicating a direct link between tectonics and volcanism. In addition, we are able to reveal several buried volcanic centers and a distinct volcanic ridge connecting the KVC with Santorini, suggesting a connection between the two volcanic centers in the past. We argue that this connection was interrupted by a major tectonic event and, as a result, the two volcanic systems now have separate, largely independent plumbing systems despite their proximity.

How to cite: Preine, J., Hübscher, C., Karstens, J., Crutchley, G., and Nomikou, P.: Seismic imaging of the submarine Kolumbo Volcanic Chain reveals its volcano-tectonic evolution and link to Santorini, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-714, https://doi.org/10.5194/egusphere-egu23-714, 2023.

EGU23-900 | ECS | Posters on site | GM2.2

Optimising passive seismic investigations of the ice-bedrock interface zone for the great ice sheets 

Ian Kelly, Anya Reading, Tobias Staal, and Andrew Bassom

The need to better predict how the great ice sheets will respond to continued atmospheric and ocean warming is paramount. Ice deformation and mechanisms for ice sliding across the bedrock underneath are both key considerations. Constraints of this critical ice-bedrock interface zone, particularly over extensive inland areas of Antarctica and Greenland, remain a major hurdle in ice-sheet modeling and estimations of future sea level rise.

Passive seismology offers a logistically-efficient avenue for such investigations, with improvements in sensor technologies, autonomous power solutions and telemetry systems encouraging the deployment of temporary arrays for subglacial mapping and real-time monitoring. Previous experiments have demonstrated the potential of techniques such as receiver functions, horizontal-to-vertical spectral ratios (HVSR) and ambient noise interferometry for characterising the depth and nature of the ice-bedrock zone. This research looks to fully explore the sensitivity range of available passive seismic methods for the ice-bedrock interface, with a view towards optimising data collection and array geometries for future applications. In this contribution, we present an optimised workflow making use of HVSR analysis and the spatial autocorrelation (SPAC) technique using numerical simulations and field data collected from East Antarctica. The results from this study provide a benchmark to guide future deployments in the polar regions.

How to cite: Kelly, I., Reading, A., Staal, T., and Bassom, A.: Optimising passive seismic investigations of the ice-bedrock interface zone for the great ice sheets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-900, https://doi.org/10.5194/egusphere-egu23-900, 2023.

Karst is a landscape with distinctive hydrology and landforms that arise when the underlying rock is soluble. Locating the flowing conduits and pathways in karst is important in terms of water resource management, groundwater flooding, geotechnical and engineering projects. Understanding flow pathways is particularly important for road and railway construction, so as not to adversely affect hydrological networks, in particular those associated with Turloughs.

The aim of this study was to develop methods for directly detecting energetic groundwater flow in sub-surface conduits through passive seismic applications, by detecting the small ground vibrations (seismic microtremor) that flowing water in the sub-surface may generate. This is in contrast to the current ‘traditional’ approach of attempting to actively image the conduits using geophysical and other methods, in order to determine the geometry of flow paths. The imagery of conduits in karst is a very difficult problem and determining if they contain flowing structures is also a very significant challenge using traditional methods, which is the motivation for developing a new approach to the problem.

We undertook experiments at two sites on karst in Ireland; one gently-sloping shallow conduit and one relatively deep and complex-structured conduit. We chose these sites as the caves had previously been dived and we had access to the shapefiles of these caves to ground-truth our findings.

We observed that subterranean flow-related micro-tremor in karst appears as persistent frequency bands on the spectrograms that vary with time and seismic station location with respect to the conduit. This persistent frequency is different than the soil resonating frequency and relates to the subterranean water flow in the conduits. Application of an Amplitude Location Method (ALM)  clearly delineated the conduit as the source of the micro-tremor.

We also conducted an active Airgun experiment at the second site to locate the conduit by tracking a pressure wave, using two arrays of surface seismic stations, as it propagated into the conduit. This combination of detecting and locating seismic microtremor generated by water flow in the conduits and the use of seismic array analysis to track active Airgun source pressure waves propagating at depth in conduits offers a new tool kit for karst hydrology determination. In the next step, we will assess the applicability of Distributed Acoustic Sensing (DAS) using fiber optic cables as sensors for detecting sub-surface water flow, where we expect unrivaled spatial resolution of the flow-induced seismic wavefield. Such a study would be the first attempt to fill the current gap regarding an understanding of karst groundwater dynamics along the entire conduit pathway, at an exceptionally high spatial scale.

How to cite: Karbala Ali, H., Bean, C. J., and Chalari, A.: Detection and source location of the groundwater-induced seismic signal in karst using a combination of passive and active seismic approaches, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1046, https://doi.org/10.5194/egusphere-egu23-1046, 2023.

EGU23-1601 | ECS | Orals | GM2.2

Groundwater Heights Prediction from Seismic Waves with Machine Learning 

Anthony Abi Nader, Julie Albaric, Marc Steinmann, Clément Hibert, Jean-Philippe Malet, Benjamin Pohl, and Christian Sue

Unlike surface water reservoirs, that can be easily quantified and monitored, underground conduits in karst systems are often inaccessible, hence challenging to monitor. Seismic noise analysis was proved to be a reliable tool to monitor ground water storage in a fractured rock aquifer (Lecocq et al. 2017). In underground karstic environments, seismic noise monitoring was able to detect hydrological cycles and monitor the groundwater-content variations (Almagro Vidal et al. 2021). The following approach relies on coupling passive seismic wavefield with hydrological data in a machine learning algorithm in order to monitor underground water heights. The studied site is the Fourbanne karst aquifer (Jura Mountains, Eastern France, Jurassic Karst observatory). The underground conduit is accessible through a drilled shaft and instrumented by two 3-component seismological stations, one located underground and the other one at the surface, and a water height probe. We applied a new approach based on the machine learning random forest (RF) algorithm and continuous seismic records (Hibert et al., 2017), to find characteristic signals to predict the underground river water height. The method consists on the computation on a sliding window of seismic signal features (waveform, spectral and spectrogram features) and using the corresponding water height at the same time window to train the algorithm, and then apply it on new data. The RF algorithm is capable of accurately detecting flooding periods and reproduce the groundwater heights with an efficiency exceeding 95% and 53% using the Nash-Sutcliffe criterion for the seismic stations located in the underground conduit and at the surface respectively. The obtained results are a first promising outcome for the remote study of water circulation in karst aquifers using seismic noise.

How to cite: Abi Nader, A., Albaric, J., Steinmann, M., Hibert, C., Malet, J.-P., Pohl, B., and Sue, C.: Groundwater Heights Prediction from Seismic Waves with Machine Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1601, https://doi.org/10.5194/egusphere-egu23-1601, 2023.

EGU23-1677 | ECS | Posters virtual | GM2.2

Event Relations and Sources of Icequakes at the Grounding Line of Rutford Ice Stream, West Antarctica 

Ian Lee, Sridhar Anandakrishnan, Richard Alley, Alex Brisbourne, and Andrew Smith

Basal icequakes are generated as a glacier slides over its underlying bedrock, and the stick-slip motion of constant loading and unloading releases shear stresses that produce these very small magnitude (ML < 0) glacial microseisms. Detecting and locating nucleation of these fine-scale icequakes can provide highly useful insights into the deformation processes occurring at the bed and consequently the mechanisms governing glacier flow. We present icequake data derived from a seismic array installed at the grounding line of the Rutford Ice Stream in West Antarctica by Penn State University and the British Antarctic Survey during the 2018/19 austral summer. The region’s natural source seismicity was first processed using the earthquake detection and location software QuakeMigrate and the events were relatively relocated using HypoDD/GrowClust. We then clustered the events into sticky spot clusters using the unsupervised clustering algorithm DBSCAN, and finally from the clusters we selected “model” waveforms to perform template matching on the original seismic traces to create methodically comprehensive high-resolution icequake catalogs at the grounding line of Rutford. We present our methodology including the complete processing pipeline (supplemented by developed supporting open-source scripts) along with key tuning parameters, and describe how our catalogs were used to resolve glacier sliding patterns and key topographical features and characteristics of the bed like sticky spots. We additionally explore the effects of tidal modulation and Rutford ice flow motion on icequake occurrences. Our seismic traces primarily contain icequake signals that derive from stick-slip sliding, but also unique waveforms that might be derived from crevassing and teleseisms that we will also explore. Our results show that stick-slip basal icequakes and these resultant icequake catalogs are valuable data-rich resources that help improve our understanding of glacier flow dynamics and will be important toward improving glacier flow models used for constraining global mean sea level rise.

How to cite: Lee, I., Anandakrishnan, S., Alley, R., Brisbourne, A., and Smith, A.: Event Relations and Sources of Icequakes at the Grounding Line of Rutford Ice Stream, West Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1677, https://doi.org/10.5194/egusphere-egu23-1677, 2023.

EGU23-2707 | Orals | GM2.2

Thermo-Acousto-Elasticity (TAE) of natural rock cliffs: toward better understanding and monitoring damage and erosion process 

Eric Larose, Antoine Guillemot, Laurent Baillet, and Pierre Bottelin

Rainfalls and freeze-thaw cycles are well known to largely contribute to rock slopes erosion, including chemical processes (dissolution, alteration) together with mechanical action (stress change in fractures due to water freezing). The role of heat waves and thermal cycles is less studied in dry conditions. Here we present a thermo-acousto-elastic (TAE) model for rock volumes exposed to cyclic (daily to seasonal) thermal forcings, as an application of environmental seismology (1).

In our model, we assume that the rock temperature is constant at depth (a few meters in general), and that the free surface is exposed to heat fluxes (radiative and convective ones). In practice, these heat fluxes can be respectively derived from solar radiation normal to the rock surface and from the air temperature, both parameters are easily measured in the field. We then develop a numerical model based on a) thermal diffusion (heat propagation in the rock in 2D or 3D models, including complex geometries as cracks, rock columns…), b) thermal expansion relating temperature to strain, and c) acousto-elasticity relating the elastic parameters to the state of stress, (2). Such a model is run, for example, with COMSOL Multiphysics with a finite element scheme. We end up with a 2D or 3D numerical model of stress and deformation of the rock volume evolving over time ranging from sub-daily to yearly time scales.

As an application we test this model on various rock columns and observe that the developed model properly reproduces field observations, including daily and seasonal cycles: the natural resonance frequency of the rock column, a proxy for its rigidity, increases with increasing heat flux (3) and the rear crack closes up. As a result of fitting our numerical model to natural rock columns, we can evaluate the acousto-elastic constant that relates the rigidity to the state of stress, a parameter that is known to mainly depend on the state of damage of the material, opening the route for rockfall risk assessment, monitoring and early warning systems. Our model also allows to shed new light into fatigue and cyclic damage process of rock slopes and cliffs, a key to rock erosion.

 

References:

  • (1) Guillemot, L. Baillet, E. Larose, P. Bottelin : Changes in resonance frequency of rock columns due to thermoelastic effects on a daily scale : observations, modeling and insights to improve monitoring, Geoph. J. Int. 231, 894-906 (2022).
  • (2) Larose, E. & Hall, S.: Monitoring stress related velocity variation in concrete with a 2.10−5 relative resolution using diffuse ultrasound, J. acoust. Soc. Am., 125, 1853–1856 (2009).
  • (3) Bottelin, P., Levy, C., Baillet, L., Jongmans, D. & Gueguen, P.: Modal and thermal analysis of Les Arches unstable rock column (Vercors massif, French Alps), Geophys. J. Int., 194, 849–858 (2013).

How to cite: Larose, E., Guillemot, A., Baillet, L., and Bottelin, P.: Thermo-Acousto-Elasticity (TAE) of natural rock cliffs: toward better understanding and monitoring damage and erosion process, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2707, https://doi.org/10.5194/egusphere-egu23-2707, 2023.

EGU23-3010 | Posters on site | GM2.2

Identification of bedrock depth and blind fault by HVSR analysis along two profiles in Pohang, South Korea considering optimal weather environment and seismometer burial depth 

Su Young Kang, Kwang-Hee Kim, Doyoung Kim, Byungmin Kim, Lanbo Liu, and Youngcheol Lee

Many deep faults do not reach the earth’s surface and thus are not recognized. Such faults are rarely mapped by standard surface geological mapping. This seriously hinders seismic risk mitigation efforts. In this study, we applied the horizontal-to-vertical spectral ratio (HVSR) method to identify blind faults invisible at the surface. Despite its simplicity and low-cost implementation, we noticed that HVSR results were unstable using data collected by exposed seismometers or under higher wind speeds. Therefore, three-component seismic sensors for ambient noise observations were buried at different depths to examine the effects of ground coupling, wind speeds, and precipitations. Results from a series of field tests under diverse conditions guided us to establish data selection criteria. The first required condition is that seismic sensors should be buried (>0.3 meters) to secure ground coupling and to avoid any direct exposure to wind or precipitations. The other is that data should be collected at low wind speeds (< 3 m/s). The requirements were applied to ambient noise data along two profiles traversing unnamed and inferred faults in Pohang, Korea. We initially estimated the resonance frequencies for each site, which varied from 0.41 to 2.52 Hz. They were then converted to bedrock depths using an empirical relationship between the resonance frequency and depth to bedrock observed at boreholes in the area. The estimated depths to bedrock along profiles ranged from 8.0 to -472.0 meters. The resulting depth profiles show significant lateral variations in the bedrock depth, including the one near the Gokgang fault at which the thickness to the major impedance contrasts decreased from 196 to 20 meters. Sudden variations were also observed at unexpected locations along the profile. We examined the details, especially for sites of apparent changes in bedrock depth, and compared their characteristics with other geophysical studies, including Vs30, MASW, Bouguer gravity anomaly, and adjacent stations correlation. Their results are all well correlated to each other and indicate rapid changes in bedrock depth. We attribute the rapid changes to vertical displacements by ancient faulting activity.

How to cite: Kang, S. Y., Kim, K.-H., Kim, D., Kim, B., Liu, L., and Lee, Y.: Identification of bedrock depth and blind fault by HVSR analysis along two profiles in Pohang, South Korea considering optimal weather environment and seismometer burial depth, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3010, https://doi.org/10.5194/egusphere-egu23-3010, 2023.

EGU23-3593 | Posters on site | GM2.2

Meteo-Seismology: Harvesting the Seismic Signals of Weather Dynamics in the Critical Zone 

Michael Dietze, Christian Mohr, Violeta A. Tolorza, Benjamin Sotomayor, and Erwin Gonzalez

Weather conditions are an important driver of Earth surface dynamics, such as gravitational mass wasting, flood propagation, biological activity events and physical interactions within the critical zone. While there are dedicated sensors to capture meteorological parameters, these sensors are comparably expensive, have a small spatial footprint and often lack the temporal resolution needed to constrain high frequency meteorological dynamics. We introduce the concept of meteo-seismology, i.e. the measurement of first-order ground motion signatures of weather conditions by decisively installed seismic sensors. While meteorological manifestations are generally considered seismic noise and it may seem odd to use seismometers instead of weather stations, geophysical sensors circumvent or complement the above caveats and add further important data to a comprehensive picture of the rapidly changing state of the atmosphere and its interaction with the landscape we live in. Based on examples from prototype forested landscapes in Central Europe and Chilean Patagonia, we demonstrate how seismic stations can be used to infer properties of the pressure and wind field and its coupling to the biosphere, constrain rain intensity and drop properties, yield temperature proxies and their propagation into the ground, and survey ground moisture trends and discharge patterns. Understanding the seismic signatures of a meteorological origin also allows to, vice versa, better handle the contaminating side of these seismic sources in records, where high frequency signals are to be used for other than meteo-seismological studies. Our approach offers an alternative and complementary way to non-invasively monitor hydrometeorological energy and matter fluxes at high temporal and spatial resolution.

How to cite: Dietze, M., Mohr, C., Tolorza, V. A., Sotomayor, B., and Gonzalez, E.: Meteo-Seismology: Harvesting the Seismic Signals of Weather Dynamics in the Critical Zone, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3593, https://doi.org/10.5194/egusphere-egu23-3593, 2023.

Large rockfalls often cause huge economic losses and casualties in densely populated mountain areas. Timely acquiring information on a large rockfall can help promptly assess the damage and residual risks and guide the emergency response. Recent works suggest that the seismic signals generated by large rockfalls can provide these key information, but most of them focused on exploring seismic signatures to understand rockfall dynamics, lacking a rapid disaster assessing scheme. Here, we establish a seismic signal-based assessment scheme and demonstrate its capability by taking a large event – the 5 April 2021 Hongya rockfall (Sichuan, China) – as a case study. This scheme consists of three components, which are rockfall identification, detection and location, and characterization. In the rockfall identification module, we show how a rockfall can be distinguished from an earthquake and a rockslide by analyzing its seismic signatures. In the detection and location module, we demonstrate how the kurtosis-based method can be used to rapidly detect the initiation of a rockfall and determine the seismic wave velocity accordingly, and how the arrival-time-based location method can be used to locate a rockfall event. In the rockfall characterization module, we show how rockfall volume can be estimated from the magnitude of radiated seismic energy and how to characterize the dynamic process of a rockfall by the signatures of seismogram, spectrum and recorded seismic energy. Our results show that the seismic signal-based scheme presented here is suitable to characterize large rockfalls and has certain potential for rapid and effective emergency management.

How to cite: Li, W., Wang, D., and Zhang, Z.: Large rockfall detection, location and characterization using broadband seismic records: A case study of Hongya rockfall, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3773, https://doi.org/10.5194/egusphere-egu23-3773, 2023.

EGU23-4500 | Orals | GM2.2

Ambient noise monitoring of the Bayou Corne sinkhole evolution 

Aurelien Mordret, Anais Lavoué, Benjamin Witten, Adam Baig, Sophie Beaupretre, Romeo Courbis, and Chloé Gradon

The collapse at depth of a cavern on the side of the Napoleonville salt dome, Assumption Parish, Louisiana, led to the formation of a large sinkhole at the surface. Besides surficial evidence from direct observations, the precise timeline of the evolution of the sinkhole is poorly known.  Here, we used two years of continuous ambient seismic vibrations recorded at 11 3-component seismic stations located around the Bayou Corne sinkhole to monitor the daily relative seismic velocity changes associated with the sinkhole activity. The sinkhole started to form in 2012 and had several phases of activity. The seismic network was installed in early 2013 and recorded the last major collapses before settling in 2014. Following standard seismic interferometry processing, we computed the full 9-component tensors of ambient vibrations cross-correlations between each pair of sensors. After a drastic quality check of the correlations, we rejected several components for which we did not have enough data or for which the data were corrupted in a way that was difficult to correct. We monitored the relative velocity variations (dv/v) during the studied period using the stretching method in the 0.9-3 Hz frequency band within the early coda of the correlations. We employed a reference-less inversion procedure to obtain a dv/v time series for each component and each pair of stations. The multi-component pairs curves are averaged to get the final time series. The results show significant velocity changes in early 2013 associated with the collapse phases of the sinkhole. The velocity recovers steadily after the second half of 2013 and all of 2014. Two seismically active periods generate smaller velocity drops. In agreement with the spatial extension of the sinkhole toward the southwest seen from the surface, the pairs of stations the most affected by large velocity drops are the ones located along the southwestern shore of the lake.
Our monitoring allows for refining the timeline of the events affecting the sinkhole and its overall activity with a daily temporal resolution. From the analysis of these two years of data, the sinkhole stabilized after intense activity in early 2013. The large velocity variations indicate a strong destructuring of the ground, with potential fracturing and water invasion.

How to cite: Mordret, A., Lavoué, A., Witten, B., Baig, A., Beaupretre, S., Courbis, R., and Gradon, C.: Ambient noise monitoring of the Bayou Corne sinkhole evolution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4500, https://doi.org/10.5194/egusphere-egu23-4500, 2023.

EGU23-5344 | Orals | GM2.2 | Highlight

Tracking storms in the Pyrenees using a dense seismic network 

Jordi Diaz, Mario Ruiz, Mireia Udina, Francesc Polls, Davis Martí, and Joan Bech

Data acquired by a dense seismic network deployed in the Cerdanya basin (Eastern Pyrenees) is used to track the temporal and spatial evolution of meteorological events such as rainfall episodes or thunderstorms. Comparing seismic and meteorological data, we show that for frequencies above 40 Hz, the dominant source of seismic noise is rainfall and hence the amplitude of the seismic data can be used as a proxy of rainfall. The interstation distance of 1.5 km provides an unprecedented spatial resolution of the evolution of rainfall episodes along the basin. Two specific episodes, one dominated by stratiform rain and the second one dominated by convective rain, are analyzed in detail, using high resolution disdrometer data from a meteorological site near one of the seismic instruments.

Seismic amplitude variations follow a similar evolution to radar reflectivity values, but in some stratiform precipitation cases, it differs from the radar-derived precipitation estimates in this region of abrupt topography where radar may suffer antenna beam blockage. Hence, we demonstrate the added value of seismic data to complement other sources of information such as rain-gauge or weather radar observations to describe the evolution of ground-level rainfall fields at high spatial and temporal resolution. The seismic power and the rainfall intensity have and exponential relationship and the periods with larger seismic power are coincident. The time periods with rain drops diameters exceeding 3.5 mm do not result in increased seismic amplitudes, suggesting that there is a threshold value from which seismic data are no longer proportional to the size of the drops.

Thunderstorms can be identified by the recording of the sonic waves generated by thunders. We show that single thunders can be recorded to distances of a few tens of kilometers. As the propagation of these acoustic waves is expected to be strongly affected by parameters as air humidity, temperature variations or wind, the seismic data could provide an excellent tool to investigate atmospheric properties variations during thunderstorms.

How to cite: Diaz, J., Ruiz, M., Udina, M., Polls, F., Martí, D., and Bech, J.: Tracking storms in the Pyrenees using a dense seismic network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5344, https://doi.org/10.5194/egusphere-egu23-5344, 2023.

EGU23-5610 | ECS | Orals | GM2.2

Evidence of sub-surface water flow dynamics within a karst conduit from ambient noise monitoring 

Axelle Pantiga, Vincent Allègre, Roland Lastennet, Nicolas Houillon, Sylvain Mateo, Fabien Naessens, and Alain Denis

Karst aquifers are characterized by their heterogeneity and complex underground geometry. A great part of the world relies on karst resources for drinkable water and understanding the functioning of karst systems is essential to assess their vulnerability and response to rainfall. Relevant continuous parameters to quantify the underground flow dynamics are still required for these studies as direct underground measurements are not possible. We used surface ambient noise measurements to estimate the seismic signature and amplitude associated with the water flow within an underground karst conduit. We combined geophysical measurements with hydro-chemical and hydrogeological data to build a multidisciplinary approach. The experimental site is the Glane spring, in Dordogne (France). The hydrogeological catchment of this Vauclusian-type spring is 75 km² and consists of upper Jurassic carbonate rocks. The Glane spring shows rapid and intense variations of discharge following rainfall events, ranging from 0.1 to 4 m3/s in 2021. Ambient noise has been continuously recorded since December 2021 using four seismic stations deployed upstream of the source and above the well-known karst terminal conduit. Hydro-chemical parameters and water level have been continuously monitored during a full hydrological cycle and a rain gauge was installed on site to monitor rainfall. During the first year of monitoring, we identified six flooding events. Each event was characterized by an increase in water flow associated with an increase in the seismic signal amplitude. We observed that the seismic amplitude standard level is higher during the high-water period than during the low water period suggesting a larger base water flow. We also observed hysteresis between the seismic power and hydro-chemical parameters. Correlations between the seismic recordings and hydrochemistry might suggest a change in water flow regime within the conduit prior to the flood. Seismic power variations associated with discharge variations are similar to what was already observed for sub-glacial melting flow. Other springs and swallow holes are currently instrumented to validate the approach in the field.

How to cite: Pantiga, A., Allègre, V., Lastennet, R., Houillon, N., Mateo, S., Naessens, F., and Denis, A.: Evidence of sub-surface water flow dynamics within a karst conduit from ambient noise monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5610, https://doi.org/10.5194/egusphere-egu23-5610, 2023.

EGU23-6049 | ECS | Posters on site | GM2.2

Towards quick clay monitoring in the city of Oslo, Norway with urban seismic noise 

Charlotte Bruland, Andreas Köhler, and Volker Oye

Historically, there is one larger quick clay landslide in Norway every year. Since 80 percent of those happen in known quick clay risk areas, it is important to monitor these sites continuously. Alna, a busy, urban area in Oslo, is an example of such a location where a quick clay slide could lead to substantial human and economical losses.

In this study we use ambient noise methods to monitor changes in the subsurface at Alna using a small array of three-component seismic sensors. To retrieve small velocity changes, we apply coda wave interferometry using 12 months of urban seismic noise (above 1 Hz).

We compare the observed day-to-day changes to air temperature, precipitation, and water levels in a nearby river, and observe environmental velocity fluctuations well correlated with air temperature and precipitation. In particular, freezing and thawing produces strong changes in seismic velocity (up to 4 percent). The surface wave-coda used here is sensitive to changes in shear wave velocity, which in turn can be used to detect changes of the sub-surface properties. Therefore, observed velocity variations at Alna could have potential for monitoring and early warning of quick clay instabilities.

How to cite: Bruland, C., Köhler, A., and Oye, V.: Towards quick clay monitoring in the city of Oslo, Norway with urban seismic noise, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6049, https://doi.org/10.5194/egusphere-egu23-6049, 2023.

EGU23-6264 | Orals | GM2.2

Stalagmites' reactions to ground motion studied using modified Raspberry Shake and nodal sensors 

Aurélie Martin, Thomas Lecocq, Ari Lannoy, Yves Quinif, Thierry Camelbeeck, and Nathalie Fagel

Karstic zones are numerous on Earth and offer a particular field of study to evaluate the ground motion levels that occurred in the past in support of regional seismic hazard assessment. Indeed, some fine and slender candlestick stalagmites are intact and therefore indicate that a certain level of ground motion has not been exceeded since they exist. Many parameters must be considered in the behaviour of stalagmites to earthquakes such as their shape, their mechanical properties and their natural frequency. A good way to better understand and characterize the reaction of these stalagmites to earthquakes is to study their reaction to the current permanent ground motion. To do this, a study based on the measurement of ambient seismic noise is underway in the cave of Han-sur-Lesse (Ardenne, Belgium). The ambient seismic noise is measured both at the surface (above the limestone massif and in the nearest village), on the floor of the cave and on the stalagmites themselves. Different three-component seismic sensors are used in parallel: three SmartSolo IGU-16HR 3C and two Raspberry Shake 3D Personal Seismographs, one of which has been adapted to be easily attached to the stalagmites. This parallel configuration during two-week recording periods made it possible to determine the eigenfrequencies and the polarization of the associated movements of 16 stalagmites. In addition, daily and weekly variations in ambient noise and transient events are measured such as earthquakes, quarry explosions or flooding in the cave. The presence of sensors in different places over the same period also makes it possible to study the possible impact of the cave's local characteristics on these measurements.

How to cite: Martin, A., Lecocq, T., Lannoy, A., Quinif, Y., Camelbeeck, T., and Fagel, N.: Stalagmites' reactions to ground motion studied using modified Raspberry Shake and nodal sensors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6264, https://doi.org/10.5194/egusphere-egu23-6264, 2023.

EGU23-6300 | Posters on site | GM2.2

Towards an unsupervised generic seismic detector for hazardous mass-movements: a data-driven approach 

Patrick Paitz, Małgorzata Chmiel, Lena Husmann, Michele Volpi, Francois Kamper, and Fabian Walter

Hazardous mass-movements pose a great danger to the population and critical infrastructure, especially in alpine areas. Monitoring and early-warning systems can potentially save many lives and improve the resilience of mountain communities to catastrophic events. Increasing coverage of seismic networks recording hazardous mass-movements opens up new warning perspectives as long as efficient algorithms screening the seismic data streams in real-time are available.

We propose to combine physical and statistical properties of seismic ground velocity recordings from geophones and seismometers as a foundation for an unsupervised workflow for mass movement detection. We evaluate the performance, consistency, and generalizability of unsupervised clustering algorithms like K-means and Bayesian Gaussian Mixture Models against supervised methods like the Random Forest classifier. Focusing on debris-flow records at the Illgraben torrent in Switzerland, we present a generic mass-movement detector with high accuracy and early-warning capability. We apply this detector to other datasets form other sites to investigate its transferability.

Since our results aim to enable mass-movement monitoring and early-warning worldwide, Open Research Data principles like Findability, Accessibility, Interoperability and Reusability (FAIR) are of high importance for this project. We discuss how using the Renku (renkulab.io) platform of the Swiss Data Science Center ensures FAIR data science principles in our investigation. This is a key step towards our ultimate goal to enable seismology-based early warning of mass-movements wherever it may be required.

How to cite: Paitz, P., Chmiel, M., Husmann, L., Volpi, M., Kamper, F., and Walter, F.: Towards an unsupervised generic seismic detector for hazardous mass-movements: a data-driven approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6300, https://doi.org/10.5194/egusphere-egu23-6300, 2023.

EGU23-6321 | ECS | Posters on site | GM2.2

Can we characterize groundwater reservoirs in central Europe from air-pressure-induced seismic velocity changes? 

Richard Kramer, Yang Lu, and Götz Bokelmann

In this study, we used coda wave interferometry to investigate four years of continuous data from AlpArray and other locations throughout Europe. We estimate the hourly Green’s function by cross-correlating ambient seismic noise recorded at pairs of stations. The results indicate short and long-term variations of the seismic velocities and show the feasibility of large-scale monitoring with ambient seismic noise at high temporal resolution. The relative seismic velocities (dv/v) show temporal variations on the order of 10-3 in a frequency band around 1 Hz. Spectra of the velocity time series contain strong daily and sub-daily behaviour, which are primarily caused by the coupling of atmospheric processes and solid Earth. The explanatory model focuses on depth variations of the groundwater table, linking atmospheric pressure (loading and unloading the Earth's surface) to variations in seismic velocity. This study aims to understand and explain differences in daily and sub-daily behaviour across Europe. This may contribute to the hydrological characterization of the near-subsurface in central Europe. 

How to cite: Kramer, R., Lu, Y., and Bokelmann, G.: Can we characterize groundwater reservoirs in central Europe from air-pressure-induced seismic velocity changes?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6321, https://doi.org/10.5194/egusphere-egu23-6321, 2023.

EGU23-7136 | ECS | Orals | GM2.2

Towards a generic clustering approach for building seismic catalogues from dense sensor networks 

Joachim Rimpot, Clément Hibert, Jean-Philippe Malet, Germain Forestier, and Jonathan Weber

In the context of climate change, the occurrence of geohazards such as landslides or rockfalls might increase. Therefore, it is important to have the ability to characterise their (spatial and temporal) occurrences in order to implement protection measures for the potential impacted populations and infrastructures. Nowadays, several methods including Machine Learning algorithms are used to study landslides-triggered micro-seismicity and the associated seismic sources (eg. rockfalls and  slopequakes). Those innovative algorithms allow the automation of the processing chains used to build micro-seismicity catalogues, leading to the understanding of the landslide deformation pattern and internal structure. Unfortunately, each landslide context has its own seismic signature which requires the use of the most complete and handmade training samples to train a Machine Learning algorithm. This is highly time consuming because it involves an expert that needs to manually check every seismic signal recorded by the seismic network, which can be thousands per day.

The aim of this study is to develop semi-supervised and unsupervised clustering methods to characterise the micro-seismicity of landslides in near real time. Here, we present the preliminary results obtained for creating a landslide micro-seismicity catalogue from the analysis of a dense network of 50 seismic stations deployed temporarily at the Super-Sauze landslide (French Alps). First, we present the performance of supervised Random Forest and XGBoost trained models on the event signals. Then, an approach aimed at processing streams of raw seismic data based on 18s-length windows is explored. Finally, we discuss the clustering results and the transferability possibilities of the approach to other landslides and even environments (glaciers, volcanoes).

How to cite: Rimpot, J., Hibert, C., Malet, J.-P., Forestier, G., and Weber, J.: Towards a generic clustering approach for building seismic catalogues from dense sensor networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7136, https://doi.org/10.5194/egusphere-egu23-7136, 2023.

EGU23-7489 | ECS | Posters on site | GM2.2

Monitoring the cryoseismic activity of the Astrolabe glacier, Terre Adélie, Antarctica 

Tifenn Le Bris, Guilhem Barruol, Emmanuel Le Meur, Florent Gimbert, and Dimitri Zigone

In coastal Antarctica, outlet glaciers exhibit complex dynamics materialized by intense internal deformation, enhanced basal sliding and strong thermo-mechanical interactions with the ocean. Here we aim to use seismic observations to unravel these various processes and their link with glacier and ocean dynamics. As part of the SEIS-ADELICE project (2020-2024) supported by the French Polar Institute IPEV, in January 2022 we deployed four permanent and six temporary (1 month long) broadband seismic stations on and around the Astrolabe Glacier (Terre Adélie, East Antarctica), as well as four ocean-bottom seismometers at sea near the terminus of the floating tongue. In January 2023 we will be supplementing this setup by a temporary network of 50 seismic nodes above the grounding line of the glacier.

Preliminary detection and classification of seismic events reveals a wide variety of cryo-seismic signals. The most pervasive events correspond to icequakes, are located close to the surface, and exhibit clear tidal modulation. We interpret these events as being generated by the brittle fracturing of ice associated with crevasse opening. We also observe numerous short and similar repetitive events of much lower amplitude that are located at few restricted locations near the ice-bedrock interface. These events are likely produced by basal stick-slip over punctual bedrock asperities. Finally, we observe glacial tremors which could result from hydraulic sources at the ice-bedrock interface, although further analysis is required to confirm this hypothesis.

This preliminary work provides useful grounds for deeper analysis to be done in the future on source characteristics and their more quantitative links with glacier dynamics.

How to cite: Le Bris, T., Barruol, G., Le Meur, E., Gimbert, F., and Zigone, D.: Monitoring the cryoseismic activity of the Astrolabe glacier, Terre Adélie, Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7489, https://doi.org/10.5194/egusphere-egu23-7489, 2023.

EGU23-7549 | ECS | Posters on site | GM2.2 | Highlight

Seabed seismometers reveal duration and structure of longest runout sediment flows on Earth 

Megan Baker, Peter Talling, Richard Burnett, Ed Pope, Sean Ruffell, Matthieu Cartigny, Michael Dietze, Morelia Urlaub, Michael Clare, Jeffrey Neasham, Ricardo Silva Jacinto, Pascal Kunath, and Christine Peirce

Seafloor sediment flows (turbidity currents) form some of the largest sediment accumulations on Earth, carry globally significant volumes of organic carbon, and can damage critical seafloor infrastructure. These fast and destructive events are notoriously challenging to measure in action, as they often damage any instruments anchored within the flow. We present the first direct evidence that turbidity currents generate seismic signals which can be remotely sensed (~1-3 km away), revealing the internal structure and remarkably prolonged duration of the longest runout sediment flows on Earth. Passive Ocean Bottom Seismograph (OBS) sensors, located on terraces of the Congo Canyon, offshore West Africa, recorded thirteen turbidity currents over an 8-month period. The occurrence and timing of these turbidity currents was confirmed by nearby moorings with acoustic Doppler current profilers.

Results show that turbidity currents travelling over ~1.5 m/s produce a seismic signal concentrated below 10 Hz with a sudden onset and more gentle decay. Comparison of the seismic signals with information on flow velocities from the acoustic Doppler current profilers demonstrates that the seismic signal is generated by the fast-moving front of the flow (frontal cell), which contains higher sediment concentrations compared to the slower-moving body. Long runout flows travelling >1000 km have a fast (3.7-7.6 m s-1) frontal cell, which can be 14 hours, and ~350 km long, with individual flows lasting >3 weeks. Flows travelling >1000 km eroded >1300 Mt of sediment in one year, yet had near-constant front speeds, contrary to past theory. The seismic dataset allows us to propose a fundamental new model for how turbidity currents self-sustain, where sediment fluxes into and from a dense frontal layer are near-balanced.

Seismic monitoring of turbidity currents provides a new method to record these hazardous submarine flows, safely, over large areas, continuously for years yet at sub-second temporal resolution. Monitoring these processes from land would considerably ease deployment efforts and costs. Thus, work is underway investigating if terrestrial seismic stations can record submarine seafloor processes in Bute Inlet, a fjord in western Canada where independent measurement of delta-lip failures and turbidity currents can be compared to a passive seismic dataset.

How to cite: Baker, M., Talling, P., Burnett, R., Pope, E., Ruffell, S., Cartigny, M., Dietze, M., Urlaub, M., Clare, M., Neasham, J., Silva Jacinto, R., Kunath, P., and Peirce, C.: Seabed seismometers reveal duration and structure of longest runout sediment flows on Earth, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7549, https://doi.org/10.5194/egusphere-egu23-7549, 2023.

EGU23-7727 | Orals | GM2.2

Using Seismic Methods to Monitor Bedload Transport Along a Desert Environment Ephemeral Tributary 

Susan Bilek, J. Mitchell McLaughlin, Daniel Cadol, and Jonathan Laronne

Use of seismic monitoring and data analysis techniques in recent years have allowed for improved understanding of several shallow earth processes, such as glacial motion, subsurface water flow, and bedload transport. Early applications using seismic data collected at high energy alpine rivers suggest that seismic energy within certain frequency bands is linked to bedload discharge.  However, study of other river systems have been more limited, even though some of these systems, such as ephemeral streams in arid environments, transport large quantities of sediment during short-lived flash flood events.  Here we present seismic and hydrologic data collected in a unique sediment observatory within an ephemeral tributary to the Rio Grande River, in the desert southwest of the U.S., combining dense seismic observations with a variety of in-channel bedload and water monitoring measurements. We have seismic records for more than a dozen floods ranging in depth from a few centimeters to over one meter, encompassing bedload flux as high as 12 kg s-1 m-1, two orders of magnitude higher than in most perennial settings. Our efforts to date focus on identifying the noise sources within the seismic record, characterization of the seismic properties of the site, and determining the seismic frequency ranges best correlated with the automatically measured bedload flux. Within the 30-80 Hz frequency range, we find a linear relationship between seismic power and bedload flux. We hypothesize that variations in linear fit statistics between flood events are due to varying bedload grain size distributions and in-channel morphological changes.

How to cite: Bilek, S., McLaughlin, J. M., Cadol, D., and Laronne, J.: Using Seismic Methods to Monitor Bedload Transport Along a Desert Environment Ephemeral Tributary, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7727, https://doi.org/10.5194/egusphere-egu23-7727, 2023.

EGU23-8127 | ECS | Posters on site | GM2.2

Benford's law in detecting rapid mass movements with seismic signals 

Qi Zhou, Hui Tang, Jens M. Turowski, Jean Braun, Michael Dietze, Fabian Walter, Ci-Jian Yang, Sophie Lagarde, and Ahmed Abdelwahab

Rapid mass movements are a major threat in populated landscapes, as they can cause significant loss of life and damage civil infrastructure. Previous work has shown that using environmental seismology methods to monitor such mass movements and establish monitoring systems offers advantages over existing approaches. The first important step in developing an early warning system for rapid mass movements based on seismic signals is automatically detecting events of interest. Though the approach, such as short-term average to long-term average ratio (STA/LTA) and machine learning model, was introduced to detect events (e.g., debris flow and rockfall), it is still challenging to calibrate input parameters and migrate existing methods to other catchments. Detection of debris flows, for instance, is similar to anomaly detection if we consider the seismic stations recording background signals as an overwhelming majority condition. 
Benford's law describes the probability distribution of the first non-zero digits in numerical datasets, which provides a functional, computationally cheap approach to anomaly detection, such as fraud detection in financial data or earthquake detection in seismic signals. In this study, seismic signals generated by rapid mass movements were collected to check the agreement of the distribution of the first digit with Benford's law. Subsequently, we develop a computationally efficient and non-site-specific model to detect events based on Benford's law using debris flows from the Illgraben, a Swiss torrent, as an example. Our results show that seismic signals generated by high-energy mass movements, such as debris flows, landslides, and lahars, follow Benford's law, while those generated by rockfall and background signals do not. Furthermore, our detector performance in picking debris-flow events is comparable to a published random forest and seismic network-based approach. Our method can be applied at other sites to detect debris-flow events without additional calibration and offers the potential for real-time warnings.

How to cite: Zhou, Q., Tang, H., Turowski, J. M., Braun, J., Dietze, M., Walter, F., Yang, C.-J., Lagarde, S., and Abdelwahab, A.: Benford's law in detecting rapid mass movements with seismic signals, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8127, https://doi.org/10.5194/egusphere-egu23-8127, 2023.

EGU23-8986 | ECS | Posters on site | GM2.2

Monitoring of an Alpine landslide using dense seismic observations: combining Distributed Acoustic Sensing and 1000 autonomous seismic nodes 

Tjeerd Kiers, Cédric Schmelzbach, Pascal Edme, Patrick Paitz, Florian Amann, Hansruedi Maurer, and Johan Robertsson

Landslides are a major natural hazard that can cause significant loss of life and property damage around the world. As global temperatures rise and weather extremes become more frequent, we can expect an increase in the hazard emanating from landslides too. In order to better understand and mitigate landslide risks, a variety of strategies have been developed to characterize and monitor landslide activity. Many established approaches provide valuable information about surface displacement and surface properties, but are not suited to inspect the subsurface parts of a landslide body. In contrast, seismic imaging and monitoring methods allow us to study subsurface structures, properties, and internal processes that control landslide behaviour.

In our project, we develop novel seismic data acquisition and interpretation approaches to characterize and monitor one of the largest active unstable slopes in the Alps, the Cuolm da Vi landslide, with an unprecedented spatial resolution. We achieve this by combining an array of over 1’000 seismic nodes with fiber-optic based monitoring techniques such as Distributed Acoustic (DAS) and Strain Sensing (DSS).

The deep-seated Cuolm da Vi landslide is located near Sedrun (Central Switzerland) and consists of approximately 100-200 million m3 of unstable rock reaching displacement rates up to 10-20 cm/yr with clear seasonal cycles. In summer 2022, we buried over 6 kilometres of fiber-optic cable in this alpine environment covering the most active part of the landslide with multiple cable orientations. Additionally, we deployed a nodal array of 1046 accelerometers in a hexagonal grid covering around 1km2 with a nominal spacing of 28 meters. Seismic data were acquired with the nodes and the DAS system continuously for four weeks. This time period included the blasting of 163 dynamite shots for calibration and active-source imaging purposes. In 2023, we plan to conduct data acquisition for longer periods using primarily fibre-optic based techniques with a focus on the temporal evolution of the landslide dynamics.

Our first goal is to resolve the internal structure of the landslide based on the controlled-source data acquired in summer 2022 to construct, for example, a seismic velocity model. Based on the models derived from the active-source seismic data, we plan to exploit the continuous seismic recordings of ambient vibrations and potential seismic signals produced by the landslide activity to complement structural models and study the landslide dynamics. We will present our current results and discuss their implications for the next steps towards monitoring this landslide over time.

How to cite: Kiers, T., Schmelzbach, C., Edme, P., Paitz, P., Amann, F., Maurer, H., and Robertsson, J.: Monitoring of an Alpine landslide using dense seismic observations: combining Distributed Acoustic Sensing and 1000 autonomous seismic nodes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8986, https://doi.org/10.5194/egusphere-egu23-8986, 2023.

EGU23-11404 | ECS | Posters on site | GM2.2

Rapid shredding of the subglacial sediment export signal by proglacial forefields 

Davide Mancini, Michael Dietze, Tom Müller, Matthew Jenkin, Floreana Marie Miesen, Matteo Roncoroni, Andrew Nicholas, and Stuart Nicholas Lane

Alpine glaciers have been rapidly retreating and at increasing rates in recent decades due to climate warming. As a consequence, large amounts of suspended- and bed-load flux are being released to proglacial environments, such as proglacial forefields. These regions are among the most unstable geomorphic systems of the Earth because they rapidly respond to changing discharge and sediment conditions. Given this, it might be hypothesized that their intense morphodynamic activity, being a complex and non-linear process, could “shred” the sediment transport signal itself, and especially that related to subglacial sediment export.

To date, our knowledge on subglacial sediment export by subglacial streams is essentially dominated by suspended sediment dynamics recorded in front of shrinking glaciers because of the limitations in measuring bedload transport. The latter is usually monitored far downstream from glacier termini by permanent stations (e.g. water intakes, geophone systems) leaving major uncertainties in the absolute amounts and temporal patterns of transport in both glacial and proglacial environments, as well as the relative importance compared to suspended sediment in case of morphodynamic filtering. Thus, the aim of this project was to investigate the evolution of the both suspended- and bedload subglacial export signals within the proglacial forefield to quantify the extent and the timescale over which proglacial morphodynamics filter them.

This work focuses on a large Alpine glacial forefield, almost 2 km in length, that has formed since the early 1980s at the Glacier d’Otemma (southern-western Swiss Alps, Valais). Data were collected over two entire melt seasons (June-September 2020 and 2021) experiencing different climatic conditions, the first year warm and relatively dry and the second cold and relatively wet. Suspended transport was recorded using conventional turbidity-suspended sediment concentration relationship, bedload transport was monitored seismically, while the morphodynamic filtering was determined using signal post-processing techniques. At present, there are no studies combining continuous measurements of both suspended- and bed-loads in such environments.

Results show that the signal of subglacial bedload export, unlike suspended load export, is rapidly shredded by proglacial stream morphodynamics, which we show is due to a particle-size dependent autogenic sorting of sediment transport at both daily and seasonal time-scales. The result is that over very short distances, the signal of subglacial bedload sediment export is lost and replaced by a signal dominated by morphodynamic reworking of the proglacial braidplain. The suspended signal is less impeded but significant floodplain storage and release of suspended sediment was observed. These results question the reliability of current inferences of glacial erosion rates from sediment transport rates often measured some way downstream of glacier margins.

How to cite: Mancini, D., Dietze, M., Müller, T., Jenkin, M., Miesen, F. M., Roncoroni, M., Nicholas, A., and Lane, S. N.: Rapid shredding of the subglacial sediment export signal by proglacial forefields, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11404, https://doi.org/10.5194/egusphere-egu23-11404, 2023.

EGU23-12107 | ECS | Orals | GM2.2

Seismic Monitoring of Permafrost Dynamics at Mt. Zugspitze (German/Austrian Alps) 

Fabian Lindner, Krystyna Smolinski, Riccardo Scandroglio, Andreas Fichtner, and Joachim Wassermann

As observed elsewhere on a global scale, mountain permafrost at the Zugspitze (German/Austrian Alps) is degrading in response to climate change, which affects the rock slope stability and thus the hazard potential. Recent studies suggest that passive seismology is a promising and emerging tool to monitor permafrost changes as the seismic velocity of rocks strongly decreases/increases upon thawing/freezing. Compared to other, more classical methods like borehole temperature logging or electrical resistivity tomography (ERT), seismology is less laborious and costly, non-invasive and allows continuous monitoring. At Mt. Zugspitze, we exploit these advantages using a permanent seismic station (installed in 2006) as well as three small seismic arrays and Distributed Acoustic Sensing (DAS; both available since summer/fall 2021), to infer permafrost dynamics with high spatio-temporal resolution. The seismic data show repeating diurnal noise generated by the operation of cable cars, which we leverage for cross-correlation analysis. Our results suggest that the dominant signal in the retrieved seismic velocity change time series is caused by the seasonal freeze-thaw cycles associated with permafrost bodies on the northern side of the mountain ridge. On the long-term, the time series show a gradual velocity decrease associated with permafrost degradation due to atmospheric warming and compare well with modeled velocity change time series using rock temperature data from a nearby borehole, which intersects the mountain ridge. We discuss differences in our seismic analysis results obtained from direct and coda waves as well as from single station to station pairs and DAS and interpret the results in the light of other measurements including ERT, rock temperature logging and meteorological parameters.

How to cite: Lindner, F., Smolinski, K., Scandroglio, R., Fichtner, A., and Wassermann, J.: Seismic Monitoring of Permafrost Dynamics at Mt. Zugspitze (German/Austrian Alps), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12107, https://doi.org/10.5194/egusphere-egu23-12107, 2023.

EGU23-12128 | Posters on site | GM2.2

Probing temporal variation of suspended load to bedload ratio using seismic saltation model 

Chao Ting Meng, Wei An Chao, and Yu Shiu Chen

Monitoring temporal and spatial changes in sediment volume in the upstream reservoir is one of the important indicators for evaluating the reservoir project life, especially the information carried by bedload and suspended load. According to field condition, direct bedload monitoring is often difficult. Thus, bedload usually can be estimated by a specific proportion of suspended load depended on the flooding magnitude, which can cause a large uncertainty in estimates of total sediment load. In recent years, riverine micro-seismic signals have been applied to study bedload transport. Our study chose the Da-Pu Dam (location: 23.296500°N, 120.644611°E), located at the upstream of the Zeng-Wen Reservoir and the junction of the Zeng-Wen river and Cao-Lan river, which is the last check dam before entering the reservoir area. Its upstream catchment area is 30,312 hectares that comprise approximately 63% of the Zeng-Wen Reservoir catchment area (48,100 hectares). The length of the monitoring section of the Da Pu Dam is 1,100 meters, with an average width of 121 meters and an average slope of 0.36 degrees. With the available data composed of riverbed cross-section survey, sediment particle size distribution, fluvial measurements (water depth, surface flow velocity), orthoimagery, and suspended load measurement, our study applies seismic saltation model to estimate the bedload flux and compares the results with the measured suspended load. Results showed that there are different ratios between bedload and suspended load under similar hydrological condition during the plum rain season(May-June) and typhoon period(July-September). In a case of flooding event considering the flow stage from medium to high discharge, significant temporal changes in the ratio between bedload and suspended load can also be observed, which imply a complex transition process between the bedload and suspension particles. The temporal changes in sediment ratio obtained in this study can be applied to estimate the total volume of sediment load entering the reservoir. Our estimated results are consistent with the survey of sediment accumulation at the end of each year in the reservoir area.

How to cite: Meng, C. T., Chao, W. A., and Chen, Y. S.: Probing temporal variation of suspended load to bedload ratio using seismic saltation model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12128, https://doi.org/10.5194/egusphere-egu23-12128, 2023.

EGU23-12687 | ECS | Orals | GM2.2

Surprising seismological signals during the October 2015 Skaftá jökulhlaup 

Thoralf Dietrich, Eva P.S. Eibl, Eyjólfur Magnússon, Daniel Binder, Sebastian Heimann, and Sigrid Roessner

Understanding the spatiotemporal details in the occurrence of jökulhlaups, also referred to as glacier lake outburst floods (GLOFs), is important for improving early warning and forecasting future events. Jökulhlaups occur in many different glacier-related settings and differ in their characteristics depending on the natural conditions: From very rapid floods (minutes-hours) originating from moraine dammed lakes in steep valleys to gradual floods (days-weeks) from subglacial lakes such as the ones beneath Vatnajökull ice cap, in Iceland. Previous studies of the October 2015 Skaftá jökulhlaup suggested that several hours of early-warning is possible based on the generated seismic tremor. Here, for the first time, we looked into all three spatial components of GNSS and seismic array data, respectively. Previous studies have already analysed the seismic events (icequakes, tremor, other migrating transient events) in detail, yet only on the z component. We reprocessed all three components of the seismic array data using frequency-wavenumber -analysis (fk-analysis) and match field processing (MFP). Both techniques allow to locate distant signal sources, either by direction only (fk) or actual location (MFP). We specifically focused on the time period when the tremor source is moving with the flood front and found two unexplained seismic signals:

  • A second migrating signal is visible on the lowermost part of the flood path 6 hours later than the passing of the first flood front.

    We compared this with a GNSS observations on top of the subglacial flood path and a hydrometric station 25 km downstream from the glacier margin in the affected Skaftá-river.

    After aligning the time series by the arrival of the pressure wave, the timing of the second seismic signal fits well with a 10 cm uplift of the glacier at the GNSS station; but also with a change in the rate of water level rise at the hydrometric station.

    We discuss this in the context of either explaining GNSS, hydrometric and seismological data individually or giving a hypothetical process that explains all three together. That could be a second intraglacial water lense draining, after the emptying of the lake deformed the overlaying glacier and connected the two water bodies. However, radio echo sounding survey over the source area in spring 2015 did not indicate a significant intraglacal water lense above the subglacial lake. The GNSS data may be cleared as noise artifact and the hydrometric data explained by flow of water out of the river course of Skaftá and onto porous lava fields between Sveinstindur, where the discharge of Skaftá is measured, and the glacier. Yet: The seismic signal then is left unexplained and open for discussion.

  • Finally, 18 hours after the first pulse, we found a sudden deceleration in horizontal motion on the GNSS that coincided with a sudden increase in seismic signals originating at the glacier terminus. We discuss if what we see is actually the glacier stopping, after losing the flood lubrication.

 

How to cite: Dietrich, T., Eibl, E. P. S., Magnússon, E., Binder, D., Heimann, S., and Roessner, S.: Surprising seismological signals during the October 2015 Skaftá jökulhlaup, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12687, https://doi.org/10.5194/egusphere-egu23-12687, 2023.

EGU23-13269 | ECS | Posters on site | GM2.2

Denoising Cryoseismological Distributed Acoustic Sensing Data Using a Deep Neural Network 

Johanna Zitt, Patrick Paitz, Fabian Walter, and Josefine Umlauft

One major challenge in Environmental Seismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Those signals potentially stay unnoticed and thus, might not be analyzed further.

Distributed acoustic sensing (DAS) is an emerging technology for measuring strain rate data by using common fiber-optic cables in combination with an interrogation unit. This technology enables researchers to acquire seismic monitoring data on poorly accessible terrain with great spatial and temporal resolution. We utilized a DAS unit in a cryospheric environment on a temperate glacier. The data collection took place in July 2020 on Rhonegletscher, Switzerland, where a 9 km long fiber-optic cable was installed, covering the entire glacier from its accumulation to its ablation zone. During one month 17 TB of data were acquired. Due to the highly active and dynamic cryospheric environment, our collected DAS data are characterized by a low signal to noise ratio compared to classical point sensors. Therefore, new techniques are required to denoise the data efficiently and to unmask the signals of interest. 

Here we propose an autoencoder, which is a deep neural network, as a denoising tool for the analysis of our cryospheric seismic data. An autoencoder can potentially separate the incoherent noise (such as wind or water flow) from the temporally and spatially coherent signals of interest (e.g., stick-slip event or crevasse formation). We test this approach on the continuous microseismic Rhonegletscher DAS records. To investigate the autoencoder’s general suitability and performance, three different types of training data are tested: purely synthetic data, original data from on-site seismometers, and original data from the DAS recordings themselves. Finally, suitability, performance as well as advantages and disadvantages of the different types of training data are discussed.

How to cite: Zitt, J., Paitz, P., Walter, F., and Umlauft, J.: Denoising Cryoseismological Distributed Acoustic Sensing Data Using a Deep Neural Network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13269, https://doi.org/10.5194/egusphere-egu23-13269, 2023.

EGU23-13334 | Posters on site | GM2.2

Ambient H/V sensitivity to the dynamics of glaciers and ice sheets 

Janneke van Ginkel, Fabian Walter, Ana Nap, Mauro Häusler, and Martin Lüthi

Climate change is causing major shifts in the dynamics of the cryosphere, leading to sea-level rise, glacier break-off events, flooding, and landslides. Geological, thermodynamic and hydraulic processes at the base of an ice mass play a central role in ice flow dynamics, and understanding these is imperative for predicting ice body behavior in a changing climate. To this end, sustained ambient vibrations in glaciated environments can be used to monitor subglacial conditions over significant spatial extent with relatively low-cost acquisition.

In earthquake seismology, a well-established methodology to investigate subsurface properties is the horizontal-to-vertical spectral ratio (H/V) of ambient seismic ground unrest. In cryoseismology, the H/V approach is already used to invert for velocity profiles of ice or firn, to obtain bedrock topography and to identify the presence of basal sediments. To date, only a few hours of seismic vibration records are typically used. Yet in such short time records, biases may arise because of the dynamic character of the glacier. Seismic resonances within the soft ice layer and resulting H/V ratios are expected to vary with changes in subglacial hydraulic conditions.

We propose to leverage temporal variations in H/V spectra to investigate subglacial processes. As a case study, we first focus on the Glacier de la Plaine Morte (Switzerland), where a seismic array was deployed for four months in summer of 2016. During this time, an ice-marginal lake formed and suddenly drained through and under the glacier, making this seismic record ideal for our purposes. This drainage event is well recorded and strongly influences the H/V in terms of amplitude and resonance frequency. We next present ambient H/V measurements of the Sermeq Kujalleq in Kangia (also known as Jakobshavn Isbræ), one of Greenland’s largest outlet glaciers. Here, the H/V spectra show multiple resonances over time, whose origin we discuss in more detail. For both our study cases, separating variations in source and medium properties is pivotal. Tackling this challenge provides glaciologists with a valuable tool to investigate the poorly accessible subglacial environment, which holds the key to our understanding of ice flow and eustatic sea level rise.

How to cite: van Ginkel, J., Walter, F., Nap, A., Häusler, M., and Lüthi, M.: Ambient H/V sensitivity to the dynamics of glaciers and ice sheets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13334, https://doi.org/10.5194/egusphere-egu23-13334, 2023.

EGU23-13383 | ECS | Posters on site | GM2.2

Using a record of bedload transport from Leverett glacier in western Greenland to understand proglacial sediment transport processes from the ice sheet   

Marjolein Gevers, Davide Mancini, Stuart Lane, and Ian Delaney

Increased glacier melt leads to a change in sediment transport capacity below glaciers, which impacts the sediment transport within proglacial areas as well as downstream ecosystems and geomorphology. Previous work on Alpine glaciers shows that strong diurnal discharge variations lead to fluctuations in sediment transport capacity such that deposition and erosion can occur in the proglacial area over the course of the melt season. However, the exact processes controlling sediment transport at the outlet glaciers of ice sheet margins and in their proglacial areas remain uncertain. Data suggest that the diurnal discharge variations are substantially reduced and baseflow discharge is much greater, likely capable of maintaining significant sediment transport throughout the melt season. This difference in the hydrological regime as compared with Alpine glacial systems may drive different rates and variations in sediment transport and, ultimately, in proglacial braid plain morphodynamics.

We measure proglacial sediment transport at Leverett glacier, a land-terminating glacier located at the western margin of the Greenland Ice Sheet. As bedload transport is exceptionally difficult to measure in situ, two seismic stations were installed to evaluate bedload transport in the glacial meltwater stream in the summer of 2022. The first station is located close to the current glacier terminus, and the second one is about 2 km from the current glacier terminus. These two stations allow for the examination of the sediment transport processes within the proglacial area. By using a Fluvial Inversion Model the recorded seismic data is converted into bedload flux. The model is calibrated using active seismic surveys and statistical approaches to evaluate the physical parameters. Outputs of the Fluvial Inversion Model are validated with available water stage data.  The results provide insight as to whether the proglacial area is aggrading or eroding as sediment transport in the two locations at Leverett glacier evolves over the summer season. We discuss the relationship between bedload transport and level of the proglacial river, as well as the seasonal variations in proglacial sediment transport and deposition in Leverett glacier’s proglacial area.

How to cite: Gevers, M., Mancini, D., Lane, S., and Delaney, I.: Using a record of bedload transport from Leverett glacier in western Greenland to understand proglacial sediment transport processes from the ice sheet  , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13383, https://doi.org/10.5194/egusphere-egu23-13383, 2023.

EGU23-16008 | ECS | Orals | GM2.2

Short-term fast ice dynamics derived from passive seismic data at a large Greenland outlet glacier 

Ana Nap, Fabian Walter, Adrien Wehrlé, Andrea Kneib-Walter, Guillaume Jouvet, and Martin P. Lüthi

Outlet glaciers and ice streams are the main channels through which ice sheets transport their mass towards the ocean. One of Greenland’s largest outlet glaciers Sermeq Kujalleq in Kangia (Jakobshavn Isbrae) has been broadly researched after experiencing a rapid retreat of the terminus and accompanying speedup to up to 40 m/day in the early 2000’s. However, such short-term ice dynamic variations remain poorly understood making numerical models difficult to constrain and predictions on future sea-level rise uncertain.

The short-term ice dynamics of Sermeq Kujalleq consists in transient states and can only be captured by in-situ measurements of high spatial and temporal resolution. Glacier seismology has proven to be a valuable tool to study these dynamics, it provides data with a high temporal resolution and can provide information on processes happening below the ice surface. Within the COEBELI project we combine passive glacier seismology with global navigation satellite system (GNSS) receivers, long-range drones, time-lapse cameras and terrestrial radar interferometry to capture processes such as calving and basal sliding at their respective timescales.

Here, we present results from a multi-array seismic deployment at Sermeq Kujalleq in Summer 2022. From May until September two arrays were deployed in the upstream part of the fast-flowing ice stream (>22 km from calving front) and one array on slower moving ice North of the main trunk. For a 3-week period in July, four more arrays were deployed on the fast-flowing ice stream closer to the calving front (<15 km). In the severely crevassed areas near the calving front (<15 km), the arrays consisted of custom-made autonomous seismic boxes whereas at more accessible upstream areas we installed borehole instruments. During the deployment we recorded multiple large calving events, glacier speedups and periodic multi-hour tremors accompanied by bursts of short-term high frequency (>50 Hz) icequakes. By studying these different signals, we are able to better constrain the processes and forces that control fluctuating ice-flow velocity and calving events.

How to cite: Nap, A., Walter, F., Wehrlé, A., Kneib-Walter, A., Jouvet, G., and Lüthi, M. P.: Short-term fast ice dynamics derived from passive seismic data at a large Greenland outlet glacier, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16008, https://doi.org/10.5194/egusphere-egu23-16008, 2023.

High-melt areas of glaciers generate a rich spectrum of ambient seismicity. These signals do not only contain information about the source mechanisms (e.g. englacial fracturing, water flow, iceberg detachment, basal stick-slip motion) but also carry information about seismic wave propagation within the glacier ice and, therefore, the mechanical properties of the ice. In the summer of 2021 two seismic arrays were deployed in Southern Spitsbergen at the vicinity of Hansbreen’s terminus, one being placed directly on the glacial ice, yielding an 8-days long time series of glacial seismicity.

The direct and scattered wave fields from tens of thousands of icequake records (few thousands per day) were used to determine seismic velocities and monitor structural changes within the ice, while the analysis of the ambient noise was leveraged to constrain the ice thickness. The surface icequakes dominate the seismograms due to an abundance of englacial fracturing. Hence, Rayleigh waves and beam-based techniques were employed to characterise the patterns of microseismicity at the transform junction of two glaciers (Tuvbreen and Hansbreen). Several clusters of various-origin seismicity being active at certain times are identified with a majority of them located on stagnant, fast-melting Tuvabreen.

How to cite: Gajek, W.: Rayleigh wave is the coolest – resolving microseismicity of a tidewater glacier in Svalbard, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16346, https://doi.org/10.5194/egusphere-egu23-16346, 2023.

Glaciers or ice-streams have many common points with tectonic faults. Glaciers can move by

stable or unstable slip or by creep within the glacier thickness. Like faults, glacier sliding can

produce “icequake” signals over a huge range of frequencies, rupture length and signal

duration, as well as tremor. But because glaciers are shallower, the sliding interface can be

accessed directly much more easily, by boreholes or cavities. And they move much faster than

tectonic faults, so that deformation is easier to estimate and icequake inter-event times are

much shorter than for earthquakes.

Here I present some observations of high- and low-frequencies repeaters of basal icequakes

in the Mont-Blanc areas. Both types of events occur as bursts lasting for a few days or weeks,

with quasi-regularly inter-events times of the order of a few minutes or hours, and progressive

changes in amplitude and inter-event times. High-frequency events (around 50 Hz) occur all

over the year, with no clear triggering mechanism, and are located on the lower-part of

glaciers, where ice is at the melting point temperature and the glacier mainly moves by stable

sliding. Low frequency events (around 5 Hz) are mainly located at higher elevations (mainly

above 3000 m asl), on steeper slopes, and have larger magnitudes (-2<m<0). They are mainly

observed during or shortly after snowfalls. At these elevations, glaciers are possibly coldbased,

or close to the melting-point temperature, so that they are stuck to their bed and

mainly deform by creep within the ice. We observe progressive changes in waveforms that

suggest slow and evolving rupture velocities. These low-frequency icequakes may be the

analog of low-frequency earthquakes, which also occur near the transition between stable and

unstable slip.

How to cite: helmstetter, A.: Clusters of low- and high-frequency repeating icequakes in the Mont-Blanc massif, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16571, https://doi.org/10.5194/egusphere-egu23-16571, 2023.

EGU23-1095 | Orals | NP5.2

Recent offline land data assimilation results and future steps towards coupled DA at Meteo-France 

Jean-Christophe Calvet, Bertrand Bonan, and Yiwen Xu

Land data assimilation aims to monitor the evolution of soil and vegetation variables. These variables are driven by climatic conditions and by anthropogenic factors such as agricultural practices. Monitoring terrestrial surfaces involves a number of variables of the soil-plant system such as land cover, snow, surface albedo, soil water content and leaf area index. These variables can be monitored by integrating satellite observations into models. This process is called data assimilation. Integrating observations into land surface models is particularly important in changing climate conditions because environmental conditions and trends never experienced before are emerging. Because data assimilation is able to weight the information coming from contrasting sources of information and to account for uncertainties, it can produce an analysis of terrestrial variables that is the best possible estimation. In this work, data assimilation is implemented at a global scale by regularly updating the model state variables of the ISBA land surface model within the SURFEX modelling platform: the LDAS-Monde sequential assimilation approach. Model-state variable analysis is done for initializing weather forecast atmospheric models. Weather forecast relies on observations to a large extent because of the chaotic nature of the atmosphere. Land variables are not chaotic per se but rapid and complex processes impacting the land carbon budget such as forest management (thinning, deforestation, ...), forest fires and agricultural practices are not easily predictable with a good temporal precision. They cannot be monitored without integrating observations as soon as they are available. We focus on the assimilation of leaf area index (LAI), using land surface temperature (LST) for verification. We show that (1) analyzing LAI together with root-zone soil moisture is needed to monitor the impact of irrigation and heat waves on the vegetation, (2) LAI can be forecasted after properly initializing ISBA. This paves the way to more interactive assimilation of land variables into numerical weather forecast and seasonal forecast models, as well as in atmospheric chemistry models.

 

How to cite: Calvet, J.-C., Bonan, B., and Xu, Y.: Recent offline land data assimilation results and future steps towards coupled DA at Meteo-France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1095, https://doi.org/10.5194/egusphere-egu23-1095, 2023.

EGU23-1846 | Posters on site | NP5.2 | Highlight

Hybrid covariance super-resolution data assimilation 

Sébastien Barthélémy, Julien Brajard, Laurent Bertino, and François Counillon

This work extends the concept of "Super-resolution data assimilation" (SRDA, Barthélémy et al. 2022)) to the case of mixed-resolution ensembles pursuing two goals: (1) emulate the Ensemble Kalman Filter while (2) benefit from high-resolution observations. The forecast step is performed by two ensembles at two different resolutions, high and low-resolution. Before the assimilation step the low-resolution ensemble is downscaled to the high-resolution space, then both ensembles are updated with high-resolution observations. After the assimilation step, the low-resolution ensemble is upscaled back to its low-resolution grid for the next forecast. The downscaling step before the data assimilation step is performed either with a neural network, or with a simple cubic spline interpolation operator. The background error covariance matrix used for the update of both ensembles is a hybrid matrix between the high and low resolution background error covariance matrices. This flavor of the SRDA is called "Hybrid covariance super-resolution data assimilation" (Hybrid SRDA). We test the method with a quasi-geostrophic model in the context of twin-experiments with the low-resolution model being twice and four times coarser than the high-resolution one. The Hybrid SRDA with neural network performs equally or better than its counterpart with cubic spline interpolation, and drastically reduces the errors of the low-resolution ensemble. At equivalent computational cost, the Hybrid SRDA outperforms both the SRDA (8.4%) and the standard EnKF (14%). Conversely, for a given value of the error, the Hybrid SRDA requires as little as  50% of the computational resources of  the EnKF. Finally, the Hybrid SRDA can be formulated as a low-resolution scheme, in the sense that the assimilation is performed in the low-resolution space, encouraging the application of the scheme with realistic ocean models.

How to cite: Barthélémy, S., Brajard, J., Bertino, L., and Counillon, F.: Hybrid covariance super-resolution data assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1846, https://doi.org/10.5194/egusphere-egu23-1846, 2023.

All-sky radiance assimilation often has non-Gaussian observation error distributions, which can be exacerbated by high model spatial resolutions due to better resolved nonlinear physical processes. For ensemble Kalman filters, observation ensemble perturbations can be approximated by linearized observation operator (LinHx) that uses the observation operator Jacobian of ensemble mean rather than full observation operator (FullHx). The impact of observation operator on infrared radiance data assimilation is examined here by assimilating synthetic radiance observations from channel 1025 of GIIRS with increased model spatial resolutions from 7.5 km to 300 m. A tropical cyclone is used, while the findings are expected to be generally applied. Compared to FullHx, LinHx provides larger magnitudes of correlations and stronger corrections around observation locations, especially when all-sky radiances are assimilated at fine model resolutions. For assimilating clear-sky radiances with increasing model resolutions, LinHx has smaller errors and improved vortex intensity and structure than FullHx. But when all-sky radiances are assimilated, FullHx has advantages over LinHx. Thus for regimes with more linearity, LinHx provides stronger correlations and imposes more corrections than FullHx; but for regimes with more nonlinearity, LinHx provides detrimental non-Gaussian prior error distributions in observation space, unrealistic correlations and overestimated corrections, compared to FullHx.

How to cite: Lei, L.: Impacts of Observation Forward Operator on Infrared Radiance Data Assimilation with Fine Model Resolutions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3011, https://doi.org/10.5194/egusphere-egu23-3011, 2023.

EGU23-3086 | Posters on site | NP5.2

Comparison of optimization methods for the maximum likelihood ensemble filter 

Takeshi Enomoto and Saori Nakashita

The Newton method, which requires the Hessian matrix, is prohibitively expensive in adjoint-based variational data assimilation (VAR). It may be rather attractive for ensemble-based VAR because the ensemble size is usually several orders of magnitude smaller than that of the state size. In the present paper the Newton method is compared against the conjugate-gradient (CG) method, which is one of the most popular choices in adjoint-based VAR. To make comparisons, the maximum likelihood ensemble filter (MLEF) is used as a framework for data assimilation experiments. The Hessian preconditioning is used with CG as formulated in the original MLEF. Alternatively we propose to use the Hessian in the Newton method. In the exact Newton (EN) method, the Newton equation is solved exactly, i.e. the step size is fixed to unity avoiding a line search. In the 1000-member wind-speed assimilation test, CG is stagnated early in iteration and terminated due to a line search error while EN converges quadratically. This behaviour is consistent with the workings of the EN and CG in the minimization of the Rosenbrock function. In the repetitive cycled experiments using the Korteweg-de Vries-Burgers (KdVB) model with a quadratic observation operator, EN performs competitively in accuracy to CG with significantly enhanced stability. These idealized experiments indicate the benefit of adopting EN for the optimization in MLEF.

How to cite: Enomoto, T. and Nakashita, S.: Comparison of optimization methods for the maximum likelihood ensemble filter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3086, https://doi.org/10.5194/egusphere-egu23-3086, 2023.

EGU23-3761 | ECS | Posters on site | NP5.2

Observation space localizations for the maximum likelihood ensemble filter 

Saori Nakashita and Takeshi Enomoto

The maximum likelihood ensemble filter (MLEF) can handle nonlinearity of observation operators more appropriately than conventional ensemble Kalman filters. Here we consider the observation space localization method for MLEF to enable application to large-scale problems in the atmosphere. Optimization of the cost function in MLEF, however, impedes local analysis, suitable for massive parallel computers, in the same manner as the local ensemble transform Kalman filter (LETKF). In this study two approaches to observation space localization for MLEF (LMLEF) are compared. The first method introduces local gradients to minimize the global cost function (Yokota et al. 2016). An alternative approach, proposed here, defines a local cost function for each grid assuming a constant ensemble weight in the local domain to enable embarrassingly parallel analysis. The two approaches are compared to LETKF in cycled data assimilation experiments using the Lorenz-96 and the SPEEDY models. LMLEFs are found to be more accurate and stable than LETKF when nonlinear observations are assimilated into each model. Our proposed method is comparable to Yokota's global optimization method when dense observations are assimilated into the Lorenz-96 model. This result is consistent with the fact that ensemble weights have high spatial correlations with those at neighboring grids. Although our method also yields similar analysis in the SPEEDY experiments with a more realistic observation network, Yokota’s global optimization method shows faster error convergence in the earlier cycles. The error convergence rate seems to be related to the difference between global and local optimization and the validity of the assumption of constant weights, which depends strongly on the observation density.

How to cite: Nakashita, S. and Enomoto, T.: Observation space localizations for the maximum likelihood ensemble filter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3761, https://doi.org/10.5194/egusphere-egu23-3761, 2023.

EGU23-4668 | ECS | Posters virtual | NP5.2 | Highlight

A particle filter based target observation method and its application to two types of El Niño events 

Meiyi Hou and Youmin Tang

The optimal observational array for improving the El Niño-Southern Oscillation (ENSO) prediction is investigated by exploring sensitive areas for target observations of two types of El Niño events in the Pacific. A target observation method based on the particle filter and pre-industrial control runs from six coupled model outputs in Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments are used to quantify the relative importance of the initial accuracy of sea surface temperature (SST) in different Pacific areas. The initial accuracy of the tropical Pacific, subtropical Pacific, and extratropical Pacific can influence both types of El Niño predictions. The relative importance of different areas changes along with different lead times of predictions. Tropical Pacific observations are crucial for decreasing the root mean square error of predictions of all lead times. Subtropical and extratropical observations play an important role in reducing the prediction uncertainty, especially when the prediction is made before and throughout the boreal spring. To consider different El Niño types and different start months for predictions, a quantitative frequency method based on frequency distribution is applied to determine the optimal observations of ENSO predictions. The final optimal observational array contains 31 grid points, including 21 grid points in the equatorial Pacific and 10 grid points in the North Pacific, suggesting the importance of the initial SST conditions for ENSO predictions in the tropical Pacific and also in the area outside the tropics. Furthermore, the predictions made by assimilating SST in sensitive areas have better prediction skills in the verification experiment, which can indicate the validity of the optimal observational array designed in this study. This result provided guidance on how to initialize models in predictions of El Niño types. 

How to cite: Hou, M. and Tang, Y.: A particle filter based target observation method and its application to two types of El Niño events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4668, https://doi.org/10.5194/egusphere-egu23-4668, 2023.

EGU23-5421 | ECS | Posters on site | NP5.2

Estimation of Spatially and Temporally Varying Biogeochemical Parameters in a Global Ocean Model 

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

Ocean biogeochemical (BGC) models are, in addition to measurements, the primary tools for investigating ocean biogeochemistry, marine ecosystem functioning, and the global carbon cycle. These models contain a large number of not precisely known parameters and are highly uncertain regarding those parametrizations.  The values of these parameters depend on the physical and biogeochemical context, but in practice values derived from limited field measurements or laboratory experiments are used in the model keeping them constant in space and time. This study aims to estimate spatially and temporally varying parameters in a global ocean BGC model and to assess the effect of those estimated parameters on model fields and dynamics. Utilizing the BGC model Regulated Ecosystem Model 2 (REcoM2), we estimate ten selected BGC parameters with heterogeneity in parameter values both across space and over time using an ensemble data assimilation technique. We assimilate satellite ocean color and BGC-ARGO data using an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF) to simultaneously estimate the BGC model states and parameters. We assess the improvement in the model predictions with space and time-dependent parameters in reference to the simulation with globally constant parameters against both assimilative and independent data. We quantify the spatiotemporal uncertainties regarding the parameter estimation and the prediction uncertainties induced by those parameters. We study the effect of estimated parameters on the biogeochemical fields and dynamics to get deeper insights into modeling processes and discuss insights from spatially and temporally varying parameters beyond parameter values.

How to cite: Mamnun, N., Völker, C., Vrekoussis, M., and Nerger, L.: Estimation of Spatially and Temporally Varying Biogeochemical Parameters in a Global Ocean Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5421, https://doi.org/10.5194/egusphere-egu23-5421, 2023.

EGU23-5506 | ECS | Posters on site | NP5.2

Empirical optimal vertical localization derived from large ensembles 

Tobias Necker, Philipp Griewank, Takemasa Miyoshi, and Martin Weissmann

Ensemble-based estimates of error covariances suffer from limited ensemble size due to computational restrictions in data assimilation systems for numerical weather prediction. Localization of error covariances can mitigate sampling errors and is crucial for ensemble-based data assimilation. However, finding optimal localization methods, functions, or scales is challenging. We present a new approach to derive an empirical optimal localization (EOL) from a large ensemble dataset. The EOL allows for a better understanding of localization requirements and can guide toward improved localization.

Our study presents EOL estimates using 40-member subsamples assuming a 1000-member ensemble covariance as truth. The EOL is derived from a 5-day training period. In the presentation, we cover both model and observation space vertical localization and discuss:

  • vertical error correlations and EOL estimates for different variables and settings;

  • the effect of the EOL compared to common localization approaches, such as distance-dependent localization with a Gaspari-Cohn function;

  • and vertical localization of infrared and visible satellite observations in the context of observation space localization.

Proper observation space localization of error covariances between non-local satellite observations and state space is non-trivial and still an open research question. First, we evaluate requirements for optimal localization for different variables and spectral channels. And secondly, we investigate the situation dependence of vertical localization in convection-permitting NWP simulations, which suggests an advantage of using adaptive situation-dependent localization approaches.

How to cite: Necker, T., Griewank, P., Miyoshi, T., and Weissmann, M.: Empirical optimal vertical localization derived from large ensembles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5506, https://doi.org/10.5194/egusphere-egu23-5506, 2023.

EGU23-6050 | ECS | Posters on site | NP5.2 | Highlight

Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation 

Lei Kong, Xiao Tang, Jiang Zhu, Zifa Wang, Yele Sun, Pingqing Fu, Meng Gao, Huangjian Wu, Jie Li, Xiaole Pan, Lin Wu, Hajime Akimoto, and Gregory R. Carmichael

The unprecedented lockdown of human activities during the COVID-19 pandemic have significantly influenced the social life in China. However, understanding of the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15km) resolutions. Subsequently, contributions of emission changes versus meteorology variations during COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations of multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measurement with NOx emissions decreased substantially by ~40%. However, emissions of other air pollutants were found only decreased by ~10%, both because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over North China Plain (NCP) during lockdown period, the emission change only accounted for 8.6% of PM2.5 reductions, and even led to substantial increases of O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over NCP, which contributed 90% of the PM2.5 reductions over most parts of NCP region. Meanwhile, our results also suggest that the local stagnant meteorological conditions together with inefficient reductions in PM2.5 emissions were the main drivers of the unexpected COVID-19 haze in Beijing. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.

How to cite: Kong, L., Tang, X., Zhu, J., Wang, Z., Sun, Y., Fu, P., Gao, M., Wu, H., Li, J., Pan, X., Wu, L., Akimoto, H., and Carmichael, G. R.: Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6050, https://doi.org/10.5194/egusphere-egu23-6050, 2023.

EGU23-7480 | ECS | Orals | NP5.2 | Highlight

Supermodelling: synchronising models to further improve predictions 

Francine Schevenhoven, Mao-Lin Shen, Noel Keenlyside, Jeffrey B. Weiss, and Gregory S. Duane

Instead of combining data from an ensemble of different models after the simulations are already performed, as in a standard multi-model ensemble, we let the models interact with each other during their simulation. This ensemble of interacting models is called a supermodel. By exchanging information, models can compensate for each other's errors before the errors grow and spread to other regions or variables. Effectively, we create a new dynamical system. The exchange between the models is frequent enough such that the models synchronize, in order to prevent loss of variance when the models are combined. In previous work, we experimented successfully with combining atmospheric models of intermediate complexity in the context of parametric error. Here we will show results of combining two different AGCMs, NorESM1-ATM and CESM1-ATM. The models have different horizontal and vertical resolutions. To combine states from the different grids, we convert the individual model states to a ‘common state space’ with interpolation techniques. The weighted superposition of different model states is called a ‘pseudo-observation’. The pseudo-observations are assimilated back into the individual models, after which the models continue their run. We apply recently developed methods to train the weights determining the superposition of the model states, in order to obtain a supermodel that will outperform the individual models and any weighted average of their outputs.

How to cite: Schevenhoven, F., Shen, M.-L., Keenlyside, N., Weiss, J. B., and Duane, G. S.: Supermodelling: synchronising models to further improve predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7480, https://doi.org/10.5194/egusphere-egu23-7480, 2023.

EGU23-7719 | ECS | Orals | NP5.2

The role of anchor observations in disentangling observation and model bias corrections in 4DVar 

Devon Francis, Alison Fowler, Amos Lawless, Stefano Migliorini, and John Eyre

Data assimilation theory relies on the assumption that the background, model, and observations are unbiased. However, this is often not the case and, if biases are left uncorrected, this can cause significant systematic errors in the analysis. When bias is only present in the observations, Variational Bias Correction (VarBC) can correct for observation bias, and when bias is only present in the model, Weak-Constraint 4D Variational Assimilation (WC4DVar) can correct for model bias. However, when both observation and model biases are present, it can be very difficult to understand how the different bias correction methods interact, and the role of anchor (unbiased) observations becomes crucial for providing a frame of reference from which the biases may be estimated. This work presents a systematic study of the properties of the network of anchor observations needed to disentangle between model and observation biases when correcting for one or both types of bias in 4DVar.

We extend the theory of VarBC and WC4DVar to include both biased and anchor observations, to find that the precision and timing of the anchor observations are important in reducing the contamination of model/observation bias in the correction of observation/model bias. We show that anchor observations have the biggest impact in reducing the contamination of bias when they are later in the assimilation window than the biased observations, as such, operational systems that rely on anchor observations that are earlier in the window will be more susceptible to the contamination of model and/or observation biases. We also compare the role of anchor observations when VarBC/WC4DVar/both are used in the presence of both observation and model biases. We find that the ability of VarBC to effectively correct for observation bias when model bias is present, is very dependent on precise anchor observations, whereas correcting model bias with WC4DVar or correcting for both biases performs reasonably well regardless of the precision of anchor observations (although more precise anchor observations reduces the bias in the state analysis compared with less precise anchor observations for all three cases). This demonstrates that, when it is not possible to use anchor observations, it may be better to correct for both observation and model biases, rather than relying on only one bias correction technique.

We demonstrate these results in a series of idealised numerical experiments that use the Lorenz 96 model as a simplified model of the atmosphere.

How to cite: Francis, D., Fowler, A., Lawless, A., Migliorini, S., and Eyre, J.: The role of anchor observations in disentangling observation and model bias corrections in 4DVar, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7719, https://doi.org/10.5194/egusphere-egu23-7719, 2023.

EGU23-8030 | Posters on site | NP5.2

Assessment of short-range forecast atmosphere-ocean cross-covariances from the Met Office coupled NWP system 

Amos Lawless, Maria Valdivieso, Nancy Nichols, Daniel Lea, and Matthew Martin

As part of the design of future coupled forecasting systems, operational centres such as the Met Office are starting to include interactions between the atmosphere and the ocean within the data assimilation system. This requires an improved understanding and representation of the correlations between short-range forecast errors in different variables. To understand the potential benefit of further coupling in the data assimilation scheme it is important to understand the significance of any cross-correlations between atmosphere and ocean short-range forecast errors as well as their temporal and spatial variability. In this work we examine atmosphere-ocean cross-covariances from an ensemble of the Met Office coupled NWP system for December 2019, with particular focus on short-range forecast errors that evolve at lead times up to 6 hours.

We find that significant correlations exist between atmosphere and ocean forecast errors on these timescales, and that these vary diurnally, from day to day, spatially and synoptically. Negative correlations between errors in sea-surface temperature (SST) and 10m wind correlations strengthen as the solar radiation varies from zero at night (local time) to a maximum insolation around midday (local time). In addition, there are significant variations in correlation intensities and structures in response to synoptic-timescale forcing. Significant positive correlations between SST and 10m wind errors appear in the western North Atlantic in early December and are associated with variations in low surface pressures and their associated high wind speeds, that advect cold, dry continental air eastward over the warmer Atlantic ocean. Negative correlations across the Indo-Pacific Warm Pool are instead associated with light wind conditions on these short timescales.

When we consider the spatial extent of cross-correlations, we find that in the Gulf Stream region positive correlations between wind speed and sub-surface ocean temperatures are generally vertically coherent down to a depth of about 100m, consistent with the mixing depth; however, in the tropical Indian and West Pacific oceans, negative correlations break down just below the surface layer. This is likely due to the presence of surface freshwater layers that form from heavy precipitation on the tropical oceans, manifested by the presence of salinity-stratified barrier layers within deeper isothermal layers that can effectively limit turbulent mixing of heat between the ocean surface and the deeper thermocline.

How to cite: Lawless, A., Valdivieso, M., Nichols, N., Lea, D., and Martin, M.: Assessment of short-range forecast atmosphere-ocean cross-covariances from the Met Office coupled NWP system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8030, https://doi.org/10.5194/egusphere-egu23-8030, 2023.

EGU23-8640 | Orals | NP5.2

Forecast error growth: A stochastic differential equation model 

Michael Ghil, Eviatar Bach, and Dan Crisan

There is a history of simple error growth models designed to capture the key properties of error growth in operational numerical weather prediction models. We propose here such a scalar model that relies on the previous ones, but captures the effect of small scales on the error growth via additive noise in a nonlinear stochastic differential equation (SDE). We nondimensionalize the equation and study its behavior with respect to the error saturation value, the growth rate of small errors, and the magnitude of noise. We show that the addition of noise can change the curvature of the error growth curve. The SDE model seems to improve substantially the fit to operational error growth curves, compared to the deterministic counterparts.

How to cite: Ghil, M., Bach, E., and Crisan, D.: Forecast error growth: A stochastic differential equation model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8640, https://doi.org/10.5194/egusphere-egu23-8640, 2023.

EGU23-9529 | Orals | NP5.2

Nonlinear Data Assimilation for State and Parameter Estimation in Earthquake Simulation 

Femke Vossepoel, Arundhuti Banerjee, Hamed Diab Montero, Meng Li, Celine Marsman, Rob Govers, and Ylona van Dinther

The highly nonlinear dynamics of earthquake sequences and the limited availability of stress observations near subsurface faults make it very difficult, if not impossible, to forecast earthquakes. Ensemble data-assimilation methods provide a means to estimate state variables and parameters of earthquake sequences that may lead to a better understanding of the associated fault-slip process and contribute to the forecastability of earthquakes. We illustrate the challenges of data assimilation in earthquake simulation with an overview of three studies, each with different objectives and experiments.

In the first study, by reconstructing a laboratory experiment with an advanced numerical simulator we perform synthetic twin experiments to test the performance of an ensemble Kalman Filter (EnKF) and its ability to reconstruct fault slip behaviour in 1D and 3D simulations. The data assimilation estimates and forecasts earthquakes, even when having highly uncertain observations of the stress field. In these experiments, we assume the friction parameters to be perfectly known, which is typically not the case in reality.

A bias in a friction parameter can cause a significant change in earthquake dynamics, which will complicate the application of data assimilation in realistic cases. The second study addresses how well state estimation and state-parameter estimation can account for friction-parameter bias. For this, we use a 0D model for earthquake recurrence with a particle filter with sequential importance resampling. This shows that in case of intermediate to large uncertainty in friction parameters, combined state-and-parameter estimation is critical to correctly estimate earthquake sequences. The study also highlights the advantage of a particle filter over an EnKF for this nonlinear system.

The post- and inter-seismic deformations following an earthquake are rather gradual and do not pose the same challenges for data assimilation as the deformation during an earthquake event. To estimate the model parameters of surface displacements during these phases, a third study illustrates the application of the Ensemble Smoother-Multiple Data Assimilation and the particle filter with actual GPS data of the Tohoku 2011 earthquake.

Based on the comparison of the various experiments, we discuss the choice of data-assimilation method and -approach in earthquake simulation and suggest directions for future research.

How to cite: Vossepoel, F., Banerjee, A., Diab Montero, H., Li, M., Marsman, C., Govers, R., and van Dinther, Y.: Nonlinear Data Assimilation for State and Parameter Estimation in Earthquake Simulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9529, https://doi.org/10.5194/egusphere-egu23-9529, 2023.

EGU23-11889 | ECS | Posters on site | NP5.2

Data Assimilation and Subsurface Flow Modeling: Interactions between Groundwater and the Vadose Zone 

Bastian Waldowski, Insa Neuweiler, and Natascha Brandhorst

Reliable estimates of soil water content and groundwater levels are essential in evaluating water availability for plants and as drinking water and thus both subsurface components (vadose zone and groundwater) are commonly monitored. Such measurements can be used for data assimilation in order to improve predictions of numerical subsurface flow models. Within this work, we investigate to what extent measurements from one subsurface component are able to improve predictions in the other one.
For this purpose, we utilize idealized test cases at a subcatchment scale using a Localized Ensemble Kalman Filter to update the water table height and soil moisture at certain depths with measurements taken from a numerical reference model. We do joint, as well as single component updates. We test strongly coupled data assimilation, which implies utilizing correlations between the subsurface components for updating the ensemble and compare it to weakly coupled data assimilation. We also update soil hydraulic parameters and examine the role of their heterogeneity with respect to data assimilation. We run simulations with both a complex 3D model (using TSMP-PDAF) as well as a more simplified and computationally efficient 2.5D model, which consists of multiple 1D vadose-zone columns coupled iteratively with a 2D groundwater-flow model. In idealized settings, such as homogeneous subsurface structures, we find that predictions in one component consistently benefit from updating the other component.

How to cite: Waldowski, B., Neuweiler, I., and Brandhorst, N.: Data Assimilation and Subsurface Flow Modeling: Interactions between Groundwater and the Vadose Zone, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11889, https://doi.org/10.5194/egusphere-egu23-11889, 2023.

EGU23-12304 | ECS | Posters on site | NP5.2

Analysis of airborne-derived sea ice emissivities up to 340 GHz in preparation for future satellite missions 

Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell

Passive microwave radiometers onboard polar-orbiting satellites provide global information on the atmospheric state. The underlying retrievals require accurate knowledge of the surface radiative properties to distinguish atmospheric from surface contributions to the measured radiance. Polar surfaces such as sea ice contribute up to 400 GHz to the measured radiance due to the high atmospheric transmissivity under cold and dry conditions. Currently, we lack an understanding of sea ice parameters driving the variability in its radiative properties, i.e., its emissivity, at frequencies above 200 GHz due to limited field data and the heterogeneous sea ice structure. This will limit the use of future satellite missions such as the Ice Cloud Imager (ICI) onboard Metop-SG and the Arctic Weather Satellite (AWS) in polar regions.

To better understand sea ice emission, we analyze unique airborne measurements from 89 to 340 GHz obtained during the ACLOUD (summer 2017) and AFLUX (spring 2019) airborne campaigns and co-located satellite observations in the Fram Strait. The Polar 5 aircraft carried the Microwave Radar/radiometer for Arctic Clouds (MiRAC) cloud radar MiRAC-A with an 89 GHz passive channel and MiRAC-P with six double-sideband channels at 183.31 GHz and two window channels at 243 and 340 GHz. We calculate the emissivity with the non-scattering radiative transfer equation from observed upwelling radiation at 25° (MiRAC-A) and 0° (MiRAC-P) and Passive and Active Microwave radiative TRAnsfer (PAMTRA) simulations. The PAMTRA simulations are based on atmospheric profiles from dropsondes and surface temperatures from an infrared radiometer.

The airborne-derived sea ice emissivity (O(0.1km)) varies on small spatial scales (O(1km)), which align with sea ice properties identified by visual imagery. High-resolution airborne-derived emissivities vary more than emissivities from co-located overflights of the GPM constellation due to the smaller footprint size, which resolve sea ice variations. The emissivity of frozen and snow-free leads separates clearly from more compact and snow-covered ice flows at all frequencies. The comparison of summer and spring emissivities reveals an emissivity reduction due to melting. We will also conduct evaluations of emissivity parameterizations (e.g. TELSEM²) and provide insights into observations at ICI and AWS frequencies over Arctic sea ice. Findings based on the field data may be useful for the assimilation of radiances from existing and future microwave radiometers into weather prediction models in polar regions.

How to cite: Risse, N., Mech, M., Prigent, C., Spreen, G., and Crewell, S.: Analysis of airborne-derived sea ice emissivities up to 340 GHz in preparation for future satellite missions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12304, https://doi.org/10.5194/egusphere-egu23-12304, 2023.

EGU23-14227 | Orals | NP5.2

Combining sea-ice and ocean data assimilation with nudging atmospheric circulation in the AWI Coupled Prediction System 

Svetlana N. Losa, Longjiang Mu, Marylou Athanase, Jan Streffing, Miguel Andrés-Martínez, Lars Nerger, Tido Semmler, Dmitry Sidorenko, and Helge F. Goessling

Assimilation of sea ice and ocean observational data into coupled sea-ice, ocean and atmosphere models is known as an efficient approach for providing a reliable sea-ice prediction (Mu et al. 2022). However, implementations of the data assimilation in the coupled systems still remain a challenge. This challenge is partly originated from the chaoticity possessed in the atmospheric module, which leads to biases and, therefore, to divergence of predictive characteristics. An additional constrain of the atmosphere is proposed as a tool to tackle the aforementioned problem. To test this approach, we use the recently developed AWI Coupled Prediction System (AWI-CPS). The system is built upon the AWI climate model AWI-CM-3 (Streffing et al. 2022) that includes FESOM2.0 as a sea-ice ocean component and the Integrated Forecasting System (OpenIFS) as an atmospheric component. An Ensemble-type Kalman filter within the Parallel Data Assimilation Framework (PDAF; Nerger and Hiller, 2013) is used to assimilate sea ice concentration, sea ice thickness, sea ice drift, sea surface height, sea surface temperature and salinity, as well as temperature and salinity vertical profiles. The additional constrain of the atmosphere is introduced by relaxing, or “nudging”, the AWI-CPS large-scale atmospheric dynamics to the ERA5 reanalysis data. This nudging of the large scale atmospheric circulation towards reanalysis has allowed to reduce biases in the atmospheric state, and, therefore, to reduce the analysis increments. The most prominent improvement has been achieved for the predicted sea ice drift. Comprehensive analyses will be presented based upon the new system’s performance over the time period 2003 – 2022.

Mu, L., Nerger, L., Streffing, J., Tang, Q., Niraula, B., Zampieri, L., Loza, S. N. and H. F. Goessling, Sea-ice forecasts with an upgraded AWI Coupled Prediction System (Journal of Advances in Modeling Earth Systems, 14, e2022MS003176. doi: 10.1029/2022MS003176.

Nerger, L. and Hiller, W., 2013. Software for ensemble-based data assimilation systems—Implementation strategies and scalability. Computers & Geosciences, 55, pp.110-118.

Streffing, J., Sidorenko, D., Semmler, T., Zampieri, L., Scholz, P., Andrés-Martínez, M., Koldunov, N., Rackow, T., Kjellsson, J., Goessling, H., Athanase, M., Wang, Q., Sein, D., Mu, L., Fladrich, U., Barbi, D., Gierz, P., Danilov, S.,  Juricke, S., Lohmann, G. and Jung, T. (2022) AWI-CM3 coupled climate model: Description and evaluation experiments for a prototype post-CMIP6 model, EGUsphere, 2022, 1—37, doi: 10.5194/egusphere-2022-32

How to cite: Losa, S. N., Mu, L., Athanase, M., Streffing, J., Andrés-Martínez, M., Nerger, L., Semmler, T., Sidorenko, D., and Goessling, H. F.: Combining sea-ice and ocean data assimilation with nudging atmospheric circulation in the AWI Coupled Prediction System, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14227, https://doi.org/10.5194/egusphere-egu23-14227, 2023.

EGU23-14826 | Posters virtual | NP5.2 | Highlight

Inverse modelling for trace gas surface flux estimation, impact of a non-diagonal B-matrix 

Ross Bannister
One of the most appealing uses of data assimilation is to infer useful information about a dynamical system that is not observed directly. This is the case for the estimation of surface fluxes of trace gases (like methane). Such fluxes are not easy to measure directly on a global scale, but it is possible to measure the trace gas itself as it is transported around the globe. This is the purpose of INVICAT (the inverse modelling system of the chemical transport model TOMCAT), which has been developed here. INVICAT interprets observations of (e.g.) methane over a time window to estimate the initial conditions (ICs) and surface fluxes (SFs) of the TOMCAT model.
This talk will show how INVICAT has been expanded from a diagonal background error covariance matrix (B-matrix, DB) to allow an efficient representation of a non-diagonal B-matrix (NDB). The results of this process are mixed. A NDB-matrix for the SF field improves the analysis against independent data, but a NDB-matrix for the IC field appears to degrade the analysis. This paper presents these results and suggests that a possible reason for the degraded analyses is the presence of a possible bias in the system.

How to cite: Bannister, R.: Inverse modelling for trace gas surface flux estimation, impact of a non-diagonal B-matrix, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14826, https://doi.org/10.5194/egusphere-egu23-14826, 2023.

EGU23-14985 | ECS | Orals | NP5.2

Reconstructing North Atlantic Ocean Heat Content Using Convolutional Neural Networks 

Simon Lentz, Dr. Sebastian Brune, Dr. Christopher Kadow, and Prof. Dr. Johanna Baehr

Slowly varying ocean heat content is one of the most important variables when describing cli-
mate variability on interannual to decadal time scales. Since observation-based estimates of
ocean heat content require extensive observational coverage, incomplete observations are often
combined with numerical models via data assimilation to simulate the evolution of oceanic heat.
However, incomplete observations, particularly in the subsurface ocean, lead to large uncertain-
ties in the resulting model-based estimate. As an alternative approach, Kadow et al (2020) have
proven that artificial intelligence can successfully be utilized to reconstruct missing climate in-
formation for surface temperatures. In the following, we investigate the possibility to train their
three-dimensional convolutional neural network to reconstruct missing subsurface temperatures
to obtain ocean heat content estimates with a focus on the North Atlantic ocean.
The network is trained and tested to reconstruct a 16 member Ensemble Kalman Filter assimi-
lation ensemble constructed with the Max-Planck Institute Earth System Model for the period
from 1958 to 2020. Specifically, we examine whether the partial convolutional U-net represents
a valid alternative to the Ensemble Kalman Filter assimilation to estimate North Atlantic sub-
polar gyre ocean heat content.
The neural network is capable of reproducing the assimilation reduced to datapoints with ob-
servational coverages within its ensemble spread with a correlation coefficient of 0.93 over the
entire time period and of 0.99 over 2004 – 2020 (the Argo-Era). Additionally, the network is
able to reconstruct the observed ocean heat content directly from observations for 12 additional
months with a correlation of 0.97, essentially replacing the assimilation experiment by an extrap-
olation. When reconstructing the pre-Argo-Era, the network is only trained with assimilations
from the Argo-Era. The lower correlation in the resulting reconstruction indicates higher un-
certainties in the assimilation outside of its ensemble spread at times with low observational
density. These uncertainties are highlighted by inconsistencies in the assimilation’s represen-
tations of the North Atlantic Current at times and grid points without observations detected
by the neural network. Our results demonstrate that a neural network is not only capable of
reproducing the observed ocean heat content over the training period, but also before and after
making the neural network a suitable candidate to step-wise extend or replace data assimilation.

How to cite: Lentz, S., Brune, Dr. S., Kadow, Dr. C., and Baehr, P. Dr. J.: Reconstructing North Atlantic Ocean Heat Content Using Convolutional Neural Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14985, https://doi.org/10.5194/egusphere-egu23-14985, 2023.

EGU23-15189 | ECS | Orals | NP5.2

A coupled data assimilation framework with an integrated surface and subsurface hydrological model 

Qi Tang, Hugo Delottier, Oliver S. Schilling, Wolfgang Kurtz, and Philip Brunner

We developed an ensemble based data assimilation (DA) system for an integrated hydrological model to facilitate real-time operational simulations of water quantity and quality. The integrated surface and subsurface hydrologic model HydroGeoSphere (HGS) (Brunner & Simmons, 2012) which simulates surface water and variably saturated groundwater flow as well as solute transport, was coupled with the Parallel Data Assimilation Framework (PDAF) (Nerger et al., 2005). The developed DA system allows joint assimilation of multiple types of observations such as piezometric heads, streamflow, and tracer concentrations. By explicitly considering tracer and streamflow data we substantially expand the hydrologic information which can be used to constrain the simulations.    Both the model states and the parameters can be separately or jointly updated by the assimilation algorithm.  

A synthetic alluvial plain model set up by Delottier et al., (2022) was used as an example to test the performance of our DA system.  For flow simulations, piezometric head observations were assimilated, while for transport simulations, noble gas concentrations (222Rn, 37Ar, and 4He) were assimilated. Both model states (e.g., hydraulic head or noble gas concentrations) and parameters (e.g. hydraulic conductivities and porosity) are jointly updated by the DA. Results were evaluated by comparing the estimated model variables with independent observation data between the assimilation runs and the free run where no data assimilation was conducted. In a further evaluation step, a real-world, field scale model featuring realistic forcing functions and material properties was set up for a site in Switzerland and carried out for numerical simulations with the developed DA system. The synthetic and real-world examples demonstrate the significant potential in combing state of the art numerical models, data assimilation and novel tracer observations such as noble gases or Radon.

How to cite: Tang, Q., Delottier, H., Schilling, O. S., Kurtz, W., and Brunner, P.: A coupled data assimilation framework with an integrated surface and subsurface hydrological model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15189, https://doi.org/10.5194/egusphere-egu23-15189, 2023.

EGU23-16806 | Orals | NP5.2

Coupled data assimilation for numerical weather prediction at ECMWF 

Patricia de Rosnay, Phil browne, Eric de Boisséson, David Fairbairn, Sébastien Garrigues, Christoph Herbert, Kenta Ochi, Dinand Schepers, Pete Weston, and Hao Zuo

In this presentation we introduce coupled assimilation activities conducted in support of seamless Earth system approach developments for Numerical Weather Prediction and climate reanalysis at the European Centre for Medium-Range Weather Forecasts (ECMWF). For operational applications coupled assimilation requires to have reliable and timely access to observations in all the Earth system components and it relies on consistent acquisition and monitoring approaches across the components. We show recent and future infrastructure developments and implementations to support consistent observations acquisition and monitoring for land and ocean at ECMWF. We discuss challenges of surface sensitive observations assimilation and we show ongoing forward operator and coupling developments to enhance the exploitation of interface observations over land and ocean surfaces. We present plans to use new and future observation types from future observing systems such as the Copernicus Expansion missions.

How to cite: de Rosnay, P., browne, P., de Boisséson, E., Fairbairn, D., Garrigues, S., Herbert, C., Ochi, K., Schepers, D., Weston, P., and Zuo, H.: Coupled data assimilation for numerical weather prediction at ECMWF, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16806, https://doi.org/10.5194/egusphere-egu23-16806, 2023.

CR3 – lce sheets, ice shelves and glaciers

EGU23-548 | ECS | Orals | CR3.1

Towards confidence in numerical modeling of ice stream cycling 

Kevin Hank, Lev Tarasov, and Elisa Mantelli

Some ice sheets and glaciers experience long quiescent periods interspersed with short periods of rapid ice advance, such as the binge-purge-type cycling hypothesized to be associated with Heinrich Events. Modeling ice stream activation/de-activation, however, is numerically challenging given the relatively abrupt changes at surge onset and the high ice velocities. In spite of this, a number of high-profile modeling papers have explored Heinrich events and ice surges, but generally with very limited consideration of numerical aspects. Here we test the ability of the 3D Glacial Systems Model (GSM) and Parallel Ice Sheet Model (PISM) to simulate binge-purge-type surges and explore the stability of the simulations with respect to relevant numerical and discretization uncertainties. 

We find surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions (order 5 km). In accordance with theoretical and experimental work, our model results suggest that the thermal activation of basal sliding should start below the pressure melting point. A resolution-dependent basal temperature ramp for the thermal activation of basal sliding as well as a subglacial hydrology model can reduce the discrepancies between high and coarse horizontal grid resolutions. Furthermore, incorporating a bed thermal and at least a minimal complexity subglacial hydrology model significantly affects surge characteristics and is, therefore, essential for modeling large-scale ice stream cycling.

How to cite: Hank, K., Tarasov, L., and Mantelli, E.: Towards confidence in numerical modeling of ice stream cycling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-548, https://doi.org/10.5194/egusphere-egu23-548, 2023.

EGU23-1401 | ECS | Posters on site | CR3.1

Ice Sheet Speed-dating: Using Expert Elicitation to identify “good” simulations of the LGM North American Ice Sheets 

Niall Gandy, Gemma Ives, Gwyneth Rivers, and Lachlan Astfalck

After running a large ensemble of palaeo ice sheet model runs, it is common to either rank the simulations or determine which simulations are an acceptable match to observations and which are ruled out. This task requires human judgement, which is usually left only to the authors of the research. Tools have been developed to compare ice sheet simulations to empirical reconstructions numerically, but even this approach requires some human input on values for match thresholds.

An alternative is to use expert elicitation to identify “good” ice sheet simulations. Expert elicitation normally captures expert’s beliefs in the form of a probability distribution; for something as complicated as ice sheet geometry this is much too arduous a task. Instead, we propose to elicit binary classifications of “good” and “bad” and find descriptions of plausible ice sheets through probabilistic inverse modelling. Experts can consider empirical ice sheet reconstructions, but also “soft-knowledge” about the sectors of the ice sheet it is most important to match, margin shapes considered to be glaciologically plausible, and an idea of the reasonable best-reconstruction a model will be able to provide. By seeking the input of many experts, it is possible to both lighten the task load of determining the quality of 100-1000s of simulations, and gain a wisdom of the crowd benefit to the results. Just like any other method of ranking ice sheet simulations, this method requires human judgement; in this case more explicitly than usual.

We are seeking expert input to rank an existing ensemble of North American Ice Sheet simulations. By asking experts at EGU 2023 to spend 3-5 minutes sorting simulations using an online interface we will build up an average community view on which LGM North American Ice Sheet simulations are “good”. This will provide a community resource to compare future ice sheet simulations against that is a justifiable representation of academic expert knowledge, adding to the current arsenal of model-data intercomparison tools.

How to cite: Gandy, N., Ives, G., Rivers, G., and Astfalck, L.: Ice Sheet Speed-dating: Using Expert Elicitation to identify “good” simulations of the LGM North American Ice Sheets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1401, https://doi.org/10.5194/egusphere-egu23-1401, 2023.

EGU23-2365 | Posters on site | CR3.1

Bayesian calibration of an ice sheet model for the Amundsen Sea Embayment region. 

Suzanne Bevan, Stephen Cornford, Inés Otosaka,, Trystan Surawy-Stepney, Lin Gilbert, and Daniel Martin

Mass loss from the Amundsen Sea Embayment of the West Antarctic Ice Sheet has been increasing over recent decades and is a major contributor to global sea level rise. Predictions of future sea level rise are increasingly modelled using ensembles of simulations within which model parameters and external forcings are varied widely then scored according to observations. Accurately reporting the uncertainty associated with these predictions is vital to enable effective planning for, and maybe construction of defences against, rising sea levels. Here we constrain, or calibrate, an ensemble of simulations of ice loss from the Amundsen Sea Embayment using the BISICLES ice sheet model with remotely sensed observations of surface elevation change and ice speed. The calibrations make it possible to reduce the 90% credibility bounds of predicted contributions to sea-level rise by 40%.

How to cite: Bevan, S., Cornford, S., Otosaka,, I., Surawy-Stepney, T., Gilbert, L., and Martin, D.: Bayesian calibration of an ice sheet model for the Amundsen Sea Embayment region., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2365, https://doi.org/10.5194/egusphere-egu23-2365, 2023.

EGU23-2607 | ECS | Orals | CR3.1

Glacier ice thickness estimation using deep-learning-driven emulation of Stokes 

Samuel Cook, Guillaume Jouvet, Romain Millan, and Antoine Rabatel

Mountain glaciers are a major source of sea-level rise and also represent an important freshwater resource in many mountainous regions. Thus, accurate estimations of their thickness and, therefore, the total ice volume are important both in predicting and mitigating the global and local effects of climate change. However, to date, only 2% of the world’s glaciers outside the ice sheets have any thickness data, due to the logistical difficulties of obtaining such measurements, creating a large and policy-relevant scientific gap.

The recent development of a global-scale ice-velocity dataset, however, provides an ideal opportunity to fill this gap and determine ice thickness across the 98% of glaciers for which no thickness data is available. This can be done by inverting an ice-dynamics model to solve for the ice thickness. For accurate thickness results, this needs to be a full-Stokes model, but such a model is far too computationally cumbersome to apply on a global scale, and simpler, quicker methods usually based on the shallow ice approximation (SIA) are too inaccurate, particularly where sliding dominates glacier motion. The only attempt that has been made to leverage the global velocity dataset to retrieve ice thickness has, though, used the SIA, simply because higher-order approaches are not computationally realistic at this scale. Consequently, most of the widely-used global glacier models have made no concerted attempt to invert for global ice thickness, owing to these limitations.

As an additional related problem, failing to fully assimilate ice-velocity data into an ice-flow model necessarily introduces a shock when initialising prognostic glacier simulations, resulting in model glaciers and predictions that may diverge substantially from their real-world counterparts.

As a solution to these problems, we present results from a deep-learning-driven inversion model that emulates the performance of state-of-the-art full-Stokes models at a thousandth of the computational cost. This model, by solving an optimisation problem, can fully use and assimilate all available input datasets (surface velocity and topography, ice thickness, etc.) as components of its cost function to simultaneously invert for and optimise multiple control parameters (here, we focus on ice thickness). This approach also gives us the possibility of using the same ice-velocity field for inversion and forward modelling, reducing the magnitude of the shock inherent in traditional modelling approaches. With a view to a large-scale application to all the world’s 200,000 glaciers, we present initial thickness-inversion results for the relatively well-documented European Alps to help constrain model parameters and provide a test bed for extension to other glaciated regions, with initial extension to the Caucasus and the Southern Alps.

How to cite: Cook, S., Jouvet, G., Millan, R., and Rabatel, A.: Glacier ice thickness estimation using deep-learning-driven emulation of Stokes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2607, https://doi.org/10.5194/egusphere-egu23-2607, 2023.

EGU23-2804 | ECS | Orals | CR3.1

Phase field modelling of glacial crevasses subject to meltwater-driven hydro-fracture 

Theo Clayton, Ravindra Duddu, Martin Siegert, and Emilio Martinez-Pañeda

Surface crevasses are predominately mode I fractures that penetrate tens of metres deep into grounded glaciers and floating ice shelves. However, elevated surrounding temperatures have resulted in the production of surface meltwater, which accumulates in neighbouring crevasses and applies additional tensile stresses to crack walls. This process is known as hydrofracture; and if sufficient, can promote full thickness crevasse propagation, and lead to iceberg calving events. Net ablation of ice sheets has become of great concern, as it has become the largest contributor to sea-level rise. To overcome the limitations of empirical and analytical approaches to crevasse predictions, we here propose a thermo-dynamically consistent phase field damage model to simulate damage growth in both ice sheets and floating ice shelves using the finite element method.

The model incorporates the three elements needed to mechanistically simulate hydrofracture of surface and basal crevasses: (i) a constitutive description of glacier flow, incorporating the non-linear viscous rheology of ice using Glen’s flow law, (ii) a phase field formulation capable of capturing cracking phenomena of arbitrary complexity, such as 3D crevasse interaction, and (iii) a poro-damage mechanics representation to account for the role of meltwater pressure on crevasse growth. To assess the suitability of the method, we simulated the propagation of surface and basal crevasses within grounded glaciers and floating ice shelves and compared the predicted crevasse depths with analytical methods such as linear elastic fracture mechanics and the Nye zero stress
method, with results showing good agreement for idealised conditions.

References
T. Clayton, R. Duddu, M. Siegert, E. Martínez-Pañeda, A stress-based poro-damage phase field
model for hydrofracturing of creeping glaciers and ice shelves, Engineering Fracture Mechanics
272 (2022) 108693

 

How to cite: Clayton, T., Duddu, R., Siegert, M., and Martinez-Pañeda, E.: Phase field modelling of glacial crevasses subject to meltwater-driven hydro-fracture, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2804, https://doi.org/10.5194/egusphere-egu23-2804, 2023.

EGU23-2844 | Orals | CR3.1

Examining the sensitivity of ice sheet models to updates in rheology (n=4) 

Daniel Martin, Samuel Kachuck, Joanna Millstein, and Brent Minchew

Ice is a non-Newtonian fluid whose rheology is typically described using Glen's flow law, a power-law relationship between stress and strain rate with a stress exponent, n, generally taken to be 3. Recent observation-based work suggests that a more accurate choice for the Glen’s law exponent in high-strain regions like ice shelves may be n=4, implying that ice viscosity is more sensitive to changes in stress than is generally assumed. The implications of a higher stress exponent for ice sheet models and their projections of ice sheet response to climate forcing are unclear and likely to be complex. Rheological parameters, such as ice viscosity, are fundamental to ice sheet dynamics and influence the evolution of marine ice sheets. 

 

Here, we present work that explores the rheological parameter space within the idealized MSIMIP+ marine ice sheet configuration using the BISICLES model. We explore the impacts of increasing  the stress exponent from 3 to 4, highlighting the considerable changes to the ice sheet system caused by increasing the stress exponent. Beyond dynamic changes in the ice behavior, changes become necessary to the other flow law parameters generally computed during initialization. For example, it may be that viscosity modifiers typically interpreted as “damage” may instead be indications of mismatches in rheology.  This study underscores the dynamic sensitivity of glacial ice to changes in the rheological parameters and calls attention to the key variables influencing ice sheet evolution. 

 

How to cite: Martin, D., Kachuck, S., Millstein, J., and Minchew, B.: Examining the sensitivity of ice sheet models to updates in rheology (n=4), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2844, https://doi.org/10.5194/egusphere-egu23-2844, 2023.

EGU23-2908 | Orals | CR3.1

Basal controls of the grounding line dynamics of buttresses marine ice sheets 

Olga Sergienko and Marianne Haseloff
The results of numerical studies suggest that the choice of sliding laws affect simulated dynamics of the grounding lines. Sliding laws that depend on the effective pressure imply that basal shear vanishes at the grounding line. Using analytical and numerical approaches we investigate what effects vanishing basal shear play in the dynamics and stability of the buttressed marine ice sheets. Our results show that the steady-state configurations of marine ice sheets with vanishing and non-vanishing basal shear do not differ greatly. In contrast, the time-variant behaviours of such marine ice sheets are drastically different. In response to a stochastic temporal variability in submarine melting, marine ice sheets with vanishing basal shear can exhibit unstoppable retreat while marine ice sheets with non-zero basal shear in the vicinity of the grounding line can exhibit intermittent advance and retreat. These results suggest that basal conditions in the vicinity of the grounding lines exhibit strong control of their dynamic behaviour. They also suggest the importance of the temporal variability of the basal shear in the dynamics of buttressed marine ice sheets.

How to cite: Sergienko, O. and Haseloff, M.: Basal controls of the grounding line dynamics of buttresses marine ice sheets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2908, https://doi.org/10.5194/egusphere-egu23-2908, 2023.

EGU23-3216 | Orals | CR3.1

Controls on the flow configuration of Vanderford Glacier, East Antarctica 

Felicity McCormack, Bernd Kulessa, and Jason Roberts

Vanderford Glacier is one of the fastest retreating glaciers in East Antarctica, with approximately 18.6 km of grounding line retreat since 1996. Together with the Totten Glacier, the Vanderford Glacier is a key outlet glacier of the Aurora Subglacial Basin (ASB), which contains approximately 7 m of global sea level equivalent, of which ~3.5 m is vulnerable to ocean driven melting, and is rapidly losing mass. While the Totten Glacier currently discharges almost twice as much ice as the Vanderford Glacier, sediment records from the Sabrina and Knox Coast Sectors indicate that the Vanderford Glacier has had sedimentation rates over twice that at Totten in the past. Here, we examine the current flow configuration between Vanderford and Totten Glaciers and drivers of it, including interactions between the subglacial topography, hydraulic potential, climate, and ice sheet dynamics. We use the Ice-sheet and Sea-level System Model (ISSM) under experiments of heightened ocean warming concentrated at Vanderford Glacier, and heightened surface mass balance at Totten Glacier, to show that the present-day flow configuration between the Totten and Vanderford Glaciers is tenuous. Rerouting towards Vanderford Glacier could occur under even minor changes in surface elevation at both glaciers. Such rerouting potentially exposes large parts of the underbelly of the ASB to enhanced ocean-driven ice shelf melting in the event of rapid retreat of Vanderford Glacier, with implications for global sea level rise.

How to cite: McCormack, F., Kulessa, B., and Roberts, J.: Controls on the flow configuration of Vanderford Glacier, East Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3216, https://doi.org/10.5194/egusphere-egu23-3216, 2023.

EGU23-3993 | ECS | Orals | CR3.1

Hysteresis of idealized marine outlet glaciers under variation of pinning-point buttressing 

Johannes Feldmann, Ricarda Winkelmann, and Anders Levermann

Ice-shelf pinning points such as ice rises or ice rumples can have an important role in regulating the ice discharge of marine outlet glaciers. For instance, the observed gradual ungrounding of the ice shelf of West Antarctica's Thwaites Glacier from its last pinning points diminishes the buttressing effect of the ice shelf and thus contributes to the destabilization of the outlet. Here we use an idealized experimental setting to simulate the response of an Antarctic-type marine outlet glacier to a successive ungrounding of its ice shelf from a pinning point. This is realized by perturbing steady states by a step-wise lowering of the pinning point, which induces a buttressing reduction. After the complete detachment of the ice shelf from the pinning point the perturbation is reversed, i.e., the pinnning point is incrementally elevated toward its initial elevation. First results show that the glacier retreat down the landward down-sloping (retrograde) bed, induced by the loss in buttressing, can be reversible in case of a relatively flat retrograde bed slope. For steeper slopes, glacier retreat and re-advance show a hysteretic behavior. Thus, if the bed depression is sufficiently deep, the glacier does not recover from its fully retreated state even for pinning-point elevations that are higher than the initial elevation.

How to cite: Feldmann, J., Winkelmann, R., and Levermann, A.: Hysteresis of idealized marine outlet glaciers under variation of pinning-point buttressing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3993, https://doi.org/10.5194/egusphere-egu23-3993, 2023.

EGU23-4200 | ECS | Posters on site | CR3.1

Impact of spatial resolution on large-scale ice cover modeling of mountainous regions 

Helen Werner, Dirk Scherler, Ricarda Winkelmann, and Guillaume Jouvet

For reconstructing paleoclimate or studying glacial isostatic effects on the Earth’s lithosphere, increasingly more studies focus on modeling the large-scale ice cover in mountainous regions over long time scales. However, balancing model complexity and the spatial extent with computational costs is challenging. Previous studies of large-scale ice cover simulation in mountain areas such as the European Alps, New Zealand, and the Tibetan Plateau, typically used 1-2 km spatial resolution. However, mountains are characterized by high peaks and steep slopes - topographic features that are crucial for glacier mass balance and dynamics, but poorly resolved in coarse resolution topography.

The Instructed Glacier Model (IGM) is a novel 3D ice model equipped with a Convolutional Neural Network which is trained from high order ice models to simulate ice flow. This results in a significant acceleration of run times, and thereby opening the possibility of running in higher spatial resolution. We use IGM to perform simulations of the entire European Alps (covering 480 240 km2), comparing models with 200 m and 2000 m resolution. We apply a linear cooling rate to today’s climate until 6 °C colder to mimic ice age conditions and model the expanding ice cover over a time period of 70,000 years.

Preliminary results indicate systematic, resolution-related differences: At the beginning of cooling, when ice accumulates at high elevations, the lower resolution yields slightly more ice volume. However, this trend reverses after ~ 41,000 years, right before the large valleys are filled with thick ice. When the Alps are fully ice covered, we find up to 15% more ice volume in the higher resolution model. The differences in ice volume are not uniformly distributed in space. The higher resolution model yields thicker and more extensive ice in some regions - mostly large valley systems - of up to 12,000 km2 , and thinner and less extensive ice in other, slightly smaller regions of the Alps. Currently, we analyze to what extent the glacier flow from steep slopes into larger, shallow valleys is represented at the different resolutions.

How to cite: Werner, H., Scherler, D., Winkelmann, R., and Jouvet, G.: Impact of spatial resolution on large-scale ice cover modeling of mountainous regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4200, https://doi.org/10.5194/egusphere-egu23-4200, 2023.

EGU23-5414 | Orals | CR3.1

A framework for quantifying parametric ice sheet model uncertainty 

James Maddison, Beatriz Recinos, and Daniel Goldberg

Ice sheet models, calibrated using observational data, provide a means of projecting our current best state of the knowledge of the system state into the future, so as to obtain information about possible future behaviour. However it is important to be able to estimate the uncertainty associated with these projections. The problem of quantifying ice sheet parametric uncertainty is considered, focusing specifically on the problem of quantifying the posterior uncertainty in inferred basal sliding and rheology coefficients. These measures of uncertainty are projected forwards in time to obtain measures of uncertainty in future quantities of interest. Automated code generation and automated differentiation tools are utilised, leading to an extensible approach. The role of the observational error model in defining parametric uncertainty is considered.

How to cite: Maddison, J., Recinos, B., and Goldberg, D.: A framework for quantifying parametric ice sheet model uncertainty, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5414, https://doi.org/10.5194/egusphere-egu23-5414, 2023.

EGU23-6272 | ECS | Posters on site | CR3.1

Identifying thresholds of ocean-induced Antarctic ice loss through idealized ice-sheet model simulations 

Lena Nicola, Julius Garbe, Ronja Reese, and Ricarda Winkelmann

The Antarctic Ice Sheet is currently losing mass through ocean induced melting at the underside of its large ice shelves. In the future, ice shelf cavities could switch from a ‘’cold” to a “warm” state, following a distinct increase in ocean temperatures e.g. by a redirection of coastal currents allowing warm circumpolar waters to access Antarctic grounding lines. With the ice-sheet model PISM, we delineate potential thresholds, at which the Antarctic Ice Sheet could experience ocean-induced non-linear ice loss. To this end, we apply circum-Antarctic ocean temperature perturbations of 1 to 5 K for different durations, ranging from tens to hundreds of years, and analyze the ice-sheet evolution after reversing the forcing over centennial to millennial timescales. Additionally, we perform ice-sheet simulations in which we slowly ramp up our forcing over similar timescales. Using these idealized overshoot scenarios, we analyze when and where critical thresholds that lead to large-scale, irreversible grounding line retreat are crossed.  We assess uncertainties of these thresholds by analysing the initial state uncertainty as well as parametric and structural uncertainties.

How to cite: Nicola, L., Garbe, J., Reese, R., and Winkelmann, R.: Identifying thresholds of ocean-induced Antarctic ice loss through idealized ice-sheet model simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6272, https://doi.org/10.5194/egusphere-egu23-6272, 2023.

EGU23-6509 | ECS | Posters on site | CR3.1

The influence of landscape evolution on Scandinavian Ice Sheet dynamics and extent 

Gustav Jungdal-Olesen, Vivi Kathrine Pedersen, Jane Lund Andersen, and Andreas Born

Ice sheets have shaped the Scandinavian landscape during numerous glacial periods throughout the Quaternary, but little is known about the effects of a changing landscape on the Scandinavian ice sheets in return. Here, we use a higher-order ice-sheet model (iSOSIA) to investigate how past morphological changes in the Scandinavian landscape may have affected ice-sheet extent and dynamics. Our preliminary results indicate that the Scandinavian ice sheet would have extended further south before the formation of the Norwegian Channel, which is believed to have been formed by glacial erosion during recent glacial periods (since ∼0.5 Ma). This suggests that landscape changes should be considered in addition to varying climate conditions, when exploring changes in ice-sheet dynamics and extent between glacial periods.

How to cite: Jungdal-Olesen, G., Pedersen, V. K., Andersen, J. L., and Born, A.: The influence of landscape evolution on Scandinavian Ice Sheet dynamics and extent, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6509, https://doi.org/10.5194/egusphere-egu23-6509, 2023.

EGU23-6947 | ECS | Orals | CR3.1

Elmer/Ice results on the CalvingMIPintercomparison project using a level-set function 

Cruz Garcia-Molina, Fabien Gillet-Chaulet, Mondher Chekki, Gael Durand, Olivier Gagliardini, and Nicolas Jourdain

Ice-calving plays a major role in the mass balance of the water-ending glaciers. Thus, it is crucial to have a well-adapted calving law for simulations over long periods. Due to its dependence on several physical parameters, this phenomenon is usually poorly parametrized in long-term numerical simulations. A worldwide model intercomparison project on ice damage and calving, CalvingMIP (see https://github.com/JRowanJordan/CalvingMIP/wiki), is proposed as part of the European project, PROTECT. The CalvingMIP project aims to evaluate the uncertainties in modelling the ice and to provide recommendations to improve the calving laws in the ice-sheet models. This intercomparison project consists of five experiments using two topographic profiles: a hill and Thule bathymetry. For the first phase, a steady-state configuration is implemented for a fixed calving position. In the second phase: the front velocity is prescribed, forcing the front to advance and, then, to retreat. Finally, the last experiment aims to test a realistic calving law. We study the experiments of this configuration by using the community finite element code, Elmer/Ice (see http://elmerice.elmerfem.org/). We study the front evolution using a level-set function (φ), defined as a signed distance to the front. Here, we present the results obtained with our model for this intercomparison experiment, discuss the sensitivity to different physical and numerical parameters, and its application to a realistic configuration.

How to cite: Garcia-Molina, C., Gillet-Chaulet, F., Chekki, M., Durand, G., Gagliardini, O., and Jourdain, N.: Elmer/Ice results on the CalvingMIPintercomparison project using a level-set function, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6947, https://doi.org/10.5194/egusphere-egu23-6947, 2023.

EGU23-6969 | ECS | Orals | CR3.1

Developments to 2D modelling of ice island deterioration using the CI2D3 Database 

Anna Crawford, Greg Crocker, Derek Mueller, and Jesse Smith

The calving of ice tongues and ice shelves can generate large, tabular icebergs that have climatological implications given their role in dispersing freshwater from the Greenland and Antarctic ice sheets. These ‘ice islands’ also pose potential risk to marine industry. It is therefore critical that influential deterioration mechanisms be accurately represented in simulations of ice island drift and deterioration, both for risk mitigation in offshore industry and for climatological studies that are focused on the Polar Regions. The majority of ice island deterioration is the result of sidewall erosion, and specifically that which results from waterline wave-erosion leading to ram growth and buoyancy-forced fracture. This study therefore focuses on the inclusion of the buoyancy-driven “footloose” calving mechanism (Wagner et al., 2014) in simulations of ice island length and areal change. Using size and lineage information of ice islands tracked in the Canadian Ice Island Drift, Deterioration and Detection (CI2D3) Database, we quantitatively assess the performance of the footloose calving model by simulating the deterioration of 172 ice islands. The mean model error was +15 (+/- 400) m over 20 d and increased to +401 (+1400/-800) m for simulations that ran to 80 d. The performance of the footloose calving model is a substantial improvement when compared to simulations that did not include this calving mechanism. For example, a thermal-melt model had mean errors of -252 and -1403 m at 20 and 80 d of simulation, respectively, and the mean error of a zero-melt model was -281 and -1545 m over the same time periods. We also present a new approach to modelling ice island areal change resulting from footloose calving. This simple, two-parameter approach simulates discrete footloose calving events and adjusts the ice island surface area to maintain a constant aspect ratio. Mean model error remained under 1 km2 over 80 d of simulation, showing that the model performs well over numerous months. Using the CI2D3 Database, we were able to conduct the first large-scale assessment of the footloose model’s performance in simulating change to the ice island length dimension. The morphological data included in the database also provided the opportunity to develop an approach for modelling areal deterioration resulting from footloose calving events. The model assessments would benefit from more observations of long-duration ice island tracks, as there were a limited number of ice islands that were tracked in the CI2D3 Database for over 40 d. Future work can look to implement the presented approaches in operational and climatological modelling while the iceberg modelling community also develops an approach to simulate larger-scale ice island fracture.

How to cite: Crawford, A., Crocker, G., Mueller, D., and Smith, J.: Developments to 2D modelling of ice island deterioration using the CI2D3 Database, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6969, https://doi.org/10.5194/egusphere-egu23-6969, 2023.

EGU23-7264 | Orals | CR3.1

Calving Model Intercomparison Project (CaMIP): Overview and preliminary results 

James Jordan and Frank Pattyn

Ice shelf calving is responsible for roughly half the mass lost by Antarctic Ice Shelves and is of vital importance for determining ice-shelf stability. Despite this, it is currently poorly represented in numerical models and as such has seen limited inclusion in numerical simulations of the future Antarctic Ice Sheet. A first step towards improving this situation is assessing the capabilities and limitations of current numerical models regarding calving. The Calving Model Intercomparison Project (CaMIP) is, therefore, being undertaken to address this issue as part of the EU funded Horizon 2020 project PROTECT.

We present here an overview of the project, as well as preliminary results from the first round of experiments by a range of participating modelling groups from across the cryospheric community.

How to cite: Jordan, J. and Pattyn, F.: Calving Model Intercomparison Project (CaMIP): Overview and preliminary results, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7264, https://doi.org/10.5194/egusphere-egu23-7264, 2023.

Jakobshavn Isbræ (JI), on the West coast of Greenland, is one of the fastest flowing outlet glaciers of the Greenland Ice Sheet, draining 7% of the ice sheet area. Since the late 1990s it has dramatically accelerated, thinned and retreated in a series of phases alternating with periods of quiescence. It exhibits strong seasonal variations in flow speed and calving front position. Between 2012 and 2015, JI attained its point of furthest retreat and flow speeds in excess of 17 km/yr. Since 2016 it has modestly thickened, concurrent with deceleration and readvance of the calving front.

The very fast flow and strong annual and interannual variability present significant challenges for ice sheet modellers. We model the evolution of JI between 2009 and 2018 using the BISICLES ice sheet model. The standard modelling technique of assimilating surface velocity observations to infer a power law basal friction coefficient for a snapshot in time fails to account for rapidly changing basal conditions, underestimating the annual variability. We implement a time-series inverse method in which regular velocity observations are assimilated throughout the study period to produce a time-evolving basal friction coefficient. This method is able to reproduce the large annual variations in flow speed much more accurately than the static method.

This reliance on regular observations to drive the model poses a problem for future projections. We compare a range of sliding laws applied with the normal snapshot inverse method. A modern regularized Coulomb friction sliding law is better able to reproduce JI’s annual variations in flow speed due to its ability to modulate the basal friction in response to movement of the grounding line. As a result, it may be a more appropriate choice of sliding law for modelling the future evolution of fast-flowing outlet glaciers.

How to cite: Trevers, M., Payne, T., and Cornford, S.: A comparison of inverse methods and basal sliding laws applied to a hindcast model of Jakobshavn Isbræ from 2009 to 2018., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8265, https://doi.org/10.5194/egusphere-egu23-8265, 2023.

EGU23-8932 | ECS | Posters on site | CR3.1

Investigation of numerical continuation methods for marine ice-sheet systems formulated as contact problems 

Thomas Gregov, Frank Pattyn, and Maarten Arnst

Marine ice sheets are complex systems whose response to external forcing is the subject of much attention in the scientific community. In particular, the West Antarctic ice sheet, which could have a significant impact on future sea-level rise, is a major concern. One method of studying the response of marine ice sheets consists in investigating the relationship between the parameters and the equilibrium states of such systems. However, this is typically done by varying these parameters and letting the ice sheets evolve to new steady states, i.e., through transient simulations, which are computationally expensive.

An alternative is to consider continuation methods, where the equilibrium state of a system is studied directly as a function of the parameters. Such an approach has already been used in glaciology to study the mechanical behaviour of 1D marine ice sheets (Mulder et al., 2018), highlighting the hysteresis phenomena that had previously been obtained theoretically (Schoof, 2007). However, this study, because it only considers 1D geometries, does not allow to take into account the effect of lateral drag and of complex bedrock geometries, which are two factors that have the potential to stabilize the grounding line (Gudmundsson, 2013, Sergienko and Wingham, 2021).

Here we consider the continuation problem in the context of 2D marine ice sheets. This introduces several mathematical difficulties, notably related to the treatment of the distinction between grounded and floating parts. Mathematically, the problem takes the form of a contact problem between the bedrock and the lower part of the ice sheet. This leads to a system of equations that is not differentiable, which is challenging to solve numerically. We address these challenges in the context of the continuation problem, and propose several solutions, including a norm-based approach that is inspired from earlier studies (Mittelmann, 1987). Finally, we present some preliminary results which show that our numerical method is promising.

How to cite: Gregov, T., Pattyn, F., and Arnst, M.: Investigation of numerical continuation methods for marine ice-sheet systems formulated as contact problems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8932, https://doi.org/10.5194/egusphere-egu23-8932, 2023.

EGU23-8982 | ECS | Orals | CR3.1

Modelling the kinematic evolution of valley-scale folding in surge-type glaciers using Elmer/Ice 

Erik Young, Gwenn Flowers, Hester Jiskoot, and Daniel Gibson

Glacier surges produce iconic valley-scale folds which encode a history of polyphase deformation resulting from switches between quiescent and surging flow. The folding is passive, resulting from disturbances to ice foliation during surging flow, and subsequently altered during quiescent flow. We investigate the kinematic evolution of these kilometre-scale folds, using Elmer/Ice, by modelling folds through multiple surge cycles using idealized synthetic glacier confluence configurations, and identifying how differences in glacier flow regimes imprint themselves onto three-dimensional fold geometry. The surge and quiescent phases are simulated by changing the basal conditions of one of the tributaries, and matching the scale of velocity variations observed in temperate glacier surges. We determine fold geometry using a particle tracking algorithm applied to the modelled velocity fields, where, mimicking a medial moraine, vertically-spaced particles are injected at the flow unit confluence and advected downglacier. Using structural analysis of the model outputs, we present an archetype of kinematic evolution that describes the transition from cylindrical, vertically plunging, gentle folds emplaced during the surge phase, to complex, non-cylindrical, depth-varying folds following multiple cycles of surging and quiescent flow. The initial fold geometry is controlled by longitudinal and lateral shear stress regimes during surging, while fold evolution is governed primarily by lateral shearing after emplacement. We examine the sensitivity of fold geometry to valley geometry, glacier dynamics, and mass balance. Finally, we illustrate the potential of our approach to reconstruct more complex fold geometries as observed in nature, by applying it to a large surge-type glacier in the St. Elias Mountains of Northern Canada.

How to cite: Young, E., Flowers, G., Jiskoot, H., and Gibson, D.: Modelling the kinematic evolution of valley-scale folding in surge-type glaciers using Elmer/Ice, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8982, https://doi.org/10.5194/egusphere-egu23-8982, 2023.

EGU23-9021 | ECS | Orals | CR3.1

Mechanical failure to drive the glacier collapse feature at Rhonegletscher, Switzerland 

Ludovic Räss, Christophe Ogier, Ivan Utkin, Mauro Werder, Andreas Bauder, and Daniel Farinotti

Rapid climate modifications perturb the long-term dynamic equilibrium of many natural systems. Polar and high-altitude regions such as alpine environments represent locations where perturbations such as glacier collapse features, become visible. Glacier collapse features are characterized by a circular depressions on the ice surface, are bounded by low-angle crevasses and are the surface expression of a cavity developing most often over a subglacial channel, commonly occurring at the glacier snout. Understanding the physical processes governing the collapse feature dynamics is essential to assess hazards and processes related to them, such as, rapid glacier length variations, snout collapses and sudden blockage of the subglacial drainage system.

Field observations from an on-going collapse feature developing at the snout of Rhonegletscher (Switzerland) in Summer 2022 suggest mechanical failure of ice lamellas from the underlying cavity roof to drive the collapse. In order to test this hypothesis, namely mechanical failure to drive glacier collapse features, we developed full-Stokes 2D and 3D mechanical models implementing a temperature and pressure dependent visco-elasto-plastic rheology. We use the extensive dataset from Rhonegletscher to constrain the numerical models to predict possible failure patterns as function of increasing cavity size. We use vertical displacement located in the centre of the collapse feature to validate our models. Preliminary results show the formation of tension failure patterns on the ice surface at locations coinciding with the low-angle circular crevasses. The model results will advance our understanding of the physics of collapse features and provide predictive tools to assess future occurrences and their related risks.

How to cite: Räss, L., Ogier, C., Utkin, I., Werder, M., Bauder, A., and Farinotti, D.: Mechanical failure to drive the glacier collapse feature at Rhonegletscher, Switzerland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9021, https://doi.org/10.5194/egusphere-egu23-9021, 2023.

EGU23-9198 | ECS | Orals | CR3.1

On the effect of damage on the recent changes in the Amundsen Sea Sector 

Cyrille Mosbeux, Nicolas Jourdain, Olivier Gagliardini, Peter Råback, and Adrien Gilbert

Ice mass loss from Antarctic Ice Sheet is increasing, accelerating its contribution to global sea level rise. In the Amundsen Sea sector, recent observations of rapid ice-shelf thinning and grounding line retreat have been attributed to increased basal melting driven by inflows of warm Circumpolar Deep Water. However, recent studies have shown that basal melting alone might not be sufficient to explain the recent acceleration, retreat and thinning of the outlet glaciers in the sector.

As part of the European Horizon 2020 research project PROTECT ­— that assesses and projects changes in the land-based cryosphere to produce robust projections of SLR we conduct numerical simulations to determine the role of damage on changes observed over the last two decades in the Amundsen Sea Sector. More particularly, we use a Stokes flow formulation combined with a Continuum Damage Mechanics model of the open-source ice flow model Elmer/Ice to simulate the ice flow evolution. We initialize our ice sheet model with data assimilation methods using 1996 observations of surface velocities as well as a corrected geometry based on the current ice-sheet geometry and the ice thickness rates of change observed over the past 20 years. From this initial state, we run forward simulations over 20 years with and without damage mechanics, and compare the model evolution to observed surface velocities and ice thickness rates of change, as well as observations of grounding line positions. Our results shed light on the importance of damage in the evolution of the region, in particular an acceleration of several hundred meters per year due to the decreasing buttressing effect of the ice shelves triggered by the increasing damage.

How to cite: Mosbeux, C., Jourdain, N., Gagliardini, O., Råback, P., and Gilbert, A.: On the effect of damage on the recent changes in the Amundsen Sea Sector, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9198, https://doi.org/10.5194/egusphere-egu23-9198, 2023.

EGU23-9343 | ECS | Posters on site | CR3.1

Patagonian Ice Sheet extent as an indicator of the regional climate regime at the Last Glacial Maximum 

Andrés Castillo-Llarena, Franco Retamal-Ramirez, Jorge Bernales, Martin Jacques-Coper, and Irina Rogozhina

During the Last Glacial Maximum (LGM, 23 to 19 thousand years ago), the Patagonian Ice Sheet (PIS) expanded along the Andes between ~ 38 °S to 55 °S. Existing paleoclimatic and paleoglacial evidence, especially that derived from glacial landforms, seems to indicate that the timing of maximum ice sheet expansions in the Southern and Northern Hemispheres was not synchronous. Moreover, significant uncertainties still exist in the onset of regional deglaciation and its major drivers. In this context, a combination of ice sheet modelling, glacial geochronology and paleoclimate reconstructions can provide important insights into the former PIS geometry and its contribution to the sea level low during the LGM. It can also help us infer likely paleoclimate scenarios and climate models that capture regional climate responses to global change in the most realistic manner.

Here we present an ensemble of numerical ice sheet simulations of the PIS at the LGM to constrain an envelope of probable atmospheric conditions derived from a range of model-based climate forcing products from the phases 3 and 4 of the Paleoclimate Modelling Intercomparison Project (PMIP). The resulting ensemble is then used as a guideline to identify sectors of the PIS where a significant disagreement between the field evidence and modelling results is obtained, highlighting a strong dependence of the PIS geometry on the uncertainties in near-surface air temperature forcing. We find that all ensemble members consistently fail to reproduce the ice sheet extent towards the northern part of Patagonia within the explored model parameter space. At the same time, the modelled PIS expands beyond its southeastern reconstructed boundary. Our analysis of the ice sheet’s mass budget seems to indicate that these discrepancies between the modelled and reconstructed PIS extents arise from poorly resolved topographic features within the global climate models and the general lack of observational data on ice thickness distribution during the LGM. We conclude that INM-CM4-8 and MPI-ESM1-2-LR produce the most realistic climate forcing across Patagonia at the LGM. It should be kept in mind that this analysis is based only on the evaluation of modelled ice sheet extents against geological evidence, as observational data on the former ice sheet thickness are still lacking. Nevertheless, our analysis suggests that the quality of the regional model-based climate reconstructions is directly linked to the horizontal resolution that must be capable of resolving topographic features of the Andes and ideally of the PIS itself.

How to cite: Castillo-Llarena, A., Retamal-Ramirez, F., Bernales, J., Jacques-Coper, M., and Rogozhina, I.: Patagonian Ice Sheet extent as an indicator of the regional climate regime at the Last Glacial Maximum, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9343, https://doi.org/10.5194/egusphere-egu23-9343, 2023.

EGU23-9497 | ECS | Posters on site | CR3.1

Basal conditions of Denman Glacier from hydrology modeling and their application to various friction laws 

Koi McArthur, Felicity S. McCormack, and Christine Dow

The key process of basal sliding in Antarctic glaciers is often incorporated into ice dynamics models via the use of a friction law, which relates the basal shear stress to the effective pressure. With few ice dynamics models actively coupled to subglacial hydrology models, the effects of subglacial hydrology often manifest in the friction coefficient – an unknown parameter in the friction law. We investigate the impact of friction coefficients for Denman Glacier, East Antarctica, by comparing Ice-sheet and Sea-level System Model (ISSM) inversion simulations using the effective pressure produced from the Glacier Drainage System (GlaDS) model compared with  a typically prescribed effective pressure using a combination of ice overburden pressure and height above sea level (NO). We apply these comparative model runs for the Budd and Schoof friction laws. In regions of fast ice flow, we find a positive correlation between the GlaDS output effective pressure and the friction coefficient for the Schoof law. In addition, using the GlaDS output effective pressure compared to  NO leads to a smoother friction coefficient as well as smaller differences between the simulated and observed surface velocity. In general we find that spatial variations in the Schoof law match more closely with the known physics of subglacial hydrology than the Budd law and therefore suggest that using the GlaDS output effective pressure compared to NO produces more realistic results. This demonstrates the need to couple ice sheet and subglacial hydrological systems to accurately represent ice flow.

How to cite: McArthur, K., McCormack, F. S., and Dow, C.: Basal conditions of Denman Glacier from hydrology modeling and their application to various friction laws, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9497, https://doi.org/10.5194/egusphere-egu23-9497, 2023.

Marine ice sheet dynamics plays a key role in the mechanical interaction between glaciers and the ocean. A steady-state marine ice sheet, due to the buttressing force of ice shelves, suppresses the glaciers from discharging into the ocean. Various atmospheric-oceanic-glacial mechanisms such as ice melting, buttressing, and snow accumulation lead to the instability of marine ice sheets. The unstable marine ice sheet cause thinning ice thickness and the resultant ice shelf collapse. Ice shelf collapse accelerates the rapid glaciers discharge and retreats, directly affecting sea level rise. The positive feedback loop of these processes significantly influences the ice sheets dynamics, especially the advance and retreat of the grounding line. The grounding line can be defined as a transition zone between the grounded ice sheet in bedrock and the floating ice shelf due to water buoyancy. Understanding grounding line dynamics may help us predict marine ice sheet instability and future sea level rise. It has been challenging to determine an accurate grounding line in the numerical simulation of the marine ice sheet due to mesh resolution. Here we performed two-dimensional ice flow modeling using open-source finite element software (i.e., Elmer/ice), to quantitatively evaluate the effect of diverse mesh sizes on the position of the grounding line. We tested a series of numerical models to precisely define varying the grounding line position influenced by bedrock topography, snow accumulation, and ice melting. The models with high mesh resolution required short time steps to obtain the accurate grounding line position. To consider both calculation efficiency and the accuracy of the position of the grounding line, we found adequate time steps corresponding to each mesh size ambient the grounding line. Our systematic results can provide inspiration for choosing a suitable mesh size and time step to determine a more accurate grounding line position.

How to cite: Lim, S. and So, B.-D.: Effect of mesh resolution on accurate grounding line definition using 2D finite element software, Elmer/ice, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10914, https://doi.org/10.5194/egusphere-egu23-10914, 2023.

Glacier mass loss related to ice flow is a quantitative factor that controls sea level rise and global warming. Ice flow is attributed to the complicated interaction between grounding line migration, calving basal friction, and topography. Previous studies using massive geophysical observation, including IPR (Ice Penetrating Radar), InSAR (Interferometric Synthetic Aperture Rada), and heat flow exploration, highlight the need to develop a sophisticated numerical model. Because it is difficult to numerically solve the full-stokes equation obtaining ice velocities, the simplified governing equation (i.e., HO, Higher Order model; SSA, Shallow Shelf Approximation; SIA, Shallow Ice Approximation) is widely used in the ice sheet dynamics community. The SSA approach, which assumes a depth-independent velocity model, has computational cost reduction based on simplified descriptions of the full-stokes equation. Here we developed the two-dimensional SSA numerical model to better understand ice dynamics using COMSOL Multiphysics® (hereafter COMSOL), which is a user-friendly finite element software providing convenient GUI, mesh generation, post-processing tools, various types of elements, and the order of shape function. To verify the application of COMSOL to ice sheet dynamics, we compared it with an open-source finite element package, Ice Sheet System Model (ISSM). The concise toy model successfully simulated the distribution of viscosity and velocity and the evolution of surface topography in both COMSOL and ISSM. Furthermore, we applied realistic bathymetry data (i.e., Bedmap2) of Pine Island Glacier, where the large ice mass loss occurs, to clarify more similarity of each model. The surface velocity patterns calculated by COMSOL and ISSM are significantly similar for various physical properties (e.g., ice viscosity, friction, and rate of accumulation and melt). We propose that COMSOL, which efficiently handles mesh generation and visualization, and various weak forms, can sufficiently be applied in ice dynamics.

How to cite: Baek, Y.-J. and So, B.-D.: Development of ice sheet model using COMSOL Multiphysics®: Comparison with Ice Sheet System Model (ISSM), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10924, https://doi.org/10.5194/egusphere-egu23-10924, 2023.

EGU23-10981 | ECS | Orals | CR3.1

Impact of crevasses on surface energy balance at an alpine glacier 

Dongqi Lin, Marwan Katurji, and Heather Purdie

Observations worldwide have shown that glaciers are receding. Thinning snow cover can result in crevasses becoming exposed at the glacier surface for longer time periods. Crevasses can increase surface roughness, change the surface wind flow fields, and impact the air temperatures within and outside the crevasses (Purdie et al., 2022). Therefore, more can be learnt about regarding the impact of crevasses on energy exchange with glacier atmospheric boundary layer. In order to understand and investigate the atmosphere-crevasse energy exchange, we carried out numerical Large Eddy Simulation (LES) experiments using the PALM model system 6.0 with crevasse-resolving grid spacings less than 1 m. The PALM model system 6.0 has been used for atmosphere and marine boundary layer studies to understand complex processes of atmospheric dynamics and energy balance. Our preliminary results show that the air inside a crevasse does not cool as fast as the air outside a crevasse resulting in net warming from the crevasse relative to the glacier surface. These results agree with the field study conducted at Tasman Glacier (Purdie et al., 2022), and confirm that crevasses could lead to heat storage and increased melting. During the daytime, air temperature inside a crevasse could be 1 °C higher than the air above the glacier surface. After sunset, the presence of the crevasse entrains and traps the warm air such that the centre of the crevasse could still be warmer than the glacier surface during the first half of the evening.  During windy evenings, our results show that turbulent heat exchange associated with eddy entrainment leads to exchange of air mass between the crevasse and the glacier surface, which then causes glacier surface warming. Our preliminary simulations only included one crevasse, while future work will include a field of crevasses to investigate the impact of crevasses in a more realistic environment. This study highlights the importance of including crevasses energy balance in glacier modelling, neglecting which would lead to significant bias in snow melt and mass balance estimations.

 

Reference:

Purdie, H., et al. (2022). "Variability in the vertical temperature profile within crevasses at an alpine glacier." Journal of Glaciology: 1-5.

How to cite: Lin, D., Katurji, M., and Purdie, H.: Impact of crevasses on surface energy balance at an alpine glacier, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10981, https://doi.org/10.5194/egusphere-egu23-10981, 2023.

EGU23-11076 | ECS | Orals | CR3.1

Physical processes driving 'switching events' 

Gabriela Clara Racz, Kevin Mirng En Yeo, Aly Thobani, Sean Henry, Maryam Zarrinderakht, Camilo Rada, and Christian Schoof

Commonly, the parts of the glacier bed that are hydraulically connected to the surface experience significant diurnal variations in water pressure, in response to cycles of surface melting. Closely spaced points on the bed often exhibit nearly identical temporal variations in water pressure, suggesting that they are connected not only to the surface but to each other through conduits along the bed. This behaviour is typically observed directly through instrumented boreholes drilled to the glacier bed. A ‘switching event’ occurs when one of a pair of boreholes abruptly changes from being connected, in the sense of exhibiting the same diurnal oscillations as the other borehole, to being disconnected, or vice versa. A switching event is indicative of a connection through a subglacial conduit being closed, or opened, and therefore provides a limited but highly specific window into the evolution of subglacial conduits and permeability.

However, in most subglacial drainage models, conduits are not represented individually but averaged over a small area of the bed to produce a macroporous continuum representation as a ‘water sheet’, quantified by a mean conduit depth h. The most common assumption is that the water sheet consists of linked cavities and that these open due to basal sliding over bed roughness, and close due to viscous creep (e.g. Hewitt, 2011). Within that framework, the simplest mechanism for a switching event is that a connection is established or closed when the sheet thickness h passes through some percolation threshold hc (Rada and Schoof, 2018).

We want to test whether the observed switching events can be explained by that mechanism, which in turn implies that two conditions must be met: water sheet depth indeed evolves according to a competition between opening due to basal sliding and creep closure, and that a simple threshold in h suffices to capture the geometric complexity involved in creating or closing connections at the bed.

In a large dataset of borehole water-pressure time series, we identify borehole pairs that exhibit strong evidence of switching behaviour. We assume that switching events can be described by the evolution of a water sheet, with connections between boreholes being opened and closed as sheet thickness passes through a threshold value as described above. We use the switching event catalogue we have created to invert for parameters in the sheet evolution model using a binary indicator function for connectedness to compute the model data mismatch in the absence of any other direct measures of sheet thickness.

This procedure allows us to capture the majority of observed switching events with plausible parameter values. The exception is a set of short-lived periods of connectedness characterized by switching events that are clustered in space and time. In a complementary study (Racz et al, 2023 in prep.), we, therefore, investigate if this class of switching events can instead be explained by an alternative mechanism in which the sudden resumption of surface water supply, following a period of snow cover, drives the propagation of a hydrofracture (e.g. Tsai and Rice 2010, 2012).

How to cite: Racz, G. C., Yeo, K. M. E., Thobani, A., Henry, S., Zarrinderakht, M., Rada, C., and Schoof, C.: Physical processes driving 'switching events', EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11076, https://doi.org/10.5194/egusphere-egu23-11076, 2023.

EGU23-11300 | ECS | Orals | CR3.1

New Antarctic spin-up method results in committed Thwaites glacier collapse 

Tim van den Akker, William H. Lipscomb, Gunter R. Leguy, Willem Jan van de Berg, and Roderik van de Wal

Projections of sea level rise are subject to large uncertainties in the contribution of the Antarctic Ice Sheet (AIS), as it is unclear how AIS dynamics will evolve over time. Ice sheet models use spin-up techniques to initialize the ice sheet to the present-day state. Previous attempts using the Community Ice Sheet Model (CISM) assumed that the ice sheet is in equilibrium at the end of the spin-up. This assumption limits the contribution of model drift, but does not match observations and might bias future projections.

 

For this reason, we have incorporated present-day thickness change rates from Smith et al. (2020) in our Antarctic spin-ups. As in previous spin-ups, we tune basal friction coefficients beneath grounded ice, and ocean temperatures beneath floating ice, to match observed present-day thickness. In the new procedure, CISM is also forced to match thickening and thinning rates, with the surface mass balance (SMB) adjusted to allow the AIS to maintain the observed thickness and grounding-line locations. This technique improves the modelled velocities in regions with substantial thinning. When the SMB adjustments are removed, the modelled ice sheet exhibits the observed thickness change rates. We use this initialised state to project AIS evolution without additional forcing (‘committed climate change’). For a range of parameter settings, this causes Thwaites glacier to collapse irreversibly, without further ocean warming. The time of initiation of collapse is sensitive to model parameters, but once initiated the collapse is largely complete within two to three centuries. The sensitivity tests are carried out for a range of parameters related to basal sliding and various ocean warming scenarios.

How to cite: van den Akker, T., Lipscomb, W. H., Leguy, G. R., van de Berg, W. J., and van de Wal, R.: New Antarctic spin-up method results in committed Thwaites glacier collapse, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11300, https://doi.org/10.5194/egusphere-egu23-11300, 2023.

EGU23-11337 | Orals | CR3.1

Large-amplitude perturbation experiments to assess the unstable behaviour of AIS in the near future 

Benoit Urruty, Olivier Gagliardini, Fabien Gillet-Chaulet, Gael Durand, and Mondher Chekki

The stability of the grounding lines of Antarctica is a fundamental question in term of sea level rise The strong mass loss of the Antarctic Ice Sheet (AIS) in recent years has raised concerns about the possibility of the ice sheet reaching a tipping point, beyond which it would experience rapid and irreversible loss of mass. Such a tipping point could be triggered by a combination of external forcing factors, including continued warming of the ocean and atmosphere, as well as changes in ice sheet dynamics. As shown by Urruty et al. (in review), the current mass loss and retreat is mainly due to external forcing such as melt induced by the ocean and current grounding lines are not yet engaged in an unstable retreat. But if forcing remains similar or increases, some irreversible and fast mass loss may occur as a result of grounding lines crossing a tipping point.

As part of the TiPACCs project, we are conducting experiments to evaluate the stability of the grounding lines of the AIS in the future. Building on the stability experiment described in Urruty et al. (in review), we are using the same initial state created with Elmer/Ice to perform a new set of experiments. In these experiments, we are applying large-amplitude perturbations to the grounding line of a steady-state AIS by increasing ocean temperature by 1°C, 3°C, and 5°C for periods ranging from 20 to 100 years in order to push the grounding line far from its current position. After the perturbation is removed, we then apply 80 years of constant forcing to see if an unstable position is reached. These experiments will help us better understand the stability of the grounding line at different positions that could be reached in the near future if current observed forcing trends continue.

How to cite: Urruty, B., Gagliardini, O., Gillet-Chaulet, F., Durand, G., and Chekki, M.: Large-amplitude perturbation experiments to assess the unstable behaviour of AIS in the near future, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11337, https://doi.org/10.5194/egusphere-egu23-11337, 2023.

EGU23-11460 | ECS | Orals | CR3.1

Modelling discontinuities in ice flow using the Material Point Method and elastoplasticity 

Hugo Rousseau, Johan Gaume, Lars Blatny, and Martin P. Lüthi

Understanding glaciers evolution is of major concern to evaluate their contribution to sea level rise in the context of global warming. Among the various processes involved in glacier dynamics, fractures like the calving of ice at the front of marine terminating glacier and crevasse formation affect the stress state, potentially modifying the glacier’s velocity. Crevasses also impact the melting rate. The fractures alter the roughness of the ground, increasing the amount of absorbed radiation, and open new networks in which the meltwater is likely to penetrate deeper toward the glacier bed.

In this work we propose to model fractures in glacier based on finite strain elastoplasticity, using the Material Point Method (Wolper et al. 2021): we solve the classical governing equations for ice deformation in an Eulerian-Lagrangian framework and we use a strain softening Drucker-Prager constitutive model to simulate plasticity. Thanks to the Lagrangian part of the model, the fractures appear explicitly where high levels of total plastic deformation are reached. The behaviour of a glacier flowing over a step in the bedrock is investigated. The simulations show that crevasse patterns appear with regular spacing between the fractures. We perform a parametric study to determine which parameters affect the length of these patterns and potential dimensionless numbers are outlined.

How to cite: Rousseau, H., Gaume, J., Blatny, L., and Lüthi, M. P.: Modelling discontinuities in ice flow using the Material Point Method and elastoplasticity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11460, https://doi.org/10.5194/egusphere-egu23-11460, 2023.

EGU23-11798 | ECS | Orals | CR3.1

Modelling the impacts of ice damage on the response of Thwaites Glacier, West Antarctica 

Yanjun Li, Javier Blasco Navarro, Frank Pattyn, and Gang Qiao

Ice shelves around the coastal portion of the Antarctic ice sheet are sensitive indicators of climate change. The thinning of ice shelves diminishes buttressing, promotes longitudinal spreading, and increases ice flux across the grounding line, leading to accelerated glacier discharge into the ocean. Thwaites Glacier in the Amundsen Sea Embayment is one of the fastest-changing outlet glaciers in Antarctica. Damaged areas with densely distributed crevasses and open fractures on Thwaites Glacier are key to future ice shelf stability, grounding line retreat and sea level contribution. The damage feedback processes should be taken into consideration when simulating the evolution of Thwaites Glacier using ice sheet models.

Here, we add the continuum damage mechanics approach to the F.ETISh/Kori ice flow model, to simulate the present-day and near future behavior of the ice sheet and ice shelf system, including brittle ice physics. The damage field is described by equating it to the total crevasse depths used in Nick et al. (2010) and Sun et al. (2017). 100 years simulations under present-day climate conditions with and without damage in different scenarios have been conducted, and the change in ice velocity, ice thickness, the grounding line retreat and the sea level contribution of Thwaites Glacier have been analysed. Moreover, the change in ice velocity along four ice flow profiles in the first 20-year simulation has been analysed and the impact of damage on velocity has been assessed by comparing the simulated velocity fields with the observations (e.g., the MEaSUREs and ITS_LIVE ice velocity products).

Results indicate that damage drastically increases the ice velocity over the ice shelves and weakens them as such that grounding line retreat ~18 km after 20 years, accelerates (~1.5 times) compared to the observed increase in flow speed and contributes around an order of magnitude to the sea level rise in 50 years. Change in ice velocity profiles of Thwaites Glacier also show that local damage may overestimate ice velocity, especially in the grounded ice near the grounding line, while it underestimates observed ice flow when local damage is omitted. Through a series of further sensitivity experiments, an analysis on the timing and magnitude of damage has been carried out to gauge the current and near future state of the Thwaites glacier basin.

 

How to cite: Li, Y., Navarro, J. B., Pattyn, F., and Qiao, G.: Modelling the impacts of ice damage on the response of Thwaites Glacier, West Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11798, https://doi.org/10.5194/egusphere-egu23-11798, 2023.

EGU23-11803 | ECS | Orals | CR3.1

Pliocene Antarctic ice sheet model ensembles with joint constraints from reconstructed sea level and margin retreat 

James ONeill, tamsin Edwards, and Lauren Gregoire

The warm Pliocene was a period of comparable atmospheric carbon dioxide concentrations to modern, but with sea levels up to ~20 m higher. High Pliocene sea level implies collapse of the West Antarctic ice sheet, and mass loss from East Antarctica. Modelling studies have sought to reproduce Pliocene deglaciation, and use sea level reconstructions as a constraint on future projections, despite their large uncertainties. We simulated the Pliocene Antarctic ice sheet under warm Pliocene climate with the BISICLES ice sheet model, capturing grounding line and ice stream dynamics down to 4 km resolution. Our perturbed parameter ensemble approach explores uncertainties in basal sliding, surface mass balance processes, bedrock-ice sheet interactions, ice shelf basal melt sensitivity to ocean forcing and choice of climate model. We simulated a mean Antarctic sea level contribution of 1.85 m and a range of -15.90 to 28.27 m, largely driven by uncertainty in the perturbed basal sliding parameter. We applied a joint calibration, combining a Pliocene Antarctic sea level contribution range and a comparison of regional grounding line with reconstructed Pliocene retreat. This reduced the mean to 1.46 m. The calibration reduced the simulated range by a factor of ~4, and was more effective in reducing uncertainty than comparing to sea level reconstructions alone. Further ensembles explored initial condition uncertainty, and the impact of perturbing the control. The Pliocene initial condition was tested for a subset of main ensemble simulations (mean = -2.35 m), increasing the mean contribution by 11.45 m with all simulations passing the joint calibration. We perturbed the control simulation for the same subset of ensemble members. This increased the mean Antarctic contribution by 7.78 m, and by 8.45 m in combination with the two Pliocene data constraints. We demonstrate a modelling framework that captures important interactions between the ice sheet and other components of the Earth system, whilst being efficient for ensemble studies. Moreover, we used two Pliocene data constraints to rule out ensemble members. This Pliocene-calibrated modelling framework can be run under future climate scenarios, to reduce uncertainty in projections of Antarctica’s long-term contribution to sea level under anthropogenic climate change.

How to cite: ONeill, J., Edwards, T., and Gregoire, L.: Pliocene Antarctic ice sheet model ensembles with joint constraints from reconstructed sea level and margin retreat, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11803, https://doi.org/10.5194/egusphere-egu23-11803, 2023.

We have modelled the influence of a supraglacial debris cover on the behavior of an idealized reference mountain glacier. A calibrated 3D coupled ice flow-mass balance-supraglacial debris cover model is used to assess the impact of the melt-altering effect of various supraglacial debris deposit rates on the overall steady state characteristics of the glacier. Additional experiments are also carried out to simulate the behavior of the debris-covered glacier in a warming future climate. The main results show that, when compared to its clean-ice version, the debris-covered version of the glacier exhibits longer but thinner ablation zones, accompanied by lower ice flow velocities, lower runoff production, as well as a dampening of the mass balance-elevation profile over the debris-covered ice. Experiments for warming climatic conditions primarily point out towards a significant delay of glacier retreat, as the dominant process for ice mass loss encompasses thinning out of the ablation zone rather than retreat. The above-mentioned effects are modelled to be increasingly pronounced with an increasing thickness and extent of the superimposed supraglacial debris cover.

How to cite: Verhaegen, Y. and Huybrechts, P.: Modelling the influence of a supraglacial debris cover on the mass balance and dynamics of mountain glaciers using a 3D higher order ice flow model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12477, https://doi.org/10.5194/egusphere-egu23-12477, 2023.

EGU23-12705 | ECS | Orals | CR3.1

Point Data Assimilation in Firedrake and Icepack 

Reuben W. Nixon-Hill, Daniel Shapero, Colin J. Cotter, and David A. Ham
We present methods and tools which significantly improve the ability to estimate quantities and fields which are difficult to directly measure at large scales, such as the fluidity of ice, using point data sources from remote sensing. Our tools work with both sparse and dense point data with estimated quantities and fields becoming more accurate as the number of measurements are increased. These are often used as input variables to mathematical models that are used to make predictions so improving their accuracy is of vital importance.
 
The tool we introduce, Firedrake, generates highly optimised code for solving PDEs via the finite element method. It is easy to use, can be integrated with other python libraries, and is scalable for use on high performance computers. As proof, we highlight Icepack, a Python library for solving the equations of motion of glacier flow, which is written using Firedrake. Firedrake has an interface to the dolfin-adjoint/pyadjoint tool which allows these data assimilation problems to be solved with just a few lines of code by automatically generating the adjoint system of PDEs.
 
By carefully considering the nature of finite element method solutions, we show, using Firedrake and Icepack, how the choice of misfit functional (i.e. objective function) significantly impacts our inferred fields. This required the development of new infrastructure not previously available in these automated code generation tools.

How to cite: Nixon-Hill, R. W., Shapero, D., Cotter, C. J., and Ham, D. A.: Point Data Assimilation in Firedrake and Icepack, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12705, https://doi.org/10.5194/egusphere-egu23-12705, 2023.

EGU23-12764 | ECS | Orals | CR3.1

Modelling the three-dimensional stratigraphy of an ice rise 

Clara Henry, Reinhard Drews, Clemens Schannwell, Vjeran Višnjević, Inka Koch, Heiko Spiegel, Leah Muhle, Olaf Eisen, Daniela Jansen, Steven Franke, and Paul Bons

The geometry of englacial isochrones is a product of the past and present ice velocity field and is useful for our understanding of steady-state ice flow dynamics, flow regime re-organisation, and calibration of models. Ice rises contain various flow regimes (divide flow, flank flow, and grounding zones) on small spatial scales, meaning they are ideal locations to study ice-flow dynamics and stratigraphy to constrain model parameters. We run full Stokes, thermo-mechanically coupled simulations of Derwael Ice Rise in East Antarctica and simulate the three-dimensional stratigraphy of the ice rise and the surrounding ice shelf using the finite element model Elmer/Ice. Over the ice rise, we derive the accumulation rate from internal reflection horizons and use RACMO2.3 surface mass balance data over the surrounding ice shelf. Simulations are run for Glen's flow law exponents of n=3 and n=4 with appropriate values derived for the Arrhenius law.

To calibrate the model, comparisons are made with the BedMachine surface elevation and density-adjusted internal reflection horizons observed in many transects recorded by AWI’s ultra-wide band radar covering the divide, the flanks, and the grounding zones. To understand ice flow dynamics where the velocity field of the ice rise and the ice shelf converge in the compressive and shear zones, we analyse the modelled englacial stress and strain rate fields. Our results allow us to investigate isochronal structures where observed internal reflection horizons are too steep or obscured to be adequately picked up by radar. A comparison between the model and observed fracturing can be used to infer threshold stress and strain rates for fracture initiation. These simulations are a blueprint for the full Stokes, three-dimensional modelling of ice rises and have further relevance in the study of three-dimensional influences on Raymond arch evolution, the constrained coupling of the anisotropy equations, comparisons with ice core data and the automated inference of ice flow parameters from internal reflection horizons.

How to cite: Henry, C., Drews, R., Schannwell, C., Višnjević, V., Koch, I., Spiegel, H., Muhle, L., Eisen, O., Jansen, D., Franke, S., and Bons, P.: Modelling the three-dimensional stratigraphy of an ice rise, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12764, https://doi.org/10.5194/egusphere-egu23-12764, 2023.

EGU23-13025 | Orals | CR3.1

The Future of Thwaites Glacier, West Antarctica. 

G. Hilmar Gudmundsson, Jan De Rydt, Sebastian Rosier, Jowan Barnes, Daniel Goldberg, and Mathieu Morlighem

We use numerical modelling to address several questions related to the future evolution of Thwaites Glacier over the next 50 years. The importance of Thwaites Ice Shelf for upstream grounded flow is investigated by quantifying the buttressing stresses along the grounding line. Removing the ice shelf changes the stress regime along the grounding line by less than 20%. This change is small compared to many, if not most, grounding lines of the Antarctic Ice Sheet, and much smaller than corresponding changes for the neighboring Pine Island and Pope, Smith and Kohler glaciers.  Transient ice-flow modelling experiments show that mass loss from Thwaites Glacier over the next 50 years is insignificantly affected by removal of the ice shelf. We then explore the consequences of the proposed marine ice-cliff instability for Thwaites Glacier. For recently proposed calving laws, where the calving rate increases sharply with cliff height, we do not observe an onset of an unstable calving front retreat. Further numerical modelling experiments for future climatic forcing scenarios will be presented, including uncertainty quantification. Interactions between the ice and the ocean are studied using a coupled ice+ocean modelling framework. As shown before in several studies, we find when simulating its future evolution, that Thwaites Glacier can enter unstable periods of self-enhancing retreat. This appears to be a very robust result, and this behavior is found in all model runs, including coupled ice+ocean simulations.

How to cite: Gudmundsson, G. H., De Rydt, J., Rosier, S., Barnes, J., Goldberg, D., and Morlighem, M.: The Future of Thwaites Glacier, West Antarctica., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13025, https://doi.org/10.5194/egusphere-egu23-13025, 2023.

EGU23-13533 | ECS | Orals | CR3.1

Fully synchronous coupled ice/ocean modelling of future changes in the Amundsen Sea Sector 

David Bett, Alexander Bradley, Rosie Williams, Paul Holland, Robert Arthern, and Daniel Goldberg

The Amundsen Sea Sector has some of the highest thinning rates of ice shelves in Antarctica, thought to be driven by high, but interannually variable, ocean driven melt rates. This thinning can lead to increased ice flow speeds, eventually leading to sea level rise. To fully represent these processes and other feedbacks, a fully coupled ice/ocean model must be used. Therefore, a fully synchronous mass conservative coupled ice-sheet/ocean model of the Amundsen Sea Sector has been developed. This new coupled model builds upon previous coupling developments and involves coupling of the WAVI ice-sheet model to the 3D ocean model MITgcm, via the Streamice ice-sheet model. Coupled model projections are presented, examining ice grounding line retreat rates and ice mass loss, along with ocean driven melt rate evolution. The sensitivity of these results to ocean forcings is shown, specifically the thickness of the relatively warm Circumpolar Deep Water layer and its variability. In addition, we discuss the impact of the present-day initialisation and tuning of the coupled model.

How to cite: Bett, D., Bradley, A., Williams, R., Holland, P., Arthern, R., and Goldberg, D.: Fully synchronous coupled ice/ocean modelling of future changes in the Amundsen Sea Sector, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13533, https://doi.org/10.5194/egusphere-egu23-13533, 2023.

EGU23-14849 | ECS | Orals | CR3.1

Increasing the largest stable time-step size in ice flow models 

André Löfgren, Josefin Ahlkrona, Thomas Zwinger, Christian Helanow, and Denis Cohen

Ice flow models often suffer from numerical instabilities that restricts time-step sizes. For higher-order models this constitutes a severe bottleneck. We present a method for increasing the largest stable time step in full Stokes models, allowing for a significant speed-up of simulations.  This type of stabilisation was originally developed for mantle-convection simulations and is here extended to ice flow problems. The method is mimicking an implicit solver but the computational cost per time step is nearly as low as for an explicit solver. As it only consists of adding a stabilisation term to the gravitational force in the full Stokes equations, it is very easy to implement. We test the method using both Elmer/Ice and FEniCS on artificial glaciers with varying bedrock roughness, slip rate and surface inclination, as well as on a real world case.

How to cite: Löfgren, A., Ahlkrona, J., Zwinger, T., Helanow, C., and Cohen, D.: Increasing the largest stable time-step size in ice flow models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14849, https://doi.org/10.5194/egusphere-egu23-14849, 2023.

EGU23-14854 | ECS | Posters on site | CR3.1

How calving could affect the future of Thwaites and Pine Island Glaciers 

Jowan Barnes and G. Hilmar Gudmundsson

Thwaites and Pine Island Glaciers in the Amundsen Sea Embayment, West Antarctica, are among the fastest evolving on the continent, and hold enough ice between them to raise sea levels by over a metre. In their present states, the two glaciers represent different configurations of floating ice, and therefore may not respond in the same way to changes in ocean forcing. Pine Island Ice Shelf is contained within a bay and provides a large amount of buttressing to its glacier. Thwaites Ice Shelf has two components; a heavily damaged ice tongue and a shelf which is only restrained by a single pinning point. Neither of these provide much buttressing. In our modelling experiments, we prescribe calving rates to the ice shelves of these two glaciers alongside thermal forcing from ISMIP6, to investigate the combined effects of warming oceans and continued calving on the future of the region. We demonstrate the potential impacts of adding the calving process into our model by using a range of constant calving rates. Examining the different responses can tell us how important the process is for each glacier, and how sensitive they are to changes in calving. This in turn can be used to determine whether or not significant effort should be invested in improving calving laws to more accurately predict the future shape of the ice front. 

How to cite: Barnes, J. and Gudmundsson, G. H.: How calving could affect the future of Thwaites and Pine Island Glaciers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14854, https://doi.org/10.5194/egusphere-egu23-14854, 2023.

EGU23-15264 | Orals | CR3.1

Predicting ocean-induced ice-shelf melt rates using deep learning 

Sebastian Rosier, Christopher Bull, Wai Woo, and Hilmar Gudmundsson

Through their role in buttressing upstream ice flow, Antarctic ice shelves play an important part in regulating future sea level change. Reduction in ice-shelf buttressing caused by increased ocean-induced melt along their undersides is now understood to be one of the key drivers of ice loss from the Antarctic Ice Sheet. However, despite the importance of this forcing mechanism, most ice-sheet simulations currently rely on simple melt-parametrisations of this ocean-driven process since a fully coupled ice-ocean modelling framework is prohibitively computationally expensive. Here, we provide an alternative approach that can capture the greatly improved physical description of this process provided by large-scale ocean-circulation models over currently employed melt-parameterisations, but with trivial computational expense.  This new method brings together deep learning and physical modelling to develop a deep neural network framework, MELTNET, that can emulate ocean model predictions of sub-ice shelf melt rates. We train MELTNET on synthetic geometries, using the NEMO ocean model as a ground-truth in lieu of observations to provide melt rates both for training and to evaluate the performance of the trained network. We show that MELTNET can accurately predict melt rates for a wide range of complex synthetic geometries, with a normalized root mean squared error of 0.11m/yr compared to the ocean model. MELTNET calculates melt rates several orders of magnitude faster than the ocean model and outperforms more traditional parameterisations for 96% of geometries tested. Furthermore, we find MELTNET's melt rate estimates show sensitivity to established physical relationships such as changes in thermal forcing and ice shelf slope. This study demonstrates the potential for a deep learning framework to calculate melt rates with almost no computational expense, that could in the future be used in conjunction with an ice sheet model to provide predictions for large-scale ice sheet models.

How to cite: Rosier, S., Bull, C., Woo, W., and Gudmundsson, H.: Predicting ocean-induced ice-shelf melt rates using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15264, https://doi.org/10.5194/egusphere-egu23-15264, 2023.

EGU23-15896 | Posters on site | CR3.1

Feedbacks between basal melt and cavity geometry in coupled ice+ocean simulations of the Amundsen Sea glaciers 

Jan De Rydt, Kaitlin Naughten, and Hilmar Gudmundsson

Ice-shelf cavities in the Amundsen Sea are expanding as the ice thins and grounding lines retreat. To sustain ice-shelf thinning, whilst accommodating a (up to) 2-fold increase in ice flux across the grounding line, basal melt and calving rates must respond. Changes in far-field ocean temperature are often evoked to explain the sustained thinning, but there is no indication of significant trends in ocean properties over the observational period. On the other hand, internal feedbacks between changes in ice-shelf geometry and basal melt could play a role in driving glacier retreat, but these processes remain poorly understood. Here we explore such melt-geometry feedbacks using a coupled ice+ocean model of the Amundsen Sea glaciers. Under present-day ocean conditions, all glaciers, including Pine Island and Thwaites glaciers, continue to retreat, and we find significant trends in ice-shelf melt rates despite the absence of trends in the far-field ocean forcing. For example, melt rates for Pine Island Glacier double over a 50-year period under constant ocean conditions. For all cavities, the trend in melt can be attributed to a reconfiguration of the ocean circulation beneath the ice shelf, in response to changes in cavity geometry. We argue that these melt-geometry feedbacks may play an important role in the evolution of the Amundsen Sea glaciers, and they should be adequately captured in numerical simulations – something simple basal-melt parameterizations are currently unable to do.

How to cite: De Rydt, J., Naughten, K., and Gudmundsson, H.: Feedbacks between basal melt and cavity geometry in coupled ice+ocean simulations of the Amundsen Sea glaciers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15896, https://doi.org/10.5194/egusphere-egu23-15896, 2023.

EGU23-16383 | ECS | Orals | CR3.1

Large-scale thermo-mechanical modelling of Greenland ice sheet 

Ivan Utkin, Ludovic Räss, and Mauro Werder

Antarctic and Greenland ice sheets lose most of their mass by a few corridors of rapidly flowing ice. These ice conveyor belts constitute fast drainage routes whose flow velocities are undoubtedly sensitive to climate perturbations directly impacting sea-level. Observations suggest the ice is rather sliding than flowing, the key being where sliding is accommodated. Commonly, sliding occurs at the ice-bedrock interface, but recent studies favour englacial sliding to explain data from Western Margin of Greenland. 

Our aim is to demonstrate that the mechanisms controlling the spontaneous formation of englacial sliding explains the transition from slow flowing to fast sliding ice over Greenland. We employ a new thermo-mechanical ice flow model to predict thermally activated creep instability leading to the spontaneous rearrangement of ice motion in three dimensions. Accurately resolving these nonlinear interactions on regional to ice sheet scales requires high spatial and temporal resolution which can only be achieved using a supercomputer.

We present a new thermo-mechanical ice flow model, FastIce.jl, that is capable of predicting the evolution of ice sheet at unprecedented scale. The model uses the full-Stokes formulation for the ice flow and the enthalpy method for describing the polythermal ice behaviour. FastIce.jl uses GPU acceleration for solving the flow equations, resulting in close to ideal scaling in distributed computing benchmarks. We compare our simulation results with other full-Stokes models, and present the results of simulating the 120x120 km regions of Greenland ice sheet at 10m resolution. High resolution allowed us to capture the transition from slow to sliding flow regimes without any simplifying assumptions.

How to cite: Utkin, I., Räss, L., and Werder, M.: Large-scale thermo-mechanical modelling of Greenland ice sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16383, https://doi.org/10.5194/egusphere-egu23-16383, 2023.

EGU23-16754 | Posters on site | CR3.1

Assessing the role of ice-shelf damage on a three-dimensional ice-sheet model 

Javier Blasco, Yanjun Li, and Frank Pattyn

As stated in the latest IPCC report, sea level will continue to rise at the end of this century and most likely well beyond, depending on future emission pathways. The Antarctic ice sheet plays an important role, as it is the largest ice sheet and thus the largest source of water storage on Earth. However, projections for Antarctica from ice-sheet models yield very mixed results due to ice-sheet-related processes that are difficult to assess. One of the main sources of uncertainty is the stability of floating ice shelves. Although ice shelves do not directly contribute to sea-level rise, they have been shown to play an important role, as they modulate the grounded ice flow via their buttressing effect. Therefore, it is necessary to assess the stability of ice shelves in a warmer climate to make more accurate predictions and define safe trajectory scenarios. Satellite images show the formation of crevasse in regions with a high deformation rate. These crevasses weaken the stability of the ice shelf, as damage enhances inland ice acceleration and promotes further shearing and retreat. However, most continental-scale ice-sheet models do not account for ice shelf damage and its consequent potential feedback mechanisms. Part of this statement is due to the fact that ice shelves at coarse resolutions show low stability to damage implementation even in simple domains. Here we force a three-dimensional ice-sheet-shelf model with various damage formulations from the literature. Given the high uncertainty in damage formation and propagation, several parameters affecting the stability of the ice shelf are evaluated. Experiments are performed in different domains to test their influence in simple and symmetric cases, such as MISMIP+, as well as in the Amundsen-Sea Embayment. Our results highlight the importance of further research on ice damage, as it has strong implications for projections but is poorly accounted for in ice-sheet models.

How to cite: Blasco, J., Li, Y., and Pattyn, F.: Assessing the role of ice-shelf damage on a three-dimensional ice-sheet model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16754, https://doi.org/10.5194/egusphere-egu23-16754, 2023.

The future evolution of the West Antarctic Ice Sheet (WAIS) will strongly influence the global sea-level rise in the coming decades. Ice shelf melting in that sector is partly controlled by the low-pressure system located off the West Antarctic coast, namely the Amundsen Sea Low (ASL). When the ASL is deep, an overall increase in ice shelf melting is noticed. Because of the sparse observational network and the strong internal variability, our understanding of the long-term climate changes in the atmospheric circulation is limited, and therefore its impact on ice melting as well. Among all the processes involved in the West Antarctic climate variability, an increasing number of studies have pointed out the strong impact of the climate in the tropical Pacific. However, most of those studies focus on the past decades, which prevents the analysis of the role of the multi-decadal tropical variability on the West Antarctic climate. Here, we combine annually-resolved paleoclimate records, in particular ice core and coral records, and the physics of climate models through paleoclimate data assimilation to provide a complete spatial multi-field reconstruction of climate variability in the tropics and Antarctic. This allows for studying both the year-to-year and multi-decadal variability of the tropical-Antarctic teleconnections. As data assimilation provides a climate reconstruction that is dynamically constrained, the contribution of the tropical variability on the West Antarctic climate changes can be directly assessed. Our results indicate that climate variability in the tropical Pacific is the main driver of ASL variability at the multi-decadal time scale, with a strong link to the Interdecadal Pacific Oscillation (IPO). However, the deepening of the Amundsen Sea Low over the 20th century cannot be explained by tropical climate variability. By using large ensembles of climate model simulations, our analysis suggests anthropogenic forcing as the primary driver of this 20th century ASL deepening. In summary, the 20th century ASL deepening is explained by the forcing, but the multi-decadal variability related to the  IPO is superimposed on this long-term trend.

How to cite: Dalaiden, Q., Abram, N., and Goosse, H.: Tropical Pacific variability and anthropogenic forcing are the key drivers of the West Antarctic atmospheric circulation variability over the 20th century, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-683, https://doi.org/10.5194/egusphere-egu23-683, 2023.

EGU23-991 | Orals | CR3.2

Future irreversible loss of Thwaites Glacier relative to global warming 

Emilia Kyung Jin, In-Woo Park, Hyun Joo Lee, and Won Sang Lee

The speed of West Antarctic melting is a very important factor in determining the degree of future global sea level rise. Loss of the Thwaites glacier due to global warming will have various regime changes in line with changes in the Earth system. The basal melting as a result of ocean warming can cause loss at an inhomogeneous rate across the underlying topography and overlying ice volume, while the change in precipitation from snow to rain as atmospheric warming can accelerate surface melting and trigger the irreversible loss.  

In this study, the ISSM model was driven with the ocean and atmospheric forcings obtained from the CMIP6 earth system model results, and future prediction experiments were performed until 2300. As a result, the accelerated period of melting of the Thwaites glacier related with forcings and the period of irreversible loss according to the structural characteristics and degree of warming are investigated. The mechanisms and timing that cause rapid ice loss are analyzed and the tipping point at which irreversible losses are triggered has been proposed as a function of warming.

How to cite: Jin, E. K., Park, I.-W., Lee, H. J., and Lee, W. S.: Future irreversible loss of Thwaites Glacier relative to global warming, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-991, https://doi.org/10.5194/egusphere-egu23-991, 2023.

EGU23-1329 | ECS | Orals | CR3.2

Characterizing the influence of idealized atmospheric forcings on firn using the SNOWPACK firn model 

Megan Thompson-Munson, Jennifer Kay, and Bradley Markle

The porous layer of snow and firn that blankets ice sheets can store meltwater and buffer an ice sheet’s contribution to sea level rise. A warming climate threatens this buffering capacity and will likely lead to depletion of the air-filled pore space, known as the firn air content. The timing and nature of the firn’s response to climate change is uncertain. Thus, understanding how the firn may evolve in different climate scenarios remains important. Here we use a one-dimensional, physics-based firn model (SNOWPACK) to simulate firn properties over time. To force the model, we generate idealized, synthetic atmospheric datasets that represent distinct climatologies on the Antarctic and Greenland Ice Sheets. The forcing datasets include temperature, precipitation, humidity, wind speed and direction, shortwave radiation, and longwave radiation, which SNOWPACK uses as input to simulate a firn column through time. We perturb the input variables to determine how firn properties respond to the perturbation, and how long it takes for those properties to reach a new equilibrium. We explore how different combinations of perturbations impact the firn to assess the effects of, for example, a warmer and wetter climate versus a warmer and drier climate. The firn properties of greatest interest are the firn air content, liquid water content, firn temperature, density, and ice slab content since these quantities help define the meltwater storage capacity of the firn layer. In our preliminary analysis, we find that with a relatively warm and wet base climatology representative of a location in southern Greenland, increasing the air temperature by 1 K yields a 48% decrease in firn air content and a 3% increase in the deep firn temperature 100 years after the perturbation. SNOWPACK also simulates near-surface, low-permeability ice slabs that inhibit potential meltwater storage in deeper firn. Conversely, decreasing the air temperature by 1 K yields a 7% increase in firn air content and a <1% decrease in the deep firn temperature in the same amount of time. In this scenario, the effects of warming are more extreme and have more adverse impacts on the firn’s meltwater storage capacity when compared to cooling. This work highlights the sensitivity of the firn to changing atmospheric variables and provides a framework for estimating the timescales and magnitude of firn responses to a changing climate.

How to cite: Thompson-Munson, M., Kay, J., and Markle, B.: Characterizing the influence of idealized atmospheric forcings on firn using the SNOWPACK firn model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1329, https://doi.org/10.5194/egusphere-egu23-1329, 2023.

EGU23-3405 | ECS | Orals | CR3.2

Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model 

Violaine Coulon, Ann Kristin Klose, Christoph Kittel, Ricarda Winkelmann, and Frank Pattyn

Recent observations show that the Antarctic ice sheet is currently losing mass at an accelerating rate in areas subject to high sub-shelf melt rates. The resulting thinning of the floating ice shelves reduces their ability to restrain the ice flowing from the grounded ice sheet towards the ocean, hence raising sea level by increased ice discharge. Despite a relatively good understanding of the drivers of current Antarctic mass changes, projections of the Antarctic ice sheet are associated with large uncertainties, especially under high‐emission scenarios. This uncertainty may notably be explained by unknowns in the long-term impacts of basal melting and changes in surface mass balance. Here, we use an observationally-calibrated ice-sheet model to investigate the future trajectory of the Antarctic ice sheet until the end of the millennium related to uncertainties in the future balance between sub-shelf melting and ice discharge on the one hand, and the changing surface mass balance on the other. Our large ensemble of simulations, forced by a panel of CMIP6 climate models, suggests that the ocean will be the main driver of short-term Antarctic mass loss, triggering ice loss in the West Antarctic ice sheet (WAIS) already during this century. Under high-emission pathways, ice-ocean interactions will result in a complete WAIS collapse, likely completed before the year 2500 CE, as well as significant grounding-line retreat in the East Antarctic ice sheet (EAIS). Under a more sustainable socio-economic scenario, both the EAIS and WAIS may be preserved, though the retreat of Thwaites glacier appears to be already committed under present-day conditions. We show that with a regional near-surface warming higher than +7.5°C, which may occur by the end of this century under unabated emission scenarios, major ice loss is expected as the increase in surface runoff outweighs the increase in snow accumulation, leading to a decrease in the mitigating role of the ice sheet surface mass balance.

How to cite: Coulon, V., Klose, A. K., Kittel, C., Winkelmann, R., and Pattyn, F.: Disentangling the drivers of future Antarctic ice loss with a historically-calibrated ice-sheet model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3405, https://doi.org/10.5194/egusphere-egu23-3405, 2023.

EGU23-4042 | Posters on site | CR3.2

Experimental design for the the 2nd marine ice sheet and ocean model intercomparison project (MISOMIP2) 

Nicolas Jourdain, Jan De Rydt, Yoshihiro Nakayama, Ralph Timmermann, and Mathias Van Caspel

The 2nd Marine Ice Sheet and Ocean Model Intercomparison Project (MISOMIP2) is a natural progression of previous and ongoing model intercomparison exercises that have focused on the simulation of ice-sheet--ocean processes in Antarctica. The previous exercises motivate the move towards more realistic configurations and more diverse model parameters and resolutions. The first objective of MISOMIP2 is to investigate the robustness of ocean and ocean--ice-sheet models in a range of Antarctic environments, through comparisons to interannual observational data. We will assess the status of ocean--ice-sheet modelling as a community and identify common characteristics of models that are best able to capture observed features. As models are highly tuned based on present-day data, we will also compare their sensitivity to abrupt atmospheric perturbations leading to either very warm or slightly warmer ocean conditions than present-day. The approach of MISOMIP2 is to welcome contributions of models as they are, but we request standardised variables and common grids for the outputs. There will be two target regions, the Amundsen Sea and the Weddell Sea, chosen because they describe two extremely different ocean environments and have been relatively well observed compared to other parts of Antarctica. An observational "MIPkit" is provided to evaluate ocean and ice sheet models in these two regions.

How to cite: Jourdain, N., De Rydt, J., Nakayama, Y., Timmermann, R., and Van Caspel, M.: Experimental design for the the 2nd marine ice sheet and ocean model intercomparison project (MISOMIP2), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4042, https://doi.org/10.5194/egusphere-egu23-4042, 2023.

EGU23-6642 | ECS | Orals | CR3.2

Snow evolution through the Last Interglacial with a multi-layer snow model 

Thi Khanh Dieu Hoang, Aurélien Quiquet, Christophe Dumas, and Didier M. Roche

The Last Interglacial period (LIG), which occurred approximately between 130 and 116 kyr BP, is characterized by similar/warmer temperatures and higher sea levels compared to the present-day conditions due to the orbital variation of the Earth. Hence, the period provides insights into the behavior of the Earth's system components under stable and prolonged warm climates and their subsequent evolution into a glacial state. 

To better understand the ice sheet's surface mass balance that ultimately drives the advance and retreat of ice-sheets, we study the snow cover changes in the Northern Hemisphere during the LIG. In order to do so, we used BESSI (BErgen Snow Simulator), a physical energy balance model with 15 vertical snow layers and high computational efficiency, to simulate the snowpack evolution. First, BESSI was validated using the regional climate model MAR (Modèle Atmosphérique Régional) as forcing and benchmark for snow cover over the Greenland and Antarctica Ice Sheets under present-day climate. Using two distinct ice sheet climates helps constrain the different processes in place (e.g., albedo and surface melt for Greenland and sublimation for Antarctica). 

For the LIG simulations, the latest version of an Earth system model of intermediate complexity iLOVECLIM was used to force BESSI in different time slices to fully capture the snow evolution in the Northern Hemisphere throughout this period. Impacts of the downscaling component of iLOVECLIM, which provides higher resolution data and accounts for the influences of the topography, on BESSI performance are also discussed.  

The results show that BESSI performs well compared to MAR for the present-day climate, even with a less complex model set-up. Through the LIG, with the ability to model the snow compaction, the change of snow density and snow depth, BESSI simulates the snow cover evolution in the studied area better than the simple snow model (bucket model) included in iLOVECLIM. 

The findings suggest that BESSI can provide a more physical surface mass balance scheme to ice sheet models such as GRISLI of iLOVECLIM to improve simulations of the ice sheet - climate interactions.  

How to cite: Hoang, T. K. D., Quiquet, A., Dumas, C., and Roche, D. M.: Snow evolution through the Last Interglacial with a multi-layer snow model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6642, https://doi.org/10.5194/egusphere-egu23-6642, 2023.

EGU23-7020 | ECS | Orals | CR3.2

Uncertainties in Greenland ice sheet evolution and related sea-level projections until 2100 

Charlotte Rahlves, Heiko Goelzer, Petra Langebroek, and Andreas Born

The Greenland ice sheet is currently one of the main contributors to sea-level rise and mass loss from the ice sheet is expected to continue under increasing Arctic warming. Since sea-level rise is threatening coastal communities worldwide, reducing uncertainties in projections of future sea-level contribution from the Greenland ice sheet is of high importance. In this study we address the response of the ice sheet to future climate change. We determine rates of sea-level contribution that can be expected from the ice sheet until 2100 by performing an ensemble of standalone ice sheet simulations with the Community Ice Sheet Model (CISM). The ice sheet is initialized to resemble the presently observed geometry by inverting for basal friction. We examine a range of uncertainties, associated to stand alone ice sheet modeling by prescribing forcing from various global circulations models (GCMs) for different future forcing scenarios (shared socioeconomic pathways, SSPs). Atmospheric forcing is downscaled with the regional climate model MAR. The response of marine terminating outlet glaciers to ocean forcing is represented by a retreat parameterization and sampled by considering different sensitivities. Furthermore, we investigate how the initialization of the ice sheet with forcing from different global circulation models affects the projected rates of sea-level contribution. In addition, sensitivity of the results to the grid spacing of the ice sheet model is assessed. The observed historical mass loss is generally well reproduced by the ensemble. The projections yield a sea-level contribution in the range of 70 to 230 mm under the SSP5-8.5 scenario until 2100. Climate forcing constitutes the largest source of uncertainty for projected sea-level contribution, while differences due to the initial state of the ice sheet and grid resolution are minor.

 

 

How to cite: Rahlves, C., Goelzer, H., Langebroek, P., and Born, A.: Uncertainties in Greenland ice sheet evolution and related sea-level projections until 2100, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7020, https://doi.org/10.5194/egusphere-egu23-7020, 2023.

The precession of the equinoxes has a strong influence on the intensity of summer insolation according to most metrics and we would therefore expect the 23-Kyr and 19-Kyr precession cycles to be strongly reflected in our records of global ice volume, if summer insolation is indeed important for pacing glacial-interglacial cycles as proposed by Milutin Milankovitch. Instead, the precession signal is reduced in amplitude compared with the obliquity cycle in the Late Pleistocene, and in the Early Pleistocene (EP) precession appears completely absent in the δ18O stack. For this reason, the ‘40-Kyr world’ of the EP has been referred to as Milankovitch's other unsolved mystery. Indeed, numerous models of the Northern Hemisphere (NH) ice sheets simulated across the Plio-Pleistocene predict both a strong precessional and obliquity variability during the EP, at odds with the δ18O record. This points to the possibility of a dynamic Antarctic Ice Sheet in the EP that varied out-of-phase with the NH ice sheets at the precession period. In the original theory proposed by Raymo et al., (2006), from 3 to 1 Ma the East Antarctic Ice Sheet may have been land-terminating between 70S to 65S and sensitive to local summer insolation forcing. As precession is out-of-phase between the hemispheres, these variations could be cancelled out in globally integrated proxies of sea-level, concealing the true precession variability of both hemispheres in the marine sediment record. While studies have demonstrated  that precession-driven variations of the Antarctic Ice Sheet could cancel out NH variations in the deep-ocean record, no studies have investigated the actual feasibility of strong precession variability of the Antarctic Ice Sheet in the EP driven by local summer insolation, and whether it would have the magnitudes necessary to offset larger variations of the NH ice sheets. The question remains under what CO2 concentrations and orbital configuration can the East Antarctic Ice Sheet realistically be sensitive to local summer insolation forcing and possibly deglaciated from 70S to 65S, as postulated by Raymo et al. (2006). Can this produce the 10-30 m of sea-level necessary to offset NH variations in ice volume? To investigate the feasibility for anti-phased precession variability between the NH ice sheets and Antarctica in the EP, we use a zonally-averaged energy balance model coupled to a 1-D ice sheet model of a northern and southern hemisphere ice sheet, forced by atmospheric CO2 concentrations and daily insolation fields. The model will simulate glacial cycles across the Quaternary for different CO2 scenarios and determine whether anti-phased precessional cycles in ice volume between the hemispheres is a viable mechanism to explain the 40-Kyr world found in the δ18O record.

How to cite: Gunning, D.: Investigating precession cancellation across the MPT using a zonally averaged energy balance model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7385, https://doi.org/10.5194/egusphere-egu23-7385, 2023.

EGU23-7422 | ECS | Orals | CR3.2 | Highlight

(Ir)reversibility of future Antarctic mass loss on multi-millennial timescales 

Ann Kristin Klose, Violaine Coulon, Frank Pattyn, and Ricarda Winkelmann

Given the potentially high magnitudes and rates of future warming, the long-term evolution of the Antarctic Ice Sheet is highly uncertain. While recent projections under Representative Concentration Pathway 8.5 estimate the Antarctic sea-level contribution by the end of this century between -7.8 cm and 30.0 cm sea-level equivalent (Seroussi et al., 2020), sea-level might continue to rise for millennia to come due to ice sheet inertia, resulting in a substantially higher long-term committed sea-level change. In addition, potentially irreversible ice loss due to several self-amplifying feedback mechanisms may be triggered within the coming centuries, but evolves thereafter over longer timescales depending on the warming trajectory. It is therefore necessary to account for the timescale difference between forcing and ice sheet response in long-term sea-level projections by (i) determining the resulting gap between transient and committed sea-level contribution with respect to changing boundary conditions, (ii) testing the reversibility of large-scale ice sheet changes, as well as (iii) exploring the potential for safe overshoots of critical thresholds when reversing climate conditions from enhanced warming to present-day.

Here, we assess the sea-level contribution from mass balance changes of the Antarctic Ice Sheet on multi-millennial timescales, as well as ice loss reversibility. The Antarctic sea-level commitment is quantified using the Parallel Ice Sheet Model (PISM) and the fast Elementary Thermomechanical Ice Sheet (f.ETISh) model by fixing forcing conditions of warming trajectories from state-of-the-art climate models available from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) at regular intervals in time. The ice sheet then evolves for several millennia under constant climate conditions. Finally, the climate forcing is reversed to present-day starting from different stages of ice sheet decline to test for the reversibility of ice loss.

Our results suggest that the Antarctic Ice Sheet may be committed to a strong grounding-line retreat or even a collapse of the West Antarctic Ice Sheet when keeping climate conditions constant at warming levels reached during this century. Fixing climate conditions later in time may additionally trigger a substantial decline of the East Antarctic Ice Sheet. We show that the reversibility of Antarctic ice loss as well as the potential for safe overshoots strongly depend on the timing of the reversal of the forcing.

How to cite: Klose, A. K., Coulon, V., Pattyn, F., and Winkelmann, R.: (Ir)reversibility of future Antarctic mass loss on multi-millennial timescales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7422, https://doi.org/10.5194/egusphere-egu23-7422, 2023.

EGU23-7507 | ECS | Posters on site | CR3.2

The influence of temperature variability on the Greenland ice sheet 

Mikkel Lauritzen, Guðfinna Aðalgeirsdóttir, Nicholas Rathmann, Aslak Grinsted, Brice Noël, and Christine Hvidberg

The projected contribution of the Greenland ice sheet to sea-level rise in response to future warming relies upon the state of the present-day ice sheet, and one of the main contributors to uncertainties in projections is due to uncertainties in the initial state of the simulated ice sheet. A previous study showed that including the inter-annual climate variability in an idealized ice sheet model leads to an increased mass loss rate, but the effect on the Greenland ice sheet is not known. Here we present a study using the PISM model to quantify the influence of inter-annual variability in climate forcing on the Greenland ice sheet. 
We construct an ensemble of climate-forcing fields that account for inter-annual variability in temperature using reanalysis data products from RACMO and NOAA-CIRES, and we investigate the steady state and the sensitivity of the simulated Greenland ice sheet under these different scenarios.
We find that the steady state volume decreases by 0.24-0.38% when forced with a variable temperature forcing compared to a constant temperature forcing, corresponding to 21.7±5.0 mm of sea level rise, and the response to abrupt warming is 0.03-0.21 mm SLE a-1 higher depending on climate scenario. The northern basins are particularly sensitive with a change in volume of 1.2-0.9%. Our results emphasize the importance of including climate variability in projections of future mass loss.

How to cite: Lauritzen, M., Aðalgeirsdóttir, G., Rathmann, N., Grinsted, A., Noël, B., and Hvidberg, C.: The influence of temperature variability on the Greenland ice sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7507, https://doi.org/10.5194/egusphere-egu23-7507, 2023.

EGU23-7553 | ECS | Orals | CR3.2

Examining Possible Retreat Scenarios for the Greenland Ice Sheet during the MIS-11c Interglacial 

Brian Crow, Lev Tarasov, Matthias Prange, and Michael Schulz

The interglacial period spanning ca. 423 to 398 ka and known as Marine Isotope Stage (MIS) 11c has been the subject of much study, due largely to the unique evolution of global temperatures, greenhouse gas levels, and sea levels relative to other interglacials of the late Pleistocene. Particularly concerning is some geological evidence and prior modeling studies which have suggested that a large majority of the Greenland ice sheet (GrIS) disappeared during this period, despite global mean air temperatures only modestly higher than those of the preindustrial period. However, uncertainty is high as to the extent and spatiotemporal evolution of this melt due to a dearth of direct geological constraints. Our study therefore endeavors to better constrain these large uncertainties by using spatiotemporally interpolated climate forcing from CESM v1.2 time slice simulations and an ensemble of ice sheet model parameter vectors derived from a GrIS history matching over the most recent glacial cycle from the Glacial Systems Model (GSM). The use of different ice sheet initialization states from simulations of the previous glacial-interglacial transition helps to capture the large initial condition uncertainty. Two different regional present-day climate modeling datasets are utilized for anomaly correction of CESM precipitation and temperature fields. 

Preliminary analysis indicates that the most robust retreat across most ensemble members happens in the northern, western, and central portions of the ice sheet, while the higher terrain of the south and east retain substantial amounts of ice. This is broadly consistent with indications that ice may have survived the MIS-11c interglacial at the Summit ice core location, but not at DYE-3. Simulations indicate a maximum MIS-11c sea level contribution from the GrIS centered between 408 and 403 ka, with minimum GrIS volumes reaching between 25% and 70% of modern-day values. In part due to the prior constraint of ice-sheet model ensemble parameters from history matching, ensemble parameters controlling downscaling and climate forcing bias correction are the largest parametric sources of output variance in our simulations.  Though CESM uncertainties are unassessed in this study, it is likely they dominate given that the choice of present-day reference temperature climatology for anomaly correction of the climate model output has the largest effect on the GrIS melt response in our simulations.

How to cite: Crow, B., Tarasov, L., Prange, M., and Schulz, M.: Examining Possible Retreat Scenarios for the Greenland Ice Sheet during the MIS-11c Interglacial, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7553, https://doi.org/10.5194/egusphere-egu23-7553, 2023.

EGU23-7920 | ECS | Orals | CR3.2

The Divergent Futures of Greenland Surface Mass Balance Estimates from Different Regional Climate Models 

Quentin Glaude, Brice Noel, Martin Olesen, Fredrik Boberg, Michiel van den Broeke, Ruth Mottram, and Xavier Fettweis

Arctic amplification is causing global warming to have a more intense impact on arctic regions, with consequences on the surface mass balance and glacier coverage of Greenland. The glaciers of Greenland are also shrinking, contributing to sea level rise as well. Projecting the future evolution of these changes is crucial for understanding the likely impacts of climate change on sea level rise.

In this study, we compared three state-of-the-art Regional Climate Models (RCMs) (MAR, RACMO, and HIRHAM) using a common grid and forcing data from Earth System Models to assess their ability to project future changes in Greenland's surface mass balance up to 2100. We also considered the impact of different Earth System Models and Shared Socioeconomic Pathways.

The results of this comparison showed significant differences in the projections produced by these different models, with a factor-2 difference in mass loss between MAR and RACMO on cumulative Surface Mass Balance anomalies. These differences are important as RCMs are often used as inputs for ice sheet models, which are used to make predictions about sea level rise. Furthermore, we aim to investigate the causes of these differences, as understanding them will be key to improving the accuracy of sea level rise projections.

The uncertainty of the RCMs projections are translated into uncertainties in Sea-Level-Rise projections. The results presented here open the door for deeper investigations in the climate modeling community and the physical reasons linked to these divergences. Our study highlighted the importance of continued research and development of RCMs to better understand the physics implemented in these models and ultimately improve the accuracy of future sea level rise projections.

How to cite: Glaude, Q., Noel, B., Olesen, M., Boberg, F., van den Broeke, M., Mottram, R., and Fettweis, X.: The Divergent Futures of Greenland Surface Mass Balance Estimates from Different Regional Climate Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7920, https://doi.org/10.5194/egusphere-egu23-7920, 2023.

EGU23-8341 | Orals | CR3.2 | Highlight

Antarctic Ice Sheet tipping points in the last 800,000 years 

David Chandler, Petra Langebroek, Ronja Reese, Torsten Albrecht, and Ricarda Winkelmann

Stability of the Antarctic Ice Sheet in the present-day climate, and in future warming scenarios, is of growing concern as increasing evidence points towards the prospect of irreversible ice loss from the West Antarctica Ice Sheet (WAIS) with little or no warming above present. Here, in transient ice sheet simulations for the last 800,000 years (9 glacial-interglacial cycles), we find evidence for strong hysteresis between ice volume and ocean temperature forcing through each glacial cycle, driven by rapid WAIS collapse and slow recovery. Additional equilibrium simulations at several climate states show this hysteresis does not arise solely from the long ice sheet response time, instead pointing to consistent tipping-point behaviour in the WAIS. Importantly, WAIS collapse is triggered when continental shelf bottom water is maintained above a threshold of 0 to 0.25°C above present, and there are no stable states for the WAIS in conditions warmer than present. Short excursions to warmer temperatures (marine isotope stage 7) may not initiate collapse (‘borrowed time’), while the more sustained interglacials (stages 11, 9, 5e) demonstrate an eventual WAIS collapse. Cooling of ca. 2°C below present-day is then required to initiate recovery. Despite the differing climatic characteristics of each glacial cycle, consistency between both the transient and equilibrium behaviour of the ice sheet through several cycles shows there is some intrinsic predictability at millennial time scales, supporting the use of Pleistocene ice sheet simulations and geological evidence as constraints on likely future behaviour.

How to cite: Chandler, D., Langebroek, P., Reese, R., Albrecht, T., and Winkelmann, R.: Antarctic Ice Sheet tipping points in the last 800,000 years, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8341, https://doi.org/10.5194/egusphere-egu23-8341, 2023.

EGU23-8690 | ECS | Orals | CR3.2

Antarctic sensitivity to oceanic melting parameterizations 

Antonio Juárez-Martínez, Javier Blasco, Marisa Montoya, Jorge Alvarez-Solas, and Alexander Robinson

Ice in Antarctica has been experiencing dramatic changes in the last decades. These variations have consequences in terms of sea level, which could have an impact on human societies and life on the planet in the future. The Antarctic Ice Sheet (AIS) could become the main contributor to sea-level rise in the coming centuries, but there is a great uncertainty associated with its contribution, which is due in part to the complexity of the coupled ice-ocean processes. In this study we investigate the contribution of the AIS to sea-level rise in the coming centuries in the context of the Ice Sheet Model Intercomparison Project (ISMIP6), but covering a range beyond 2100, using the higher-order ice-sheet model Yelmo. We test the sensitivity of the model  to basal melting parameters using several forcings and scenarios for the atmosphere and ocean, obtained from different GCM models. The results show a strong  dependency on variations of the parameter values of the basal melting laws and also on the forcing that is chosen. Higher values of the heat exchange velocity between ice and ocean lead to higher sea-level rise, varying the contribution depending on the forcing. Ice-ocean interactions therefore can be expected to contribute significantly to the uncertainty associated with the future evolution of the AIS.

 

How to cite: Juárez-Martínez, A., Blasco, J., Montoya, M., Alvarez-Solas, J., and Robinson, A.: Antarctic sensitivity to oceanic melting parameterizations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8690, https://doi.org/10.5194/egusphere-egu23-8690, 2023.

EGU23-8853 | ECS | Orals | CR3.2

Sensitivity of Heinrich events to boundary forcing perturbations in a coupled ice sheet-solid Earth model 

Clemens Schannwell, Uwe Mikolajewicz, Marie Kapsch, and Florian Ziemen

Heinrich events are one of the prominent signals of glacial climate variability. They are characterised as abrupt, quasi-periodic episodes of ice-sheet instabilities during which large numbers of icebergs are released from the Laurentide ice sheet. These events affect the evolution of the global climate by modifying the ocean circulation through the addition of freshwater and the atmospheric circulation through changes in ice-sheet height. However, the mechanisms controlling the timing and occurrence of Heinrich events remain enigmatic to this day. Here, we present simulations with a coupled ice-sheet solid Earth model that aim to quantify the importance of different boundary forcings for the timing of Heinrich events. We focus the analysis on two prominent ice streams of the Laurentide ice sheet with the land-terminating Mackenzie ice stream and the marine-terminating Hudson ice stream. Our simulations identify different surge characteristics for the Mackenzie ice stream and the Hudson ice stream. Despite their different glaciological and climatic settings, both ice streams exhibit responses of similar magnitude to perturbations to the surface mass balance and the geothermal heat flux. However, Mackenzie ice stream is more sensitive to changes in the surface temperature. Changes to the ocean temperature and the global sea level have a negligible effect on the timing of Heinrich events in our simulations for both ice streams. We also show that Heinrich events for both ice streams only occur in a certain parameter space. Transitioning from an oscillatory Heinrich event state to a persistent streaming state can lead to an ice volume loss of up to 30%. Mackenzie ice stream is situated in a climate that is particularly close to this transition point, underlining the potential of the ice stream to have contributed to prominent abrupt climate events during glacial-interglacial transitions.

How to cite: Schannwell, C., Mikolajewicz, U., Kapsch, M., and Ziemen, F.: Sensitivity of Heinrich events to boundary forcing perturbations in a coupled ice sheet-solid Earth model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8853, https://doi.org/10.5194/egusphere-egu23-8853, 2023.

EGU23-8973 | ECS | Posters on site | CR3.2

How does the Greenland ice sheet respond on a medium-term time scale to various levels of warming? 

Alison Delhasse, Johanna Beckmann, and Christoph Kittel

The Greenland ice sheet is considered as one of the main causes of sea level rise (SLR) at the end of the 21st century. But what if it is already too late to reverse the loss of ice from the Greenland ice sheet? The mass balance (MB) resulting from the coupling between the Regional Atmospheric Model (MAR, ULiège) and the Parallel Ice Sheet Model (PISM, PIK) over Greenland following the CESM2 ssp585 climate indicates that even if we stop the CESM2 warming in 2100 and continue with a +7°C climate until 2200 with respect to the reference period (1961-1990), the GrIS continues to lose mass up to a contribution equivalent to 60 cm of SLR in 2200. From this coupling experiment, we ran several coupled simulations by stabilizing the warming at different thresholds (+ 1, 2, 3, ... °C) with respect to our reference period in order to highlight a kind of tipping point of the ice sheet with respect to atmospheric warming. Other experiments have been launched by reversing the climate imposed by CESM2 from 2100 to 2000, for example, with the aim of identifying whether the GrIS could gain ice mass again with a climate as warm as the present one.

How to cite: Delhasse, A., Beckmann, J., and Kittel, C.: How does the Greenland ice sheet respond on a medium-term time scale to various levels of warming?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8973, https://doi.org/10.5194/egusphere-egu23-8973, 2023.

EGU23-9449 | Posters on site | CR3.2

Interactive coupling of the Antarctic Ice Sheet and the global ocean 

Moritz Kreuzer, Willem Huiskamp, Torsten Albrecht, Stefan Petri, Ronja Reese, Georg Feulner, and Ricarda Winkelmann

Increased sub-shelf melting and ice discharge from the Antarctic Ice sheet has both regional and global impacts on the ocean and the overall climate system. Additional meltwater, for example, can reduce the formation of Antarctic Bottom Water, potentially affecting the global thermohaline circulation. Similarly, increased input of fresh and cold water around the Antarctic margin can lead to a stronger stratification of coastal waters, and a potential increase in sea-ice formation, trapping warmer water masses below the surface, which in turn can lead to increased basal melting of the ice shelves.

So far these processes have mainly been analysed in simple unidirectional cause-and-effect experiments, possibly neglecting important interactions and feedbacks. To study the long-term and global effects of these interactions, we have developed a bidirectional offline coupled ice-ocean model framework. It consists of the global ocean and sea-ice model MOM5/SIS and an Antarctic instance of the Parallel Ice Sheet Model PISM, with the ice-shelf cavity module PICO representing the ice-ocean boundary layer physics. With this setup we are analysing the aforementioned interactions and feedbacks between the Antarctic Ice Sheet and the global ocean system on multi-millenial time scales.

How to cite: Kreuzer, M., Huiskamp, W., Albrecht, T., Petri, S., Reese, R., Feulner, G., and Winkelmann, R.: Interactive coupling of the Antarctic Ice Sheet and the global ocean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9449, https://doi.org/10.5194/egusphere-egu23-9449, 2023.

EGU23-9747 | Orals | CR3.2

Climate variability as a major forcing of recent Antarctic ice-mass change 

Matt King, Kewei Lyu, and Xuebin Zhang

Antarctica has been losing ice mass for decades, but its link to large-scale modes of climate forcing is not clear. Shorter-period variability has been partly associated with El Niño Southern Oscillation (ENSO), but a clear connection with the dominant climate mode, the Southern Annular Mode (SAM), is yet to be found. We show that space gravimetric estimates of ice-mass variability over 2002-2021 may be substantially explained by a simple linear relation with detrended, time-integrated SAM and ENSO indices, from the whole ice sheet down to individual drainage basins. Approximately 40% of the ice-mass trend over the GRACE period can be ascribed to increasingly persistent positive SAM forcing which, since the 1940s, is likely due to anthropogenic activity. Similar attribution over 2002-2021 could connect recent ice-sheet change to human activity.

How to cite: King, M., Lyu, K., and Zhang, X.: Climate variability as a major forcing of recent Antarctic ice-mass change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9747, https://doi.org/10.5194/egusphere-egu23-9747, 2023.

EGU23-9842 | ECS | Orals | CR3.2

The choice of present-day climate forcing can significantly affect modelled future and past Antarctic Ice Sheet evolution 

Christian Wirths, Johannes Sutter, and Thomas Stocker

Model simulations of past and future Antarctic ice sheet (AIS) evolution depend on the applied climatic forcing. To model the present and future Antarctic ice sheet, several different forcings from regional climate models are available. It is therefore critical to understand the influence and the resulting model differences and uncertainties associated with the choice of present-day reference forcing.  

We apply present-day climatic forcings from regional models (RACMO2.3p2, MAR3.10, HIRHAM5 and COSMO-CLM2) combined with climate anomalies from a global climate model (HadGEM2-ES). With this setup, we investigate the future evolution of the AIS under the RCP2.6, RCP4.5 and RCP8.5 scenarios using the Parallel Ice Sheet Model (PISM). We find substantial differences in the future evolution of the AIS depending on the choice of the present-day reference field even under an extreme scenario such as RCP8.5. We discuss the influence of those forcing choices on the projected future AIS dynamics and sea-level contribution, considering a variety of ice sheet model parameterizations. 

With this analysis, we aim to gain a better understanding of the role of climate forcing choices and parameterization-induced uncertainties of sea-level rise projections. 

 

How to cite: Wirths, C., Sutter, J., and Stocker, T.: The choice of present-day climate forcing can significantly affect modelled future and past Antarctic Ice Sheet evolution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9842, https://doi.org/10.5194/egusphere-egu23-9842, 2023.

EGU23-9904 | Orals | CR3.2

Response of the Greenland Ice Sheet to temperature overshoot scenarios  

Michele Petrini, Heiko Goelzer, Petra Langebroek, Charlotte Rahlves, and Jörg Schwinger

As there is no evidence for the implementation of sufficiently ambitious global CO2 emission reductions, it is very unlikely that we will be able to keep the global mean warming at the end of the century below the 1.5 C limit set in the Paris Agreement. However, the development of CO2 removal techniques could potentially allow us to reach the 1.5 C target after a period of temperature overshoot, by offsetting past and current high levels of emissions with net-negative emissions in the future. To assess the effectiveness and the risks associated to such mitigation options, we need to better understand the impact of temperature overshoot scenarios on various components of the Earth System.  

Here, we focus on the Greenland Ice Sheet. We force an ice-sheet model (CISM2) with Surface Mass Balance (SMB) from an ensemble of 400 years-long idealized overshoot simulations, carried out with the Norwegian Earth System Model NorESM2. The SMB, which is calculated in NorESM2 using an energy balance scheme at multiple elevation classes, is downscaled during runtime to the ice-sheet model grid, thus allowing to account explicitly for the SMB-height feedback. In this presentation, we will assess the sea-level contribution of the Greenland Ice Sheet for overshoot pathways, compared to reference pathways without overshoot. Moreover, we will assess the impact of individual processes, such as the SMB-height feedback and the ocean-driven mass loss at marine-terminating margins, on the sea-level contribution of the Greenland Ice Sheet.  

How to cite: Petrini, M., Goelzer, H., Langebroek, P., Rahlves, C., and Schwinger, J.: Response of the Greenland Ice Sheet to temperature overshoot scenarios , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9904, https://doi.org/10.5194/egusphere-egu23-9904, 2023.

EGU23-10165 | Orals | CR3.2

Competing climate feedbacks of ice sheet freshwater discharge in a warming world 

Dawei Li, Robert DeConto, and David Pollard

Earth's polar ice sheets are projected to undergo significant retreat in the coming centuries if anthropogenic warming were to continue unabated, injecting freshwater stored on land over millennia into oceans and raise the global mean sea level. Ice sheet freshwater flux alters the status of ocean stratification and ocean-atmosphere heat exchange, inducing oceanic surface cooling and subsurface warming, hence an impact on the global climate. How the climate effects of ice sheet freshwater would feedback to influence the retreat of ice sheets, however, remains unsettled. Here we develop a two-way coupled climate-ice sheet modeling tool to assess the interactions between retreating polar ice sheets and the climate, considering a variety of greenhouse gas emission scenarios and modeled climate sensitivities. Results from coupled ice sheet-climate modeling show that ice sheet-ocean interactions give rise to multi-centennial oscillations in ocean temperatures around Antarctica, which would make it challenging to isolate anthropogenic signals from observational data. Future projections unveil both positive and negative feedbacks associated with freshwater discharge from the Antarctic Ice Sheet, while the net effect is scenario-dependent. The West Antarctic Ice Sheet collapses in high-emission scenarios, but the process is slowed significantly by cooling induced by ice sheet freshwater flux.

How to cite: Li, D., DeConto, R., and Pollard, D.: Competing climate feedbacks of ice sheet freshwater discharge in a warming world, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10165, https://doi.org/10.5194/egusphere-egu23-10165, 2023.

EGU23-10204 | ECS | Orals | CR3.2

Parameter ensemble simulations of the North American and Greenland ice sheets and climate of the Last Glacial Maximum with Famous-BISICLES 

Sam Sherriff-Tadano, Niall Gandy, Ruza Ivanovic, Lauren Gregoire, Jonathan Owen, Charlotte Lang, Jonathan Gregory, Robin Smith, and Tamsin Edwards
Testing the ability of climate-ice sheet coupled models to simulate past ice sheets and climates can provide a way to evaluate the models. One example is the Last Glacial Maximum (LGM), when huge ice sheets covered the Northern Hemisphere, especially over the North America. Here, we perform 200 ensemble member simulations of the North American and Greenland ice sheets and climate of the LGM with an ice sheet-atmosphere-slab ocean coupled model Famous-BISICLES. 16 parameters associated with climate and ice dynamics are varied. The simulated results are evaluated against the LGM global temperature, the total ice volume and the ice extent at the southern margin of the North American ice sheet. In the ensemble simulations, the global temperature is controlled by the combination of precipitation efficiency in the large-scale condensation and entrainment rate in the cumulus convection. Under reasonable LGM global temperature, we find that the surface albedo and Weertman coefficient in the basal sliding law control the North American ice volume. In contrast, the ice volume of Greenland is found to be controlled by the Weertman coefficient. Based on the constraints, the model produces 6 good simulations with reasonable global temperature and North American ice sheet. We also find that warm summer surface temperature biases at the ice sheet interior as well as downscaling of surface mass balance based on altitude can cause strong local ice melting. This implies the need of better representing the atmospheric conditions and surface mass balance in the ice sheet interior.

How to cite: Sherriff-Tadano, S., Gandy, N., Ivanovic, R., Gregoire, L., Owen, J., Lang, C., Gregory, J., Smith, R., and Edwards, T.: Parameter ensemble simulations of the North American and Greenland ice sheets and climate of the Last Glacial Maximum with Famous-BISICLES, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10204, https://doi.org/10.5194/egusphere-egu23-10204, 2023.

EGU23-10231 | ECS | Orals | CR3.2

The effect of an evolving Greenland ice sheet in NorESM2 projections 

Konstanze Haubner, Heiko Goelzer, Petra Langebroek, and Andreas Born

The Greenland ice sheet's mass loss is increasing and so is its impact to the climate system. Yet, Earth System models mostly keep ice sheets at a constant extent or treat interactions with the ice sheets fairly simple.

Here, we present the first simulations of NorESM2 coupled to the ice sheet model CISM over Greenland. We compare NorESM2 simulations from 1850 to 2300 with and without an evolving ice sheet over Greenland based on the ssp585 scenario and its extension to 2300. Ocean and atmosphere horizontal resolution are on 1deg, while the coupled ice sheet module CISM is running on 4km. The coupling setup is based on CESM2. Ice extent and elevation are provided to the atmosphere every 5years and the land model every year. Whereas the ice sheet receives updated surface mass balance every year.
We show the evolution of the Greenland ice sheet and changes in atmosphere, ocean and sea ice.

Overall global mean surface air temperatures (SAT) change from 14°C to 24°C by 2300 with the steepest increase between 2070-2200.
Over the Southern ocean and Antarctica, SAT are increasing by 10°C, while over the Northern hemisphere we see a change of 15-28°C by 2300. 
At the end of the simulations (year 2300), SAT over Greenland are 6°C warmer when including an evolving ice sheet. In contrast, the ocean surrounding Greenland shows SAT that are 2°C colder in the coupled system, compared to the simulation with a fixed Greenland ice sheet. Sea surface temperatures show the same ~2°C difference around Greenland in coupled and uncoupled simulation. The overall change in sea surface temperatures is 12°C.
Minimum and maximum sea ice extent differs only slightly with and without the coupling, indicating that the overall warming seems to dictate speed of the sea ice retreat.

How to cite: Haubner, K., Goelzer, H., Langebroek, P., and Born, A.: The effect of an evolving Greenland ice sheet in NorESM2 projections, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10231, https://doi.org/10.5194/egusphere-egu23-10231, 2023.

The mid-Pleistocene Transition (MPT) from 41 kyr to 100 kyr glacial cycles was one of the largest changes in the Earth system over the past 2 million years. The transition happened in the absence of a relevant change in orbital forcing. As such, it presents a challenge for the Milankovitch theory of glacial cycles. A change from a low to high friction bed under the North American Ice Complex through the removal of pre-glacial regolith has been hypothesized to play a critical role in the transition. For testing, this hypothesis requires constraint on pre-glacial regolith cover and topography as well as mechanistic constraint on whether the appropriate amount of regolith can be removed from the required regions to enable MPT occurrence at the right time. To date, however, Pleistocene regolith removal has not been simulated for a realistic, 3D North American ice sheet fully resolving relevant basal processes. A further challenge is very limited constraints on pre-glacial bed elevation and sediment thickness.

Herein, we address these challenges with an appropriate computational model and ensemble-based analysis addressing parametric and initial mean sediment cover uncertainties. We use the 3D Glacial Systems Model that incorporates relevant glacial processes. Specifically, it includes: 3D thermomechanically coupled hybrid SIA/SSA ice physics, fully coupled sediment production and transport, subglacial linked-cavity and tunnel hydrology, isostatic adjustment from dynamic loading and erosion, and climate from a 2D non-linear energy balance model and glacial index. The sediment model includes quarrying and abrasion for sediment production with both englacial and subglacial transport. The coupled system is driven only by atmospheric CO2 and insolation.

We show that the ice, climate, and sediment processes encapsulated in this fully coupled glacial systems model enables capture of the evolution of the Pleistocene North American glacial system. Specifically and within observational uncertainty, our model captures: the shift from 41 to 100 kyr glacial cycles, early Pleistocene extent, LGM ice volume, deglacial ice extent, and the broad present-day sediment distribution. We also find that pre-glacial sediment thickness and topography have a strong influence on the strength and duration of early Pleistocene glaciations.

How to cite: Drew, M. and Tarasov, L.: The pre-Pleistocene North American bed from coupled ice-climate-sediment physics and its strong influence on glacial cycle evolution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10318, https://doi.org/10.5194/egusphere-egu23-10318, 2023.

EGU23-10677 | Orals | CR3.2 | Highlight

Impacts of regional sea-level changes due to GRD effects on multi-centennial projections of Antarctic Ice Sheet under the ISMIP6-2300 experimental protocol  

Holly Han, Matt Hoffman, Xylar Asay-Davis, Trevor Hillebrand, and Mauro Perego

Evolution of ice sheets contribute to sea-level change globally by exchanging mass with the ocean, and regionally by causing the solid Earth deformation and perturbation of the Earth’s rotation and gravitational field, so-called “gravitational, rotational and deformational (GRD) effects”. In the last decade, much work has been done to establish the importance of coupling GRD effects particularly in modeling of marine-based ice sheets (e.g., West Antarctic Ice Sheet; WAIS) to capture the interactions between ice sheets, sea level and the solid Earth at the grounding lines. However, coupling of GRD effects has not yet been done widely within the ice-sheet modeling community; for example, GRD effects were not included in any of the ice sheet models that contributed to the most recent recent ice-sheet model intercomparison through 2100 (Ice Sheet Model Intercomparison Project for CMIP6: ISMIP6-2100; Serrousi et al., 2020) cited by the latest IPCC AR6 report.

In this work, we couple the US Department of Energy’s MPAS-Albany Land Ice model (which was one of the models that participated in the ISMIP6-2100 project) to a 1D sea-level model and perform coupled simulations of Antarctica under the new ISMIP6-2300 protocol in which climate forcing is extended beyond 2100 to 2300. Comparing to the standalone ice-sheet simulations with fix bed topography without GRD effects, the results from our coupled simulations show multi-decadal to centennial-scale delays in the retreat of the Thwaites glacier in the West Antarctica. Our results further suggest that the strength of the negative feedback of sea-level changes on the WAIS retreat becomes weaker as the strength of the applied forcing increases, implying the pertinence of our commitment to limiting greenhouse gas emissions. In addition, within our coupled ice sheet-sea level modeling frame, we introduce a new workflow work in which the ISMIP6 protocol-provided ocean thermal forcing is re-extrapolated based on the updated ocean bathymetry. Our preliminary results indicate that bedrock uplift due to ice mass loss can block the bottom warm ocean, providing additional negative feedback, but also can block cold water when/if the vertical ocean temperature profile gets inverted due to climate change (e.g., as represented in the UKESM model - SSP585 scenario results).

How to cite: Han, H., Hoffman, M., Asay-Davis, X., Hillebrand, T., and Perego, M.: Impacts of regional sea-level changes due to GRD effects on multi-centennial projections of Antarctic Ice Sheet under the ISMIP6-2300 experimental protocol , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10677, https://doi.org/10.5194/egusphere-egu23-10677, 2023.

EGU23-11678 | ECS | Posters on site | CR3.2

Antarctic ice sheet response to AMOC shutdowns during the penultimate deglaciation 

Maxence Menthon, Pepijn Bakker, Aurélien Quiquet, and Didier M. Roche

According to geological records, the sea level during the Last Interglacial (∼ 129–116 ka) peaked 6 to 9 m higher than during the pre-industrial with a major contribution from the Antarctic ice sheet (Dutton et al. 2015). According to Clark et al. 2020, a longer period of reduced Atlantic Meridional Overturning Circulation (AMOC) during the penultimate deglaciation compared to the last deglaciation could have led to greater subsurface warming and subsequent larger Antarctic Ice Sheet retreat.

Here we study the response of the Antarctic ice sheet to climate forcing with a forced AMOC shutdown at different timing and duration during the penultimate deglaciation (∼ 138–128 ka). The simulations are done with the Earth System Model of Intermediate Complexity iLOVECLIM (Roche et al. 2014) and the ice sheet model GRISLI (Quiquet et al. 2018), using the recently implemented sub-shelf melt module PICO (Reese et al. 2018). In the present simulations the GRISLI is forced with the iLOVECLIM simulations and is a step towards a fully coupled climate - ice sheet set up to take into account the climate - ice sheet interactions in a physical way.

We hypothesize that both the duration and timing of reduced AMOC can significantly affect the sensitivity of the Antarctic Ice Sheet. A longer period of AMOC reduction will lead to a larger subsurface warming in the Southern Ocean and subsequently a larger ice sheet retreat. On the other hand, an AMOC reduction earlier (later) in the deglaciation implies that the ice sheet that is affected by this subsurface warming is still fairly large (already small). We will discuss both the individual as well as combined effect of duration and timing on the ice sheet evolution.

How to cite: Menthon, M., Bakker, P., Quiquet, A., and Roche, D. M.: Antarctic ice sheet response to AMOC shutdowns during the penultimate deglaciation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11678, https://doi.org/10.5194/egusphere-egu23-11678, 2023.

EGU23-11845 | ECS | Orals | CR3.2

An Adimensional Ice-Sheet-Climate Model for glacial cycles 

Sergio Pérez-Montero, Jorge Alvarez-Solas, Alexander Robinson, and Marisa Montoya

Although the ultimate trigger of glacial cycles is Milankovitch insolation cycles, there are still uncertainties concerning their timing and transitions. These unknowns are believed to be due to intrinsic nonlinearities in the climate system, and there is a deep interest in their solution. However, the longer timescales involved make it infeasible to use comprehensive climate models because of the large computational cost involved. In this context, conceptual models are built to mimic complex processes in a simpler, computationally efficient way. Here we present an adimensional ice-sheet–climate model (AMOD), which aims to study these outstanding paleoclimatic topics. AMOD represents ice sheet dynamics by using common assumptions as in state-of-the-art ice-sheet models, adapted to its dimensionless nature, and it solves surface mass balance processes and the aging of snow and ice. In this way, AMOD is able to run several glacial cycles in seconds and produces results comparable to those of paleoclimatic proxies. Preliminary results indicate nonlinearities related to both ice dynamics and snow aging that determine the timing and shape of deglaciations.

How to cite: Pérez-Montero, S., Alvarez-Solas, J., Robinson, A., and Montoya, M.: An Adimensional Ice-Sheet-Climate Model for glacial cycles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11845, https://doi.org/10.5194/egusphere-egu23-11845, 2023.

EGU23-12206 | ECS | Orals | CR3.2

The Glacier-climate Interaction over the High-Mountain Asia during the Last Glacial Maximum 

Qiang Wei, Yonggang Liu, Yongyun Hu, and Qing Yan

Glacier advances affect the local climate, and in turn, can either promote or prohibit its own growth. Such feedback has not been considered in modeling the High-Mountain Asia (HMA) glaciers during the Last Glacial Maximum (LGM; ~28-23 ka), which may contribute to the large spread in some of the published modeling work, with some notable discrepancy with existing reconstruction data. By coupling an ice sheet model (ISSM) with a climate model (CESM1.2.2), we find that the total glacial area is reduced by 10% due to the glacier-climate interaction; glacier growth is promoted along the western rim of HMA, and yet reduced in the interior. Such changes in spatial pattern improve model-data comparison. Moreover, the expansion of glaciers causes an increase in the winter surface temperature of the eastern Tibetan Plateau by more than 2 K, and a decrease of precipitation almost everywhere, especially the Tarim basin, by up to 60%. These changes are primarily due to the increase in surface elevation, which blocks the water vapor brought by westerlies and southwesterlies, reducing precipitation and increasing surface temperatures to the east and northeast of the newly grown glaciers.

How to cite: Wei, Q., Liu, Y., Hu, Y., and Yan, Q.: The Glacier-climate Interaction over the High-Mountain Asia during the Last Glacial Maximum, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12206, https://doi.org/10.5194/egusphere-egu23-12206, 2023.

The Greenland ice sheet comprises a volume of 7.4 m sea level equivalent and is losing mass rapidly as a result of global warming. It is widely thought that the ice sheet will exhibit tipping behaviour in a warmer climate. In other words, due to ice sheet – climate feedbacks (some of) its contribution to sea level rise may become irreversible once critical thresholds are crossed. This would severely affect the increasing number of people living in low-lying coastal areas worldwide. However, the current understanding of such thresholds and tipping behaviour is very limited, because most modelling studies up to date do not include (local) interactions or feedbacks between the ice sheet (topography and ice extent) and other climate system components (surface mass balance and atmosphere).

To investigate the irreversibility of Greenland’s ice mass loss and the associated processes, we coupled our high-resolution Greenland Ice Sheet Model (GISM) with a renowned high-resolution regional climate model, the Modèle Atmosphérique Régional (MAR). The two-way coupling between both models provides a (more) realistic representation of (local) ice sheet – climate interactions for future ice sheet simulations.

Like all regional climate models, MAR needs 6 hourly atmospheric forcing from a global climate model (GCM). Several coupled model runs with forcing from different GCMs are envisioned over the coming months and years. As they are computationally intensive, simulations up to the end of the century and beyond take several weeks to a few months to complete.

The poster will present the preliminary results from our first coupled model run in an envisioned series of experiments: a two-way coupled MAR-GISM run forced by the IPSL-CM6 6 hourly output, which is available up to 2300. For this timescale, our coupled models can still be run in fully interactive mode, which means the information (surface mass balance and ice sheet extent/topography) between both models can be exchanged on a yearly basis. In addition to its long duration, the IPSL forcing is of particular interest as it is on the high end of the CMIP6 model ensemble projections regarding warming over Greenland. We thus expect the experiment to provide valuable insights regarding Greenland’s potential contribution to future sea-level rise and the associated ice sheet – climate interactions or feedbacks.  

How to cite: Paice, C. M., Fettweis, X., and Huybrechts, P.: Quantifying the response of the Greenland ice sheet in a high-end scenario until 2300 from a coupled high-resolution regional climate and ice sheet model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12281, https://doi.org/10.5194/egusphere-egu23-12281, 2023.

EGU23-13350 | Orals | CR3.2

Large effects of ocean circulation change on Greenland ice sheet mass loss 

Miren Vizcaino, Julia Rudlang, Laura Muntjewerf, Sotiria Georgiou, Raymond Sellevold, and Michele Petrini

The Greenland ice sheet (GrIS) is currently losing mass at an accelerated rate, due to atmospheric and ocean warming causing respectively enhanced melt and ice discharge to the ocean. A large part of the uncertainty on future GrIS contribution to sea level rise relates to unknown atmospheric and ocean circulation change. For the later, AR6 models project a weakening of the North Atlantic Meridional Overturning Circulation (NAMOC) during the 21st century. The magnitude of this weakening depends on the greenhouse gas scenario and model, but none of the models project a complete collapse.

Projections of future GrIS evolution in the last IPCC report AR6 are mostly based on simulations with ice sheet models forced with the output of climate models (e.g., Goelzer et al. (2020)). This method permits large ensembles of simulations, however the coupling between climate and GrIS is not represented. Here, we use a coupled Earth System and Ice Sheet Model (ESM-ISM), the CESM2-CISM2 (Muntjewerf et al. 2021) to examine the multi-millennial evolution of the GrIS surface mass balance for a middle-of-the-road CO2 scenario. The model couples realistic simulation of global climate (Danabasoglu et al. 2020), surface processes (van Kampenhout et al. 2020) and ice dynamics (Lipscomb et al. 2019). We use an idealized scenario of 1% CO2 increase until stabilization at two times pre-industrial values.  compare our results with pre-industrial and 1% to 4xCO2 simulations (Muntjewerf et al. 2020).

We find small increases and even reduction of annual temperatures in the GrIS area in connection with strong NAMOC weakening in the first two centuries of simulation. Summer temperatures and surface melt increase moderately with respect to pre-industrial. From simulation year 500, the NAMOC recovers, resulting in strong increases in GrIS melt rates and contribution to sea level rise. We compare the deglaciation pattern over a period of 3,000 years with deglaciation simulations with the same model for the last interglacial (Sommers et al. 2021).

 

How to cite: Vizcaino, M., Rudlang, J., Muntjewerf, L., Georgiou, S., Sellevold, R., and Petrini, M.: Large effects of ocean circulation change on Greenland ice sheet mass loss, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13350, https://doi.org/10.5194/egusphere-egu23-13350, 2023.

EGU23-13907 | ECS | Orals | CR3.2

First results of RACMO2.4: A new model version with updated surface and atmospheric processes 

Christiaan van Dalum and Willem Jan van de Berg

In recent years, considerable progress in surface and atmospheric physics parameterizations has been made by the scientific community that could benefit regional climate modelling of polar regions. Therefore, we developed a major update to the Regional Atmospheric Climate Model, referred to as RACMO2.4, that includes several new and updated parameterizations. Most importantly, the surface and atmospheric processes from the European Center for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), which are embedded in RACMO, are updated to cycle 47r1. This includes, among other changes, updates in the cloud, aerosol and radiation scheme, a new lake model, and a new multilayer snow module for non-glaciated regions. Furthermore, a new spectral albedo and radiative transfer scheme in snow scheme, which has been introduced and evaluated in a previous, yet inoperative version, is now operational. Here, we shortly introduce the aforementioned changes and present the first results of RACMO2.4 for several domains, particularly of the Greenland ice sheet.

How to cite: van Dalum, C. and van de Berg, W. J.: First results of RACMO2.4: A new model version with updated surface and atmospheric processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13907, https://doi.org/10.5194/egusphere-egu23-13907, 2023.

EGU23-14088 | Posters on site | CR3.2

Reconstructing the Greenland ice sheet in past warm climates 

Christine S. Hvidberg, Mikkel Lauritzen, Nicholas M. Rathmann, Anne M. Solgaard, and Dorthe Dahl-Jensen

The stability of the Greenland ice sheet through past glacial-interglacial cycles provides knowledge that can contribute to understanding the future mass loss and contribution to sea level from the Greenland ice sheet in a warmer climate. Paleo-climatic records from ice cores provide constraints on the past climate and ice sheet thickness in Greenland through the current interglacial, the Holocene, 11.7 kyr to present, but is limited to a few ice cores from the central areas. In the previous interglacial period, the Eemian, 130 kyr to 110 kyr before present, the ice core constraints are sparse, and beyond the Eemian, the climate evolution is known from Antarctic ice cores and marine sediments. The limited constraints on the past climate in Greenland presents a challenge for reconstructions based on ice flow modelling. Here we present initial results from an ice flow modelling study using the PISM ice flow model to simulate the evolution of the Greenland ice sheet in the Eemian and the Holocene periods. We discuss how paleo-climatic data from ice cores and marine sediments can be combined with ice flow modelling. We find that the Greenland ice sheet retreated to a minimum volume of up to ∼1.2 m sea-level equivalent smaller than present in the early or mid-Holocene, and that the ice sheet has continued to recover from this minimum up to present day. In all our runs, the ice sheet is approaching a steady state at the end of the 20th century. Our studies show that the Greenland ice sheet evolves in response to climate variations on shorter and longer timescales, and that assessment of future mass loss must take into account the history and current state.

How to cite: Hvidberg, C. S., Lauritzen, M., Rathmann, N. M., Solgaard, A. M., and Dahl-Jensen, D.: Reconstructing the Greenland ice sheet in past warm climates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14088, https://doi.org/10.5194/egusphere-egu23-14088, 2023.

EGU23-14236 | ECS | Orals | CR3.2

Sensitivity of future projections of ice sheet retreat to initial conditions 

Tijn Berends, Jorjo Bernales, Caroline van Calcar, and Roderik van de Wal

Both the Greenland and Antarctic ice sheets are expected to experience substantial mass loss in the case of unmitigated anthropogenic climate change. The exact rate of future mass loss under high warming scenarios remains uncertain, depending strongly on physical quantities that are difficult to constrain from observations, such as basal sliding and sub-shelf melt. We apply a novel model initialisation protocol, that combines elements from existing approaches such as the equilibrium spin-up, basal inversion, and palaeo spin-up, to models of both the Greenland and Antarctic ice sheets. We show the results in term of sea-level projections including the uncertainties, under different warming scenarios, following the ISMIP6 protocol.

This abstract is a companion to “On the initialisation of ice sheet models: equilibrium assumptions, thermal memory, and present-day states” by Bernales et al. We hope that, if both abstracts are lucky enough to be accepted, the conveners can program the two talks in sequence.

How to cite: Berends, T., Bernales, J., van Calcar, C., and van de Wal, R.: Sensitivity of future projections of ice sheet retreat to initial conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14236, https://doi.org/10.5194/egusphere-egu23-14236, 2023.

EGU23-14412 | ECS | Orals | CR3.2

Self-adaptive Laurentide Ice Sheet evolution towards the Last Glacial Maximum 

Lu Niu, Gregor Knorr, Uta Krebs-Kanzow, Paul Gierz, and Gerrit Lohmann

Northern Hemisphere summer insolation is regarded as a main control factor of glacial-interglacial cycles. However, internal feedbacks between ice sheets and other climate components are non-negligible. Here we apply a state-of-the-art Earth system model (AWI-ESM) asynchronously coupled to the ice sheet model PISM, focusing on the period when ice sheet grows from an intermediate state (Marine isotope stage 3, around 38 k) to a maximum ice sheet state (the Last Glacial Maximum). Our results show that initial North American ice sheet differences at 38 k are erased by feedbacks between atmospheric circulation and ice sheet geometry that modulate the ice sheet development during this period. Counter-intuitively, moisture transported from the North Atlantic warm pool during summer is the main controlling factor for the ice sheet advance. A self-adaptative mechanism is proposed in the development of a fully-grown NA ice sheet which indicates how the Earth system stabilizes itself via interactions between different Earth System components.

How to cite: Niu, L., Knorr, G., Krebs-Kanzow, U., Gierz, P., and Lohmann, G.: Self-adaptive Laurentide Ice Sheet evolution towards the Last Glacial Maximum, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14412, https://doi.org/10.5194/egusphere-egu23-14412, 2023.

EGU23-14469 | ECS | Orals | CR3.2 | Highlight

Has the (West) Antarctic Ice Sheet already tipped? 

Ronja Reese, Julius Garbe, Emily A. Hill, Benoît Urruty, Kaitlin A. Naughten, Olivier Gagliardini, Gael Durand, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, David Chandler, Petra M. Langebroek, and Ricarda Winkelmann

Observations of ocean-driven grounding line retreat in the Amundsen Sea Embayment in Antarctica raise the question of an imminent collapse of the West Antarctic Ice Sheet. Here we analyse the committed evolution of Antarctic grounding lines under the present-day climate. To this aim, we run an ensemble of historical simulations with a state-of-the-art ice sheet model to create model instances of possible present-day ice sheet configurations. Then, we extend the simulations to investigate their evolution under constant present-day climate forcing and bathymetry. We test for reversibility of grounding line movement at different stages of the simulations to analyse when and where irreversible grounding line retreat, or tipping, is initiated.

How to cite: Reese, R., Garbe, J., Hill, E. A., Urruty, B., Naughten, K. A., Gagliardini, O., Durand, G., Gillet-Chaulet, F., Gudmundsson, G. H., Chandler, D., Langebroek, P. M., and Winkelmann, R.: Has the (West) Antarctic Ice Sheet already tipped?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14469, https://doi.org/10.5194/egusphere-egu23-14469, 2023.

EGU23-14648 | ECS | Orals | CR3.2

On the initialisation of ice sheet models: equilibrium assumptions, thermal memory, and present-day states 

Jorjo Bernales, Tijn Berends, Caroline van Calcar, and Roderik van de Wal

A significant portion of the spread in future projections of ice sheet volume changes is attributed to uncertainties in their present-day state, and the way this state is represented in ice-sheet models. The scientific literature already contains a variety of classic initialisation approaches used by modelling groups around the globe, each with its own advantages and limitations. We propose a generalised protocol that allows for the quantification of the impact of individual initialisation choices, such as steady-state assumptions, the inclusion of internal paleoclimatic thermal signals, sea level and glacial isostatic effects, and calibration methods. We then apply this protocol to an ensemble of multi-millennia model spin-ups of the present-day Greenland and Antarctic ice sheets and show the importance of the choices made during initialisation.

[This abstract is a companion to “Sensitivity of future projections of ice sheet retreat to initial conditions” by Berends et al. We hope that, if both abstracts are lucky enough to be accepted, the conveners can program the two talks in sequence.]

How to cite: Bernales, J., Berends, T., van Calcar, C., and van de Wal, R.: On the initialisation of ice sheet models: equilibrium assumptions, thermal memory, and present-day states, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14648, https://doi.org/10.5194/egusphere-egu23-14648, 2023.

EGU23-14666 | Orals | CR3.2

Sensitivity of of coupled climate and ice sheet of modern Greenland to atmospheric, snow and ice sheet parameters 

Charlotte Lang, Tamsin Edwards, Jonathan Owen, Sam Sherriff-Tadano, Jonathan Gregory, Ruza Ivanovic, Lauren Gregoire, and Robin S. Smith

As part of a project working to improve coupled climate-ice sheet modelling by studying the response of ice sheets to changes in climate across different periods since the Last Glacial Maximum, we present an analysis of an ensemble of coupled climate and ice sheet simulations of the modern Greenland using the FAMOUS-BISICLES model and statistical emulation.

FAMOUS-BISICLES, a variant of FAMOUS-ice (Smith et al., 2021a), is a low resolution (7.5°X5°) global climate model that is two-way coupled to a higher resolution (minimum grid spacing of 1.2 km) adaptive mesh ice sheet model, BISICLES. It uses a system of elevation classes to downscale the lower resolution atmospheric variables onto the ice sheet grid and calculates surface mass balance using a multilayer snow model. FAMOUS-ice is computationally affordable enough to simulate the millennial evolution of the coupled climate-ice sheet system as well as to run large ensembles of simulations. It has also been shown to simulate Greenland well in previous work using the Glimmer shallow ice model (Gregory et al., 2020).

The ice sheet volume and area are sensitive to a number of parametrisations related to atmospheric and snow surface processes and ice sheet dynamics. Based on that, we designed a perturbed parameters ensemble using a Latin Hypercube sampling technique and ran simulations with climate forcings appropriate for the late 20th century.

Gaussian process emulation allows us explore parameter space in a more systematic and faster way than with more complex earth system models and make predictions at input parameter values that are not evaluated in the simulations. We find that the mass balance is most correlated to three parameters:

  • n, the exponent in Glen’s flow law, and beta, the coefficient of the basal drag law, both influencing the amount of ice lost through discharge
  • rho_threshold, a parameter setting the minimum value the dense firn albedo can possibly reach

Finally, using a history matching approach, we built an implausibility metric (based on surface mass balance, ice volume loss, near-surface and sea-surface temperature) to identify the regions of the parameter space that produce plausible runs.

How to cite: Lang, C., Edwards, T., Owen, J., Sherriff-Tadano, S., Gregory, J., Ivanovic, R., Gregoire, L., and Smith, R. S.: Sensitivity of of coupled climate and ice sheet of modern Greenland to atmospheric, snow and ice sheet parameters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14666, https://doi.org/10.5194/egusphere-egu23-14666, 2023.

EGU23-15230 | Posters on site | CR3.2

Antarctic RINGS to characterize the Antarctic Ice Sheet coastal zone and Antarctic contribution to the global sea-level rise 

Kenichi Matsuoka, Xiangbin Cui, Fausto Ferraccioli, Rene Forsberg, Tom Jordan, Felicity McCormack, Geir Moholdt, and Kirsty Tinto and the Antarctic RINGS

Regions where the Antarctic Ice Sheet reaches the coast are fundamental to our understanding of the linkages between Antarctica and the global climate system. These coastal regions contain multiple potential tipping points for the Antarctic Ice Sheet in the ongoing 2oC warming world, which must be better understood to predict future sea-level rise. The Antarctic Ice Sheet constitutes the largest uncertainty source in future sea-level projections, and this uncertainty is mainly rooted in poorly known bed topography under the ice sheet. Bed topography matters the most in the coastal regions as it controls the stability of the ice sheet. Together with an overview of the current multidisciplinary understandings of the Antarctic coastal regions, we present ensemble analysis of published datasets to present data and knowledge gaps, and their regional distribution is discussed in the context of ice-sheet evolution and instability. Finally, we identify outstanding science priorities and discuss protocols of airborne surveys to develop a comprehensive dataset uniformly all-around Antarctica.

How to cite: Matsuoka, K., Cui, X., Ferraccioli, F., Forsberg, R., Jordan, T., McCormack, F., Moholdt, G., and Tinto, K. and the Antarctic RINGS: Antarctic RINGS to characterize the Antarctic Ice Sheet coastal zone and Antarctic contribution to the global sea-level rise, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15230, https://doi.org/10.5194/egusphere-egu23-15230, 2023.

EGU23-15361 | ECS | Posters virtual | CR3.2

Sea ice extent and subsurface temperatures in the Labrador Sea across Heinrich events during MIS 3 

Henrieka Detlef, Mads Mørk Jensen, Rasmus Andreasen, Marianne Glasius, Marit-Solveig Seidenkrantz, and Christof Pearce

Heinrich events associated with millennial-scale climate oscillations during the last glacial period are prominent events of ice-sheet collapse, characterized by the dispersal of ice(berg) rafted debris and freshwater across the North Atlantic. Hudson Strait has been suggested as one of the predominant iceberg source regions. One potential mechanism triggering iceberg release invokes cryosphere-ocean interactions, where subsurface warming destabilizes the Laurentide ice sheet. Subsurface warming is facilitated by the expansion of sea ice in the Labrador Sea in combination with a slow down of the Atlantic Meridional Overturning Circulation, which prevents the release and downward mixing of heat in the water column.

Here we present high-resolution reconstructions of sea ice dynamics in the outer Labrador Sea at IODP Site U1302/03 between 30 ka and 60 ka. Sea ice reconstructions are based on a suite of sympagic and pelagic biomarkers, including highly branched isoprenoids and sterols. The results suggest a transition from reduced/seasonal to extended/perennial sea ice conditions preceding the onset of iceberg rafting associated with Heinrich event 3, 4, 5, and 5a by ~0.9 ± 0.5 ka. Ongoing work on the same core and sample material will have to confirm the timing and extent of subsurface warming compared to sea ice advances. 

How to cite: Detlef, H., Mørk Jensen, M., Andreasen, R., Glasius, M., Seidenkrantz, M.-S., and Pearce, C.: Sea ice extent and subsurface temperatures in the Labrador Sea across Heinrich events during MIS 3, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15361, https://doi.org/10.5194/egusphere-egu23-15361, 2023.

EGU23-16930 | ECS | Orals | CR3.2 | Highlight

Multistability and transient response of the Greenland ice sheet to anthropogenic CO2 emissions 

Dennis Höning, Matteo Willeit, and Andrey Ganopolski

Ongoing CO2 emissions into the atmosphere and associated temperature rise have dramatic consequences for the ice sheets on our planet. In this presentation, we focus on the Greenland ice sheet, which holds so much ice that a complete melting would cause the global sea level to rise by seven meters. However, a prediction of future mass loss of the Greenland ice sheet is challenging because it is a strongly non-linear function of temperature and occurs over very long timescales. With the fully coupled Earth system model of intermediate complexity CLIMBER-X, we study the stability of the Greenland ice sheet and its transient response to CO2 emissions over the next 20 kyr. We find two bifurcation points within a global mean surface air temperature anomaly of 1.5°C. Each of these bifurcation points corresponds to a critical ice volume. If the Greenland ice sheet volume decreases below these critical values, returning to a previous atmospheric CO2 concentration would not cause the ice sheet to grow back to its previous state. We also find increased mass loss rates and increased sensitivity of mass loss to cumulative CO2 emission in the vicinity of these critical ice volumes. Altogether, our results suggest that global warming near the lower 1.5°C limit of the Paris agreement would already cause the Greenland ice sheet to irreversibly melt, although a complete melting would take thousands of years.

How to cite: Höning, D., Willeit, M., and Ganopolski, A.: Multistability and transient response of the Greenland ice sheet to anthropogenic CO2 emissions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16930, https://doi.org/10.5194/egusphere-egu23-16930, 2023.

EGU23-81 | ECS | Orals | CR3.3

A model of the weathering crust and microbial activity on an ice-sheet surface 

Tilly Woods and Ian Hewitt

Shortwave radiation penetrating beneath an ice-sheet surface can cause internal melting and the formation of a near-surface porous layer known as the weathering crust, a dynamic hydrological system that provides home to impurities and microbial life. We develop a mathematical model, incorporating thermodynamics and population dynamics, for the evolution of such layers. The model accounts for conservation of mass and energy, for internal and surface-absorbed radiation, and for logistic growth of a microbial species mediated by nutrients that are sourced from the melting ice. It also accounts for potential melt-albedo and microbe-albedo feedbacks, through the dependence of the absorption coefficient on the porosity or microbial concentration. We investigate one-dimensional steadily melting solutions of the model, which give rise to predictions for the weathering crust depth, water content, melt rate, and microbial abundance, depending on a number of parameters. In particular, we examine how these quantities depend on the forcing energy fluxes, finding that the relative amounts of shortwave (surface penetrating) radiation and other heat fluxes are particularly important in determining the structure of the weathering crust. The results explain why weathering crusts form and disappear under different forcing conditions, and suggest a range of possible changes in behaviour in response to climate change. Time-dependent solutions of the model will also be discussed.

How to cite: Woods, T. and Hewitt, I.: A model of the weathering crust and microbial activity on an ice-sheet surface, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-81, https://doi.org/10.5194/egusphere-egu23-81, 2023.

EGU23-269 | ECS | Posters on site | CR3.3

Rain-induced transient variations in glacier dynamics characterized by a continuous and dense GPS network at the Glacier d’Argentière 

Anuar Togaibekov, Andrea Walpersdorf, and Florent Gimbert

The motion of glaciers with a temperate base is highly variable in time and space, mainly as a result of glacier basal sliding being strongly modulated by subglacial hydrology. Although transient friction laws have recently been established in order to predict short-term sliding velocity changes in response to water input changes, yet little observations enable fully constraining these laws. Here we investigate short-term changes in glacier dynamics induced by transient rainwater input on the Glacier d’Argentière (French Alps) using up to 13 permanent GPS stations. We observe strong surface acceleration events materialized by maximum downglacier velocities on the order of 2 to 3 times background velocities and associated with significant glacier surface uplift of 0.03 m to 0.1 m. We demonstrate that uplift strikingly coincides with water discharge. In contrast, horizontal speed-up occurs over a timescale shorter than discharge and uplift changes, with a maximum occurring concomitantly with maximum water pressure but prior to maximum discharge or uplift. Our findings suggest that transient acceleration and uplift of the glacier are not necessarily modulated by the same mechanism. We also observe that the horizontal speed-ups propagate downglacier at migrating speeds of 0.04 m s-1 to 0.13 m s-1, suggesting an underlying migration of subglacial water flows through the inefficient, distributed system. We demonstrate that the temporal relationship between water discharge, water pressure, and three-dimensional glacier motions are complex and cannot be directly interpreted by changes in the subglacial water pressure through cavity formation and water storage. 

How to cite: Togaibekov, A., Walpersdorf, A., and Gimbert, F.: Rain-induced transient variations in glacier dynamics characterized by a continuous and dense GPS network at the Glacier d’Argentière, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-269, https://doi.org/10.5194/egusphere-egu23-269, 2023.

EGU23-1820 | Posters on site | CR3.3

Subglacial drainage across Kamb Ice Stream’s Grounding Zone, West Antarctica. 

Huw Horgan, Gavin Dunbar, Christine Hulbe, Britney Schmidt, Craig Stevens, Craig Stewart, and Mauro Werder and the KIS2 Science Team

Kamb Ice Stream in West Antarctica is a poster child for the natural variability of ice sheet flow. This major ice stream ceased flowing approximately 160 years ago and mass gain in its catchment currently offsets a significant portion of the mass loss occurring elsewhere in West Antarctica. Hypotheses explaining why Kamb shut down include changes in water routing at the ice stream bed. Here I report on our exploration of the main subglacial drainage channel crossing Kamb’s grounding zone and entering the ocean cavity beneath the Ross Ice Shelf. We find that the subglacial channel transitions into a large sub ice shelf channel. Oceanographic observations detect subglacial discharge within the channel, although the channel shape and surface elevation change suggest greater discharge rates in the past. Sediment coring of the channel substrate shows evidence of repeated high-velocity discharge events. The provenance of these sediments, combined with subglacial routing constraints indicate the subglacial catchment varies in time. Together with observations of surface change, these findings indicate that the subglacial hydrologic network beneath Kamb Ice Stream varies temporally, with background flow punctuated by fast flow events, and also changes spatially, spanning catchments of variable size.   

How to cite: Horgan, H., Dunbar, G., Hulbe, C., Schmidt, B., Stevens, C., Stewart, C., and Werder, M. and the KIS2 Science Team: Subglacial drainage across Kamb Ice Stream’s Grounding Zone, West Antarctica., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1820, https://doi.org/10.5194/egusphere-egu23-1820, 2023.

EGU23-2279 | ECS | Orals | CR3.3

Modelling lateral meltwater flow atop the GreenlandIce Sheet’s near-surface ice slabs 

Nicole Clerx and Horst Machguth

The Greenland ice sheet is losing mass. Thereby, the location of the runoff limit, the highest elevation from which meltwater finds its way off the ice sheet, plays an important role. Above the runoff limit all meltwater refreezes and does not contribute to mass loss. In recent years surface runoff has increasingly occurred from higher elevations, thereby expanding the area of mass loss: between 1985 and 2020, the maximum runoff limit rose by on average 194 metres, expanding the visible runoff area by around 29%.

The observed rise in the runoff limit might be related to intensive meltwater refreezing within the firn which leads to the formation of thick ice layers, also called ice slabs. Our field experiments, carried out at around 1750 m a.s.l. on the K-Transect, have shown that meltwater generated over ice slabs is generally forced to flow laterally: initially through a near-surface slush matrix and then forming streams and rivers. It remains unclear, however, how much of the meltwater contributes to runoff, and which percentage refreezes and contributes to ice slab formation or expansion.

Here we present a conceptual quasi 2D-model of runoff, that simulates lateral meltwater flow on top of an ice slab using firn hydrological properties measured on the southwest Greenland ice sheet. We adapted a gridded linear-reservoir runoff routing model to calculate (i) the distance meltwater can travel within one melt season, and (ii) when meltwater breakthrough at the snow surface (i.e. slush formation) occurs. First results provide insight into the evolution of the water table height over time that matches observations made during our summer field campaign. We are exploring ways to incorporate meltwater refreezing, to better understand ice slab evolution and their impact on the fate of meltwater between vertical percolation, refreezing and lateral runoff.

How to cite: Clerx, N. and Machguth, H.: Modelling lateral meltwater flow atop the GreenlandIce Sheet’s near-surface ice slabs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2279, https://doi.org/10.5194/egusphere-egu23-2279, 2023.

EGU23-3558 | ECS | Orals | CR3.3

Temporal activity of subglacial channels around the grounding line of Roi Baudouin Ice Shelf, from ice-penetrating radar 

Yan Zhou, Steven Franke, Thomas Kleiner, Reinhard Drews, Angelika Humbert, Daniela Jansen, and Olaf Eisen

The existence of ice-shelf basal channels has a significant impact on both buttressing ability and basal melting of ice shelves in Antarctica. Although they can provide a unique perspective of processes for the mass transfer from grounded ice sheet to floating ice shelf, their origination and evolution are still not fully understood.

Here we used airborne and ground-based radar to investigate the subglacial channel features at the grounding line (GL). We determined the geometry of five channels (two of which are characterized for the first time) in a set of flow-perpendicular radar profiles, also perpendicular to the channels. We found that the evolution from grounded subglacial channels to the ice-shelf basal channels mainly goes through three stages: (1) The grounded subglacial channels appear 4 to 5 km upstream of the GL and their incision into the ice sheet increases while approaching the GL; (2) as the subglacial channels extend into the grounding zone (1 to 2 km downstream of GL), their inner walls started melting, also they keep their roof-top features; (3) on the shelf interaction with the ocean, surface accumulation and ice dynamics further lead to flattening and widening, progressively turning them into generally known ice-shelf basal channels.

Additionally to radar observations, we investigated the role of subglacial hydrology with two modelling approach (subglacial water flux with CUAS-MPI and water routing with CiDRE). A comparison shows that most channel locations in the radar profiles match with areas of higher subglacial water presence, consequently implying that subglacial water flux could mainly be responsible for maintaining the presence of subglacial channels.

Based on the already earlier proposed relation that the presence of a sub-ice shelf basal channel is linked to a corresponding channel at the GL, we identify one now active channel at the GL to be related to one which was earlier until 59 years ago. This indicates that basal channels and consequently basal water flux across the GL can change at least on the scale of centuries. Our observed reactivation of a subglacial channel confirms the suitability of basal channels in ice shelves to be used as proxies of past subglacial hydrological activities and other potentially larger events.

How to cite: Zhou, Y., Franke, S., Kleiner, T., Drews, R., Humbert, A., Jansen, D., and Eisen, O.: Temporal activity of subglacial channels around the grounding line of Roi Baudouin Ice Shelf, from ice-penetrating radar, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3558, https://doi.org/10.5194/egusphere-egu23-3558, 2023.

EGU23-3647 | Posters on site | CR3.3

Significant Temporal and Spatial Differences in Greenland Ice Sheet Surface and Subsurface Meltwater Persistence Revealed by Multi-Frequency Radiometry 

Andreas Colliander, Mohammad Mousavi, John Kimball, Julie Miller, and Mariko Burgin

Increasingly more significant portions of the Greenland ice sheet are undergoing seasonal melting-refreeze cycles due to climate warming. The process begins with the arrival of warm temperatures and increased solar radiation in the spring and summer seasons generating meltwater on the ice sheet’s surface. Meltwater percolates to deeper ice layers, either refreezing within the firn, creating longer-term meltwater pockets (firn aquifers), or generating peripheral runoff. Depending on the location and climate, the refreeze duration, the depth of infiltration, and meltwater persistence are temporally and spatially complex. Multi-frequency passive microwave measurements in the 1.4 GHz to 36.5 GHz range can distinguish seasonal meltwater between the immediate surface and the deeper firn layers, as demonstrated at experiment sites on the Greenland ice sheet. Here we explored the multi-frequency melt response at the pan-Greenland scale. We employed 1.4 GHz brightness temperature (TB) measurements from the NASA Soil Moisture Active Passive (SMAP) satellite and 6.9, 10.7, 18.9, and 36.5 GHz TB measurements from the JAXA Global Change Observation Mission-Water Shizuku (GCOM-W) satellite. The results show that the frequency-dependent response was consistent across the ice sheet. The multi-frequency melt indications match with lasting seasonal subsurface meltwater with delayed refreezing compared to the surface. These results suggest persistent seasonal subsurface meltwater occurrences that are spatially and temporally significant but concealed from the high-frequency observations. Similar to the surface melt with significant interannual variations, the results show that the subsurface meltwater cycle exhibits substantial spatial and temporal variations from year to year.

How to cite: Colliander, A., Mousavi, M., Kimball, J., Miller, J., and Burgin, M.: Significant Temporal and Spatial Differences in Greenland Ice Sheet Surface and Subsurface Meltwater Persistence Revealed by Multi-Frequency Radiometry, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3647, https://doi.org/10.5194/egusphere-egu23-3647, 2023.

EGU23-3843 | ECS | Orals | CR3.3

Mapping moulins on the southwestern Greenland Ice Sheet 

Yuhan Wang, Kang Yang, Jinyu Liu, Wensong Zhang, Mengtian Man, and Dinghua Chen

Moulins are vertical meltwater pathways formed by hydro-fracturing of crevasses or supraglacial lakes. On the southwestern Greenland Ice Sheet (GrIS), moulins can efficiently drain surface meltwater into the ice sheet, drive subglacial drainage system development, control variations in subglacial water pressure, and eventually influence ice motion. Thereby, moulins are essential to understand the connection between supraglacial, englacial and subglacial drainage systems. However, most of existing studies focused on moulins in small areas or short time periods, leaving moulin spatiotemporal variations still unclear. In this study, we extracted moulins on the southwestern GrIS from 2016 to 2021 using multi-temporal Sentinel-2 satellite images and ArcticDEM, and analyzed their interannual spatiotemporal variations. Results show that: (1) Moulin distribution varies significantly in different years. In general, moulin numbers increase linearly with the rise of accumulative annual meltwater runoff, while the terminal supraglacial lake numbers decreased exponentially. (2) The annual moulin distribution can affect the distributions in subsequent years, and the smaller the intervals, the stronger the effect. (3) There are significant variations in the meltwater runoff draining into the ice sheet in different years, especially in high elevation region (>1600 m).

How to cite: Wang, Y., Yang, K., Liu, J., Zhang, W., Man, M., and Chen, D.: Mapping moulins on the southwestern Greenland Ice Sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3843, https://doi.org/10.5194/egusphere-egu23-3843, 2023.

EGU23-3994 | ECS | Orals | CR3.3

Vulnerability of Firn to Hydrofracture, Part I: Poromechanical Modeling 

Yue Meng, Riley Culberg, and Ching-Yao Lai

Ice slabs are multi-meter thick layers of solid reforzen ice that form on top of the porous firn layer in Greenland’s wet snow zone. Recent observations in Northwest Greenland highlight the ability of this relict firn layer to store meltwater in its pores after surface meltwater drains rapidly through cracks in the overlying ice slab. Current fracture mechanics (i.e., LEFM) assumes that the stored elastic energy in an impermeable solid matrix is instantaneously dissipated by creating new crack surfaces, which only holds for impermeable solid media. To better understand the fate of meltwater in the porous firn layer beneath ice slabs, we develop a two-dimensional, poroelastic continuum model to quantify the stress and pressure changes in the porous firn during meltwater penetration.

We extend Biot’s poroelastic theory to two-phase immiscible flow by introducing meltwater saturation as an extra variable. By coupling the fluid continuity and force balance equations, we resolve the spatiotemporal evolution of 1) matrix deformations and effective stresses, 2) the water saturation field, and 3) the water pressure field. We adopt a fracture criterion for the cohesive porous firn layer: the maximum tensile effective stress should exceed the material tensile strength to generate fractures. We study the maximum tensile effective stress induced by water injection as a function of firn’s mechanical and hydraulic properties (bulk modulus, porosity, and permeability), and the infiltration conditions (constant infiltration pressure or flow rate). Our results show that the maximum tensile effective stress in the firn layer is no more than a quarter of that predicted for an equivalent solid ice column, because the imposed load is mostly transmitted into the pore pressure. Therefore our model predicts that surface-to-bed hydrofracture is unlikely to form if meltwater can leak into the firn layer. In “Vulnerability of Firn to Hydrofracture, Part II: Greenland’s Ice Slab Regions”, we apply this model to assess the vulnerability of Greenland’s ice slab regions.

How to cite: Meng, Y., Culberg, R., and Lai, C.-Y.: Vulnerability of Firn to Hydrofracture, Part I: Poromechanical Modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3994, https://doi.org/10.5194/egusphere-egu23-3994, 2023.

EGU23-3996 | ECS | Posters on site | CR3.3

Vulnerability of Firn to Hydrofracture, Part II: Greenland’s Ice Slab Regions 

Riley Culberg, Yue Meng, and Ching-Yao Lai

Hydrofracture and rapid lake drainage can transport surface meltwater to the bed of the Greenland Ice Sheet, thereby coupling surface mass balance processes and dynamic mass loss. Vertical hydrofracture is widely assumed to occur in the ablation zone, where abundant surface runoff that can fill fractures. However, in Greenland, the runoff line has expanded into the accumulation zone due to the development of ice slabs in the firn. It remains unclear whether surface runoff from these ice slab regions also drains locally to the bed. Recent observations in Northwest Greenland suggest that when meltwater penetrates ice slabs via surface fractures, it leaks off into a relict firn layer and does not initiate unstable vertical hydrofracture that propagates throughout the ice thickness. At the same time, buried supraglacial lakes have been observed to drain to the ice sheet bed this same region. Therefore, to assess the mass balance impact of ice slab expansion, it is important to understand if and when surface-to-bed hydrofracture may occur in these regions.

In “Vulnerability of Firn to Hydrofracture, Part I: Poromechanical Modeling”, we developed an analytic expression for the maximum tensile effective stress within the firn layer beneath a water-filled fracture in an ice slab. Here we apply this model to Greenland’s ice slab regions. We use an ensemble of in situ and remote sensing observations to constrain the physical, mechanical, and hydraulic parameters in our model. We then run a Monte Carlo analysis to constrain the physically-plausible range of maximum tensile effective stress in the firn for two scenarios: a water-filled crevasse in an ice slab or a supraglacial lake over a fractured ice slab. Our results show that the maximum stress in the firn layer is always less than in an equivalent solid ice column, and typically remains compressive, because the imposed load is partially accommodated by a change in pore pressure. An overlying lake further stabilizes the system by increasing the lithostatic stress that acts to close the fracture. Therefore, in Greenland, the relict firn layer can be an important stabilizing factor that suppresses surface-to-bed hydrofracture under ice slabs, despite the abundance of both surface crevassing and meltwater.

How to cite: Culberg, R., Meng, Y., and Lai, C.-Y.: Vulnerability of Firn to Hydrofracture, Part II: Greenland’s Ice Slab Regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3996, https://doi.org/10.5194/egusphere-egu23-3996, 2023.

EGU23-4220 | ECS | Posters on site | CR3.3

Validation of a subglacial hydrology model with analytical and semi-analytical solutions 

Jeremie Schmiedel and Roiy Sayag

Subglacial hydrology is a key component in ice sheet dynamics. The presence of subglacial water at the ice-bed interface can significantly reduce basal friction and facilitate rapid ice flow, which could lead to the formation of surges and ice streams. The action of the subglacial network on ice flow is simulated by a range of numerical models. Validating these models is essential to ensure that the important physical processes are included, and that the numerical methods provide accurate solutions. In addition to field measurements, which are challenging to obtain at the ice-bed interface, a common validation technique includes inter-comparison with other numerical models. Even though such a technique is beneficial, it does not provide an equivalent validation as exact solutions. Here we present analytical and semi-analytical solutions for confined and unconfined flows in porous layers to validate subglacial hydrology models, which are based on an effective porous medium (EPM) approach. We then apply them to validate the confined-unconfined aquifer scheme (CUAS) model. We find that the numerical results are consistent with the analytical solutions, which provides more confidence that CUAS accurately solves the hydrology equations. Because of their generality, our solutions are readily applicable to other subglacial hydrology models that are based on an EPM approach. We anticipate that a validated hydrology model with our solutions can achieve more credible results in subglacial network simulations.

How to cite: Schmiedel, J. and Sayag, R.: Validation of a subglacial hydrology model with analytical and semi-analytical solutions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4220, https://doi.org/10.5194/egusphere-egu23-4220, 2023.

EGU23-4630 | Posters on site | CR3.3

Imaging the structures of a subglacial lake D2 in the Antarctic David Glacier catchment from the multi-channel reflection seismic records 

Seung-Goo Kang, Hyeon Tae Ju, Yeonjin Choi, Hoje Kwak, Joohan Lee, Yeadong Kim, Jong Kuk Hong, and Jong Ik Lee

                  David Glacier is a significant East Antarctic outlet glacier through the Transantarctic Mountains and into the western Ross Sea. The six active subglacial lakes (D1~D6) were identified in the David Glacier catchment based on NASA’s Ice, Cloud, and land Elevation Satellite (ICESat) laser altimeter dataset for 4.5 years (2003-2008). Since 2016, Korea Polar Research Institute (KOPRI) has been preparing the hot-water drilling project in the David Glacier, starting with the geophysical surveys. KOPRI’s geophysical research team confirmed the change rate of the surface elevation of the David glaciers based on the analysis of the Double-Differential Interferometric synthetic aperture radar (DDinSAR) from satellite (Sentinel-1A) images for July to August 2015 and March 2017, then selected the D2 subglacial lake as the target for the potential hot-water drilling project. KOPRI’s Antarctic traverse team developed ground routes for logistics from the JBS to the D2 lake site in the 2017-18 season. In the 2018-19 season, a radar survey was conducted on the D2 site, and the lake's global structures and scale were confirmed. Then, in the 2021-2022 season, a multi-channel seismic survey was conducted on the D2 site to image the detailed subglacial structures of the lake. The final goal of this seismic survey is to get information on the optimal site selection for the hot-water-drilling location for subglacial sampling.  The seismic survey was performed for about two months on the ice. Dynamite is used to generating the seismic source; 1.6 kg of dynamites were used per the charging hole. The charging depth is 25 m. 90 m and 180 m shot intervals were used for 8- and 4-fold data acquisition. Four sets of the Geometric Geode and a 96-channel GEOROD system were employed to record the seismic signal from the ice. The group spacing of the receiver (GEOROD) is 15 m. The seismic data were recorded for 4 seconds with two milliseconds sampling rates. The total length of the acquired seismic data is 17.2 km, consisting of 4 survey lines: two south-to-north and two east-to-west lines. The maximum and minimum fold numbers are 8 and 4, respectively. We got high-quality seismic migrated images containing actual structural and geophysical information about the subglacial lake through seismic data processing with advanced denoise and de-ghosting algorithms. We confirmed the thickness of the ice, which can estimate by the depth of the reversed-polarity reflections on the boundaries between the ice and lake water from the migrated seismic sections for each survey line for D2 lake. Also, the 200 m lake water depth, structures, and geophysical characteristics of the subglacial lake were confirmed, and then, we found the optimal hot-water drilling location for the subglacial lake D2 in the David Glacier, Antarctica.

How to cite: Kang, S.-G., Ju, H. T., Choi, Y., Kwak, H., Lee, J., Kim, Y., Hong, J. K., and Lee, J. I.: Imaging the structures of a subglacial lake D2 in the Antarctic David Glacier catchment from the multi-channel reflection seismic records, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4630, https://doi.org/10.5194/egusphere-egu23-4630, 2023.

EGU23-6444 | ECS | Posters on site | CR3.3

Subglacial discharge as a driver of fjord circulation in Ilulissat Icefjord 

Karita Kajanto, Basile de Fleurian, Helene Seroussi, Fiammetta Straneo, and Kerim Nisancioglu

The ice-ocean interface of Greenlandic outlet glaciers is the main source of uncertainty in the sea level contribution estimates from the Greenland ice sheet in the coming century. Subglacial discharge of surface meltwater is a currently understudied process that connects atmospheric forcing to the marine terminus. At the terminus, subglacial discharge drives buoyant plumes that enhance melt of the glacier. Surface meltwater from the ice sheet is often assumed to be directly and instantaneously transported to the gounding line as subglacial discharge. However, the subglacial drainage network evolves as a response to changes in surface meltwater volume, thus moderating and distributing the discharge along the grounding line. The early-season and late-season networks are likely to have different transport properties, leading to different properties of the buoyant subglacial discharge plume, and the accompanying melt rate.


We model the subglacial hydrologic network of Sermeq Kujalleq (Jakobshavn Isbræ) glacier in West Greenland with GlaDS in ISSM. We characterize the evolution of subglacial discharge into the fjord throughout the runoff season, and compare different runoff years. Furthermore, we use the MITgcm ocean model of Ilulissat Icefjord to characterize the impact of seasonality of the subglacial discharge to fjord properties, circulation and submarine melt of the glacier front.

How to cite: Kajanto, K., de Fleurian, B., Seroussi, H., Straneo, F., and Nisancioglu, K.: Subglacial discharge as a driver of fjord circulation in Ilulissat Icefjord, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6444, https://doi.org/10.5194/egusphere-egu23-6444, 2023.

EGU23-6493 | ECS | Posters on site | CR3.3

Statistical downscaling increases Antarctic ice sheet surface melt rate 

Brice Noël, Melchior van Wessem, Bert Wouters, Luke Trusel, Stef Lhermitte, and Michiel van den Broeke

Although recent mass loss of the Antarctic ice sheet (AIS) is predominantly driven by ice shelf thinning and increased solid ice discharge, surface processes also directly affect mass changes. Snowfall fluctuations control the variability in surface mass balance (SMB) of the grounded AIS, while meltwater ponding threatens the viability of floating ice shelves. Surface processes are thus essential to quantify present and project future AIS mass loss, but remain poorly represented in climate models running at 25-100 km spatial resolution. Here we present new, daily Antarctic SMB products at 2 km resolution, statistically downscaled from the output of RACMO2.3p2 at 27 km resolution, for the contemporary climate (1979-2021) and a low, moderate and high-end warming scenario until 2100. We show that statistical downscaling to 2 km resolution modestly enhances contemporary SMB (+8%) but strongly increases melt (+50%), notably in the vicinity of the grounding line, in better agreement with both in situ and remote sensing records. The melt increase in the downscaled products persists in the future projections irrespective of the scenario, suggesting a systematic underestimate in low-resolution (regional) climate models.

How to cite: Noël, B., van Wessem, M., Wouters, B., Trusel, L., Lhermitte, S., and van den Broeke, M.: Statistical downscaling increases Antarctic ice sheet surface melt rate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6493, https://doi.org/10.5194/egusphere-egu23-6493, 2023.

EGU23-6497 | ECS | Posters on site | CR3.3

Seasonal and inter annual effects of meltwater runoff on ice dynamics in Northeast Greenland 

Ilaria Tabone, Johannes J. Fürst, and Thomas Mölg

The large amount of meltwater reaching the base of a glacier at the beginning of the melt season accelerates its ice flow, as meltwater pressurised in cavities acts as bed lubricant. Proceeding in the melt season, if the erosive power of basal water becomes high enough to prevail on the creep-induced closure of such cavities, channels may form. In this regime, water is efficiently drained towards the ice front, basal pressure abates and ice flow decelerates. The temporary speed-up and deceleration throughout the melt season has been observed in many glaciers in Greenland, especially in the southwest. There, more observations are available and the ablation zone extends hundreds of km inland. Yet, there is no evidence that Northeast Greenland follows this hydrology-induced dynamic behaviour. In fact, very few observations of flow variability during the melt season are available for this area, hampering our understanding of impacts of meltwater on the ice dynamics in these remote arctic regions. In this work we run a fully coupled ice-flow-hydrology model (Elmer/Ice coupled to GlaDS) to explore the feedbacks between surface meltwater and ice-flow variations over the whole basin of Northeast Greenland. We address both seasonal and annual timescales by running the coupled model for successive melt seasons (from May 2016 to the end of September 2018). To do so, we make use of daily surface mass balance and runoff data computed by a fully-fledged surface energy balance model (COSIPY) resolving snowpack processes. Our simulations show a seasonal speed-up due to increase in melt water pressure at the base, followed by a decrease in velocities due to the activation of a channelised system beneath the ice sheet. Our results suggest that the Northeast Greenland presents a complex hydrological system that is comparable to other regions of the ice sheet and hint at hydrology-dynamics mechanisms to be a potential controlling factor in the evolution of the area. 

How to cite: Tabone, I., Fürst, J. J., and Mölg, T.: Seasonal and inter annual effects of meltwater runoff on ice dynamics in Northeast Greenland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6497, https://doi.org/10.5194/egusphere-egu23-6497, 2023.

EGU23-6723 | ECS | Posters on site | CR3.3

An assessment of satellite-derived supraglacial lake depth measurements on the Greenland ice sheet 

Laura Melling, Amber Leeson, Mal McMillan, Jennifer Maddalena, Emily Glen, Louise Sandberg Sørensen, Mai Winstrup, and Rasmus Arildsen

Supraglacial lakes (SGLs) form when meltwater collects in glacial surface depressions. On the Greenland ice sheet (GrIS), SGL position is controlled by bedrock topography, meaning SGLs form in approximately the same locations each melt season. SGLs provide surface water storage and mediate the transfer of surface water to the ice sheet bed. Drainage water from SGLs has been shown to infiltrate to the base of the ice sheet and lubricate the ice sheet-bedrock interface, causing transient increases in basal sliding at the glacier margin. SGL depth is used to calculate the volume of water contained within the basin and thus the likelihood of hydrofracture caused by increased pressure from overlying water. As overlying water pressure is a function of water depth, determining the accuracy of depth estimation techniques is of the utmost importance in obtaining reliable estimates of hydrofracture likelihood and in determining SGL drainage impacts on ice sheet velocity.

Here, we present the results of an intercomparison of SGL depth measurements focused on the Watson River region of the GrIS. Previous studies have derived SGL depth by applying the Philpot (1989) radiative transfer equation to satellite-derived optical imagery. These results proved difficult to validate until recent advancements in remote sensing which allowed us to compare the radiative transfer-derived depths to laser altimetry measurements and high-resolution digital elevation modelling. Previous research has separately investigated the use of the Philpot (1989) radiative transfer equation, laser altimetry and digital elevation models, but none have intercompared all three.

This research compares estimates of SGL depths in the Watson River region in southwest Greenland that have been derived using three satellite-based approaches; 1) by applying the radiative transfer equation proposed by Philpot (1989) to Sentinel-2 optical satellite imagery, 2) using ICESat-2 laser altimetry and 3) from ArcticDEM digital elevation models.

Using the radiative transfer equation, we find the green band overestimates SGL depth and the red band underestimates SGL depth (with caveats) compared to ICESat-2 transects and digital elevation models. In summary, we achieve the first comprehensive intercomparison of these methods and provide insight into the strengths and potential limitations of each method, including levels of agreement between datasets, and associated uncertainties. This work helps to improve confidence in radiative transfer-derived estimates of SGL depth and volume and, consequently, quantitative estimates of meltwater storage on the surface of the GrIS. This research is associated with ESA’s Polar+ 4DGreenland study.

How to cite: Melling, L., Leeson, A., McMillan, M., Maddalena, J., Glen, E., Sandberg Sørensen, L., Winstrup, M., and Arildsen, R.: An assessment of satellite-derived supraglacial lake depth measurements on the Greenland ice sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6723, https://doi.org/10.5194/egusphere-egu23-6723, 2023.

EGU23-8335 | ECS | Posters on site | CR3.3

Antarctic firn air content depletion for different climate change scenarios 

Sanne Veldhuijsen, Willem Jan van de Berg, Peter Kuipers Munneke, and Michiel van den Broeke

Firn covers about 99% of the Antarctic ice sheet, providing, among other things, pore space in which most of the surface meltwater refreezes. Under anticipated future warming, surface melt, densification rates and the formation of impermeable ice layers are expected to increase, resulting in the depletion of reachable firn air content and consequently accelerated firn saturation by melt water. Firn saturation on Antarctica’s floating ice shelves is especially important, as this can potentially lead to their disintegration by hydrofracturing. On the other hand, snowfall is expected to increase as well, which will add pore space to the firn.

       In this study, we simulate the historical (1950-present) and future (present until 2100) transient evolution of the Antarctic firn layer under three different climate change scenarios. For this we use the IMAU Firn Densification Model driven by outputs of the Community Earth System Model (CESM2), dynamically downscaled to 27 km resolution with the Regional Atmospheric Climate MOdel (RACMO), version 2.3p2. We analyze the dominant atmospheric and firn processes and investigate under which conditions firn air content depletion is expected to occur. We show that ice shelves around the Antarctic Peninsula and the Roi Baudouin ice shelf are most vulnerable to firn air content depletion, whereas strong firn air content loss on the Ross and Filchner-Ronne ice shelves is not expected to occur before 2100 under all climate change scenarios. We discuss potential reasons for the differences between this ‘transient’ modelling approach from recently applied ‘diagnostic’ studies.

How to cite: Veldhuijsen, S., van de Berg, W. J., Kuipers Munneke, P., and van den Broeke, M.: Antarctic firn air content depletion for different climate change scenarios, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8335, https://doi.org/10.5194/egusphere-egu23-8335, 2023.

EGU23-8743 | ECS | Orals | CR3.3

Perturbation of ice sheet surface stress via basal lubrication 

Joshua Rines, Yao Lai, and Yongji Wang

Surface meltwater on the Greenland Ice Sheet (GrIS) can lubricate the bottom of the ice sheet via surface-to-bed pathways, such as moulins and vertical hydrofracture. The basal lubrication reduces the friction between the ice and the bed, which leads to perturbations in the velocity, strain rate, and stress fields that are felt laterally away from the location of the basal water as well as through the entire thickness of the ice column up to the surface.  In some instances, the induced surface stress may be sufficient to break new cracks, leaving the GrIS more vulnerable to rapid lake drainage via hydrofracture.  It is therefore important to understand the dominant physical parameters which control the magnitude and spatial extent (i.e. the coupling lengthscale) of stress perturbations induced by the basal meltwater lubrication.  To constrain the importance of surface slope, bed slope, and ice thickness as controls on this stress response, we used a 2D simulation of Stokes flow over a slippery patch with various basal boundary conditions, simulating the presence of meltwater lubrication.  We found that the magnitude of the stress response scales with the surface slope while the coupling lengthscale scales with the ice thickness.  This indicates that inland ice may experience a weaker but longer-range stress perturbation in response to water lubrication at the bed.

How to cite: Rines, J., Lai, Y., and Wang, Y.: Perturbation of ice sheet surface stress via basal lubrication, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8743, https://doi.org/10.5194/egusphere-egu23-8743, 2023.

EGU23-9491 | Orals | CR3.3

Antarctic firn aquifers detection algorithm based on satellite and regional climate model data 

Valeria Di Biase, Peter Kuipers Munneke, Bert Wouters, and Sophie de Roda Husman

In the past decade, localized in-situ observation and radar measurements have revealed the presence of firn aquifers on the Antarctic Ice Sheet. They are believed to be an important component of the hydrological system of the ice sheet, but currently no large-scale observational studies exist, hampering a detailed assessment of their contribution to runoff and sea-level rise.
We present a probability map of firn-aquifer presence around the Antarctic Ice Sheet, based on a combination of remote sensing and climate modeling data. C-band radar imagery from the Sentinel-1 and Advanced Scatterometer (ASCAT) missions, together with climate data from the regional atmospheric climate model RACMO2.3p2, are combined to map the probability of detecting seasonal and perennial firn aquifers at 5.5x5.5 km2 resolution in the period 2017 to 2020.
Our method is based on Monte Carlo simulations: its algorithm predicts the probability of aquifer presence based on a set of fixed inputs, to which dedicated thresholds and weights are assigned.
In agreement with observation from previous studies, we find a high probability of firn aquifer presence in the north and northwest of the Antarctic Peninsula, and on the Wilkins and George VI ice shelves. A low probability is found in the higher elevation areas of the central Antarctic Peninsula, where the presence of aquifers is not expected due to the absence of surface melt. Thanks to the large spatio-temporal availability of the input datasets, the presence of aquifers in different regions of Antarctica has been estimated. The methodology has been validated in selected regions of Greenland, where the presence of aquifers has been observed using in-situ and remotely sensed data.

How to cite: Di Biase, V., Kuipers Munneke, P., Wouters, B., and de Roda Husman, S.: Antarctic firn aquifers detection algorithm based on satellite and regional climate model data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9491, https://doi.org/10.5194/egusphere-egu23-9491, 2023.

Meltwater retention on the surface of ice shelves may lead to hydrofracturing and ultimately their breakup and collapse. Several methods have been used to map surface meltwater on ice shelves from satellite imagery, but a comparison of the methods and a systematic analysis of the results has not been undertaken. Here we bring together two recent inventories of surface meltwater features that use machine learning technologies to map: i) ponds from both Sentinel-2 optical and Sentinel-1 SAR imagery; and ii) both ponds and slush from Landsat 8 optical imagery. We analyse the meltwater products at a bi-monthly (twice a month) timescale over six austral summers between November 2015 and March 2021 for the George VI Ice Shelf, Antarctic Peninsula. We investigate the data sets to reveal the seasonal evolution of surface and shallow subsurface meltwater in terms of the onset, cessation and therefore duration of slush and ponded water, as well as the persistency of slush and ponded water areas from year to year. We find areas where the evolution follows similar patterns from year to year, but also highly anomalous patterns and timings in other years. Finally, a systematic analysis of Sentinel 1 imagery throughout the winter seasons reveals several perennial shallow surface water bodies.

How to cite: Willis, I., Deakin, K., Dell, B., and Dirscherl, M.: Intra- and inter-annual melt water patterns on George VI Ice Shelf, Antarctic Peninsula, from synthetic aperture radar and optical satellite imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9677, https://doi.org/10.5194/egusphere-egu23-9677, 2023.

EGU23-9972 | Posters on site | CR3.3

Observed and modelled surface meltwater-induced flexure and fracture on north George VI Ice Shelf, Antarctica 

Alison Banwell, Douglas MacAyeal, Ian Willis, Laura Stevens, and Rebecca Dell

Hundreds of surface lakes are known to form each summer on north George VI Ice Shelf, Antarctic Peninsula. To investigate surface-meltwater induced ice-shelf flexure and fracture, we obtained Global Navigation Satellite System (GNSS) observations and ground-based timelapse photography over north George VI for three melt seasons from November 2019 to November 2022

In particular, we used these field observations to characterize the flexure and fracture behaviour of a pre-existing doline (i.e. drained lake basin) on north George VI during the record-high melt season of 2019/2020. The GNSS displacement timeseries shows a downward vertical displacement of the doline centre with respect to the doline rim of ~80 cm in response to loading from the development of a central meltwater lake. Viscous flexure modelling indicates that this vertical displacement likely generates flexure stresses of ~> 75 kPa. The GNSS data also show a 10s of days episode of rapid-onset, exponentially decaying horizontal displacement where the horizontal distance from the rim of the doline with respect to its center increases by ~70 cm. We interpret this event as the initiation and/or widening of a single fracture, possibly aided by the availability of surface meltwater (i.e. hydrofracture). Our observations document for the first time the initiation and/or widening of a “ring fracture” on an ice shelf, equivalent to those fractures proposed to be part of the chain reaction lake drainage process involved in the breakup of Larsen B Ice Shelf in 2002.

How to cite: Banwell, A., MacAyeal, D., Willis, I., Stevens, L., and Dell, R.: Observed and modelled surface meltwater-induced flexure and fracture on north George VI Ice Shelf, Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9972, https://doi.org/10.5194/egusphere-egu23-9972, 2023.

EGU23-10070 | Posters on site | CR3.3

Tipping point behaviour of submarine melting at ice sheet grounding zones 

Ian Hewitt and Alex Bradley

Ice sheets are highly sensitive to melting in their grounding zones, where they transition from grounded ice to floating. Recent models of the interaction between warm salty ocean water and cold fresh subglacial discharge in the grounding zone suggest that warm water can intrude kilometres beneath the ice sheet, with important consequences for ice dynamics.

Here, we couple a model for warm water intrusion to a simple model of melting and, in doing so, capture a previously ignored feedback between geometry and subglacial water flow that occurs as the grounding zone responds to melting. This feedback enhances the potential for warm water to intrude beneath the grounded ice sheet, and therefore makes high melting in grounding zones more likely.

Intriguingly, our results also suggest that increases in ocean temperature can lead to a tipping point being passed, beyond which ocean water intrudes indefinitely beneath the ice sheet by a process of runaway melting, suggesting a candidate mechanism for dramatic changes in grounding-zone behaviour that are not currently included in ice sheet models, and which may enable them to reproduce previous high warm-period sea levels.

How to cite: Hewitt, I. and Bradley, A.: Tipping point behaviour of submarine melting at ice sheet grounding zones, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10070, https://doi.org/10.5194/egusphere-egu23-10070, 2023.

EGU23-10253 | ECS | Orals | CR3.3

Sea-level rise projections of Petermann Glacier, Greenland, modeled using synchronously coupled subglacial hydrology and ice flow dynamics 

Shivani Ehrenfeucht, Mathieu Morlighem, Eric Rignot, and Christine Dow

Greenland ice shelves are known to display seasonal speedups of ice velocity which can be attributed to ice front position or to meltwater runoff, depending on which glacier is being examined. However, it remains uncertain if the seasonality of glacier speed will be impacted by climate change in the coming century. Current projections of glacier dynamics under 21st century climate forcings do not include subglacial hydrology, so it also remains unknown if it will play any important role in evolving glacier dynamics under different climate change scenarios, or ultimately have an impact on sea-level rise projections. Here we present a model with synchronous coupling of ice dynamics and subglacial hydrology applied to Petermann Glacier in northern Greenland. Petermann exhibits a summer-time acceleration of roughly 15% as compared to its baseline winter velocity, which is likely the result of subglacial hydrology. Although it has been relatively stable in recent years, as one of the largest marine terminating glaciers in northern Greenland, whether or not Petermann remains stable will have a significant impact on the sea-level contribution of the northern sector of the ice-sheet. We use climate through 2100 to investigate how the subglacial hydrologic system may evolve in a warmer climate and to test if including hydrology changes the stability of Petermann under future climate scenarios using the Ice-sheet and Sea-level System Model (ISSM) which includes the Glacier Drainage System (GlaDS) model. We compare glacier evolution and projected sea-level rise for three model configurations: one with synchronously coupled subglacial hydrology and ice dynamics, a second with asynchronous coupling where subglacial hydrology is calculated with static ice geometry and velocity but ice dynamics are calculated using effective pressure from GlaDS output, and a third where subglacial hydrology is excluded entirely from the model setup.  Results show a significant increase in projected sea-level rise by the end of the century and differing patterns of grounding line migration and ice thinning when subglacial hydrology is included in the model configuration for Petermann.

How to cite: Ehrenfeucht, S., Morlighem, M., Rignot, E., and Dow, C.: Sea-level rise projections of Petermann Glacier, Greenland, modeled using synchronously coupled subglacial hydrology and ice flow dynamics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10253, https://doi.org/10.5194/egusphere-egu23-10253, 2023.

EGU23-10614 | ECS | Posters on site | CR3.3

Detection of lake drainage events in Antarctica from SAR imagery 

Mariapina Vomero, Sarah Thompson, Sue Cook, and Bernd Kulessa

Supraglacial lakes are expected to play a crucial role in determining the ice sheet mass balance in a warming climate. Water ponding lowers the albedo of the ice surface, establishing a positive feedback of melting processes that might be further enhanced by the projected rising temperatures. Lake drainages can have particularly large impacts on ice shelves depending on their location and surrounding topography. Drainage events on grounded ice can transport water to the ice/bedrock interface, affecting the sliding of the ice sheet. On floating ice shelves, lake drainage events have been linked to fracture formation potentially leading to ice shelf collapse.

Over the past decade, observations of supraglacial lake drainage events have mainly been gathered from the Greenland ice sheet, while observations of drainage events remain rare in Antarctica. While some limited examples have been reported in the literature, it is not yet known how common these events are, the likelihood of their formation from the grounding line, and how their recurrence could impact Antarctic ice shelves. Observations of Antarctic supraglacial lake drainages are challenging as the lakes often have lids of ice covering liquid water, and drainages can occur in winter when low light levels preclude the use of optical sensors. Since Sentinel-1 SAR imagery works independently from light and cloud conditions, it enables continuous monitoring throughout the year providing further insights into their spatial and temporal evolution. The use of Google Earth Engine (GEE) platform for analyzing SAR images and detecting drainage events has shown the value of this platform as a tool to monitor changes over several locations and to efficiently deal with the increasing workload of satellite data. Here we demonstrate the use of SAR backscatter to reliably detect drainage events to map their location also during the winter months and to locate their prevalence around the Antarctic coastline.

How to cite: Vomero, M., Thompson, S., Cook, S., and Kulessa, B.: Detection of lake drainage events in Antarctica from SAR imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10614, https://doi.org/10.5194/egusphere-egu23-10614, 2023.

EGU23-11012 | ECS | Posters on site | CR3.3

Relaxation of water-filled blisters via flow through the subglacial drainage system 

Ching-Yao Lai, Laura Stevens, Mark Behn, and Sarah Das

The drainage efficiency of the subglacial water system evolves during the melt season. However, direct measurement of drainage efficiency under 1000-meters thick ice is challenging. We previously demonstrated that surface observations of rapid lake drainage induced uplifts can be used to assess subglacial transmissivity beneath the ice sheet. When a lake drains, the water reaches the interface between the ice and bed and forms a water-filled blister. This water then drains through the subglacial drainage system. In this study, we use mathematical models to examine the behavior of surface uplift relaxation resulting from different types of drainage systems, including a laminar water sheet, a turbulent water sheet, and a turbulent subglacial channel. Combined with surface GPS observations of five lakes, we showed that the model can be used to study the evolution of subglacial drainage efficiency.

How to cite: Lai, C.-Y., Stevens, L., Behn, M., and Das, S.: Relaxation of water-filled blisters via flow through the subglacial drainage system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11012, https://doi.org/10.5194/egusphere-egu23-11012, 2023.

EGU23-11493 | Posters on site | CR3.3

Impact of subglacial hydrology recharge location and intensity on ice dynamics. 

Basile de Fleurian

As temperatures continues to increase, a larger amount of snow and ice is melting at the surface of ice sheets and glaciers. It is now clear that this increase in meltwater availability has a direct impact on ice dynamics on a seasonal timescale, and there are strong presumption that this is also the case on longer timescales. The recent evolution of subglacial hydrology models now allows them to be directly coupled to ice dynamics model to perform long term simulation of this systems and provide the link between ice dynamics and the evolution of climate. There is however still a large number of open questions to answer in order to provide long term realistic simulations of this coupled system. One of those questions is the impact of the recharge location an intensity of the subglacial hydrological system on ice dynamics, or whether the injection of water in the system needs to pass through Moulins or can be approximated as a uniform source. The impact of the recharge strategy on subglacial hydrology model alone has yielded contrasted results with a potential impact on short timescale but a limited influence once integrated over a full season. Here we want to investigate the direct effect of this recharge scenarios on the ice dynamics itself.

We apply the Ice-sheet and Sea-level System Model (ISSM) to a synthetic glacier with a geometry similar to a Greenland ice sheet land terminating glacier. Using a range of moulin density (or uniform input) for the recharge of the subglacial hydrology model we observe the response of the ice dynamics itself both on short and longer timescales. Those simulations provide an insight into the importance of recharge location and intensity of the subglacial drainage system directly on the ice dynamics, and so provide a baseline for the choice of recharge style for more realistic simulations.

How to cite: de Fleurian, B.: Impact of subglacial hydrology recharge location and intensity on ice dynamics., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11493, https://doi.org/10.5194/egusphere-egu23-11493, 2023.

EGU23-12179 | Orals | CR3.3

Numerical modelling of rapidly-rising glacier outburst floods 

Tómas Jóhannesson, Thomas Zwinger, Peter Råback, and Juha Ruokolainen

Glacier outburst floods, or jökulhlaups, from subglacial geothermal areas, marginal lakes and subglacial volcanic eruptions are common in Iceland and they pose a substantial hazard to settled areas as well as to roads, communication lines and other infrastructure near glaciers. Jökulhlaups have attracted increasing attention in recent years in many glacier areas because of an increased frequency due to the formation of terminus and marginal lakes in connection with global warming and the associated glacier downwasting. Jökulhlaups can be categorized into two groups, slowly and rapidly rising, with marked differences in the flood hydrographs. Slowly-rising jökulhlaups are traditionally explained by the theory of Nye (1976) through a conduit-melt–discharge feedback mechanism. The initial subglacial propagation and the development of the flood hydrograph of rapidly-rising jökulhlaups is, on the other hand, not quantitatively understood. We present observations of glacier outburst floods from W-Vatnajökull in Iceland that may be interpreted in terms of a conceptual theory for such floods as a pressure wave in the basal hydraulic system that propagates downglacier and creates the initial flood path by lifting the glacier from its sole. This theory is being implemented as a numerical model for rapidly-rising jökulhlaups in the Elmer/Ice Open-Source Finite-Element Software. The model describes the subglacial propagation of the jökulhlaup front using visco-elastic plate dynamics for the overlying glacier ice combined with a turbulent sheet model for the subglacial water flood. The evolution of the subglacial flooded area is simulated numerically through the solution of a contact problem that represents the lifting of the ice from the underlying glacier bed where the subglacial water pressure exceeds the normal stress in the ice at the sole of the glacier. We hence can identify 4 crucial components of the model: 1) A visco-elastic ice-deformation model, 2) a two-dimensional pressurized water-sheet model based on Manning’s law for turbulent friction in water flow, 3) the solution of a contact problem induced by hydraulic jacking of the glacier, and 4) the consistent (in terms of the spatial stress distribution) solution of the fluid–structure interaction between the ice and the water-sheet. We present and discuss these different aspects in terms of their numerical implementation in Elmer/Ice. The aim of the model is to explain the speed of propagation of the subglacial flood front at the beginning of the flood as well as the time-dependent flood hydrograph after the flood bursts out from under the glacier at the ice margin.

How to cite: Jóhannesson, T., Zwinger, T., Råback, P., and Ruokolainen, J.: Numerical modelling of rapidly-rising glacier outburst floods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12179, https://doi.org/10.5194/egusphere-egu23-12179, 2023.

EGU23-12647 | ECS | Orals | CR3.3

Factors driving Supraglacial Lake variability on ice shelves in Dronning Maud Land, East Antarctica 

Anirudha Mahagaonkar, Geir Moholdt, and Thomas V. Schuler

We have identified large spatiotemporal variability in the extent of supraglacial lakes on ice shelves in Dronning Maud Land, East Antarctica. While large lakes were found over Nivlisen and Roi Baudouin ice shelf areas, only small lakes were mapped over Fimbulisen and Muninisen. Preliminary analyses reveal a positive correlation between supraglacial lake extents (over specific ice shelves) and mean summer (December, January, February) temperatures and Positive Degree Days from ERA5. However, these correlations do not explain the large variability in overall Dronning Maud Land assessments. For instance, Fimbulisen area has the warmest summer temperatures and highest sum Positive Degree Days with very low lake extents. In contrast, the adjacent Nivlisen area with similar summer statistics has large spread of supraglacial lakes over the ice shelf area. We also identified that over specific melt years (e.g., 2016-2017, high lake extents; 2020-2021, low lake extents), all ice shelf areas with supraglacial lakes in Dronning Maud Land had relatively similar local lake extents, indicating the role of both regional and local factors influencing the ponding of meltwater. In this work, we attempt to identify the factors influencing melting and ponding in Dronning Maud Land. For this purpose, we use outputs from ERA5-Land and Modèle Régional Atmosphèrique (MAR) which have a higher spatial resolution (~10 km) than ERA5 (~31 km). At coarser resolutions climate models are deficient to capture localized processes that may have a crucial role in influencing surface melting and ponding – e.g., the katabatic winds. Outputs at higher resolutions may better constrain such small-scale phenomena and by using these datasets we may resolve the knowledge gap surrounding the controlling factors. Atmospheric factors in consideration are katabatic winds, cloudiness, precipitation, and albedo. Environmental factors such as blue ice, firn air content and surface topography will also be assessed to ascertain their influencing role. The results of this analysis will help in understanding the near-future evolution of supraglacial lakes on Dronning Maud Land and provide important insight into the future ice shelf stability in the region and also for improving estimates of the Antarctic ice sheet mass budget.

How to cite: Mahagaonkar, A., Moholdt, G., and Schuler, T. V.: Factors driving Supraglacial Lake variability on ice shelves in Dronning Maud Land, East Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12647, https://doi.org/10.5194/egusphere-egu23-12647, 2023.

EGU23-12649 | ECS | Orals | CR3.3

Spatio-Temporal Variations of Blue Slush and Water Flow in the Percolation Zone of Greenland: the Role of Local Topography 

Federico Covi, Mark Fahnestock, Regine Hock, Jing Xiao, Åsa Rennermalm, Martin Truffer, Matthew Sturm, and Carl Benson

The presence of thick ice layers in firn (so-called ice slabs) has the potential to increase the contribution to sea-level rise of the Greenland ice sheet. These impermeable ice layers prevent water percolation in the firn, leading to more efficient runoff by favoring lateral movement of water on top of the ice slabs. Here we use optical images from the Sentinel-2 satellites to track the seasonal and interannual evolution of snow fully saturated with water to the surface (blue slush) in southwest Greenland. Furthermore, we use a high resolution digital elevation model to assess the role of local topography on the formation of ice slabs and on lateral movement of water. 

We find that blue slush can reach elevations up to 1900 m a.s.l. in years with above average melt with maxima in August. Blue slush appears preferentially in areas where the surface slope approaches 0°, which is also where the ice slabs are thicker. The propagation of blue slush to lower elevation following local slope indicates water movement on top of the impermeable layer. Thus, we suggest that the process of formation of thick ice slabs is a self-sustaining positive feedback system.

How to cite: Covi, F., Fahnestock, M., Hock, R., Xiao, J., Rennermalm, Å., Truffer, M., Sturm, M., and Benson, C.: Spatio-Temporal Variations of Blue Slush and Water Flow in the Percolation Zone of Greenland: the Role of Local Topography, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12649, https://doi.org/10.5194/egusphere-egu23-12649, 2023.

EGU23-12684 | ECS | Posters on site | CR3.3

Sensitivity of Pine Island and Thwaites drainage basins to subglacial hydrology 

Elise Kazmierczak and Frank Pattyn

Subglacial processes in Antarctica are difficult to directly observe and are one of the sources of uncertainty when modelling the response of ice sheets to environmental forcing. Subglacial processes pertain to the type of basal sliding or friction law used, where especially the contrast between viscous (linear) and plastic (Coulomb) sliding makes the latter far more responsive to changes at the marine boundary (Ritz et al., 2015; Brondex et al., 2019; Bulthuis et al., 2019; Sun et al., 2020; Kazmierczak et al., 2022).

Besides the type of sliding, physical basal conditions, such as basal temperatures, bed properties (hard or soft), subglacial water flow and drainage, till properties, and mechanics, also directly affect the ice sheet flow (Clarke, 2005, Cuffey and Patterson, 2010, Kazmierczak et al., 2022),  by affecting the effective pressure (Bueler and Brown, 2009, Winkelmann et al., 2011, van der Wel et al., 2013).

In this study, we investigate how variations in effective pressure determines the evolution of the main marine basins of the West Antarctic Ice Sheet (Pine Island and Thwaites glacier) which are currently exhibiting the largest ice mass loss of the Antarctic ice sheet.

For this purpose, we employ different types of subglacial hydrology for soft and hard bed configurations, which we adapt to simulate individual drainage basins of the Antarctic ice sheet at a km-scale resolution, thus allowing for proper migration of the grounding line (e.g., Pattyn et al., 2013). These hydrological representations are included in a generalized basal sliding law (Zoet et al., 2020), implemented in the the f.ETISh/Kori model (Pattyn, 2017; Sun et al., 2020). Results are compared to results from a pan-Antarctic ice sheet model (Kazmierczak et al., 2022) and demonstrate the importance of detailed bed topography influencing the subglacial conditions upstream of the grounding line.

How to cite: Kazmierczak, E. and Pattyn, F.: Sensitivity of Pine Island and Thwaites drainage basins to subglacial hydrology, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12684, https://doi.org/10.5194/egusphere-egu23-12684, 2023.

EGU23-12985 | ECS | Orals | CR3.3

Semi-Automatic Active Subglacial Lake Detection in Antarctica 

George Malczyk, Noel Gourmelen, Carolyn Michael, and Oskar Krauss

Most of the ice in the Antarctic ice sheet drains from the continent to the ocean through fast-flowing ice streams and glaciers. The high velocity of these features is thought to be maintained by water at the ice sheet's base, which reduces friction. Subglacial water moving has been linked to transient glacier flow acceleration and enhanced melt at the grounding line. Therefore, the presence, location, and movement of water at the ice sheet's base are likely significant controls on the mass balance of Antarctica.

The transport of subglacial water from the interior of Antarctica to the grounding line was once thought to be a steady-state process. It is now known that subglacial water collects in hydrological sinks, which store and release water in episodic events. These features can be detected and quantified by satellite altimetry. This behaviour is interpreted as water moving in and out of 'active' subglacial lakes.

Detecting active subglacial lakes with satellite altimetry commonly involves searching for localized regions of surface elevation change over short temporal time frames. In practice, this can be incredibly cumbersome due to the large amounts of data that need to be processed and a high degree of guesswork regarding where potential lakes might be located.

Here we present a semi-automatic active subglacial lake approach for detecting and classifying drainage and filling events across Antarctica from an entire archive of satellite altimetry. We first use CryoSat-2 altimetry to produce time-dependent rate of elevation change maps for the whole of the Antarctic continent. From these maps, we search for localized regions of elevation change indicative of subglacial lake activity. We then extract time series for these features and perform change point analysis to automatically detect subglacial lake activity and extract important parameters such as discharge volumes and recharge rates. This approach reveals several new lakes previously undetected.

For example, five new lakes are found over the Thwaites glacier in addition to the four previously recorded. Here we present the approach and the resulting updated inventory of subglacial lake activity across Antarctica.

How to cite: Malczyk, G., Gourmelen, N., Michael, C., and Krauss, O.: Semi-Automatic Active Subglacial Lake Detection in Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12985, https://doi.org/10.5194/egusphere-egu23-12985, 2023.

EGU23-14438 | ECS | Orals | CR3.3

Satellite-derived estimates of slush and ponded water extent across Antarctica's ice shelves, 2013-2021 

Rebecca Dell, Ian Willis, Neil Arnold, and Alison Banwell

Surface meltwater on Antarctic ice shelves is comprised of slush (saturated firn), and ponded water (lakes and streams). Often, slush forms as a precursor to ponded water, and its formation leads subsequently to water collecting in basins or flowing across ice shelf surfaces. Where slush and/or ponded water refreeze at the end of a melt season, the firn air content of ice shelves may be lowered. This can increase ice shelves’ susceptibility to future meltwater ponding, making them more vulnerable to potential hydrofracture and break-up. Slush and ponded water also have a lower albedo than snow or dry firn, further increasing ice-shelf surface melt under warmer climates. To date, most satellite-derived estimates of surface water on ice shelves have identified only ponded water, potentially underestimating the extent of surface meltwater. Here, we use a previously developed random forest classifier to produce a novel, continent-wide dataset of slush and ponded water extent across all Antarctic ice shelves between 2013 and 2021. Our dataset is comprised of monthly meltwater products for the austral summers (November-March where data availability allows), from which continent-wide, regional, and individual ice-shelf trends are investigated.

The continent-wide total meltwater coverage (assessed between November and February) was greatest during January 2017, reaching 6078 km2. Notably, we find that including the slush extent in total meltwater calculations increases surface water extent by a mean of 56% during the melt-season peak (January). However, we identify marked inter-regional variation, with slush accounting for 71% of January’s total surface meltwater extent in Dronning Maud Land, but only 46% in the Antarctic Peninsula. This indicates that until now, the extent of surface meltwater across Antarctica’s ice shelves has been largely under-estimated on ice shelf, regional, and continent-wide scales, which has significant repercussions for calculations of the surface and sub-surface energy and mass balance of ice shelves, the long-term storage of meltwater on ice shelves, and predictions of future ice shelf stability.

How to cite: Dell, R., Willis, I., Arnold, N., and Banwell, A.: Satellite-derived estimates of slush and ponded water extent across Antarctica's ice shelves, 2013-2021, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14438, https://doi.org/10.5194/egusphere-egu23-14438, 2023.

EGU23-15825 | Posters on site | CR3.3

Hydrological changes in Bayelva catchment (Western Svalbard-Norway): water discharge quantification and water-driven biogeochemical fluxes 

Ilaria Baneschi, Marco Doveri, Marianna D'Amico, Linda Franceschi, Matteo Lelli, Angelina Lo Giudice, Giovanna Maimone, Matia Menichini, Francesco Norelli, Luisa Patrolecco, Tanita Pescatore, Ciro Alessandro Rappazzo, Jasmin Rauseo, Francesca Spataro, Gabriella Caruso, Sandra Trifirò, Maurizio Azzaro, and Marco Vecchiato

Surface water and groundwater are changing rapidly because of significant climate warming in the Arctic region [1,2]. Arctic amplification has intensified the melting of snow cover and glaciers, as well as widespread permafrost degradation, leading to a prominently increase of the annual discharge of some Arctic rivers [3,4]. This results in dramatic impacts on the surface water transition and freshwater circulation that, in turn, can cause localized permafrost thaw [5], allowing greater connection between deep groundwater and surface water pathways. Groundwater is a crucial component of the hydrological cycle, affecting ecosystems and human communities in Arctic regions.

In high-latitude regions, evaluating groundwater flux and storage and river discharge is challenging due to a lack of trusted and publicly available hydrogeological data. Changes in river flows and groundwater discharge will alter fluxes of freshwater and terrigenous material (e.g., sediment, nutrients, and carbon), with implications for biodiversity in both freshwater and marine ecosystems. The rapid glacier melting affects weathering processes, resulting in the mobilization-transport of pollutants, microorganisms stored for a long time, and turbid meltwaters. Consequently, more timely and accurate evaluation of surface and groundwater is urgently required.

Thanks to its geographical characteristics, the retreating glaciers, the research stations and infrastructures, and the studies carried out in the past and present, the Bayelva catchment near Ny-Ålesund (Western Svalbard-Norway) is an ideal site for surveys aimed at increasing knowledge on hydrology dynamics and associated effects, in the continuum from glaciers to the fjord.

In this framework, within the ICEtoFLUX project (MUR/PRA2021 project-0027) field campaigns were conducted in the spring and summer of 2022 in the Bayelva River catchment, from its glaciers and periglacial/proglacial systems up to the Kongsfjorden sector significantly affected by the river.  The activities were aimed at quantifying hydrologic processes and related transport of pollutants and microbial biomass and activities. Suprapermafrost groundwater was monitored by four piezometers installed along a hillslope to investigate how subsurface and surface waters interact during active layer development.

Water samples were repeatedly collected for analysing physical-chemical-isotopic-biological parameters. Main rain events and monthly total precipitation were sampled for stable isotopes.

The first results suggest that, in general, electrical conductivity and total suspended solids increase from the glacier to the Bayelva monitoring station, which is located less than 1 km far from the coast. Seasonal evolution of physical-chemical features was also observed. Results from piezometers indicate that the underground flow is spatially and temporally heterogeneous, both quantitatively and from a physical-chemical-isotopic-biological point of view. A general increase of electrical conductivity over the melt season was registered for groundwater and streamwater. First evidence on organic pollutants and microbe transport are also discussed.

[1] Fichot, C.G. et al. 2013. Sci. Rep., 3, 1053.

[2] Morison, J. et al. 2012. Nature, 481, 66–70.

[3] McClelland, J.W. et al. 2006. Geophys. Res. Lett., 33, L06715.

[4] Wang, P. et al. 2021. Res. Lett., 16, 034046.

[5] Zheng, L. et al. 2019. J. Geophys. Res. Earth Surf., 124, 2324–2344.

How to cite: Baneschi, I., Doveri, M., D'Amico, M., Franceschi, L., Lelli, M., Lo Giudice, A., Maimone, G., Menichini, M., Norelli, F., Patrolecco, L., Pescatore, T., Rappazzo, C. A., Rauseo, J., Spataro, F., Caruso, G., Trifirò, S., Azzaro, M., and Vecchiato, M.: Hydrological changes in Bayelva catchment (Western Svalbard-Norway): water discharge quantification and water-driven biogeochemical fluxes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15825, https://doi.org/10.5194/egusphere-egu23-15825, 2023.

EGU23-15826 | ECS | Posters on site | CR3.3

Estimating Supraglacial Lake Area for Greenland using Sentinel-2 Images and Deep Learning 

Zahra Bahrami, Katrina Lutz, and Matthias Braun

Ice loss from the Greenland ice sheet is one of the main sources of global sea level rise. Surface meltwater is one of the drivers of Greenland ice sheet mass loss. Supraglacial lakes are formed when meltwater accumulates in topographic depressions on glaciers or ice sheets during the melt season. The development and rapid drainage of supraglacial lakes in Greenland have been linked to the collapse of floating ice shelves. This can then lead to increased discharge of ice from outlet glaciers and increased ice velocity. Supraglacial lakes in Greenland are studied using Sentinel-2 images with daily observation intervals and high spatial resolution. The objective of this study is to estimate the maximum area of supraglacial lakes using Sentinel-2 L1C images between 2019 and 2022 in the months of July and August. After pre-processing Sentinel-2 L1C images, the detection and semantic segmentation of supraglacial lakes is carried out using a deep learning algorithm. As large labeled Sentinel-2 images are not available and labeling the training data is time-consuming, the F-mask algorithm is used for the training data labels. The deep learning algorithm consists of several stages, and the model is validated with manually labeled data at every stage. The training labels for the next stages are generated from the most successful model of its previous stage. After that, labels are generated for all acquired images, and the maximum area of the lakes in the months of July and August between 2019 and 2022 is calculated for supraglacial lakes in Greenland sub-regions.

How to cite: Bahrami, Z., Lutz, K., and Braun, M.: Estimating Supraglacial Lake Area for Greenland using Sentinel-2 Images and Deep Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15826, https://doi.org/10.5194/egusphere-egu23-15826, 2023.

EGU23-15919 | Posters on site | CR3.3

Antarctica's subglacial hydrology 

Mauro A. Werder, Daniel Goldberg, George Malczyk, Kenneth D. Mankoff, and Martin Wearing

The components Antarctica's subglacial hydrology -for instance water sources, sinks, flow paths and catchments- are still poorly constrained.  In this work we assess the subglacial hydrology of the continent at high resolution (500m) using (1) water inputs from frictional melting derived with a higher order ice flow model combined with existing geothermal heat flux products, (2) BedMachine topography, and (3) a water flow routing model taking uncertainties into account.

We present the following modelling results: probabilistic maps of water flow paths and catchments, potential subglacial lake locations, fluxes across the grounding line, and melt or freeze-on rates due to water flow.  We also present locations where the drainage system configuration, such as the catchment size, is sensitive to model inputs and thus where future field investigations would be particularly valuable.

How to cite: Werder, M. A., Goldberg, D., Malczyk, G., Mankoff, K. D., and Wearing, M.: Antarctica's subglacial hydrology, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15919, https://doi.org/10.5194/egusphere-egu23-15919, 2023.

EGU23-16569 | ECS | Posters on site | CR3.3

R-channel laboratory experiment: data evaluation and numerical simulations 

Stefanie Börsig, Mauro Werder, Alexander Jarosch, Yuri Prohaska, and Daniel Farinotti

Englacial and subglacial drainage substantially controls glacier dynamics. However, because of the inaccessible glacier bed, few actual measurements exist and empirical relations in current models are either adopted from other research fields or based on theoretical arguments.

This study focuses on the channelized drainage system and determines the flow properties of R-channels: we evaluate a set of laboratory experiments and complementary computational fluid dynamics simulations of their final geometries. These experiments make use of channels with water flow in ice blocks and represent pressurized englacial R-channels. Simulation and measurements only partially agree on pressure gradients and the resulting hydraulic friction factor. However, the results are within the published range of variability.

How to cite: Börsig, S., Werder, M., Jarosch, A., Prohaska, Y., and Farinotti, D.: R-channel laboratory experiment: data evaluation and numerical simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16569, https://doi.org/10.5194/egusphere-egu23-16569, 2023.

EGU23-16781 | Orals | CR3.3

Exploring a subglacial channel beneath Kamb Ice Stream with Icefin 

Britney Schmidt, Peter Washam, Justin Lawrence, Huw Horgan, Craig Stevens, Craig Stewart, Gavin Dunbar, Linda Balfoort, Christina Hulbe, Benjamin Hurwitz, Andrew Mullen, Enrica Quartini, Daniel Dichek, Veronica Hegelein, Francis Bryson, and Darcy Mandeno

Kamb Ice Stream (KIS) is one of the largest tributaries to the Ross Ice Shelf, grounded near the southermost edge of the massive ice shelf.  Stagnant for some 150 years, the rerouting of subglacial water beneath the ice stream and others in the region is likely critical to the stagnation of the ice stream, as well as present and future dynamics.  As part of the Antarctic New Zealand-led Antarctic Science Platform, our NSF- and NASA- funded team was able to participate in two field seasons accessing below KIS.  In Austral summer 2021-2022,  a hot water drilled borehole was made through the ice and into the major subglaciall channel upstream of the grounding line that carries subglacial water in to the ocean.  The access hole allowed for ice, ocean, sediment, and environemental observations inside the channel.  We deployed the Icefin ROV, which is a novel platform that provides hydrographic, imaging, and sonar exploration in situ below the ice.  Here, we report the first Icefin observations from within the channel.  We report channel geometry, ice-ocean interactions at the top and side walls of the channel, and sonar and imaging data of the sediment along a 500m mission extending upstream of the borehole.  In particular, we report bathymetric observations of the bed of the channel, which varied by 10s of me in width over the mission, and into which a small, meandering ~4m deep channel was incised into the sediments.  We discuss observations of boulder and sediment drape and suspended particulates in the water colum, and discuss implications for hydrological activity within the channel.

How to cite: Schmidt, B., Washam, P., Lawrence, J., Horgan, H., Stevens, C., Stewart, C., Dunbar, G., Balfoort, L., Hulbe, C., Hurwitz, B., Mullen, A., Quartini, E., Dichek, D., Hegelein, V., Bryson, F., and Mandeno, D.: Exploring a subglacial channel beneath Kamb Ice Stream with Icefin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16781, https://doi.org/10.5194/egusphere-egu23-16781, 2023.

EGU23-16804 | ECS | Posters on site | CR3.3

Deep Clustering in Subglacial Reflections Reveals New Insight into Subglacial Lakes 

Sheng Dong and Lei Fu

Radar images imply subglacial features, including distinct reflections from ice bottom. Different from bedrock interfaces, subglacial lakes generally display smooth and continuous highlights as a special type of ice bottom reflectors in radar images. In this study, we construct a dataset of ice bottom reflectors based on CReSIS radar sounder dataset. A deep learning method is applied to downsample and convert peak ice bottom reflectors to latent space. Unsupervised clustering later separates different types of subglacial reflectors. One reflector type with a sharp shape and high reflect power reveals smooth and continuous distributions in the radar images. The spatial distribution of this reflector type also matches the known subglacial lake distribution. We further applied this workflow to indicate candidate groups of subglacial reflectors similar to the conventional lakes. Results show more lakes are marked in the same radar sounder dataset. This method can automatically indicate subglacial lakes in radar images with high efficiency. The other types of subglacial reflectors can also provide potential references for subglacial studies.

How to cite: Dong, S. and Fu, L.: Deep Clustering in Subglacial Reflections Reveals New Insight into Subglacial Lakes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16804, https://doi.org/10.5194/egusphere-egu23-16804, 2023.

EGU23-1109 | ECS | Posters virtual | CR3.4

Seasonal enhanced melting under Ekström Ice Shelf, Antarctica 

Ole Zeising, Olaf Eisen, Sophie Berger, M. Reza Ershadi, Reinhard Drews, Tanja Fromm, Tore Hattermann, Veit Helm, Niklas Neckel, Frank Pattyn, and Daniel Steinhage

Ice–ocean interaction is crucial for the integrity of ice shelves and thus ice sheet stability. Warm ocean currents lead to enhanced basal melting of ice shelves, which is the dominant component of mass loss for the Antarctic Ice Sheet. Knowing the current melt rates and predicting those under future climate scenarios is thus of great importance. In the course of the ­MIMO-EIS (Monitoring melt where Ice Meets Ocean) Project, we deployed a continuously measuring ApRES (Autonomous phase-sensitive Radio-Echo Sounding) device in the center of Ekström Ice Shelf, recording an hourly time series since April 2020. The continuous time series reveals a seasonal onset of enhanced melt rates, abruptly increasing from <0.5 to 2 m/a in July/August. High melt rates with around weekly to bi-weekly fluctuations last until November/December. In addition, we performed annual point measurements to determine the spatial pattern of basal melt rates. The majority of these sites show yearly averaged melt rates of <0.5 m/a. These measurements allow the evaluation of future ocean-simulations and are in good agreement with satellite remote sensing estimates.

How to cite: Zeising, O., Eisen, O., Berger, S., Ershadi, M. R., Drews, R., Fromm, T., Hattermann, T., Helm, V., Neckel, N., Pattyn, F., and Steinhage, D.: Seasonal enhanced melting under Ekström Ice Shelf, Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1109, https://doi.org/10.5194/egusphere-egu23-1109, 2023.

EGU23-1190 | Orals | CR3.4

A periodic visco-elastic model for crevasses propagation in marine ice shelves 

Maryam Zarrinderakht, Thomas Zwinger, and Christian Schoof
Calving is a key mechanism that controls the length of floating ice shelves, and therefore their
buttressing effect on grounded ice. A fully process-based model for calving is currently still not
available in a form suitable for large-scale ice sheet models. Here we build on prior work that
treats crevasse growth in the run-up to calving as an example of linear elastic fracture growth.
Purely elastic behaviour is confined to short time intervals, much less than a single Maxwell
time (the ratio of viscosity to Young’s modulus) in duration: this is typically hours to a few days
for cold polar ice shelves, depending on temperature and state of stress. We explicitly recognize
that the elastic stresses occurring during fracture propagation act on an ice-mass subject to a
pre-stress created by long-term viscous deformation. By coupling a boundary element solver
for instantaneous elastic stress increments and the resulting fracture propagation with the
Elmer/Ice Stokes flow solver that computes the pre-stress and is able to model the long-term
evolution of the domain, we are able to show how viscous deformation end elastic fracture
mechanics interact. We show that viscous deformation is in general an essential part of calving,
and as a result, viscous deformation ultimately sets the time scale for calving. The geometric
changes resulting from that deformation are necessary to cause continued growth to calving
of fractures that initially propagate only part-way through the domain. We identify two distinct
modes of fracture propagation: either fractures propagate episodically, the crack lengthening in
each instance by a finite difference over short (elastic) time scales. Alternatively, fractures grow
gradually in such a way as to keep the viscous pre-stress near the crack tip from becoming
tensile, with elasticity playing a secondary role. Our results point to the purely instantaneous
stress-based calving laws that have become popular in large-scale ice sheet mechanics being
too simplistic.
  • Figure1: ice shelf geometry evolution and crevasse propagation
 
 

How to cite: Zarrinderakht, M., Zwinger, T., and Schoof, C.: A periodic visco-elastic model for crevasses propagation in marine ice shelves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1190, https://doi.org/10.5194/egusphere-egu23-1190, 2023.

EGU23-3165 | ECS | Orals | CR3.4

Antarctic ice-shelf advance driven by anomalous atmospheric and sea-ice circulation 

Frazer Christie, Toby Benham, Christine Batchelor, Wolfgang Rack, Aleksandr Montelli, and Julian Dowdeswell

The disintegration of the eastern Antarctic Peninsula’s Larsen A and B ice shelves has been attributed to atmosphere and ocean warming, and increased mass losses from the glaciers once restrained by these ice shelves have increased Antarctica’s total contribution to sea-level rise. Abrupt recessions in ice-shelf frontal position presaged the break-up of Larsen A and B, yet, in the ~20 years since these events, documented knowledge of frontal change along the entire ~1,400-km-long eastern Antarctic Peninsula is limited. Here, we show that 85% of the seaward ice-shelf perimeter fringing this coastline underwent uninterrupted advance between the early 2000s and 2019, in contrast to the two previous decades. We attribute this advance to enhanced ocean-wave dampening, ice-shelf buttressing and the absence of sea-surface slope-induced gravitational ice-shelf flow. These phenomena were, in turn, enabled by increased near-shore sea ice driven by a Weddell Sea-wide intensification of cyclonic surface winds around 2002. Collectively, our observations demonstrate that sea-ice change can either safeguard from, or set in motion, the final rifting and calving of even large Antarctic ice shelves.

How to cite: Christie, F., Benham, T., Batchelor, C., Rack, W., Montelli, A., and Dowdeswell, J.: Antarctic ice-shelf advance driven by anomalous atmospheric and sea-ice circulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3165, https://doi.org/10.5194/egusphere-egu23-3165, 2023.

EGU23-3240 | ECS | Posters on site | CR3.4

Anomalous mass gain of a tidewater outlet glacier with rapidly thinning ice sheet margin in Greenland 

Armin Dachauer and Andreas Vieli

In response to the general warming ocean-termination outlet glaciers of the Greenland ice sheet are generally thinning and retreating rapidly. However, the glacier system of Qajuuttap Sermia (also known as Eqalorutsit Kangilliit Sermiat), at the southwestern basin of the greenland ice sheet, shows a strongly contrasting and highly heterogenous dynamical behaviour. Detailed analysis of elevation changes (AeroDEM, GIMP, ArcticDEM) and front positions between the years 1985 and 2021 show slight but significant advance and thickening over at least the last 35 years, whereas its neighboring ocean- and land-terminating glaciers and more interestingly its three direct northwestern tributaries all show rapid thinning. The data indicates that effects of fjord geometry alone cannot explain this anomaly and we therefore further investigate potential reasons using operational continuous time series of solid ice flux (PROMICE) and surface mass balance from regional climate models (RACMO, MAR). 

How to cite: Dachauer, A. and Vieli, A.: Anomalous mass gain of a tidewater outlet glacier with rapidly thinning ice sheet margin in Greenland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3240, https://doi.org/10.5194/egusphere-egu23-3240, 2023.

EGU23-4043 | Posters on site | CR3.4

Impacts of tide and cavity geometry on ocean-driven melting beneath the Nansen Ice Shelf 

Taekyun Kim, Ji-Seok Hong, Jae-Hong Moon, and Emilia Kyung Jin

Mass loss from ice shelves occurs through ocean-driven melting regulated by dynamic and thermodynamic processes in sub-ice shelf cavities. However, the understanding of these oceanic processes is quite limited because of the scant observations under ice shelves. Here, a regional coupled sea-ice/ocean model that includes physical interactions between the ocean and the ice shelf is used as an alternative tool for exploring ocean-driven melting beneath the Nansen Ice Shelf (NIS), Terra Nova Bay (TNB), Antarctica.

We will first show the spatiotemporal variability signatures for different modes of ocean-driven melting at the base of the NIS. Our model includes detailed bathymetry and ice shelf base topography based on in-situ observation and has been run with and without tides. We have also investigated how tide and model geometries (i.e., cavity geometry) affect the water mass transformations and ice shelf melting/freezing regimes at the base of the ice shelf which significantly affect the ice shelf stability.

How to cite: Kim, T., Hong, J.-S., Moon, J.-H., and Jin, E. K.: Impacts of tide and cavity geometry on ocean-driven melting beneath the Nansen Ice Shelf, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4043, https://doi.org/10.5194/egusphere-egu23-4043, 2023.

EGU23-4541 | ECS | Posters on site | CR3.4

Can rifts alter ocean dynamics beneath ice shelves? 

Mattia Poinelli, Michael Schodlok, Eric Larour, Miren Vizcaino, and Riccardo Riva

The ongoing ablation of Antarctic ice shelves - to a large extent due to enhanced melting at the grounding line - is known to accelerate the outflow of upstream glaciers into the world oceans, rising the global sea level. A better understanding of ocean heat intrusion under the ice base is therefore essential to accurately estimate basal melt and the consequent impact on ice sheet dynamics. Observations also show that most ice shelves are crossed by full-thickness ice rifts. Nevertheless, their impact on ocean circulation around and below ice shelves has been largely unexplored as ocean models are commonly characterized by resolutions that are too coarse to resolve km-sized features in the ice draft. In this work, we investigate ocean circulation under rifted ice-shelves using the Massachusetts Institute of Technology ocean general circulation model. We find that the rift presence modulates oceanic heat transport toward the grounding line with potential repercussion in the dynamics of the most vulnerable portions of the ice shelf.

How to cite: Poinelli, M., Schodlok, M., Larour, E., Vizcaino, M., and Riva, R.: Can rifts alter ocean dynamics beneath ice shelves?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4541, https://doi.org/10.5194/egusphere-egu23-4541, 2023.

EGU23-4728 | Posters on site | CR3.4

NECKLACE: A circum-Antarctic dataset of basal melt 

Sue Cook, Keith W. Nicholls, Irena Vaňková, Sarah S. Thompson, and Craig L. Stewart

Ocean-driven melt at the base of floating ice shelves is a major mass loss process from the Antarctic ice sheet, and a key component in accurately predicting its contribution to future sea level rise. Observations of basal melt are important tools for testing and improving models of ice shelf-ocean interaction. While many of these observations come from satellite methods, field observations of melt are valuable for validating satellite-derived data products, and to provide higher-temporal resolution timeseries of melt.

The NECKLACE project aims to collate field measurements of ice shelf melt to create a standardised data product that can be used by glaciologists, oceanographers, and ice sheet modellers for testing and validation. Field measurements of melt can use a range of techniques, including range finding from under-ice moorings and surface radar instruments, but the most commonly used instrument in recent years is the Autonomous phase-sensitive Radio Echo Sounder (ApRES) due to its low cost and ease of deployment. The project will combine data contributions from multiple international teams to create a continent-wide, open-access database of timeseries of basal melt rates. The initial dataset will contain contributions from over 40 sites on 12 ice shelves. Beyond the collation of existing data, the project team also aims promote the collection of new field data by providing assistance with equipment procurement, set-up, and data processing. We hope that this data product can provide the basis for an ongoing monitoring network observing basal melt around Antarctica.

How to cite: Cook, S., Nicholls, K. W., Vaňková, I., Thompson, S. S., and Stewart, C. L.: NECKLACE: A circum-Antarctic dataset of basal melt, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4728, https://doi.org/10.5194/egusphere-egu23-4728, 2023.

EGU23-4949 | ECS | Orals | CR3.4

Hydraulic control of the submarine glacier melt in Greenlandic sill fjords 

Jonathan Wiskandt, Inga Monika Koszalka, and Johan Nilsson

The oceanic forcing of basal melt remains a major source of uncertainty in climate ice sheet modelling.
Several factors such as ice and fjord geometry, ambient water properties and subglacial discharge in-
fluence the submarine melt processes. We use a high resolution, non-hydrostatic configuration of the
Massachusetts Institute of Technology general circulation model (MITgcm, see Wiskandt et al., 2022)
to investigate the dependence of basal melt rates and melt driven circulation in the Sherard Osborn
Fjord (SOF) under the floating ice tongue of Ryder Glacier (RG), northwest Greenland, on the fjords
bathymetry in connection with variable subglacial discharge. In SOF, a sill in front of the floating ice
shields the cavity underneath the ice from warm Atlantic water (AW) penetrating towards the grounding
line, providing an effective shielding of the glacier from oceanic thermal forcing. The volume flux of the
AW inflow is controlled by the sill height and the melt water outflow. The outflow volume flux is in turn
dependent on basal melting, subglacial discharge and the mixing of the two with the AW. For sufficiently
strong outflow, hydraulic control at the sill crest can limit the available glacier–ward flux and therefore
the available oceanic thermal forcing for basal melting creating a stabilizing feedback. In this study
we investigate the sensitivity of the AW inflow into an idealized fjord to the presence of a sill, variable
sill height and seasonal forcing from subglacial discharge. The model results are compared to theory of
hydraulic control (Nilsson et al., 2022).

References
Nilsson, J., van Dongen, E., Jakobsson, M., O’Regan, M., and Stranne, C. (2022).
Hydraulic suppression of basal glacier melt in sill fjords. EGUsphere, 2022:1–33.
Wiskandt, J., Koszalka, I. M., and Nilsson, J. (2022). Basal melt rates and ocean
circulation under the Ryder Glacier ice tongue and their response to climate warming: a high resolution
modelling study. EGUsphere, 2022:1–29.

How to cite: Wiskandt, J., Koszalka, I. M., and Nilsson, J.: Hydraulic control of the submarine glacier melt in Greenlandic sill fjords, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4949, https://doi.org/10.5194/egusphere-egu23-4949, 2023.

EGU23-5472 | ECS | Orals | CR3.4

Ocean and atmospheric forcing of ice dynamic variability of west Antarctic Peninsula glaciers 

Benjamin Wallis, Anna Hogg, J. Melchior van Wessem, Benjamin Davison, Michiel van den Broeke, and Michael Meredith

In Antarctica changes to ice dynamics dominate the ice sheet’s contribution to rising sea-levels. The Antarctic Peninsula (AP), has undergone the greatest atmospheric warming of any southern hemisphere terrestrial area in the 20th century. Over the last three decades, the AP has experienced significant change; floating ice shelves have collapsed and retreated, and the loss of ice shelf buttressing strength has led to an acceleration in ice speed and dynamic thinning of the grounded ice. On the west coast warming ocean water at depth has been linked to glacier terminus retreat, acceleration, and grounding line retreat.

In this study, we use feature tracking of Sentinel-1 synthetic aperture radar (SAR) imagery to measure ice speed of the Antarctic Peninsula’s west coast tidewater glaciers from 2014-2022 at 6-12 day temporal resolution.

Our results show widespread patterns of increased summertime ice speed over a study area of 105 tidewater glaciers. We observe average seasonal speed variability of 12.4 ± 4.2 %, with maximum speed change of 22.3 ± 3.2 % on glaciers with the most pronounced seasonality. We also measure ice dynamic changes on inter-annual timescales on the west AP coast in this period. We study one example, Cadman Glacier, in detail, which has increased speed by 1025 ± 83 m/yr (41.6%) from October 2018 to November 2019. This increased flow speed has been maintained until at least May 2022 causing terminus retreat, increased ice discharge, and dynamic thinning of grounded ice by 20.3 ± 2.1 m/yr.

We investigate forcing mechanisms which may cause the seasonal and long-term dynamic variability we observe using a regional climate model, ocean temperature reanalysis data and remote sensing observations of terminus position. We find that summertime speed increases may be explained by a combination of perennial firn aquifer modulated meltwater runoff and seasonal patterns of terminus position change, revealing that these glaciers can respond to forcings on seasonal timescales. For the longer-term speed change, we find that the large acceleration of Cadman glacier is coincident with a period of anomalously high ocean temperatures on the west AP shelf.

How to cite: Wallis, B., Hogg, A., van Wessem, J. M., Davison, B., van den Broeke, M., and Meredith, M.: Ocean and atmospheric forcing of ice dynamic variability of west Antarctic Peninsula glaciers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5472, https://doi.org/10.5194/egusphere-egu23-5472, 2023.

EGU23-5510 | ECS | Orals | CR3.4

A 3D glacier dynamics-line plume model to estimate the frontal ablation of Hansbreen, Svalbard 

José Manuel Muñoz Hermosilla, Eva De Andrés, Kaian Shahateet, Jaime Otero, and Francisco J. Navarro

Frontal ablation is responsible for a large fraction of the mass loss from marine-terminating glaciers. The main contributors to frontal ablation are iceberg calving and submarine melting, being calving the largest one. However, submarine melting, in addition to its direct contribution to mass loss, also promotes calving through the changes induced in the stress field at the glacier terminus, so both processes should be jointly analysed. Among the factors influencing submarine melting, the formation of a buoyant plume due to the emergence of fresh subglacial water at the glacier grounding line plays a key role. 

In this study we use Elmer/Ice, an open-source, parallel, finite-element software which solves the full-Stokes system, to develop a 3D glacier dynamics model including calving and subglacial hydrology coupled with a line-plume model fed by the subglacial discharge that accounts for the submarine melting at the calving front. The ice flow model provides the calving front position at every time-step. 

We apply this model to the Hansbreen–Hansbukta glacier–fjord system in Southern Spitsbergen, Svalbard, where a large set of data are available for both glacier and fjord. The evolution of the modelled front positions are in agreement in terms of advance and retreatment with those observed from time-lapse images of the glacier front, and, in general, the modelled is always ahead of the observed due to an underestimation of calving.

How to cite: Muñoz Hermosilla, J. M., De Andrés, E., Shahateet, K., Otero, J., and Navarro, F. J.: A 3D glacier dynamics-line plume model to estimate the frontal ablation of Hansbreen, Svalbard, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5510, https://doi.org/10.5194/egusphere-egu23-5510, 2023.

EGU23-5617 | Posters on site | CR3.4

Ocean variability beneath the Filchner-Ronne Ice Shelf inferred from basal melt rate time series 

Keith Nicholls and Irena Vankova

We deployed multiple phase-sensitive radars (ApRES) on Filchner-Ronne Ice Shelf (FRIS) to measure and characterize variability in its basal melt rate under present-day climatic conditions.
Sites along the western portion of the ice shelf show primarily seasonal variations, consistent with the propagation dynamics of seasonal dense water from the western FRIS front into the cavity.
Fifteen years of melt rate estimates from instruments moored beneath the ice at sites further from the western Ronne Ice Front are remarkably uniform in that melting is bounded between 0 and 1 m/a throughout the record. Here, inter-annual melt rate variability is expressed as a suppression or delay in the arrival of a seasonal melt rate minimum, which can be understood in terms of inter-annual stratification changes and variable inflow pathways towards the western Ronne sites.
Elsewhere in the cavity, along a direct flow pathway connecting the western FRIS front and the southwestern tip of Berkner Island, the lower frequency inter-annual signal is superimposed on a regular seasonal signal, with year-to-year melt rate variations as high as 1 m/a. Anomalously low summer sea-ice concentrations in front of the ice shelf, such as in 1998 and 2017, cause higher melting along this pathway with a year's delay.
Long term mean ApRES melt rates agree with estimates from satellite data over eastern FRIS. However, the satellite estimates overstate the area of active basal freezing in the western part of the ice shelf. The temporal melt rate variability from the satellite estimates dramatically overstates the range of variability at both seasonal and inter-annual time scales and only one site, on the eastern Ronne Ice Shelf, shows any correspondence between the in-situ and remotely derived inter-annual variability.

How to cite: Nicholls, K. and Vankova, I.: Ocean variability beneath the Filchner-Ronne Ice Shelf inferred from basal melt rate time series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5617, https://doi.org/10.5194/egusphere-egu23-5617, 2023.

EGU23-5827 | Posters on site | CR3.4

Processes driving the speedup of Pine Island Ice Shelf between 2017 and 2020 

Sainan Sun and Gudmundur Hilmar Gudmundsson

From 2017 to 2020, three significant calving events took place on Pine Island Glacier, West Antarctica. Ice-shelf velocities changed over this period and the calving events have been suggested as possible drivers. However, satellite observations also show significant changes in the areal extent of fracture zones, especially in the marginal areas responsible for providing lateral support to the ice shelf. Here we conduct a model study to identify and quantify drivers of recent ice-flow changes of the Pine Island Ice Shelf. In agreement with recent studies, we find that the calving events caused significant velocity changes over the ice shelf. However, calving alone cannot explain observed velocity changes. Changes in the structural rigidity, i.e., ice damage, further significantly impacted ice flow. We suggest that ice damage evolution of the ice-shelf margins may have influenced recent calving events, and these two processes are linked.

How to cite: Sun, S. and Gudmundsson, G. H.: Processes driving the speedup of Pine Island Ice Shelf between 2017 and 2020, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5827, https://doi.org/10.5194/egusphere-egu23-5827, 2023.

EGU23-7094 | Orals | CR3.4

Precursor of disintegration of Greenland's largest floating ice tongue 

Angelika Humbert, Veit Helm, Niklas Neckel, Ole Zeising, Martin Rückamp, Khan Shfaqat Abbas, Loebel Erik, Dietmar Gross, Rabea Sondershaus, and Ralf Müller

The largest floating tongue of Greenland’s ice sheet, Nioghalvfjerdsbrae, has so far been relatively stable with respect to areal retreat. Curiously, it experienced significant less thinning and ice flow acceleration than its neighbour Zacharias Isbrae. Draining more than 6% of the ice sheet, Nioghalvfjerdsbrae might become a large contributor to sea level rise in the future. Therefore, the stability of the floating tongue is a focus of this study. We employ a suite of observational methods to detect recent changes. We found that the calving style has changed at the southern part of the eastern calving front from normal tongue-type calving to a crack evolution initiated at frontal ice rises reaching 5-7km and progressing further upstream compared to 2010. The calving front area is further weakened by a substantial increase of a zone of fragments and open water at the tongue’s southern margin, leading to the formation of a narrow ice bridge. These geometric and mechanical changes are a precursor of instability of the floating tongue. We complement our study by numerical ice flow simulations to estimate the impact of future break-up or disintegration events on the ice discharge. These idealised scenarios reveal that a loss of the south-eastern area would lead to 1% of increase of ice discharge at the grounding line, while a sudden collapse of the frontal area (46% of the floating tongue area) will enhance the ice discharge by 8.3% due to loss in buttressing.

Humbert, A., Helm, V., Neckel, N., Zeising, O., Rückamp, M., Khan, S. A., Loebel, E., Gross, D., Sondershaus, R., and Müller, R.: Precursor of disintegration of Greenland's largest floating ice tongue, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-171, in review, 2022

How to cite: Humbert, A., Helm, V., Neckel, N., Zeising, O., Rückamp, M., Shfaqat Abbas, K., Erik, L., Gross, D., Sondershaus, R., and Müller, R.: Precursor of disintegration of Greenland's largest floating ice tongue, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7094, https://doi.org/10.5194/egusphere-egu23-7094, 2023.

EGU23-7096 | ECS | Posters on site | CR3.4

Decadal grounding line migration and ice shelf melt regime of Petermann Glacier, North-West Greenland, from high-resolution InSAR data. 

Enrico Ciracì, Eric Rignot, Bernd Scheuchl, Valentyn Tolpekin, Michael Wollersheim, Lu An, Pietro Milillo, Jose Luis Bueso-Bello, Paola Rizzoli, and Luigi Dini

Petermann Glacier (80.75N, 60.75W) terminates in one of the most extensive remaining ice tongues of the Greenland Ice Sheet. The glacier is grounded 600 meters below sea level on a downsloping bed and could significantly contribute to sea level rise during the 21st century. Recent observations showed an ongoing acceleration in ice flow and kilometric-scale grounding line retreat after decades of stable dynamic conditions. Warming of the ocean waters surrounding Greenland has been indicated as the main driver of this process. However, the melting regime of the glacier at the interface between ocean waters and grounded ice is not well known and needs to be investigated.

In this study, we achieve this goal by employing a time series of satellite radar interferometry data available between 2011 and 2022. We document grounding line migration using high-frequency observations from the Italian COSMO-Skymed constellation and the Finnish ICEYE constellation. Furthermore, we use time-tagged digital elevation models from the German TanDEM-X mission to assess the ice shelf basal melt regime in a Lagrangian framework.

InSAR observations reveal kilometer-size grounding line migrations - (2-6 km) grounding zones - during tidal cycles, with preferential seawater intrusions of 6 km along pre-existing subglacial channels. In addition, results from the Lagrangian approach indicate that the highest ice shelf melt rates occur at these locations, with values reaching peaks ranging from 60 to 80 meters per year.

Such high melt rates concentrated in kilometer-wide grounding zones contrast with the traditional plume model adopted by physical models with zero melt at a fixed grounding line. Their inclusion in physical models will increase the glacier's sensitivity to ocean warming and double the projections of sea level rise from the glacier.

This work was supported by a grant from NASA.

How to cite: Ciracì, E., Rignot, E., Scheuchl, B., Tolpekin, V., Wollersheim, M., An, L., Milillo, P., Bueso-Bello, J. L., Rizzoli, P., and Dini, L.: Decadal grounding line migration and ice shelf melt regime of Petermann Glacier, North-West Greenland, from high-resolution InSAR data., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7096, https://doi.org/10.5194/egusphere-egu23-7096, 2023.

EGU23-7200 | ECS | Orals | CR3.4 | Highlight

Un-pinning of Antarctic ice shelves over the past 5 decades 

Bertie Miles and Robert Bingham

Pinning points form when floating ice shelves locally reground on bathymetric highs. The anchoring of ice shelves onto these pinning points buttresses ice flow from the interior of the ice sheet, meaning they play a vital role in the mass balance of the ice sheet. However, we do not know how the hundreds of pinning points that fringe the Antarctic coastline have changed over recent decades. By utilizing the historic Landsat satellite image archive, we show that there has been an acceleration in pinning point loss over the past 5 decades, and in doing so help resolve the timeline of the onset of widespread ice shelf thinning in Antarctica. Between 1974 and 1990, only ice shelves in isolated regions were thinning and unanchoring from their pinning points, with 11% of all mapped pinning points reducing in size. Pinning point loss spreads from these isolated regions in the 1990s, with the proportion of pinning points reducing in size across the ice sheet more than doubles to 23%, before further increasing to 35% between 2000 and 2022. Pinning point loss is concentrated along the western Antarctic Peninsula, West Antarctic and Wilkes Land coastlines, but we do also observe the rapid growth and break-up of some large ice rises outside of these regions. Continued acceleration in pinning point loss will reduce the buttressing potential of ice shelves and ultimately result in enhanced discharge of ice into the Southern Ocean and contribute to sea level rise.

How to cite: Miles, B. and Bingham, R.: Un-pinning of Antarctic ice shelves over the past 5 decades, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7200, https://doi.org/10.5194/egusphere-egu23-7200, 2023.

EGU23-7295 | ECS | Orals | CR3.4

Simulating northern hemisphere glacier – ocean interactions using the Open Global Glacier Model and the Nucleus for European Modelling of the Ocean 

Jan-Hendrik Malles, Fabien Maussion, Lizz Ultee, Will Kochtitzky, Luke Copland, Paul Myers, and Ben Marzeion

Marine-terminating glaciers cover roughly one-third of the Northern Hemisphere's glacierized area (outside the Greenland ice sheet) and their direct freshwater export to the oceans has the potential to change not only global mean sea level (GMSL), but also local and regional ocean circulation patterns. Due to the interrelation of surface and frontal mass budgets, the dynamics of marine-terminating  glaciers are distinct from those of land-terminating glaciers forced only by the atmosphere. Here, were present recent advances in large-scale modeling of marine-terminating glaciers in the Open Global Glacier Model (OGGM). These include an enhanced representation of frontal processes and an independent calibration of surface and frontal mass balance. Further, we do a first investigation of coupling effects with an ocean general circulation model (Nucleus for European Modelling of the Ocean; NEMO). Including an explicit treatment of frontal processes (but so far ignoring future changes in ocean climate), we find that the spread between different emission scenarios at the end of the 21st century is reduced. Cumulative GMSL rise contribution projected for Northern Hemisphere glaciers is reduced by ca. 8 % in 2100, while the reduction for marine-terminating glaciers is ca. 23 %. Utilizing temperature and salinity output of NEMO, configured for the Arctic and Northern Hemisphere Atlantic (NEMO-ANHA4), to force a newly implemented submarine melt parameterization in OGGM, we estimate that 12 (6 - 22) % of the total frontal ablation was caused by submarine melt between 2010 and 2020. Finally, we explore differences in the ocean model’s output between runs that include the freshwater forcing from northern hemisphere glaciers and those that do not. The two main findings considering NEMO runs that include the freshwater forcing derived from OGGM output compared to those that do not are: i) an increased heat transport into Baffin Bay, and ii) changes in the pathways of Atlantic water to the Arctic Ocean, with less transport into the Barents Sea and more through Fram Strait.

How to cite: Malles, J.-H., Maussion, F., Ultee, L., Kochtitzky, W., Copland, L., Myers, P., and Marzeion, B.: Simulating northern hemisphere glacier – ocean interactions using the Open Global Glacier Model and the Nucleus for European Modelling of the Ocean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7295, https://doi.org/10.5194/egusphere-egu23-7295, 2023.

EGU23-7332 | ECS | Orals | CR3.4 | Highlight

Antarctic ice shelf front dynamics between 2015 and 2023 

Celia A. Baumhoer, Andreas J. Dietz, and Claudia Kuenzer

The Antarctic ice sheet is fringed by ice shelves regulating the ice flow into the ocean. The fronts of these ice shelves are constantly moving and are sensitive indicators of glaciological and environmental change. Previously, Antarctic ice shelf front change was not observed regularly due to limited availability of satellite imagery and time-consuming manual front delineations. The era of freely available SAR satellite data and recent developments in image processing with artificial intelligence created new opportunities for monitoring ice shelf front dynamics on a regular basis. Here, we present the IceLines dataset providing continuous time series of calving front dynamics for 36 major Antarctic ice shelves since 2015. The dataset consists of over 19,000 front positions extracted from Sentinel-1 satellite data by using a convolutional neural network called HED-Unet. The automatically extracted front positions can deviate from manual delineated fronts due to fast ice, mélange and icebergs close to the front by 209±12 m (5.2 pixel) on dual polarized imagery and 432±21 m (8.8 pixel) for single-polarized imagery whereas the frontal movement can be determined with higher accuracies of 63±68 m (1.6 pixel) for dual and 107±126 m (2.7 pixel) for single polarized imagery. To minimize errors and enhance quick usability, automatic separation of unreliable front positions (still accessible) is applied for an easy analysis of the dataset. This contribution features the analysis of the IceLines dataset providing new insights into Antarctic calving front dynamics by investigating intra-annual calving front dynamics, changing advance rates of ice shelf fronts, recent calving events and overall calving front change.

How to cite: Baumhoer, C. A., Dietz, A. J., and Kuenzer, C.: Antarctic ice shelf front dynamics between 2015 and 2023, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7332, https://doi.org/10.5194/egusphere-egu23-7332, 2023.

A 3D modelling of glacial bay hydrodynamics has been performed in Admiralty Bay (AB), King George Island, Antarctica, using Delft3D Flow model with tides and density gradients as its drivers. It was conducted in multiple scenarios of varied glacial input - a baseline case without meltwater added, and scenarios with meltwater input divided into two modes: drained through entire glacial front and with glacial input solely added to the surface layers of the ocean. Each mode was further divided into three cases dependent on the volume of  freshwater added, with estimated small, medium and large volume input case per each mode. Through these seven studied scenarios a character of glacial impact on overall glacial bay flow patterns, water level changes and salinity was shown. Results revealed general circulation pattern in AB, consisting of two cyclonic circulation cells that control water exchange between the bay and the ocean. Cells are separated by a boundary area, located approximately 7 km from the bay’s opening, dividing Admiralty Bay into waters primarily driven by the ocean, and inner waters significantly influenced by glacial input. This pattern is consistent in all studied cases, however its intensity and specific location is controlled by the volume of glacial input and tidal phases. Although water level changes have been found to be overall predominantly driven by tides, areas within the boundary and top 50-60 m of the water column are substantially influenced by glacial forcing, regardless of the scenario mode. Salinity distribution showed strong water column stratification, classifying AB as a salt-wedge estuary. Gathered results have been confronted with abundant in situ measurements consisting of ADCP probing validating water flow velocities and CTD+ profile measurements consistently carried out in 31 locations in AB, throughout three-year long period. Modelled calculations compared with measurement dataset allowed an estimation of summerly glacial inflow volume into AB from adjacent twenty tidewater glaciers. These values contrasted with CTD+ data from different seasons permitted assessment of glacial input volume variability during the course of the year. Altogether results of the study give first in this scale and detail image of seasonally changing impact of glaciers on Antarctic bay waters.

How to cite: Osińska, M.: Features and extent of meltwater impact on glacial bay’s flow pattern, water level and salinity revealed through multi-scenario 3D modelling and in situ measurements in Admiralty Bay, Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7613, https://doi.org/10.5194/egusphere-egu23-7613, 2023.

EGU23-7811 | Posters on site | CR3.4

Impact of sliding laws and surface mass projections on Greenland outlet glacier dynamics at 100-year timescales 

Rachel Carr, Emily Hill, and Hilmar Gudmundsson

The Greenland Ice Sheet (GrIS) contributed to 10.6 mm to global sea level rise between 1992 and 2018, making it crucial to accurately forecast its near future ice losses. Here, we assess the relative importance of two major sources of uncertainty in GrIS ice loss, namely the choice of sliding law and SMB forecasts. To do this we use the ice flow model Úa to perform a series of model experiments using different formulations of the sliding law, and different projections of future surface mass balance (SMB). We conducted this work at three major Greenland outlet glaciers, to assess the variability in the importance of sliding laws and/or SMB forecasts between different types of glacier. Our results show that the choice of sliding law had a small impact on ice loss from our study glaciers, whereas the choice of SMB forecast produced major differences in sea level rise estimates.

How to cite: Carr, R., Hill, E., and Gudmundsson, H.: Impact of sliding laws and surface mass projections on Greenland outlet glacier dynamics at 100-year timescales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7811, https://doi.org/10.5194/egusphere-egu23-7811, 2023.

EGU23-8124 | Orals | CR3.4

Strong ocean melting feedback during the recent retreat of Thwaites Glacier 

Paul Holland, Suzanne Bevan, and Adrian Luckman

The accelerating ice loss from Thwaites Glacier is making a substantial contribution to global sea-level rise, and could add tens of centimetres to sea level over the coming centuries. This ice loss is associated with rapid thinning and disintegration of the floating sections of Thwaites Glacier, and retreat of its grounding line. In this study, we use a high-resolution ocean model and a series of Digital Elevation Models of the floating part of Thwaites Glacier from 2011 to 2022 to simulate the evolution of sub-ice melting during this rapid retreat.

The results show that the ice evolution induces a strong geometrical feedback onto melting. The ice thinning and retreat provide a larger melting area, thicker and better-connected sub-ice water column, and steeper ice base. This leads to stronger sub-ice ocean currents, increasing melting by ~50% without any change in forcing from wider ocean conditions. This geometrical feedback over just 12 years is stronger than the melting changes expected from century-scale changes in ocean conditions and subglacial meltwater input. The strength of this feedback implies that greenhouse gas emissions policy may have a very weak influence over future ocean-driven ice loss from Thwaites Glacier.

How to cite: Holland, P., Bevan, S., and Luckman, A.: Strong ocean melting feedback during the recent retreat of Thwaites Glacier, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8124, https://doi.org/10.5194/egusphere-egu23-8124, 2023.

EGU23-8261 | ECS | Orals | CR3.4

Rift Initiation via Unstable Basal Crevasses 

Niall Coffey, Ching-Yao Lai, and Yongji Wang

Ice shelves, the floating extensions of ice sheets, can reduce the rate of sea level rise by buttressing the upstream grounded ice. However, calving, or the fracturing that creates icebergs, can cut out regions that were resisting flow, and allow for increased ice flux and thus sea level contribution. In this work, I focus on the transition from basal crevasses, or seawater-filled fractures on the bottom surface, to full thickness fractures called rifts. Using RACMO ice shelf surface temperatures and holding the ice-ocean interface at -2℃, I find good agreement between observed rifts on the Larsen C and Ross Ice Shelves and rifts predicted to evolve from basal crevasses through 2D Mode I Linear Elastic Fracture Mechanics (LEFM). I also explore the influence of ice shelf geometry in rift formation by solving the Shallow Shelf Approximation (SSA) equations for idealized ice shelves with COMSOL’s Finite Element Analysis software. Using the stress field outputs with LEFM’s rift initiation criteria, I find qualitative agreement in the rift orientation between the predicted unstable basal crevasses and the observed rifts on the left margin of Pine Island Ice Shelf.

How to cite: Coffey, N., Lai, C.-Y., and Wang, Y.: Rift Initiation via Unstable Basal Crevasses, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8261, https://doi.org/10.5194/egusphere-egu23-8261, 2023.

EGU23-8393 | ECS | Orals | CR3.4

Constraining the overall future projection of Upernavik Isstrøm by observations 

Eliot Jager, Fabien Gillet-Chaulet, Nicolas Champollion, and Romain Millan

The uncertainty of the future contribution to sea level rise of the Antarctic and Greenland polar ice sheet remains important, as shown by the latest multi-model intercomparison (ISMIP6). We can summarise three main sources of uncertainties that are related to the ice flow model, the atmospheric and oceanic forcing and final to the Shared Socioeconomic Pathways (SSP). Results for the Greenland Ice Sheet (Goelzer and al., 2020) show that the model uncertainty explains a similar part of the ensemble spread (40 mm of sea-level rise by 2100) compared to the atmospheric forcing uncertainty (36 mm) or the SSP uncertainty (48 mm) and two times more than the ensemble spread due to the oceanic forcing uncertainty (19 mm). 

Uncertainties in ice flow models are mainly due to different assumptions in numerical models and parameterisation, as well as model initialisation (spin-up, data assimilation). Here, we investigate the sensitivity of a single ice flow model (Elmer/Ice) to different sources of uncertainties for Upernavik Isstrøm, a tidewater glacier in the North-West sector of Greenland. To achieve this goal, we have identified potential sources of uncertainties: parameters related to the initialization of the model by inverse method (ice stiffness, friction law, regularization, input observations), those related to the dynamics (ice flow law, friction law) and finally those related to the forcing (sensitivity to the ocean, global climate model, regional climate model, SSP). To evaluate their influence we run a 200-member ensemble that samples these different sources of uncertainty. Each member is initialised to a state close to 1985 and evaluated during a historical simulation from 1985 to 2015 where the front positions are forced using observations (Wood et al., 2021). We then use the ISMIP6 protocol where the front position is parametrized as a function of ocean temperature and runoff to perform projections to 2100.

We then evaluate the sensitivity of this ensemble to our different sources of uncertainty using Sobol indices. Based on this novel approach, we define several metrics that allow us to score individual ensemble members using a comprehensive record of ice velocity, surface elevation and mass loss over the period 1985-2015. We then evaluate the possibility of reducing the uncertainty in Upernavik Isstrøm's contribution to sea level rise using these scores. 

How to cite: Jager, E., Gillet-Chaulet, F., Champollion, N., and Millan, R.: Constraining the overall future projection of Upernavik Isstrøm by observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8393, https://doi.org/10.5194/egusphere-egu23-8393, 2023.

EGU23-8902 | ECS | Orals | CR3.4

Baroclinic Ocean Response to Climate Forcing Regulates Decadal Variability of Ice-Shelf Melting in the Amundsen Sea 

Alessandro SIlvano, Paul Holland, Kaitlin Naugthen, Oana Dragomir, Pierre Dutrieux, Adrian Jenkins, Yidongfang Si, Andrew Stewart, Beatriz Peña Molino, Gregor Janzing, Tiago Dotto, and Alberto Naveira Garabato

Warm ocean waters drive rapid ice-shelf melting in the Amundsen Sea. The ocean heat transport toward the ice shelves is associated with the Amundsen Undercurrent, a near-bottom current that flows eastward along the shelf break and transports warm waters onto the continental shelf via troughs. Here we use a regional ice-ocean model to show that, on decadal time scales, the undercurrent's variability is baroclinic (depth-dependent). Decadal ocean surface cooling in the tropical Pacific results in cyclonic wind anomalies over the Amundsen Sea. These wind anomalies drive a westward perturbation of the shelf-break surface flow and an eastward anomaly (strengthening) of the undercurrent, leading to increased ice-shelf melting. This contrasts with shorter time scales, for which surface current and undercurrent covary, a barotropic (depth-independent) behavior previously assumed to apply at all time scales. This suggests that interior ocean processes mediate the decadal ice-shelf response in the Amundsen Sea to climate forcing.

How to cite: SIlvano, A., Holland, P., Naugthen, K., Dragomir, O., Dutrieux, P., Jenkins, A., Si, Y., Stewart, A., Peña Molino, B., Janzing, G., Dotto, T., and Naveira Garabato, A.: Baroclinic Ocean Response to Climate Forcing Regulates Decadal Variability of Ice-Shelf Melting in the Amundsen Sea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8902, https://doi.org/10.5194/egusphere-egu23-8902, 2023.

EGU23-9001 | ECS | Orals | CR3.4

Accessing ice effective viscosity using physics-informed deep learning 

Wang Yongji and Ching-Yao Lai

Ice flows in response to stresses according to the flow law that involves ice viscosity. An accurate description of effective ice viscosity is essential for predicting the mass loss of the Antarctic Ice Sheet, yet measurement of ice viscosity is challenging at a continental scale. Lab experiments of polycrystalline ice shows that the effective viscosity of ice obeys a power-law relation with the strain rate, known as Glen’s flow law. However, it remains unclear how processes at ice-shelf scales impact the effective viscosity of glacial ice. Here, we leverage the availability of remote-sensing data and physics-informed deep learning to infer the effective ice viscosity and examine the rheology, i.e. flow law,  of glacial ice in Antarctic Ice Shelves. We find that the rheology of ice shelves differs substantially between the compression and extension zones. In the compression zone near the grounding line the rheology of ice closely obeys power laws with exponents in the range 1<n<3, consistent with prior laboratory experiments. In the extension zone, which comprises most of the total ice-shelf area, ice performs complex rheological behavior, deviating from laboratory findings. We also discover the areas where ice viscosity appears non-isotropic.

How to cite: Yongji, W. and Lai, C.-Y.: Accessing ice effective viscosity using physics-informed deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9001, https://doi.org/10.5194/egusphere-egu23-9001, 2023.

EGU23-9993 | ECS | Orals | CR3.4

Seasonal land-ice-flow variability and its drivers in the Antarctic Peninsula 

Karla Boxall, Frazer D. W. Christie, Ian C. Willis, Jan Wuite, and Thomas Nagler

Ice flow of the Antarctic Ice Sheet has experienced multi-annual acceleration in response to increased rates of ice thinning and retreat. Despite the well-documented seasonality of ice flow in Arctic and Alpine regions, little to no observations exist of seasonal ice-flow variability in Antarctica, due largely to a lack of systematic, high temporal-resolution satellite imagery. Accordingly, the mechanisms driving any such seasonality remain similarly undetermined. Such information is critical for understanding, modelling, and ultimately refining projections of the ongoing and future contribution of Antarctica to global sea-levels.

Here, we use high spatial- and temporal- (6/12-daily) resolution Copernicus Sentinel-1a/b synthetic aperture radar (SAR) observations spanning 2014 to 2020 to provide evidence for seasonal flow variability of land ice feeding George VI Ice Shelf (GVIIS), Antarctic Peninsula. Between 2014 and 2020, the flow of glaciers draining to GVIIS from Palmer Land and Alexander Island increased during the austral summertime (December – February) by ~15% relative to baseline rates of flow. This speedup is corroborated by independent observations of ice flow as imaged by the Landsat 8 Operational Land Imager.

To identify the likely drivers of this seasonality, we carry out statistical time-series analyses on an array of remotely sensed and reanalysis datasets of potential environmental forcing mechanisms. We find that both surface and oceanic forcing act as statistically significant precursors to summertime ice-flow acceleration. Ultimately, these findings imply that seasonality may be present elsewhere in Antarctica where comparable forcing mechanisms exist.

How to cite: Boxall, K., Christie, F. D. W., Willis, I. C., Wuite, J., and Nagler, T.: Seasonal land-ice-flow variability and its drivers in the Antarctic Peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9993, https://doi.org/10.5194/egusphere-egu23-9993, 2023.

EGU23-10142 | ECS | Orals | CR3.4

Drivers and rarity of the strong 1940s westerly wind event in the Amundsen Sea, West Antarctica 

Gemma O'Connor, Paul Holland, Eric Steig, Pierre Dutrieux, and Greg Hakim

Ice loss in the Amundsen Sea Embayment is occurring primarily via exposure to warm ocean water, which varies in response to local wind variability. There is evidence that glacier retreat in this region was initiated in the mid-20th century, however the perturbation that may have triggered retreat remains unknown, leaving the climatic mechanisms driving retreat highly uncertain. A leading hypothesis is that a large atmospheric circulation anomaly in the Amundsen Sea occurred in the 1940s, driving a strong oceanic ice-shelf melting perturbation. However, the characteristics and drivers of this 1940s event remain poorly constrained, and the expected occurrence of such events in a natural climate has not yet been evaluated. We investigate this hypothesis using paleoclimate reconstructions and climate model simulations. The reconstructions show that a large multi-year westerly wind anomaly occurred from ~1938-1942, likely as a combined response to the very large El Niño event from 1940-1942 and variability sourced from outside the tropical Pacific starting years earlier. In climate model simulations we find evidence that events of similar magnitude and duration are unusual but may have occurred tens to hundreds of times throughout the Holocene. Our results suggest that the strong westerly event in the 1940s is unlikely to be exceptional enough to initiate glacier retreat on its own; naturally driven climatic/oceanic trends preceding the event or perhaps anthropogenically driven trends following the event are needed to explain retreat. Our analyses provide novel constraints on the significance of the 1940s westerly event in the Amundsen Sea and highlight outstanding uncertainties in our understanding of the mechanisms driving glacier retreat. 

How to cite: O'Connor, G., Holland, P., Steig, E., Dutrieux, P., and Hakim, G.: Drivers and rarity of the strong 1940s westerly wind event in the Amundsen Sea, West Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10142, https://doi.org/10.5194/egusphere-egu23-10142, 2023.

EGU23-10704 | Orals | CR3.4

Community-based monitoring to understand changing tidewater glacier-ocean interactions in the Canadian Arctic Archipelago 

Maya Bhatia, Stephanie Waterman, Erin Bertrand, Paul Myers, Andrew Hamilton, Terry Noah, David Burgess, Eric Brossier, France Pinczon du Sel, Claire Parrott, Patrick White, Patrick Williams, Megan Roberts, Maria Cavaco, Jenifer Spence, and Ana Heras Duran

Tidewater glaciers are defining coastal features in Canadian high Arctic marine systems. Rapid Arctic climate warming is dramatically altering the nature of these coastlines and adjacent waters through changing atmospheric forcing, a lengthening open-water season, and accelerating glacier retreat. These changes have a broad range of impacts enhancing glacier meltwater discharge, shifting coastal biological productivity patterns, and changing upper ocean freshwater variability and circulation. For the community of Aujuittuq (Grise Fiord), Canada’s northernmost community and ‘the place that never thaws’, these impacts have critical implications for local infrastructure, travel safety and food security. Over the last decade, Aujuittuq community members have noted significant recession of glaciers, as well as changes in the fjords surrounding their home and hunting grounds. To better understand these changes, for the last several years, we have been collaborating with the community to collect year-round marine observations in Jones Sound, home of the Inuit of Aujuittuq. Our observations span the nearshore coastal zone to the open Sound, comparing glacierized and non-glacierized fjords and multiple glaciers of varying type (land-terminating, tidewater), grounding line depth, and size draining surrounding ice caps. In total these observations represent over 400 casts measuring water column temperature, salinity, turbidity, dissolved oxygen, and chlorophyll a, with paired bottle samples characterizing carbon, nutrient, metal, and phytoplankton community composition and activity to elucidate how these properties evolve with distance from the shore. In 2022, we worked with 12 local youth, adults, and elders to make these observations. Our efforts aim to establish a long-term, community-led monitoring program centered around the co-consideration of Indigenous and scientific knowledge to understand ongoing change in high Arctic coastal environments. Results from this study substantially further our holistic understanding of glacier-ocean impacts in the sparsely sampled Canadian Arctic Archipelago and beyond, while also providing data critical to accurate future projections of high-latitude marine change in regions that are a hotspot for tidewater glacial retreat and meltwater runoff to the ocean.

How to cite: Bhatia, M., Waterman, S., Bertrand, E., Myers, P., Hamilton, A., Noah, T., Burgess, D., Brossier, E., Pinczon du Sel, F., Parrott, C., White, P., Williams, P., Roberts, M., Cavaco, M., Spence, J., and Heras Duran, A.: Community-based monitoring to understand changing tidewater glacier-ocean interactions in the Canadian Arctic Archipelago, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10704, https://doi.org/10.5194/egusphere-egu23-10704, 2023.

EGU23-11256 | ECS | Orals | CR3.4

Mass balance of the northern Antarctic Peninsula Ice Sheet 

Thorsten Seehaus, Christian Sommer, Philipp Malz, Thomas Dethinne, Francisco Navarro, and Kaian Shahateet

Some of the highest specific mass change rates in Antarctica are reported for the Antarctic Peninsula. However, the existing estimates for the northern Antarctic Peninsula (<70°S) are either spatially limited or are affected by considerable uncertainties. Within this study, the first assessment of the geodetic mass balance throughout the ice sheet of the northern Antarctic Peninsula is carried out employing bi-static SAR data from the TanDEM-X satellite mission. Repeat coverages from austral-winters 2013 and 2017 are employed. An overall coverage of 96.4% of the study area by surface elevation change measurements is revealed. The spatial distribution of the surface elevation and mass changes points out, that the former ice shelf tributary glaciers of the Prince-Gustav-Channel, Larsen-A&B, and Wordie ice shelves are the hotpots of ice loss in the study area, and highlights the long-lasting dynamic glacier adjustments after the ice shelf break-up events. The highest mass change rate is revealed for the Airy-Seller-Fleming glacier system and the highest average surface elevation change rate is observed at Drygalski Glacier. The comparison of the ice mass budget with anomalies in the climatic mass balance indicates, that for wide parts of the southern section of the study area the mass changes can be partly attributed to changes in the climatic mass balance. The previously reported connection between mid-ocean warming along the southern section of the west coast and increased frontal glacier recession does not repeat in the pattern of the observed glacier mass losses, excluding Wordie Bay.

How to cite: Seehaus, T., Sommer, C., Malz, P., Dethinne, T., Navarro, F., and Shahateet, K.: Mass balance of the northern Antarctic Peninsula Ice Sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11256, https://doi.org/10.5194/egusphere-egu23-11256, 2023.

EGU23-11569 | Posters on site | CR3.4

Basal melt rates and mechanical properties of the Ekström Ice Shelf, East Antarctica, inferred from repeat, quad-polarimetric radar data 

Reinhard Drews, Falk Oraschewski, Mohammadreza Ershadi, Clara Henry, Vjeran Višnjević, Paul Bons, Inka Koch, Jonathan Hawkins, and Olaf Eisen

Ice shelves buttress ice flow from Antarctica’s interior and provide the interface for ice-ocean interactions. Here, we present a comprehensive dataset collected with autonomous phase-sensitive radio-echo sounders (ApRES) on the Ekström Ice Shelf in East Antarctica. The data include a one year time series of basal melt near the grounding zone and > 40 repeat and quad-polarimetric observations covering the entire ice shelf with the transition to grounded ice and multiple lateral shear zones. The inferred melt rates are put into context in terms of their seasonality and with respect to spatial patterns which were mapped in the internal ice stratigraphy. The polarimetric backscatter show signs of anisotropic ice fabric and spatial changes can be traced coherently from the grounding line to the ice-shelf front. We investigate those signatures in conjunction with the vertical strain rates inferred from ApRES and ice-flow modelling to learn more about ice-shelf rheology, particularly with respect to the stress exponent n.

How to cite: Drews, R., Oraschewski, F., Ershadi, M., Henry, C., Višnjević, V., Bons, P., Koch, I., Hawkins, J., and Eisen, O.: Basal melt rates and mechanical properties of the Ekström Ice Shelf, East Antarctica, inferred from repeat, quad-polarimetric radar data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11569, https://doi.org/10.5194/egusphere-egu23-11569, 2023.

EGU23-11610 | ECS | Posters on site | CR3.4

Frontal processes of Sermeq Kujalleq in West Greenland observed with repeated UAV surveys 

Andrea Kneib-Walter, Guillaume Jouvet, Adrien Wehrlé, Ana Nap, Fabian Walter, and Martin P. Lüthi

Outlet glaciers and ice streams transport ice from the ice sheets to the ocean, where the glaciers lose mass by iceberg calving. Sermeq Kujalleq (SKK, Jakobshavn Isbræ) is one of the largest and most dynamic ice streams of the Greenland Ice Sheet with ice flow velocities up to 40 m/day. With extensive fieldwork and detailed repeated UAV surveys we aim at understanding the complex processes occuring at the ice stream margins and at the calving front of SKK. Such processes are often neclected in numerical models inducing uncertainties in projections of the ice sheet evolution.

Within the framework of the COEBELI project we conducted drone photogrammetry surveys in July 2022 at SKK along other field measurements including in-situ GPS, GPRI, seismometers, and time-lapse imagery. Despite challenging weather conditions and constraints due to flying restrictions, we acquired 17 repeated flight surveys over the calving front of SKK during two weeks. As a result, we produced a large imagery data set, which was processed to infer high-resolution ortho-images and digital elevation models (DEM). Comparing the different products enables us to estimate changes in surface topography and ice dynamics. During the observation period several large calving events occurred allowing us to investigate the interaction between frontal processes and ice flow dynamics. With the very detailed data we can study crevasse opening, acceleration at the front, weaknesses in the ice and their origin, and the reaction of the glacier to large calving events.

How to cite: Kneib-Walter, A., Jouvet, G., Wehrlé, A., Nap, A., Walter, F., and Lüthi, M. P.: Frontal processes of Sermeq Kujalleq in West Greenland observed with repeated UAV surveys, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11610, https://doi.org/10.5194/egusphere-egu23-11610, 2023.

EGU23-11661 | ECS | Posters on site | CR3.4

Revisiting the impact of anomalous precipitation on the mass budget of the Amundsen Sea Embayment ice streams 

Benjamin Davison, Anna Hogg, Richard Rigby, Sanne Veldhuijsen, Melchior van Wessem, Michiel van den Broeke, Paul Holland, Heather Selley, and Pierre Dutrieux

Mass loss from the West Antarctic Ice Sheet is dominated by glaciers draining into the Amundsen Sea Embayment (ASE). The majority of that mass loss is driven by decadal variations in submarine melt rates. However, periods of extremely high or low precipitation can compound or mitigate ocean-driven mass losses, yet the impact of anomalous precipitation on the mass balance of the ASE is poorly known. We present a 25-year (1996-2021) record of ASE input-output mass balance and evaluate how two periods of anomalous precipitation affected its sea level contribution. Since 1996, the ASE has lost 3331±424 Gt ice, contributing 9.2±1.2 mm to global sea level. Overall, surface mass balance changes contributed just 7.7 % to total mass loss; however, two anomalous precipitation events had a larger, albeit short-lived, impact on rates of mass change. During 2009-2013, persistently low snowfall, due to anomalously zonal circulation, led to an additional 51±4 Gt yr-1 mass loss in those years (contributing positively to the total loss of 195±4 Gt yr-1). Contrastingly, extreme precipitation in the winters of 2019 and 2020 decreased mass loss by 60±16 Gt yr-1 during those years (contributing negatively to the total loss of 107±15 Gt yr-1). These results demonstrate that extreme snowfall variability can have a substantial impact on the short-term sea level contribution from West Antarctica and show that mass changes do not necessarily scale with grounding line discharge anomalies.

How to cite: Davison, B., Hogg, A., Rigby, R., Veldhuijsen, S., van Wessem, M., van den Broeke, M., Holland, P., Selley, H., and Dutrieux, P.: Revisiting the impact of anomalous precipitation on the mass budget of the Amundsen Sea Embayment ice streams, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11661, https://doi.org/10.5194/egusphere-egu23-11661, 2023.

EGU23-11695 | ECS | Orals | CR3.4

Calving response to the propagation of a speedup pulse through the ice stream of Sermeq Kujalleq in Kangia (Jakoshavn Isbræ), Greenland 

Adrien Wehrlé, Martin P Lüthi, Ana Nap, Andrea Kneib-Walter, Guillaume Jouvet, Hugo Rousseau, and Fabian Walter

Sermeq Kujalleq in Kangia (Jakobshavn Isbræ), Greenland is one of the most studied glaciers in the world mainly due to its recent retreat associated with extremely fast ice stream flow and high solid ice discharge. However, large limitations remain in the understanding of its short-term ice dynamics as the study of sub-daily variations, generally undetectable in spaceborne observations, requires high-rate field measurements that are challenging to acquire. Here, we present glacier surface velocities determined in Post-Processed Kinematic (PPK) mode from eight autonomous Global Navigation Satellite System (GNSS) stations deployed in July 2022 along the ice stream at a distance of 4 to 30 kilometers from the calving front. During this field campaign, we identified an 8-hour-long glacier speedup which was recorded at all GNSS stations and reached up to 11% of the pre-event velocity, followed by a 12-hour-long slowdown of similar magnitude. We further found the peak velocity was first measured at a GNSS station 16 kilometers away from the calving front, then recorded consecutively at each of the three other downstream GNSS stations with a positive time lag corresponding to a ~3 km/h wave propagation speed. At the station closest to the calving front, the timing of peak velocity corresponded to the occurrence of large-scale calving events. We further present line-of-sight glacier surface velocities measured along three shear margin transects with a terrestrial radar interferometer deployed simultaneously with the GNSS array. Across all profiles, we observed a widespread and simultaneous response of fast- and slow-moving ice suggesting a strong coupling between the main trunk and the shear margins of the ice stream.

How to cite: Wehrlé, A., Lüthi, M. P., Nap, A., Kneib-Walter, A., Jouvet, G., Rousseau, H., and Walter, F.: Calving response to the propagation of a speedup pulse through the ice stream of Sermeq Kujalleq in Kangia (Jakoshavn Isbræ), Greenland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11695, https://doi.org/10.5194/egusphere-egu23-11695, 2023.

EGU23-12358 | ECS | Orals | CR3.4

Mapping the ratio of meteoric and continental ice in Antarctic Ice Shelves as a metric for susceptibility to future climate change 

Vjeran Visnjevic, Reinhard Drews, Guy Moss, and Clemens Schannwell

Ice shelves encircling the Antarctic perimeter buttress ice flow from the continent towards the ocean, and their evolution and integrity are governed by surface accumulation, basal melting, and ice dynamics. The disintegration of ice shelves, caused by future changes in the climate, leads to an increase in ice discharge towards the ocean and a consequent increase in global sea level – making their future stability important.

In this study we focus on the structure and composition of ice shelves. We model ice shelf stratigraphy for all ice shelves around Antarctica using a simplistic and observationally driven ice-dynamic forward model (validated on the Roi Baudouin Ice Shelf, Visnjevic et al., 2022), and map spatial variations in the percentage of locally accumulated ice on the ice shelf (local meteoric ice - LMI) compared to the ice inflowing from the continental ice sheet (continental meteoric ice - CMI). We investigate differences between LMI and CMI dominated ice shelves in the context of ice shelf stability, and discuss their susceptibility to future atmospheric and oceanic changes in climate. Expanding the analysis to the continental scale allows us to identify zones where future changes in climate might strongly impact ice shelf geometry and composition.

How to cite: Visnjevic, V., Drews, R., Moss, G., and Schannwell, C.: Mapping the ratio of meteoric and continental ice in Antarctic Ice Shelves as a metric for susceptibility to future climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12358, https://doi.org/10.5194/egusphere-egu23-12358, 2023.

EGU23-13003 | Orals | CR3.4

Timescales for Ice Shelf Collapse and MISI Initiation in the Filchner-Ronne Sector 

Michael Wolovick, Angelika Humbert, Thomas Kleiner, Martin Rückamp, and Ralph Timmermann

The Filchner-Ronne sector of Antarctica contains a number of deep-bedded ice streams and glaciers potentially vulnerable to the Marine Ice Sheet Instability (MISI).  Previous work has shown that, in a warming climate, the ocean circulation in the cavity underneath the Filchner-Ronne Ice Shelf (FRIS) could switch from its present cold state to an alternate warm mode, in which intrusions of Circumpolar Deep Water (CDW) cause high basal melt rates near the deep grounding lines of potentially vulnerable glaciers.  However, less work has been done on modeling the response of the ice sheet and ice shelf system to such a mode switch in the cavity circulation.  Here, we use the Ice-sheet and Sea-level System Model (ISSM) to simulate the response of the Filchner-Ronne sector of Antarctica over multi-centennial timescales to changes in basal melt rate caused by a mode switch in the cavity circulation.  We force ISSM with both melt rates directly calculated by the cavity-resolving Finite-Element Sea ice-Ocean Model (FESOM) and with parameterized melt rate forcing derived from CMIP6 global models.  We find that parameterized melt rates in high-emissions scenarios cause rapid grounding line retreat at almost all of the major glaciers and ice streams feeding the FRIS beginning in the 22nd century, followed by ice shelf collapse and rapid sea level rise in the 23rd.  Using FESOM simulated melt rates the destabilization of the FRIS sector proceeds more slowly. During the 22nd century retreat is concentrated in specific ice streams, reflecting the more heterogeneous distribution of melt rate in the ocean model as opposed to the parameterized forcing.  In the 23rd century retreat becomes more widespread, culminating in ice shelf collapse and rapid sea level rise in the 24th and 25th centuries.  

How to cite: Wolovick, M., Humbert, A., Kleiner, T., Rückamp, M., and Timmermann, R.: Timescales for Ice Shelf Collapse and MISI Initiation in the Filchner-Ronne Sector, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13003, https://doi.org/10.5194/egusphere-egu23-13003, 2023.

EGU23-13220 | ECS | Orals | CR3.4

Submarine melting of glaciers in Greenland amplified by atmospheric warming 

Donald Slater and Fiamma Straneo

The retreat and acceleration of Greenland's marine-terminating glaciers since the 1990s is responsible for approximately half of Greenland's sea level contribution over the same period. A warming ocean, and the associated increase in submarine melting of calving fronts, is understood to be the most likely driver of this retreat. Yet atmospheric variability can also affect submarine melting by modulating subglacial discharge, which plays a role in driving fjord circulation and enhancing the transfer of heat from ocean to ice. The relative importance of atmospheric and oceanic sources of variability in submarine melting have, however, not been quantified.

We use atmospheric and oceanic reanalyses to estimate submarine melt rate at Greenland's marine-terminating glaciers since 1979, finding that in southeast Greenland the ocean has driven the majority of variability in submarine melt, while in northwest Greenland it is the atmosphere that has played the greater role. A simple two-stage glacier model is then used to map submarine melting onto dynamic mass loss, suggesting that although submarine melting is intuitively an ocean process, a warming atmosphere has amplified the impact of the ocean on the Greenland ice sheet.

How to cite: Slater, D. and Straneo, F.: Submarine melting of glaciers in Greenland amplified by atmospheric warming, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13220, https://doi.org/10.5194/egusphere-egu23-13220, 2023.

EGU23-13326 | ECS | Orals | CR3.4

Taxonomy of Cliff Failure Criteria: Phase Field Modelling and Parallels with Other Models 

Jakub Stocek, Robert Arthern, and Oliver Marsh

Ice loss from glaciers and ice sheets is the largest contributor to sea level rise. Damaged ice is central to the stability of the Antarctic Ice Sheet and calving of tabular icebergs from ice shelves accounts for more than half of all the ice lost from Antarctica each year. The processes driving calving and fracture are complex but not yet well understood. The aim of this talk is to present a physically based modelling of fracture.

The timing of calving is currently difficult to predict and is only included in some ice sheet models. Calving and cliff retreat rates are based on heuristic arguments or limited observations scaled up to the whole of Antarctica. There is no guarantee that current methods accurately capture the sea level contributions and physically based modelling is needed.

Recently, phase field models for fracture have gained a large following due to their ability to predict complex cracking phenomena such as crack branching and coalescence, or crack nucleation and have been applied to ice sheets for example by Clayton et al. (2022). We employ a phase field formulation of fracture for Maxwell viscoelastic materials capable of capturing the creep of glacial ice over longer periods as well as instantaneous elastic deformation.

In this talk we present different failure criteria and fracture driving forces used in phase field modelling and their impact on cliff retreat rates. We draw parallels with existing models and commonly used failure criteria and expand on the possibilities of using phase field modelling in large scale domains.


Clayton, T., Duddu, R., Siegert, M., Martínez-Pañeda, E. (2022). A stress-based poro-damage phase field model for hydrofracturing of creeping glaciers and ice shelves. Engineering Fracture Mechanics, 272, 108693.

How to cite: Stocek, J., Arthern, R., and Marsh, O.: Taxonomy of Cliff Failure Criteria: Phase Field Modelling and Parallels with Other Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13326, https://doi.org/10.5194/egusphere-egu23-13326, 2023.

EGU23-14118 | Orals | CR3.4

Modelling the source of glacial earthquakes: numerical modelling of the response of a tide-water glacier to the capsize of an instable iceberg 

Anne Mangeney, Pauline Bonnet, Vladislav Yastrebov, Olivier Castelnau, Alban Leroyer, Patrick Queutey, Martin Rueckamp, and Amandine Sergeant

One current concern in Climate Sciences is the estimation of the annual amount of ice lost by glaciers and the corresponding rate of sea level rise. Greenland ice sheet contribution is significant with about 30% to the global ice mass losses. The processes that control ablation at tidewater glacier termini, glacier retreat and calving are complex, setting the limits to the estimation of dynamic mass loss and the relation to glacier dynamics. It involves interactions between bedrock – glacier – icebergs – ice-mélange – water – atmosphere. Moreover, the capsize of cubic kilometer scale icebergs close to a glacier front can destabilize the glacier, generate tsunami waves, and induce mixing of the water column which can impact both the local fauna and flora.

 

We aim to improve the physical understanding of the response of glacier front to the force of a capsizing iceberg against the terminus. For this, we use a mechanical model of iceberg capsize against the mobile glacier interacting with the solid earth through a frictional contact and we constrain it with measured surface displacements and seismic waves that are recorded at teleseismic distances. Our strategy is to construct a solid dynamics model, using a finite element solver, involving a deformable glacier, basal contact and friction, and simplified iceberg-water interactions. We simulate the response of a visco-elastic near-grounded glacier to the capsize of an iceberg close to the terminus. The influence of the glacier geometry, the type of capsize, the ice properties and the basal friction on the glacier dynamic and the observed surface displacements are assessed. The surface displacements simulated with our model are then compared with measured displacements for well documented events. We show the surface and basal displacements of the glacier are significantly different in the case of to a top-out and a bottom-out (the two possible rotations) iceberg capsize.  This suggests different basal forces in both types of capsize, and thus probably a different signature in the seismic waves generated at the basal surface during capsize. To reproduce the vertical displacements of the glacier, our results suggest a higher hydrodynamic force on the glacier tongue than suggested in previous studies.

How to cite: Mangeney, A., Bonnet, P., Yastrebov, V., Castelnau, O., Leroyer, A., Queutey, P., Rueckamp, M., and Sergeant, A.: Modelling the source of glacial earthquakes: numerical modelling of the response of a tide-water glacier to the capsize of an instable iceberg, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14118, https://doi.org/10.5194/egusphere-egu23-14118, 2023.

EGU23-14409 | ECS | Posters on site | CR3.4

Ice shelf buttressing – a comparison of Antarctic ice shelves in a transient evolution 

Simon Schöll, Ronja Reese, and Ricarda Winkelmann

The accelerating loss of grounded ice in Antarctica at present is mainly caused by a thinning of the surrounding ice shelves and a subsequent reduction in buttressing. The adjacent ice streams speed-up due to the decrease in back-pressure from the weakened ice shelves. Most methods typically used to quantify the buttressing of ice shelves analyze the state at individual locations along the grounding line or within the shelf. Based on the stress-balance at the grounding line, we here present a method to quantify shelf-wide buttressing values in Antarctica. The Parallel Ice Sheet Model (PISM) and Úa are used in diagnostic as well as in transient experiments to compare the buttressing effect of major ice shelves in Antarctica. We show an increase in buttressing in more confined ice shelves and a decrease for higher basal melt rates. The buttressing decreases consistently across different ice shelves and idealized ocean warming scenarios. The newly-developed, shelf-wide buttressing metrics can be used to understand the role of ice shelves in changing climate conditions.

How to cite: Schöll, S., Reese, R., and Winkelmann, R.: Ice shelf buttressing – a comparison of Antarctic ice shelves in a transient evolution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14409, https://doi.org/10.5194/egusphere-egu23-14409, 2023.

EGU23-14665 | ECS | Orals | CR3.4

Exploring the variability of freshwater inputs from tidewater glacier-ocean systems in the Canadian Arctic Archipelago 

Claire Parrott, Stephanie Waterman, Paul Myers, Maya Bhatia, Erin Bertrand, Andrew Hamilton, David Burgess, Terry Noah, Eric Brossier, and David Didier

Tidewater glaciers, numerous in the Canadian Arctic Archipelago (CAA), are an important and dynamic source of freshwater to the Arctic freshwater system, with glacial inputs modifying ocean structure, stimulating vertical mixing, enhancing biogeochemical delivery near-terminus, as well as contributing to regional freshwater budgets, storage, transport and export. Despite their abundance, we lack important knowledge regarding glacier-ocean systems across the CAA, and these systems are often omitted in regional studies of freshwater transport or storage.

In this study, we examine the nature and spatial extent of glacial meltwater influence on freshwater dynamics in Jones Sound, a tidewater glacier-rich region in the CAA. Our goals are to better understand the influences of glacier inputs on upper ocean water column structure and mixing processes near the glacier terminus, as well as the role of tidewater glaciers in the regional oceanic freshwater system. We use summertime,  near-shore  in situ observations at both glacierized and non-glacierized sites, collected using the sailing yacht Vagabond and local vessels operated by community members from Ausuittuq (Grise Fiord, NU) over a 4-year timespan. This novel dataset provides fjord-scale and interannual resolution of water column properties from glacier terminus to open ocean. Further, we employ a high-resolution regional model (Nucleus for European Modelling of the Ocean (NEMO) framework of the Arctic and Northern Hemisphere Atlantic at 1/12 degree resolution) to examine regional freshwater transport and storage.

In this presentation we will present results detailing notable year-to-year and site-to-site variation in upper ocean structure at the glacierized sites.  These results suggest that there is important spatial and temporal variability of the influences of glacially-sourced freshwater to Jones Sound that should be considered in near-shore ocean functioning and the regional freshwater budget.

How to cite: Parrott, C., Waterman, S., Myers, P., Bhatia, M., Bertrand, E., Hamilton, A., Burgess, D., Noah, T., Brossier, E., and Didier, D.: Exploring the variability of freshwater inputs from tidewater glacier-ocean systems in the Canadian Arctic Archipelago, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14665, https://doi.org/10.5194/egusphere-egu23-14665, 2023.

Here, we present detailed ice and ocean data from beneath Thwaites Eastern Ice Shelf, Antarctica, collected with the underwater vehicle Icefin as part of the ITGC MELT project. The observations are a subset of the full data set that focus on the ice-ocean interactions within several well-sampled terrace formations occupying the ice base. These terraces range from 0.50 to 6 m in height and up to 100 m in width. We present measurements of ocean conditions to within centimeters of the ice surface along flat terrace roofs and their steeply sloping sidewalls. The ocean observations are combined with ice base elevations and scaled morphological melt patterns in the ice to understand the dominant mechanisms driving ice-ocean interactions within these features. We then input these data into the three-equation melt parameterization to estimate spatial variability in melt rates within these topographic features. We test various parameterizations for ocean heat flux into the flat and sloped ice surfaces, and compare the results to melt rates sampled along a nearby terrace sidewall and roof with a phase sensitive radar. This work in progress aims to better understand how ocean conditions interact with ice slope on small scales to drive variable melting in warm, highly stratified environments. We expect regions beneath much of the ice shelves occupying West Antarctica to interact similarly with the underlying ocean to what we observe beneath Thwaites Glacier. Hence, our observations hold relevance for how ice sheet models parameterize ocean-driven melting in this type of melt-driven regime.

How to cite: Washam, P. and the ITGC MELT Team: Direct observations of coupled interactions between near-ice ocean stratification and ice slope and morphology in basal terraces beneath Thwaites Glacier, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15683, https://doi.org/10.5194/egusphere-egu23-15683, 2023.

EGU23-15930 | ECS | Orals | CR3.4

Variability in circulation in Cumberland Bay, South Georgia, and implications for glacier retreat 

Joanna Zanker, Emma Young, Ivan Haigh, Paul Holland, and Paul Brickle

Mass loss from marine-terminating glaciers in high-latitude fjords is increasing globally, contributing to sea-level rise. It is widely cited that oceanic melting of these glaciers is enhanced by turbulent plumes rising in contact with the submarine face. Increasing evidence suggests fjord-wide horizontal circulation also enhances melting outside of plumes. The influence of buoyancy-driven outflow arising from submarine plumes on fjord-wide circulation is complex and subject to fjord geometry.  There are many studies of fjord systems in Greenland and Antarctica, but relatively little is known about fjords on sub-Antarctic islands such as South Georgia. This study uses observations and a new high-resolution model of Cumberland Bay, South Georgia, to study the interactions between fjord geometry and buoyancy-driven outflow on the circulation regime. We examine how this varies seasonally and the implications for glacier retreat. Cumberland Bay is a fjord system with two arms, each with a large marine-terminating glacier at the head. These glaciers have shown contrasting retreat rates over the past century.   In the shallower fjord arm (~70 m) the plume reaches the surface year-round, whereas in the deeper fjord arm (~160 m) the plume terminates sub-surface for ~3 months of the year. The addition of a shallow submarine sill in the deeper fjord arm leads to warmer and fresher water properties in the inner basin by blocking colder, higher salinity waters at depth. This change in water properties results in the plume reaching the surface year-round and the strength of the circulation outside of the plume is increased by recirculation of the buoyancy-driven outflow bouncing off the sill. The increase in temperature and energetic fjord-wide circulation both increases the plume-driven melt by as much as 2 m per day, and the potential for melt outside of the plume. Our results give the first detailed description of the oceanography of Cumberland Bay and highlight the importance of the interaction between fjord geometry and buoyancy-driven outflow influencing the rate of glacier retreat. 

How to cite: Zanker, J., Young, E., Haigh, I., Holland, P., and Brickle, P.: Variability in circulation in Cumberland Bay, South Georgia, and implications for glacier retreat, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15930, https://doi.org/10.5194/egusphere-egu23-15930, 2023.

EGU23-15957 | Orals | CR3.4

Long- and Short-term Damage Changes on Antarctic Ice Shelves 

Maaike Izeboud and Stef Lhermitte

The contribution of the Antarctic ice sheet to sea level rise remains uncertain due to the potential instability of ice shelves. Damage areas in the shear zone of an ice shelf are a first sign of mechanical weakening, which can lead to speed-up of the ice and additional damage development. This damage feedback can precondition ice shelves for disintegration and enhanced grounding line retreat but remains one of the least understood processes, mainly since we lack a quantification of damage and its changes on large spatiotemporal domains.

Recent efforts have resulted in a new, automated approach to detect damage. The NormalisEd Radon Transform Damage (NeRD) detection method allows to robustly detect damage features from multi-source, high-resolution satellite imagery. We have made both long-term (25 years) and short-term (annual) assessments from SAR images, based on both RAMP Radarsat (1997) and Sentinel-1 datasets (2015-2021).

We produce, for the first stime, damage state and damage change maps of Antarctic ice shelves. Over the past two decades we detect a general damage increase on ice shelves, most evident on fast flowing ice shelves in the West Antarctic (Thwaites, Pine Island, Crosson) and the Peninsula (Wilkins).  On short time scales the detected damage changes are governed by new damage development versus calving events, imposing fluctuations on its increase or decrease from year to year. A strong decrease in damage is observed on ice shelves that have retreated significantly, thereby removing all damaged parts. This gives attention to small, retreated ice shelves that are otherwise overlooked.  We furthermore detect areas with stable damage states across the Antarctic. We detect this for both initially intact and initially damaged ice shelves, showing that the amount of damage itself is no indication for damage-induced instability.

Our results provide new insights in Antarctic wide damage change, identifying regions that are (not) sensitive to a potential damage feedback and/or are vulnerable to retreat in combination with other forcings such as ocean warming or surface melt.  This large-scale damage change assessment is a first and important step in identifying ice shelf weakening and potential instability.

How to cite: Izeboud, M. and Lhermitte, S.: Long- and Short-term Damage Changes on Antarctic Ice Shelves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15957, https://doi.org/10.5194/egusphere-egu23-15957, 2023.

EGU23-16400 | Orals | CR3.4

The triggers for Conger Ice Shelf demise: long-term weakening vs. short-term collapse 

Stef Lhermitte, Bert Wouters, and HiRISE Team

Ice shelf instability is a key uncertainty in future sea level rise projections, as several small-scale processes leading to ice shelf collapse remain poorly quantified. Historical large scale ice shelf collapses, like the Conger Ice Shelf collapse in March 2022, therefore, provide unique insights in the processes leading to ice shelf instability.

In this study, we assess the long- and short-term changes on Conger Ice Shelf in historical satellite records (Landsat, Sentinel, MODIS, ICESat) and model output of ocean and climate conditions (HYCOM, RACMO, IMAU-FDM and ERA-5). Based on both observations and model output we determine the role of known ice shelf instability processes like hydrofracturing, basal melting and damage changes. Moreover, we evaluate the role of extreme weather and ocean conditions in the sudden Conger Ice Shelf collapse.

The longer satellite record shows that Conger Ice Shelf has been weakening for years and then collapsed in two abrupt events (2 and 15 of March 2022). The long-term weakening is the result of damage processes and calving events due to extreme ocean/weather conditions that gradually abate the ice shelf. The abrupt Conger Ice Shelf collapse, however, coincides with extreme atmospheric and ocean conditions (e.g., ocean slope and wave conditions) that trigger the weakened ice shelf into a sudden collapse. Our results show that the known ice shelf instability processes like hydrofracturing and basal melting do not play a key role in the abrupt Conger Ice Shelf collapse, but that gradual weakening followed by extreme weather and ocean conditions triggered the ice shelf collapse.

Our results stress the importance of separating ice shelf weakening from ice shelf collapse in studies of ice shelf stability. Moreover, they imply that extreme weather and oceanic conditions need to be to considered when assessing the future vulnerability of Antarctic ice shelves to collapse.

How to cite: Lhermitte, S., Wouters, B., and Team, H.: The triggers for Conger Ice Shelf demise: long-term weakening vs. short-term collapse, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16400, https://doi.org/10.5194/egusphere-egu23-16400, 2023.

EGU23-16435 | Orals | CR3.4

Currents, mélange and iceberg calving in Greenland fjords: new insights to a self-organised critical system 

Poul Christoffersen, Seungbong Lee, Samuel Cook, and Martin Truffer

Flow and mass balance of the Greenland Ice Sheet are largely controlled by marine-terminating glaciers that deliver large quantities of ice into fjords and coastal seas. The interaction of these glaciers with the ocean is crucial because heat and circulation in fjords drive high rates of melting. However, the links between warm ambient fjord water, subaqueous melting and iceberg calving are poorly understood. Here, we report a detailed record of surface circulation in Ikerasak Fjord, West Greenland, by tracking the displacements of icebergs in radar imagery acquired with a terrestrial radar interferometer, which also produced a detailed record of iceberg calving from Store Glacier. With images captured every three minutes, we derived fjord circulation and calving rates with unusually high temporal resolution. In the first of three periods, we observed low-speed surface currents (<0.15 m/s) together with high calving activity (around 50 events per hour) as a response to the break-up of proglacial winter melange. We subsequently observed faster surface currents (up to 0.57 m/s) but much less calving (<20 icebergs per hour). Later, as currents intensified and a large eddy formed, we observed a combination of fast fjord circulation (around 0.4 m/s) and high calving activity (20-40 events per hour). The record shows that calving is a self-organised critical system, with small icebergs produced continuously in a critical state, whereas large icebergs were produced mostly when calving becomes super-critical. A super-critical state was reached when the melange broke up and later as the eddy formed in front of the glacier. In this state, we found stronger fjord circulation to drive more frequent calving events, while more frequent calving in general caused a higher flux of ice to the ocean.

How to cite: Christoffersen, P., Lee, S., Cook, S., and Truffer, M.: Currents, mélange and iceberg calving in Greenland fjords: new insights to a self-organised critical system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16435, https://doi.org/10.5194/egusphere-egu23-16435, 2023.

EGU23-16803 | ECS | Orals | CR3.4

Rapid Grounding Line Retreat of Ryder Glacier, Northern Greenland, from 1992 to 2021 

Yikai Zhu, Chunxia Zhou, and Dongyu Zhu

Ice losses from the Greenland Ice Sheet (GrIS) have expanded rapidly in recent decades. The Ryder Glacier (RG) is one of the major marine-terminating outlet glaciers located on the northwestern GrIS. Paying attention to its dynamic changes is of great significance to the study of the mass balance in the whole GrIS. We utilize the Double Differential Synthetic Aperture Radar Interferometry (DDInSAR) to detect the change of grounding line (GL) position in RG from 1992 to 2021. It is found that the GL has retreated significantly (1-8 km) during this period and its rate on the eastern and western flanks is nearly eight times different. To explore the reasons for the retreat, we combine the ice-shelf thickness variation, surface and bed topography, and potential subglacial drainage-pathway to discover that the basal melt governs the severe migration in RG. The uneven melting dominates the asymmetric retreat on the eastern and western flanks, which is caused by the disparity of ocean heat near the GL at different depths and the bed topography slope. The higher the ocean heat and the greater the slope are, the more intense the basal melt is, leading to further GL retreat and threatening the stability of the ice shelf. The experimental results also demonstrate that RG may continue to retreat, with a more drastic change in the west, in the coming decades.

How to cite: Zhu, Y., Zhou, C., and Zhu, D.: Rapid Grounding Line Retreat of Ryder Glacier, Northern Greenland, from 1992 to 2021, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16803, https://doi.org/10.5194/egusphere-egu23-16803, 2023.

EGU23-17325 | Orals | CR3.4

Glacial plumes drive widespread subsurface warming in northwest Greenland’s fjords 

Tom Cowton, Donald Slater, and Mark Inall

Greenland’s glacial fjords modulate the exchange of heat and freshwater between the ice sheet and ocean, with the ocean properties adjacent to tidewater glaciers influencing the rate of submarine glacier melting and the properties of glacially modified waters exported to the shelf. Here we use a numerical plume model in conjunction with observations from close to 14 glaciers in northwest Greenland to assess the impact of subglacial-runoff-driven plumes on near-glacier ocean properties. We find that at depths where plumes most commonly find neutral buoyancy (~75-300m), intruded plume waters frequently make up the largest component of the near-glacier water composition. These plume waters register predominantly as a warm anomaly relative to waters of equivalent density on the shelf, and will thus serve to increase submarine melting at intermediate depths. Our findings demonstrate the key role played by plumes in driving water modification in Greenland’s fjords, the importance of accounting for this process when studying ice-sheet/ocean interactions, and the potential for simple models to capture these impacts across a range of settings.

How to cite: Cowton, T., Slater, D., and Inall, M.: Glacial plumes drive widespread subsurface warming in northwest Greenland’s fjords, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17325, https://doi.org/10.5194/egusphere-egu23-17325, 2023.

CR4 – Sea, Lake and River Ice

EGU23-3215 | Posters on site | CR4.2

Implementation of form drag scheme into NEMO sea ice model SI3 

David Schroeder and Danny Feltham

The efficiency of air-sea momentum depends on top and bottom sea ice surface roughness which varies with ice types and conditions, but constants are applied in most climate models. Future sea ice reduction will entail an increase in efficiency of air-sea momentum transfer. A high physical process fidelity will be a requirement for realistic model predictions. Within the CANARI project (Climate change in the Arctic-North Atlantic Region and Impacts on the UK) the form drag scheme from the sea ice model CICE is implemented into the NEMO sea ice model SI3. Based on parameters of the ice cover such as ice concentration, size, and frequency of the sails and keels, freeboard and floe draft, and size of floes and melt pond fraction, the total form drag can be computed as a sum of form drag from sails and keels, form drag from floe edges, form drag from melt pond edges, and a reduced skin drag due to a sheltering effect. Historical simulations are presented discussing the impact on sea ice dynamics and mass balance separating the contributions from modified momentum and heat transfer.

How to cite: Schroeder, D. and Feltham, D.: Implementation of form drag scheme into NEMO sea ice model SI3, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3215, https://doi.org/10.5194/egusphere-egu23-3215, 2023.

The rate of reduction of Arctic Ocean sea ice cover and its change is an important key issue in the study of global climate change. Wave climate variability and the wave effects in the Arctic Ocean plays important roles in influencing the rate of sea ice melting. Aiming to improve the parameterization of wave numerical model that considers the presence of sea ice in polar region and to develop the associated satellite remote sensing technologies, in situ wave observations at the sea-ice edge and Marginal Ice Zones are essential. Currently the data and observations are scarce.

This study developed a low-cost miniature wave drifting buoy, its shape is 50 cm diameter dish, built-in IMU, Iridium satellite modem and temperature and salinity sensor, etc., to monitor the wave height, period, direction and wave spectral shape, sea surface Mean Square Slope (MSS), surface ocean temperature (SST) and GPS positioning.

The signal sampling frequency is 10Hz, the spectral analysis is carried out on-board using ARM single chip computer in the buoy. The data is then encoded to Iridium satellite in real-time. In this study, in August 2021 and 2022, 8 and 10 sets of miniature buoys were deployed in Fram Strait off the western Svalbard Islands, respectively. The buoys were deployed in cluster and placed 15 km apart from each other, forming a rectangular spatial array, and drifting with West Spitzbergen Current, transported northward into the ice edge area of Svalbard northwest sea.

The observation took place every two hours for about three months. This report presents the time series of the observation data. First, we analyzed the drift trajectories of the buoy cluster, estimated the sea surface dispersion coefficient, Lyapunov index from the spatial array shape change rate of the buoy cluster. Secondly, sea surface temperature variation along the meridional trajectories was investigated. The results showed that the surface water mass was converged around Molloy Abyss, and the surrounding water body was accompanied by rapid temperature drop. On the other hand, in the wave analysis of the MIZ, the SAR images were used to identify the sea ice edge and ice concentration and to investigate the attenuation of wave spectral shape between the sea ice zone and the open ice-free waters in the vicinity.

How to cite: Chien, H., Chen, Y.-C., and Chang, H.-M.: On the wave-ice attenuation and WSC variation in Fram Strait using clusters of miniature wave drifting buoy in 2021 & 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4106, https://doi.org/10.5194/egusphere-egu23-4106, 2023.

EGU23-4483 | ECS | Orals | CR4.2

Drivers of very rapid sea ice loss on sub-seasonal timescales 

Jake Aylmer, Daniel Feltham, John Methven, and Ambrogio Volonté

Very rapid ice loss events (VRILEs) are extreme, local reductions in Arctic sea ice extent on timescales of days to weeks. They are poorly captured in operational forecasts that are used, for instance, to inform shipping through the Arctic Ocean. A better understanding of the drivers and underlying processes is thus critical to a range of stakeholders. We analyse summertime (May–September) VRILEs occurring in a simulation (1980–2022) with the sea ice model CICE forced by atmospheric reanalyses. Our configuration includes novel marginal ice physics such as a prognostic floe size distribution and an explicit form drag scheme. Most VRILEs are dominated by thermodynamic processes. However, many events occurring near the start or end of the melt season are driven by advective redistribution, often associated with the presence of a cyclone. We illustrate this with key case studies and generalise the results to all simulated VRILEs using simple metrics quantifying the dominant contributions to the sea ice concentration tendencies and atmospheric conditions in each event. Finally, a suite of parameter sensitivity studies highlights factors with potential to improve forecasting of VRILEs.

How to cite: Aylmer, J., Feltham, D., Methven, J., and Volonté, A.: Drivers of very rapid sea ice loss on sub-seasonal timescales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4483, https://doi.org/10.5194/egusphere-egu23-4483, 2023.

EGU23-5055 | ECS | Posters on site | CR4.2

Arctic rapid sea ice loss events in CMIP6 simulations 

Annelies Sticker, François Massonnet, and Thierry Fichefet

The summer Arctic sea ice is projected to disappear completely by the middle of the century in response to anthropogenic greenhouse gas emissions, according to simulations conducted with the latest global climate models. The decrease in summer Arctic sea ice extent is marked by periods of rapid ice loss, known as rapid ice loss events (RILEs), which are expected to become more frequent in the coming decades. However, the causes of RILEs are not well understood and it is difficult to predict their occurrence a season to several years ahead. It is essential to improve our understanding of these events and their potential impacts on ecosystems and societies, as the rate of sea ice decline can affect the ability to adapt to rapid change. To gain a better understanding of RILEs, we conducted an analysis using climate simulations from the Coupling Model Intercomparison Project phase 6 (CMIP6). Our results show that the frequency of RILEs increases as the Arctic sea ice extent diminishes, and the probability of observing a RILE is highest during the period from 2025 to 2030. Moreover, the observed September Arctic sea ice extent is critically approaching the value corresponding to the peak of probability of occurrence of RILEs. This suggests that we may be on the verge of a new RILE, following a slowdown since the early 2010s. In the future, we plan to identify the climatic conditions that are favorable for the formation of RILEs, with the goal of predicting the probability of their occurrence in real-time. We also aim to study the impacts of these rapid ice loss events on the wider climate system. 

How to cite: Sticker, A., Massonnet, F., and Fichefet, T.: Arctic rapid sea ice loss events in CMIP6 simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5055, https://doi.org/10.5194/egusphere-egu23-5055, 2023.

EGU23-6428 | Posters on site | CR4.2

Observations and modeling of areal surface albedo and surface types in the Arctic 

Evelyn Jäkel, Tim Sperzel, Manfred Wendisch, Hannah Niehaus, Gunnar Spreen, Marcel Nicolaus, Ran Tao, Wolfgang Dorn, Lara Footh, and Annette Rinke

The spread of climate model results quantifying the snow–ice surface albedo feedback is partly caused by the significant sensitivity of the simulated sea ice surface albedo with respect to surface warming. Therefore, the accurate representation of the Arctic sea ice and its evolution throughout the year, particularly in the melting period, is crucial to obtain reliable climate model projections.

Here we evaluate the results of the surface albedo scheme of the coupled regional climate model HIRHAM–NAOSIM against airborne and ground-based measurements. The corresponding observations were conducted during the MOSAiC expedition in 2020 and during five aircraft campaigns within the framework of the (AC)3 project in different seasons between 2017 and 2022.  

The comparison of measured and modeled surface albedo was based on observed fractions of four surface types (melt ponds, snow, sea ice, bare ice), which were classified using fisheye camera imagery and the measured skin temperatures along the flight track. From the modeling side, we applied the full surface albedo scheme, together with the ice sub-type fractions. We found a seasonal-dependent degree of agreement between measured and modeled surface albedo for cloud-free and cloudy situations. The current albedo scheme has projected an earlier onset of melting and a more realistic width of surface albedo frequency distributions in summer than the former albedo scheme. In spring, however, the cloud effect on surface albedo was overestimated by the model, while the albedo scheme for cloudless cases showed a smaller bias than the former scheme without cloud-depending parameters.

How to cite: Jäkel, E., Sperzel, T., Wendisch, M., Niehaus, H., Spreen, G., Nicolaus, M., Tao, R., Dorn, W., Footh, L., and Rinke, A.: Observations and modeling of areal surface albedo and surface types in the Arctic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6428, https://doi.org/10.5194/egusphere-egu23-6428, 2023.

EGU23-8780 | ECS | Orals | CR4.2

Arctic and Antarctic sea ice thickness and volume changes during the last 29 years from satellites 

Marion Bocquet, Sara Fleury, Frédérique Rémy, Florent Garnier, and Thomas Moreau


Sea ice is both a key witness and driver of climate change. While sea ice extent and area is well described with observations during the last four decades, sea ice thickness and volumes changes remain poorly known. However, thickness is a mandatory variable to fully understand the past, present and future changes of sea ice. Despite improvements in sea ice thickness estimation from altimetry during the past few years thanks to SAR and laser altimetry, former radar altimetry missions such as Envisat and especially ERS-1 and ERS-2 have remained under exploited so far. ERS-2 arctic sea ice thickness has been recently retrieved thanks to a machine learning approach aiming at calibrating ERS-2 and Envisat against CryoSat-2. We are now able to extend the time series from ERS-1 for both polar oceans, allowing to propose a 29 years-long sea ice thickness and volume time series. Estimates are combined with uncertainties derived from a Monte Carlo methodology. Nearly 30 years of sea ice volume time series reveals that Arctic sea ice is melting by 120 +/- 45 km³/year up to 81.5 °N (-13.1  +/- 5.1 %/decade). Antarctic sea ice evolution has no significant trends along the whole period, but a volume drop is observed since 2016. For both hemispheres, prominent regional changes have been identified with a strong heterogeneity of trends across regions. Finally, comparisons between observations and models show increasing negative bias while going back in time.

How to cite: Bocquet, M., Fleury, S., Rémy, F., Garnier, F., and Moreau, T.: Arctic and Antarctic sea ice thickness and volume changes during the last 29 years from satellites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8780, https://doi.org/10.5194/egusphere-egu23-8780, 2023.

EGU23-9077 | Posters on site | CR4.2

Seasonal and interannual variability in Fram Strait sea ice ridge statistics during 2006-2019 from the data of Ice Profiling Sonars. 

Dmitry Divine, Hiroshi Sumata, Laura de Steur, Olga Pavlova, Mats Granskog, and Sebastian Gerland

Fram Strait is the major gateway connecting the Arctic Ocean and North Atlantic Ocean, where nearly 90% of the sea ice export from the Arctic Ocean takes place. The transported ice represents a broad range of thicknesses and types and exhibits an integrated history of thermodynamic growth/decay and deformation on its way across the Arctic. The present study utilizes high resolution sea ice draft data from ice profiling sonars (IPS) from the four moorings of the Fram Strait Outflow Observatory at 78.85 N and 3W to 6.5 W over the period 2006-2019. The analysis focuses on the identification of deformed ice/sea ice ridges and analysis of the seasonal and interannual variability in the number, geometry and shape of ridges. The study demonstrates a pronounced seasonal cycle in the number, probability density function of keel drafts and shape of ridges traversing FS with a maximum ridge count in March-April and minimum in August-September. An overall decline found in the annual ridge number is accompanied by a general shallowing of ridge keels. The observed changes are most pronounced in the easternmost mooring at 3W, and linked to continuing sea ice retreat in the FS over the studied period. The results are further compared with previous studies on ridge statistics from the area and placed in the context of the observed changes in Arctic sea ice over the last two decades.

How to cite: Divine, D., Sumata, H., de Steur, L., Pavlova, O., Granskog, M., and Gerland, S.: Seasonal and interannual variability in Fram Strait sea ice ridge statistics during 2006-2019 from the data of Ice Profiling Sonars., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9077, https://doi.org/10.5194/egusphere-egu23-9077, 2023.

EGU23-9088 | Posters virtual | CR4.2

The role of brittle fracture in determining sea ice floe size distribution 

Adam Bateson, Daniel Feltham, David Schröder, Yanan Wang, and Byongjun Hwang

There have been several recent efforts to develop parameterisations of the sea ice floe size distribution (FSD) for use in sea ice models such as CICE and SI3. These models aim to capture the key processes that determine the evolution of floe sizes, including melting at the edges of floes, welding together of floes, and break-up of floes by waves. However, several fragmentation processes are not yet accounted for in these models. For example, in-plane brittle fracture events can have a direct impact on the size of larger floes and potentially also smaller floes. Plausible indirect mechanisms also exist. It has been observed that thermodynamic weakening of cracks and other linear features in the sea ice cover can in some cases drive the break-up of sea ice in the central Arctic. These observations imply that linear features in the sea ice that form in winter from in-plane brittle fracture before freezing up can then determine the fragmentation of sea ice in summer as it thins and weakens. 


Here we will present results from sea ice simulations including a prognostic model of sea ice FSD to show that the inclusion of brittle fracture-derived impacts on floe size improves the performance of the FSD model in simulating observed FSD shape for mid-sized floes. We will use these results to motivate the development of a more physically derived parameterisation of floe breakup via thermal weakening of floes along existing linear features. Finally, we will discuss how we can combine novel observations and recent advancements in modelling techniques such as discrete element methods applied to sea ice to aid in the development of parameterisations of these floe-scale processes for subsequent application in continuum models.  

How to cite: Bateson, A., Feltham, D., Schröder, D., Wang, Y., and Hwang, B.: The role of brittle fracture in determining sea ice floe size distribution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9088, https://doi.org/10.5194/egusphere-egu23-9088, 2023.

EGU23-11086 | ECS | Orals | CR4.2

Past and future of the Arctic sea ice in HighResMIP 

Julia Selivanova and Doroteaciro Iovino

Arctic sea-ice area and volume have dramatically decreased since the beginning of the satellite era. This alarming rate of ice decline raises a key scientific question: how soon will the Arctic meet the first “ice-free” summer? Coupled climate models are the primary tools to provide projections of future sea ice conditions. Increasing the horizontal resolution of general circulation models is a widely recognized way to improve the representation of the complex processes at high latitudes, and to obtain trustworthy predictions of ice variability. Here, we investigate the past and future changes of sea ice cover at hemispheric and regional scales using model outputs from the High Resolution Model Intercomparison Project (HighResMIP, Haarsma et al. 2016) of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). 

The main objective is to investigate the impact of ocean/atmosphere model resolution on the representation of Arctic sea ice area (SIA) and volume (SIV)  and their seasonal, interannual variability, and trends in the recent past. Model results over the period 1950–2014 are compared to a set of observational datasets. 

All models project substantial sea ice shrinking: from 1950 to 2050 the Arctic loses nearly 92% of SIV from 1950 to 2050. The individual models simulate the first summer ice-free Arctic as early as 2019 and as late as 2050. The ensemble mean of the three best performing models suggests the event to happen by 2044. Along with the overall reduction of sea ice cover, there are changes in the structure of sea ice cover: the marginal ice zone (MIZ) dominates the ice cover by the mid-XXI which implies the shift to a new sea ice regime closest to the Antarctic conditions. The MIZ-dominated Arctic might suggest to adapt and modify model physics parameterizations and sea ice rheology.

Our analysis does not present a strong relationship between ocean/atmosphere spatial resolution and sea ice cover representation: the impact of horizontal resolution rather depends on the model used and the examined variables. However, the refinement of the ocean mesh has a more prominent effect compared to the atmospheric one, mainly due to a more realistic representation of the sea ice edges as a result of better simulated ocean currents and heat transports in the Northern Atlantic Ocean. 

How to cite: Selivanova, J. and Iovino, D.: Past and future of the Arctic sea ice in HighResMIP, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11086, https://doi.org/10.5194/egusphere-egu23-11086, 2023.

The thermodynamic growth of sea ice is governed by heat transfer through the ice together with appropriate boundary conditions at the interfaces with the atmosphere and ocean. Several different representations of this process have been used in climate modelling, including the simplest zero-layer models (Semtner, 1976) and more complex partial-differential-equation-based models (Maykut & Untersteiner, 1971; Bitz & Lipscomb, 1999). Recently, these latter models have been extended to include a representation of the dynamic evolution of the salinity of sea ice based on mushy-layer theory (Turner et al., 2013; Griewank & Notz, 2013; Rees Jones and Worster, 2014). Salinity variation might be expected to have a significant effect on ice growth given that it controls the relative proportions of solid ice and liquid brine, and these materials have different thermal properties. 

In this study, we develop a simplified framework to investigate the effects of variations in the thermal properties of sea ice. We develop and test a quasi-static simplification. In this simplification, we apply a transformation to the underlying heat equation such that the spatial coordinate scales with the ice thickness. We then neglect the explicit time dependence. This procedure reduces the full partial differential equation to an ordinary differential equation. The solution is exact for constant forcing conditions. 

We show that ice salinity has only a modest effect on the growth rate, notwithstanding its large effect on the thermal properties of sea ice. The model allows us to unpick the physical causes, which are related to the trade-off between the effect of salinity on thermal conductivity and latent heat release. We calculate the growth of ice under steady and time-dependent forcing. Under steady forcing, the ice growth equation admits an analytical approximate solution, which compares well to numerical solutions. We show that saltier ice initially grows slightly faster but subsequently grows slightly slower, a further trade-off explaining the relatively weak sensitivity of ice growth to salinity.

Under time-dependent forcing, we show that the quasi-static model compares well to full partial-differential-equation-based models. So our approach offers intermediate complexity between zero-layer Semtner models and full models based on partial differential equations such as Maykut-Untersteiner/Bitz-Lipscomb/mushy-layer models.

References:
Semtner, A. J. (1976) J. Phys. Oceanogr. 6 (3), 379–389.
Maykut, G. A. & Untersteiner, N. (1971) J. Geophys. Res. 76 (6), 1550–1575.
Bitz, C. M. & Lipscomb, W. H. (1999) J. Geophys. Res. – Oceans 104 (C7), 15669–15677.
Turner, A. K., Hunke, E. C. & Bitz, C. M. (2013) J. Geophys. Res. – Oceans 118 (5), 2279–2294.
Griewank, P. J. & Notz, D. (2013) J. Geophys. Res. – Oceans 118 (7), 3370–3386.
Rees Jones, D. W. & Worster, M. G. (2014) J. Geophys. Res. – Oceans 119 (9), 5599–5621.

How to cite: Rees Jones, D.: Modelling the thermodynamic growth of sea ice: insights from quasi-static models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11105, https://doi.org/10.5194/egusphere-egu23-11105, 2023.

EGU23-11652 | ECS | Orals | CR4.2

Unsupervised statistical classification of the Antarctic marginal ice zone. 

Noah Day, Luke Bennetts, and Siobhan O'Farrell

This work presents a new method for quantifying the Antarctic marginal ice zone (MIZ). The MIZ acts as an interface between the open Southern Ocean and the consolidated inner pack, and is generally described as area of sea ice affected by ocean surface waves. We use standalone CICE6, which includes a floe size distribution with atmospheric, oceanic and wave forcing, to simulate the evolution of Antarctic sea ice from 2010 – 2020. CICE output variables were categorised as static (sea ice concentration, age, thickness, etc.), thermodynamic, and dynamic. Unsupervised statistical methods were used to classify distinct sea ice regions and then to identify the dominant processes which contribute to the spatial and temporal variance of Antarctic sea ice cover. The unsupervised sea ice classification agrees with recent MIZ extent estimations using altimetry observations of wave attenuation. The addition of floe size information enhanced our MIZ classification to include high concentration pancake fields (which are promoted by waves). These results support the inclusion of floe size within sea ice modelling, and the importance of multi-variate approaches to describe sea ice.

How to cite: Day, N., Bennetts, L., and O'Farrell, S.: Unsupervised statistical classification of the Antarctic marginal ice zone., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11652, https://doi.org/10.5194/egusphere-egu23-11652, 2023.

EGU23-12844 | ECS | Orals | CR4.2

Evolution of heat fluxes at the Arctic sea-ice edge 

Julia Steckling, Markus Ritschel, Prof. Dr. Johanna Baehr, and Prof. Dr. Dirk Notz

We analyze the evolution of heat fluxes and the resulting surface energy balance at the Arctic sea-ice edge in CMIP6 model simulations. We build on the study of Notz and Stroeve (2016), in which they show the existence of a strong linear relationship between Arctic sea-ice area and cumulative anthropogenic CO2 emissions. In explaining this linear relationship, the authors claim that the surface energy balance at the sea-ice edge remains constant, following the conceptual idea of the sea-ice edge retreating northwards to compensate for the increasing longwave radiative input due to global warming by a decrease in shortwave radiation at higher latitudes. We examine the validity of this hypothesis by first identifying the sea-ice edge in the model data, and then scrutinize whether or not the surface energy balance at that location stays constant under future sea-ice retreat. Furthermore, we decompose the energy balance into its constituents to explore dynamical effects and oceanic influence.

We find that the annual mean surface energy balance shows stronger spatial than temporal variations. Looking at individual months, we find that the surface energy balance is negative in winter and positive in summer along the ice edge. Towards the end of the 21st century, the surface energy balance enters a new regime, becoming less negative in winter, and more positive in summer. By showing a consistently negative relation between downwelling shortwave radiation and downwelling longwave radiation, we finally confirm the idea of the compensation of increasing longwave input by a decrease in shortwave incoming radiation due to a northward migration of the sea-ice edge.

How to cite: Steckling, J., Ritschel, M., Baehr, P. Dr. J., and Notz, P. Dr. D.: Evolution of heat fluxes at the Arctic sea-ice edge, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12844, https://doi.org/10.5194/egusphere-egu23-12844, 2023.

EGU23-14000 | ECS | Orals | CR4.2

Driving Mechanisms of an Extreme Winter Sea Ice Breakup Event in the Beaufort Sea 

Richard Davy, Jonathon Rheinlaender, Pierre Rampal, Clemens Spensburger, Anton Korosov, Timothy Williams, and Thomas Spengler

The loss of thick multiyear sea ice in the Arctic leads to weaker sea ice that is more easily broken up by strong winds. As a consequence, extreme sea ice breakup events may become more frequent, even during the middle of winter when the sea ice cover is frozen solid. This can lead to an earlier onset of the melt season and potentially accelerate Arctic sea ice loss. Such extreme breakup events are generally not captured by climate models, potentially limiting our confidence in projections of Arctic sea ice. We investigated the driving forces behind sea ice breakup events during winter and how they change in a future climate. Our sea ice model is the first to reproduce such breakup events and reveals that the combination of strong winds and thin sea ice is a key factor for these breakups. We found that winter breakups have a large effect on local heat and moisture transfer and cause enhanced sea ice production, but also increase the overall movement of the sea ice cover, making it more vulnerable. Finally, we show that if the Arctic sea ice continues to thin, these extreme breakup events could become even more frequent.

How to cite: Davy, R., Rheinlaender, J., Rampal, P., Spensburger, C., Korosov, A., Williams, T., and Spengler, T.: Driving Mechanisms of an Extreme Winter Sea Ice Breakup Event in the Beaufort Sea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14000, https://doi.org/10.5194/egusphere-egu23-14000, 2023.

EGU23-14770 | ECS | Orals | CR4.2

Fractal properties of Arctic sea ice floe fragmentation processes 

Rajlaxmi Basu and Byongjun Hwang

Seasonal evolution of Arctic sea ice floe is caused by various fragmentation and melt processes. Those include melt fragmentation in summer due to weaker part of floe by melt ponds, legacy re-frozen leads or cracks, as well as mechanical breakup in spring due to ice deformation forcing. Understanding these fragmentation processes is important not only to evaluate recent Arctic sea ice decline, but also to improve climate models for the Arctic. The objective of this study is to investigate those fragmentation processes at individual floe scales, with hypothesis that fractal properties may differ between melt fragmentation and mechanical breakup. With that in mind, we collected the “floe-scale” data set of 1-m MEDEA images that contain floe-scale imagery before and after fragmentation, and calculated the floe size, perimeter and fractal properties at the floe scale. In this presentation, we will share preliminary results of those analysis, including the role of melt ponds and legacy refrozen leads or cracks in melt fragmentation and difference in fractal properties between melt fragmentation and mechanical breakup.

How to cite: Basu, R. and Hwang, B.: Fractal properties of Arctic sea ice floe fragmentation processes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14770, https://doi.org/10.5194/egusphere-egu23-14770, 2023.

EGU23-15061 | ECS | Posters on site | CR4.2

Sea Ice Growth, Melt and Dynamics in an Increasingly Marginal Arctic 

Rebecca Frew, Danny Feltham, David Schroeder, and Adam Bateson

As summer Arctic sea ice extent has retreated, the marginal ice zone (MIZ) has been widening and making up an increasing percentage of the summer Arctic sea ice. The MIZ is defined as the region of the ice cover that is influenced by waves, and for convenience here is defined as the region of​ the ice cover between ice concentrations of 15-80%. The MIZ is projected to become a larger percentage of the summer ice cover, as the Arctic transitions to ice free summers. We use​ a sea ice-mixed layer model that has a prognostic floe size distribution model including brittle fracture and form drag. The model has been compared and calibrated to FSD observations, satellite observation of sea ice extent and PIOMAS. We compare the processes of ice volume gain and loss in the ice pack to those in the​ MIZ to establish and contrast the relative importance of processes in the pack and MIZ, and the changes as the summer MIZ fraction increases and the amplitude of the seasonal sea ice growth/melt cycle increases. We compare the components of the sea ice volume budget in the 1980s and 2010s and then between the 2010s and the 2040s where almost the entirety summer sea ice cover has become MIZ.  

How to cite: Frew, R., Feltham, D., Schroeder, D., and Bateson, A.: Sea Ice Growth, Melt and Dynamics in an Increasingly Marginal Arctic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15061, https://doi.org/10.5194/egusphere-egu23-15061, 2023.

EGU23-15852 | ECS | Posters on site | CR4.2

A new HPLC-MS method for fatty acid detection in sea ice 

Siobhan Johnson, Chiara Giorio, and Elizabeth Thomas

The presence of marine-sourced fatty acids1,2,3, in Antarctic ice cores has been linked to changes in sea ice conditions2,3. It has been proposed that the phytoplankton within and around the sea ice produce these fatty acids3 which are then released into the atmosphere upon sea-ice retreat and deposited onto the continental ice sheet2,3. While fatty acids show great potential as a proxy to reconstruct past sea ice, their transport, deposition and preservation within the ice sheet is poorly understood.  Few studies have investigated sea ice as a source of fatty acids and even fewer have investigated Antarctic sea ice4,5,6. Here we present a new study exploring the methods of detecting fatty acids in sea ice, including new results from pancake ice collected from the Antarctic Marginal Ice Zone in 2022.

Analyses of fatty acids are typically carried out using gas chromatography (GC) coupled with mass spectrometric (MS) techniques6,7,8,9. With the rise of liquid chromatography methods in the past few decades, their use have become more common. High performance liquid chromatography (HPLC) has an advantage over GC methods with its lower temperatures during analysis, thus reducing the risk of altering or destroying the fatty acids10. A resultant HPLC-MS method, using electrospray ionisation, is presented for the detection and analysis of fatty acids in sea ice.

[1] K. Kawamura et al., “Ice core record of fatty acids over the past 450 years in Greenland,” Geophysical Research Letters, vol. 23, pp. 2665-2668, 1996.

[2] A. King et al., “Organic compounds in a sub-Antarctic ice core: A potential suite of markers” Geophysical Research Letters, vol. 46, pp. 9930-9939, 2019.

[3] E. Thomas et al., “Antarctic Sea Ice Proxies from Marine and Ice Core Archives Suitable for Reconstructing Sea Ice over the past 2000 Years,” Geosciences, vol. 9, pp. 506-539, 2019.

[4] K. Fahl and G. Kattner, "Lipid Content and fatty acid composition of algal communities in sea-ice and water ffom the Weddell Sea (Antarctica)," Polar Biology, vol. 13, pp. 405-409, 1993.

[5] P. Nichols et al., "Occurence of an isoprenoid C25 diunsaturated alkene and high neutral lipid content in antarctic sea-ice diatom communities," Journal of Phycology, vol. 24, pp. 90-96, 1988.

[6] D. Nichols et al., "Fatty acid, sterol and hydrocarbon composition of Antarctic sea ice diatom communities during the spring bloom in McMurdo Sound," Antarctic Science, vol. 5, pp. 271-278, 1993.

[7] S. Wang et al., "Fatty acid and stable isotope characteristics of sea ice and pelagic particulate organic matter in the Bering Sea: tools for estimating sea ice algal contribution to Arctic food web production," Oecologia, vol. 174, pp. 699-712, 2014.

[8] S. Wang et al., "Importance of sympagic production to Bering Sea zooplankton as revealed from fatty acid-carbon stable isotope analyses," Marine Ecology Progress Series, vol. 518, pp. 31-50, 2015.

[9] E. Leu et al., "Spatial and Temporal Variability of Ice Algal Trophic Markers—With Recommendations about Their Application," Journal of Marine Science and Engineering, vol. 8, pp. 676, 2020.

[10] E. Lima and D. Abdalla, "High-performance liquid chromatography of fatty acids in biological samples," Analytica Chimica Acta, vol. 465, pp. 81-91, 2002.

How to cite: Johnson, S., Giorio, C., and Thomas, E.: A new HPLC-MS method for fatty acid detection in sea ice, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15852, https://doi.org/10.5194/egusphere-egu23-15852, 2023.

EGU23-16687 | Orals | CR4.2

Understanding recent changes in Antarctic sea ice seasonality 

Kenza Himmich, Sharon Stammerjohn, Martin Vancoppenolle, and Gurvan Madec

Changes in the timing of Antarctic sea ice retreat and advance have been analyzed over 1979-2012, based on satellite sea ice concentration retrievals. The Ross Sea showed large trends towards earlier sea ice advance and later retreat whereas the Bellingshausen and Amundsen Seas showed opposite trends.

Since 2016, however, we find the occurrence of anomalously late advance and early retreat in the Ross and Weddell Seas and anomalously early advance and late retreat, west of the Peninsula. Trends in the timing of sea ice retreat and advance are consequently weaker over 1979-2022, than over 1979-2012. Here, we investigate the possible role of ocean thermodynamics and wind-driven ice drift in causing such anomalies and resulting trend weakening, using satellite and reanalysis data.

In most of the seasonal zone, anomalies in the date of advance strongly correlate with anomalies in the previous seasonal maximum sea surface temperature (SST) and in the previous date of retreat. This suggests that anomalies in the date of advance are caused by summer ocean heat uptake anomalies, themselves constrained by anomalies in the previous date of retreat. In a large outer band of the seasonal ice zone, however, anomalies in the timing of sea ice advance seem linked to anomalies in the magnitude of winter southerlies, suggesting a possible role for ice drift anomalies there.

By contrast, we find no clear correspondence between anomalies in the date of retreat and anomalies in winds or SST. We will provide more analysis to disentangle the thermodynamic and dynamic mechanisms causing anomalies in the date of retreat, based on a sea ice concentration budget decomposition.

How to cite: Himmich, K., Stammerjohn, S., Vancoppenolle, M., and Madec, G.: Understanding recent changes in Antarctic sea ice seasonality, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16687, https://doi.org/10.5194/egusphere-egu23-16687, 2023.

EGU23-234 | ECS | Posters on site | OS1.4

Future Arctic Ocean atmosphere-ice-ocean momentum transfer and impacts on ocean circulation 

Morven Muilwijk, Tore Hattermann, Sigrid Lind, and Mats Granskog

Over the last few decades, the Arctic has experienced surface warming at more than twice the global rate and extensive sea ice loss. The reduced sea ice cover affects the mechanical and thermodynamical coupling between the atmosphere and the ocean. A commonly repeated hypothesis is that a thinner and more mobile sea ice cover will increase momentum transfer, resulting in a spin-up of upper Arctic Ocean circulation and enhanced vertical mixing. In general, sea ice protects the ocean from interaction with the atmosphere, and a thinning and shrinking sea ice cover implies a more direct transfer of momentum and heat. For example, several observational studies show a more energetic ocean after strong wind events over open water than wind events over ice-covered water. However, previous modeling studies show that seasonality is very important and that the total momentum transfer can decrease with more open water because the ice surface provides greater drag than the open water surface. We here present numerical simulations of future scenarios with the Norwegian Earth System Model (NorESM) and show how the momentum transfer is projected to change with changing sea ice and wind conditions in various regions of the Arctic Ocean. We then compare our results with output from other CMIP6 models and present how different wind conditions and the diminishing ice cover impacts the upper ocean circulation. 

How to cite: Muilwijk, M., Hattermann, T., Lind, S., and Granskog, M.: Future Arctic Ocean atmosphere-ice-ocean momentum transfer and impacts on ocean circulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-234, https://doi.org/10.5194/egusphere-egu23-234, 2023.

EGU23-2446 | ECS | Orals | OS1.4

Drivers of Laptev Sea interannual variability in salinity and temperature from satellite data 

Phoebe Hudson, Adrien Martin, Simon Josey, Alice Marzocchi, and Athanasios Angeloudis

Arctic surface air temperatures are warming twice as fast as global average temperatures. This has caused ocean warming, an intensification of the hydrological cycle, snow and ice melt, and increases in river runoff. Rivers play a central role in linking the components of the water cycle and Russian rivers alone contribute ~1/4 of the total freshwater to the Arctic Ocean, maintaining the halocline that covers the Arctic and dominates circulation. Increases in river runoff could further freshen this layer and increase Arctic Ocean stratification. However, the increase in atmosphere-ocean momentum transfer with sea ice loss could counteract or alter this pattern of circulation, mixing this cold fresh water with the warm salty water that currently sits below it. Understanding the interplay between these changes is crucial for predicting the future state of the Arctic system. Historically, studies trying to understand the interplay between these changes have been challenged by the difficulty of collecting in situ data in this region.

 

Over most of the globe, L-band satellite acquisitions of sea surface salinity (SSS), such as from Aquarius (2011–2015), SMOS (2010- present), and SMAP (2015-present), provide an idea tool to study freshwater storage and transport. However, the low sensitivity of L-band signal in cold water and the presence of sea ice makes retrievals at high latitudes a challenge. Nevertheless, retreating Arctic sea ice cover and continuous progress in satellite product development make the satellite based SSS measurements of great value in the Arctic. This is particularly evident in the Laptev Sea, where gradients in SSS are strong and in situ measurements are sparse. Previous work has demonstrated a good consistency of satellite based SSS data against in situ measurements, enabling greater confidence in acquisitions and making satellite SSS data a truly viable potential in the Arctic. Therefore, this project aims to combine satellite data, particularly SMAP and SMOS sea surface salinity (SSS) data, with model output to improve our understanding of interactions between the components of the Arctic hydrological cycle and how this is changing with our changing climate.

 

The Laptev Sea was chosen as an initial region of focus for analysis as the Lena river outflows as a large, shallow plume, which is clearly observable from satellite SSS data. The spatial pattern of the Lena river plume varies considerably interannually, responding to variability in atmospheric and oceanic forcing, sea ice extent, and in the magnitude of river runoff.  Numerical model output and satellite products confirm what has previously been suggested from in-situ data: wind forcing is the main driver of river plume variability.

How to cite: Hudson, P., Martin, A., Josey, S., Marzocchi, A., and Angeloudis, A.: Drivers of Laptev Sea interannual variability in salinity and temperature from satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2446, https://doi.org/10.5194/egusphere-egu23-2446, 2023.

EGU23-3244 | Orals | OS1.4

Stable oxygen isotopes from the MOSAIC expedition show vertical and horizontal variability of sea-ice and river water signals in the upper Arctic Ocean during winter 

Dorothea Bauch, Nils Andersen, Ellen Damm, Alessandra D'Angelo, Ying-chih Fang, Ivan Kuznetsov, Georgi Laukert, Moein Mellat, Hanno Meyer, Benjamin Rabe, Janin Schaffer, Kirstin Schulz, Sandra Tippenhauer, and Myriel Vredenborg

Our aim is to better understand how local winter modification and advected signals from the Siberian Shelf affect the structure of the upper Arctic Ocean along the Transpolar Drift (TPD). Hereto we use stable oxygen isotopes of the water (δ18O) in combination with salinity to quantify river water contributions (fr) and changes due to sea-ice formation or melting (fi) in the upper ~150m of the water column during the MOSAIC drift. Furthermore, ratios of fi/fr at identical salinities can be used to distinguish waters remnant from the previous summer and those modified locally.

Within the ongoing winter we observed salinification and deepening of the mixed layer (ML) due to sea-ice related brine release together with interleaving waters at the base of the ML and within the main halocline. These interleaving waters with variable sea-ice and river water signals are observed for the first time and have not been observed during summer expeditions before.

The MOSAIC floe drifted in and out of the freshwater-rich part of the TPD and into the Atlantic regime throughout the winter. Despite these strong regime changes the sea-ice related brine content accumulated during the ongoing winter remained visible within the water column. Budgets derived by integration of signals over the upper 100m result in ~1 to 5 m of pure sea-water (34.92 salinity and 0.3‰ δ18O) removed from the water column for ice formation and are much higher than ice thicknesses of ~0.5 to 2 m observed for the MOSAIC floe. For further evaluation scaling factors have to be considered accounting e.g. for the different densities of ice and water as well as for the lower salinity in the halocline relative to pure sea-water. Therefore, our analysis indicates a lower limit of the advected signal relative to local winter modification within the Arctic Ocean halocline.

How to cite: Bauch, D., Andersen, N., Damm, E., D'Angelo, A., Fang, Y., Kuznetsov, I., Laukert, G., Mellat, M., Meyer, H., Rabe, B., Schaffer, J., Schulz, K., Tippenhauer, S., and Vredenborg, M.: Stable oxygen isotopes from the MOSAIC expedition show vertical and horizontal variability of sea-ice and river water signals in the upper Arctic Ocean during winter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3244, https://doi.org/10.5194/egusphere-egu23-3244, 2023.

EGU23-3465 | Orals | OS1.4

Ocean heat increase and sea ice reduction in the Fram Strait conveys Arctic Ocean change 

Laura de Steur, Hiroshi Sumata, Dmitry Divine, Mats Granskog, and Olga Pavlova

The sea ice extent in the Arctic Ocean has reduced dramatically with the last 16 years (2007-2022) showing the 16 lowest September extents observed in the satellite era. Besides a declining sea ice cover and increase in ocean heat content in summer, the winter sea ice concentration and thickness have also become more vulnerable to changes. We present results from the Fram Strait Arctic Outflow Observatory showing that the upper ocean temperature in the East Greenland Current in the Fram Strait has increased significantly between 2003 and 2019. While the cold Polar Water now contains more heat in summer due to lower sea ice concentration and longer periods of open water upstream, the warmer returning Atlantic Water has shown a greater presence in winter the central Fram Strait, impacting the winter sea ice thickness and sea ice extent. These processes combined result in a reduced sea ice cover downstream along the whole east coast of Greenland both in summer and winter, which has consequences for winter-time ocean convection in the Greenland Sea.

How to cite: de Steur, L., Sumata, H., Divine, D., Granskog, M., and Pavlova, O.: Ocean heat increase and sea ice reduction in the Fram Strait conveys Arctic Ocean change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3465, https://doi.org/10.5194/egusphere-egu23-3465, 2023.

EGU23-3547 | Orals | OS1.4

An increasingly turbulent Arctic Ocean? 

Tom P. Rippeth

Historically, the Arctic Ocean has been considered an ocean of weak turbulent mixing. However, the decline in seasonal sea ice cover over the past couple of decades has led to increased coupling between the atmosphere and the ocean, with potential enhancement of turbulent mixing. Here, we review studies identifying energy sources and pathways that lead to turbulent mixing in an increasingly ice-free Arctic Ocean. We find the evolution of wind-generated, near-inertial oscillations is highly sensitive to the seasonal sea ice cycle, but that the response varies greatly between the continental shelves and the abyssal ocean. There is growing evidence of the key role of tides and continental shelf waves in driving turbulent mixing over sloping topography. Both dissipate through the development of unsteady lee waves. The importance of the dissipation of unsteady lee waves in driving mixing highlights the need for parameterization of this process in regional ocean models and climate simulations.

How to cite: Rippeth, T. P.: An increasingly turbulent Arctic Ocean?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3547, https://doi.org/10.5194/egusphere-egu23-3547, 2023.

EGU23-4159 | ECS | Orals | OS1.4

Modes of decadal variability in observed Arctic sea-ice concentration 

Jakob Dörr, Marius Årthun, David B. Bonan, and Robert C. J. Wills

The Arctic sea ice cover is strongly influenced by internal variability on decadal time scales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but the contributions of distinct modes of decadal variability to regional and pan-Arctic sea-ice trends has not been quantified in a consistent manner. The relative contribution of forced and unforced variability in observed Arctic sea ice changes also remains poorly quantified. Here, we identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record and their underlying mechanisms using a novel technique called low-frequency component analysis. The identified patterns account for most of the observed regional sea ice variability and trends, and thus help to disentangle the role of forced and unforced sea ice changes since 1979. In particular, we separate a mode of decadal ocean-atmosphere-sea ice variability, with an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30-50% of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012. For winter, we find that internal variability has so far dominated decadal trends in the Bering Sea, while it plays a smaller role in the Barents and Kara Seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to decadal trends in sea ice, suggest a lower estimate of the internal variability contribution than most model-based assessments.

How to cite: Dörr, J., Årthun, M., Bonan, D. B., and Wills, R. C. J.: Modes of decadal variability in observed Arctic sea-ice concentration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4159, https://doi.org/10.5194/egusphere-egu23-4159, 2023.

EGU23-4822 | ECS | Orals | OS1.4

Ocean response to reduced Arctic sea ice in PAMIP simulations. 

Sourav Chatterjee, Julia Selivanova, Tido Semmler, and James A. Screen

Arctic Amplification (AA) – the greater warming of the Arctic than the global average - is a prominent feature of past and projected future climate change. AA exists due to multiple positive feedbacks involving complex interactions among different components of Arctic atmosphere, ocean, and cryosphere. The loss of sea ice is a key driver of AA. Sea ice loss and resultant AA can influence the global climate system, way beyond the Arctic. The atmospheric response to sea ice loss has been studied extensively. In comparison, the oceanic response has received less attention and our understanding of it is imprecise. Here, we utilize the fully coupled model simulations from the Polar Amplification Model Comparison Project (PAMIP) to explore the oceanic response to projected Arctic sea ice loss at 2o C global warming.

The sea surface warming signal is maximum in the Barents-Kara Sea region in all three models analysed. Results suggest that the observed northward propagation of the Arctic ‘cooling machine’ (region of intensive heat loss to the atmosphere) is largely driven by the reduced sea ice over the northern Barents Sea. Simultaneously, the atmospheric response with stronger south-westerlies over the Norwegian Seas and southern Barents Sea reduces the heat loss therein. This may partly explain the bipolar spatial structure of heat loss in the Norwegian seas and the Northern Barents-Kara Sea. This seesaw heat loss pattern can result in a warmer inflow of Atlantic Waters from the Norwegian Sea to the northern Barents Sea as projected by CMIP6 models. The mixed layer depth response in these regions is consistent with the heat loss patterns, with a deepening of the mixed layer in regions of enhanced heat loss and vice versa. The surface ocean dynamic response is most prominent in the Beaufort Sea. With reduced sea ice, the Beaufort gyre circulation is strengthened due to larger wind forcing and accumulates freshwater within. As a result, surface salinity response shows maximum freshening in this region. In summary, preliminary results from the coupled simulations under the PAMIP protocol indicate that the observed and projected changes in the Arctic Ocean during the 21st century are strongly driven by the reduction in sea ice.

How to cite: Chatterjee, S., Selivanova, J., Semmler, T., and Screen, J. A.: Ocean response to reduced Arctic sea ice in PAMIP simulations., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4822, https://doi.org/10.5194/egusphere-egu23-4822, 2023.

EGU23-4972 | Posters on site | OS1.4

An 8-year time series of mesozooplankton fluxes in Kongsfjorden, Svalbard 

Patrizia Giordano, Alessandra D'Angelo, Kyle Mayers, Jasmin Renz, Ilaria Conese, Stefano Miserocchi, Federico Giglio, and Leonardo Langone

In Arctic regions, the food availability for epi-pelagic fauna is strictly influenced by environmental stressors, such as solar radiation, ice cover, glacial and watershed runoffs. This study presents an 8-year time-series (2010-2018) of mesozooplankton collected from an automatic sediment trap in the inner part of Kongsfjorden, Svalbard, at ~87m depth. The aim of this study is to observe the temporal variability in the abundance of epipelagic mesozooplankton species, collected as active flux (swimmers). Reference meteorological and hydrological data are also presented as environmental stressors, to evaluate possible relationships with zooplankton populations. A principal component analysis (PCA) applied to the dataset revealed that the physical and chemical characteristics of seawater affected the mesozooplankton abundance and composition. Collectively, this result highlighted the role of the thermohaline characteristics of the water column on the Copepods behavior, and the correlation between siliceous phytoplankton and Amphipods. Overall, the zooplankton within inner Kongsfjorden did not show a clear seasonal trend, suggesting their high adaptivity to extreme environmental conditions. Although the swimmer fluxes have decreased from 2013 onwards, an increase in community diversity has nevertheless been observed, probably due to copepods decline and subsequent higher food availability. Despite the decreasing magnitude of the zooplanktonic community over time, we recorded the intrusion of subarctic boreal species, such as Limacina retroversa, since 2016. The uniqueness of this dataset is an 8-year uninterrupted time series, which provides correlations between environmental and biological parameters in a poorly studied region. Under a warming Kongsfjorden scenario, with increasing submarine and watershed runoff, and the rapid Atlantification of the fjord, major changes in mesozooplankton communities are expected in the medium to long-term due to their adaptation to environmental changes and the introduction of alien species.

How to cite: Giordano, P., D'Angelo, A., Mayers, K., Renz, J., Conese, I., Miserocchi, S., Giglio, F., and Langone, L.: An 8-year time series of mesozooplankton fluxes in Kongsfjorden, Svalbard, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4972, https://doi.org/10.5194/egusphere-egu23-4972, 2023.

EGU23-4988 | ECS | Posters on site | OS1.4

Spatial and temporal distribution of organic matter in central Arctic: Insights from biomarker proxy 

Akanksha Singh, Sze Ling Ho, and Ludvig Löwemark

Studies have shown that Arctic sea-ice conditions influence the earth’s energy budget by affecting its albedo and global ocean circulation. It also exerts a strong control on the local primary productivity. In addition, by drifting sea ice, it facilitates the transport of sediment and organic matter (OM) from marginal seas across the Arctic Ocean. Over the past decades, there have been several studies on sediment cores from Central Arctic where the major source of OM was shown to be terrigenous. The presence of this elevated terrigenous OM is driven by the transport of sediments and OM from marginal seas to the Central Arctic via drifting ice. However, our understanding of the processes involved in the transport of OM to the central Arctic is still limited. In this study, in order to better understand the pathways of OM transport, we examine spatial and temporal variations in OM flux to the central Arctic. We use organic carbon and biomarker proxies, namely n-alkanes and Glycerol dialkyl glycerol tetraether (GDGT) to explore the spatial and temporal (Marine Isotope Stage 1, 2 and 3) variation of terrigenous input versus marine primary productivity in the central Arctic. To understand the transport of OM in the Central Arctic, biomarkers in 100 samples collected from 9 central Arctic cores were investigated. The presence of terrestrial organic matter in the central Arctic region was confirmed by the high values of the BIT index, which virtually all reached above 0.5 with a maximum of 0.9. The spatial pattern of both terrestrial and marine OM showed higher concentrations at the central Lomonosov ridge and reduced values towards the Lomonosov Ridge off Greenland, with lowest concentrations from the cores located at Morris Jesup Rise (MJR). The pattern of declining terrestrial biomarker concentrations from the central Arctic to MJR, which is closer to the Fram Strait and marks the exit of the Arctic Ocean, are likely caused by sea-ice drift patterns. The sea ice would have been transported by the Transpolar Drift, which allows terrigenous material entrained in the dirty sea ice to get transported towards central Arctic. This spatial pattern remains same for all three studied Marine Isotope Stages. Looking at the temporal variation of the OM into the central Arctic, compared to MIS 3 and MIS 2, TOC as well as both marine and terrestrial biomarkers show enhanced concentration during MIS 1 all over the central Arctic. These increased biomarker concentrations reflect that MIS 1 was warmer with less extensive sea-ice cover than MIS 2 and MIS 3.

How to cite: Singh, A., Ho, S. L., and Löwemark, L.: Spatial and temporal distribution of organic matter in central Arctic: Insights from biomarker proxy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4988, https://doi.org/10.5194/egusphere-egu23-4988, 2023.

EGU23-5197 | ECS | Orals | OS1.4

A high-resolution view on mesoscale eddy activity in the Eurasian Basin 

Vasco Müller, Qiang Wang, Sergey Danilov, Nikolay Koldunov, Xinyue Li, and Thomas Jung

Mesoscale eddies might play a substantial role for the dynamics of the Arctic Ocean, making them crucial for understanding future Arctic changes and the ongoing ‘atlantification’ of the Arctic Ocean. However, simulating high latitude mesoscale eddies in ocean circulation models presents a great challenge due to their small size and adequately resolving mesoscale processes in the Arctic requires very high resolution, making simulations computationally expensive.

Here, we use a seven-year simulation from the unstructured‐mesh Finite volumE Sea ice-Ocean Model (FESOM2) with 1-km horizontal resolution in the Arctic Ocean. This very high-resolution model setup can be considered eddy resolving and has previously been used to investigate the distribution of eddy kinetic energy (EKE) in the Arctic. Now, with a simulation spanning several years, we evaluate the changes of EKE in the Eurasian Basin and the connection to other properties like sea-ice cover, baroclinic conversion rate and stratification. EKE seasonality is influenced predominantly by sea-ice changes, while monthly anomalies have different drivers for different depths levels. The mixed layer is strongly linked to the surface and thus to sea-ice variability. Deeper levels on the other hand are shielded from the surface by stratification and influenced more strongly by baroclinic conversion.

How to cite: Müller, V., Wang, Q., Danilov, S., Koldunov, N., Li, X., and Jung, T.: A high-resolution view on mesoscale eddy activity in the Eurasian Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5197, https://doi.org/10.5194/egusphere-egu23-5197, 2023.

EGU23-5605 | Posters on site | OS1.4

CMIP6/OMIP simulations of the Arctic Ocean and the impact of resolutions 

Chuncheng Guo, Qi Shu, Qiang Wang, Aleksi Nummelin, Mats Bentsen, Alok Gupta, Yang Gao, and Shaoqing Zhang

Underlying the polar climate system are a number of closely coupled processes that are interconnected through complex feedbacks on a range of temporal and spatial scales. Observations are limited in these inaccessible and remote areas, and understanding of these processes often relies on regional and global climate modelling. However, large uncertainties remain due to unresolved key processes in both the regional and global contexts.

In this presentation, we first show that large model spread and biases exist in simulating the Arctic Ocean hydrography from the latest CMIP6/OMIP experiments. Our results indicate that there are almost no improvements compared with the previous CORE-II experiments (with similar OMIP-like protocol) which were thoroughly assessed by the ocean modelling community. The model spread and biases are especially conspicuous in the simulation of subsurface halocline and Atlantic Water, the latter often being too warm, too thick, and too deep for many models. The models largely agree on the interannual/decadal variabilities of key metrics, such as volume/heat/salt transport across main Arctic gateways, as dictated by the common atmospheric forcing reanalysis.

We then examine a hierarchy of global models with horizontal resolutions of the ocean on the order of 1-deg, 0.25-deg, and 0.1-deg. For the 0.1-deg resolution, we take advantage of a recent unprecedented ensemble of high-resolution CESM simulations, as well as NorESM simulations of similar ocean resolution but of shorter integration. High(er) resolutions show signs of improvements and advantages in simulating the Arctic Ocean, but certain biases remain, which will be discussed together with the challenges of high-resolution simulations in the region.

How to cite: Guo, C., Shu, Q., Wang, Q., Nummelin, A., Bentsen, M., Gupta, A., Gao, Y., and Zhang, S.: CMIP6/OMIP simulations of the Arctic Ocean and the impact of resolutions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5605, https://doi.org/10.5194/egusphere-egu23-5605, 2023.

EGU23-5780 * | ECS | Orals | OS1.4 | Highlight

Marine Heatwaves in the Arctic Ocean: drivers, feedback mechanisms and interactions with sea ice 

Benjamin Richaud, Eric C.J. Oliver, Xianmin Hu, Sofia Darmaraki, and Katja Fennel

Arctic regions are warming at a rate faster than the global average. Superimposed on this trend, marine heatwaves and other extreme events are becoming more frequent and intense. Simultaneously the sea ice phenology with which these events interact is also changing. While sea ice can absorb atmospheric heat by melting and therefore acts as a heat buffer for the ocean, meltwater-induced stratification and albedo changes can provoke positive feedbacks on the heat content of the upper ocean. Disentangling those effects is key to better understanding and predicting the present and future state of the Arctic Ocean, including how it responds to forcing by extreme events. Using a three-dimensional regional ice-ocean coupled numerical model, we calculate a two-layer heat budget for the surface mixed layer of the Arctic Ocean, using a novel approach for the treatment of residuals. We present a statistical overview of the dominant drivers of marine heatwaves at the regional scale as well as more in-depth analyses of specific events in key regions of interest. The characteristics of marine heatwaves under different sea ice conditions is also considered, to identify anomalous ice-ocean interactions. Finally, potential feedback mechanisms are investigated to verify their existence and quantify their importance.

How to cite: Richaud, B., Oliver, E. C. J., Hu, X., Darmaraki, S., and Fennel, K.: Marine Heatwaves in the Arctic Ocean: drivers, feedback mechanisms and interactions with sea ice, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5780, https://doi.org/10.5194/egusphere-egu23-5780, 2023.

EGU23-6012 | ECS | Posters on site | OS1.4

Winter Atlantic Water intrusions in Kongsfjorden: atmospheric triggering and oceanic preconditioning 

Francesco De Rovere, Jacopo Chiggiato, Leonardo Langone, Angelo Rubino, and Davide Zanchettin

Kongsfjorden is an Arctic fjord in Svalbard facing the West Spitsbergen Current (WSC) transporting warm and salty Atlantic Water (AW) through the Fram Strait to the Arctic. In this work, winter AW intrusions in Kongsfjorden occurring in the 2010-2020 decade are assessed by means of oceanographic and atmospheric observations, provided by in-situ instrumentations and reanalysis products. Winter AW intrusions are relatively common events, bringing heat and salt from the open ocean to the fjord interior; they are characterized by water temperatures rising by 1-2 °C in just a few days. Several mechanisms have been proposed to explain winter AW intrusions in West Spitsbergen fjords, tracing back to the occurrence of energetic wind events along the shelf slope. Here we demonstrate that the ocean plays a fundamental role as well in regulating the inflow of AW toward Kongsfjorden in winter.

Winter AW intrusions in 2011, 2012, 2016, 2018 and 2020 occurred by means of upwelling from the WSC, triggered by large southerly winds blowing on the West Spitsbergen Shelf (WSS) followed by a circulation reversal with northerly winds. Southerly winds are generated by the setup of a high pressure anomaly over the Barents Sea. In these winters, fjord waters are fresher and less dense than the AW current, resulting in the breakdown of the geostrophic control mechanism at the fjord mouth, allowing AW to enter Kongsfjorden. The low salinity signal is found also on the WSS and hence is related to the particular properties of the Spitsbergen Polar Current (SPC). The freshwater signal is hypothesized to be linked to the sea-ice production and melting in the Storfjorden and Barents Sea regions, as well as the accumulation of glaciers’ runoff. The freshwater transport toward West Spitsbergen is thus the key preconditioning factor allowing winter AW intrusions in Kongsfjorden by upwelling, whilst energetic atmospheric phenomena trigger the intrusions. 

Winter 2014 AW intrusion shows a different dynamic, i.e., an extensive downwelling of warm waters in the fjord lasting several weeks. Here, long-lasting southerly winds stack surface waters toward the coast. The fjord density is larger than the WSC density, forcing the AW intrusion to occur near the surface, then spreading vertically over the water column due to heat loss to the atmosphere. We hypothesize the combination of sustained Ekman transport and the shallower height of the WSC on the water column to be the key factor explaining the AW intrusion in this winter. 

After mixing with the initial AW inflow, fjord waters undergo heat loss to the atmosphere and densification. The water column becomes denser than the WSC, restoring the geostrophic control mechanism and blocking further intrusions of AW.

How to cite: De Rovere, F., Chiggiato, J., Langone, L., Rubino, A., and Zanchettin, D.: Winter Atlantic Water intrusions in Kongsfjorden: atmospheric triggering and oceanic preconditioning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6012, https://doi.org/10.5194/egusphere-egu23-6012, 2023.

EGU23-6564 | ECS | Orals | OS1.4

Impact of an isolated summer storm on sea ice and ocean conditions in the Canadian Basin 

Emma Bent, Camille Lique, and Peter Sutherland

The Arctic Ocean has undergone a rapid decrease of sea ice extent for decades and studies have shown that the storm activity has increased in the Arctic. Regions that are seasonally ice-opened experience a greater forcing at the surface, which affects the upper-ocean through mixing, turbulence and air-sea interactions. Previous studies have shown the local and short term impacts of wind and waves on sea ice through negative or positive feedback mechanisms. For instance, increased air-sea flux during the freezing season can lead to a cooling of the upper-ocean and favor ice formation, while an increase in wind forcing can modify the vertical profile of the mixed layer, leading to melting or formation of ice. Given the potential of the mixed layer properties to be modified locally by an increased wind/wave forcing, we question whether this type of forcing could have a seasonal effect on the mixed layer and therefore on the sea ice.

We thus use a 1D coupled ocean-sea ice model (NEMO1D-SI³) to study, in the seasonal ice zone of the Beaufort Sea, the immediate change and the seasonal evolution of the mixed layer when forced by an idealized summer storm. The response of sea ice is also examined. We conduct the experiment for a range of storms varying in intensity, duration and date of forcing. Compared to a situation with no increased forcing, we first find that summer storms thicken the mixed layer through mixing which increases the upper-ocean heat content. In the fall, ice formation is consequently delayed for a maximum of 2 weeks compared to a situation with no increased forcing. Secondly, we show that storm-induced thick mixed layers isolate the sea ice from sub-surface warm waters, allowing for efficient ice growth. Ice is consequently thicker at the end of winter compared to a situation with no increased forcing (maximum difference of 10 cm). Thirdly, we find that these results are amplified for storms that happen earlier in summer and have a strong momentum input to the ocean. Our results suggest that localized storms could be a significant driver of the seasonal evolution of the mixed layer and the sea ice.

How to cite: Bent, E., Lique, C., and Sutherland, P.: Impact of an isolated summer storm on sea ice and ocean conditions in the Canadian Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6564, https://doi.org/10.5194/egusphere-egu23-6564, 2023.

EGU23-6699 | ECS | Orals | OS1.4

Investigating ventilation and saturation dynamics in the Arctic Ocean using noble gas tracer techniques 

Yannis Arck, Lennart Gerke, Edith Engelhardt, Florian Freundt, Julian Robertz, Stanley Scott, David Wachs, Markus Oberthaler, Toste Tanhua, and Werner Aeschbach

Timescales of ventilation of the Arctic Ocean are still only poorly known. The commonly used tracers for ocean ventilation studies like CFCs and SF6 are limited to young water masses that are either close to the surface or in highly ventilated deep waters. The radioisotope 39Ar with its half-life of 269 years covers time scales of 50 to 1000 years, perfectly suitable to investigate ventilation timescales of deep and intermediate water masses within the Arctic Ocean. The new measurement technique called Argon Trap Trace Analysis (ArTTA) only requires samples sizes of a few liters of ocean water, instead of the previous low-level counting method, which required about 1000 liters of water. The benefit for ocean studies is evident, much more samples can be taken during one cruise if ArTTA is applied. This enables a better resolution of the water column in great depths at the desired sampling location in the Arctic Ocean. Combined with the additional data of the CFC-12 and SF6 measurements, ventilation timescales of the complete water column from surface to bottom are obtained by constraining transit time distributions via this multi-tracer approach.

Another focus of this study is the saturation of all gaseous transient tracers. It is determined by surface conditions as well as interior mixing processes. Measurements of stable noble gas isotopes (He, Ne, Ar, Kr, Xe) are used to determine possible saturation anomalies that arise during air bubble dissolution, rapid cooling and subduction, or ice formation and subsequent interior mixing of water masses. These saturation distortions for different boundary conditions are of key importance to correct the input function for gas tracers in the Arctic Ocean and hence to constrain the ventilation timescales. The uncertainty of the age distributions will be reduced, and ocean circulation models can be improved.

This contribution presents first stable and radioactive noble gas results of the project Ventilation and Anthropogenic Carbon in the Arctic Ocean (VACAO), which is part of the Synoptic Arctic Survey carried out in summer 2021 on the Swedish icebreaker Oden.

How to cite: Arck, Y., Gerke, L., Engelhardt, E., Freundt, F., Robertz, J., Scott, S., Wachs, D., Oberthaler, M., Tanhua, T., and Aeschbach, W.: Investigating ventilation and saturation dynamics in the Arctic Ocean using noble gas tracer techniques, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6699, https://doi.org/10.5194/egusphere-egu23-6699, 2023.

EGU23-6724 | Orals | OS1.4

On the realism of Arctic Ocean transports in CMIP6 

Susanna Winkelbauer, Michael Mayer, and Leopold Haimberger

This contribution evaluates key components of the Arctic energy budget as represented by the Coupled Model Intercomparison Project Phase 6 (CMIP6) against reanalyses and observations.

The Arctic regions are characterized by a net energy loss to space, which is balanced by northward heat transports in atmosphere and ocean. Mean and variability in the oceanic northward heat transports have major impacts on the state and change of the Arctic Ocean and sea ice. Therefore, an accurate representation of oceanic transports in climate models is a key feature to realistically simulate the Arctic climate. However, the nature of curvilinear ocean model grids and the variety of different grid types used in the CMIP ensemble, make the calculation of oceanic transports on their native grids difficult and time consuming. We developed new tools that enable the precise calculation of volume, heat, salinity and ice transports through any desired oceanic sections or straits for a large number of CMIP6 models as well as ocean reanalyses. Our tools operate on native grids and hence avoid biases that often arise from interpolation to regular grids. Those tools will be made available as open-source Python package enabling easy and effortless calculations of oceanic transports.

In the work presented here, we use the newly developed tools to compare oceanic heat transports (OHT) through the main Arctic gateways from CMIP6 models and reanalyses to those gained from observations and analyze them concerning their annual means, seasonal cycles and trends. We find strong connections between the Arctic’s mean state and lateral OHT, with variations in OHT having major effects on the sea ice cover and ocean warming rate.

Results help us to understand typical model biases. For instance, many models feature systematic biases in oceanic transports in the Arctic main gateways, e.g., some models feature to high sea ice extents due to the underestimation of heat transports entering the Arctic through the Barents Sea Opening. Using those results it is possible to generate physically based metrics to detect outliers from the model ensemble, which may be useful in reducing the spread of future projections of Arctic change.

How to cite: Winkelbauer, S., Mayer, M., and Haimberger, L.: On the realism of Arctic Ocean transports in CMIP6, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6724, https://doi.org/10.5194/egusphere-egu23-6724, 2023.

EGU23-7774 | ECS | Posters on site | OS1.4

Upper Arctic Ocean properties and water mass pathways during the year-round MOSAiC expedition in the context of historical observations 

Myriel Vredenborg, Wiebke Körtke, Benjamin Rabe, Maren Walter, Sandra Tippenhauer, and Oliver Huhn

The Arctic Ocean is characterized by complex processes coupling the atmosphere, cryosphere, ocean and land, and undergoes remarkable environmental changes due to global warming. To better understand this system of physical, biogeochemical and ecosystem processes, as well as recent changes was the aim of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) ice drift conducted year-round from autumn 2019 to autumn 2020. Here, we focus on the properties and circulation pathways of upper Arctic Ocean water masses that have been found to change in recent decades, likely in response to changes in sea ice, surface fluxes, and advection of air masses under Arctic amplification.

We use hundreds of hydrographic profiles obtained with two Conductivity Temperature Depth (CTD) systems mounted to rosette water samplers from the drifting ship and at a remote location on the ice to investigate the properties of the polar mixed layer, halocline waters and warm water of Atlantic origin (“Atlantic Water”) in the Eurasian Arctic during the MOSAiC campaign. Additionally, we analyse chemical tracers (noble gases and anthropogenic tracers CFC-12 and SF6) measured from water samples taken with both CTD/Rosette systems to identify pathways of the water masses. We compare these observations with a comprehensive dataset of historical hydrographic data from the region to put our findings into a long-term context.

We find a shoaling and thickening of the Atlantic-Water layer compared to historical observations, as well as signatures of interleaving at the core of the warm Atlantic Water that slowly get eroded during the drift. Along the MOSAiC track the hydrographic data show convective lower halocline waters that are typically formed north of Fram Strait and further downstream, as well as advective-convective lower halocline waters typically formed in the Barents Sea. We see a change in lower halocline properties in the eastern Amundsen Basin compared to historical observations, that could either be caused by local formation or a change in circulation. Further, we use the chemical tracers to investigate possible pathways and formation regions of the observed water masses.

How to cite: Vredenborg, M., Körtke, W., Rabe, B., Walter, M., Tippenhauer, S., and Huhn, O.: Upper Arctic Ocean properties and water mass pathways during the year-round MOSAiC expedition in the context of historical observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7774, https://doi.org/10.5194/egusphere-egu23-7774, 2023.

EGU23-8320 | ECS | Posters on site | OS1.4

Tracing Atlantic water exiting the Fram Strait and its transit in the Arctic Ocean by isolating reprocessing-derived 236U and colored dissolved organic matter 

Gang Lin, jixin Qiao, Rafael Gonçalves‐Araujo, Peter Steier, Paul Dodd, and Colin Stedmon

The Fram Strait, located between Svalbard and Greenland is an important gateway for exchange of salt and heat between the Arctic Ocean and the North Atlantic Ocean and is also a geographically crucial region for investigating Atlantic water transport pathways and transit times, which are necessary to understand the progress of environmental changes in the Arctic. 236U from the two European nuclear reprocessing plants (RPs) at La Hague (LH) and Sellafield (SF) provides a unique signal in Atlantic water for studying its circulation pattern in the Arctic Ocean. In this study we first isolate RP-derived 236U (236URP) using the characteristic 233U/236U signature and then use colored dissolved organic matter (CDOM) to indicate transit pathways and therefore constrain the selection of appropriate 236URP input functions. High CDOM absorbance in the Fram Strait reflects the passage of Atlantic water transported to the Arctic by the Norwegian Coastal Current (NCC) and subsequently along the Siberian shelf where the Ob, Yenisei and Lena rivers supply terrestrial organic matter with high CDOM levels. Conversely low CDOM water represents Atlantic water that has remained off the shelf. Based on CDOM absorbance, potential temperature (θ) and water depth the path of a given body of Atlantic water could be determined and an appropriate RP input function selected so that transit times could be estimated. Waters with high CDOM levels sourced from the NCC and Barents Sea branch water (BSBW) had an average Atlantic water transit time of 12 years. Waters with low CDOM,  θ < 2 °C, and depth < 1500 m were sourced from the Norwegian Atlantic Current (NwAC), had little interaction with riverine freshwater with an advective Atlantic water transit time of 26 years.

How to cite: Lin, G., Qiao, J., Gonçalves‐Araujo, R., Steier, P., Dodd, P., and Stedmon, C.: Tracing Atlantic water exiting the Fram Strait and its transit in the Arctic Ocean by isolating reprocessing-derived 236U and colored dissolved organic matter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8320, https://doi.org/10.5194/egusphere-egu23-8320, 2023.

EGU23-9367 | ECS | Posters on site | OS1.4

Wind forcing and tides mediate transport of ocean heat from Storfjordrenna to the Arctic domain of the Barents Sea 

Kjersti Kalhagen, Ragnheid Skogseth, Ilker Fer, Till M. Baumann, and Eva Falck

The Barents Sea is undergoing changes with impacts on the physical environment, e.g., the seasonal sea ice formation and extent and with large consequences for the ecosystems. There are knowledge-gaps concerning the complex pathways of Atlantic Water (AW) through the Barents Sea and the associated distribution of heat and nutrients. Records from a mooring deployed between September 2018 and November 2019 on the 70 m deep saddle between Edgeøya and Hopen islands in the Svalbard archipelago show sporadic exchange between the AW-influenced trough Storfjordrenna and the Arctic domain of the north-western Barents Sea. Forced by sea surface anomalies, the observed currents show a tendency for eastward transport across the saddle year-round. However, the eastward overflow into the Barents Sea is strongly mediated by wind forcing: The predominant north-northeasterly winds with corresponding geostrophic adjustment to Ekman transport tend to hamper and sometimes even reverse this cross-saddle current. Weaker and/or southerly winds on the other hand tend to enhance the eastward flow into the Barents Sea. The strength and shape of the overflow current vary substantially on seasonal and sub-seasonal timescales: during autumn and winter, the current is strong and barotropic, while during summer, the current is weaker and more baroclinic. On shorter time scales, the strongest oscillations occur during the ice-free autumn with a periodicity of a few days. When the area has a partial sea ice cover in winter, the strength decreases and the periodicity increases to a week or more. Further analysis of variability in temperature and current velocity shows that cross-saddle transport of positive temperature anomalies (indicating heat from waters of Atlantic origin) is evident in frequency bands associated with various drivers of mesoscale variability, such as eddies, synoptic events, and tides. There are indications that the studied area will become an increasingly important location for heat transport into the interior of the Barents Sea: A comparison between historical and recent hydrographic records show that AW is warming and shoaling in the water column in Storfjordrenna, which suggests that AW will be more easily transported across the saddle by the mentioned drivers. Furthermore, the ongoing changes in the large-scale weather patterns resulting in more southerly and southwesterly winds is hypothesized to affect the strength and persistence of the overflow on the saddle between Edgeøya and Hopen islands.

How to cite: Kalhagen, K., Skogseth, R., Fer, I., Baumann, T. M., and Falck, E.: Wind forcing and tides mediate transport of ocean heat from Storfjordrenna to the Arctic domain of the Barents Sea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9367, https://doi.org/10.5194/egusphere-egu23-9367, 2023.

EGU23-9887 | ECS | Posters virtual | OS1.4

An updated observational record of Davis Strait ocean transports, 2004-2017 

Jed Lenetsky, Craig Lee, Clark Richards, and Alexandra Jahn

The Davis Strait, located in Southern Baffin Bay between Greenland and the Canadian Arctic Archipelago, is a key gateway of oceanic exchange between the Arctic and North Atlantic Oceans. Large fluxes of fresh Arctic Waters through the Davis Strait potentially influence deep-water formation in the Labrador Sea, with implications for the strength of the Atlantic Meridional Overturning Circulation. From 2004-2017, and 2020-present, ocean temperatures, salinities, and velocities have been measured along a moored array spanning the entire strait, allowing for ocean transports to be assessed over both the continental shelves and central channel. Here we will present new data from 2011-2017, extending the previously published data for 2004-2010. Furthermore, the whole record has been updated, filling spatial and short temporal data gaps using average temperature, salinity, and velocity sections from high resolution Seaglider surveys from 2004 to 2010. These updated volume, freshwater, and watermass transports will increase understanding of changing oceanic conditions in Baffin Bay, as well as local and remote physical mechanisms that govern the Davis Strait throughflow on synoptic to interannual timescales.

How to cite: Lenetsky, J., Lee, C., Richards, C., and Jahn, A.: An updated observational record of Davis Strait ocean transports, 2004-2017, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9887, https://doi.org/10.5194/egusphere-egu23-9887, 2023.

Everything that happens in the Arctic Ocean, be it of physical, biological, or chemical nature, is constrained by the vertical distribution of heat and salt. In this talk, I will share recent results and on-going work aimed at examining questions directly related to vertical mixing below sea ice: (1) How accurately are the physical properties of the Canada Basin simulated in climate models? (2) How do observed changes to the size and speed of a sea ice floe and ocean stratification impact ocean mixing in 2D numerical simulations? (3) Can we, for the first time, examine seasonal ice-ocean boundary layer dynamics in a 20 m × 10 m × 3 m outdoor saltwater pool?

Our results indicate that the majority of climate models do not accurately simulate the surface freshening trend observed in the Canada Basin between 1975 and 2006-2012, nor do they simulate heat from Pacific Water in the same region. We suggest that both of these biases can be partly attributed to unrealistically deep vertical mixing in the models. We next explore one possible source of this model bias related to decadal changes to the underside of ice floes, called ice keels. Results from idealized numerical simulations highlight the importance of ice keel depth, which controls the range over which ocean mixing occurs, as well as ice keel speed and ocean stratification. Further, we estimate that observational uncertainties related to ice keel depth may translate into uncertainties in the sign of current and future changes to below-ice momentum transfer into the ocean. Lastly, we present the instrument setup for our 2022-2023 pilot experiment and on-going outreach work at the Sea-ice Environmental Research Facility (SERF) in Canada. This is a unique facility centres around an outdoor saltwater pool where sea ice evolves under natural atmospheric conditions in a semi-idealized and well-instrumented setting.

How to cite: Rosenblum, E. and the Team: Exploring ice-ocean boundary layer dynamics in climate models, idealized simulations, and outdoor lab experiments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10302, https://doi.org/10.5194/egusphere-egu23-10302, 2023.

EGU23-10365 | Orals | OS1.4

Causal Mechanisms of Rising Sea Level and Increasing Freshwater Content of the Beaufort Sea 

Ichiro Fukumori, Ou Wang, and Ian Fenty

Over the last two decades, sea-level across the arctic’s Beaufort Sea has been rising an order of magnitude faster than its global mean. This rapid sea-level rise is mainly a halosteric change, reflecting an increase in Beaufort Sea’s freshwater content. The rising volume of freshwater is greater than that associated with the Great Salinity Anomaly of the 1970s, raising the prospect of future disruptions in large-scale ocean circulation and climate. Here we provide a new perspective of this Beaufort Sea variation using a global data-constrained ocean and sea-ice model of the Estimating the Circulation and Climate of the Ocean (ECCO) consortium. Causal relationships are quantified using the model’s adjoint. Controlling processes are elucidated analyzing property budgets.

The study reveals the multi-decadal variation to be driven jointly by change in wind stress and sea-ice melt. Strengthening anticyclonic winds surrounding the Beaufort Sea intensify the ocean’s lateral Ekman convergence of relatively fresh near-surface waters. The strengthening winds also enhance convergence of sea-ice and ocean heat that increase the amount of Beaufort Sea’s sea-ice melt. Whereas the region’s direct wind-driven kinematic anomalies equilibrate over weeks, sea-ice-melt-driven diabatic changes persist for years owing to Beaufort Sea’s semi-enclosed gyre circulation. The growing disparity between where sea-ice forms and where it melts results in this rare example of melting floating ice causing large-scale sea-level rise. The spin-up difference suggests that, on their own, the sea-ice-melt-driven diabatic change will last much longer than the direct wind-driven kinematic anomaly.

The study highlights the importance of observations and the utility of ECCO’s modeling system. While ocean and sea-ice observations are essential in diagnosing the change, the study also points to a need for expanded observations of the atmosphere, especially the winds that act on the ocean/sea-ice system. ECCO is implementing a novel “point-and-click” interface for analyzing its modeling system, such as conducted here, without requirements for expertise in numerical modeling, and invites exploitation of its new utility (https://ecco-group.org).

How to cite: Fukumori, I., Wang, O., and Fenty, I.: Causal Mechanisms of Rising Sea Level and Increasing Freshwater Content of the Beaufort Sea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10365, https://doi.org/10.5194/egusphere-egu23-10365, 2023.

Black carbon (BC) is one of the most important absorbing particles in the atmosphere. BC can reduce the albedo of snow/ice and enhance the absorption of solar radiation at ultraviolet (UV) and visible wavelengths when it deposited on snow/ice surface. The deposition of BC can lead to an acceleration of the melting of snow/ice. To quantify the changing process of BC in snow/ice and its contribution to the melting of snow/ice, a series of sensitivity numerical experiments including the impacts of BC species (hydrophobic and hydrophilic), deposition rate, and scavenging efficiency of BC was completed using the Icepack one-dimensional column model of CICE. Further, we evaluate the effects of BC deposition on Arctic albedo and ice thickness, forced by ERA5 reanalysis data and BC deposition rate from CMIP6, including two simulation results of the historical experiments with GISS-E2 model and EC-Earth3 model. The results indicate that the hydrophobic BC can cause a reduction of snow/ice albedo by 0.43% in the melting season, which is 35% larger than hydrophilic BC with the same deposition rate. When only the hydrophilic BC was considered, the impact on scavenging efficiency halved to BC content in snow/ice is similar to double the deposition rate in the melting season. Additionally, the 2D model results indicate that the existence of BC in snow could enhance the absorption of solar radiation in the snow layer and reduce the transmittance of radiation to the ice layer, leading to a thicker ice thickness before the melting season. The thermodynamic impact of BC is more significant in the marginal ice zone than that in the central Arctic, especially from Barents Sea to Laptev Sea. In this paper, we quantify the effects of BC on the melting of Arctic snow and sea ice and discuss the problems of the parameterizations of BC’s effect. This may contribute to the improvement of the sea ice model.

Key words: Black carbon; CICE model; Sensitivity experiment; Scavenging efficiency; Albedo

How to cite: Wang, Y. and Su, J.: Sensitivity study of the effects of black carbon on Arctic sea ice using CICE sea-ice model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10572, https://doi.org/10.5194/egusphere-egu23-10572, 2023.

EGU23-10826 | Posters on site | OS1.4

13-Year Observation of the CH4 across the sea surface in the Western Arctic Ocean 

Tae Siek Rhee, Young Shin Kwon, Mi-Seon Kim, Scott Dalimore, Charles Paull, Jong Kuk Hong, and Young Keun Jin

Methane (CH4) is one of the most important greenhouse gases on Earth. Recent finding of the strong CH4 emissions in the Arctic Seas with shrinking the sea ice may amplify the Arctic warming leading to the positive feedback in the Arctic climate. Korea Polar Research Institute (KOPRI) has ongoing interest in Arctic environmental conditions including the potential release of the CH4 from the seabed to the water column and finally, further to the atmosphere. During the last 13 years throughout a series of campaigns on the Korean ice-breaker, R/V Araon, we measured CH4 concentrations at the surface ocean and overlying air in summer season to estimate the emissions from the western arctic seas including the Chukchi Sea, the Beaufort Sea, and the East Siberian Sea. We compare each of these seas and the Central Arctic Ocean covering the deep Arctic Ocean basin. The surface ocean showed super-saturation almost everywhere with respect to the CH4 in the overlying air. Nonetheless, we have insufficient regional coverage to assess any possible saturation anomaly trend in each sea. Flux densities of outgassing CH4 are modestly larger than the global mean value of the continental shelf except for the Central Arctic Ocean where the CH4 emission is slightly lower. Our estimate of CH4 emission in the East Siberian Sea is far larger than other Arctic Seas abiding by the previous observations, but its magnitude is far lower due likely to the distance from the hot spot area. Future methane flux studies should be extended to shallow, nearshore environments where rate of permafrost degradation should be greatest in response to ongoing marine transgression.

How to cite: Rhee, T. S., Kwon, Y. S., Kim, M.-S., Dalimore, S., Paull, C., Hong, J. K., and Jin, Y. K.: 13-Year Observation of the CH4 across the sea surface in the Western Arctic Ocean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10826, https://doi.org/10.5194/egusphere-egu23-10826, 2023.

EGU23-10840 | Posters on site | OS1.4

Upper Arctic Ocean Properties and Relationships with Sea Ice in CMIP6 Historical Simulations 

Wei Cheng, Cecilia Bitz, Lettie Roach, Edward Blanchard-Wriggleworth, Mitch Bushuk, and Qiang Wang

While current-generation CMIP and OMIP models have clear biases in their upper Arctic Ocean hydrography, it is less clear how these biases impact the models' ability to simulate the observed Arctic sea ice mean state and trends. In this study we seek to quantify cross-relationship between sea ice and ocean states in CMIP6 historical simulations and identify common model behaviors. Multi-model mean (MMM) simulations exhibit accelerated changes in the ice and ocean system since the late 20th century. Underlying the MMM is strong inter-model variation in the simulated ice and ocean mean states and their temporal variability including trends. Despite such inter-model differences, all models show a similar ratio between sea ice reduction and upper ocean warming such that models with higher ocean warming also have higher SIE reduction and vice versa. Our results also highlight the urgent needs of reliable Arctic Ocean observations or data products in order to better contextualize modeling results.

How to cite: Cheng, W., Bitz, C., Roach, L., Blanchard-Wriggleworth, E., Bushuk, M., and Wang, Q.: Upper Arctic Ocean Properties and Relationships with Sea Ice in CMIP6 Historical Simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10840, https://doi.org/10.5194/egusphere-egu23-10840, 2023.

EGU23-10871 | Orals | OS1.4

A First Look at Surface Ocean Measurements during the SASSIE Field Campaign in 2022 

Julian Schanze and the Salinity and Stratification at the Sea Ice Edge (SASSIE)

The NASA Salinity and Stratification at the Sea Ice Edge (SASSIE) field campaign took during place between August and October of 2022. Using three major components, the aim is to understand the relationship between both haline and thermal stratification and sea-ice advance, and to test the hypothesis that a significant fresh layer at the surface can accelerate the formation of sea ice by limiting convective processes. The three components of the field campaign include: 1) A one-month shipboard hydrographic and atmospheric survey in the Beaufort Sea, 2) A concurrent airborne campaign to observe ocean salinity, temperature, and other parameters from a low-flying aircraft, and 3) The deployment of autonomous assets, buoys, and floats that are able to observe both the melt season and the sea ice advance.

Here, we focus on the novel results from the month-long research cruise aboard the R/V Woldstad that took place during September and October of 2022, particularly measurements of salinity and temperature at radiometric depths (1-2 cm) from the salinity snake instrument. These measurements will be contextualized with all other components of the cruise, including uCTD, air-sea flux, airborne, and satellite data to examine the effects of stratification on ocean dynamics in the Beaufort Sea near at the sea ice edge.

How to cite: Schanze, J. and the Salinity and Stratification at the Sea Ice Edge (SASSIE): A First Look at Surface Ocean Measurements during the SASSIE Field Campaign in 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10871, https://doi.org/10.5194/egusphere-egu23-10871, 2023.

EGU23-11483 | ECS | Posters on site | OS1.4

The Sea Ice Drift Forecast Experiment (SIDFEx): Introduction and applications 

Valentin Ludwig and Helge Gößling and the SIDFEx Team

We introduce the Sea Ice Drift Forecast Experiment (SIDFEx) database. SIDFEx is a collection of close to 180,000 lagrangian drift forecasts for the trajectories of specified assets (mostly buoys) on the Arctic and Antarctic sea ice, at lead times from daily to seasonal scale and mostly daily resolution. The forecasts are based on systems with varying degrees of complexity, ranging from free-drift forecasts to forecasts by fully coupled dynamical general circulation models. Combining several independent forecasts allows us to construct a best-guess consensus forecast, with a seamless transition from systems with lead times of up to 10 days to systems with seasonal lead times. The forecasts are generated by 13 research groups using 23 distinct forecasting systems and sent operationally to the Alfred-Wegener-Institute, where they are archived and evaluated. Many systems send forecasts in near-real time.

One key purpose when starting SIDFEx in 2017 was to find the optimal starting position for the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC). Over the years, more applications evolved: During MOSAiC, the SIDFEx forecasts were used for ordering high-resolution TerraSAR-X images in advance, with a hit rate of 80%. During the Endurance22 expedition, we supported the onboard team with near-real time forecasts, contributing to the success of the mission. Currently, we evaluate drift forecasts for several buoys of the MOSAiC Distributed Network (DN). We know that there is skill in predicting the location of single buoys. Now, we extend this to studying the deformation of the polygon spanned by the DN buoys. Deformation is derived from the spatial velocity derivatives of the buoy array. We find low correlation coefficients between the deformation in the models and the observed deformation for a small-scale DN configuration, but larger and significant correlations around 0.7 for larger configurations and an Arctic-wide buoy array.

How to cite: Ludwig, V. and Gößling, H. and the SIDFEx Team: The Sea Ice Drift Forecast Experiment (SIDFEx): Introduction and applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11483, https://doi.org/10.5194/egusphere-egu23-11483, 2023.

EGU23-12014 | Posters on site | OS1.4

Summer Net Community Production in the northern Chukchi Sea: Comparison between 2017 and 2020 

Doshik Hahm, Soyeon Kwon, Inhee Lee, Keyhong Park, Kyoung-Ho Cho, Jinyoung Jung, Taewook Park, Youngju Lee, Chanhyung Jeon, and Seongbong Seo

The Arctic Ocean experiences warming-induced processes, such as the decrease in sea-ice extent and freshening of the surface layer. While these processes have the potential to alter primary production and carbon export to the deep layer, the changes that will likely occur in them  are still poorly understood. To assess the potential changes in net community production (NCP), a measure of biological carbon export to the deep layer, in response to climate change, we observed the O2/Ar at the surface of the northern Chukchi Sea in the summers of 2017 and 2020. The NCP estimates derived from O2/Ar measurements were largely in the range of 1 -- 11 mmol O2 m-2 d-1 in the northern Chukchi and Beaufort Seas, close to the lower bounds of the values in the global oceans. The average NCP of 1.5 ± 1.7 mmol O2 m-2 d-1 in 2020 was substantially lower than 7.1 ± 7.4  mmol O2 m-2 d-1  in 2017, with the most pronounced decrease occurring in the ice-free region of the northern Chukchi Sea; the NCP of the ice-free region in 2020 was only 12% of that in 2017. The decrease in 2020 was accompanied by a lower salinity of >2, which resulted in shallower mixed layer depths and stronger stratification. We speculated that the anomalously low pressure near the east Russian coast and the lack of strong winds contributed to the strong stratification in 2020. With a continuing decrease in the extent of sea ice, the northern Chukchi Sea will likely experience earlier phytoplankton blooms and nitrate exhaustion. Unless winds blow strong enough to break the stratification, the biological carbon export in late summer is likely to remain weak.  

How to cite: Hahm, D., Kwon, S., Lee, I., Park, K., Cho, K.-H., Jung, J., Park, T., Lee, Y., Jeon, C., and Seo, S.: Summer Net Community Production in the northern Chukchi Sea: Comparison between 2017 and 2020, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12014, https://doi.org/10.5194/egusphere-egu23-12014, 2023.

EGU23-12032 | ECS | Orals | OS1.4

Anthropogenic Carbon in the Arctic Ocean: Perspectives from different TTD Approaches and Tracer Pairs 

Lorenza Raimondi, Anne-Marie Wefing, and Núria Casacuberta Arola

At present, it is well-known that the fast increase in atmospheric carbon dioxide (CO2) concentrations resulting from human activities (Cant), drives the dramatic changes observed in our environment such as global warming and ocean acidification. The Arctic Ocean has been identified as one of the fastest-changing regions of the world ocean and can therefore be considered as a sentinel for future global scenarios.

Here, Cant-rich waters coming from the Atlantic Ocean become isolated from the atmospheric input of CO2 as they flow at an intermediate depth below the mixed layer, making the Arctic Ocean a key region for intermediate-to-long-term storage of Cant. Despite having such an important role, the magnitude of the Cant inventory and its change over time in the region is yet not fully understood, particularly if we are to consider future changes in ice coverage and therefore ocean circulation.

A way of estimating oceanic Cant inventories is by applying the so-called Transit Time Distribution (TTD) method, which implies the use of transient tracers such as the anthropogenically produced CFC-12 and SF6.

In this work we present a new estimate of Cant inventory for the Arctic Ocean in 2015 assessed with the TTD method using both well-established tracers (CFC-12 and SF6, both having a global source) as well as novel ones (anthropogenic radionuclides 129I and 236U, both having primarily a point-like source represented by European nuclear reprocessing plants, as well as a global one represented by the global fallout from nuclear bomb testing).

The TTD was here applied following a relatively novel approach to infer the statistical parameters that describe the age distribution within a water sample, the mean (G) and the width (D). Unlike the “classical TTD” approach, the one used in this study allows the statistical parameters of the TTD to be constrained for each individual sample rather than finding values that are most representative of the region and time studied. We first show a comparison of the two TTD approaches by comparing mean and mode ages as well D/G ratios of this study (new TTD method) to those presented in Rajasakaren et al. 2019 (classical TTD method), using CFC-12 and SF6 as our tracers’ pair. We then compare TTD results obtained from the two tracers’ pairs, CFC-12/SF6 and 129I-/236U, using the new TTD method.

Finally, we estimate and compare Cant concentrations and inventories obtained with the two pairs of transient tracers to one-another as well as to previous estimates of Cant in the region by Rajasakaren et al (2019) obtained with the “classical TTD”. This study demonstrates for the first time the feasibility of using anthropogenically produced radionuclides with input functions and chemical properties different than CO2 as proxies for Cant estimates.  

How to cite: Raimondi, L., Wefing, A.-M., and Casacuberta Arola, N.: Anthropogenic Carbon in the Arctic Ocean: Perspectives from different TTD Approaches and Tracer Pairs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12032, https://doi.org/10.5194/egusphere-egu23-12032, 2023.

EGU23-12592 | ECS | Posters on site | OS1.4

Seasonality and regionality of the vertical structure of the water column in the Arctic Ocean. 

Lucia Gutierrez-Loza and Siv K. Lauvset

The Arctic Ocean is rapidly changing in response to high temperatures and increased atmospheric greenhouse gas concentrations.  As part of these changing conditions, sea-ice loss and increased freshwater inputs are expected to impact the mixing processes and the characteristics of water column in the Arctic region, directly modulating the nutrient availability and primary productivity in the surface water.

Here, we investigate the spatial and temporal variations of the vertical structure of the water column using high-resolution model outputs for the period 2000-2099. We focus on the Atlantic sector of the Arctic, an increasingly temperature-stratified region, where we evaluate the changes on nutrient availability and carbonate chemistry in the upper ocean. Changes in the regionality and seasonality under a medium- to high-end emission scenario (SSP3-7.0), transitioning towards a sea-ice free Arctic, will be used to further understand the upper ocean mixing processes and their impacts on the local and regional biogeochemistry.

How to cite: Gutierrez-Loza, L. and Lauvset, S. K.: Seasonality and regionality of the vertical structure of the water column in the Arctic Ocean., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12592, https://doi.org/10.5194/egusphere-egu23-12592, 2023.

EGU23-12658 | Orals | OS1.4

Arctic Ocean mixing maps inferred from pan-Arctic observations 

Stephanie Waterman, Hayley Dosser, Melanie Chanona, Nicole Shibley, and Mary-Louise Timmermans

Quantifying ocean mixing rates in the Arctic Ocean is critical to our ability to predict upwards oceanic heat flux, freshwater distribution, and circulation. However, direct ocean mixing measurements in the Arctic are sparse and cannot characterize the high spatiotemporal variability typical of ocean mixing. Further, latitude, ice, and stratification make the Arctic Ocean mixing environment unique, with all of double-diffusive (DD), internal wave (IW)-driven and non-turbulent mixing processes playing a role.

In this work, we use year-round temperature and salinity data from Ice-Tethered Profilers (ITPs), as well as an archived record of ship-based measurements, to construct highly-resolved, pan-Arctic maps characterizing the relative prevalence of DD, IW-driven and non-turbulent mixing mechanisms based on thermohaline staircase identification and estimations of turbulence intensity. We next quantify pan-Arctic maps of estimates of average effective vertical diffusivity inferred from these observations that account for all of DD, IW-driven, and non-turbulent mixing processes. Finally, focusing on the water column segment directly above the Atlantic Water (AW) temperature maximum, we use this mixing regime characterization and regime-specific estimates of effective diffusivity to compute estimates of the pan-Arctic distributions of average vertical heat and buoyancy flux from the AW layer.

We find that estimates of effective vertical diffusivities are highly variable in both space and time. Although variability in diffusivity reflects both variations in the prevalence of the various mixing processes and variability in the strength of IW-driven mixing, the prevalence of the mixing mechanisms (predominantly DD and non-turbulent in the basins vs. IW-driven on the shelf) sets the dominant large-scale spatial patterns and the notable shelf-basin contrast. Estimated heat fluxes out of the AW layer also exhibit distinct regional patterns set by mixing mechanism prevalence and regional patterns in the vertical temperature gradient. Buoyancy fluxes from DD mixing compete with the destabilizing effects of IW-driven mixing in the basins, a competition that may be an important control on stratification in the Arctic Ocean interior.

These results are significant as they show that mixing mechanism prevalence is an important consideration in computing robust estimates of average effective diffusivity. They further suggest that the sensitivity of mixing rates to changing environmental conditions may have important regional dependencies owing to differing prevalence of the various mixing processes.

How to cite: Waterman, S., Dosser, H., Chanona, M., Shibley, N., and Timmermans, M.-L.: Arctic Ocean mixing maps inferred from pan-Arctic observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12658, https://doi.org/10.5194/egusphere-egu23-12658, 2023.

EGU23-13807 | Posters on site | OS1.4

The MOSAiC webODV: Interactive online data exploration, visualization and analysis 

Sebastian Mieruch, Ingrid Linck Rosenhaim, and Reiner Schlitzer

In the frame of the M-VRE (The MOSAiC virtual research environment, https://mosaic-vre.org) project we have set up a webODV application, to serve data from the arctic MOSAiC (https://mosaic-expedition.org) expedition.

webODV is deployed at AWI's computing center under https://mvre.webodv.cloud.awi.de. MOSAiC data have been retrieved from the long-term archive Pangaea (https://pangaea.de). To get the most out of the data with webODV, we have harmonized, aggregated and compiled the datasets into different separated and interdisciplinary data collections.

webODV is operated interactively in the browser via the mouse and keyboard (no programming), it's fast, efficient and easy to use for exploring, visualizing, analyzing, downloading data, creating map projections, scatter plots, section plots, surface plots and station plots and many more.

webODV supports the FAIR data principles and analyses and visualizations are fully reproducible using our so-called "xview" files that can be shared among colleagues or attached to publications. We provide real-time sharing, full author traceability and downloadable lists of all the DOI's used in the analysis or the respective .bib or .ris files including all citations. Extensive documentation is available at https://mosaic-vre.org/docs as well as video tutorials at https://mosaic-vre.org/videos/webodv.

How to cite: Mieruch, S., Linck Rosenhaim, I., and Schlitzer, R.: The MOSAiC webODV: Interactive online data exploration, visualization and analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13807, https://doi.org/10.5194/egusphere-egu23-13807, 2023.

EGU23-14133 | ECS | Posters on site | OS1.4

Seasonal and regional sensitivity of Arctic sea ice 

Markus Ritschel and Dirk Notz

We examine the seasonal and regional evolution of sea-ice coverage in the Arctic in response to changes in the forcing. Using satellite and reanalysis data in combination with CMIP6 model simulations, we build on previous studies that have found a strong linear relationship between the September sea-ice area of the northern hemisphere and global atmospheric air temperature (TAS) as well as anthropogenic CO2 emissions. Instead of focusing on the whole Arctic and September sea ice only, we perform sensitivity analyses on higher-resolved regional and seasonal scales, aiming to identify the atmospheric and oceanic drivers that govern the evolution of sea-ice coverage on these scales and to derive simple empirical relationships that describe the impact of these processes. We find clear linkages also on these higher-resolved scales, with different regions and different seasons showing diverse sensitivities of sea-ice area evolution with respect to TAS and anthropogenic CO2. Furthermore, we use a multivariate metric to quantify the "quality" of a single simulation matching the observations, thereby considering the different sensitivities of all seasons of the year. Building the combined covariance matrix of observations and simulations as a measure of the joint uncertainties, we can determine how "close" to the observations every single member of the simulations is. This allows us to separate models whose sensitivities are in overall good agreement with the observations from those that are apparently not capable of properly simulating the response of the sea ice to the forcing throughout all months. Based on our findings we can infer the dominant drivers that force Arctic sea-ice evolution on a regional and seasonal scale and also derive projections for the future evolution of Arctic sea ice for different climate scenarios based on simple empirical relationships that can directly be estimated from observational records.

How to cite: Ritschel, M. and Notz, D.: Seasonal and regional sensitivity of Arctic sea ice, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14133, https://doi.org/10.5194/egusphere-egu23-14133, 2023.

EGU23-16107 | Posters on site | OS1.4

Oceanic gyres in the Arctic 

Yevgeny Aksenov, Stefanie Rynders, Alex Megann, A.J. George Nurser, Chris Wilson, and Andrew C. Coward

The Arctic can be seen as a two-layer ocean: thin (<100m) mixed layer at the surface, and the rest of the weakly-stratified ~5-km water column, separated from the surface waters by the Arctic halocline. The weak subsurface ocean stratification results in most of the ocean flow being depth-uniform and guided by bathymetry. One way to look at the Arctic long-term, large-scale ocean circulation is examining the Arctic gyres and cross-ocean currents, such as the Trans-Polar Drift. Wilson et all 2021[1] show how gyres, saddle points and flow separation structures “separatrices” in the surface ocean circulation changes between years and how these affect cross-basin Arctic oceanic connectivity. We extend the method to the subsurface oceanic flow and examine barotropic circulation in the present-day Arctic Ocean using global NEMO model (Nucleus for European Modelling of the Ocean) at 3-km horizontal resolution. The closed-gyre detection method allows us to map positions of the principal Arctic gyres and quantify their strength. The Montgomery potential analyses complements the study by giving us an insight in the geostrophic flows of the Atlantic and Pacific waters. The results suggest a large year-to-year variability of the Arctic gyres and the changes in the Arctic – the Nordic Sea connectivity, which impacts exports of the freshwater, heat, and biogeochemical tracers from the Arctic.

This work has been funded from LTS-S CLASS (Climate–Linked Atlantic Sector Science, grant NE/R015953/1), from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 820989 (project COMFORT), from the project EPOC, EU grant 101059547 and UKRI grant 10038003 and from the UK NERC project CANARI (NE/W004984/1).

Reference

[1] Wilson, C., Aksenov, Y., Rynders, S. et al. Significant variability of structure and predictability of Arctic Ocean surface pathways affects basinwide connectivity. Commun. Earth. Environ. 2, 164 (2021). https://doi.org/10.1038/s43247-021-00237-0.

How to cite: Aksenov, Y., Rynders, S., Megann, A., Nurser, A. J. G., Wilson, C., and Coward, A. C.: Oceanic gyres in the Arctic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16107, https://doi.org/10.5194/egusphere-egu23-16107, 2023.

EGU23-419 | ECS | Orals | OS1.9 | Highlight

Contributions of atmospheric forcing and ocean preconditioning in the 2016 Antarctic sea ice extent drop 

Bianca Mezzina, Hugues Goosse, Pierre-Vincent Huot, Sylvain Marchi, and Nicole Van Lipzig

The observed evolution of Antarctic sea ice extent is marked by an abrupt decrease in 2016/2017. After several years of gradual increase culminated in an all-time record high in 2014/2015, a rapid decline in 2016 led to an unprecedented minimum, and unusual low extents have been observed since then. Even though this record has now been beaten, the sudden drop from extreme high values to a minimum in less than two years is unique to this event, whose dynamics are still uncertain. While it was likely triggered by anomalous atmospheric conditions in the prior months, the contribution of the ocean conditions, as a preconditioning which amplified the response of the sea ice or helped to maintain the anomalies for a longer period, still needs to be quantified. 

To evaluate the respective influences of the atmosphere and ocean on this 2016 event, we have performed sensitivity experiments using the circum-Antarctic fully coupled model (ice-sheet–ocean–sea-ice–atmosphere) PARASO. First, a control experiment with the model forced by lateral boundary conditions derived from observations (ERA5 in the atmosphere, ORAS5 in the ocean) is performed over the period 1985-2018. In such a set-up, the model correlates well with the observations and is able to capture the 2016 drop. Then, the model is integrated again between 2016 and 2018 with the same atmospheric boundary forcing, but with different initial conditions in the ocean: namely, ocean conditions from previous years in the control run are used as initial state in 2016 in the sensitivity experiments, producing an ensemble of 5 members.

Preliminary results indicate that the 2016 drop is captured by all members, suggesting the atmospheric boundary forcing as the dominant driver and confirming that the event is induced by large-scale atmospheric dynamics. However, some variability is present in the amplitude and timing of the drop, as well as in the evolution and recovery of the sea ice in the following months, which may be influenced by the different states of the ocean. Related processes are further investigated by examining different oceanic and atmospheric fields, focussing on the role of ocean preconditioning by identifying the differences between the members and their impact. 

How to cite: Mezzina, B., Goosse, H., Huot, P.-V., Marchi, S., and Van Lipzig, N.: Contributions of atmospheric forcing and ocean preconditioning in the 2016 Antarctic sea ice extent drop, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-419, https://doi.org/10.5194/egusphere-egu23-419, 2023.

EGU23-480 | ECS | Orals | OS1.9

Spatial variations in the sea ice-mixed layer depth relationship in the West Antarctic Peninsula 

Milo Bischof, Daniel Goldberg, Sian Henley, and Neil Fraser

The impacts of upper-ocean mixing on primary productivity are complex and range from an entrainment of nutrients to modulating light limitations. Sea ice in turn plays an important role in determining mixing conditions through its cycles of formation and melt, and by moderating wind forcing. With sea ice conditions in the Southern Ocean projected to undergo large changes over the course of the century, understanding the relationship between sea ice and upper-ocean mixing is crucial for understanding the impacts of climate change on biological production in this region. Due to the inaccessibility of sea ice-covered waters however, mixed layer depth observations are often not available at a high temporal and spatial resolution. Here we present an analysis of sea ice-mixed layer depth relationships during a 40-year regional ocean-sea ice simulation of the  West Antarctic Peninsula (WAP) and Bellingshausen Sea, a highly biologically productive region of global importance. The relationship between winter sea ice and spring mixed layer depth shows clear differences on and off the WAP continental shelf, with decadal variations in the location of the boundary between negative and positive correlations. Potential mechanisms causing this effect are considered in detail, including the nonlinear relationship between sea ice cover and turbulent mixing, the transport of sea ice within the region, and a difference in the timing of the sea ice seasonal cycle between the two regions. The transport of warm Circumpolar Deep Water onto the shelf is also discussed. The presented findings have implications for the spatial distribution of primary producers in a more ice-free future WAP.

How to cite: Bischof, M., Goldberg, D., Henley, S., and Fraser, N.: Spatial variations in the sea ice-mixed layer depth relationship in the West Antarctic Peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-480, https://doi.org/10.5194/egusphere-egu23-480, 2023.

EGU23-3111 | Posters on site | OS1.9

On the 2018-2020 Ice Shelf Water outflow event in the southeastern Weddell Sea 

Markus Janout, Mathias van Caspel, Elin Darelius, Tore Hattermann, Svein Østerhus, Jean-Baptiste Sallée, and Nadine Steiger

The southern Weddell Sea features a vast perennially ice-covered continental shelf with polynyas, strong sea ice formation, first- and multi-year ice. Sea ice and the general ocean circulation maintain predominantly near-freezing waters on the shelf, which help to maintain the comparatively moderate basal melt rates of the Filchner-Ronne Ice Shelf (FRIS), Antarctica’s largest ice shelf by volume. In contrast to FRIS, other West Antarctic ice shelves show strong basal melt rates, caused by warm intruding ocean waters. In the southern Weddell Sea, however, warm water inflows occur episodically and spatially limited, when modified warm deep water enters the continental shelf through incisions in the shelf break and flows southward towards the FRIS front. Overall, the majority of the shelf is dominated by dense and cold water masses such as High Salinity Shelf Water (HSSW) and Ice Shelf Water (ISW), which are precursors of Antarctic Bottom Water and thus relevant for the global ocean circulation. In 2018, a comprehensive CTD survey found unprecedented (in the available observations) volumes of ISW in Filchner Trough. The ISW was exported from underneath the Filchner Ice Shelf (FIS) following a shift to enhanced cavity circulation due to strong sea ice formation in front of the Ronne Ice Shelf. These Filchner Trough conditions are summarized as the “Ronne-mode”, which is in contrast to the “Berkner-mode”, characterized by a greater influence of locally-formed waters. In this presentation, we introduce new multi-year time series from an international mooring network from various Southeast Weddell Sea locations (sub-FIS, Filchner Trough and Sill), to highlight the temporal and spatial extent of the recent Ronne-mode event, which lasted from 2018-2020, before shifting back into a Berkner-mode. The dominance of either circulation mode is controlled by large-scale atmospheric forcing and has implications on ice shelf basal melt and dense water export into the Weddell Sea. 

How to cite: Janout, M., van Caspel, M., Darelius, E., Hattermann, T., Østerhus, S., Sallée, J.-B., and Steiger, N.: On the 2018-2020 Ice Shelf Water outflow event in the southeastern Weddell Sea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3111, https://doi.org/10.5194/egusphere-egu23-3111, 2023.

EGU23-3627 | ECS | Posters on site | OS1.9

Sub-ice shelf circulation and melt rate variability in the Energy Exascale Earth System Model 

Irena Vankova, Xylar Asay-Davis, and Stephen Price

In-situ observations from the Filchner-Ronne Ice Shelf (FRIS) have uncovered dominant time scales of variability in basal melting and circulation beneath this extensive ice shelf. In particular, the data characterize mechanisms of seasonal and inter-annual variability in sub-ice shelf properties, and show that the amplitude of the variability over the past thirty years is very modest.
Because accurate representation of variability under present-day climate is an obvious prerequisite for earth system models that aim to project climate under a future change, this new observational understanding presents an opportunity for critical evaluation and improvement of existing models. We focus on the Energy Exascale Earth System Model (E3SM) and through a series of simulations we investigate the impact of ocean mixing parameterizations on the variability in the FRIS cavity.

How to cite: Vankova, I., Asay-Davis, X., and Price, S.: Sub-ice shelf circulation and melt rate variability in the Energy Exascale Earth System Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3627, https://doi.org/10.5194/egusphere-egu23-3627, 2023.

EGU23-4468 | ECS | Posters on site | OS1.9

The role of the Pacific-Antarctic Ridge in establishing the northward extent of Antarctic sea-ice 

Antonino Ian Ferola, Yuri Cotroneo, Peter Wadhams, Giannetta Fusco, Pierpaolo Falco, Giorgio Budillon, and Giuseppe Aulicino

Monitoring the Antarctic sea-ice is essential for improving our knowledge of the Southern Ocean. We used satellite sea-ice concentration data for the 2002-2020 period to retrieve the sea-ice extent (SIE) and analyze its variability in the Pacific sector of the Southern Ocean. Results provide observational evidence of the recurring formation of a sea-ice protrusion that extends to 60° S at 150° W during the winter season. These activities are carried on in the framework of the ACCESS and SWIMMING projects of the PNRA.
Our findings show that the northward deflection of the southern Antarctic Circumpolar Current front is driven by the Pacific Antarctic Ridge (PAR) and is associated with the enhanced sea-ice advance. The PAR also constrains anticyclonic and cyclonic eddy trajectories, limiting their interaction with the sea-ice edge. These factors, within the 160° W - 135° W sector, determine an average SIE increase of 61,000 km2 and 46,293 km2 per year more than the upstream and downstream areas, respectively.

How to cite: Ferola, A. I., Cotroneo, Y., Wadhams, P., Fusco, G., Falco, P., Budillon, G., and Aulicino, G.: The role of the Pacific-Antarctic Ridge in establishing the northward extent of Antarctic sea-ice, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4468, https://doi.org/10.5194/egusphere-egu23-4468, 2023.

EGU23-5080 | ECS | Orals | OS1.9 | Highlight

Multi-decadal trends in Antarctic deep convection from satellite-derived steric height 

Jennifer Cocks, Alessandro Silvano, Alice Marzocchi, Alberto Naveira-Garabato, and Anna Hogg

Deep convection from dense water formation in the Southern Ocean drives the lower limb of the global overturning circulation, sequesters anthropogenic heat and carbon from the atmosphere and ventilates the abyssal ocean. The rate and location of dense water formation and its trajectory to the deep ocean is determined by changes in ocean density and stratification and influenced by ocean-ice-atmosphere interactions such as polynya openings (both open-ocean and coastal), sea ice formation and ice shelf collapse.

Signatures of deep convection are logistically difficult to measure. The highest-quality observations of water column density are currently provided by in-situ moorings and profiles from Argo floats or CTDs mounted on elephant seals (MEOP data[1]), but these data are spatially and temporally sparse. Satellite products providing complete coverage of high latitudes at regular repeat periods are becoming more readily available and offer an alternative method for capturing changes the extent and variability of deep-water formation in polar regions.

 

We compute steric height anomalies in the Southern Ocean from 2002-2018 using a novel method combining satellite altimetry and gravimetry data. We use these to explore density changes, focussing on deep water formation regions including the Weddell and Ross seas, the Adelie coastline and Amery shelf region, and infer multi-decadal changes in deep convective processes. Long term changes in the steric height anomalies can be linked to recorded ocean-ice events, such as the 2010 collapse of the Mertz glacier, the 2017 Maud Rise polynya and recent recovery of Ross Sea Bottom Water. The satellite-derived steric height anomalies have been validated against in-situ Argo and MEOP profiles and show good agreement in regions with a high data density.


[1]https://meop.net/meop-portal/

How to cite: Cocks, J., Silvano, A., Marzocchi, A., Naveira-Garabato, A., and Hogg, A.: Multi-decadal trends in Antarctic deep convection from satellite-derived steric height, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5080, https://doi.org/10.5194/egusphere-egu23-5080, 2023.

EGU23-6731 | ECS | Orals | OS1.9

The ice-cavity feedback in an Earth system model 

Pengyang Song, Patrick Scholz, Gregor Knorr, Dmitry Sidorenko, Ralph Timmermann, and Gerrit Lohmann

The melting of the Antarctic ice shelves becomes critical in a warming climate. However, the ocean component of climate models do not consider the effect of the Antarctic ice-shelf cavities. Here, we implement ice-shelf cavity features into the new AWI Earth system model (AWI-ESM2) based on unstructured meshes allowing for varying resolution in a multi-scale approach. We create a global mesh explicitly resolving the Antarctic ice-shelf cavities and evaluate the effect of the cavities under global warming scenarios. The new mesh provides a more realistic freshwater input into the Antarctic coast and the Southern Ocean. In an extreme warming climate scenario, the melting of the Antarctic ice shelves gets stronger by a factor of ~3, affecting the North Atlantic salinity and the overturning circulation. We conclude that the incorporation of ice-cavity feedback is essential to study the past, present, and future. Our approach might be seen as a prototype for the next phase of the Coupled Model Intercomparison Project.

How to cite: Song, P., Scholz, P., Knorr, G., Sidorenko, D., Timmermann, R., and Lohmann, G.: The ice-cavity feedback in an Earth system model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6731, https://doi.org/10.5194/egusphere-egu23-6731, 2023.

EGU23-9657 | Orals | OS1.9 | Highlight

Spatial and temporal variability of water masses in the Southern Ross Sea 

Karen J. Heywood, Esther Portela, Walker Smith, Gillian Damerell, Peter Sheehan, and Meredith Meyer

Relatively warm modified Circumpolar Deep Water accesses the southern Ross Sea steered by bathymetric troughs. There it provides nutrients to support phytoplankton blooms in spring, and heat to melt the Ross Ice Shelf.  Here we present new observations collected by two ocean gliders during December 2022 and January 2023, in the Ross Sea polynya adjacent to the Ross Ice Shelf.  The gliders surveyed the full depth of the water column (about 700 m depth) carrying sensors measuring temperature, salinity, dissolved oxygen, chlorophyll fluorescence and optical backscatter, and also yielded estimates of the dive-average-current which we use to reference geostrophic shear.  Repeated quasi-meridional high resolution (profiles approximately every 1.5 km) sections along the sea ice edge allow analysis of the spatial and temporal variability, as well capturing the dynamic field of eddies, tides and coastal current. We discuss the influence of the sea ice and the atmospheric forcing on the water properties. One glider made an unauthorised foray beneath the Ross Ice Shelf, surveying the upper 200 m of the water column in high resolution beneath an ice shelf base at about 80 m depth. We observe solar-warmed water penetrating beneath the ice shelf with significant signatures of elevated chlorophyll fluorescence and optical backscatter, and low oxygen and salinity. We discuss the likely mechanisms involved in advecting this water beneath the ice shelf and its importance for physical and biogeochemical processes of ocean-ice interaction.



How to cite: Heywood, K. J., Portela, E., Smith, W., Damerell, G., Sheehan, P., and Meyer, M.: Spatial and temporal variability of water masses in the Southern Ross Sea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9657, https://doi.org/10.5194/egusphere-egu23-9657, 2023.

EGU23-10081 | ECS | Orals | OS1.9

Exploring oceanic heat pathways along the George V Land continental shelf 

Eliza Dawson and Earle Wilson

Ocean circulation patterns along the continental shelf in the Australian Antarctic Basin remain poorly understood due to the scarcity of in-situ observations and limited modeling studies. In this dynamically complex and climatically important region, the Ross Gyre, Antarctic Slope Current, and Antarctic Circumpolar Current converge just offshore of the George V Land continental shelf. If warm deep water could access the continental shelf and increase basal melt rates along the George V Land coastline, marine-terminating glaciers in the region could retreat and threaten the stability of the vast Wilkes Subglacial Basin. Here, we explore potential pathways for warm deep water to access the shelf along the George V Land coastline using output from the Southern Ocean State Estimate (SOSE) model. We use the SOSE output to map bottom temperatures and identify where warm bottom water could come close to the grounding line due to bathymetric steering. While SOSE provides observationally constrained hydrographic estimates along the George V Land continental shelf, there are substantial discrepancies between the model’s estimates and observations. Most notably, SOSE does not reproduce the dense, high salinity shelf waters observed in the region. SOSE is a model-generated best fit to Southern Ocean observations, so biases could be present in sparsely sampled regions like this one. To further examine the dynamics of this region, we also present preliminary results from an idealized ocean circulation model that explores the sensitivity of cross-shelf heat transport to changes in local heat and wind forcing.

How to cite: Dawson, E. and Wilson, E.: Exploring oceanic heat pathways along the George V Land continental shelf, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10081, https://doi.org/10.5194/egusphere-egu23-10081, 2023.

EGU23-11464 | ECS | Posters on site | OS1.9

Identification of ventilated and submarine glacial meltwaters in the Amundsen Sea, Antarctica, using noble gases 

DongYoub Shin, Doshik Hahm, Tae-Wan Kim, Tae Siek Rhee, SangHoon Lee, Keyhong Park, Jisoo Park, Young Shin Kwon, Mi Seon Kim, and Tongsup Lee

To estimate the glacial meltwater distribution, we used five noble gases as tracers for optimum multiparameter analysis (OMPA) of the water masses in the Amundsen Sea, Antarctic. The increased number of tracers allowed us to define additional source waters at the surface, which have not been possible with a limited number of tracers. The highest fraction of submarine meltwater (SMW, ~0.6%) was present at the depth of 400 -- 500 m near the Dotson Ice Shelf. The SMW appeared to travel along an isopycnal layer to the continental shelf break >300 km away from the ice shelf. Ventilated SMW (VMW) and surface melts (up to 1.5%) were present in the surface layer <100 m. The distribution of SMW indicates that upwelled SMW, known as an important carrier of iron to the upper layer, amounts for 29% of the SMW in the Dotson Trough. The distinction between SMW and VMW made it possible to clearly distinguish the locally-produced SMW since the previous Winter Water formation from the fresh water (VMW) originated from the upstream; the production rate of the former was estimated as 53-94 G ton yr-1. The Meteoric Water fractions, consisted of SMW and VMW, comprised 24% of those derived from oxygen isotopes. This indicates that the annual input from basal melting is far less than the inventory of meteoric water derived from oxygen istopes.

How to cite: Shin, D., Hahm, D., Kim, T.-W., Rhee, T. S., Lee, S., Park, K., Park, J., Kwon, Y. S., Kim, M. S., and Lee, T.: Identification of ventilated and submarine glacial meltwaters in the Amundsen Sea, Antarctica, using noble gases, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11464, https://doi.org/10.5194/egusphere-egu23-11464, 2023.

EGU23-11864 | ECS | Orals | OS1.9

Evolution of warm water intrusions in the Filchner Trough, Antarctica 

Vanessa Teske, Ralph Timmermann, and Tido Semmler

The Filchner Trough on the continental shelf in the southern Weddell Sea is a region of great importance for the water mass exchange between the open ocean and the Filchner Ronne Ice Shelf cavity. Observations of the last 20 years and modelling studies show seasonal variations and longer lasting pulses of warm water intruding into the trough and reaching the Filchner Ice Shelf front. In this study, we evaluate the evolution of these intrusions in four climate scenarios defined for CMIP6 and simulated with the AWI Climate Model. We show that a warming climate will lead to more frequent pulses in the mitigation scenarios SSP1-2.6 and SSP2-4.5. For the high emission scenarios SSP3-7.0 and SSP5-8.5, hydrography in Filchner Trough will shift to a substantially warmer state during the second half of the 21st century with a temperature rise of 2°C in the trough until 2100. We demonstrate that the system‘s tipping into a warmer state is primarily caused by changes in the local sea ice formation and the depth of the Antarctic Slope Front. Our results show that a regime shift can be avoided by reaching the 2°C climate goal.

How to cite: Teske, V., Timmermann, R., and Semmler, T.: Evolution of warm water intrusions in the Filchner Trough, Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11864, https://doi.org/10.5194/egusphere-egu23-11864, 2023.

The local temperarturecannot explain the inter-annual variation in δ18Oprecip in the coastal Antarctic in past few decades. To understand this enigmatic variation, we have used long-term modern δ18Oprecip value of three coastal Antarctic sites. Using the δ18O-d-excess relationship and modelled δ18O value of vapor at source, we have shown that δ18Oprecip inherits the signature of moisture source parameters (MSPs). Furthermore, the wavelet analysis suggests that the variation in the MSPs impacts the seasonal cycle of δ18Oprecipwhich lead to disparity in the seasonal isotope-temperature relationship. The Southern Ocean surface stratification, due to increase in the freshwater flux by glacier melting, led to alignment of MSPs in such a manner that altogether significantly lowered the isotopic composition of initially formed vapor, which is reflected in δ18Oprecip at inter-annual scale.Our observations suggest that the palaeothermometry will underestimate the Antarctic temperature change for the periods characterized by warming and high glacier-melt.

How to cite: Sanyal, P. and ajay, A.: The Imprint of Southern Ocean Stratification on the Isotopic Composition of Antarctic Precipitation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12482, https://doi.org/10.5194/egusphere-egu23-12482, 2023.

EGU23-12791 | ECS | Orals | OS1.9

Observed Seasonal Evolution of the Antarctic Slope Current System at the Coast of Dronning Maud Land, East Antarctica 

Julius Lauber, Laura de Steur, Tore Hattermann, and Elin Darelius

The Antarctic Slope Front and the associated Antarctic Slope Current shield the continental shelves in East Antarctica from offshore warm water that holds the potential for considerable ice shelf melting and, consequently, sea level rise. Here, we present two-year-long records of temperature, salinity, and velocity (2019-2020), obtained from two oceanographic moorings located within the slope front/current over bathymetries of around 1000m and 2000m slightly east of the prime meridian. The two-year data record reveals clear differences in the seasonality of the thermocline depth and the baroclinicity of the current between the deep and shallow mooring locations. In combination with climatologies of hydrography and satellite-derived surface geostrophic currents, we use the new data to refine the baroclinic seasonality of the ASF. The results highlight the role of surface buoyancy fluxes via seasonal sea ice melt and freeze. Finally, the slope current is shown to control flow into and out of the cavity of the close-by Fimbulisen Ice Shelf on seasonal time scales depending on the orientation of the entrances of the cavity. Our findings contribute to a better understanding of the processes controlling the slope front/current seasonality and resulting inflow into the East-Antarctic ice shelf cavities.

How to cite: Lauber, J., de Steur, L., Hattermann, T., and Darelius, E.: Observed Seasonal Evolution of the Antarctic Slope Current System at the Coast of Dronning Maud Land, East Antarctica, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12791, https://doi.org/10.5194/egusphere-egu23-12791, 2023.

EGU23-13222 | Posters on site | OS1.9

The role of WDW density for a regime shift in the FRIS cavity 

Verena Haid, Ralph Timmermann, Simon Schöll, Torsten Albrecht, and Hartmut H. Hellmer

A potential tipping point on the Antarctic continental shelves, in which cold shelf water is replaced by (modified) Circumpolar Deep Water (CDW) / Warm Deep Water (WDW), is currently the subject of many studies. Such a regime shift entails a drastic increase of basal melt for the fringing ice shelves and could ultimately destabilize large portions of the Antarctic ice sheet.

From the results of a large suite of experiments conducted with the Finite Element Sea ice-Ocean Model (FESOM), we identified for the Weddell Sea the density balance between the densest shelf water produced on the continental shelf and the WDW present on the continental slope at sill depth (shallowest depth of deepest connection to the cavity) as the crucial criterion for a shift in on-shelf circulation leading to a substantially increased heat flux into the cavity. This finding holds true for model runs using both z-level and sigma vertical coordinates as well as ocean-ice sheet (with the Parallel Ice Sheet Model, PISM) coupled model runs. We also find evidence that the same principle is valid in other Antarctic regions with a backward-sloping continental shelf.

Apart from the shelf water characteristics that largely depend on sea ice formation, the development of CDW/WDW characteristics  is crucial, but often neglected, in this context, especially in regional model studies. If under the influence of the globally warming climate the continental slope current becomes warmer and fresher, the associated density decrease could keep the continental shelf stable. Even if none of the on-shelf water classifies as High Salinity Shelf Water any more, as long as it is denser than the off-shelf CDW/WDW, it will block access to the cavity and prevent a regime shift.

How to cite: Haid, V., Timmermann, R., Schöll, S., Albrecht, T., and Hellmer, H. H.: The role of WDW density for a regime shift in the FRIS cavity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13222, https://doi.org/10.5194/egusphere-egu23-13222, 2023.

EGU23-13262 | Posters on site | OS1.9

Ice sheet-ocean coupling in an Earth System Model 

Xylar Asay-Davis, Carolyn Begeman, Darren Engwirda, Holly Han, Matthew Hoffman, and Stephen Price

We present our approach to coupling an ocean component (MPAS-Ocean, Model for Prediction Across Scales-Ocean) to an ice sheet component (MALI, MPAS-Albany Land Ice) within an Earth System Model (E3SM, the Energy Exascale Earth System Model) developed by the US Department of Energy.  First, we present an extrapolation technique, similar to the ISMIP6 (Ice Sheet Modeling for CMIP6) protocol, that can be used in the absence of evolving grounding lines in the ocean component.  This technique, while crude, can be used in both Greenland fjords and ice-shelf cavities as a stop-gap in situations where the ocean component cannot capture the topographic evolution (e.g. because the ocean grid is too coarse or full coupling has not yet been completed).  Second, we demonstrate progress on a fully conservative wetting-and-drying technique using the idealized MISOMIP1 (Marine Ice Sheet-Ocean Intercomparison Project, phase 1) experiments within E3SM.

How to cite: Asay-Davis, X., Begeman, C., Engwirda, D., Han, H., Hoffman, M., and Price, S.: Ice sheet-ocean coupling in an Earth System Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13262, https://doi.org/10.5194/egusphere-egu23-13262, 2023.

EGU23-15622 | ECS | Orals | OS1.9 | Highlight

Ice shelf-ocean interaction at shallow depths needs more attention 

Ole Richter, Ben Galton-Fenzi, Kaitlin Naugthen, and Ralph Timmermann

Understanding the processes involved in basal melting of Antarctic ice shelves is important to quantify the rate at which Antarctica will lose mass. Current research of ice shelf-ocean interaction highlights deep warm water intrusions and melting along narrow grounding lines. The majority of the ice, however, lies in much shallower waters. Here we analyse the vertical structure of previously published Antarctic-wide estimates of ice shelf basal melting derived from satellites and ice shelf buttressing derived from ice sheet flow modelling. The results show that ice shelf regions with a draft shallower than 500 m account for more than 60 % of the total basal mass loss and more than 30 % of the total buttressing flux response. The oceanic processes that drive melting in shallow regions might be very different compared to the ones at depth and how well these are represented in large-scale models of Antarctic ice shelf-ocean interaction is not clear. This gap should be addressed for more accurate predictions of the Antarctic response to climate change.

 

How to cite: Richter, O., Galton-Fenzi, B., Naugthen, K., and Timmermann, R.: Ice shelf-ocean interaction at shallow depths needs more attention, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15622, https://doi.org/10.5194/egusphere-egu23-15622, 2023.

EGU23-16106 | Posters on site | OS1.9

Weddell Watch 

Svein Østerhus

Long term observations of the flow of dense waters from their area of formation to the abyss of the World Ocean, and the return flow of warm waters, are central to climate research. For the Weddell Sea an important component of such a system entail monitoring the formation of High Salinity Shelf Water (HSSW) on the continental shelf north of Ronne Ice Front, the transformation to Ice Shelf Water (ISW) beneath the floating Filchner-Ronne ice shelf, and the flux of ISW overflowing the shelf break to the deep Weddell Sea. Equally important is the return flow of warm water toward the Filchner-Ronne Ice Shelf system.

We operate several monitoring stations in the southern Weddell Sea. The systems build upon techniques and methods developed over several decades and have a proven record of high data return. Here we present plans for extending, integrating, and operating the existing long-term observatories to increase our knowledge of the natural variability of the ocean-ice shelf system, and to allow early identification of possible changes of regional or global importance.

How to cite: Østerhus, S.: Weddell Watch, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16106, https://doi.org/10.5194/egusphere-egu23-16106, 2023.

CR5 – Frozen ground, debris-covered glaciers and geomorphology

EGU23-661 | ECS | Posters virtual | CR5.2

Morphometric parameters of retrogressive thaw slumps as of 2021 in West Siberia  

Nina Nesterova, Maxim Altukhov, and Marina Leibman

Retrogressive thaw slumps (RTS, also referred to as thermocirques) are dynamic polycyclic landforms resulting from ground ice melting. Initiation of RTS causes organic carbon emissions into the atmosphere and hydrosphere, as well as changes in topography and vegetation. West Siberia's Arctic zone is characterized by continuous permafrost and the presence of tabular ground ice close to the surface. These factors result in widespread RTS occurrence over the region. Since the majority of RTS studies in West Siberia have been limited to fieldwork at a few key sites, there is still no understanding of true RTS distribution, as well as morphometric and topographical parameters in the region. Remote sensing approaches help gain more knowledge of RTS characteristics over vast areas. This research presents preliminary results of the actual morphometric characteristics of 97 lake-associated RTSs located on the Yamal and Gydan peninsulas. The area of each modern RTS that are possible to identify on Sentinel-2 satellite images taken in 2021 was obtained. Elevation profile for several transects over the digitized RTS were collected using ArcticDEM data. The largest RTS was found in the northern part of the Gydan peninsula with an area of 38 ha. The smallest identified RTS based on the 10 m spatial resolution of Sentinel-2 satellite images was located in central Yamal with an area of 6 ha. The median area was found to be 2,5 ha. Around 70% of RTS had elongated shapes along the coastline with a width larger than the length. This can be caused by either merging neighboring RTSs or by widthwise enlargement. Around 21% of the RTSs were found to have approximately equal width and length. And only 9% of RTS were found to expand inland with a width much less than length. According to our estimates, the average elevation of studied RTS edges was 26 meters above sea level. The smallest difference between the edge and front line heights of the RTS was evaluated at ~ 0,2 meters and the largest appeared to be ~ 5,6 meters. Data collected from the Yamal and Gydan peninsulas enable further analysis of the morphometric parameters of RTSs.

This research was funded by the Russian Science Foundation, grant number № 22-27-00644.

 

How to cite: Nesterova, N., Altukhov, M., and Leibman, M.: Morphometric parameters of retrogressive thaw slumps as of 2021 in West Siberia , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-661, https://doi.org/10.5194/egusphere-egu23-661, 2023.

EGU23-1041 | ECS | Posters on site | CR5.2 | Highlight

Rock Glacier Surface Change Detection Based on UAV- and Tristereo Pléiades Data (Agua Negra, Argentina) 

Melanie A. Stammler, Rainer Bell, Xavier Bodin, Jan Blöthe, and Lothar Schrott

Glacial and periglacial landforms in the semi-arid Andes represent an essential water storage and feed regional river runoff. Glacial and periglacial systems are undergoing change; with signs of permafrost degradation such as thermokarst being visible in the study area of the Agua Negra catchment in the Desert Andes of Argentina. Surface changes are often indicators of thawing and freezing processes and/or permafrost degradation. The analysis of surficial changes provides local patterns and indicates potential meltwater contribution to runoff. It is important to understand such changing processes to assess their future input to the hydrological system. Analyses that exceed landform scale are, however, rare due to limited accessibility and high demand on fieldwork and resources.

Glaciers and permafrost in the Agua Negra catchment exist within close proximity, suggesting (de)coupling effects. Glaciers and permafrost features can act as thermal and mechanical entity with water functioning as agent of transient glacier-permafrost interaction. Investigating (de)coupling is essential to better understand landscape (in)stability and changing water storages within the system. We hypothesize that periglacial systems directly interacting with glacial landscapes display diverging surface processes compared to non-glacially impacted periglacial systems. They differ in terms of magnitude and pattern, e.g. due to meltwater (re)routing.

We derive high-resolution digital elevation models (DEMs) for one talus-derived and one glacially impacted rock glacier and assess surface change based on repeated UAV flights in 2017, 2018, 2022 and 2023 for the talus-derived, and 2022 and 2023 for the glacially impacted rock glacier; both georeferenced by DGPS measurements. We increase the spatial scale of the analysis and use tristereo Pléiades data for Pléiades-based surface change detection of two glacially impacted rock glaciers between 2014 and 2022. Here, we use the UAV-based DEMs as validation datasets. We envision that combining the two data sources allows us to investigate change signals over larger spatial areas which might provide new insight in our process-response understanding of the high Andean (peri)glacial landscape and its hydrological significance.

First results from UAV based DEM comparison indicate net negative surface changes of the talus-derived rock glacier. Preliminary analysis of the Pléiades data shows a net negative mass balance of Agua Negra glacier and highlights the need for improved co-registering of the Pléiades data for rock glacier surface change detection.

How to cite: Stammler, M. A., Bell, R., Bodin, X., Blöthe, J., and Schrott, L.: Rock Glacier Surface Change Detection Based on UAV- and Tristereo Pléiades Data (Agua Negra, Argentina), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1041, https://doi.org/10.5194/egusphere-egu23-1041, 2023.

EGU23-1340 | Posters on site | CR5.2

Monitoring lake ice phenology from CYGNSS: Algorithm development and assessment using Qinghai Lake, Tibet Plateau, as a case study 

Yusof Ghiasi, Claude Duguay, Justin Murfitt, Milad Asgarimehr, and Yuhao Wu

This study introduces the first use of Global Navigation Satellite System Reflectometry (GNSS-R) for monitoring lake ice phenology. This is demonstrated using Qinghai Lake, Tibetan Plateau, as a case study. Signal-to-Noise Ratio (SNR) values obtained from the Cyclone GNSS (CYGNSS) constellation over four ice seasons (2018 to 2022) were used to examine the impact of lake surface conditions on reflected GNSS signals during open water and ice cover seasons. A moving t-test (MTT) algorithm was applied to time-varying SNR values allowing for the detection of lake ice at daily temporal resolution. Strong agreement is observed between ice phenology records derived from CYGNSS and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Differences during freeze-up (i.e., the period starting with the first appearance of ice on the lake until the lake becomes fully ice covered) ranged from 3 to 21 days with a mean bias error (MBE) and mean absolute error (MAE) of 10 days, while those during breakup (i.e., the period beginning with the first pixel of open water and ending when the whole lake becomes ice-free) ranged from 3 to 18 days (MBE and MAE:  6 and 7 days, respectively). Observations during the breakup period revealed the sensitivity of GNSS reflected signals to the onset of surface (snow and ice) melt before the appearance of open water conditions as determined from MODIS. While the CYGNSS constellation is limited to the coverage of lakes between 38° S and 38° N, the approach presented herein will be applicable to data from other GNSS-R missions that provide opportunities for the monitoring of ice phenology from large lakes globally (e.g., Spire constellation of satellites).

How to cite: Ghiasi, Y., Duguay, C., Murfitt, J., Asgarimehr, M., and Wu, Y.: Monitoring lake ice phenology from CYGNSS: Algorithm development and assessment using Qinghai Lake, Tibet Plateau, as a case study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1340, https://doi.org/10.5194/egusphere-egu23-1340, 2023.

EGU23-1675 | Orals | CR5.2

Mapping Retrogressive Thaw Slumps Using Satellite Data With Deep Learning 

Yili Yang, Brendan M. Rogers, Greg Fiske, Jennifer Watts, Stefano Potter, Tiffany Windholz, Andrew Mullen, Ingmar Nitze, and Sue Natali

Retrogressive thaw slumps (RTS) are thermokarst features in ice-rich hillslope permafrost terrain and can cause dynamic changes to the landscape. Their occurrence in the Arctic has become increasingly frequent. RTS can significantly impact permafrost stability and generate substantial carbon emissions. Understanding the spatial distribution of RTS is critical to understanding and modelling global warming factors from permafrost thaw. Mapping RTS using conventional Earth observation approaches is challenging due to the highly dynamic nature and often small scale of RTS in the Arctic. In this study, we trained deep neural network models to map RTS across several landscapes in Siberia and Canada. Convolutional neural networks were trained with 965 RTS features, where 509 were from the Yamal and Gydan peninsulas in Siberia, and 456 from six other pan-Arctic regions including Canada and Northeastern Siberia. We used 4-m Maxar commercial imagery as the base map, 10-m NDVI derived from Sentinel-2 as the vegetation feature and 2-m ArcticDEM as the elevation feature. The best-performing model reached a validation Intersection over Union (IoU) score of 0.74 and a test IoU score of 0.71. Compared to past efforts to map RTS features, this represents one of the best-performing models and generalises well for mapping RTS in different permafrost regions, representing a critical step towards pan-Arctic deployment. Our experiments shed light on the impact of within-class and between-class variances of RTS in different regions on the model performance and provided critical implications for our follow-up study. We propose this method as an effective, accurate and computationally undemanding approach for RTS mapping.

How to cite: Yang, Y., M. Rogers, B., Fiske, G., Watts, J., Potter, S., Windholz, T., Mullen, A., Nitze, I., and Natali, S.: Mapping Retrogressive Thaw Slumps Using Satellite Data With Deep Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1675, https://doi.org/10.5194/egusphere-egu23-1675, 2023.

EGU23-3784 | ECS | Posters virtual | CR5.2 | Highlight

Remotely Sensed Water Color as a Proxy for Monitoring Water Quality in Inland Lakes 

Wondwosen Seyoum and Andrew Dooley

Constituents in water control the amount of light reflected from and absorbed by natural water bodies. This interaction is used as a basis for water quality monitoring using remotely sensed data. Recent studies have shown that water color derived from satellite data can be used to investigate water quality changes due to human and climate change impacts. However, how the change in satellite-based water color corresponds with specific water quality variables needs to be better understood. We analyzed timeseries (2013-2022) satellite-derived water color. We compared it with in-situ measured water quality variables (Secchi depth, turbidity, chlorophyll a, and total suspended solids) for lakes in the Midwest and Northeast regions of the USA. One of the focuses of this study, Lake Erie, observed for size, movement, and toxicity of harmful algal blooms (HABs) at multiple stations. Four bands (ultra blue, blue, green, and red) were extracted from harmonized Landsat and Sentinel-2 data to obtain the tristimulus values. These values are mapped on a chromaticity diagram to get the dominant color wavelength and the Forel–Ule Index (FUI). Results showed a strong relationship between in-situ water quality variables (e.g., Secchi depth and turbidity) and satellite-based FUI. Spatially, the relationship between in-situ water quality variables and water color is not consistent, as there is high variability in the concentration of the observed variables between the sampling locations. For example, measurement stations characterized by yellow to brown colors exhibited a strong relationship with TSS. However, generally, peak chl a concentration corresponds with yellowish green color. Typically, stations with blue to green water color are characterized by lower Chl a concentrations. This in-situ validation is used to infer the water quality of water bodies with no available in-situ monitoring. 

How to cite: Seyoum, W. and Dooley, A.: Remotely Sensed Water Color as a Proxy for Monitoring Water Quality in Inland Lakes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3784, https://doi.org/10.5194/egusphere-egu23-3784, 2023.

EGU23-4015 | Posters on site | CR5.2

Observational studies of water surface Evaporation on inland lake over the classical Tibetan Plateau 

Weiqiang Ma, Yaoming Ma, Binbin wang, Rongmingzhu Su, Weiyao Ma, Zhipeng Xie, Wei Hu, Jianan He, Nan Yao, Longtengfei Ma, and Ling Bai

To understand how the changing process of lake water level and area in Tibetan Plateau effects on the dynamic process of water resources in the surrounding area is very important. This project intends to focus on the study of surface evaporation observation of typical inland lake in Tibetan plateau and makes full use of the "water - ice - atmosphere -biology " multi-spheres comprehensive observation and various professional networks on the Tibetan plateau and surrounding regions which were establish by the leading of institute of the Tibetan plateau research, CAS, to carry out the research of synchronous comprehensive observation. Meanwhile, it collects the existing comprehensive observation data combined with the inversion method of satellite remote sensing to carry out multi-disciplinary comprehensive analysis and research. The aim is to reveal the longer time scale change of lake level by the lake expansion and withdrawal process in different areas of Tibetan plateau. By improving the method of remote sensing and observation, it will get the hourly and daily long-term data of the water level of inland lakes in different areas of Tibetan plateau which is lacking in previous studies. And by combining the analysis of meteorological factors and flow data in lake regions to research daily water cycle, it will help us to clearly understand the change rule of lake water level in different areas of Tibetan plateau. This time some general in-situ observation and model results will be show here.

How to cite: Ma, W., Ma, Y., wang, B., Su, R., Ma, W., Xie, Z., Hu, W., He, J., Yao, N., Ma, L., and Bai, L.: Observational studies of water surface Evaporation on inland lake over the classical Tibetan Plateau, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4015, https://doi.org/10.5194/egusphere-egu23-4015, 2023.

EGU23-4024 | ECS | Orals | CR5.2

Variation characteristics of mesoscale lakes in the Tibetan Plateau 

Weiyao Ma, Lin Bai, Weiqiang Ma, Wei Hu, Zhipeng Xie, Rongmingzhu Su, Binbin Wang, and Yaoming Ma

Known as “Water Tower of Asia”, the Tibetan Plateau (TP) is widely distributed with numerous inflow lakes. Lakes on the TP are less affected by human actives and can be considered as a sensitive indicator of climate change, changes of lakes can well reflect the change of regional climate. However, due to the harsh environment, data acquisition is not easy, studies on the response of lake change to climate mainly focus on several typical lakes (Nam Co, Selin Co Ngoring lake, etc.), but less attention is paid to variation characteristics of mesoscale lake (~100km2). To compensate for this deficiency, we selected three typical mesoscale lakes (Bamu Co, Langa Co and Longmu Co) in different climate zones and studied the lake changes and their responses to climate change using in-situ observations data and remote sensing data. By using multisource remote sensing and water level observation data, this study systematically analyzed inter-annual changes from 1970 to 2021 and monthly changes from 2019 to 2021. The main conclusions are as follows: (1) The changes to lakes in different climatic regions are different: lakes in the monsoon-dominated region showed a significant trend of expansion from 2000 to 2014, but the trend slowed down and stabilized after 2014; lakes in the westerlies-dominated region showed a small expansion trend; lakes in the region affected by both westerlies and the monsoon showed an overall shrinking trend. (2) The monthly variation of lake water volume showed a periodical trend of first increasing and then decreasing, with the largest relative change of lake water volume in August and September. (3) Temperature and precipitation are dominant meteorological elements affecting the variation of lakes, and with the warming of the TP, temperature plays an increasingly important role. Combining observational data and remote sensing data, the study of mesoscale lakes changes can increase the understanding of relationship between lake change and climate change, provide help for further study of lake - atmosphere interaction and climate effect and climate change in the TP.

Key words: Tibetan Plateau; mesoscale lakes; change of lake water volume; multisource altimetry data; in-situ observation; climate zones

How to cite: Ma, W., Bai, L., Ma, W., Hu, W., Xie, Z., Su, R., Wang, B., and Ma, Y.: Variation characteristics of mesoscale lakes in the Tibetan Plateau, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4024, https://doi.org/10.5194/egusphere-egu23-4024, 2023.

EGU23-6486 | ECS | Posters on site | CR5.2

From pixels to charts – using remote sensing for the climate change indicator “lake ice” in alpine lakes 

Katja Kuhwald, Marcel König, Kerstin Brembach, and Natascha Oppelt

Lake ice is an important feature for many limnic ecosystems. Periodic ice cover influences biological, chemical and physical processes in lakes during the cold climate period. Additionally, ice cover also affects processes outside the ice period, for instance, lake water temperature, timing of spring bloom, primary productivity and mixing regimes. In the face of climate change, many regions experience shifting lake ice cover. The observed and projected loss significantly affects lake ecology but also cultural ecosystem services. In the alpine region of Germany, people associate personal memories, sportive activities and many other aspects with lake ice. Lake ice is connected to society and also strongly affected by climate change. Therefore, it well suits as an indicator to communicate climate change. The region, however, lacks systematic measurements and data on lake ice cover.

In our project, we therefore aimed at developing a remote sensing approach to create a comparable data basis for a climate change indicator on lake ice. Our case study analysed six lakes between 700 and 2000 m AMSL. We generated a data set on lake surface characteristics (water, ice, snow, transparent ice etc.) using public webcam imagery as independent source. The data set was used to train and validate random forest classifiers for Setinel-1 A/B, Sentinel-2 A/B and Landsat 8 imagery. We excluded Sentinel-1 data, which were acquired at wind speeds > 1 m/s (ERA5-LAND). Thus, we prevented erroneous classification of rough waters. The validation revealed very high accuracies with balanced overall accuracies around 0.99, which is misleading. The high accuracies result from how we designed the ground the data since we only used data with labelling under high certainty. In this region, mapping lake ice faces the challenge of multiple freezing in thawing processes within the ice period. We therefore, implemented an air temperature (ERA5-LAND) filter to check the plausibility of classification results.

The final classification results differentiated binary between ice and no-ice pixels. From this data, we defined ice-days with at least 80 % ice cover on a lake. To build the indicator, we divided the monthly sum of ice days by the number of valid image acquisitions. Thus, the indicator also accounts for varyingly available satellite data.

With covering currently seven ice periods, the time series is relatively short for a climate change indicator. The approach may also be transferred to archived imagery whereas lacking ground truth data remain challenging. The small size of (0.2 - 3 km²) complicates the usage of large scale sensors such as MODIS. Thus, combining data from five satellites resolving at 10 – 30 m allowed to generate comparable and spatially explicit data on ice cover of these lakes for the first time.

How to cite: Kuhwald, K., König, M., Brembach, K., and Oppelt, N.: From pixels to charts – using remote sensing for the climate change indicator “lake ice” in alpine lakes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6486, https://doi.org/10.5194/egusphere-egu23-6486, 2023.

EGU23-6816 | ECS | Orals | CR5.2

Mapping and inventorying rock glaciers on the Tibetan Plateau from Planet Basemaps using deep learning 

Zhangyu Sun, Yan Hu, Lin Liu, Adina Racoviteanu, and Stephan Harrison

Rock glaciers are geomorphologically valuable indicators of permafrost distribution and form potentially important hydrological resources in the context of future climate change. Despite the widespread distribution of permafrost on the Tibetan Plateau and its reputation as the "water tower of Asia", this region lacks a complete inventory and systematic investigation of rock glaciers. In this study, we develop a deep-learning-based approach for mapping rock glaciers on the Tibetan Plateau. A powerful deep learning network, DeepLabv3+, is trained using Planet Basemaps as training imagery and multi-source rock glacier inventories as training labels. The well-trained model is then used to map new rock glaciers. The visually consistent and cloud-free properties of Planet Basemaps are crucial for developing comprehensive maps of rock glacier distribution; and the rock glacier inventories from multiple regions can improve the volume and diversity of the training dataset. The deep learning mapped results present strong identification and acceptable boundary delineation of rock glaciers, indicating that the deep learning model could serve as a useful tool for facilitating the inventory of rock glaciers over vast regions. Based on the deep learning outputs, we compile 4233 rock glaciers on eight subregions of the Tibetan Plateau, which are widespread in the surrounding regions while being scarcely distributed in inner areas. Talus- and glacier-connected rock glaciers are two major classes, which are dominant on the southeastern and densely distributed on the northwestern Tibetan Plateau, respectively. The regions with steep slopes are favored by rock glacier clusters with high density, and glacier-abundant regions tend to breed large rock glaciers. The proposed rock glacier mapping method effectively speeds up inventorying efforts, which will be used to map and inventory rock glaciers on the entire Tibetan Plateau. The complete inventory will offer a significant contribution to the global catalog and serves as a benchmark dataset for modeling and monitoring the state of permafrost in a changing climate.

 

How to cite: Sun, Z., Hu, Y., Liu, L., Racoviteanu, A., and Harrison, S.: Mapping and inventorying rock glaciers on the Tibetan Plateau from Planet Basemaps using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6816, https://doi.org/10.5194/egusphere-egu23-6816, 2023.

EGU23-7162 * | ECS | Posters on site | CR5.2 | Highlight

Greenland Lake Ice Breakup Detection from Sentinel-1 SAR 

Christoph Posch and Jakob Abermann

The timing of lake ice formation and breakup are relevant climate indicators. In this study, we explore the potential of utilizing Sentinel-1 synthetic aperture radar (SAR) data for identifying the timing of lake ice breakup across Greenland between 2016 and 2022 and assess its latitudinal and vertical gradients. We retrieve average backscatter data of lakes in peripheral Greenland with a surface area > 1km2 (n = 1842). Data with a low number of acquisitions for the entire study period (n < 1000) or exhibiting strong uniformal annual characteristics (backscatter difference between 95th and 5th quantile < 5dB) are excluded from the analysis. We apply a locally weighted scatterplot smoothing (LOWESS) filter to remove outliers. A dynamic numerical threshold (backscatter decline within 3 consecutive acquisitions > 25% of the annual backscatter range) is applied for each respective year to identify the timing of ice breakup. The study area is divided into 6 main regions of Greenland (N, NE, SE, S, SW, NW) to explore spatio-temporal statistics. The data exhibits a temporal resolution of about 2 days during the relevant period. We validate the breakup detection (n = 10) by utilizing daily time-lapse images of 3 lakes between 2016 and 2020. The detection of the timing from SAR data proves to be conservative (i.e., later) compared to time-lapse camera data and allows characterizing lake ice breakup with a mean error of 7 days. We find that only SAR data in West Greenland (S, SW, NW), i.e., > 43°W and < 70°N, exhibits characteristics for breakup detection (97%, 77% and 57% suitable) while coverage for North and East Greenland (N, NE, SE) lacks necessary radiometric and temporal characteristics (only 2%, 3% and 2% suitable). Our preliminary results indicate that no significant trend (α = 0.05) of breakup timing between 2016 and 2022 can be identified. Annual median DOYs range between June 8 (2019) and July 11 (2022). Ice breakup timing increases with latitude and elevation, however, strong correlations (up to r = 0.81) can only be identified for limited years. Correlations are in the order of 2 to 5 DOY/°lat. and 2 to 7 DOY/100m. Based on these preliminary results, we aim to explore statistical relations in greater detail to assess the role of extreme events and global climate change. Furthermore, we intend to apply this automated algorithm for an analysis of lake ice breakup timing on a global scale.

How to cite: Posch, C. and Abermann, J.: Greenland Lake Ice Breakup Detection from Sentinel-1 SAR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7162, https://doi.org/10.5194/egusphere-egu23-7162, 2023.

EGU23-7720 | ECS | Posters on site | CR5.2 | Highlight

Generation of an improved land surface temperature time series to support permafrost modelling in the northern high latitudes 

Sonia Dupuis, Frank-Michael Göttsche, and Stefan Wunderle

Northern high latitudes have experienced pronounced warming throughout the last decades with particularly high temperatures during winter and spring. Due to Arctic Amplification, the Arctic region is warming thrice as fast as anywhere else. The warming affects the sensible ecosystem, vegetation dynamics and the cryosphere (sea ice, snow and permafrost). Permafrost, which is a crucial component of arctic ecosystems, is particularly sensitive to increasing air temperatures and changes in the snow regime. Climate change has a high impact in these regions because thawing affects the stability of the bedrock, damages infrastructures and releases massive quantities of organic carbon. Permafrost cannot directly be observed from space, but permafrost models link physical surface variables such as land surface temperature (LST) to the thermal ground regime. Models are an important addition to boreholes to monitor the status of the permafrost at hemispheric scale. On a global scale, observation of LST is only available from very few in-situ stations or climate models with coarse spatial resolution. Both data sources are not sufficient to model fine-scaled features. In contrast, LST information retrieved from satellite data has high spatiotemporal coverage.

To compute LST on a hemispheric scale, we use the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data set starting in 1981. The AVHRR on board the NOAA and MetOp satellite series now covers more than four decades. AVHRR’s two thermal infrared channels allow applying the split-window (SW) method to reduce the atmospheric effect and retrieve LST. Split-window algorithms (SWA) performances depend on the quality of SW coefficients. These are empirical coefficients, which are retrieved by fitting the SWA to a calibration database. Here, the calibration data is generated by running a radiative transfer (RT) model. The input profiles of the RT are selected to cover typical atmospheric conditions occurring in permafrost regions. The coefficients are adjusted for different water vapour and satellite viewing conditions. Cloud and water masks as well as fractional snow cover information from the ESA CCI snow project and emissivity data are included in the final LST retrieval algorithm. Besides, a machine learning algorithm was applied to improve the spatial resolution of the GAC data to generate a 40-year time series with a spatial resolution of 1km. The first validation results of the LST time series are shown.

How to cite: Dupuis, S., Göttsche, F.-M., and Wunderle, S.: Generation of an improved land surface temperature time series to support permafrost modelling in the northern high latitudes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7720, https://doi.org/10.5194/egusphere-egu23-7720, 2023.

Microwave remote sensing can provide effective monitoring of landscape FT dynamics. Its sensitivity to surface permittivity, which is predominantly influenced by the phases of water, can be used to measure landscape freeze/thaw state information. The technique of Interferometric Synthetic Aperture Radar (InSAR) enables to map the ground movement through the use of Synthetic Aperture Radar (SAR). Compared to optical imagery, microwave data has advantages that it would not be affected by cloud cover, smoke or daylight and exhibits useful penetration depths of soil and vegetation.  
Both active and passive microwave remote sensing with different wavelengths have shown their principal capacity in many studies and have complementary advantages to each other. While many passive sensors (such as SMAP and SMOS) are providing observations with high temporal resolution and good worldwide coverage at the deca-kilometer scale, there are a series of active sensors providing observations with worse temporal resolution but much better spatial resolution at the scale from a few meters to a few deca-meters, for example, the Sentinel-1 mission from the European Space Agency (ESA) with 5 x 5 m spatial resolution and 6-12 days repeat cycle. Hence, the combined use of different microwave data can be expected further to promote the monitoring of permafrost-related phenomena and permafrost-dominated landscapes.
An assumption of near-linear relation between the measurements from the passive and active sensors has been used in NASA’s Soil Moisture Active Passive (SMAP) active-passive baseline algorithm for downscaling coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter (σ 0). Recent research proved that a good linear relationship could be found at a global scale (Zeng et al., 2021). However, the relation is significantly affected by environmental factors, for example, the density of vegetation cover. 
Based on the findings, we attempt to explore the possibility of merging microwave remote-sensing data from different platforms in this work. We are committed to exploring suitable data sources for merging, as well as the possibility of taking environmental factors into consideration. The capacity and limitation of the merging process will be discussed.  

 

 

How to cite: Chen, Y. and Ludwig, R.: Exploring the merging potential of high temporal resolution and high spatial resolution microwave remote sensing data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8999, https://doi.org/10.5194/egusphere-egu23-8999, 2023.

EGU23-9035 | ECS | Orals | CR5.2 | Highlight

Unraveling fire-permafrost interactions in Northeastern Siberian tundra using InSAR and machine learning 

Sonam Wangchuk, Kevin Schaefer, Roger Michaelides, Jorien Vonk, and Sander Veraverbeke

Permafrost soils in boreal forests and tundra store more than two atmospheres worth of carbon, yet the vigorous permafrost-carbon-climate feedback loop remains poorly understood. In addition to ongoing strong warming, fires can further accelerate permafrost degradation and trigger the release of ancient carbon into the atmosphere. Despite the urgency after the recent Arctic fire seasons of 2019, 2020 and 2021, fire-permafrost interactions are currently not included in Earth system models from the sixth assessment of the Intergovernmental Panel on Climate Change (IPCC). This is because large-scale observations of fire-induced permafrost degradation are lacking. Therefore, we studied fire-induced permafrost degradation using the Interferometric Synthetic Aperture Radar (InSAR) technique and time series of Sentinel-1 (S-1) imagery. In this pilot study, we tested our approach on fires from 2019 and 2020 in the Chokurdakh area, Northeastern Siberia. We processed time series S-1 SAR data from a snow-free season (June-October) where S-1 SAR image selection was automated by using the Moderate Resolution Imaging Spectroradiometer snow cover products. To understand the drivers of InSAR-derived subsidence, we applied the XGBoost regression algorithm using subsidence as a response variable and ten other environmental variables as predictor variables. First, we found that the time series InSAR technique is suitable for deriving subsidence over fire-affected permafrost terrain. Second, the fire-affected permafrost terrain exhibited four to five times greater subsidence compared to the surrounding unburned area. Third, the XGBoost regression model revealed land surface temperature (LST) and albedo (derived from Landsat data) as the primary predictor variables  of surface subsidence, accounting for more than 50% of the predictive power. The permafrost degradation in many tundra areas is likely dominated by fire-induced changes in the surface energy balance.  From this pilot study, we conclude that our approach has the potential to study fire-permafrost interaction and environmental drivers of surface subsidence at the northern circumpolar scale. Models can also use our data to parameterize subsidence and thermokarst processes associated with permafrost degradation due to fire.

How to cite: Wangchuk, S., Schaefer, K., Michaelides, R., Vonk, J., and Veraverbeke, S.: Unraveling fire-permafrost interactions in Northeastern Siberian tundra using InSAR and machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9035, https://doi.org/10.5194/egusphere-egu23-9035, 2023.

EGU23-9092 | Orals | CR5.2

Seasonal and annual kinematics of active rock glaciers under different climate regimes in the Western USA 

Alexander L. Handwerger, Camryn Kluetmeier, George Brencher, and Jeffrey S. Munroe

Rock glaciers are common landforms in many alpine permaforst landscapes that play an important role in alpine hydrology and landscape evolution, principally through the release of seasonal meltwater and the downslope transport of coarse material. Here, we use satellite-based interferometric synthetic aperture radar (InSAR) to identify and monitor rock glaciers in the Western USA. We focus on the movement of active and transitional rock glaciers in Utah (Uinta, Wasatch, and La Sal Mountains), and Wyoming (Wind River Mountains) between 2015 and 2022. Using the new framework established by the International Permafrost Association (IPA) Action Group, we identified 255 active and transitional rock glaciers in the ~3500 km2 Uinta Mountains, 45 rock glaciers in the ~200 km2 La Sal Mountains, 55 rock glaciers in the ~135 km2 Wasatch Mountains, and 120 rock glaciers in the ~3000 km2 Wind River Mountains. These rock glaciers currently occur under different climatic regimes based on data from the 30 year (1991-2020) normal Parameter-elevation Relationships on Independent Slopes Model (PRISM). The La Sals and Wasatch are warmer and wetter with a mean annual air temperature (MAAT) of ~3.0± 1.9 ˚C and  2.7 ± 1.1 ˚C and a mean annual precipitation (MAP) of ~92 ± 13 cm and ~130 ± 17 cm, respectively, whereas the Uintas and Wind Rivers are cooler and drier with a MAAT of ~0.24 ± 1.4 ˚C and  -0.87 ± 1.4 ˚C and a MAP of ~87 ± 11 cm and ~81 ± 10 cm. The mean line-of-sight (LOS) velocities for individual rock glaciers range from ~1 to 10 cm/yr. We also examined the time-dependent relationship between the motion of the rock glaciers and local climatic drivers such as temperature and precipitation. We found that rock glaciers exhibit seasonal and annual velocity changes, likely driven by liquid water availability (from snowmelt and rainfall), with accelerated motion during summers and during wetter years. Our findings demonstrate the ability to use satellite InSAR to monitor rock glaciers over large areas and provide insight into the environmental factors that control their kinematics.

How to cite: Handwerger, A. L., Kluetmeier, C., Brencher, G., and Munroe, J. S.: Seasonal and annual kinematics of active rock glaciers under different climate regimes in the Western USA, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9092, https://doi.org/10.5194/egusphere-egu23-9092, 2023.

EGU23-9630 | ECS | Orals | CR5.2

Comparison of land surface temperatures from Landsat with soil freeze/thaw measurements in permafrost peatlands 

Aida Taghavi Bayat, Markus Gerke, and Björn Riedel

Permafrost is an important component of sub-Arctic environments and is extremely vulnerable to the impact of climate change. During the last decades, permafrost regions in northern high latitudes have been exposed to greater temperature changes than other regions worldwide. Increased temperatures cause rapid thawing of permafrost which can lead to changes in hydrological processes. Therefore, capturing dynamics of land surface temperature (LST) as one of the key factors affecting the thermal regime of permafrost landscapes at high spatial resolution is crucial for better monitoring these areas under drastic warming projected due to climate change. 
Landsat imagery at 30 m resolution offers the potential to obtain a consistent coverage of near-surface temperature values. In this study LST values from Landsat were compared with in-situ based soil freeze/thaw (F/T) index, air and soil temperature measurements obtained at the Abisko peatland site in the permafrost areas of northern Sweden. The soil F/T index is an important proxy that describes the relationship between the unfrozen soil water content and the soil temperature in freezing soils.  From 2017 to 2022, comparisons between Landsat LST and soil F/T index show high similarity between them in identifying frozen state, thawed state, and transition periods. In addition, Landsat LST values were found to be better correlated with air temperature (R2 > 90%) than with soil temperature (R2 > 80%) measurements. Overall, it is concluded that Landsat LST offers great potential for monitoring surface temperature changes in high-latitude permafrost regions and provides a promising source of input data for developing models to determine the spatial heterogeneity of freezing and thawing cycles.

How to cite: Taghavi Bayat, A., Gerke, M., and Riedel, B.: Comparison of land surface temperatures from Landsat with soil freeze/thaw measurements in permafrost peatlands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9630, https://doi.org/10.5194/egusphere-egu23-9630, 2023.

EGU23-10182 | ECS | Orals | CR5.2

Modelling SAR Backscatter from Lake Ice under Wet Conditions using the Snow Microwave Radiative Transfer (SMRT) model 

Justin Murfitt, Claude Duguay, Ghislain Picard, and Juha juha.lemmetyinen@fmi.fi

Lake ice plays a critical role in local energy balances and provides crucial socio-economic services such as travel between communities and transportation of goods during winter months. However, over the past 40 years, the number of in situ observations has declined. Additionally, increasing temperatures lead to an increasing number of melt events throughout the season, resulting in the formation of more snow ice and slush layers. The increase in wet ice conditions poses a challenge in monitoring lake ice using active microwave technologies (e.g., synthetic aperture radar) and can be a risk to those who use ice cover as an essential travel route. This study focuses on Lake Oulujärvi in Finland during the 2020-2021 ice season. Using the snow microwave radiative transfer (SMRT) model, backscatter was modelled using observations of dry and wet conditions from the field. Snow density, snow depth, microstructure data, and ice thickness data collected during the field campaign helped parameterize the Snow Microwave Radiative Transfer (SMRT) model. Simulations under dry conditions showed that increasing roughness at the ice-water interface had the largest increase in backscatter. However, when the water content of the overlying snow layers increased, the roughness of the interface with the highest moisture content became the dominant interface impacting backscatter. Melt-freeze events throughout the ice season had a prolonged impact on backscatter resulting in increases of >3.69 dB. Larger increases in backscatter due to higher moisture were a result of larger dielectric contrasts created between overlying dry snow on slush layers. Improved understanding of the impact of wet conditions on backscatter can help to improve the monitoring of hazardous lake ice conditions and aid in the further development of inversion models for lake ice properties.

How to cite: Murfitt, J., Duguay, C., Picard, G., and juha.lemmetyinen@fmi.fi, J.: Modelling SAR Backscatter from Lake Ice under Wet Conditions using the Snow Microwave Radiative Transfer (SMRT) model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10182, https://doi.org/10.5194/egusphere-egu23-10182, 2023.

EGU23-10950 | ECS | Posters on site | CR5.2

A Deep Learning-based Toolbox for Automated Monitoring of Central Asian Glacial Lakes from Space 

Manu Tom, Holger Frey, Simon Allen, Alessandro Cicoira, Laura Niggli, and Christian Huggel

In recent decades, climate change has intensified the melting of glaciers in high mountain regions around the world, leading to the formation of new glacial lakes. These lakes can cause damage up to several hundred kilometres downstream when an outburst flood occurs. While more and more glacier lake inventories are becoming available to the research community, high-frequency mapping and monitoring of these lakes are still essential to assess hazards and estimate Glacial Lake Outburst Flood (GLOF) risks, particularly for lakes with high seasonal variations. In Central Asia, new lakes have been known to develop quickly, and non-stationary lakes can expand or regrow within a matter of weeks to months. Monitoring these lakes is crucial to understanding and mitigating the risks they pose.

Detecting glacial lakes using satellite sensors is difficult due to their small size and the fact that they are often frozen for much of the year. Furthermore, optical satellite imagery can be hindered by clouds. Additionally, cast and cloud shadows, as well as increasing lake and atmospheric turbidity, make it challenging to accurately observe and monitor these lakes using optical satellite imagery. On the other hand, using a SAR satellite sensor to monitor these lakes is difficult during windy scenarios and changes in backscattering due to variations in turbidity and the presence of cast shadows.

We have developed a Python-based toolbox for mapping potentially dangerous glacial lakes in Central Asia and for monitoring the dynamics of these lakes over time and space. The proposed analytical toolbox uses a combination of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical satellite data as input. Satellite data fusion allows high temporal resolution, while SAR can penetrate cloud cover and allow year-round monitoring. The user interface for the toolbox is designed to a