CR2.3

CR2 EDI
Remote sensing of the Cryosphere 

This session will focus on recent and upcoming advances in satellite remote sensing of the global cryosphere. We welcome presentations providing new insights into cryospheric processes in the broadest sense, ranging from ice sheets, glaciers, snow cover and its properties, frozen soil, sea ice and extraterrestrial glaciology. While the advent of remote sensing has revolutionized the field of glaciology, a vast reservoir of potential remains to be unlocked by using these observations in concert with other data sets. We particularly encourage presentations discussing multi-platform data merging, integration of GIS and ground validation data, integration of remote sensing data into earth system models, as well as cloud computing and processing of super large data sets. We also encourage contributions focusing on historic satellite data re-analysis, novel processing approaches for upcoming satellite missions, and presentations outlining pathways to next-generation satellite missions for the coming decades.

Convener: Bas AltenaECSECS | Co-conveners: Stephen ChuterECSECS, Sara Fleury, Kathrin NaegeliECSECS
Presentations
| Thu, 26 May, 15:10–18:12 (CEST)
 
Room L3

Presentations: Thu, 26 May | Room L3

15:10–15:15
Snow cover
15:15–15:21
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EGU22-467
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ECS
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Presentation form not yet defined
An autonomous multi-band albedometer for long-term monitoring and calibration of optical satellites
(withdrawn)
Sara Arioli, Ghislain Picard, Laurent Arnaud, Vincent Favier, and Baptiste Vandecrux
15:21–15:27
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EGU22-1867
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ECS
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Virtual presentation
Arvind Pandey, Deepanshu Parashar, Sarita Palni, and Ajit Partap Singh

The cryosphere plays a vital role in the climate system by changing the energy and mass transfer between the atmosphere and the surface, and it is the most important land cover type in the Himalayan and Polar Regions, which act as an important source of freshwater for rivers. This study examines Snow-Covered Area (SCA) and Snowline variations in the Uttarakhand Himalaya using the Normalized Difference Snow Index (NDSI). Three Landsat series imagery for 1990, 2000, 2010, and Sentinel Data from 2015 to 2021 were processed and analyzed through open-source software. In order to estimate the average elevation of the snowline, a digital elevation model was used and an area estimation study using multispectral imagery. The research focuses on the snowline and snow cover variations over the Uttarakhand Himalaya from 1990 to 2021 and the average snow line-height, respectively. This study shows that in the years 1990, 2000, 2010, and 2015 to 2021, snow line variations and area estimation differences in Uttarakhand, Central Himalaya, and snow line at above sea level (a.s.l.) in the western and eastern part of the study area. This study deals with the analysis of snow line shifting and cover. It suggests that the snow cover area in the Uttarakhand, Central Himalaya, is depleting steadily, which will have adverse impacts, especially upon water resources causing various economic and social disruptions in the near future.

How to cite: Pandey, A., Parashar, D., Palni, S., and Singh, A. P.: Application of Remote Sensing and GIS to Assess Snow Cover and its Dynamics: A Case Study of Uttarakhand Himalaya, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1867, https://doi.org/10.5194/egusphere-egu22-1867, 2022.

15:27–15:33
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EGU22-12419
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Virtual presentation
Cemal Melih Tanis, Kari Luojus, Miriam Kosmale, Simon Gascoin, Gabriele Schwaizer, Markus Hetzenecker, Lina Zschenderlein, Michäel Ablain, and Joel Dorandeu

Copernicus Land Monitoring Service has recently launched a group of high-resolution snow cover products which are derived from Sentinel-1 and Sentinel-2 constellations. High- Resolution Snow and Ice Monitoring (HRSI) products include Fractional Snow Cover (FSC) from the Sentinel-2 constellation and Wet and Dry Snow (WDS) covering Europe and SAR Wet Snow (SWS) products for selected mountain regions derived from the Sentinel-1 constellation. The FSC and WDS products have gaps in the snow cover data due to cloud presence and the SWS product provides only information on the melting snow extent, but dry snow areas and snow-free areas cannot be discriminated by means of SAR data. In the same portfolio, we provide the daily cumulative Gap-filled Fractional Snow Cover (GFSC) product, which is a fusion of those three products. In this product, we gap-fill the FSC product using the wet snow presence detected by the SWS in the spatial domain. In the temporal domain, all recent data in the last 7 days are used for gap-filling by temporal composition. The product aims to have a complete snow cover map of Europe. 

The quality of the product is assessed using in-situ data and gap simulation, for the period of 09.2017 - 08.2018 for mountain ranges in the Pyrenees, Alps, Scandinavia , East Turkey and Corsica, covered by 34 Sentinel-2 tiles. In-situ snow depth information is converted to binary snow cover information using a snow depth threshold. For the gap simulation method, as first step, FSC products with observed snow information are selected. Then, an artificial cloud mask is overlaid on these products, and the gap-filling method is run to generate GFSC products. The resulting GFSC products are compared with the corresponding observed FSC products, considering them as reference data. This comparison shows the agreement between the FSC product and the gap-filling methods. For both comparison methods, FSC values in GFSC and FSC products are converted to binary snow cover information using an FSC threshold. Resulting binary snow cover information is used in contingency tables and performance metrics are calculated for the product and for different gap-filling methods. 

We have found that the gap-filling provides 5 times more pixels with snow cover information and the quality is fairly good. The comparison with in-situ data shows an accuracy over 88% in temporal gap-filling and precision over 87% in spatial gap-filling. The comparison of the gap simulated GFSC and the FSC products shows an accuracy over 97% in temporal gap-filling and precision over 83% in spatial gap-filling. Temporal gap-filling performance is consistent throughout the seasons, although it is less accurate in the accumulation season for the spatial gap-filling, which is expected as the wet snow algorithm is developed for the melting season conditions. The assessment shows that the methods are working well and 7 days old FSC, WDS and SWS data are still valid to fill the gaps in the data. 

How to cite: Tanis, C. M., Luojus, K., Kosmale, M., Gascoin, S., Schwaizer, G., Hetzenecker, M., Zschenderlein, L., Ablain, M., and Dorandeu, J.: Gap-filled snow cover fraction from Sentinel-1 and Sentinel-2 constellations , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12419, https://doi.org/10.5194/egusphere-egu22-12419, 2022.

15:33–15:39
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EGU22-12911
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ECS
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Virtual presentation
Freddy Saavedra, Ana Hernandez, Daniela Gonzalez, Yael Aguirre, Valentina Contreras, Alexis Caro, and Carlos Romero

The Andes Mountains span a length of 7000 km and are important for sustaining regional water supplies in South America. Rivers flow from the west side of the Andes to the Pacific Ocean and are the main source of water supply for energy generation, irrigation, and drinking water. Snow variability across this region has not been studied in detail due to sparse and unevenly distributed instrumental climate data. The optical remote sensing approach has been developed as a great tool to avoid this limitation. However, cloud cover reduces the ability to use it in the northernmost and southernmost portions of the Andes and the winter and spring in the central part of the Andes. We tested the performance of temporal, spatial algorithms in consecutive and simultaneous steps over daily MODIS snow cover products (Aqua and Terra). We evaluated the cloud reduction (effectiveness) and accuracy using simulated experiments from MODIS data by selecting low cloud cover images (“truth”) and cover with artificial clouds, then we ran all the algorithms and tested them based on the “truth” dataset. On clear sky days, we include higher spatial remote sensing data (Landsat and Sentinel) and in-field data from UAV. The combination of Aqua and Terra reduced the cloud cover by 10-15% on a yearly scale. The temporal combination with previous and following days yielded a substantial improvement in cloud removal but is usually less effective for large-area cloud cover. Developing a new dataset with cloud reduction can help to increase the performance of the snowmelt runoff model and extend a large latitude range across the Andes Mountains to use optical remote sensing data for seasonal snow studies. The use of machine learning, fusion with other snow products (e.g. radar), and more intense use of UAVs point to the next research.

How to cite: Saavedra, F., Hernandez, A., Gonzalez, D., Aguirre, Y., Contreras, V., Caro, A., and Romero, C.: Validation of Cloud Reduction Algorithms Over MODIS Snow Products on Andes Mountain, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12911, https://doi.org/10.5194/egusphere-egu22-12911, 2022.

15:39–15:45
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EGU22-2394
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Virtual presentation
Horst Machguth, Andrew Tedstone, and Enrico Mattea

Streams and lakes develop each summer over the marginal regions of the Greenland ice sheet. These hydrological systems reach well into the accumulation area and indicate that surface runoff of meltwater is an important component of the mass balance of the Greenland ice sheet. Here we map the slush limit, a proxy for the extent of surface runoff, using daily MODIS data (500 m spatial resolution) for the 22 melt seasons from 2000 to 2021. We develop an automated algorithm capable of detecting daily slush limits, provided sufficient image quality. The algorithm is applied to Greenland's west coast. Albeit MODIS' spatial resolution is too coarse to resolve streams, slush fields or lakes, the results highly agree to surface runoff mapping from better resolution satellite imagery. The data document the evolution of the slush limit across latitudes and during the individual melt seasons. We find significant increasing trends in slush limits until the year 2012, but not thereafter. We show that the slush limit typically rises quickly early in the melt season, but upward migration halts before melting ceases. The reasons behind this behaviour remain somewhat enigmatic. For the year 2012, we are able to demonstrate that upward migration of surface runoff stopped early in the melt season, at the upper margin of the ice slabs. These thick and continuous ice layers are located close to the surface, in the firn, and prevent percolation of melt into the otherwise porous firn. Had the ice slabs extended further into the accumulation area, the summer 2012 saw sufficient energy to raise the slush limit by another ~300 m in elevation.

How to cite: Machguth, H., Tedstone, A., and Mattea, E.: West Greenland surface runoff extent, mapped from daily MODIS imagery 2000 to 2021, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2394, https://doi.org/10.5194/egusphere-egu22-2394, 2022.

15:45–15:51
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EGU22-5343
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ECS
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On-site presentation
Zhongyang Hu, Peter Kuipers Munneke, Stef Lhermitte, Mariel Dirscherl, Chaonan Ji, and Michiel van den Broeke

Antarctic Blue Ice Areas (BIAs) are a sensitive indicator for climate change. They can be formed either by wind and sublimation or by surface melt, and vary over time. In this regard, distinguishing different blue ice types and observing their change over time can enhance our understanding of climate change in Antarctica. Presently, the areal extent of BIA is retrieved using Earth observation satellites. However, such products rarely provide time series of BIA extent over the entire continent. To fill this gap, we derived blue ice fraction over Antarctica from the moderate resolution imaging spectroradiometer (MODIS) using spectral mixture analysis. Blue ice fraction is defined as the fraction of each MODIS pixel that is covered by blue ice. The results provide a continuous time series of blue ice fraction during the austral summers 2000 to 2021. This time series shows Antarctic blue ice abundance and exposure over time, and indicates that melt-induced BIAs are more variable in time than wind-induced.  According to the accuracy assessment based on high-resolution Sentinel-2 images over six selected test sites in coastal East Antarctica, the blue ice fraction results have an overall uncertainty of around 15% and 25% in wind- and melt-induced BIAs, respectively. The uncertainties mainly arise due to the very similar spectral profiles among melt streams, lakes, and ponds. Overall, our results show great potential in (1) generating annual BIA maps, (2) separating wind-and melt-induced BIAs, (3) evaluating (regional) climate model outputs, and (4) deriving temporal variations in blue ice abundance and exposure.

How to cite: Hu, Z., Kuipers Munneke, P., Lhermitte, S., Dirscherl, M., Ji, C., and van den Broeke, M.: Tracking blue ice in time: deriving Antarctic blue ice fraction from MODIS images using spectral unmixing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5343, https://doi.org/10.5194/egusphere-egu22-5343, 2022.

15:51–15:57
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EGU22-5913
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On-site presentation
Thomas Dethinne, Christoph Kittel, Quentin Glaude, Xavier Fettweis, and Anne Orban

Melting ice sheets are a major contributor to the rising sea level. At the Liège University, the Regional Climate Model MAR (Modèle Atmosphérique Régional) has been developed to monitor and study the current and future evolution of various properties of ice sheets. However, uncertainties remain on the surface melt extent upon Antarctic ice sheets as models are subject to error propagation and need some external data to model the climate.

In Antarctica, unlike Greenland, the produced surface meltwater does not leave the ice sheet through visible rivers in which the quantity of meltwater can be estimated. Remote sensing is then the only product able to provide an estimation of the surface melt extent with a satisfying spatial and temporal coverage. The assimilation of melt spatial extent estimated by remote sensing allows the mitigation of the uncertainties linked to the models as well as a better quantification of the melt quantity.

In this research, active (Sentinel-1) and passive (AMSR2 & SSMIS) microwave satellite data are assimilated into MAR model over the Antarctic Peninsula, where surface melt has caused hydrofracturing and destabilization of ice shelves in the past. The assimilation of the different satellite products is also conducted to study the effect of spatial resolution on melt detection, Sentinel-1 having a pixel size of a few meters while passive satellites are at the 10km scale. This difference can be crucial upon the Peninsula as Foehn effects are occurring locally and can generate local surface melt, not detectable while using a coarser resolution.

How to cite: Dethinne, T., Kittel, C., Glaude, Q., Fettweis, X., and Orban, A.: Interest of the Assimilation of Surface Melt Extent Derived From Passive and Active Microwave Satellites Into the Regional Climate Model MAR Over the Antarctic Peninsula, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5913, https://doi.org/10.5194/egusphere-egu22-5913, 2022.

15:57–16:03
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EGU22-7452
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ECS
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On-site presentation
Bertie Miles and Rob Bingham

The Landsat-1 satellite provides sporadic coverage of coastal Antarctica between 1972 and 1975.  This dataset is a highly valuable scientific resource but has yet to be utilized to its full potential. The imagery are of reasonable quality and have a spatial resolution of 60 m, but are often difficult to process owing to their poor geolocation accuracy, where most images are displaced by >10 km. This requires a time-consuming manual correction which can be especially tricky over the featureless sections of the ice sheet (e.g. Ross Ice Shelf). Here we report on progress towards creating a geolocated mosaic over coastal Antarctica that preliminary analysis indicates will have near-complete coverage with only limited cloud cover. Potential glaciological uses for the mosaic include coastline change both in terms of fast flowing outlet glaciers and the slower flowing regions of the coastline, ice shelf damage, basal channel evolution and migration, changes in ice rises and also any changes in bedrock exposure. We highlight these potential uses with a few small-scale examples.

How to cite: Miles, B. and Bingham, R.: Landsat-1 mosaic of Antarctica from the 1970s, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7452, https://doi.org/10.5194/egusphere-egu22-7452, 2022.

Ice sheet wide analysis
16:03–16:09
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EGU22-3624
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Presentation form not yet defined
Anders Bjørk, Enze Zhang, Mathieu Morlighem, Mathilde Dunk, Amanda Fleischer, Kathrine Thage, Jeremie Mouginot, and Shfaqat Abbas Khan

While great improvements in our understanding of the subglacial landscape has occurred in recent years, the majority of the land beneath the Greenland Ice Sheet is still unmapped. With this study we map newly emerging land masses using Landsat 8 and Planet Scope satellite imagery. By including new islands, nunataks, and ice-contact outcrops in the current bed elevation model BedMachine we are able to improve the reliability of both pro glacial bathymetry as well as subglacial topography in all areas where new land is emerging.

The previous official Greenland wide mapping occurred in 1978-1987 and was done on the basis of aerial photographs recorded at scale 1:150.000. Here, we manually update new island - and ice contact bedrock outcrops using false color pan-sharpened Landsat 8 (15m) from 2019 and verifying our results with Planet Scope satellite images (3m) from 2019 and 2021. With the mapping of newly emerged islands and bedrock outcrops, existing bathymetric and ice thickness products can be updated. As existing models (eg. BedMachineV3) is limited by the available input, it is common to see large assumed ice sheet thicknesses where nunataks are now exposed. Likewise, many newly mapped islands are appearing in places where fjord depths were expected to be several hundreds of meters.

While traditional methods fcor collecting bedrock elevations below the ice and ocean surfaces are associated with extremely high logistical costs, our approach can in a quick and affordable manner update existing med models with valuable data. This addition will result in more accurate ice flow and fjord circulation models, which will ultimately give us better predictions of future sea-level rise. We argue that with the ongoing retreat and downwasting, these systematic mapping efforts should ideally take place on a biannual basis.  

How to cite: Bjørk, A., Zhang, E., Morlighem, M., Dunk, M., Fleischer, A., Thage, K., Mouginot, J., and Abbas Khan, S.: Mapping newly emerging islands and nunataks in Greenland improves existing bed elevation datasets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3624, https://doi.org/10.5194/egusphere-egu22-3624, 2022.

16:09–16:15
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EGU22-10065
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ECS
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Virtual presentation
Inès Otosaka, Andrew Shepherd, Andreas Groh, Jeremie Mouginot, and Xavier Fettweis

About a third of Greenland’s total ice losses come from the Northwest sector, a sector that includes a large number of marine-terminating outlet glaciers, which have all experienced widespread retreat triggered by ocean-induced melting. Here, we measure changes in surface elevation in the Northwest sector of the Greenland Ice Sheet from CryoSat-2 between July 2010 and July 2021 and find that the surface has lowered at a rate of 21.9 ± 1.1 cm/yr on average over this period, with rapid thinning occurring at the ice sheet margins at a rate of 46.9 ± 5.9 cm/yr. We further compute mass change by combining our CryoSat-2 surface elevation change dataset with firn densities from a regional climate model, and we show that the Northwest sector lost 456 ± 5.7 Gt of ice between July 2010 and July 2021.

To evaluate our altimetry-based mass balance solution, we compare our solution to independent estimates derived from satellite gravimetry and the mass budget method. We show that our altimetry estimate is the least negative for the Northwest sector as a whole, in contrast, the mass budget method leads to the largest ice losses. However, when partitioning these three estimates into sub-regions of the Northwest sector, we show that the spatial pattern of differences between mass balance estimates is complex, suggesting that discrepancies between techniques do not solely originate from one single region or technique.

Thanks to the higher spatial resolution afforded by satellite altimetry retrievals and the mass budget method, we examine the mass balance of the Northwest sector within its 74 glacier basins and find that differences between the two techniques greater than 0.5 Gt/yr  are recorded in 19 basins, with the largest disagreement recorded at Steenstrup-Dietrichson and Kjer Gletscher.

Comparing altimetry, gravimetry and the mass budget estimates at different spatial scales is critical to isolate the differences between geodetic techniques as well as the drivers of these differences. Previous studies, such as the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE), have demonstrated that combining independent estimates of ice sheet mass balance can lead to greater certainty. Here, aggregating the altimetry, gravimetry and mass budget method estimates results in a rate of mass loss of 55.6 ± 1.5 Gt/yr for the Northwest sector between June 2010 and June 2019.

How to cite: Otosaka, I., Shepherd, A., Groh, A., Mouginot, J., and Fettweis, X.: A Regional Mass Balance Assessment of the Northwest Sector of the Greenland Ice Sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10065, https://doi.org/10.5194/egusphere-egu22-10065, 2022.

16:15–16:21
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EGU22-12244
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Virtual presentation
Louise Sandberg Sørensen and the 4DGreenland team

Our ability to monitor and quantify ice sheet runoff is essential for a better understanding of the hydrology of the Greenland Ice Sheet and its contribution to global sea-level rise in a future warming climate. The amount of liquid water at the surface of the Greenland Ice Sheet has particularly increased due to large regional warming that this region has experienced over the last decades.

Here, we present the initial results of monthly runoff estimates from the Watson drainage basin in West Greenland, developed within the 4DGreenland project. 4DGreenland is a 2-year project funded by  the European Space Agency (ESA), which is focused on quantifying and analyzing the dynamic variations in the hydrological components of the ice sheet, while maximizing the use of Earth Observation (EO) data. 

The basin scale runoff estimate is derived from adding each component of the hydrological cycle: We have used a Random Forest approach (Supervised Learning algorithm) to map supraglacial hydrology ice sheet wide from optical imagery, and used ICESat-2 derived lake bathymetry as calibration to derive storage volumes. To map meltwater we have developed algorithms for generating maps of surface melt extent from high-medium resolution C-band backscatter measurements, and Low resolution PMW data. The subglacial melt is inherently difficult to monitor. The melt produced by friction heat though is derived from a model run of an ice sheet model (Elmer/Ice) which is tuned to assimilate observed ice velocities. Lastly, we have further developed a firn model to quantify the amounf of the meltwater that is retained in the snow/firn column.

By adding these component we are able to present monthly estimated of runoff from the drainage basin, and specifically show how each of the hydrologically components change over time. 

How to cite: Sandberg Sørensen, L. and the 4DGreenland team: Maximizing the use of remote sensing to quantify Greenland ice sheet runoff , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12244, https://doi.org/10.5194/egusphere-egu22-12244, 2022.

16:21–16:27
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EGU22-6961
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On-site presentation
Ingo Sasgen, Eva Boergens, Christoph Dahle, Thorben Döhne, Andreas Groh, Henryk Dobslaw, Sven Reißland, and Frank Flechtner

The German Research Centre for Geosciences (GFZ), together with the Technische Universität Dresden and the Alfred-Wegener-Institute (AWI), maintains the ‘Gravity Information Service’ portal (GravIS, gravis.gfz-potsdam.de). GravIS facilitates the dissemination of user-friendly data of mass variations in the Earth system, based on observations of the GFZ and NASA/JPL satellite gravimetry mission GRACE (Gravity Recovery and Climate Experiment, 2002-2017) and its successor mission GRACE-FO (GRACE-Follow-On, since 2018).

The portal provides mass changes of the Greenland and Antarctic ice sheets on a regular 50 km by 50 km Polar stereographic grid and as basin averages accompanied by empirical uncertainties. Both ice mass balance products rely on the same input data of GRACE/GRACE-FO spherical harmonic coefficients, generated and post-processed by GFZ. Corrections applied to the data include the insertion of estimates of the geocentre motion, replacement of the C20 and C30 coefficients, and the correction for glacial isostatic adjustment with the ICE-6G model.

The gridded data product is processed with sensitivity kernels, tailored explicitly to resolving mass changes of the ice sheets. A regional integration applies these sensitivity kernels to the unfiltered GRACE and GRACE-FO spherical harmonic coefficients. The sensitivity kernels optimise a trade-off between leakage errors and propagated GRACE solution errors.

The basin-average product consists of continent-wide estimates of ice sheet mass changes, and basin averages for seven basins in Greenland and 25 basins in Antarctica. The regional time series are calculated using a forward-modelling  inversion approach, which considers the typical spatial anomalies of the surface-mass balance and dynamic ice discharge.

In addition to the ice mass change data, GravIS provides terrestrial water storage (TWS) variations over the continents and ocean bottom pressure (OBP) variations from which global mean barystatic sea-level rise can be estimated. These data sets are provided either on 1° grids or as regional averages.

The data sets of all Earth system domains can be interactively displayed with the portal and are freely available for download. This contribution aims to show the features and possibilities of the GravIS portal to cryosphere researchers.

How to cite: Sasgen, I., Boergens, E., Dahle, C., Döhne, T., Groh, A., Dobslaw, H., Reißland, S., and Flechtner, F.: GravIS Portal: User-friendly Ice Mass Variations in Greenland and Antarctica from GRACE and GRACE-FO, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6961, https://doi.org/10.5194/egusphere-egu22-6961, 2022.

16:27–16:33
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EGU22-7677
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Virtual presentation
Jan Wuite, Thomas Nagler, Markus Hetzenecker, Ludivine Libert, Stefan Scheiblauer, and Helmut Rott

The limited availability of Synthetic Aperture Radar (SAR) data over glaciers and ice sheets in the past, which formed a major obstacle for obtaining consistent climate data records, has been overcome by the Copernicus Sentinel-1 (S-1) mission, launched in 2014. S-1 SAR data in Greenland, Antarctica and other polar regions have since been regularly acquired every 6 to 12 days, allowing for the operational monitoring and time series analysis of key climate variables at a high spatial and temporal resolution. Exploiting the extensive archive of S-1 acquisitions, we have developed algorithms for retrieving dense time series of glacier and ice sheet velocities, ice discharge and surface melt processes, facilitated by the ESA Climate Change Initiative (ESA CCI), ESA Polar Science Cluster (ESA POLAR+) and EU Copernicus Climate Change Service (C3S) programs.

In order to improve existing ice velocity products, we have implemented an InSAR processing line for generation of high-resolution velocity fields from crossing orbits and included a tide correction module to the offset-tracking processing line which accounts for the vertical motion of floating ice shelves and ice tongues due to ocean tides and pressure differences. We present synergistic InSAR and offset tracking ice velocity products, derived from repeat pass S-1 Interferometric Wide (IW mode) swath data, for the Greenland Ice Sheet and report on the performance of the products using in-situ GPS data. Additionally, we show velocity variations of major outlet glaciers in Greenland and Antarctica and other polar ice bodies. The generated ice velocity maps, complemented with ice thickness and other Earth observation datasets, form the basis for deriving ice flow and discharge fluctuations and trends at sub-monthly to multi-annual time scales.

To evaluate snowmelt dynamics in Greenland and Antarctica, we have also developed an algorithm for generating maps of snowmelt extent based on multitemporal S-1 SAR and Advanced Scatterometer (ASCAT) data. The dense backscatter time series yields a unique temporal signature that is used to identify the different stages of the melt/freeze cycle and to estimate the melting intensity of the surface snowpack. The high-resolution melt maps form the main input for deriving value-added products on annual melt onset, ending and duration. Intercomparisons with in-situ weather station data and melt products derived from regional climate models (RCMs) and passive microwave radiometers confirm the ability of the algorithm to detect short-lived and longer melt events.

Our results demonstrate the excellent capability of the S-1 mission in combination with other sensors for comprehensive monitoring of key climate variables on glaciers and ice sheets, providing essential input for various applications such as ice dynamic and climate modelling.

How to cite: Wuite, J., Nagler, T., Hetzenecker, M., Libert, L., Scheiblauer, S., and Rott, H.: Monitoring of Key Climate Variables on Glaciers and Ice Sheets using Sentinel-1 SAR, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7677, https://doi.org/10.5194/egusphere-egu22-7677, 2022.

Coffee break
Glacier dynamics
17:00–17:06
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EGU22-7646
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ECS
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On-site presentation
Paul Halas, Jeremie Mouginot, Basile de Fleurian, and Petra Langebroek

When assessing ice velocity trends in Greenland, optical feature-tracking has previously been used to derive one-year velocity averages. Indeed, this technique requires pairs of images separated by approximately one year, and usually all possible pairs are used in order to achieve the best spatial coverage for every year. But this implies averaging pairs that start at different time in the year, and it is common to also use pair of images that are separated by shorter or longer time (ranging from 336 days up to 400 days between images).

Since ice velocities display strong seasonal variations, we argue that combining all pairs may impact the yearly ice velocities estimations by sampling differently summer and winter velocities, and therefore impacting the long-term trends.

Here we assess this impact by reproducing the work done from previous studies (Tedstone et al. 2015, Williams et al. 2020) using optical feature-tracking on Landsat-5, 7 and 8 as well as Sentinel-2 constellation, focusing on land-terminating parts of the Southwest of Greenland Ice sheet, and by comparing obtained velocity trend maps with trends of the same area obtained when operating a precise selection of the data.

 

How to cite: Halas, P., Mouginot, J., de Fleurian, B., and Langebroek, P.: Impact of satellite image pairs selection when deriving ice velocities trends, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7646, https://doi.org/10.5194/egusphere-egu22-7646, 2022.

17:06–17:12
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EGU22-5932
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On-site presentation
Frank Paul, Livia Piermattei, Désirée Treichler, Lin Gilbert, Luc Girod, Andreas Kääb, Ludivine Libert, Thomas Nagler, Tazio Strozzi, and Jan Wuite

In the Karakoram, dozens of glacier surges occurred in the past two decades, making the region one of the global hotspots. Detailed analyses of dense time series from available optical and radar satellite images revealed a wide range of surge behaviours in this region: from slow advances characterized by slow ice flow over periods longer than a decade to short, pulse-like advances with high velocity over one or two years.

In this study, we present an analysis of three glaciers that are currently surging in the same region of the central Karakoram: North Chongtar, South Chongtar and an unnamed glacier referred to as NN9. A full suite of optical and SAR satellite sensors and digital elevation models (DEMs) are used to (1) obtain comprehensive information about the evolution of the surges between 2000 and 2021 and (2) to compare and evaluate capabilities and limitations of the different satellite sensors for monitoring small glaciers in steep terrain. 

The analysis for (1) reveals a contrasting evolution of advances rates and flow velocities for the three glaciers, while the elevation change pattern is more similar. South Chongtar Glacier showed advance rates of more than 10 km y-1, velocities up to 30 m d-1 and surface elevations raised by 200 m. In comparison, the three times smaller North Chongtar Glacier has a slow and almost-linear increase of advance rates (up to 500 m y-1), flow velocities below 1 m d-1 and elevation increases of up to 100 m. The even smaller glacier NN9 changed from a slow advance to a full surge within a year, reaching advance rates higher than 1 km y-1, but showing the typical surface lowering higher up only recently. It seems that, despite a similar climatic setting, different surge mechanisms are at play in this region and that the surge mechanisms can change in the course of a single surge. 

On topic (2) we found that sensor performance is dependent on glacier characteristics (size, flow velocity, amplitude of changes). In particular velocities derived from Sentinel-1 performed poorly on small, narrow glaciers in steep environment. The comparison of elevation changes revealed that all considered DEMs have a sufficient accuracy to detect the mass transfer during the surges and that elevations from ICESat-2 ATL06 data fit neatly. 

How to cite: Paul, F., Piermattei, L., Treichler, D., Gilbert, L., Girod, L., Kääb, A., Libert, L., Nagler, T., Strozzi, T., and Wuite, J.: Three different glacier surges at a spot: What satellites observe and what not, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5932, https://doi.org/10.5194/egusphere-egu22-5932, 2022.

17:12–17:18
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EGU22-12059
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Virtual presentation
Laura Edwards

Calving at marine terminating glaciers accounts for at least 40 % of mass loss from the Greenland ice sheet, the current largest land ice sea level rise (SLR) contributor. Research suggests that glacier meltwater plumes play an important role in glacier calving front retreat and so studying their extent (2D and 3D) and temporal and spatial variability is critical for estimating potential SLR.

This work presents the novel use of satellite synthetic aperture radar (SAR) to study glacial meltwater plume extent and compares this approach to the more standard use of cloud and light-limited multispectral data for observations of plume extent. SAR data have often been used in oceanography to isolate ocean fronts, large-scale upwelling and estuarine plumes but have not thus far been used for studying glacial meltwater plumes. A SAR intensity image is retrieved from the backscatter signal which is a function of wind, wave and current interactions on a water surface, therefore, any ocean or river features which modify these have the potential to be identified within the SAR image. Higher resolution SAR data have more recently become available providing the opportunity to study meltwater plumes which present as an upwelling in the glacier fjord or lake in front of the glacier.

An initial study location of Breiðamerkurjökull glacier and Jökulsárlón proglacial lake, in Iceland, was chosen for the study which will involve fieldwork in 2022 to allow 3D analysis of plumes. This location is logistically easier and less expensive than Greenland yet provides an ideal analogue for study of Greenland glacier meltwater plumes in fjord-ocean systems. This is due to the connection of Jökulsárlón glacial lake water with the North Atlantic Ocean via a channel through which all tidal and residual flows in and out of the lake occur (Brandon et al., 2017) much like a glacier-fjord system in Greenland. Breiðamerkurjökull glacier calving front terminates in Jökulsárlón just like a marine terminating glacier in Greenland terminates in a fjord. Here initial results from quantifying the 2D extent of surface plumes using SAR are presented. 

How to cite: Edwards, L.: Satellite investigations of glacial meltwater plumes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12059, https://doi.org/10.5194/egusphere-egu22-12059, 2022.

17:18–17:24
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EGU22-9837
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ECS
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Presentation form not yet defined
Floriane Provost, Dimitri Zigone, Jean-Philippe Malet, Emmanuel Le Meur, and Clément Hibert

Better understanding the behaviour of tidewater outlet glaciers fringing marine ice sheets is of paramount importance to simulate Antarctica‘s future response to global warming. Addressing the processes underlying these glaciers dynamics (ice motion, crack propagation, basal melting, sea ice interaction, calving events, etc) is a mean of constraining their ice discharge to the sea and hence the ice sheet global mass balance. We here focus on the Astrolable glacier located in Terre Adélie (140°E, 67°S) near the Dumont d'Urville French research station. In January 2019, a large crack of around 3km length was observed in the western shore of the glacier potentially leading to a calving of ca. 28 km2.The fissure has progressively grown until November 2021 when an iceberg of 20km2 was eventually released. 

The location of the glacier outlet at the proximity of the Dumont DUrville French research station is an asset to collect in-situ observations such as GNSS surveys and seismic monitoring. Satellite optical imagery also provides numerous acquisitions from the early nineties till the end of 2021 thanks to the Landsat and Sentinel-2 missions. We used two monitoring techniques: optical remote sensing and seismology to analyze changes in the activity of the glacier outlet. We computed the displacement of the ice surface with MPIC-OPT-ICE service available on the ESA Geohazards Exploitation Platform (GEP) and derived the velocity and strain rates from the archive of multispectral Sentinel-2 imagery from 2017 to the end of 2021. The images of the Landsat mission are used to map the limit of the ice front in order to retrieve the calving cycle of the Astrolabe. We observe that the ice front had significantly advanced toward the sea (4 km) since September 2016 and such an extension has not been observed in the previous years (since 2006) despite minor calving episodes.

The joint analysis of the seismological data and the velocity and strain maps are discussed with the recent evolution of the glacier outlet. The strain maps show complex patterns of extension and compression areas. The number of calving events detected in the seismological dataset significantly increased during 2016-2021 in comparison with the period 2012-2016. Since the beginning of 2021, both datasets show an acceleration. The number of calving events increased exponentially from June 2021 until the rupture in November 2021 and the velocity of the ice surface accelerated from 1 m.day-1 to 4 m.day-1 in the part of the glacier that detached afterward. This calving event is the first one of this magnitude ever documented over the Astrolabe glacier.

How to cite: Provost, F., Zigone, D., Malet, J.-P., Le Meur, E., and Hibert, C.: Monitoring ice-calving at the Astrolabe glacier (Antarctica) with seismological and optical satellite, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9837, https://doi.org/10.5194/egusphere-egu22-9837, 2022.

Sea ice and ice bergs
17:24–17:30
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EGU22-8267
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ECS
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Presentation form not yet defined
Ben Evans, Andrew Fleming, Anita Faul, Scott Hosking, Jan Lieser, and Maria Fox

Accurate estimates of iceberg populations, disintegration rates and iceberg movements are essential to fully understand ice sheet contributions to sea level rise and freshwater and heat balances. Understanding and prediction of iceberg distributions is also of paramount importance for the safety of commercial and research shipping operations in polar seas. Despite their manifold implications the operational monitoring of icebergs remains challenging, largely due to difficulties in automating their detection at scale.  

Synthetic Aperture Radar (SAR) data from satellites, by virtue of its ability to penetrate cloud cover and strong sensitivity to the dielectric properties of the reflecting surface, has long been recognised as providing great potential for the identification of icebergs. Many existing studies have developed algorithms to exploit this data source but the majority are designed for open water situations, require significant operator input, and are susceptible to the substantial spatial and temporal variability in backscatter characteristics within and between SAR scenes that result from meteorological, geometric and instrumental differences. Further ambiguity arises when detecting icebergs in dense fields close to the calving front and in the presence of sea ice. For detection to be fully automated, therefore, adaptive iceberg detection algorithms are required, of which few currently exist. 

Here we propose an unsupervised classification procedure based on a recursive implementation of a Dirichlet Process Mixture Model that is robust to inter-scene variability and is capable of identifying icebergs even within complex environments containing mixtures of open water, sea ice and icebergs of various sizess. The method exploits freely available dual-polarisation Sentinel 1 EW imagery, allowing for wide spatial coverage at a high temporal density and providing scope for near-real-time monitoring.  It overcomes many of the limitations of existing approaches in terms of environments to which it may be applied as well as requirements for labelled training datasets or determination of scene-specific thresholds. Thus it provides an excellent basis for operational monitoring and tracking of iceberg populations at a continental scale to inform both scientific and navigational priorities. 

How to cite: Evans, B., Fleming, A., Faul, A., Hosking, S., Lieser, J., and Fox, M.: Unsupervised detection and quantification of iceberg populations within sea ice from dual-polarisation SAR imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8267, https://doi.org/10.5194/egusphere-egu22-8267, 2022.

17:30–17:36
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EGU22-7013
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ECS
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Virtual presentation
Laust Færch, Wolfgang Dierking, Anthony P. Doulgeris, and Nick Hughes

Images from satellite Synthetic Aperture Radar (SAR) systems are widely used for iceberg monitoring. Normally, icebergs are detected in SAR images by utilizing constant false alarm rate (CFAR) filters, which compare each pixel or cluster of pixels against its background and adaptively set a threshold based on several assumptions regarding the statistical distribution of the background clutter. CFAR algorithms are currently being applied on images from the C-band SAR Sentinel-1 and RADARSAT missions by the operational ice services responsible for Canadian and Greenland waters. Previous studies have shown that imagery from wide-swath C-band SAR is unsuitable for detecting icebergs surrounded by sea ice, but other studies have indicated that icebergs in sea ice may be detected in high-resolution L-band SAR images. Additionally, it is well known that L-band SAR is less sensitive to sea surface roughness than C-band SAR. Therefore, a future operational L-band SAR mission is currently being investigated by the European Space Agency (ESA) since it is expected that L-band images are valuable complements to current C-band imagery for iceberg detection in areas with drift ice and in rough windy seas.

In this project, we investigate the backscatter intensity contrast between icebergs and their surroundings using ALOS-2 PALSAR-2 (L-band) ScanSAR, and Sentinel-1 (C-band) extra wide swath imagery. The investigations are concentrated on SAR images from two test sites, one in the Labrador Sea, where we – for further analysis - identified 256 icebergs in open water, and another site in the region of Belgica Bank with 1013 icebergs embedded in fast ice. The investigation shows that the two SAR sensors performed similarly for the open water site, with a backscatter intensity contrast between icebergs and the background of 5-6 dB in both the HH and HV band.  But for icebergs surrounded by sea ice, the contrast between icebergs and background at both C- and L-band is greatly reduced to around 2 dB for the HH channel and 4-5 dB for the HV channel.  By further manually classifying the sea ice types around the icebergs, we show that the backscatter contrast between icebergs and background is similar at C- and L-band for icebergs embedded in smooth sea ice. However, for rough sea ice, the C-band contrast is decreasing, while remaining high at L-band. Our results indicate that L-band data will lead to better performance for detecting icebergs surrounded by sea ice.

How to cite: Færch, L., Dierking, W., Doulgeris, A. P., and Hughes, N.: A comparison of Backscatter Intensity of Icebergs in C- and L-band SAR Imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7013, https://doi.org/10.5194/egusphere-egu22-7013, 2022.

17:36–17:42
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EGU22-2491
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ECS
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Virtual presentation
Anne Braakmann-Folgmann, Andrew Shepherd, Laura Gerrish, Jamie Izzard, and Andy Ridout

Icebergs impact the physical and biological properties of the ocean along their drift trajectory by releasing cold fresh meltwater and nutrients. This facilitates sea ice formation, fosters biological production and influences the local ocean circulation. The intensity of the impact depends on the amount of meltwater. A68 was the sixth largest iceberg ever recorded in satellite observations, and hence had a significant potential to impact its environment. It calved from the Larsen-C Ice Shelf in July 2017, drifted through the Weddell and Scotia Sea and approached South Georgia at the end of 2020. Finally, it disintegrated near South Georgia in early 2021. Although this is a common trajectory for Antarctic icebergs, the sheer size of A68A elevates its potential to impact ecosystems around South Georgia through release of fresh water and nutrients, through blockage and through collision with the benthic habitat.

In this study we combine satellite imagery data from Sentinel 1, Sentinel 3 and MODIS and satellite altimetry from CryoSat-2 and ICESat-2 to chart changes in the A68A iceberg’s area, freeboard, thickness, volume and mass over its lifetime to assess its disintegration and melt rate in different environments. We find that A68A thinned from 235 ± 9 to 168 ± 10 m, on average, and lost 802 ± 35 Gt of ice in 3.5 years. While the majority of this loss is due to fragmentation into smaller icebergs, which do not melt instantly, 254 ± 17 billion tons are released through melting at the iceberg’s base - a lower bound estimate for the fresh water input into the ocean. Basal melting peaked at 7.2 ± 2.3 m/month in the Northern Scotia Sea. In the vicinity of South Georgia we estimate that 152 ± 61 Gt of freshwater were released over 96 days, potentially altering the local ocean properties, plankton occurrence and conditions for predators. The iceberg may also have scoured the sea floor briefly. Our detailed maps of the A68A iceberg thickness change will be useful to investigate the impact on the Larsen-C Ice Shelf, and for more detailed studies on the effects of meltwater and nutrients released off South Georgia. Our results could also help to model the disintegration of other large tabular icebergs that take a similar path and to include their impact in ocean models.

How to cite: Braakmann-Folgmann, A., Shepherd, A., Gerrish, L., Izzard, J., and Ridout, A.: Observing the Disintegration of the A68A Iceberg from Space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2491, https://doi.org/10.5194/egusphere-egu22-2491, 2022.

17:42–17:48
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EGU22-7584
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Presentation form not yet defined
Clément Soriot, Catherine Prigent, Frédéric Frappart, and Ghislain Picard

The Copernicus Imaging Microwave Radiometer (CIMR) [Kilic et al. 2018] is a wide-swath conically-scanning multi-frequency microwave radiometer from 1.4 to 36 GHz. It will to provide a wide range of sea-ice information, including sea ice concentration, thin sea ice thickness and snow depth over sea ice. 
The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) [Kern et al. 2020] will carry a multi-frequency radar altimeter and microwave radiometer to monitor sea ice thickness and overlying snow depth. Both missions are Copernicus high priority to respond to the Integrated European Union Policy for the Arctic. At the same time, MetOp-SG will carry the ASCAT instrument, that shows sensitivity to sea ice properties, especially the ice type.
Here, we propose to analyze the potential synergies of these instruments, using existing observations with similar characteristics (although less optimal). 

The combination of AMSR2 and SMAP can mimic CIMR, SARAL and Sentinel-3 are proxies for CRISTAL, and ASCAT is already available on MetOp-A and -B. A data set of coincident AMSR2, SMAP, SARAL, Sentinel-3 and ASCAT observations is constructed, over the Poles, over a year. It includes both the Level 1 and Level 2 products. We concentrate first on the study of the complementarity between the observations, at Level 1. It has been previously shown that the exploitation of the observation synergy at Level 1 is more efficient than a posteriori combinations of products, independently estimated from different instruments [Aires 2011]. Then, in order to analyze results of this database, the Snow Microwave Radiometric Transfer (SMRT) [Picard et al. 2018] model is used.  It is an up-to-date radiative transfer model that is tailored to handle snow and sea ice in a plane-parallel configuration, and it can simulate both passive and active microwave responses.

A first study [Soriot et al. 2021] has shown that the use of CIMR-like data with the SMRT model can explain temporal and spatial distribution of microwave signatures over the whole North Pole during all year long. From this interpretation, a realistic characterization of the sea ice and its snow cover has been provided. Correlation and causalities, between microwave signatures and geophysical properties (such as sea ice type, sea ice thickness, snow depth or snow microstructure), have been classified. 

Here, we extend this study to the Austral Ocean and to altimetric data, southern sea ice being more covered by current altimeters than northern sea ice.
Both height and radiometric signals are exploited from the altimeters, using a unique dataset altimetric points space-time colocated. 
Recent developments in SMRT have made it able to simulate altimetric signal [Larue et al. 2021, Sandells et al. 2021], and are used to interpret CRISTAL-like data. Synergies between CIMR-like and CRISTAL-like data are highlighted for an improved sea ice and snow characterization.

How to cite: Soriot, C., Prigent, C., Frappart, F., and Picard, G.: Sea ice characterization from combined passive microwave, scatterometers and altimeters observation and radiative transfer modelling., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7584, https://doi.org/10.5194/egusphere-egu22-7584, 2022.

17:48–17:54
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EGU22-11
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Virtual presentation
Craig Donlon, Rolv Midthassel, Marcello Sallusti, Mariel Trigganese, Benedetta Fiorelli, and Christophe Accadia

This presentation describes the Copernicus Imaging Microwave Radiometer (CIMR) Sentinel expansion mission. The mission is designed to provide measurement evidence in support of developing, implementing, and monitoring the impact of the European Integrated Policy for the Arctic. Since changes in the Polar regions have profound impacts globally, CIMR will provide a new view of the cryosphere using a suite of unique low-frequency, yet high spatial resolution, microwave radiometer measurements over the high latitudes and the entire global domain. Products will be provided within 3 hours of sensing and for specific operational activities, within 1 hour over specific regions. CIMR will serve users in the Copernicus Ocean, Land and Climate Services fueling down-stream cryosphere applications. The primary instrument is a conically scanning low-frequency, high spatial resolution multi-channel microwave radiometer. A dawn-dusk orbit and large swath width of ~2000 km ensures 95% global coverage each day with a single satellite. Channels centred at L-, C-, X-, Ku- and Ka-band are fully polarised with effective spatial resolution of ≤60 km, ≤15km, ≤5 km and <5 km (goal:4km) respectively. On-board processing provides robustness against radio frequency interference and enables the computation of modified 3rd and 4th Stokes parameters for all channels. This paper reviews the CIMR mission, anticipated performances and the expected Level-2 products that will be provided including sea ice concentration, sea surface temperature, thin sea ice thickness, sea surface salinity and wind speed over the ocean amongst others . In addition, synergies with other Copernicus missions, notably the CRISTAL mission, will be highlighted for cryosphere applications.

How to cite: Donlon, C., Midthassel, R., Sallusti, M., Trigganese, M., Fiorelli, B., and Accadia, C.: The Copernicus Imaging Microwave Radiometer (CIMR): A new view of the Cryosphere, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11, https://doi.org/10.5194/egusphere-egu22-11, 2022.

17:54–18:00
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EGU22-888
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Presentation form not yet defined
Andrew Shepherd, Sinead Farrell, and Sara Fleury

With the accelerated melting of the ice sheets and the sea ice cover, Earth’s Polar Regions are major witnesses to global warming. Arctic Amplification already modifies lifestyles, economies, ecologies, industries, and transportation across the region. But as a result of teleconnections with the climate system, the Polar Regions also impact on a global scale, affecting sea level rise, ocean circulation and weather patterns, which, in turn, disrupt the natural environment and society. Because of their scale and inaccessibility, observation of the Polar Regions requires a collection of space-based techniques.  Satellite altimetry provides a unique capability to monitor changes in the thickness of land ice and sea ice, and in the Polar Oceans. This information is essential for charting the response of the Polar Regions to climate change, and for predicting their future interactions with, and impacts on, the global climate system.

Although at least 7 satellite altimeters are in orbit today, only two reach polar latitudes: CryoSat-2 and ICESat-2.  CryoSat-2 was launched in 2010 and although it is still operational, it is projected to reach end of life between 2024 and 2026 due to known fuel leakage and battery degradation. ICESat-2 was launched in 2018 with a design-life of 3 years. Other satellite altimeters in lower inclination orbits, including Sentinel-3, survey only minor fractions of the Arctic sea ice pack and the polar ice sheets, and are therefore unable to provide observations of their overall imbalance. The European Commission has initiated the CRISTAL polar altimeter as a high priority candidate mission in partnership with the European Space Agency, in view of their Arctic policy, and based on user requirements. The earliest launch date for CRISTAL is in the final quarter of 2027.

Without successful mitigation, there will be a gap of between 2 and 5 years in our polar satellite altimetry capability. This gap will introduce a decisive break in the long-term records of ice sheet and sea ice thickness change and polar oceanography and this, in turn, will degrade our capacity to assess and improve climate model projections. These capabilities are of major societal importance. In order to ensure the continuity of polar altimetry, there is an urgent need to consider mitigation measures. This paper aims to stimulate a community discussion and position on possible solutions, including extending the lifetime of CryoSat-2 or ICESat-2, manoeuvring an alternative satellite into a high-inclination orbit, accelerating the launch of CRISTAL, and initiating a systematic airborne measurement programme as a bridging capability. 

 

 

 

How to cite: Shepherd, A., Farrell, S., and Fleury, S.: Bridging the gap in polar altimetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-888, https://doi.org/10.5194/egusphere-egu22-888, 2022.

18:00–18:06
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EGU22-11829
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ECS
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On-site presentation
Thomas Johnson, Michel Tsamados, Jan-Peter Muller, and Julienne Stroeve

Surface roughness is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer melt pond extent, while also closely related to ice age and thickness. At a local scale, roughness in the form of ridges, hummocks, rafted ice can slow down and hinder safe transport on the ice as well as be a hazard for ice strengthened vessels and structures. High resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness have remained elusive and do not extend over multi-decadal time-scales. The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles sampling specular anisotropy, since 1999. Extending on previous work to model sea ice surface roughness from MISR angular reflectance signatures, a training dataset of cloud-free pixels and coincident roughness from coincident operation IceBridge (OIB) airborne laser data is generated. Surface roughness, defined as the standard deviation of the within-pixel lidar elevations to a best-fit plane, is modelled using several techniques and Support Vector Regression with a Radial Basis Function kernel selected. Hyperparameters are tuned using grid optimisation, model performance is assessed using blocked k-fold cross-validation. We present a derived sea ice roughness product at 1.1km resolution over the period of operation (April 2000 – 2020) and a corresponding time series analysis. To demonstrate the validity of the derived product, we first evaluate our roughness product against independent LiDAR characterisations of surface roughness consistent with our training data. We also evaluate our derived roughness product with known proxies of surface roughness on a pan-Arctic basis (AWISMOS CS2-SMOS sea ice thickness.) Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable capacity in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads.

How to cite: Johnson, T., Tsamados, M., Muller, J.-P., and Stroeve, J.: Mapping Arctic sea ice surface roughness with Multi-angle Imaging Spectro-radiometer., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11829, https://doi.org/10.5194/egusphere-egu22-11829, 2022.

18:06–18:12
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EGU22-11674
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ECS
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On-site presentation
Yoon Taek Jung, Yeji Lee, and Sang-Eun Park

Due to large temperature variations between the summer and winter seasons the active layer of permafrost undergoes repetitive thawing and freezing, and the increase of global temperature has accelerated permafrost degradation related to surface deformation seasonally and annually. Repeated freezing and thawing causes frost heave and thaw settlement, which results in displacement in the activity layer of permafrost. This surface displacement is also associated with ground ice and soil moisture content, and these factors in permafrost region could be observed through timely-dense SAR data. In particular, since the revisit time of Sentinel-1 (C-band) is relatively dense, timeseries SAR interferometry could be useful tools for monitoring and mapping subsurface soil properties over such a wide area. In this study, since the degree of freezing and thawing is very different spatially and temporally, we propose the method to indirectly estimate the ground ice content of the freezing period and the moisture content of the thawing period by quantifying the displacement using timeseries InSAR measurements in the Lena-river floodplain, Siberia.

How to cite: Jung, Y. T., Lee, Y., and Park, S.-E.: Monitoring Frost Heave and Thaw Settlement of Permafrost Using Timeseries InSAR Measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11674, https://doi.org/10.5194/egusphere-egu22-11674, 2022.