EGU General Assembly 2022  

Accurate mapping of subglacial bedrock topography is of prime importance to correctly simulate the past and future evolution of glaciers and ice sheets. As ocean warming is a major driver of recent changes in Greenland and Antarctica, mapping the bathymetry of the ocean seafloor in fjords and underneath ice shelves is crucial to accurately model warm water pathways up to the ice margins and grounding lines. A good knowledge of this bedrock topography also allows to better understand the past extent of the ice sheets and identify vulnerable regions that are sitting on retrograde bed slopes, hence that might be prone to the marine ice sheet instability. For mountain glaciers, accurately mapping the bedrock topography is mandatory to estimate ice thicknesses, which are used to simulate the contribution of glaciers to sea level rise, but also to quantify the amount of freshwater resources stored in glaciers. Because of their large number, remote locations, and difficult access conditions, only scarce in-situ data exists for bedrock topography. Hence, while being a fundamental variable for glacier modeling, it remains poorly constrained at the time. Here, we present how the use of multiple sensors remote sensing techniques has helped us to unravel the hidden relief beneath glaciers and ice sheets. In Greenland and Antarctica, we use airborne gravimetry measurements along with multibeam and radar echoe sounder to map the bathymetry in fjords and below ice shelves. We show that the use of these new bathymetric products help us to understand the retreat history of glaciers, revealing pathways for warm water, and contributes to better modeling ocean circulation up to the grounding lines of glaciers. For mountain glaciers, we mapped the ice velocity worldwide at an enhanced sampling resolution of 50 m, using massive cross correlation techniques on image pairs from both optical (ESA’s Sentinel-2; USGS/NASA’s Landsat-7/8) and radar imagery (ESA’s Sentinel-1a/b). Finally, we combine this mapping with airborne and ground penetrating radar to recover the ice thickness of all glaciers on Earth. These estimations reveal a different picture of the bedrock topography beneath glaciers, with a modified ice thickness distribution. Using these new estimations as initial state in the Open Global Glacier Model, we show the important impact on the evolution of freshwater resources, and specifically on the timing of the peak water.

How to cite: Millan, R., Mouginot, J., Morlighem, M., Rabatel, A., Gimenes, L., Champollion, N., Rignot, E., An, L., and Bjørk, A.: Insights of multiple sensors remote sensing techniques for the mapping of subglacial valleys beneath glaciers and ice shelves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1321, https://doi.org/10.5194/egusphere-egu22-1321, 2022.

Concurrent with atmospheric warming, glaciers around the world are rapidly retreating with direct consequences for global sea level and streamflow. Projections indicate considerable mass losses over the 21st century, however, mass losses vary strongly between regions and emission scenarios. In some regions with little ice cover projections forced by high emission scenarios show almost complete deglaciation by the end of the 21st century while in high-polar regions the relative mass losses are generally in the order of a few tenths of percent relative to year 2015. The mass losses alter local runoff regimes and lead to glacier runoff increases in some regions but to decreases in others. Global glacier changes are linearly correlated with global mean temperature increase indicating that limiting global warming has a direct effect on future glacier mass changes.

How to cite: Hock, R.: Future global glacier mass changes and their impact on sea level and streamflow, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6431, https://doi.org/10.5194/egusphere-egu22-6431, 2022.

With the ongoing, rapid glacier retreat, high-alpine landscapes are poised to change drastically over the coming decades. The newly exposed areas will not only give rise to new environments that can be eventually colonized by plants and organisms, but also to characteristic landforms. Amongst these, future glacier lakes forming in topographical depressions left behind by glacier retreat, have already been in the focus of earlier studies. The interest in these features is given by a number of factors, ranging from the ecological significance of such high-alpine lakes, over the potential hazards posed by such newly emerging water bodies, to their optical appeal in terms of landscape elements.

Here, we add to the existing body of literature dealing with the formation of new glacier lakes, and do so by leveraging both (1) a recently released, measurement-based estimate for the subglacial topography of all glaciers in the Swiss Alps, and (2) the results of a regional-scale glacier evolution model driven by different climate scenarios. Whilst the first point significantly increases the robustness of our projections, the second allows for a first quantification of the timing by which such new glacier lakes are expected to emerge. In this time-dependent analysis, we also include the possibility for newly emerging lakes to disappear again due to re-filling with sediments – a process neglected by studies so far.

Our results indicate that, if glaciers were to disappear entirely from the Swiss Alps, up to 683 new glacier lakes could emerge. These hold the potential of storing up to 1.16 ± 0.16 km³ of water, for a total lake area of 45 ± 9 km². For a middle-of-the-road climate scenario, we estimate that about 14% of the total volume (i.e. 0.16 ± 0.07 km³) could emerge by 2050. For 2100, the number changes to 57% (0.66 ± 0.17 km³), indicating a substantial increase in the pace by which new lakes will emerge after mid-century. Our first-order assessment of lake re-sedimentation indicates that about 45% of the newly emerging glacier lakes (ca. 260 out of ca. 570) could disappear again before the end of the century, and that between 12 to 20% of the newly emerging lake volume could be lost again due to this process. This suggests that sedimentation processes have to be taken into account when aiming at anticipating how future glacier landscapes will look like.

How to cite: Farinotti, D., Steffen, T., Huss, M., Estermann, R., and Hodel, E.: Future glacier lakes in the Swiss Alps: a projection of their evolution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-53, https://doi.org/10.5194/egusphere-egu22-53, 2022.

In contrast to the general retreat of glaciers across the globe, the glaciers of the Karakoram (KR) region of Karakoram-Himalayas (KH) have displayed an anomalous divergent response, with some glaciers remaining either stable or surging. This phenomenon is known as the "Karakoram Anomaly." Although many factors are reported to have control over it, the present study tries to decipher the role of Western Disturbances (WDs) in establishing and sustaining the anomaly. These upper-tropospheric extra-tropical cyclones impact the region during the boreal winter. WDs are the major contributor of winter snowfall over KR, dictating the mass-balance variability of the region, as reported by previous studies. Therefore, to achieve the study's objectives, a tracking algorithm is applied to 39-seasons (1980-2019; Nov-Mar) of the ERA5 reanalysis dataset. Initial simulations suggest that the tracking algorithm has the potential to capture nearly ~90% of the reported tracks accurately in terms of their time of occurrence. Furthermore, the associated statistics generated for tracks passing through KR revealed a ~10% increase in the WD-associated precipitation intensity. The results shall be further analyzed to quantify the contribution of WD-associated snowfall in modulating the regional mass-balance anomaly. Additionally, the various mechanisms involved in WDs' formation and intensification will also be investigated.

How to cite: Javed, A. and Kumar, P.: Deciphering the changes associated with Western Disturbances impacting “Karakoram Anomaly”, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-155, https://doi.org/10.5194/egusphere-egu22-155, 2022.

Surface energy balance (SEB) is the most comprehensive way to explain the atmosphere-glacier interactions but requires extensive data. We analyse an 11-year (2009-2020) record of the meteorological dataset from an automatic weather station installed at 4863 m a.s.l., on a lateral moraine of the Chhota Shigri Glacier in the western Himalaya. The study was carried out over the winter months (December to April) to understand the SEB drivers and snow sublimation. Further, we examine the role of cloud cover on SEB and turbulent heat fluxes. The turbulent heat fluxes were calculated using the bulk aerodynamic method, including stability corrections. The net short-wave radiation is the primary energy source. However, a significant amount of energy is dissipated by the turbulent heat fluxes. The cloud cover plays an important role in limiting the incoming short-wave radiation by up to 75%. It also restricts the turbulent heat fluxes by around 50%, consequently less snow sublimation. During the winter period, turbulent latent heat flux contributed the largest (63%) in the total SEB, followed by net all-wave radiation (29%) and sensible heat flux (8%). Dry air, along with the high snow surface temperature and wind speed, favours sublimation. We also observe that strong and cold winds, possibly through mid-latitude western disturbances, impede sublimation by bringing high moisture content in the region and cooling the snow surface. The estimated snow sublimation fraction is 18 to 42% of the total winter snowfall at the study site, indicating that the snow sublimation is an essential parameter in the surface mass balance and hydrological modelling at the high mountain Himalayan catchments.

How to cite: Mandal, A., Angchuk, T., Azam, M. F., Ramanathan, A., Wagnon, P., Soheb, M., and Singh, C.: Surface energy balance and sublimation of the winter snow cover at 4863 m a.s.l. on Chhota Shigri Glacier moraine (western Himalaya, India) between 2009 and 2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-366, https://doi.org/10.5194/egusphere-egu22-366, 2022.

Globally, glaciers are losing dramatic volumes of ice, especially in Alaska, which dominates sea-level rise from glaciers. Plateau icefields may be especially sensitive to climate change due to the non-linear controls their topography imparts on their response to climate change. However, Alaskan plateau icefields have been subject to little structural glaciological or regional geomorphological assessment, which makes the controls on their present and former mass balance difficult to ascertain. 

We inventoried 1050 glaciers and 401 lakes of the Juneau Icefield region for the year 2019. We found that 63 glaciers had disappeared since the 2005 inventory, with a reduction of glacier area of 422 km². We also present the first structural glaciological and geomorphological map for an entire plateau icefield in Alaska. Glaciological mapping of nearly 20,000 features included crevasses, debris cover, foliation, ogives, medial moraines and, importantly, areas of glacier fragmentation, where glaciers either separated from tributaries via lateral recession (n=59), and disconnected within areas of former icefalls (n=281). Geomorphological mapping of >10,000 landforms included glacial moraines, glacial lakes, trimlines, flutes and cirques. These landforms were generated by a temperate icefield during the “Little Ice Age” neoglaciation. These data demonstrate that the present-day outlet glaciers, which have a similar thermal and ice-flow regime, have undergone largely continuous recession since the “Little Ice Age”.

These data document the interactions between topography and glacier change. Importantly, disconnections are occurring within glaciers can separate accumulation and ablation zones, increasing rates of glacier mass loss. We show that glacier disconnections are widespread across the icefield and should be critically taken into consideration when icefield vulnerability to climate change is considered.

How to cite: Davies, B., Bendle, J., Carrivick, J., McNabb, R., McNeil, C., Pelto, M., Campbell, S., Holt, T., Ely, J., and Markle, B.: Topographic controls on ice flow and recession for Juneau Icefield (Alaska/British Columbia), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-777, https://doi.org/10.5194/egusphere-egu22-777, 2022.

Mountain glaciers and ice caps are undergoing rapid mass loss but rates of contemporary change lack long-term (centennial-scale) context. Future projections of glacier changes require spin up to present day conditions and thus baseline ice extents and ice volumes are a prerequisite for model validation. Here, we reconstruct the Little Ice Age maximum glacier extent and ice surface of Jostedalsbreen, which is the largest ice mass in mainland Europe. Jostedalsbreen had its largest Little Ice Age (LIA) maximum about 1740 to 1860. The LIA ice-covered area was 568 km² and the LIA ice volume was between 61 km³ and 91 km³. We show that the major outlet glaciers have lost at least 110 km² or 19 % of their LIA area and 14 km³ or 18 % of their LIA volume until 2006. The largest proportional changes are associated with the loss of ice falls and consequent disconnection of tributaries. Glacier-specific hypsometry changes suggest a mean rise in ELA of 135 m but there is wide inter-glacier variability. A median date for the LIA of 1755 suggests that the long-term rate of ice mass loss has been 0.05 m w.e. a^-1. Comparison of that long-term rate of mass loss with our other published analyses of changes to mountain glaciers and ice caps since the LIA shows that Jostedalsbreen is unusual in not exhibiting an acceleration in mass loss since the LIA. Indeed, we have reported a 23 % acceleration of glacier mass loss in NE Greenland and a doubling for the Southern Alps of New Zealand. Others have reported a doubling of the rate of mass loss for the Vatnajökull ice cap and for Patagonia since the LIA. We have very recently reported a ten-fold increase for ~ 15,000 glaciers across the Himalaya. A synthesis of these long-term analyses reveals a latitudinal effect, regional climate effects and local controls on long-term glacier mass balance. For example, local rates of loss across the Himalaya were enhanced with the presence of surface debris cover (by 2 times vs clean-ice) and/or a proglacial lake (by 2.5 times vs land-terminating). Overall, we highlight the utility of geomorphological-based reconstructions of glaciers for understanding and quantifying long-term (centennial-scale) responses of mountain glaciers and ice caps to climate and hence for understanding of meltwater production and proglacial landscape evolution.

How to cite: Carrivick, J., Yde, J., Andreassen, L., James, W., Sutherland, J., Lee, E., Quincey, D., Boston, C., and Grimes, M.: Glaciers and ice caps under climate change since the Little Ice Age, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1170, https://doi.org/10.5194/egusphere-egu22-1170, 2022.

The artificial reduction of glacier melt is gaining increased attention due to accelerated ice wastage with atmospheric warming. In Switzerland, active coverage of glaciers using geotextiles is performed at currently ten sites and since more than 15 years. The measures represent an efficient method to locally safeguard the operability of ski slopes or other touristic attractions. Furthermore, ideas for large-scale technical interventions to save glaciers using artificially produced snow were proposed, with considerable resonance in the international media.

Here, an assessment of the benefit and applicability, as well as the costs and the drawbacks of different techniques to artificially reduce glacier melt is presented. On the one hand, observational data (in situ and remote sensing) across the Swiss Alps are used to analyze the efficiency and the spatial extent of the applied technical measures in the past. On the other hand, an integrative model approach is presented for investigating the potential of large-scale artificial snow production to limit the retreat of an entire glacier over the 21^st century, including an evaluation of the related costs and risks.

Presently, about 0.18 km², or 0.02% of the total Swiss glacier area, are covered by geotextiles, with a doubling of the covered area since 2012. Up to 350,000 m³ of ice melt per year have been mitigated by this technique. It is estimated that 1 m³ of saved glacier ice comes at a cost of 0.6 to 7.9 CHF m^-3 yr^-1, depending on the type of installation and its location on the glacier. These relatively high costs are an indication for the considerable economic value attributed to glacier ice.

It is shown that artificial melt reduction is not scalable. Whilst local interventions can be efficient and profitable, climate scenario-based model results for large-scale interventions indicate that saving Alpine glaciers by technological solutions is neither achievable nor affordable. It is a challenge to adequately communicate this gap between feasible local-scale ice-melt reduction, and the impractical technological 'saving' of entire glaciers to a broader public.

How to cite: Huss, M.: On the benefit and cost of artificial glacier melt reduction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1808, https://doi.org/10.5194/egusphere-egu22-1808, 2022.

Glacier monitoring has been internationally coordinated for more than 125 years. Despite this long history there is no unambiguous answer to the popular question: which are the world’s largest glaciers?

In this study, we present a first scientific assessment of the largest glaciers in the world – distinct from the two ice sheets in Greenland and Antarctica – and in the 19 regions used for the current Randolph Glacier Inventory. Ranking glaciers by size is non-trivial since it depends on how an individual glacier is defined and mapped. It is also important to differentiate between individual glaciers and glacier complexes, which are contiguous glaciers that meet at ice divides and might form an ice cap or ice field.

We find that the largest glacier complexes cover areas larger than ten thousand square kilometres, whereas the largest individual glaciers cover up to several thousand square kilometres. The world’s largest glaciers and glacier complexes are located on the Antarctic Peninsula, on sub-Antarctic Islands, in the Arctic, and in Patagonia. As such, the largest glacier complexes cover areas the size of smaller countries (e.g., Switzerland or Austria) or of smaller US states (e.g. New Jersey or South Carolina), but are still orders of magnitudes smaller than the Greenland and Antarctic Ice Sheets.

In addition, we show that the ranking of glaciers requires not only clear definitions but depends on the availability, quality, and consistency of digital glacier outlines at global scale. Corresponding additional metadata are required in the available inventories to fully automate a glacier ranking by area, and to extend such a study to rankings by length, volume/mass, and other parameters.

How to cite: Zemp, M., Windnagel, A., Hock, R., Maussion, F., Paul, F., Rastner, P., and Raup, B.: Which are the largest glaciers in the world outside the ice sheets?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2367, https://doi.org/10.5194/egusphere-egu22-2367, 2022.

The thermal regime of glaciers and ice caps represents the internal distribution of ice temperatures. Accurate knowledge of the thermal regime is crucial to understand the dynamics and response of ice masses to climate change, and to model their evolution. The ice temperature for example strongly controls the plasticity and the deformation rate of the ice with higher temperatures encouraging movement, and whether a glacier can slide over its base. Although the assumption is that most ice masses in the Inner Tien Shan are polythermal, this has not been examined in appropriate detail so far. In this research, we investigate the thermal regime of the Grigoriev ice cap and the Sary-Tor glacier, both located in the Inner Tien Shan in Kyrgyzstan. A 3D thermo-mechanical higher-order model is applied. Input data and boundary conditions are inferred from a surface energy mass balance model, a historical air temperature and precipitation series, ice thickness reconstructions, and digital elevation models. Calibration and validation of the englacial temperatures is performed using historical borehole measurements on the Grigoriev ice cap, radar measurements for the Sary-Tor glacier and temperature measurements on other glaciers in the area. The results of this study reveal a polythermal structure of the Sary-Tor glacier and a cold structure of the Grigoriev ice cap. The difference is related to the larger amount of snow (insulation) and superimposed ice (release of latent heat) for the Sary-Tor glacier resulting in higher surface temperatures, especially in the accumulation area, which are subsequently advected downstream. Further, ice velocities are much lower for the Grigoriev ice cap compared to the Sary-Tor glacier with consequent lower advection rates. Since the selected ice masses are representative examples of the (Inner) Tien Shan glaciers and ice caps, our findings can be generalised allowing this to improve the understanding of the dynamics and future evolution of the studied ice masses as well as other glaciers and ice caps in the region.

How to cite: Van Tricht, L. and Huybrechts, P.: Thermal regime of the Grigoriev ice cap and the Sary-Tor glacier in the Inner Tien Shan, Kyrgyzstan., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2818, https://doi.org/10.5194/egusphere-egu22-2818, 2022.

Icelandic glaciers have been losing mass since the Little Ice Age in the mid-to-late 1800s, with higher mass loss rates in the early 21^st century, followed by a slowdown since 2011. As of yet, it remains unclear whether this mass loss slowdown will persist in the future. By reconstructing the contemporary (1958-2019) surface mass balance of Icelandic glaciers, we show that the post-2011 mass loss slowdown coincides with the development of the Blue Blob, an area of regional cooling in the North Atlantic Ocean to the south of Greenland. This regional cooling signal mitigates atmospheric warming in Iceland since 2011, in turn decreasing glacier mass loss through reduced meltwater runoff. In a future high-end warming scenario, North Atlantic cooling is projected to mitigate mass loss of Icelandic glaciers until the mid-2050s. High mass loss rates resume thereafter as the regional cooling signal weakens. 

How to cite: Noël, B., Aðalgeirsdóttir, G., Pálsson, F., Wouters, B., Lhermitte, S., Haacker, J. M., and van den Broeke, M. R.: North Atlantic cooling is slowing down mass loss of Icelandic glaciers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3021, https://doi.org/10.5194/egusphere-egu22-3021, 2022.

The excess meltwater that results from climate change induced mass loss of mountain glaciers is an important contributor to sea level rise (SLR). Up to now, large scale glacier observations and models have been used to estimate the amount of generated excess meltwater and its transient contribution to SLR under the assumption that meltwater is added to the ocean instantaneously and in its entirety. However, hydrological processes and water consumption during the transit from glacier to the ocean may affect the amount and timing of glacier runoff that eventually drains into the ocean. We hypothesize that some of the lost glacier ice may not reach the ocean at all or only at a much later stage.

In this study, we assessed the impacts of the hydrological pathway of meltwater from the glacier snouts to the ocean in the Indus Basin. With its large glacier ice reserves, relatively arid climate and large irrigation scheme, this basin provides the optimal case study for such an assessment. We coupled output from a detailed glacier model to the fully distributed hydrological model PCR-GLOBWB 2, and forced the models with bias-corrected historical and future climate data from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3).

Our findings show that, particularly in (periods of) dry years, considerable fractions of excess glacier meltwater do not enter the ocean. The changes in hydrological stores indicate that much of it is withdrawn for surface water irrigation of cropland and eventually evaporates as a result. The increased surface water availability due to the presence of excess glacier meltwater leads to a lowering of groundwater irrigation and a reduction of the unsustainable depletion of the basin’s groundwater store. In the future, increased availability of excess glacier meltwater and increased water withdrawals due to continued climate change and socioeconomic developments exacerbate these effects. Up to the end of century, depending on the specific climate scenario, around 12% of excess glacier meltwater does not enter the ocean directly.

We conclude that not all glacier mass loss can be assumed to contribute (directly) to SLR, which may lead to overestimation of future sea level rise. Further research is necessary to estimate the breadth of these effects at a global scale, but we hypothesize that this may also play a role in other glacierized basins with semi-arid downstream regions and considerable distances between the glaciers and the ocean.

How to cite: Kraaijenbrink, P., Sutanudjaja, E., van de Wal, R., Bierkens, M., and Immerzeel, W.: Mass loss of mountain glaciers does not translate directly to sea level rise, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4166, https://doi.org/10.5194/egusphere-egu22-4166, 2022.

Glaciers are considered to be a biome with diverse microbial life, and their meltwaters are highly influential to downstream ecosystems by creating a unique riverine habitat template and providing resources such as nutrients and organic matter. Yet, despite unprecedented rates of glacial retreat globally, not much is known about the fate of microbial cells exported from glaciers, despite their potential to colonize and reside in downstream ecosystems. The influence of glacial meltwater on these downstream ecosystems may persist far downstream, but other sources of nutrients, organic matter, and microbial cells within the hydrological catchment likely gain influence with distance from the glacier. These include soils and thawing permafrost - partly via eroding stream banks - and benthic stream biofilms residing both within and outside the glacial environment (e.g. in tributary streams).

In this work, we ask how suspended microbial assemblages change with increasing distance from the source glacier, especially in terms of their composition and corresponding with abiotic environmental factors. We hypothesize that OTU richness will increase with distance from source glaciers as the importance of other catchment sources increase. Specifically, we expect ‘cryospheric’ OTUs to decrease in relative abundance, and more ‘generalist’ freshwater OTUs to increase. We sampled five glacier-fed streams (3 in the Austrian Alps, 1 in Iceland and 1 in Greenland) from the glacier terminus until the ocean or major riverine outlet. DNA was extracted from samples, and 16s rRNA gene amplicons were sequenced to characterize the assemblage structure. These preliminary observations improve our knowledge of the fate of glacially-exported microbial assemblages, and help us to understand the extent of their potential impact for downstream ecosystems, especially in the current age of deglaciation.

How to cite: Jachnická, K., Kohler, T. J., Vinšová, P., Falteisek, L., Singer, G., Vrbický, T., and Stibal, M.: Longitudinal patterns of suspended microbial assemblages in glacier-fed streams., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4281, https://doi.org/10.5194/egusphere-egu22-4281, 2022.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital glacier outlines, excluding the two ice sheets. It has become a pillar of glaciological research at global and regional scales for estimates of recent and future glacier changes, glacier mass balance, glacier contribution to sea-level rise, among others. The latest RGI version (V6) was released in July 2017.

Here, we present a new version of the RGI (version 7.0), which is our best estimate of global glacier outlines around the year 2000. Unlike previous versions which were compiled by an ad-hoc manual process using different sources, RGI7.0 is generated directly from the Global Land Ice Measurements from Space (GLIMS) glacier database, ensuring full traceability of single outlines to their original authors. The dataset is generated automatically with Python scripts parsing the GLIMS database and selecting outlines according to community decisions (based on data availability, quality and closeness to the year 2000). Prior to its release, the dataset was available for open review from the scientific community, and further refined as necessary.

About 70% of the outlines (30% of the total area) in RGI7.0 are obtained from new inventories that were submitted to GLIMS since the last release of RGI6.0 by different groups around the world. This led to considerable quality improvements especially in High Mountain Asia, Northern Canada, northern Greenland, Caucasus and Middle East, South America and New Zealand. RGI7.0 includes updated topographical and geometrical glacier attributes generated with a new community software. The new RGI generation process is open-source, fully reproducible and easily adaptable, making future updates straightforward to generate.

How to cite: Maussion, F., Hock, R., Paul, F., Rastner, P., Raup, B., Zemp, M., and Consortium, T. R.: The Randolph Glacier Inventory (RGI) version 7, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4484, https://doi.org/10.5194/egusphere-egu22-4484, 2022.

European Alpine glaciers have strongly shrunk over the last 150 years in response to climate warming. Glacier retreat is expected to persist and even intensify in future projections. This work aims at evaluating how much of the glacier retreat can be attributed to anthropogenic atmospheric forcings. For this purpose, we quantify the evolution of the Argentière glacier in the Mont Blanc area under different climate reconstructions over the period 1850-present. The different reconstructions are extracted from 4 ensemble experiments conducted with the IPSL-CM6-LR General Circulation Model (GCM), excluding and including natural and anthropogenic atmospheric forcings. These 6-member experiments are statistically corrected and downscaled with a quantile mapping approach that ensures consistent long term tendencies and precipitation-temperature relationship. These data feed a three-dimensional ice flow model coupled with a surface mass balance model to simulate changes in the glacier geometry over time. Over 1850-2014, historical simulations show a significant warming whereas there is no clear trend of precipitation at the annual scale. The glacier appears to be highly sensitive to individual anthropogenic forcings, with a glacier volume loss around 45% in the greenhouse gases-only experiment and a growth of about 5% in the aerosols-only experiment in 2014 relative to 1850, compared to the 32% volume loss over the same period in the historical experiment. Moreover, the natural-only experiment reveals the great impact of anthropogenic forcings with a much lower volume loss of about 10%. The latter also confirms that the end of the Little Ice Age would have occurred even without human activities. Finally, the simulations highlight a strong influence of natural internal variability and show that Argentiere Glacier definitively left its possible natural pathway only during the last decade.

How to cite: Clauzel, L., Gilbert, A., Ménégoz, M., and Gagliardini, O.: Sensitivity of Alpine glaciers to anthropogenic atmospheric forcings, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5099, https://doi.org/10.5194/egusphere-egu22-5099, 2022.

Fedchenko Glacier, located in the central Pamir in Tajikistan, is the longest glacier in Asia. Due to its prominent location and its large size, it attracted scientific interest over the course of the twentieth and twenty first centuries, providing thus a rare legacy of historical data in Central Asia. In this study, we investigate a series of topographic data from 1928 to 2019. We use topographic maps collected during historical expeditions in 1928 and 1958. We take advantage of modern satellite data, such as KH-9 spy satellite (1980), SPOT5 (2010) and Pléiades (2017 and 2019). We also rely on ICESat campaign of 2003 and numerous GNSS surveys conducted in 2009, 2015, 2016 and 2019, which ensures a proper co-registration of the satellite data.

We calculate a mean rate of elevation change of -0.40 m/yr for 1928-2019, with a maximum thinning at the lowermost locations (approaching -0.90 m/yr). Despite this long-term thinning trend, we observe large contrasts between the sub-periods. The thinning rate of the tongue doubled for two sub-periods (1958-1980 and 2010-2017) compared to the long-term average. The ERA5 reanalysis (1950-2020) and the Fedchenko meteorological station records (1936-1991) reveal a dry anomaly in 1958-1980, followed by a wet anomaly in 1980-2010, which might have compensated for the temperature increase and thus mitigated mass losses. This wet anomaly could be an important feature of the “Pamir-Karakoram” anomaly, characterized by limited glacier mass losses in this region during the early twenty-first century. Our work contributes to better constrain the decadal glacier thickness changes, with unprecedented temporal resolution. This opens the way for more sophisticated approaches that link the glacier response to climate variability over decades.

How to cite: Brun, F., Lambrecht, A., Mayer, C., Berthier, E., Dehecq, A., Rezaei, J., and Deschamps-Berger, C.: Multi-temporal elevation changes of Fedchenko Glacier, Tajikistan (1928-1958-1980-2010-2017-2019), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5833, https://doi.org/10.5194/egusphere-egu22-5833, 2022.

Glaciers in Central Asia provide essential water resources for an increasing socio-economic water demand. However, glacier ablation is spatio-temporally highly heterogeneous, revealing hot-spots of the complex glacier response to climate change. A darkening of glacier surfaces caused by varying sources ranging from light absorbing mineral particles and black carbon to organic matter such as algal bloom, impacts the surface energy balance of glaciers. The albedo of the bare-ice surface is particularly crucial in regard to the ablation magnitude.

In this study, we present across scale results of the dependence of glacier mass balance on surface albedo for a large number of glaciers in the Tien Shan and Pamir Mountains. We used over 3000 surface reflectance scenes from the Landsat suite over the last two decades to produce distributed albedo maps. Annual mass balance time series are modelled using a temperature-index and distributed accumulation model for each glacier and year individually. The modelled estimates are annually calibrated with transient snowlines and further constrained by multiyear geodetic mass balances.

A comprehensive analysis of albedo variability and trends is performed at varying scales, ranging from pixel to catchment. A relationship between the distributed albedo information and the detected trends with the mass balance rates and variabilities is established. We highlight the sensitivity of glacier mass balance on surface albedo and stress the importance of the enhanced albedo feedback that will be amplified due to atmospheric warming and suspected darkening of glacier surfaces in the near future. This feedback will accelerate glacier melt and thus put the availability of melt water to river run off at sustainable risk. 

How to cite: Naegeli, K. and Barandun, M.: The Albedo-Ablation couple: a complex relationship with severe consequences, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6364, https://doi.org/10.5194/egusphere-egu22-6364, 2022.

We have investigated the source and role of light absorbing impurities (LAI) deposited on the glaciers of the Olivares catchment, in Central Chile. LAI can considerably darken (lower ice albedo) the glacier surface, enhancing their melting. We combined chemical and mineralogical analyses of surface ice samples with field-based spectral reflectance measurements and laboratory analysis to investigate the nature and properties of LAI on the glacier surface. Using remote sensing-based albedo maps, we upscaled local information to glacier-wide coverage. We then used a model to evaluate the sensitivity of surface mass balance to a change in ice albedo. The across-scale surface sample analysis revealed a history of over half a century of LAI deposition. We found traces of mining residuals in glacier surface samples. The glaciers with highest mass loss in the catchment present enhanced concentrations of surface dust particles with low reflectance properties. Our results indicate that dust particles with strong light-absorbing capacity have been mobilized from anthropogenic sources and deposited on the nearby glacier surfaces, thus lowering their surface reflectance. Large scale assessment from satellite-based observations revealed darkening (ice albedo lowering) at most investigated glacier tongues from 1989 to 2018. Mass balance is sensitive to ice albedo changes. However, we believe that an accelerated winter and spring snow albedo decrease, triggered by surface impurities, might be responsible for the above-average mass balances encountered in this catchment.

How to cite: Barandun, M., Bravo, C., Grobety, B., Jenk, T., Fang, L., Naegeli, K., Rivera, A., Cisternas, S., Münster, T., and Schwikowski, M.: Anthropogenic Influence on Surface Changes at Olivares Glaciers, Central Chile, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7864, https://doi.org/10.5194/egusphere-egu22-7864, 2022.

The quantification of snow accumulation and the temporal evolution of the snow pack is essential when investigating the mass balance conditions of mountain glaciers. In particular, accumulation regions become smaller due to the gradual increase of the equilibrium line, thus reducing mass input into the glacier system. This will have severe consequences on ice flux and thus the mass balance conditions across many mountain regions worldwide. The mass redistribution within the accumulation regions is considerably influenced by migration of melt water in the snow and firn pack and the induced mass and density changes. Here, we study snow and firn processes at a high mountain accumulation plateau on 3470 m asl at Vernagtferner, Austria. Vernagtferner is a major glacier in the drainage basin of Rofenache, with an area of about 6.9 km², covering altitudes between 2900 m and 3550 m. A snow monitoring station, including an upward-looking ground penetrating radar (upGPR) was installed at the highest accumulation basin in 2018. This station allows the continuous determination of the snow pack stratigraphy and of the snow water equivalent (SWE) (Heilig et al. (2009, 2010), Schmid et al. 2014, Heilig et al., 2015). We compare numerical simulations of the 1-dimensional snow cover model SNOWPACK (Bartelt and Lehning, 2002), driven by automatic weather station data, with continuous observations of the installed upGPR system and bi-annual in-situ data. The analysed upGPR data enable continuous evaluation of the SNOWPACK simulations over several melt and accumulation seasons. The upGPR data show that even at high elevations frequent melt-freeze crusts develop during the accumulation period. Even though the crusts are several centimetres, melt water rapidly percolates trough these layers, once the snow pack reaches isothermal conditions in late spring. The simulation results demonstrate, that SNOWPACK is able to reproduce this fast advance of the melt front accurately, while the up-GPR measurements provide an independent proof of the model performance. These measurements also show that firn layers (previous summer surfaces) block water infiltration into depth only for a very short period, indicating that SWE measurements of glacier accumulation only provide realistic values, if carried out before or just at the onset of spring melt. This feasibility study provides important indication on how to extend such studies to larger glacier systems, also in less monitored regions, where in-situ data might be sparse.

How to cite: Lambrecht, A., Heilig, A., and Mayer, C.: Comparison of simulated and radar-determined accumulation and melt at a high glacier accumulation site in the Alps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8724, https://doi.org/10.5194/egusphere-egu22-8724, 2022.

Glaciers are a representative icon of the current climate change. They embody the three main aspects of this global phenomenon. They are (1) victims of the climate change, (2) an instrument of knowledge which allowed to better understand and address what is happening today, and (3) an important source of impact from climate change, both with respect to natural ecosystems and socio-economic activities.

One aspect related to the retreat of glaciers that is currently poorly investigated, is the consequence on the perception of the mountain environment and on our cultural heritage. Our approach to glaciers has deeply changed with time. During the Little Ice Age, they were regarded as a menace capable of destroying pastures and the highest settlements because of their advance. Then the view changes and glaciers became a sublime component of the landscape, interesting to know and study. Finally, glaciers turned into a source of entertainment for alpinists and tourists. Despite these different perspectives have somehow partially survived the passing of time, now the dominant perception of glaciers regards them as an endangered species. This is because of climate change and in many regions of Earth this vision will change soon: from endangered to extinct species (Carey, 2007).

Among the many environmental and socio-economic consequences, there is also the risk that with melting ice we will lose an important part of our culture. Retreating glaciers are sharing with us important messages, significantly contributing to strengthen the environmental awareness, what will happen when glaciers will be completely disappeared from whole mountain ranges? Will we be able to preserve what they have taught?

From this point of view the Dolomite represent an interesting laboratory to explore, ahead of other Alpine sectors, the effects of deglaciation in a renowned mountain range, with emphasis on the cultural impacts of glacier disappearance. These mountains, among the most famous and frequented of the Earth, hosted several small glaciers characterized by a notable morphological variety, but this glaciological heritage will soon disappear, as the Dolomites are expected to be ice-free in a few decades (Santin et al., 2019). There is a real risk that the Dolomite glaciers will vanish into silence and that with them we will also lose the stories of those who discovered, studied and attended those same glaciers. The aim of the present work is to oppose this fate, reviewing the recent history of Dolomitic glaciers and discussing the human and scientific significance of their demise.

References

• Carey (2007) The history of ice: how glaciers became an endangered species, Environmental History 12:497-527.
• Santin et al. (2019) Recent evolution of Marmolada glacier (Dolomites, Italy) by means of ground and airborne GPR surveys, Remote Sensing of the Environment 235:111442.

How to cite: Baccolo, G. and Varotto, M.: Mountains with no ice: deciphering the disappearance of glaciers in a renowned mountain range, the Dolomite case (Eastern Alps), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9821, https://doi.org/10.5194/egusphere-egu22-9821, 2022.

Glacier extents are mainly mapped by a semi-automated classification of multispectral satellite images (e.g. Landsat, Sentinel-2) with manual corrections of unmapped regions (e.g. ice in cast shadow or under debris cover). The quality of such corrections improve towards higher spatial resolution sensors, but such data were so far only seldom available for direct digitizing in a GIS. With the increasing availability of web map services (wms) such as the ESRI image service or national services the situation has strongly changed and first studies already analysed the potential of such services in geoscience.

The ESRI wms can be embedded into the professional mapping environment of ArcMap or QGIS. It provides mostly cloud and snow free mosaics of very high-resolution (0.31 - 0.5 m) GeoEye and Worldview images up to a scale of 1:5000. The images can be shown in the background as an information layer, but not further processed. The user has no control over the images provided (e.g. their acquisition date) or how they are mosaiced and orthorectified, locally resulting in snow covered or shifted images. The acquisition date and sensor used for each image part can be extracted using the information tool. Due to its recent availability, the ESRI wms has not yet been widely used and its huge potential especially for geomorphological and paleoglaciological mapping has still to be explored.

In this study, which is performed in the framework of the EU Horizon 2020 project PROTECT (protect-slr.eu) we present (1) a workflow for mapping Little Ice Age (LIA) glacier extents using the ESRI wms, (2) a detailed uncertainty analysis and (3) first results of glacier area changes since the LIA for selected regions in Alaska, Baffin Island, Novaya Zemlya and the tropics. Additionally to the ESRI wms, we used Sentinel-2 images, the ArcticDEM and modern glacier outlines from the Randolph Glacier Inventory (RGI). Geomorphological indicators (trim lines, moraines, vegetation free zones) and glaciological considerations were considered to guide the digitizing. Geolocation uncertainties were determined against independent data sources and the interpretation and reproduction uncertainties were quantified by multiple digitising experiments. The possible timing of the former LIA maximum extents was obtained to the extent possible from the literature, but here large uncertainties remain.

In total, outlines for 371 LIA glaciers were created and compared to today relative area changes of -20%, -15%, -26% and -58% were found for Alaska, Baffin Island, Novaya Zemlya and the tropics, respectively. Reproduction uncertainties were calculated for a sample of 18 glaciers to be on average 1.4 ±1.3%, interpretation uncertainties for a sample of 17 glaciers 1.9 ±10%. The digitization of LIA glacier extents with 10 m Sentinel-2 images is only rarely possible due to the difficulties identifying small scale moraines and resulted in much higher . We conclude that wms such as the ESRI World imagery layer provide, despite their shortcomings, an excellent opportunity to precisely map LIA maximum extents of glaciers around the world.

How to cite: Reinthaler, J. and Paul, F.: On the use of the ESRI image service for mapping Little Ice Age glacier extents, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9884, https://doi.org/10.5194/egusphere-egu22-9884, 2022.

Rocky debris covers 30% of glacier ablation areas in High Mountain Asia and generally suppresses melt. However, remote sensing observations have shown no statistical difference in glacier thinning rates between areas with and without debris cover; the ‘debris cover anomaly’. This pattern is apparent at subregional and regional scales, even after controlling for the elevation differences between debris-covered and clean ice. 

Two primary hypotheses to explain this behaviour have interpreted the thinning patterns in terms of melt or ice supply differences. First, rapid melt at supraglacial ponds and ice cliffs could enhance ablation in debris-covered areas, and therefore thinning as well. These features cannot entirely compensate for the melt reduction under debris, so a second hypothesis interprets the anomaly to indicate differences in emergence velocity between debris-covered and clean ice. However, complete understanding of the problem is challenged by a scale gap: the prior process studies have focused on single glaciers, whereas the anomaly has been identified for subregional- to regional spatial scales. Furthermore, these hypotheses neglect numerous other differences between debris-covered and clean glaciers (e.g. topo-climatic situation, accumulation mechanisms), which could bias this comparison.

We overcome these limitations through a direct assessment leveraging diverse large datasets and modelling. We firstly estimate emergence velocities and map ice cliffs and supraglacial ponds on a glacier-by-glacier basis across High Mountain Asia. We additionally assess other factors that could contribute to unexpected specific mass balance patterns: thin debris melt enhancement, distinct topo-climatic settings and the importance of avalanching for debris-covered ice. To determine the contribution of each factor to the debris-cover anomaly, we develop a statistical metric of how anomalous sub-debris ablation rates are, based on the difference in ablation rates between debris-covered and clean ice, as well as its altitudinal pattern. We use this metric and systematically remove the influence of the above hypothesized controls from each glacier’s emergence-corrected thinning data (specific mass balance) in a full-factorial investigation.

Our results firstly demonstrate that although emergence velocity differences between clean and debris-covered ice are systematic across the region, they do not resolve the debris-cover anomaly at the subregional or regional scale (altitudinal ablation rates are more negative for debris than clean ice). We find that accounting for any additional factor reduces the strength of the debris anomaly at regional and subregional scales, and our full-factorial analysis suggests that multiple factors combine to explain the debris cover anomaly.  Our results indicate that both hypotheses are correct in their process understanding at the glacier scale (reduced emergence velocity under debris, substantial ice cliff and pond ablation contribution), but not in their interpretation of the debris cover anomaly. Rather, our results underline previous suggestions that debris-covered glaciers fundamentally differ from clean ice glaciers in terms of mass supply mechanisms (i.e. supported by avalanching) and ablation patterns, leading to distinctive geometric expression and dynamics, and that the debris anomaly results from the integration of these patterns across scales.

How to cite: Miles, E., Kneib, M., McCarthy, M., Fugger, S., and Pellicciotti, F.: Disentangling the debris-cover anomaly in High Mountain Asia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11054, https://doi.org/10.5194/egusphere-egu22-11054, 2022.

The glacier katabatic wind system represents a feedback mechanism (de)coupling the glacier and the overlying atmosphere, altering the glacier microclimate.

Still, there are only a limited number of distributed measurements of the atmospheric conditions above the glacier surface. In August 2018, eight weather stations, partly with turbulence measurements at two levels, were installed in the middle and lower part of the Hintereisferner valley glacier in Austria, yielding three weeks of data on the near-surface spatial pattern of atmospheric conditions. These data are used to (a) quantify the observed properties of the glacier wind with regard to its spatial variability, persistence, and the synoptic conditions that erode it and (b) assess how well methods to extrapolate near-surface air temperature over glacier surfaces are influenced by the existence of the glacier wind and match the available observations on Hintereisferner.

Despite data limitations and uncertainties, results show that the glacier wind persists under most synoptic conditions, and deepens and speeds up downglacier. However, significant disturbances such as cold front passages and rain events can cause erosion of katabatic wind for periods from minutes to days. Representations of near-surface temperature distribution over the glacier using classical lapse rates and the along flow-line modified Greuell-Böhm model showed variable agreement to the measured data, with evidence for dependency on ambient atmospheric conditions. However, interpretations of the performance of temperature extrapolations should be viewed with caution due to the absence of observations in the upper glacier. We consider how these findings can be included in surface energy balance models of future glacier evolution, and conceptually how this aspect of the glacier microclimate, and the wider valley circulation, can be expected to evolve with continued glacier shrinkage.

How to cite: Nicholson, L., Stiperski, I., and Kehl, A.: Air temperature distribution and structure of katabatic wind on a shrinking mountain glacier, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12934, https://doi.org/10.5194/egusphere-egu22-12934, 2022.

Glaciers play a crucial role in the hydrological cycle, providing water in summer when it is most needed for irrigation. Global warming is leading to glacier retreat and enhanced summer runoff in the short-term, which should occur until glaciers become small enough that there is an end to this glacial subsidy and a reduction of summer runoff. However, debris accumulation, as it may alter the surface energy balance, will modify the rate at which this happens and may represent an important negative feedback. For this reason, quantifying and explaining glacier change in the Hindu Kush Himalaya (HKH) region, notably its relation to changing debris cover, is of paramount importance, especially for a country like Afghanistan with water resources highly dependent on glacial meltwater. This study assessed changes in glaciers of Afghanistan using data for 2000, 2007, 2017 and 2020 based upon the analysis of country-wide Landsat data and innovative indices for mapping both ice and debris-covered glacier extent.

Results showed 2862.5±47.8 km² of total glacier area in the year 2000, decreasing by 45.9 km²to 2007 (i.e. 6.55 km² per year), by a further 112.0 km² by 2017 (i.e. 11.2 km² per year), and by a further 73 km² (i.e. 24.3 km² per year) by 2020; that is there is a progressive increase in retreat rates. Of the 231.2 km² (8.07 %) loss of glacier surface area between 2000 and 2020, almost 81% related to glaciers with a size ≤ 2.01 km², which accounted for 50% of the total glacier area in the year 2000. Decreases were more dominant in center and northern regions of the country, whilst the northeastern region, the most glaciated part of the country, showed lesser changes. Increases in total debris cover area were found in the northeastern region of the country where there were lower decreases in total glacier area, whilst there were noticeable decreases in total debris cover area observed in southern and southeastern regionss and higher decreases in total glacier area. This suggested that the ability of the glaciers to produce debris cover has regional significance in explaining whether glacier loss occurs.

Ice elevation significantly changed over the time; changes in minimum ice elevations were up to +53 m, higher in the north, south, and southeastern regions. Maximum ice elevations decreased by -88 m, suggesting loss of accumulation zones. However, the northeastern part had a positive increase in maximum accumulation zone heights +23 m, this indicates possibility of increases in accumulation area.  

These results revealed differences in the regional response of Afghan glaciers to climate change. In the next stage of this work, we will link the spatial distributions of glacier response to downstream populations to identify those regions most exposed to the effects of these climate changes.

How to cite: Shokory, J. A. N. and Lane, S.: Glacier retreat and debris cover evolution in the Afghan Hindu Kush Himalaya between 2000 and 2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12976, https://doi.org/10.5194/egusphere-egu22-12976, 2022.

Glaciers in every region on Earth have lost mass over the past two decades as global temperature has risen 0.5C. Retreating glaciers symbolize climate change and present societal challenges across the globe. To better quantify the consequences of climate change, previous studies have established methods to calculate the anthropogenic component of extreme weather and climate events. Previously, we established a framework using existing event attribution methods together with glacier mass balance modeling to determine the increase in probability of extreme annual mass loss of New Zealand glaciers occurring with climate change. Here, we look to expand our developed attribution framework to calculate the change in probability and amount of extreme annual mass loss for glaciers around the world.

We develop the attribution method though application to several glaciers around the world, including South Cascade Glacier (USA), Gries Glacier (Switzerland), and Brewster Glacier (New Zealand). Our initial results show large increases in probability and amount of annual glacier mass loss occurring due to climate change for all three glaciers. Difficulties in applying the attribution framework to glaciers globally include accessing modern glacier outlines and reconciling differences between glaciological and geodetic measurements of glacier mass change.

How to cite: Vargo, L., Dadic, R., Anderson, B., Hock, R., Horgan, H., King, A., Mackintosh, A., and Marzeion, B.: Attribution of extreme annual glacier mass loss to anthropogenic forcing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13209, https://doi.org/10.5194/egusphere-egu22-13209, 2022.

Global climate model projections suggest that 21st century climate change will bring significant drying in the terrestrial midlatitudes. Recent glacier modeling suggests that runoff from glaciers will continue to provide substantial freshwater in many drainage basins, though the supply will generally diminish throughout the century. In the absence of dynamic glacier ice within global climate models (GCMs), a comprehensive picture of future drought conditions in glaciated regions has been elusive. We evaluate glacial buffering of droughts in the Standardized Precipitation-Evapotranspiration Index (SPEI), which we calculate by combining CMIP5 climate model output with glacial runoff projections from GloGEM.

We find that accounting for glacial runoff tends to increase multi-model ensemble mean SPEI (wetter baseline) and reduce drought frequency and severity, even in basins with glacier cover of <2% by area.  We also find that the strength and future trend of glacial drought buffering depends on basin aridity index and glacial cover, and does not depend on other characteristics such as total basin area or latitude.  Glacial drought buffering persists even as glacial runoff is projected to decline through the 21st century.

How to cite: Ultee, L., Coats, S., and Mackay, J.: Glacial drought buffering through the 21st century, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13295, https://doi.org/10.5194/egusphere-egu22-13295, 2022.

Satellite observations show rapid retreat of many outlet glaciers in West Antarctica, corresponding to a significant proportion of the contributions to global sea level rise in recent years. These changes have not been formally attributed to anthropogenic climate change, primarily because of the potential for positive feedbacks on ice sheet mass loss, which may have been triggered even within the limits of natural variability. This naturally leads to the attribution question: “how much more (or less) likely have anthropogenic changes made a specified contribution to sea level rise?” In this talk, I shall describe a Bayesian framework to address this question, which uses ensembles of many simulations with independent realizations of ice-sheet forcing with, and without, anthropogenic changes. Enhanced melting of ice shelves is thought to be the key forcing contribution responsible for recent retreat of the West Antarctic Ice Sheet; we include a consideration of the accuracy of melt rates in this framework by updating our prediction of sea level rise according to the agreement between the parametrized melt rate in the simulations and the output from a numerical ocean circulation model, at various time points. Experiments in an idealized setup point elucidate the dependence on the forcing timescale in the changes in likelihood of various contributions and demonstrate the feasibility of attribution studies for the Antarctic ice sheet.

How to cite: Bradley, A. T.: A Bayesian Framework for Anthropogenic Attribution of Sea Level Rise Contributions from the West Antarctic Ice Sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1118, https://doi.org/10.5194/egusphere-egu22-1118, 2022.

Stratospheric aerosol injection (SAI) has been proposed as a potential method of mitigating some of the adverse effects of anthropogenic climate change, including sea-level rise from the ice sheets. In this study, we use the SICOPOLIS (www.sicopolis.net) and Elmer/Ice (elmerice.elmerfem.org) dynamic models driven by changes in surface mass balance, surface temperature and ocean temperature (similar to ISMIP6-Greenland; Goelzer et al., 2020, doi: 10.5194/tc-14-3071-2020) to estimate the sea-level-rise contribution from the Greenland ice sheet under the IPCC RCP4.5, RCP8.5 and GeoMIP G4 (Kravitz et al., 2013, doi: 10.1002/2013JD020569) scenarios. The G4 scenario adds 5 Tg/yr sulfate aerosols to the equatorial lower stratosphere to the IPCC RCP4.5 scenario.

We simulate the mass loss of the Greenland ice sheet for the period 2015-2090 under the three scenarios with four earth system models, using SICOPOLIS with hybrid shallow-ice-shelfy stream dynamics and Elmer/Ice in the Elmer/Ice-sheet set-up with shelfy stream dynamics. For atmosphere-only forcing, the results from the two ice-sheet models are very similar. Relative to the constant-climate control simulations (CTRL), the losses from 2015 to 2090 are 64 [53, 80] mm SLE for RCP8.5, 46 [38, 53] mm SLE for RCP4.5 and 28 [18, 39] mm SLE for G4 (mean and full range). Thus, the mean mass loss under G4 is about 38% smaller than that under RCP4.5. For both models, the accumulated SMB is larger than the actual ice loss because, as the ice sheet recedes further from the coast, the mass loss due to calving is reduced. We　will also investigate the response of the ice sheet to ocean-only forcing and combined atmospheric and oceanic forcing.

How to cite: Greve, R., Moore, J. C., Zwinger, T., Gillet-Chaulet, F., Yue, C., Zhao, L., and Goelzer, H.: Reduced mass loss from the Greenland ice sheet under stratospheric aerosol injection, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3279, https://doi.org/10.5194/egusphere-egu22-3279, 2022.

Surface mass balance (SMB) is computed from regional climate models (RCM) using reanalysis data. Estimates of the SMB vary between RCMs due to differences such as the model set-up, physical parameterizations, and topography as well as ice mask. The ice mask in a model defines the surface covered by glacier ice. The differences in ice masks appear small, however it is here shown that it leads to important differences in SMB when integrated over the continent. To circumvent this area-dependent bias, intercomparison studies of modelled SMB use a common ice mask (Mottram et al., 2021). The SMB in areas outside the common ice mask is discarded. By comparing the native ice masks with the common ice mask used in Mottram et al. 2021 we find differences in integrated SMB of between 20.1 and 102.4 Gt per year over the grounded ice sheet when compared to the ensemble mean from Mottram et al. 2021. These differences are nearly equivalent to the entire Antarctic ice sheet mass imbalance identified in the IMBIE study.
SMB is particularly essential when estimating the total mass balance of an ice sheet via the input-output method, by subtracting ice discharge from the SMB to derive the mass change. We use the RCM HIRHAM5 to simulate the Antarctic climate and force a newly develop offline subsurface firn model, to simulate the Antarctic SMB from 1980 to 2017. We use discharge estimates from two previously published studies to calculate the regional scale mass budget. To validate the results from the input-output method, we compared the results to the gravimetry-derived mass balance from the GRACE/GRACE-FO mass loss time series, computed for the period 2002–2020. We find good agreement between the two input-output results and GRACE in West Antarctica, however, there are large disagreements between the two input-output methods in East Antarctica and over the Antarctic Peninsula. Over the entire grounded ice sheet, GRACE detects a mass loss of 900 Gt for the period 2002-2017, whereas the two input-output results show a mass gain of 500 Gt and a mass loss of 4000 Gt, depending on which discharge dataset is used. These results are integrated over the native HIRHAM5 ice mask. If we instead integrate over the common ice mask from Mottram et al. 2021, the results change from a mass gain of 500 Gt to a mass loss of 500 Gt, and a mass loss of 4000 Gt to a mass loss of 5000 Gt, over the grounded ice sheet for the period 2002-2017. While the differences in ice discharge remain the largest sources of uncertainty in the Antarctic ice sheet mass budget, our analysis shows that even a small area bias in ice mask can have a huge impact in high precipitation areas and therefore SMB estimates. We conclude there is a pressing need for a common ice mask protocol, to create an accurate harmonized updated ice mask.

How to cite: Hansen, N., Simonsen, S. B., Boberg, F., Forsberg, R., and Mottram, R.: Errors in Mass Balance estimates of Antarctica from ice mask and input-output inconsistencies, pinpointed by GRACE, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3838, https://doi.org/10.5194/egusphere-egu22-3838, 2022.

Surface Mass Balance (SMB) is the key driver of ice sheet mass budget. It delivers the snow that nourishes ice sheets and the surface melt that balances snowfall and, along with ocean interactions, drives ice flow. We here present alternative future projections for both the Greenland and Antarctic ice sheets, driven by two different earth system models (ESMs), EC-Earth and CESM2, for two different emissions pathways (SSP585, 245) and in the case of EC-Earth for two different CMIP versions (EC-EARTH2 inCMIP5 and EC-EARTH3 in CMIP6).

We use the regional climate model (RCM) HIRHAM5 to downscale the global models to 5.5km resolution over the Greenland ice sheet and 12km resolution over Antarctica. HIRHAM5 output is then used to drive a surface mass budget model for both ice sheets.

The matrix of models and scenarios gives us the opportunity to examine how different factors, including atmospheric circulation indices, model resolution, ocean dynamics, sea ice and SMB components affect mass budget and sea level rise estimates over the course of the 21^st century. About half the difference between CMIP5 and CMIP6 SMB estimates is related to differences in the scenarios compared to the SSPs and about half is related to differences in the driving models. In addition, we compare with other published downscaled SMB estimates from different RCMs (MAR and RACMO) to assess the envelope of likely ice sheet evolution out to 2100. Both CESM2 and EC-EARTH3 have high equilibrium climate sensitivity, and our study correspondingly shows high ice sheet mass loss particularly from Greenland by the end of the century, in line with other published estimates under high emissions scenarios. Melt is increasingly important in both ice sheets, but especially Greenland over the course of the 21^st century and scales by temperature and therefore emissions pathway. All model projections show an increase in precipitation, but internal variability in circulation in the Southern Ocean still dominates the patterns in Antarctica and masks to some extent climate change signal in SMB.

Future work will extend the ensemble of SMB estimate with a direct statistically based method, that allows fast downscaling of ESM output directly to SMB using the Copenhagen Ice Sheet Surface Energy and Mass Balance modEL (CISSEMBEL) and we also present some early preliminary results comparing different downscaling techniques.

How to cite: Mottram, R., Boberg, F., Hansen, N., Langen, P., Yang, S., Larsen, M., and Rodehacke, C.: A Tale of Two Ice Sheets, SSPs, CMIPs and global models: future climate and surface mass balance projections for Greenland and Antarctica, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4154, https://doi.org/10.5194/egusphere-egu22-4154, 2022.

Sea-level projections are usually calculated from numerical simulations using complex long-term numerical models (or a chain of models) as part of multi-model ensemble studies. Because of their importance in supporting the decision-making process for coastal risk assessment and adaptation, improving the interpretability of these projections is of great interest. Specifically, it is assumed that clear and transparent explanations of projected sea-level changes can increase the trust of the end-users, and ultimately favor their engagement in coastal adaptation. To this end, we adopt the local attribution approach developed in the machine learning community, and we combine the game-theoretic approach known as ‘SHAP’ (SHapley Additive exPlanation, Lundberg & Lee, 2017) with tree-based regression models. We applied our methodology to sea-level projections for the Greenland ice sheet computed by the ISMIP6 initiative (Goelzer et al., 2020) with a particular attention paid to the validation of the procedure. This allows us to quantify the influence of particular modelling decisions and to express the influence directly in terms of sea level change contribution. For Greenland, we show that the largest predicted sea level change, 19cm in 2100, is primarily attributable to >4.5cm (i.e. nearly 25%) to the choice of the model parameter that controls the retreat of marine-terminating outlet glaciers, i.e. to the modelling of the retreat rate of tidewater glaciers; other modelling decisions (choice of global climate model, formulation of the ice sheet model ISM, model grid size, etc.) have only a low-to-moderate influence for this case (with contribution of 1-2cm). This type of diagnosis can be performed on any member of the ensemble, and we show how the aggregation of all local attribution analyses can help guide future model development as well as scientific interpretation, particularly with regard to model spatial resolution or the selection of a specific model formulation.

This study was supported by the PROTECT project, which received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 869304.

References

Goelzer, H., et al. (2020). The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6. The Cryosphere 14, 3071-3096.

Lundberg, S.M., & Lee, S.I. (2017). A unified approach to interpreting model predictions. In Proceedings of the 31st international conference on neural information processing systems (pp. 4768-4777).

How to cite: Rohmer, J., Thieblemont, R., Le Cozannet, G., Goelzer, H., and Durand, G.: Improving interpretation of sea-level projections through a machine-learning-based local explanation approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5252, https://doi.org/10.5194/egusphere-egu22-5252, 2022.

Full-Stokes (FS) ice sheet models provide the most sophisticated formulation of ice sheet flow. However, their ap- plicability is often limited due to the high computational demand and numerical challenges. To balance computational demand and accuracy, the so-called Blatter-Pattyn (BP) stress regime is frequently used. Here, we explore the dynamic consequences by solving FS and the BP stress regime applied to the Northeast Greenland Ice Stream. To ensure a consistent comparison, we use one single ice sheet model to run the simulations under identical numerical conditions. A sensitivity study to the horizontal grid resolution (from 12.8 down to 0.1 km) reveals that velocity differences between the FS and BP solution emerge below ∼1 km horizontal resolution and continuously increase with resolution. Over the majority of the modelling domain both models reveal similar surface velocity patterns. At the grounding line of 79° North Glacier the simulations unveil considerable differences whereby BP overestimates ice discharge of up to 50% compared to FS. A sensitivity study to the friction type reveals that differences are stronger for a power-law friction than a linear friction law. Model differences are attributed to topographic variability and the basal drag since neglected stress terms in BP become important.

How to cite: Humbert, A., Kleiner, T., and Rückamp, M.: Comparison of ice dynamics using full-Stokes and Blatter-Pattyn approximation: application to the Northeast Greenland Ice Stream, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5459, https://doi.org/10.5194/egusphere-egu22-5459, 2022.

Over the last decades, the Antarctic Ice Sheet (AIS) has been losing mass, mainly through ice discharge and sub-shelf melting (Rignot et al., 2019). More specifically, recent observations show that the AIS is currently losing mass at an accelerating rate in areas subject to strong ocean-induced melt. At the same time, no long-term trend in snowfall accumulation changes can be detected in the interior of the ice sheet. Due to these current trends, basal melting has often been considered as the main driver of future Antarctic mass loss. However, even though stronger basal melting of ice shelves is projected to drive future AIS mass loss, recent studies (e.g. Seroussi et al., 2020) have shown that surface mass balance (SMB, the balance of accumulation through snowfall and ablation through erosion, sublimation and runoff) has a strong potential in controlling the future stability and evolution of the Antarctic Ice Sheet. With increasing temperatures, SMB is expected to increase in Antarctica in the future as a result of enhanced snowfall. As long as the warming remains modest, other AIS SMB components (such as runoff) will likely continue to play a minor role in future SMB changes (Lenaerts et al., 2019; Kittel et al., 2021). Under high-emission scenarios, however, future runoff is likely to significantly compensate for mass gain through snowfall (Kittel et al 2021). The balance between these competing processes is still a matter of debate and, as of yet, there is no consensus on estimates of the future mass balance of the Antarctic Ice Sheet (Seroussi et al., 2020).

Here, we investigate the relative importance of SMB changes and ocean-induced melt on the long-term (multi-centennial to multi-millennial) AIS response as well as their associated uncertainties. To do so, we force two ice sheet models (fETISh and PISM) with atmospheric and oceanic projections inferred from a subset of models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under the Shared Socioeconomic Pathways (SSP) 5-8.5 and SSP1-2.6. Changes in precipitation rate and air temperature are corrected for elevation changes and used as inputs to a positive degree-day scheme which estimates changes in snowfall, rainfall and surface runoff. Climate projections are used as forcing until the year 2300 and afterwards no climate trend is applied, allowing to investigate the long-term impacts of early-millennia warming (often called sea-level commitment).

Taking into account key uncertainties in both atmospheric and oceanic forcing, our results predict that atmosphere-ice surface interactions will have an important role on the AIS stability under high-end future emission scenarios. We also show the increasingly important role of the melt-elevation feedback for multi-centennial projections of the AIS. Finally, we find that modelling choices regarding the atmosphere forcing have a significant influence on the future sea-level contribution from the AIS under high-end emission scenarios, leading to a spread from a few centimeters to several meters contribution over the coming millennia.

How to cite: Coulon, V., Klose, A. K., Kittel, C., Pattyn, F., and Winkelmann, R.: Influence of surface mass balance on the high-end sea-level commitment from the Antarctic Ice Sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5983, https://doi.org/10.5194/egusphere-egu22-5983, 2022.

The elevation feedback on melt has been identified as a key process to explain (Gregoire et al, Nature, 2012) and project (Aschwanden et al., Sci Advances, 2019; Ridley et al, J Clim, 2005; Vizcaino et al, Clim Dyn, 2008) long-term deglaciation. It is also central to the theory of ice sheet evolution hysteresis, deglaciation thresholds/tipping points, and the problem of reversibility (Garbe et al, Nature, 2020; Gregory et al, TC, 2020; Gregory et al, Nature, 2004, Robinson et al, Nature Clim. Change, 2012). Ice-sheet-model-only estimates of this feedback rely on a largely unexplored parameter, the so-called “lapse rate”. This parameter is defined as the rate at which the near-surface atmospheric temperature changes strictly due to surface elevation change.

In this work, we use coupled an uncoupled ice sheet and climate simulations with two different General Circulation/Earth System Models (Vizcaino et al, GRL, 2015; Muntjewerf et al, JAMES, 2019) to estimate the temperature lapse rate over the Greenland ice sheet as it deglaciates. We find that this lapse rate is highly variable over seasons, with much reduced lapse rates during summer over melting surfaces. We propose that uncoupled  state-of-the-art projections are likely overestimating deglaciation rates due to too high summer lapse rates over the ablation area.

How to cite: Vizcaino, M., Mikolajewicz, U., and Sellevold, R.: Overestimation of elevation-melt feedback in uncoupled projections of ice sheet mass loss, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7420, https://doi.org/10.5194/egusphere-egu22-7420, 2022.

Mass loss from the Greenland Ice Sheet ice sheet has increased sixfold since the 1990s. With accelerated ice mass loss rates, it could become the largest contributor to sea-level rise in the 21^stcentury. Both the surface mass balance and outlet glacier retreat control this ice mass loss. The latter is decomposed between ice flow changes in the lower trunks of outlet glaciers (discharge) and calving of marine-terminating outlet glaciers. Partitioning between SMB and retreat contributors evolved through the last decade. It is uncertain how much they will contribute individually in the future. While a coupled RCM-ice sheet model helps to improve the SMB contribution, future glacier retreat contribution modelling is in its early stages. Using the RCM MAR, fully coupled to the GISM ice sheet model, we investigate the impact of the surrounding ocean on the outlet glaciers. Our parameterization, based on oceanic basins temperature and subglacial ice sheet runoff changes, simulates individual outlet glacier retreat rate. By forcing our atmosphere – GrIS – ocean-retreat-like model by several CMIP6 GCM models, we assess the 21^stcentury Greenland ice mass loss. Partitioning between mass loss from SMB and outlet glacier retreat forced by various CMIP6 SSP scenarios is estimated both at the regional and large Greenland scale.

How to cite: Le clec'h, S., Fettweis, X., and Huybrechts, P.: Quantifying 21st century Greenland ice mass loss from outlet glacier retreat and surface mass balance changes from coupled MAR-GISM simulations., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7883, https://doi.org/10.5194/egusphere-egu22-7883, 2022.

With a sea-level rise potential of 58 m sea-level equivalent, the future evolution of the Antarctic Ice Sheet under progressing warming is of importance for coastal communities, ecosystems and the global economy. Short-term projections of the sea-level contribution from Antarctica in the recent ice sheet model intercomparison ISMIP6 range from a slight mass gain (-7.8 cm) to a mass loss of up to 30.0 cm sea-level equivalent at the end of the century under Representative Concentration Pathway 8.5 (Seroussi et al., 2020, Edwards et al., 2021). However, due to high inertia of the system, the ice sheet response to perturbations in its climatic boundary conditions are rather slow. Consequences of potentially triggered unstable ice loss due to positive feedback mechanisms may therefore play out over long timescales (on the order of millennia).  Projections of the committed sea-level change at a given point in time, that is the sea-level change which arises by fixing the climatic boundary conditions and letting the ice sheet evolve over several millennia, might differ substantially from the sea-level change expected at that point in time (Winkelmann et al., 2022).

Previous assessments of the long-term contribution to sea-level rise from the Antarctic Ice Sheet have been primarily restricted to a single model and have rarely explored the full range of intra- and inter-model parameter uncertainties. Here, we determine the long-term, multi-millennial sea level contribution from mass balance changes of the Antarctic Ice Sheet by means of two ice sheet models, the Parallel Ice Sheet Model (PISM) and the fast Elementary Thermomechanical Ice Sheet (f.ETISh) model. More specifically, we assess the response of the Antarctic Ice Sheet to atmospheric and oceanic forcing conditions derived from state-of-the-art climate model projections available from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under the Shared Socioeconomic Pathways SSP5-8.5 and SSP1-2.6 available until the year 2300. The sea-level commitment from the Antarctic Ice Sheet is quantified by branching off at regular intervals in time and running the ice sheet models for several millennia under fixed climate conditions. Key uncertainties related to ice dynamics as well as to interactions with the bed, atmosphere and ocean are taken into account in an ensemble approach.

How to cite: Klose, A. K., Coulon, V., Pattyn, F., and Winkelmann, R.: The long-term sea-level commitment from Antarctica, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7964, https://doi.org/10.5194/egusphere-egu22-7964, 2022.

We are using an ice sheet model that explicitly represents individual layers of accumulation that are fixed in time (isochronal). With progressing time, new layers are added on the top, while older layers subside and become thinner as ice flows towards the margins. This approach eliminates unwanted diffusion and faithfully represents the englacial stratification.

The isochronal model is coupled uni-directionally to a full ice sheet model (“host model”), which provides the ice physics and dynamics. Via the isochronal model’s layer tracking, the host model’s output can be evaluated throughout the interior using the radiostratigraphy data set of the Greenland ice sheet.

We investigate the stability and resolution-dependence of this coupled modeling system in simulations of the last glacial cycle with yelmo as the host model. One key question concerns how frequent updates from the host model must be to ensure a reliable simulation. This could enable offline forcing of the isochronal model with output from a range of existing ice sheet models.

The long-term goal is to make the isochronal model flexible and easily adaptable enough to effectively force it with existing full ice sheet models and to provide it to the community as a new way to assess the models’ performance. 

How to cite: Rieckh, T., Born, A., and Robinson, A.: Layer Tracing of the Greenland Ice Sheet Interior: A Coupled Model Approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8432, https://doi.org/10.5194/egusphere-egu22-8432, 2022.

There are large uncertainties in model predictions of the Antarctic Ice Sheet (AIS) contribution to future sea level rise. One source of model uncertainty is the description of basal friction. Here, we implement a new basal sliding law, developed by Zoet and Iverson (2020), in an updated version of the Community Ice Sheet Model (CISM). The Zoet-Iverson sliding law combines properties of two previously used sliding laws: power-law behavior in areas with slow-moving ice and coulomb-law behavior in fast-moving ice streams and outlet glaciers. We adress the behavior and performance of the Zoet-Iverson law in CISM using AIS spin-up procedures developed for the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We invert a non-dimensional coefficient in the Zoet-Iverson law to match modelled and observed thickness for grounded ice. Ocean temperatures are tuned to nudge ice-shelf thickness via the basal melt rates. These tuning processes are Antarctic-wide, but we focus on the Amundsen Sea region. We then advance the model forward to better represent the present-day Thwaites glacier, by inverting for observed ice velocity and by changing the ocean forcing. The main results from this run are the sub-shelf ocean temperature perturbation, thickness, and velocity profile of Thwaites glacier. Results are compared with different sliding laws to demonstrate the effect of the Zoet-Iverson law on the representation of the ongoing retreat. 

Zoet, L.K. & Iverson, N.R. (2020). A slip law for glaciers on deformable beds. In: Science 368 (6486), pages 76-78. DOI: 10.1126/science.aaz1183

How to cite: van den Akker, T., Lipscomb, W. H., Leguy, G. R., van de Berg, W. J., and van de Wal, R. S. W.: Implementation of the Zoet-Iverson basal sliding law in CISM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8882, https://doi.org/10.5194/egusphere-egu22-8882, 2022.

We use the diurnal Energy Balance Model (dEBM) in combination with ERA5 reanalysis forcings to simulate the surface mass balance (SMB) of the Greenland Ice Sheet (GrIS). The dEBM (Krebs-Kanzow et al., 2021) is based on the energy balance of glaciated surfaces. In contrast to most empirical schemes, it is physics based and accounts for variations in the radiative forcing due to changes in the Earth's orbit and atmospheric composition. The dEBM scheme only requires monthly atmospheric forcing (precipitation, temperature, shortwave and longwave radiation and cloud cover) and is computationally inexpensive, which makes it particularly suitable to investigate the response of ice sheets to long-term climate change. After calibration and validation, we investigate the contribution of individual climate forcings (temperature, precipitation, clouds and radiation) to the interannual SMB variability.                     

Furthermore, we compare 1979-2016 ERA5 and ERA-Interim with respect to the main atmospheric drivers of the melt season over the GrIS. In summer, ERA5 differs remarkably from ERA-Interim: averaged over the lower parts of the GrIS, the mean near-surface temperature is 1 K lower while mean downward shortwave radiation at the surface is on average 15W/m^2 higher than in ERA-Interim. In consequence those methods which hitherto have estimated the GrIS surface mass balance from the ERA-Interim surface energy balance need to be carefully recalibrated before they can be progressed to ERA5 forcing.

Krebs-Kanzow, U., Gierz, P., Rodehacke, C. B., Xu, S., Yang, H., and Lohmann, G., 2021: The diurnal Energy Balance Model (dEBM): a convenient surface mass balance solution for ice sheets in Earth system modeling, The Cryosphere, 15, 2295–2313, https://doi.org/10.5194/tc-15-2295-2021.

How to cite: Krebs-Kanzow, U., Rodehacke, C., and Lohmann, G.: The 1950-2020 variability of the Greenland Ice Sheet surface mass balance, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9957, https://doi.org/10.5194/egusphere-egu22-9957, 2022.

How to cite: Oien, R., Nowicki, S., and Csatho, B.: The pre-industrial digital elevation model of the Greenland Ice Sheet from the 17th and 18th Centuries , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10811, https://doi.org/10.5194/egusphere-egu22-10811, 2022.

Large efforts are engaged to model climate-ice sheet interactions in order to estimate Antarctic and Greenland ice sheets’ contribution to sea level in the next decades to centuries. Here we present a first-order evaluation of CMIP5 and CMIP6 climate models over both polar regions. We focus on large-scale atmospheric fields and surface ocean variables only. Our goal is to provide a first overview of climate model biases in polar regions, in order to use their outputs on an informed basis. We particularly target the use of climate model outputs for forcing ice sheet models and regional atmospheric models.

We consider 9 (non-independent) variables : 850 hPa and 700 hPa annual and summer temperature, annual integrated water vapor, annual sea level pressure, annual 500hPa geopotential height, summer sea surface temperature, and winter sea ice concentration; over the Arctic (> 50°N) and the Antarctic (<40°S) regions. We use the ERA5 reanalysis as a reference, but we also include 5 other reanalyses in the intercomparison in order to estimate uncertainty coming from this choice. We define two sets of metrics. The first set of metrics, called “scaled rmse”, is the spatial root mean square error (RMSE) of time-mean variables for each region, that we divide by the median RMSE among all CMIP models. The second set of metrics, called “implausible fraction”, is the portion of the region where the difference between time-mean CMIP model and time-mean ERA5 is greater than three times the local interannual standard deviation. We find a strong relationship between the two sets of metrics. In addition, using the implausible fraction, we find that CMIP variables are significantly more implausible in the Antarctic than in the Arctic. It might be because of badly resolved processes or because of higher decadal variability in the South. Further work should include estimates of decadal variability in the implausibility computation.

How to cite: Agosta, C., Kittel, C., Amory, C., Edwards, T., and Davrinche, C.: Evaluation of CMIP5 and CMIP6 global climate models in the Arctic and Antarctic regions, atmosphere and surface ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11363, https://doi.org/10.5194/egusphere-egu22-11363, 2022.

This study investigates the sensitivity of modeled surface melt and subsurface heating on the Antarctic ice sheet to a new spectral snow albedo and radiative transfer scheme in the Regional Atmospheric Climate Model (RACMO), version 2.3p3 (Rp3). We tune Rp3 to observations by performing several sensitivity experiments and assess the impact on temperature and melt by incrementally changing one parameter at a time. When fully tuned, Rp3 compares well with in situ and remote sensing observations of surface mass and energy balance, melt, near-surface and (sub)surface temperature, albedo and snow grain specific surface area. Near surface snow temperature is especially sensitive to the prescribed fresh snow specific surface area and fresh dry snow metamorphism. These processes, together with the refreezing water grain size and subsurface heating, are important for melt around the margins of the Antarctic ice sheet. Moreover, small changes in the albedo and the aforementioned processes can lead to an order of magnitude overestimation of melt, locally leading to runoff and a reduced surface mass balance.

How to cite: van Dalum, C., van de Berg, W. J., and van den Broeke, M.: Antarctic surface climate in RACMO2.3p3, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12543, https://doi.org/10.5194/egusphere-egu22-12543, 2022.

Over recent decades, a significant increase in the amount and the size of glacier lakes has been observed. These lakes enhance glacier mass loss but also present societal hazard as they may retain large volumes of water. When large lakes drain, the downstream valleys can severely be impacted by the resulting glacial lake outburst floods (GLOFs), potentially leading to infrastructural damage and ecological impacts. Most studies assessing the future evolution and potential hazards from glacial lakes focus on proglacial lakes, i.e. lakes that are dammed by either moraines or bedrock. Albeit typically more hazardous, ice-dammed lakes including supraglacial lake are generally neglected in such assessments. 

Here, we assess for the first time the formation and development of potential ice-dammed lakes for all glaciers in High Mountain Asia. To do so, we model the geometry of each glacier by linking past digital elevation models to outputs of the combined glacier mass balance, ice flow and debris evolution model GloGEMflow. We identify potential ice-dammed lakes in depressions at the surface and margins of glaciers, and model their geometrical evolution by accounting for the enhanced melt caused by the lakes’ presence. The model is calibrated and evaluated with independent datasets. 

To analyze the ice-dammed lakes’ sensitivity to climate change, we model the evolution of glaciers and their ice-dammed lakes under different Shared Socioeconomic Pathways (SSPs). Our results indicate that the total number of potential ice-dammed lakes will first increase through time, and then diminish as glaciers shrink, reducing confining barriers. Compared to 2000, a moderate warming scenario (SSP126) anticipates approx. 42% more lakes by 2050, whilst in a strong warming scenario (SSP585), the increase is of ~46%. By the end of this century, the number of ice-dammed lakes will diminish compared to the 2050 peak by approx. 16%  (SSP126) and ~42% (SSP585) due to glacier shrinkage. The same pattern is also expected for the lakes’ volume evolution, which is expected to increase compared to 2000 between ~79% (SSP119) and ~87% (SSP585) by 2050, for then diminish by about 8% by the end of the century for SSP585 compared to 2050.  Finally, by investigating the largest ice-dammed lakes, we highlight regions that could be of particular relevance when aiming at anticipating future GLOFs from ice-dammed lakes.

How to cite: Compagno, L., Huss, M., Zekollari, H., Miles, E., and Farinotti, D.: Anticipating future ice-dammed lakes across High Mountain Asia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-24, https://doi.org/10.5194/egusphere-egu22-24, 2022.

In the northern Japanese Alps, more than 100 perennial snow patches exist (Higuchi and Iozawa, 1971)．Recently, several groups measured the ice thickness and horizontal flow velocity of seven perennial snow patches in the region, finding them to be active glaciers (e.g., Arie et al., 2019). As they are less than 0.5 km² in area, they are classified as very small glaciers (VSGs). According to Arie et al. (2021), who observed the mass balance using geodetic methods from 2015 to 2019, 1) the fluctuation of the annual mass balance of Japanese VSGs was highly dependent on yearly fluctuation in accumulation depth, 2) the mass balance amplitude was the largest of all glaciers in the world recorded by WGMS, 3) VSGs can be formed only in terrains where avalanches and snowdrifts can acquire more than double the snowfall. However, for avalanches and snowdrifts in 3), the specific topographic conditions that indicate the magnitude of these contributions are not clear. Hughes (2009) found that the contribution of avalanches to the glacier is large where the "avalanche ratio," which is the ratio of total avalanche discharge area to total glacier area, is high.
 
In this study, we compared the avalanche ratio, distribution altitude, and slope direction of the seven confirmed VSGs, seven large perennial snow patches (over 10,000 m²), and three small perennial snow patches (under 1000 m²) to show the topographic conditions for the formation of glaciers and perennial snow patches in the northern Japanese Alps. As a result, there was a positive correlation between the average snow depth of VSGs calculated by the geodetic method from 2015 to 2021 and the avalanche ratio. A negative correlation was seen between the avalanche ratio and distribution altitude in the VSGs, and the lower the altitude, the higher the avalanche ratio. In addition, the relationship between avalanche ratio and distribution altitude showed that the avalanche ratio of VSGs and large perennial snow patches were larger than that of small perennial snow patches at the same altitude. The avalanche ratio of Ikenotan Glacier, which is the only glacier on the windward slope with no snowdrift, was more than twice as large as that of VSGs at the same altitude. These results suggest that the magnitude of the contribution of avalanche and snowdrift deposition and the distribution altitude determine the size of glaciers and perennial snow patches.
 
Arie, K., Narama, C., Fukui, K., Iida, H. and Takahashi, K.: Ice thickness and flow of the Karamatsuzawa perennial snow patch in the northern Japanese Alps, Journal of the Japanese Society of Snow and Ice, 81(6), 283–295, doi:10.5331/seppyo.81.6_283, 2019.
Arie, K., Narama, C., Yamamoto, R., Fukui, K. and Iida, H.: Characteristics of mountain glaciers in the northern Japanese Alps, cryosphere, 1–28, doi:10.5194/tc-2021-182, 2021.
Higuchi, K. and Iozawa, T.: Atlas of perennial snow patches in central Japan, Water Research Laboratory. Faculty of Science, Nagoya University., 1971.
Hughes, P. D.: Twenty-first Century Glaciers and Climate in the Prokletije Mountains, Albania, Arct. Antarct. Alp. Res., 41(4), 455–459, 2009.

How to cite: Arie, K. and Narama, C.: Topographic conditions for the formation of glaciers and perennial snow patches in the northern Japanese Alps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-131, https://doi.org/10.5194/egusphere-egu22-131, 2022.

Alpine glacier retreat has increased markedly since the late 1980s and is commonly linked to the effects of rising air temperature on surface melt. Less considered are processes associated with glacier snout-marginal surface collapse. A survey of 22 retreating Swiss glaciers suggests that collapse events have increased in frequency since the early 2000s, driven by ice thinning and reductions in glacier-longitudinal ice flux.

Detailed measurement of a collapse event at one glacier with Uncrewed Aerial Vehicles and ablation stakes showed 0.02 m/day vertical surface deformation above a meandering main subglacial channel, the planform of which was mapped with Ground Penetrating Radar measurements. However, with low rates of longitudinal flux (<1.3 m/year), ice creep was insufficient to close the channel in the snout marginal zone. We hypothesize that an open channel maintains contact between subglacial ice and the atmosphere, allowing greater incursion of warm air up-glacier, thus enhancing melt from below. The associated meandering of subglacial channels at glacier snouts leads to surface collapse due to erosion and internal melt as well as removal of ice via fluvial processes.

How to cite: Egli, P., Belotti, B., Ouvry, B., Irving, J., and Lane, S.: Subglacial channels, climate warming and increasing frequency of alpine glacier snout collapse, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3638, https://doi.org/10.5194/egusphere-egu22-3638, 2022.

Various techniques exist to model the evolution from glaciers at regional to global scales. Whereas pioneering efforts typically relied on volume-area scaling approximations or parameterizations based on observed glacier changes (retreat parameterization), more recent approaches now also explicitly incorporate ice-dynamical processes. In these latter studies, glaciers are typically represented through central flowlines. Such flowline approaches are particularly suited for mountain glaciers that span over a large elevation range, i.e. valley-glaciers with an elongated shape. However, flowline approaches are not ideal to represent the geometry of ice caps (large glaciers) that generally have a dome-shaped geometry. For ice caps, a model representation that explicitly accounts for the glacier’s 3D geometry and that allows for the glacier to lose and gain mass in all directions, both through mass balance and ice dynamic processes, is needed.

Here we present simulations performed with a coupled surface mass balance – ice flow model that explicitly accounts for the 3D geometry of individual glaciers. The model, written in Python, relies on the shallow ice approximation to describe ice flow, allowing to run large ensembles of simulations. The goal is to simulate the temporal evolution of glaciers with distinct shapes and situated in various climatic regimes, i.e. having a model that allows for an automated intialization and that is suited for regional to global-scale applications.

In this contribution, we present simulations performed with this new large-scale model for regions with mountain glaciers (e.g. European Alps and Scandinavia), as well as regions with large ice caps (e.g. Iceland). Through this, we highlight various challenges that relate to model initialization or the choice of model settings, for instance. We also explore how simulated glacier evolutions compare to those simulated with a retreat parameterzation and through flowline modelling, thereby shedding light on the need for a 3D modelling approach.

How to cite: Zekollari, H., Huss, M., Compagno, L., Pattyn, F., Goelzer, H., Lhermitte, S., Wouters, B., and Farinotti, D.: Modelling the 3-D evolution of glaciers at regional to global scales: challenges and opportunities, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4231, https://doi.org/10.5194/egusphere-egu22-4231, 2022.

Concepts and ideas related to implementation of calving in large-scale ice-sheet models are presented and discussed, and new model verification experiments proposed. For unconfined ice shelves, any calving law where the calving rate increases with cliff height (free board) must lead to an unstable advance or retreat. No other solutions are possible and all calving front positions are always unstable. If in contrast, calving rate is a monotonically decreasing function of cliff height, both stable and unstable positions are possible. An example of such a configuration and simple analytical solution for the transient evolution of the calving front is provided, which can be used for numerical verification purposes. It is argued that cliff-height based calving laws are, at least for the case of buttressed ice shelves, arguably unphysical as they can result in a multi-valued function for the calving rate as a function of local state of stress. Implementation of a new variational form of the level-set method, involving forward-and-backward diffusion, for capturing the evolution of calving fronts is discussed and several applications to Pine Island and Thwaites glacier shown.

How to cite: Gudmundsson, G. H.: Implementation of calving processes in large-scale ice sheet models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4263, https://doi.org/10.5194/egusphere-egu22-4263, 2022.

In most models of large-scale ice-sheet dynamics, horizontal density variations within the ice are largely ignored. Ice-sheets typically comprise a core of meteoric ice, and an overlying layer of lower-density firn of variable thickness. This gives rise to spatial variation in the average density of the ice at each point on the surface, which in principle will modify the glacial dynamics.  A common approach to handle density-variation in the ice is to adjust the thickness of the glacier to the equivalent height of constant-density meteoric ice. We refer to this as the density-to-thickness (D2T) adjustment method. While this approximation preserves the total mass of the ice-column at each spatial coordinate, it introduces additional unwanted terms in the momentum equations, and misses other correction terms. 

In this study, we investigate the D2T adjustment approximation in detail, and consider a number of alternative formulations to handle the density variation in the ice-sheet, based around the modified field equations that we derive in the presence of a variable density field. The alternative formulations include: a static density distribution in which accumulation and compactfication of the firn layer counteracts the advection of the density field so that the time-evolution of the density field can be ignored; or alternatively a time-evolving density distribution with advects with the ice-flow, such that the material derivative of the density field is zero. 

These different formulations are studied in detail within the framework of perturbation analysis. We derive transfer functions for the induced perturbations in both the glacial thickness and velocity, in response to a small perturbation in the density field. We study the frequency profile of the response and its temporal evolution. This helps us gain a deeper understanding of the different formulations, and their impact on glacial dynamics. Within the numerical ice-flow model Úa, we compare the D2T adjustment method to an approach which explicitly includes the density variations, applied to numerical simulations of the Western Antarctic region containing Pine Island and Thwaites Glaciers.

How to cite: Schelpe, C. and Gudmundsson, H.: Treatment of Density Variations in Ice-Flow Models using the Shallow Ice Stream Approximation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4937, https://doi.org/10.5194/egusphere-egu22-4937, 2022.

Crunching satellite imagery or Digital Elevation Models (DEMs) is part of your weekly routine?

You are desperate to calculate glacier volume changes despite gappy observations?

Your head blows up just trying to provide those errors bars for your mass balance estimates?

You think science should be fully reproducible?

Python is one of your favourite programming languages?

If you answered yes to any two of those questions, you should definitely attend this presentation!

Remote sensing is becoming increasingly important in our understanding of global glacier changes. Dozens of studies each year aim at estimating geodetic glacier mass balance from the regional to the global scale, providing factual numbers behind glacier retreat. But how reliable are those numbers? Current approaches raise several problems:

• External data, such as glacier outlines can be updated regularly, as is done e.g. with the RGI outlines, potentially making previous estimates obsolete.
• Data processing techniques, such as DEM coregistration or gap-filling, evolve over time in the community.
• Many results are not reproducible or cannot even be updated, because access to the data or code is not granted.

All these issues make the comparison and validation of older vs newer studies challenging and questions the reliability of glacier change estimates. Why not team-up and create the tools we all dream of?

Here we present xDEM, an open-source, community-built and easy-to-use set of tools for DEMs postprocessing and volume change calculation. The tool is designed as a set of Python modules, built on top of popular libraries (rasterio, geopandas, GDAL). It will ultimately provide all that is needed to turn individual raw DEMs into a geodetic volume change and its uncertainties: coregistration, bias correction, gap-filling, volume change calculation and spatial statistics (e.g. variograms). The concept behind xDEM is:

Ease of use: Python modules developed by glaciologists, (mostly) for glaciologists.

Flexibility and modularity: We offer a set of options, rather than favouring a single method and make it straightforward to combine them.

Reproducibility: Version-controlled; releases saved with DOI; test-based development ensures our code always performs as expected.

The progress of the project can be followed at https://github.com/GlacioHack/xdem.

We illustrate the use of xDEM for various test cases and on-going projects to post-process DEMs obtained from ~1930 terrestrial images of the Swiss Alps, American reconnaissance KH-9 satellite images, modern ASTER and Pleiades images or the recent RAGMAC intercomparison experiment.

How to cite: Dehecq, A., Mannerfelt, E., Hugonnet, R., and Tedstone, A.: xDEM - A python library for reproducible DEM analysis and geodetic volume change calculations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5781, https://doi.org/10.5194/egusphere-egu22-5781, 2022.

Energy budget-based distributed modelling in glacierized catchments is important to examine glaciological-hydrological regimes and compute flow rates in current and projected scenarios. Trends in ablation of snow and glaciers retreat depend upon snow and ice reserves, meteorological parameters and geographical features which vary across sub-basins in Upper Indus Basin. This study attempts to address these issues by employing [1] regional climate models (RCMs) and the Physical based Distributed Snow Land and Ice Model (Ranzi and Rosso 1991; Grossi et al. 2013) in the Naltar catchment (area of 242.41 km2, with 42 km2 glacierized), located in the Hunza river basin (Upper Indus Basin), to project snow and glacier melt and daily streamflow. The calibration and validation of the model were successfully carried out using observed historical meteorological data at hourly time resolution from high altitude meteorological stations (Liaqat et al. 2021). For each of the climate simulations, projections of near future (2040-2059) and far future (2080-2099) under three Representative Concentration Pathways (RCPs) namely RCP2.6, RCP4.5, and RCP8.5 are presented[2]  with respect to corresponding present climate (1991-2010). We used all relevant meteorological variables from an ensemble of 37 simulations in total, which were performed by 3 RCMs driven by 11 different global climate models (GCMs) and were developed under the CORDEX Experiment, (Giorgi et al. 2009)-South Asia initiative. RCMs often present systematic biases and, despite their rather high spatial resolution (here approximately 50km x 50km) they are still too coarse for hydrological impact assessments. In order to produce localized and unbiased climate projections, we scaled the observed climate according to the simulated changes by means of the delta change method as described in Räisänen and Räty (2013) and Räty et al. (2014). Correction factor [3]  in the mean and standard deviation for all for all meteorological variables were obtained for the near and far future periods compared to the historical period (1991-2010) for each simulation. [4] T[5] he joint analysis of climate projections and hydrological modelling, spanning different scenarios and other sources of uncertainty is essential to predict future changes in water resources availability to satisfy mainly irrigation demand in the downstream areas.

References

Giorgi F, Jones C, Asrar GR (2009) Addressing climate information needs at the regional level: the CORDEX framework World Meteorological Organization Bulletin 58:175

Grossi G, Caronna P, Ranzi R (2013) Hydrologic vulnerability to climate change of the Mandrone glacier (Adamello-Presanella group, Italian Alps) Advances in water resources 55:190-203

Liaqat MU, Grossi G, Ansari R, Ranzi R Modeling Hydrological Vulnerability to Climate Change in the Glacierized Naltar Catchment (Pakistan) Using a Distributed Energy Balance Model. In: AGU Fall Meeting 2021, 2021. AGU,

Räisänen J, Räty O (2013) Projections of daily mean temperature variability in the future: cross-validation tests with ENSEMBLES regional climate simulations Climate dynamics 41:1553-1568

Ranzi R, Rosso R (1991) Physically based approach to modelling distributed snowmelt in a small alpine catchment IAHS PUBL, IAHS, WALLINGFORD:141-150

Räty O, Räisänen J, Ylhäisi JS (2014) Evaluation of delta change and bias correction methods for future daily precipitation: intermodel cross-validation using ENSEMBLES simulations Climate dynamics 42:2287-2303

How to cite: Liaqat, M. U., Casanueva, A., Grossi, G., and Ranzi, R.: Future climate and runoff projections in the Naltar Catchment, Upper Indus Basin from CORDEX-South Asia regional climate models and hydrological modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6030, https://doi.org/10.5194/egusphere-egu22-6030, 2022.

The geodetic method has become a popular tool to measure glacier elevation changes over large glacierized regions with high accuracy for multi-annual to decadal time periods. In contrast, the glaciological method provides annually to seasonally resolved information on glacier evolution, but only for a small sample of the world’s glaciers (less than 1%). Various methods have been proposed to bridge the gap on spatio-temporal coverage of glacier change observations and to provide annually-resolved glacier mass balances using the geodetic sample as calibration. Thanks to a new global and near-complete (96% of the world glaciers) dataset of geodetic mass balance observations, this goal has become feasible at the global scale. Inspired by previous methodological frameworks, we developed a new approach to combine the glacier distribution 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 changes and related uncertainties for every individual glacier during the 2000–2020 period. The glacier-specific time series can then be integrated into an annually-resolved global gridded glacier change product at any user-requested spatial resolution, useful for comparison with gravity-based products, calibration or validation of glacier mass balance models operating at a global scale and to improve calculations of the glacier contribution to regional hydrology and global sea-level rise.

How to cite: Dussaillant, I., Hugonnet, R., Huss, M., Berthier, E., Paul, F., and Zemp, M.: An annual mass balance estimate for each of the world’s glaciers based on observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6338, https://doi.org/10.5194/egusphere-egu22-6338, 2022.

We give an overview of the Instructed Glacier Model (IGM) -- a new framework to model the evolution of glaciers at any scale by coupling ice dynamics, surface mass balance, and mass conservation. The key novelty of IGM is that it models the ice flow by a Convolutional Neural Network (CNN), which is trained from physical high-order ice flow mechanical models. Doing so has major advantages in both forward and inverse modelling.

In forward modelling, the most computationally demanding model component (the ice flow) is substituted by a very cheap CNN emulator. Once trained with representative data, IGM permits to model individual mountain glaciers several orders of magnitude faster than high-order ones on CPU with fidelity levels above 90 % in terms of ice flow solutions leading to nearly identical transient thickness evolution. Switching to Graphics Processing Unit (GPU) permits additional significant speed-ups, especially when modelling large-scale glacier networks and/or high spatial resolutions.

In inverse modelling, the substitution by a CNN emulator does not only speed up but facilitates dramatically the data assimilation step, i.e. the search for optimal ice thickness and ice flow parameter spatial distributions that match spatial observations at best (such as ice flow, surface topography or ice thickness profiles) while being consistent with the high-order ice flow mechanics. The reason is that inverting a CNN can take great benefit from the tools used for its training such as automatic differentiation, stochastic gradient methods, and GPU.

IGM is an open-source Python code (https://github.com/jouvetg/igm), which deals with two-dimensional gridded input and output data. Together with a companion library of trained ice flow emulators, IGM permits user-friendly, computationally highly-efficient, easy-to-customize, and mechanically state-of-the-art glacier forward and inverse modelling at any scale. We illustrate its potential by replicating a simulation of the great Aletsch Glacier, Switzerland, from 1880 to 2100, based on a Stokes model. The complete workflow (data assimilation and 220 years long forward modelling) at 100 m of resolution takes about 1-2 min on the GPU of a laptop and can be replicated and adapted easily using an online Colab notebook.

How to cite: Jouvet, G., Cordonnier, G., Kim, B., Luethi, M., Vieli, A., and Aschwanden, A.: IGM, a glacier evolution model accelerated by deep-learning and GPU, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7271, https://doi.org/10.5194/egusphere-egu22-7271, 2022.

A common disadvantage of almost all global glacier models is that they ignore the explicit description of the debris cover on the heat exchange of the glacier surfaces with the atmosphere. Debris cover plays a key role in the regulation of melt processes. A debris cover more than a few centimeters reduces melting, since it isolates the underlying ice. In this way, debris covered areas are thought to be less exposed to rising temperatures, thereby reducing glacier retreat and mass loss.        

In the foothills of the North Caucasus, an important agricultural region, the problem of expected changes in mountain glaciation is particularly acute, since fluctuations in the flow regime of local rivers depend on the evolution of glaciers: the contribution of glacial runoff to total discharge is very significant.

Here, we present the assessment of debris cover influence on the glacier evolution of the Northern Caucasus on a regional scale (Terek and Kuban river basins). The aim is to determine how much the characteristics of mountain glaciation (its mass balance, area, volume, position of the glacier fronts) of the Northern Caucasus depend on the debris cover evolution. In order to accomplish this goal, we use the GloGEMflow model and a newly created debris cover dynamic module, which is calibrated using newly mapped debris cover outlines. The debris thickness evolution is simulated with a steady deposit model adapted from Verhaegen et al. (2020) and Anderson & Anderson (2016), where debris input onto the glacier is generated from a fixed point on the flow line.

The results reveal that the debris cover evolution pattern differ significantly for Terek and Kuban glacierized basins. Lower elevated Kuban basin glaciers undergo a rapid retreat and lose the debris covered glacier tongues while the Terek basin glaciers experience supraglacial debris expansion with a six times larger effect of debris cover on glacier volume evolution. From 2000 to 2016 the mass loss in the Terek ice basin reached 47834 Mt with an influence of the debris cover module and 50435 Mt  under debris-free conditions. Therefore, we can expect that by the end of the current century the mass loss of the Terek glaciers will be significantly overestimated in case debris cover influence will be ignored in model calculations. On the contrary, in the Kuban basin, calculated mass loss in 2000-2016 with and without debris cover were 1249 Mt  and 1258 Mt  respectfully. Committed loss experiments (constant mean climate for 1990-2015) show that the glaciers of the Terek basin lose ~35% of ice if debris cover is not taken into account and ~29% if debris cover module is turned on (~2  ice volume difference). For the Kuban basin glaciers, the difference of ice volume is only ~0.1  in debris-free vs. debris-covered modes.

The reported study was funded by the RFBR and RS grant 21-55-10003, the work of T. Postnikova was supported by the RFBR grant 20-35-90042.

How to cite: Postnikova, T., Rybak, O., Zekollari, H., Huss, M., Gubanov, A., and Nosenko, G.: Debris cover effect on the evolution of glaciation in the Northern Caucasus, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7736, https://doi.org/10.5194/egusphere-egu22-7736, 2022.

The number and extent of glacial lakes in mountain regions worldwide has increased over recent decades as glaciers have lost mass. These ice-contact lakes modify the dynamic response of glaciers to climate change, presenting a challenge to projecting their future evolution. In High Mountain Asia (HMA) glacial lakes have expanded by more than 45% in the last 30 years. Previous studies have demonstrated the contrasting dynamic evolution of lake- and land-terminating glaciers in the Eastern Himalaya, although it was previously unclear if this was a localised phenomenon. Using existing and manually derived datasets, we observed glacier surface velocity, surface elevation, terminus position and glacial lake area change across HMA’s differing climatic regimes over a twenty-year period (2000–2020) to investigate the dynamic evolution of ~60 lake- and land-terminating glaciers.

Our results show that lake-terminating glaciers in the Himalaya, Karakoram and Pamir experienced faster ice flow in the ablation zone, significant surface thinning and extensive terminus recession in comparison to land-terminating glaciers over the same period. The majority of lake-terminating glacier population experienced a glacier-wide increase in velocity during the twenty-year observation period, including 58% of individual glaciers. In comparison, 62% of land-terminating glaciers experienced a decrease in velocity during the same period. This result suggests that lake-induced dynamic changes are occurring irrespective of the regional climatic regime. Our observations also revealed that lake-terminating debris-covered glaciers experienced a greater magnitude of change in velocity, surface elevation and terminus position, than their clean ice counterparts. These results are important for making projections of future glacier change in HMA where many debris-covered glaciers are pre-disposed to the development of terminal lakes in the next few decades.

How to cite: Scoffield, A., Rowan, A., and Sole, A.: The influence of ice-contact lakes and supraglacial debris on glacier change in High Mountain Asia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7754, https://doi.org/10.5194/egusphere-egu22-7754, 2022.

Ice flow models include processes which cannot be determined from observational data, but must be represented mathematically in order to simulate the physical system. One such process is the interaction between the ice and the bedrock, basal sliding, which enters models in the form of a sliding law describing the relationship between basal drag and velocity. Basal sliding is a major factor in ice flow, and therefore how it is represented is one of the most important modelling decisions.

Several sliding laws have been proposed and used in ice flow models, representing different types of bed. In general, these comprise some combination of a Weertman-style power law and Coulomb friction. The equations for sliding laws contain parameters which are usually given constant, uniform values. The responses to perturbations in the ice flow system differ depending on the sliding law used, and on the parameter choices made.

In this study, we use the ice flow model Úa to run experiments for a range of different sliding laws, and different values for parameters within these laws. In each case, we test the response of the model to perturbations in the ice shelf melt rate. We investigate the differences between our model outputs, and assess the relationships between sliding law parameter choices and the resulting changes in ice flow.

Our model domain covers the Amundsen Sea Embayment, which we break down into separate catchment areas during our analysis in order to capture localised variation in our results.

How to cite: Barnes, J. and Gudmundsson, H.: The effects of parameter choices in basal sliding laws on a modelled ice flow response to perturbations in forcing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8658, https://doi.org/10.5194/egusphere-egu22-8658, 2022.

The purpose of the research is to assess the influence of the random weather fluctuations on the estimates of the model-based surface mass balance (SMB) components of the mountain glacier. The common approach in the modeling studies is to use meteorological records (measured or modelled) – surface air temperature and precipitation rate – as weather forcing in numerical experiments. The results of the calculations are normally very sensitive to the parameter choice and the model should be carefully calibrated against measured SMB to obtain correct results. What is usually ignored within the frameworks of this approach is that forcing records at e.g. daily resolution contain internal weather variability which after being integrated by the model can yield in a random walk type trend of SMB.   

To evaluate uncertainty in SMB calculations we force an energy balance model of Djankuat glacier in the Central Caucasus with surrogate series of surface air temperature and precipitation rate. The surrogate series of several model decades duration each are produced by a stochastic weather generator WGEN basing on the observed meteorological series at the weather stations located nearby. In WGEN, precipitation events are simulated by a first-order Markov chain, and the intensity of precipitation is represented using independent gamma distribution. Air temperature is calculated by fitting the appropriate distributions and harmonic functions separately for wet and dry days. Seasonality is reproduced by an estimate of individual sets of model parameters for different periods of the year.

Statistical analysis of the generated ensemble of SMB components revealed that relative standard deviation (RSD) of SMB components (accumulation rate, melting, evaporation, melt water retention) vary within the limits 3-6%, but RSD of the specific mass balance is several times higher.

Our approach enables to filter out reaction of the modeled glaciers induced by the weather noise from systematic reply on climate change.

The reported study was funded by the RFBR and RS grant 21-55-10003.

How to cite: Rybak, O. and Rybak, E.: Evaluating uncertainties in modelled surface mass balance components of a mountain glacier, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8893, https://doi.org/10.5194/egusphere-egu22-8893, 2022.

GLIMS, Global Land Ice Measurements from Space, is an initiative that involves ~250 analysts from 34 countries and has the purpose of mapping all glaciers in the world (excluding the Greenland and Antarctic ice sheets) on a periodic basis.  The GLIMS Glacier Database, which became an official product of the NASA NSIDC DAAC (Distributed Active Archive Center) in 2019, contains time series of glacier outlines from different data sources.  Various parts or facies of glaciers are mapped, including the full glacier extent, debris-covered parts, internal rock outcrops, and glacial lakes.  The Randolph Glacier Inventory (RGI) is a snapshot map of glaciers, with one outline per glacier, as close as possible to a target date.

In the last year, GLIMS and the RGI working group have been working closely together to ingest new data into GLIMS and to improve GLIMS and RGI software tools. The goal is to improve data completeness and quality and to make the creation of the RGI smoother and more transparent (Maussion et al., EGU22-4484).  New data include approximately 60,000 outlines from 14 regions in all parts of the Earth, with times ranging from the Little Ice Age to 2018. Software improvements include more quality-control checks and constraints, such as separating multi-polygons into individual ones. 

The presentation will provide an overview on the latest data additions and software developments in GLIMS and the synergy with RGI production.

How to cite: Raup, B., Maussion, F., Paul, F., Berthier, E., Bolch, T., Kargel, J., and Racoviteanu, A.: More data and increased automation leads to better quality for GLIMS and RGI glacier data sets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10563, https://doi.org/10.5194/egusphere-egu22-10563, 2022.

Within the earth system, glaciers serve as important indicators of climate change, being principally governed by temperature and precipitation. Additionally, they provide essential freshwater storage on various scales, ranging from long-term in firn and ice, to short-term storage in snow cover. By preventing precipitation from immediately turning into runoff, glaciers fulfill a buffering role within their basins, providing downstream runoff during melt season. With changes in glacier mass balance in response to changes in climate, a glacier's buffering capacity is altered simultaneously. In order to predict the evolution of runoff on temporal scales relevant to water resource management (5-15 years), it is essential to observe and simulate glacier mass balance on the same scale. The current research presents a global modelling approach using the Open Global Glacier Model (OGGM), forced with a multi-model, multi-member retrospective ensemble of monthly temperature and precipitation re-forecasts (hindcasts) from the Decadal Climate Prediction Project (DCPP), part of the Coupled Model Intercomparison Project, phase 6 (CMIP6). The decadal hindcasts are initialized each year in the period 1960-2010 and are bias corrected for model drift, while retaining the period's warming trend, using a lead-time based correction. The hindcasts are then downscaled to the glacier scale and used to compute the climatic mass balance with OGGM, with fixed glacier geometries. The method is validated using 274 reference glaciers, which have a >5 year observational record. It is then applied globally, to all land-terminating glaciers in the Randolph Glacier Inventory (RGI), outside the Greenland Ice Sheet and Antarctica. The results indicate merit in using decadal re-forecasts to model glacier mass balance, paving the way for reliable decadal scale runoff predictions on regional and global scales.

How to cite: van der Laan, L., Förster, K., Scaife, A., Vlug, A., and Maussion, F.: Assessing skill and use of CMIP6 decadal re-forecasts in global glacier mass balance modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11942, https://doi.org/10.5194/egusphere-egu22-11942, 2022.

The evolution of glaciers and ice sheets depends sensitively on the processes occurring at their boundaries such as, e.g., the ice-bedrock interface or shear margins. These boundary regions share as common characteristics the transition from flow to no flow over a relatively short distance, resulting in a complex and fundamentally non-hydrostatic stress field. The localised intense shearing may further induce weakening of the ice owing to thermomechanical interactions, ultimately accelerating and potentially destabilising the bulk of the ice. Better understanding the sensitivity of these near-boundary processes is vital and challenging as it requires non-linear and coupled full Stokes models that can afford very high resolution in three-dimensions.

We present recent development of a thermomechanical coupled numerical model that leverages graphical processing units (GPUs) acceleration to resolve the instantaneous stress and velocity fields within ice flow over complex topography in three dimensions. We apply the model to various glaciers of the Swiss Alps resolving the complex flow field in three dimensions and at very high spatial resolution. We further use the model to assess the competition between basal sliding and internal sliding, the latter referring to the formation of a near-basal internal shear zone within the ice owing to thermomechanical feedback. We finally provide some insights in GPU-based high-performance computing model development using the Julia language and the ongoing development of efficient implicit iterative solvers based on the accelerated pseudo-transient method.

How to cite: Räss, L., Utkin, I., and Omlin, S.: Resolving thermomechanical ice flow on Alpine topography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12741, https://doi.org/10.5194/egusphere-egu22-12741, 2022.

The future contributions of the Antarctic Ice Sheet to sea level rise will be highly dependent on the evolution of its surface mass balance (SMB), which can offset increased ice discharge at the grounding line. In-situ SMB constraints over annual to multi-decadal timescales come mostly from firn and ice cores. However, although they have a high temporal resolution, ice cores are local measurements of SMB with a surface footprint on the order of cm². Post depositional processes (e.g. wind driven redistribution) can change the initial snowfall record locally and therefore affect our interpretation of the SMB signal recovered. On the other hand, regional climate models have a high temporal resolution but may miss some of the processes at work as a result of their large spatial footprint, on the order of km². Comparisons of ice core and model SMB records often show large discrepancies in terms of trends and variability.

We investigate the representativeness of a single shallow core record of SMB of the area surrounding it. For this, we use ice-penetrating radar data, co-located with the ice core records examined, to obtain a multi-annual to decadal radar-derived SMB record. We then compare the radar-derived SMB records to the ice core SMB records to determine the surface area that the ice core record is representative of, in terms of mean SMB as well as SMB temporal variability on historical timescales. We examine ice core records situated over the coastal ice rises of East Antarctica, where SMB is high and spatially heterogeneous, as well as over the interior of the West Antarctic Ice Sheet, where SMB is more uniform spatially. By comparing these two contrasting regions in terms of SMB, we will determine whether a general rule of thumb can be obtained to determine the spatial representativeness of an ice core SMB record.

How to cite: Cavitte, M. G. P., Goosse, H., Wauthy, S., Medley, B., Kausch, T., Tison, J.-L., Van Liefferinge, B., Lenaerts, J. T. M., and Pattyn, F.: Quantifying the spatial representativeness of ice core surface mass balance records using ground-penetrating radar data in Antarctica, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-649, https://doi.org/10.5194/egusphere-egu22-649, 2022.

The growth and decay of marine ice sheets act as important controls on regional and global climate and sea level. The Wilkes Subglacial Basin ice sheet appears to have undergone thinning and ice discharge events during recent decades, but its past dynamics are still under debate. The aim of our study is to investigate ice margin retreat of the Wilkes Subglacial Basin ice sheet during late Pleistocene interglacials with the help of new high-resolution records from the TALDICE ice core. Here we present a multiproxy approach associated with modelling sensitivity experiments.

The novel high-resolution δ¹⁸O signal reveals that interglacial periods MIS 7.5 and 9.3 are characterized by a unique double-peak feature, previously observed for MIS 5.5 (Masson-Delmotte et al., 2011), that is not seen in other Antarctic ice cores. A comparison with our GRISLI modelling results indicates that the Talos Dome site has probably undergone elevation variations of 100-400 m during past interglacials, with a major ice thickness variation during MIS 9.3, likely connected to a relevant margin retreat of the Wilkes Subglacial Basin ice sheet. To validate this elevation change hypothesis, the modelling outputs are compared to the ice-rafted debris record (IBRD) and the neodymium isotope signal from the U1361A sediment core (Wilson et al., 2018), which show that during MIS 5.5 and especially MIS 9.3, the Wilkes Subglacial Basin ice sheet has been subjected to ice discharge events.

Overall, our results indicate that the interglacial double-peak δ¹⁸O signal could reflect decreases in Talos Dome site elevation during the late stages of interglacials due to Wilkes Subglacial Basin retreat events. These changes coincided with warmer Southern Ocean temperatures and elevated global mean sea level, confirming the sensitivity of the Wilkes Subglacial Basin ice sheet to ocean warming and its potential role in sea-level change.

How to cite: Crotti, I., Quiquet, A., Landais, A., Stenni, B., Frezzotti, M., Wilson, D., Severi, M., Mulvaney, R., Wilhelms, F., and Barbante, C.: Response of the Wilkes Subglacial Basin Ice Sheet to Southern Ocean Warming During Late Pleistocene Interglacials, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1281, https://doi.org/10.5194/egusphere-egu22-1281, 2022.

Tracing the provenance of Antarctic sediments yields unique insights into the form and flow of past ice sheets. However, sediment provenance studies are typically limited to qualitative interpretations by uncertainties regarding subglacial geology, glacial erosion, and transport of sediment both subglacially and beyond the ice sheet margin. Here, we forward model marine geochemical sediment provenance data, in particular neodymium isotope ratios. Numerical ice-sheet modelling predicts the spatial pattern of subglacial erosion rates for a given ice sheet configuration, then ice flow paths are traced to the ice sheet margin. For the modern ice sheet, simple approximations of glacimarine sediment transport processes produce a good agreement with Holocene surface sediments in many areas of glaciological interest. Calibrating our model to the modern setting permits application of the approach to past ice sheet configurations, which show that large changes to sediment provenance over time can be reconstructed around the West Antarctic margin. This first step towards greater integration of Antarctic sediment provenance data with numerical modelling offers the potential for advances in both fields.

How to cite: Marschalek, J., Gasson, E., van de Flierdt, T., Hillenbrand, C.-D., and Siegert, M.: A Path to Quantitative Interpretation of Antarctic Sediment Provenance Records, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1667, https://doi.org/10.5194/egusphere-egu22-1667, 2022.

Recent sea-level rise from the Antarctic icesheet has been dominated by contributions from Pine Island and Thwaites Glaciers of the Amundsen Sea Embayment (ASE). Much of the ASE ice is grounded below sea level and is therefore likely to be highly sensitive to ongoing oceanic and atmospheric warming.

Confidence in model-based predictions of the future contributions of the ASE region to sea-level rise requires an understanding of the sensitivity of the predictions to input data, such as bedrock topography, and to chosen parameters within, for example, sliding laws.

We will present results from using the BISICLES adaptive mesh refinement ice-sheet model to explore the sensitivity of modelled ASE 2050 grounded ice loss. We test a regularized Coulomb friction sliding law, varying the regularization parameter, and we test the sensitivity to bedrock elevation by adding gaussian noise of different wavelengths to MEaSUREs BedMachine Version 2 elevations. However, within our experiments, we find the greatest sensitivity in modelled 2050 sea-level contributions is to the imposed ice-shelf thinning or damage rates, which we vary between spatially uniform values of 0 to 15 m/year.

We will also present a comparison of the modelled annual evolution of surface velocity and surface elevation change with observations. Observed surface velocities are based on Sentinel 1 feature tracking, and surface elevation change rates are derived from satellite radar altimetry.

How to cite: Bevan, S., Cornford, S., Luckman, A., Hogg, A., Otosaka, I., and Surawy-Stepney, T.: Exploring the sensitivity of modelled sea-level rise projections from the Amundsen Sea Embayment of the Antarctic Ice Sheet to model parameters, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2310, https://doi.org/10.5194/egusphere-egu22-2310, 2022.

Since the beginning of the 20^th century, various countries have carried out expeditions to Antarctica with the aim of claiming territory, reconnaissance as well as capturing aerial images for topographic mapping. Many of these image inventories has since then been forgotten and never used for scientific purposes. We have gained access to a unique dataset of aerial images captured in 1936-1937 as a part of the Norwegian Thorshavn IV expedition surveying and mapping large parts of the East Antarctic coastline. The images have a stereo overlap of approximate 60% and are digitized using a photogrammetry-grade scanner, enabling us to produce the earliest known digital elevation models and orthophotos of Antarctica.

Expanding the observational records of Antarctic glaciers are vital for better understanding and modelling how changes in climatic parameters affects the ice. Currently, we know very little about the behaviour of Antarctic glaciers prior to the 1990s, due to a lack of large-scale observations. Several studies has proven the effectiveness of using digitally-scanned historical aerial images in studying ice mass losses of the pre-satellite era, but very few such studies exist for Antarctica. In this study, we explore Norwegian and Australian historical aerial expedition images collected between 1937 and 1997 to extensively expand the records and provide the earliest regional-scale Antarctic glacier records. The images are processed using structure-from-motion photogrammetry, which enables us to construct accurate, high-resolution digital elevation models and orthophotos. By combining expedition images with modern satellite data, we are creating a unique time-series dataset to study the changes of multiple glaciers along the East Antarctic coastline in Mac Robertson and Kemp Land between 1937 and 2017.

How to cite: Dømgaard, M., Huiban, F., Schomacker, A., Mouginot, J., and Bjørk, A.: Dynamics of East Antarctic glaciers from 1937-2017 analyzed using historical aerial expedition images, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4161, https://doi.org/10.5194/egusphere-egu22-4161, 2022.

Changes in ice load over time deform the Earth’s crust and mantle. This effect, Glacial Isostatic Adjustment (GIA), induces vertical deformation of the bedrock of the Antarctic continent and impacts the grounding line position which is critical for the dynamical state of the Antarctic Ice Sheet (AIS). GIA introduces a negative feedback and stabilizes the ice sheet evolution, hence GIA modelling is important for transient studies. Most ice dynamic models use a two-layer flat Earth approach with a laterally homogenous relaxation time or a layered Earth approach with a laterally homogenous viscosity (1D) to compute the bedrock deformation. However, viscosity of the Earth’s interior varies laterally (3D) and radially with several orders of magnitude across the Antarctic continent. Here we present a new coupled 3D GIA – ice dynamic model which can run over hundred thousands of years with a resolution of 500 years. The method is applied using various 1D and 3D rheologies. Results show that the present-day ice volume is 3 % lower when using a 1D viscosity of 10²¹ Pa·s than using a 3D viscosity. However, local differences in grounding line position maybe up to a hundred kilometres around the Ronne and the Ross Ice Shelfs, and ice thickness differences are up to a kilometre for present day conditions when comparing 1D rheologies and 3D rheologies. The difference between the use of various 3D rheologies is significantly smaller. These results underline and quantify the importance of including local GIA feedback effects in ice dynamic models when simulating the Antarctic Ice Sheet evolution over the Last Glacial Cycle.

How to cite: van Calcar, C., van de Wal, R., Blank, B., de Boer, B., and van der Wal, W.: Simulating the evolution of the Antarctic Ice Sheet including 3D GIA feedback during the Last Glacial Cycle, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4786, https://doi.org/10.5194/egusphere-egu22-4786, 2022.

The future evolution of the Antarctic Ice Sheet (AIS), particularly the West Antarctic Ice Sheet (WAIS), will strongly influence global sea-level rise during the 21^st century and beyond. However, because of the sparse observational network in concert with the strong internal variability, our understanding of the long-term climate and ice sheet changes in the Antarctic is limited. Among all the processes involved in Antarctic climate variability and change, an increasing number of studies have pointed out the strong relationship between the climate in the tropics and Antarctic (also called tropical-Antarctic teleconnections), especially between the Pacific Ocean and the West Antarctic region. Most of those studies focus only on the past decades, but to fully understand the long-term Antarctic climate changes associated with tropical variability longer time-series are needed. This is achieved here by using annually-resolved paleoclimate records (ice core and coral records) that cover at least the last two centuries to study both the year-to-year and multi-decadal variability of tropical-Antarctic teleconnections. These records are incorporated into a data assimilation framework that optimally combines the paleoclimate records with the physics of the climate model. As data assimilation provides a climate reconstruction that is dynamically constrained – through the spatial covariance in the climate model – the contribution of tropical variability on Antarctic climate changes can be directly assessed. Different sensitivity tests are performed to isolate the contribution of each tropical basin. Additionally, the roles of multi-decadal and year-to-year variability are compared by averaging the annual paleoclimate records at a lower temporal resolution. This new method of combining the two time-scales is proposed in order to preserve the multi-decadal variability in the annual climate reconstruction.

How to cite: Dalaiden, Q., Abram, N., and Goosse, H.: Contribution of tropical variability on Antarctic climate changes over the past centuries, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5596, https://doi.org/10.5194/egusphere-egu22-5596, 2022.

How to cite: Wirths, C., Sutter, J., and Stocker, T.: Exploring the impact of different past- and present-day climatic forcings on Antarctic Ice sheet evolution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7213, https://doi.org/10.5194/egusphere-egu22-7213, 2022.

The stability of the grounding lines of Antarctica is a fundamental question in glaciology, because current grounding lines in some locations are at the edge of large marine basins, and have been hypothesized to potentially undergo irreversible retreat in response to climate change. This could have global consequences and raise sea levels by several metres. If the Antarctic grounding lines in their current configuration are close to being unstable, a small change in external forcing, e.g. a reduction in ice shelf buttressing resulting from an increase in ice shelf melt rates, would lead to continued retreat of the grounding line due to the marine ice sheet instability hypothesis, even after the melt perturbation is reverted. Alternatively, if the system state reverts to its previous value after the perturbation is removed, we can consider the current grounding line positions to be reversible. 

Here, we initialise the ice sheets models Úa and Elmer/Ice to closely replicate the current configuration of the Antarctic Ice Sheet, in particular, the current position of the grounding lines. Under control conditions, state fluxes and ice volume changes are forced to be in balance. Using these quasi-steady state ice sheet configurations, we apply a small amplitude perturbation in ice shelf melt rates by imposing an increase for 20 years in the far-field ocean temperature. After 20 years the melt rate perturbation is returned to zero, and model simulations are continued for a further 80-year recovery period. During this recovery period we examine the trend in ice flux and grounding line position, i.e. do they tend towards their previous values, or do they move further away from their initial state? Our results suggest that the global grounding line around Antarctica begins to reverse to its former state after the perturbation is removed. However, we find the reversibility and response times of grounding lines to a small perturbation in ice shelf buttressing varies between individual basins across the ice sheet.

This work is part of the TiPACCs project and complements an overview presentation on the reversibility of present-day Antarctic grounding lines (EGU22-5176) as well as a presentation exploring long-term reversibility experiments (EGU22-7885).

How to cite: Hill, E. A., Urruty, B., Reese, R., Garbe, J., Gagliardini, O., Durand, G., Gillet-Chaulet, F., Gudmundsson, G. H., Winkelmann, R., Chekki, M., Chandler, D., and Langebroek, P.: Reversibility experiments of present-day Antarctic grounding lines: the short-term perspective, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7802, https://doi.org/10.5194/egusphere-egu22-7802, 2022.

The stability of the grounding lines of Antarctica is a fundamental question in glaciology, because current grounding lines are in some locations at the edge of large marine basins, and have been hypothesized to potentially undergo irreversible retreat in response to climate change. This could have global consequences and raise sea levels by several metres. However, their long-term reversibility for the current ice sheet geometry has not yet been questioned, i.e., if the present-day climatology is kept constant, will the grounding lines remain close to their currently observed position or will they retreat substantially? 

Here we focus on the long-term evolution of Antarctic grounding lines over millennial time scales. Using the Parallel Ice Sheet Model, an initial equilibrium state is created for historic climate conditions around 1850. Then the model is run forward until 2015 with atmospheric and oceanic changes from ISMIP6 to reflect recent trends in the ice sheet. After 2015, we keep the present-day climatology constant and let the ice sheet evolve towards a new steady state, which takes several thousand years. An ensemble over model parameters related to sliding and ocean forcing allows us to analyse the sensitivity of the grounding line evolution to model uncertainties. Since we start from a historic equilibrium state, we can use this approach to assess if the increase from historic to present-day climatology might push Antarctic grounding lines across a tipping point into a different basin of attraction that is characterised by a substantially retreated steady-state grounding line position. 

This work is part of the TiPACCs project and complements an overview presentation on the reversibility of present-day Antarctic grounding lines (EGU22-5176) as well as a presentation exploring the short-term reversibility experiments in more detail (EGU22-7802).

How to cite: Reese, R., Urruty, B., Hill, E. A., Garbe, J., Gagliardini, O., Durand, G., Gillet-Chaulet, F., Gudmundsson, G. H., Winkelmann, R., Chekki, M., Chandler, D., and Langebroek, P.: Reversibility experiments of present-day Antarctic grounding lines: the long-term perspective, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7885, https://doi.org/10.5194/egusphere-egu22-7885, 2022.

Ice shelf basal melt accounts for about half the present-day ice loss from the Antarctic Ice Sheet, and is important for both ice sheet mass balance and as a source of fresh water into the Southern Ocean. In Antarctic Ice Sheet simulations over Quaternary glacial cycle time scales, neither basal melt rate nor its principal oceanographic controls (temperature and salinity of waters adjacent to ice shelves) can be reconstructed directly from proxy records. Given the strong ice-ocean-atmosphere interactions, the ideal solution is a coupled ice-ocean-atmosphere model, but computational demands currently limit this approach to short time scales. Stand-alone ice sheet simulations can cover much longer time scales at reasonable resolution, but require an alternative estimate of ocean temperatures. Here we compare the strengths and weaknesses of three options: (i) proxy reconstructions of North Atlantic and circumpolar deep water temperatures, from marine sediment cores north of 43°S; (ii) an ice sheet air temperature reconstruction, damped and lagged by a linear response function; and (iii) a glacial index method which interpolates between CMIP6 lig127k (interglacial) and lgm (glacial) end-member ocean states. We find considerable differences in the rates and magnitudes of the Antarctic Ice Sheet's contribution to past sea-level changes when applying the three methods in simulations over the last two glacial cycles, particularly during the last interglacial and Holocene. Therefore, the ocean temperature forcing remains as an important but poorly-constrained modelling choice, whether investigating past warm climates or using long simulations as a spin-up for future projections. 

How to cite: Chandler, D., Langebroek, P., Reese, R., Albrecht, T., and Winkelmann, R.: Ocean temperature forcings in glacial-interglacial Antarctic Ice Sheet simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8215, https://doi.org/10.5194/egusphere-egu22-8215, 2022.

Sea-level rise projections under climate change exhibit large uncertainty related to the contribution of ice sheets. A major source of uncertainty is the Antarctic Ice-Sheet (AIS) due to the marine-based nature of the West Antarctic Ice-Sheet (WAIS). Part of the WAIS is grounded under sea level and thus in contact with the surrounding ocean via the floating ice shelves. Melting of ice shelves does not directly contribute to sea level rise but it modulates the ice flow towards the sea by controlling the discharge through the grounding line. However, the processes that regulate the dynamics are not fully well understood and represented in state-of-the-art models due to the complexity of the various feedbacks involved. In addition, the basal friction or sliding law that should be employed is not well known. In this context arose the Antarctic BUttressing Intercomparison Project (ABUMIP, Sun et al., 2020) with the aim of studying the response of the AIS to a sudden and maintained collapse of its ice shelves. Here we show the results obtained while performing experiments extending those of Sun et al., (2020) with the thermomechanical ice-sheet model Yelmo and assessing the effect of using different friction laws.

How to cite: Pérez-Montero, S., Blasco, J., Robinson, A., Montoya, M., and Alvarez-Solas, J.: Assessment of the Antarctic ice-sheet response to ice-shelf collapse as a function of the friction law employed, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9447, https://doi.org/10.5194/egusphere-egu22-9447, 2022.

Sea-level rise represents one of the biggest threats that humankind has to face in the
coming centuries. Antarctica hosts today's largest ice sheet on Earth, the Antarctic Ice Sheet
(AIS). In the mid-long term, the AIS could become the main contributor to sea-level rise,
especially as a result of the West Antarctic Ice Sheet (WAIS) being marine-based and
therefore strongly exposed to the ocean. Nonetheless, there is substantial uncertainty in the
future contribution of the AIS to sea-level rise, mainly as a result of poor understanding of
physical processes, such as ice-sheet dynamics or ice-ocean interactions. In order to
overcome the problem of different Antarctic sea-level projections with different experimental
setups, the Ice Sheet Model Intercomparison Project for CMIP6 was organized (ISMIP6).
The first results showed that at higher emission scenarios the AIS melts more. Nonetheless,
the WAIS response to this warming varies widely among the models. We herein investigate
the contribution of the higher-order ice-sheet model Yelmo. Results
with Yelmo show a strong sensitivity of the AIS contribution to sea-level rise to the calibration
of the basal-melting parametrization, particularly remarkable in the WAIS, but being in the
range of the results reached by other ice-sheets models in the context of the ISMIP6
intercomparison project.

How to cite: Juárez-Martínez, A., Blasco, J., Montoya, M., Álvarez-Solas, J., and Robinson, A.: Antarctic contribution to future sea-level rise with a three-dimensional ice-sheet model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9448, https://doi.org/10.5194/egusphere-egu22-9448, 2022.

Thwaites Glacier in West Antarctica may be the single largest contributor to sea level rise in the coming centuries, but existing projections over such timescales are highly uncertain. A number of factors contribute to this uncertainty and robust predictions involve many complex processes through the interaction between ice, ocean and atmosphere. Here, we use the Úa ice-flow model in conjunction with an uncertainty quantification approach to provide uncertainty estimates for the future (100 years’ time scale) mass loss from Thwaites, and the relative contribution of individual model parameters to that uncertainty. In a first step, we simulate Thwaites glacier from 1997 to present day for a wide variety of uncertain model parameters and compare key outputs from each simulation to observations.  Using a Bayesian probability framework we sample the model parameter space, using informed priors, to build up a model emulator, allowing us to provide uncertainty estimates for a range of future emission scenarios. We show how this framework can be used to quantify the relative contribution of each model parameter to the total variance in our estimation of the future mass loss from the area. This, furthermore, allows us to make clear quantitative statements about different sources of uncertainty, for example, those related to external forcing parameterizations (e.g. surface mass balance) as compared to uncertainties in ice-flow parameters (e.g. basal sliding).    

How to cite: Rosier, S. and Gudmundsson, H.: Estimating the future sea level rise contribution of Thwaites glacier, Antarctica, using an uncertainty quantification approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9736, https://doi.org/10.5194/egusphere-egu22-9736, 2022.

As the atmosphere warms in response to increasing greenhouse gas emissions, snow accumulation over the Antarctic Ice Sheet is projected to increase over the next century. Furthermore, short-term emissions scenarios are also expected to have long-term impacts on ice sheet mass balance for centuries to come. Here, we analysed the extended runs of the Coupled Model Intercomparison Project’s Sixth Phase (CMIP6) to investigate the consequences of emissions scenarios on Antarctic surface mass balance until 2300. Unlike the Arctic, which shows a regime shift from snow-dominated precipitation to rain-dominated precipitation, snow accumulation continues to outpace ablation over the Antarctic Ice Sheet through the year 2300, even under the high emissions Shared Socioeconomic Pathway 5-8.5 scenario. The positive relationship between precipitation and temperature increases through time at both high elevation in the continental interior as well as at the coastal margins of the ice sheet. Under high emissions, although rainfall is projected in some vulnerable regions, such as Thwaites Glacier, overall surface mass balance remains positive and increases through time. In corresponding ice sheet model experiments using the Parallel Ice Sheet Model, the sea level compensation of this increased surface mass balance is as high as 10 cm by 2100 and 1.8 m by 2300, though considerable intermodel spread exists. These model results suggest that mass loss of the ice sheet will continue to be dominated by ocean driven-melting rather than melting of the ice sheet surface for the next centuries.

How to cite: Trayling, N., Lowry, D., and Dadic, R.: Projected increases in Antarctic snow accumulation from CMIP6 to 2300, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10547, https://doi.org/10.5194/egusphere-egu22-10547, 2022.

We will present the design of a permittivity sensor that can be attached to a melting probe and measure the respective ice properties during the melting process, yielding in a comprehensive permittivity profile. Melting probes were already successfully applied in terrestrial cryospheres, such as alpine glaciers and Antarctica. Further applications to cross the ice shield on Dome C in Antarctica or even on icy moons in the outer solar system, such as Europa, are already planned e.g. within the TRIPLE project line funded by the German aerospace center. A sensor measuring the permittivity of the surrounding ice in situ during melting could provide valuable data about the ice properties. The respective density of the ice is correlated with the permittivity, or volcanic ash layers can be identified through permittivity measurements. Another usage of the data could be to correct distance measurements from radar travel times within the ice.

The sensor is designed to operate in the frequency range of 0.1 - 1.5 GHz and works in the range of the near field, which is defined to be within one wavelength, corresponding to the frequency. The concept of this sensor is based on an open coaxial probe, which is connected to the medium of interest. The measurement principle and calibration techniques, as well as first lab measurement results of ice and other materials will be presented. A comprehensive data set on effects of porosity, salinity and impurities of lab-manufactured ice samples on the permittivity will also be given. These data will help to interpret the taken permittivity profiles of glaciers on further missions.

We will also show how the device can be integrated into a melting probe, such as the TRIPLE melting probe. One major challenge is to ensure good contact to the ice during measurement. The diameter of a melting hole often results to be several cm larger in diameter than the melting probe itself. A mechanism that extends the sensors of the melting probe and press it onto the ice for measurements is being developed. 

How to cite: Becker, F., Friend, P., and Helbing, K.: Development of a permittivity sensor for melting probes to explore terrestrial and extraterrestrial cryospheres, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-574, https://doi.org/10.5194/egusphere-egu22-574, 2022.

In recent decades, the Greenland Ice Sheet (GrIS) has experienced a significant increase in surface melting and meltwater runoff, which is now the main contributor to GrIS mass loss. In areas covered by firn, meltwater percolation and refreezing processes can significantly buffer meltwater runoff to the ocean. However, this process leads to the formation of ice layers and an overall firn densification, which is predicted to limit the firns’ meltwater storage capacity in the future. Additionally, the high spatial and temporal variability of ice layer formation and subsequent firn densification can cause large uncertainties in altimetry-derived mass balance estimates. Thus, understanding the spatial and vertical extent of ice layers in the firn is important to estimate the GrIS contribution to sea-level rise.

Due to limited direct observations of firn properties, modeling future meltwater runoff and processes over the rapidly changing GrIS firn facies remains challenging. Here, we present a prospective new technique that leverages concurrent airborne radar sounding and laser altimetry measurements to characterize near-surface firn over spatially extensive areas. We hypothesize that due to their different depth sensitivities, the presence of ice layers in the firn yields an offset between radar sounding- and laser-derived surface elevations (differential altimetry). We compare existing airborne radar and laser measurements to in-situ firn observations and use one-dimensional radar sounding simulations to investigate 1) the sensitivity of the differential altimetry technique to different firn facies, and 2) the techniques’ capability to estimate firn density and firn ice content. Preliminary results over the western GrIS show good correlations between differential altimetry signatures and areas of firn affected by percolation and refreezing processes.

Through this technique, we explore the potential to leverage a wealth of radar sounding measurements conducted at low frequencies (< 200 MHz), that typically do not resolve the firn structure, to derive near-surface firn properties. Finally, we apply the differential altimetry technique to data collected as part of NASA’s Operation IceBridge between 2009-2019 to derive spatio-temporal changes in the GrIS firn in response to climatic conditions, in particular the formation of ice layers and changes in firn ice content. Our results can help reduce uncertainties in satellite-derived mass balance measurements and improve firn models, which both contribute to reducing uncertainties in current and projected GrIS contributions to global sea-level rise.

How to cite: Rutishauser, A., Ahlstrøm, A. P., Fausto, R. S., Karlsson, N. B., Vandecrux, B., Scanlan, K. M., Picard, G., and Andersen, S. B.: Using offsets in airborne radar sounding and laser altimetry to characterize near-surface firn properties over the Greenland ice sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-612, https://doi.org/10.5194/egusphere-egu22-612, 2022.

The internal ice stratigraphy as imaged by radar is an integrated archive of the atmospheric- oceanographic, and ice-dynamic history that the ice sheet has experienced. It provides an observational constraint for ice flow modeling that has been used for instance to predict age-depth relationships at prospective ice-coring sites in Antarctica’s interior. The stratigraphy is typically more disturbed and more difficult to image in coastal regions due to faster ice flow. Yet, knowledge of ice stratigraphy across ice shelf grounding lines and further seawards is important to help constrain ocean-induced melting and associated stability.

Here, we present preliminary results of synthesizing information from radar stratigraphic characteristics from airborne and ground-based radar surveys that have been collected for specific projects starting from the 1990s onwards focusing on ice marginal zones of Antarctica. The key data is based on airborne surveys from the German Alfred Wegener Institute’s polar aircrafts equipped with a 150 MHz radar. In the meantime this system has been replaced by an ultra-wide band 150-520 MHz radar. The older data will provide a baseline with extensive coverage that can be used for model calibration and change detection over time. We aim to provide metrics of the radio stratigraphy (e.g. shape and slope of internal reflection horizons) as well as classified prevalent stratigraphy types that can be used to calibrate machine learning approaches such as simulation based inference. The data obtained will be integrated in coordination efforts within the SCAR AntArchitecture Action Group.

How to cite: Drews, R., Koch, I., Oraschewski, F., Ershadi, M., Muhle, L. S., Spiegel, H., Visnjevic, V., Moss, G., Macke, J., Franke, S., Jansen, D., Steinhage, D., and Eisen, O.: Towards assembling the internal ice stratigraphy in coastal regions of Dronning Maud Land, East Antarctica, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-942, https://doi.org/10.5194/egusphere-egu22-942, 2022.

Temporally continuous measurements of the snow water equivalent (SWE) are a key variable in many hydrological, meteorological and glaciological studies and are of particular importance in high mountain regions. Obtaining temporally continuous, accurate and reliable SWE observations in these harsh environments, however, remains a challenge. Recently, promising results have been achieved by using a neutronic cosmic ray snow gauge (n-CRSG). The n-CRSG device is deployed below the seasonal snowpack and counts fast neutrons from the secondary cascades of cosmic rays, which are efficiently moderated and absorbed by the hydrogen atoms contained in the snowpack. Based on the exponential relationship between neutrons and hydrogen atoms, we can infer SWE from the neutron count rate. We have installed and evaluated a n-CRSG on the Swiss Glacier de la Plaine Morte. Our validation with 22 manual measurements over five winter seasons (2016/17-2020/21) showed an average underestimation of -2% ±10% (one standard deviation).
In the present study, we explore the use of muons instead of neutrons to infer SWE. To this end, we deployed two muonic cosmic ray snow gauges (µ-CRSG), one below and one above the seasonal snowpack, for the winter season 2020/21 on the same glacier site in Switzerland. The difference in count rates between the top and bottom device can be related to the SWE of the snowpack. We derive a first-cut conversion function based on manual SWE observations by means of snow pits and snow cores. To evaluate the measurements by the µ-CRSG, we also compare them to SWE estimates by the n-CRSG. Over the winter season 2020/21, almost up to 2000 mm w.e. were observed. Overall, the µ-CRSG agrees well with the n-CRSG on the evolution of the snowpack at a high temporal resolution and thus demonstrates its great potential. Also, the inferred SWE measurements lie within the uncertainty of manual observations. Furthermore, the µ-CRSG has several advantages over the n-CRSG; It is cheaper, lighter and promises a higher measurement precision due to the improved counting statistics of the muon count rates. We conclude that the µ-CRSG has even greater potential than the n-CRSG to monitor SWE in remote high mountain environments.

How to cite: Gugerli, R., Desilets, D., and Salzmann, N.: Application of cosmic ray snow gauges to monitor the snow water equivalent on alpine glaciers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1002, https://doi.org/10.5194/egusphere-egu22-1002, 2022.

In recent years radar polarimetry has re-surfaced as an ideal tool to determine ice-fabric patterns and linked mechanical ice anisotropy. The leap forward was facilitated by coherent data processing often collected by phase-sensitive Radio-Echo-Sounding (pRES) systems at fixed locations. The polarimetric response can either be synthesized from a set of quad-polarimetric measurements or obtained by manually rotating the antennas. Specifics of the data collection in the field varied between the different surveys, and no set of best practices has yet emerged.  Here we present a systematic study that includes more than fifty different combinations of how polarimetric data can be acquired, including:

• different distances between the transmitter and receiver (2, 4 and 8 m)
• different combinations in polarization orientation (22.5 deg)
• a comparison between discrete full azimuthal data collected every 22.5 degrees and synthesized data collected in a quad-pole setup
• the effect of 180-degree polarization orientation on repeat measurements, e.g., basal melt rate and polarimetric analysis, e.g., coherence phase
• definition of Horizontal (H) and Vertical (V) orientation is pRES antenna setup and its impact on synthesizing and analyzing data
• 90-degree fabric orientation ambiguity in polarimetric data

This study aims to provide best practices, considering that observation time in the field is limited. Ideally, this will lead to a unified setup and nomenclature, facilitating better compatibility from data collected by different groups on ice sheets, shelves, and glaciers.

How to cite: Eisen, O., Ershadi, R., Drews, R., Berger, S., Gong, D., Li, Y., Martin, C., and Zeising, O.: Best practices for collecting polarimetric data with ApRES for constraining ice-fabric orientation and its spatial variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1021, https://doi.org/10.5194/egusphere-egu22-1021, 2022.

Glaciers on Svalbard have been shrinking in recent decades in response to current climate change. Most of them have decreased in size, area and surface elevation with stable negative or even accelerated changes in mass balance. Many of them are of the polythermal type, and as they shrink, their thermal regime might also change significantly depending on climate and local parameters, such as distribution of ice facies, firn thickness, and other factors affecting hydrology and glacier movement. In this study, we used data from repeated GPR surveys in 2010/12 and 2020/21 to identify likely changes in the thermal regime of the two polythermal glaciers Fridtjovbreen and Erdmanbreen in the western part of the Nordenskiöldland. These changes we have identified by comparison of changes in the depth of the internal reflection horizon (IRH) which corresponds to the cold-temperate transition surface (CTS) in polythermal glaciers.

Comparison of radio-echo sounding (RES) data obtained along the same transverse and longitudinal transects shows that in the last decade the most prominent CTS changes have occurred in the upper western basin of the Fridtjovbreen, where the mean total ice thickness decreased with rate −0.76 m a^-1 (from 151 to 144 m in 9 years), meanwhile the thickness of the temperate ice core decreased with rate −2.52 m a^-1 (from 115 to 92 m). As a result, with a general reduction in the thickness of the glacier, its upper cold layer increased from 36 to 52 m. These changes we attribute to the reduction of the firn area in this basin, which resulted in less thermal insulation and water retention and internal refreezing, and, therefore, in the fast cold front penetration into the glacier body with rates more than 3 times higher than the glacier thinning.

How to cite: Borisik, A., Novikov, A., Lavrentiev, I., and Glazovsky, A.: Changes in the internal structure of polythermal glaciers over the last decade: the case study of Fridtjofbreen and Erdmanbreen from 2010 to 2021, Svalbard, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1852, https://doi.org/10.5194/egusphere-egu22-1852, 2022.

How to cite: Semmling, M., Wickert, J., Kreß, F., Hoque, M., Divine, D., Gerland, S., and Spreen, G.: Arctic Sea-Ice Permittivity Derived from GNSS Reflectometry Data of the MOSAiC Expedition, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3030, https://doi.org/10.5194/egusphere-egu22-3030, 2022.

Thanks to the excellent propagation characteristics of radar waves in ice, ground-penetrating radar (GPR) has been one of the key geophysical methods used in the field of glaciology over the last 50 years. Alpine glacier GPR surveys are typically performed either directly on the glacier surface (e.g., on foot, skis, or with snowmobiles), or by helicopter several tens of meters above the surface. Helicopter-based surveys allow the coverage of large areas safely and efficiently, but this comes at the expense of reduced resolution of glacier internal structures, particularly in the context of 3D surveys. On the other hand, ice-based acquisitions offer high-resolution opportunities, but are very time-consuming, often risky, and can be physically exhausting to perform. Recent advances in the development of drone technologies open new data acquisition possibilities for glacier GPR data, combining the advantages of both ice and air-based methods.

We have developed a drone-based GPR system that allows for safe and efficient high-resolution 3D and 4D data acquisition on alpine glaciers. Our custom-built GPR instrument uses real-time sampling to record traces of length 2800 ns, which corresponds to a depth of over 200 m in glacier ice. Each trace is stacked over 5000 times and acquired using a sampling frequency of 320 MHz, the latter of which is just enough to avoid aliasing with our single lightweight 70-MHz-center-frequency antenna. Traces are recorded at a rate of 14 Hz, meaning that a drone speed of at least 4 m/s can be considered while maintaining a sufficiently high trace density for high-resolution studies. This is at least four times faster than a conventional survey on foot. The total weight of our GPR system plus single transmit/receive antenna is around 2 kg. The drone used in our work has a maximum payload capacity of about 6 kg and is equipped with a radar-based ground sensor which enables us to follow the glacier surface topography during the flights. An independent differential GPS allows us to locate each recorded GPR trace with decimeter precision.

We performed initial testing of the above-described system in August 2021 on the Otemma glacier and successfully acquired around 70-line kilometers of 3D GPR data, over an 8-day period, covering a large portion of the glacier. In September 2021, we undertook additional fieldwork on the Tsanfleuron and Sex-Rouge glaciers and recorded 30-line kilometers of 3D GPR data in less than 3 days. We could then determine and model with high-precision the ice-thickness distribution over the Tsanfleuron pass. These first field results show the concrete benefit of drone-based GPR glacier surveys and motivate further development towards 3D and 4D studies.

How to cite: Ruols, B., Baron, L., and Irving, J.: Drone-based GPR system for 4D glacier data acquisition, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3073, https://doi.org/10.5194/egusphere-egu22-3073, 2022.

We have developed a method to determine shear-modulus (rigidity) structure for the upper 20-50m of the Earth. The method is based on the analysis of co-located pressure and seismic instruments. We applied this method to about 200 (co-located) stations in Alaska and examined seasonal variation in shallow shear-modulus structure at each site; in this report we quantify this seasonal change by taking the ratio (R) of the highest shear-modulus to the lowest throughout a year and use it as a characteristic feature for each station.

R is smaller than 2 at many stations but there are some stations in and near the Arctic zone that have R larger than 10. Such a large seasonal change implies that there occurs massive melting of shallow permafrost and a significant development of the active layer every summer. As a side product, because of such a huge reduction in near-surface shear-modulus, horizontal amplitudes in seismic noise become 30 times larger in summer than amplitudes in winter.

These seasonal changes may not be surprising because thawing of ice is common every summer in the permafrost region. But regions with large R show a systematic geographic pattern on a large-scale map; large-R stations are typically found near the coast (ocean) and tend to decrease toward the interior of the continent (Alaska and NW Canada). Large R stations are found in the NW Territories in Canada, the North Slope region northern side of the Brooks Range, near the Seaward Peninsula (west), and the Yukon-Kuskokwim Delta (west). These locations suggest a strong influence by the nearby ocean on the climate at each station. Proximity to the ocean (coast) seems to be an important factor in evaluating periglacial hazards.

There are a few exceptions in the northernmost coastal stations as they show small R despite the fact that they are at the coast. But the ICEsat-2 (satellite) data show that sea ice seems to remain thick near the peninsula (near Barrow, Alaska) much longer than other coastal areas in this study; temperature is colder because of thicker sea ice and the amount of melting at these exception sites remains low. This would strengthen the hypothesis that near-coastal ocean has strong influence on the climate of continental interior.

How to cite: Tanimoto, T. and Wang, J.: Strong Ocean Influence on Seasonal Changes in Shallow Shear-Modulus Structure in Alaska, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3192, https://doi.org/10.5194/egusphere-egu22-3192, 2022.

Estimation of snow SWE using passive RFID tags as radar reflectors

Mathieu Le Breton^(1,2), Éric Larose⁽¹⁾, Laurent Baillet⁽¹⁾, Alec van Herwijnen⁽³⁾, Yves Lejeune⁽⁴⁾

⁽¹⁾ Univ. Grenoble Alpes, CNRS, ISTerre, Grenoble, France
⁽²⁾ Géolithe Innov, Géolithe, Crolles, France
⁽³⁾ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
⁽⁴⁾ CEN-CNRM, Météo-France, CNRS, Saint Martin d’Heres, France

Passive radio-frequency identification (RFID) tags are used massively to remotely identify industrial goods, and their capabilities offer new ways to monitor the earth’s surface already applied to coarse sediments, landslides, rock fissures and soils (Le Breton et al., 2910, 2020, 2021b). We introduce a method to estimate the variations in snow water equivalent (SWE) of a snowpack using an 865–868 MHz (RFID) system based on commercial off-the-shelf devices. The system consists of a vertical profile of low-cost passive tags installed before the first snowfall, on a structure that is minimally disruptive to the snowpack. The tags are interrogated continuously and remotely by a fixed reader located above the snow. The key measured value is the increase of phase delay, induced by the new layers of fresh snow which slow down the propagation of the waves. The method is tested both in a controlled laboratory environment, and outdoors on the Col de Porte observation site, in order to cross-check the results with a well-documented reference dataset (Lejeune et al., 2019). The experiments demonstrate that SWE can be estimated by this non-contact and non-destructive RFID technique. However, multipath interferences in the snowpack can generate errors up to 40 mm of SWE. This error is mitigated by using multiple tags and antennas placed at different locations, allowing the RFID measurements to remain within +/-10% of the cumulated precipitations (outdoor) and snow weighting (laboratory). In complement, the system can also estimate whether the snow is wet or dry, using temperature sensors embedded in the tags combined with the received signal strength. Using this approach with a mobile reader could allow the non-destructive monitoring of snow properties with a large number of low-cost, passive sensing tags.

Publications related to the project:

Le Breton, M., Baillet, L., Larose, E., Rey, E., Benech, P., Jongmans, D., Guyoton, F., Jaboyedoff, M., 2019. Passive radio-frequency identification ranging, a dense and weather-robust technique for landslide displacement monitoring. Eng. Geol. 250, 1–10. http://doi.org/10.1016/j.enggeo.2018.12.027

Le Breton, M., Grunbaum, N., Baillet, L., Larose, É., 2021a. Monitoring rock displacement threshold with 1-bit sensing passive RFID tag (No. EGU21-15305). Presented at the EGU21, Copernicus Meetings. http://doi.org/10.5194/egusphere-egu21-15305

Le Breton, M., Liébault, F., Baillet, L., Charléty, A., Larose, É., Tedjini, S., 2021b. Dense and long-term monitoring of Earth surface processes with passive RFID -- a review. Submitted. Preprint at: https://arxiv.org/abs/2112.11965v1

Lejeune, Y., Dumont, M., Panel, J.-M., Lafaysse, M., Lapalus, P., Le Gac, E., Lesaffre, B., Morin, S., 2019. 57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m of altitude). Earth Syst. Sci. Data 11, 71–88. http://doi.org/10.5194/essd-11-71-2019

How to cite: Le Breton, M., Larose, É., Baillet, L., van Herwijnen, A., and Lejeune, Y.: Estimation of snow SWE using passive RFID tags as radar reflectors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3205, https://doi.org/10.5194/egusphere-egu22-3205, 2022.

In the Pyrenees, as in other mid latitude mountain ranges, sub alpine areas have a long lasting snow cover that affect different mountain processes, including river discharge timing, soil erosion, primary production or animal and plant phenology. This work presents and analyzes a complete snow depth and Normalized Difference Vegetation Index (NDVI) spatial distribution dataset, generated by Unmanned Aerial Vehicles (UAV) over two years. This study aims to increase the knowledge and understanding of the relationship of the duration and timing of snowmelt and vegetation cover and its annual cycle.

The dataset was obtained in Izas Experimental Catchment, a 55 ha study area located in Central Spanish Pyrenees ranging between 2000 to 2300 m a.s.l., which is mostly covered by grasslands. A total of 18 UAV snow depth and 14 NDVI observations were obtained by a fixed wing UAV equipped with RGB and multispectral cameras during 2020 and 2021. The melt out date for the different areas of the catchment has been obtained from the snow depth distribution dataset, which in turn has been used to analyze the NDVI evolution. The NDVI values for each UAV flight have been correlated with the snow depth distribution observed in previous dates and with different topographic variables as elevation, solar radiation, curvature (through the Topographic Position Index) or slope.

The maximum seasonal NDVI happens throughout the study area simultaneously in the entire study area; however those zones with the latest snow disappearance do not reach NDVI values as high as those observed in areas with earlier snow disappearance. Oppositely areas with the soonest snow melting (in late February) have lower maximum NDVI values that those observed in areas with snow melting occurring later (around May).  NDVI correlations have shown that the snow depth distribution observed about one month prior to each NDVI acquisition has a very important control on pasture phenology. This correlation is particularly evident on the free-snow areas during first melting weeks, with a lower influence in those areas where snow melts at the end of the snow season. This field study exemplifies how intensive UAV acquisitions allow understanding snow processes over extended areas with an unprecedented spatial resolution.

How to cite: Revuelto, J., Sobrino, J., Gómez, D., Rodriguez-López, G., Alonso-González, E., Rojas-Heredia, F., Izagirre, E., Montorio-Lloveria, R., Pérez-Cabello, F., and López-Moreno, J. I.: Annual development of subalpine grassland observed with UAV: how NDVI evolution is controlled by snow melting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3248, https://doi.org/10.5194/egusphere-egu22-3248, 2022.

Calving of tidewater glaciers is a key driver of glacier mass loss as well as a significant contribution towards sea level rise. However, this dynamic process is still challenging to quantify. In addition, there are very few direct measurements of calving activity in Svalbard at daily to sub-daily resolution due to the requirement of continuous human labour at the calving front for field studies. Seismic instruments in the vicinity of glaciers offer the potential to circumvent this issue since they record ground motion signals, including those generated by calving events, with an unprecedented sub-second resolution. Such data sets are not affected by site conditions like poor visibility or darkness and, in the case of permanent regional seismological stations, already offer long-term datasets. Despite this, a knowledge gap remains which prevents making a direct link between precise calving volumes and seismic records. This study presents our effort made towards obtaining an estimate of volumetric ice loss from integrating seismic records with 3D millimetre-wave radar measurements of a tidewater glacier calving front. In the summer of 2021, an 8-day long time series of integrated measurements was acquired at the calving front of Hansbreen, South Spitsbergen. It included remote sensing observations from a millimetre-wave radar (AVTIS2), Terrestrial Laser Scanner and time-lapse cameras correlated with a seismic dataset from two local arrays deployed at direct vicinity of calving front and a closeby regional permanent seismological station in Hornsund. Integrating these datasets brings an opportunity to correlate visual observations of calving including volumetric ice loss derived from radar scans with seismic signatures registered at nearby seismic arrays. We explore various parameters that characterize observed calving events and develop a model linking chosen parameters with ice loss using machine learning techniques. Local arrays were installed for a limited time and the calibrated parameters are expected to change spatially. Therefore, we further transfer our approach and integrate decade long records from nearby permanent seismological station. Limiting data to a single station record reduces both the accuracy of estimated ice volume and spatial resolution. However, it enables us to apply detection algorithm trained using observed calvings to decade long records and, consequently, to revisit a decade long history of Hansbreen's calving.

How to cite: Gajek, W., Harcourt, W., and Pearce, D.: Hansbreen’s calving-driven ice loss derived from seismic data supported by millimetre-wave radar scans and neural networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4179, https://doi.org/10.5194/egusphere-egu22-4179, 2022.

Ground and space based GNSS-IR (Interferometric Reflectometry) has been used in the last 20 years for characterizing the Earth Surface, together with other remote sensing techniques. Among the physical quantities which can be monitored using these techniques, the characterization of the snow cover is of particular interest since it is an important source of freshwater. The increase of the global temperature due to anthropogenic climate changes is threatening the seasonal recharging, and for this reason monitoring the snow cover is crucial. Ground based GNSS-IR can be used for obtaining information on the height of the snowpack, with a precision of 0.04 m by using geodetic-grade GNSS instruments (such those involved in Continuously Operating Reference Stations - CORS). In the present study, the sensitivity of the retrieval of the snowpack height from data acquired with low cost non-geodetic grade instruments with the GNSS-IR technique is evaluated. The analysis is applied to a flat alpine area in the Lavarone plateau in the Province of Trento, Italy (1400 m above sea level), where GNSS field campaigns were carried out in 2018, 2019 for short time periods (90, 120 minutes) due to constraints of the study area. Single-frequency GPS observations were collected with u-blox M8T GNSS receivers and patch u-blox and Tallysman antennas. Leica antenna and receiver were also used for collecting GPS data in double frequency, in order to acquire reference data with geodetic grade instruments. Given the characteristics of the area, it is possible to consider that GPS signals reflect with specular reflection, and thus modelling the Signal to Noise Ratio (SNR) as a function of the distance between the reflecting snow surface above solid ground and the antenna. Multipath frequency associated with snowpack height is retrieved by applying the Lomb Scargle Periodogram on SNR data. The results show that, by applying GNSS-IR technique to data acquired with low-cost receivers and antennas, it is possible to retrieve the height of the snow pack with a standard deviation of about 0.05 m. This demonstrates the feasibility of GNSS-IR also with non-geodetic grade instruments.

How to cite: Graldi, G., Rover, S., and Vitti, A.: Single-frequency GNSS-IR for estimating snowpack height with consumer grade receivers and antennas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4573, https://doi.org/10.5194/egusphere-egu22-4573, 2022.

Ice thickness is a key parameter for predictive ice sheet modeling, geological interpretation of the underlying bed rock, and site selection for deep ice sheet and bed rock sampling.  However, the uncertainties typically reported are in terms of crossover statistics, and ice thickness uncertainties are generally not formally integrated into ice sheet models.  Here we examine what crossover statistics reveal and conceal for the actual uncertainty in reported ice thickness, examine the impact of system and geometric parameters on uncertainties, and place these parameters in the context of the observed subglacial roughness.  We provide a predictive model for uncertainties as a function of ice thickness, sensor height, and subglacial roughness parameters, evaluate it from the perspective of ground based, airborne and orbital sounding and make recommendations for parameters that should be reported in ice thickness data products.

How to cite: Young, D., Kempf, S., and Ng, G.: Beyond crossovers: Predicting ice thickness uncertainties in ice penetrating radar data from geometric controls, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5506, https://doi.org/10.5194/egusphere-egu22-5506, 2022.

Over the past decades, ground-penetrating radar (GPR) has become a fundamental tool in glaciological studies thanks to its tremendous capacity to provide high-resolution images in snow and ice. 3D acquisitions in particular can give detailed information on the internal structure, properties, and dynamics of glaciers. For imaging and highlighting important englacial and subglacial features such as meltwater tunnels and voids, an analysis of the spatial distribution of diffractions in the data holds great potential. However, the diffracted wavefield typically has low amplitude and is often masked by more prominent arrivals. Diffraction separation and imaging procedures have already become topics of significant interest in the field of exploration seismology, and may potentially open new possibilities for the analysis of glacier GPR data.

Here, we explore the potential of recent advances in diffraction imaging for the analysis of alpine glacier GPR data. To this end, we consider a 3D data set acquired on the Haut Glacier d’Arolla (Valais, Switzerland) using a 70-MHz single-antenna real-time-sampling GPR system. The approach we use coherently approximates the dominant reflected wavefield and subtracts it from the data. The remaining diffracted wavefield is then enhanced using local coherent stacking. We find that this methodology is highly effective at isolating diffractions in glacier GPR data and provides clean images of the diffracting structures. Current work includes investigation of the correlation between these structures and the englacial and subglacial hydrological network.

How to cite: Klahold, J., Schwarz, B., Bauer, A., and Irving, J.: Diffraction imaging of alpine glacier GPR data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5865, https://doi.org/10.5194/egusphere-egu22-5865, 2022.

The radar detection of bedrock interface and internal ice layers is a widely used technique for observing interiors and bottoms of ice sheets, which is also an important indicator of inferring the evolution of glaciers and explaining subglacial topographies. The conventional methods, such as the filtering denoise, are limited by the low contrast in ice radar image with noise and interferes and thus the automatic method in tracing and extracting layers' features is trapped. The manual and semiautomatic methods are widely applied but with large time-consuming especially for the large-scale radar image with continuous bedrock and internal layers. To extract and identify the bedrock interface and internal ice layers automatically, we propose EisNet, a fusion system consisting of three sub neural networks. Because of the limitations of conventional manual methods, it is relatively rare that the high-precision extraction of layer features, which can be applied as labels in training. To obtain sufficient radar images with high-quality training labels, we also propose a novel synthetic method to simulate the not only visual texture of the bedrock interface and internal layers but also the artifact noise and interference to match the feature in field data. EisNet is first verified on synthetic data and shows capacity on the extraction of multi types of layer targets. Second, the application on observational radar images reveals EisNet’s generalized performance from synthetic data to the CHINARE data. EisNet is also applied to extract bedrock interfaces from the radar film from the Antarctic. EisNet is now open open-accessing. We hope that EisNet could be applied in more ice radar images from other regions and different forms to promote glacial research.

How to cite: Dong, S., Tang, X., and Fu, L.: Using EisNet to Extract Bedrock and Internal layers from Digital and Analog Radiostratigraphy in Ice Sheets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6377, https://doi.org/10.5194/egusphere-egu22-6377, 2022.

Atmospheric warming has led to increased surface melting on glaciers in the Arctic. This meltwater can percolate into firn and refreeze to form ice layers. Depending on their thickness, low-permeability ice layers can act as barriers that inhibit subsequent vertical meltwater infiltration in deeper firn pore space and favor lateral meltwater runoff. Thus, characterizing ice layers in firn is key for understanding the near-surface hydrological conditions that could promote surface meltwater runoff and its contribution to sea level rise.

Airborne ice-penetrating radar (IPR) is a powerful tool for imaging subsurface structure, but only recently have these systems been applied to direct observations of the bulk properties of the near-surface. To evaluate the bulk permeability of the near-surface firn system of Devon Ice Cap (DIC), Canadian Arctic, we use the Radar Statistical Reconnaissance (RSR) technique, originally developed for accumulation studies in West Antarctica. This method utilizes both the coherent and incoherent components of the total surface return, which are predominately sensitive to near-surface permittivity/structure within the system’s vertical range resolution and surface roughness, respectively. Here, we apply RSR to IPR data collected over DIC with the High-Capability Airborne Radar Sounder 2 (HiCARS) system (60 MHz center-frequency, 15 MHz bandwidth), operated by the University of Texas Institute for Geophysics (UTIG). Guided by ground-based ice-penetrating radar data and firn core density measurements, we show that the near-surface heterogeneous firn structure, featuring ice layers, mainly affects the observed coherent component.

We further compare the coherent component of HiCARS with that derived from IPR data collected with the University of Kansas Multichannel Coherent Radar Depth Sounder (MCoRDS) 3 system (195 MHz center-frequency; 30 MHz bandwidth), to evaluate the utility of dual-frequency IPR for characterizing near-surface ice layers. We expect that each radar system is sensitive to a different scale of near-surface bulk properties (i.e., depth and thickness of ice layers of different vertical extents), governed by each radar systems’ center frequency and bandwidth-limited range resolution. We leverage these differences in range resolution to derive ice layer thickness constraints in the DIC firn zone containing meter-thick ice layers, which are consistent with ground-based observations. Our results suggest this dual-frequency approach does indeed show that ice layers are vertically resolvable, spatially extensive, and mostly impermeable to surface meltwater. Thus, we hypothesize that lateral flow over high elevation meter-thick ice layers may contribute to the total surface runoff routed through supraglacial rivers down-glacier in the ablation zone.

How to cite: Chan, K., Grima, C., Rutishauser, A., Young, D. A., Culberg, R., and Blankenship, D. D.: Ice layer detection, distribution, and thickness in the near-surface firn on Devon Ice Cap: a new dual-frequency radar characterization approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6414, https://doi.org/10.5194/egusphere-egu22-6414, 2022.

Electrical resistivity tomography (ERT) is a geophysical method that produces an estimate of subsurface resistivity distribution, which can be used to infer the presence and extent of frozen ground. Repeated ERT surveys indicate how subsurface temperature and ground ice conditions are changing over time, which is particularly important for evaluating the changes and risks associated with climate change. However, there is no existing framework for sharing ERT data and datasets are rarely published, making it difficult to find and use historical data to assess subsurface changes. To facilitate data sharing, we are developing a Canadian database for ERT surveys of permafrost.

A key component of this project is the development of an automated ERT data processing workflow to prepare datasets. Establishing best practices for data processing ensures that ERT results are optimized and standardized, which is essential so that changes in subsurface conditions can be reasonably interpreted. We also present our web-based data visualization tool that allows for targeted searching of surveys and plotting of selected results. By storing ERT data in a standardized and accessible way, our goal is to facilitate interpretations of permafrost change on a range of spatial and temporal scales and guide future research in permafrost science.

How to cite: Herring, T. and Lewkowicz, A.: Creating a database of electrical resistivity tomography surveys of permafrost in Canada and establishing best practices for data processing and sharing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6575, https://doi.org/10.5194/egusphere-egu22-6575, 2022.

The thermal regime under lakes, ponds, and shallow near shore zones in permafrost zones in the Arctic is predominantly determined by the temperature of the overlying water body throughout the year.   Where the temperatures of the water are warmer than the air, unfrozen zones within the permafrost, called taliks, can form below the water bodies.

However, the presence of bottom-fast ice can decrease the mean annual bed temperature in shallow water bodies and significantly slow down the thawing or even refreeze the lake or sea bed in winter. Small changes in water level have the potential to drastically alter the sub-bed thermal regime between permafrost-thawing and permafrost-forming. The temperature regime of lake sediments is a determining factor in the microbial activity that makes their taliks hot spots of methane gas emission. Measurements of the sediment temperature below shallow water bodies are scarce, and single temperature-chains in boreholes are not sufficient to map spatial variability.

We present a new device to measure in-situ temperature-depth profiles in saturated soils or sediments, adapting the functionality of classic Bullard-type heat flow probes to the special requirements of the Arctic. The measurement setup consists of 30 equally spaced (5cm) digital temperature sensors housed in a 1.5 m stainless steel lance. The lance is portable and can be pushed into the sediment by hand either from a wading position, a small boat or through a hole in the ice during the winter. Measurements are taken continuously and 15 minutes in the sediment are sufficient to acquire in-situ temperatures within the accuracy of the sensors (0.01K after calibration at 0°C). The spacing of the sensors yield a detailed temperature-depth-profile of the near-surface sediments, where small-scale changes in the bottom water changes dominate the temperature field of the sediment. The short time needed for a single measurement allows for fine-meshed surveys of the sediment in areas of interest, such as the transition zone from bottom-fast to free water.

Test campaigns in the Canadian Arctic and on Svalbard have proven  the device to be robust in a range of environments. We present data acquired during winter and summer, covering non-permafrost, thermokarst lake and offshore measurements.

How to cite: Miesner, F., Cable, W., Boike, J., and Overduin, P. P.: In-situ measurements of sediment temperature under shallow water bodies in Arctic environments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7154, https://doi.org/10.5194/egusphere-egu22-7154, 2022.

Measurements of the seismic properties of Antarctic ice streams are critical for constraining glacier dynamics and future sea-level rise contributions. In 2020, passive seismic data were acquired at the Rutford Ice Stream, West Antarctica, with the aim of imaging the near-surface firn layer. A DAS (distributed acoustic sensing) interrogator and 1 km of optic fibre were supplemented by 3-component geophones. Taking advantage of transient seismic energy from a petrol generator and seismicity near the ice stream shear margin (10s of km away from the DAS array), which dominated the ambient seismic noise field,  we retrieve Rayleigh wave signals from 3 to 50 Hz. The extracted dispersion curve for a linear fibre array shows excellent agreement with an active seismic surface wave survey (Multichannel Analysis of Surface Waves) but with lower frequency content. We invert the dispersion curves for a 1D S-wave velocity profile through the firn layer, which shows good agreement with the previously acquired seismic refraction survey. Using a triangular-array geometry we repeat the procedure and find no evidence of seismic anisotropy at our study site. Our study presents challenges and solutions for processing noisy but densely sampled DAS data, for noise interferometry and imaging. 

How to cite: Zhou, W., Butcher, A., Kendall, J. M., Kufner, S.-K., and Brisbourne, A.: S-wave velocity profile of an Antarctic ice stream firn layer with ambient seismic recording using Distributed Acoustic Sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7409, https://doi.org/10.5194/egusphere-egu22-7409, 2022.

Geophysical characterization of rock glaciers commonly relies on electrical resistivity tomography (ERT) and seismic refraction tomography (SRT). Yet, large blocks make the installation of geophones and electrodes time consuming, while bad contacts lead to reduced signal-to-noise ratios in both methods. Additionally, ERT and SRT campaigns require rather heavy equipment and need long profiles to reach large depths of investigation. Transient electromagnetic (TEM) measurements offer diverse advantages, as they do not require a galvanic contact with the ground, and can be conducted with light instruments for simplified field procedures. We propose the application of TEM measurements with a single-loop configuration for the collection of extensive data sets in alpine environments. We hypothesize that TEM measurements provide the same information as SRT and ERT, yet field procedures of the TEM method are much more efficient permitting to cover larger areas in reduced time. In particular, we present investigations conducted on the Gran Sometta rock glacier (above Cervinia, Aosta Valley, Italian Alps). The study area consists of a large active rock glacier complex composed of two main lobes with varying ice content. Our surveys aimed at: (i) estimating the depth to the bedrock below the rock glacier, (ii) identifying the degree of weathering in the underlying bedrock, and (iii) evaluating spatial variations of ice content of the rock glacier. We collected TEM data with a TEM-FAST 48 system using 4 A current and a 50 m by 50 m single loop configuration. The experimental setup fits in a single backpack and our 3-person team covered an area of approximately 75’000 m² in 2.5 days, despite the difficult terrain. We measured 28 soundings distributed over the entire site and repeated two sounding locations with a larger 75 m square loop. Complementary spectral induced polarization (SIP) data were measured using 64 electrodes with a separation of 2.5 m between electrodes along two perpendicular profiles to validate our TEM results. We used separated transmitter and receiver instruments as well as cables to reduce EM coupling effects in our SIP data. TEM data reveal sign reversals, which are caused by the induced polarization effect due to the ice content in the rock glacier. We model the TEM response with the open-source algorithm empymod assuming a layered media. We observe that including a layer with a frequency-dependent polarization results in the signal reversals, while the geometry of such a layer also influences the TEM response. Furthermore, we observe that resistivity variations in the layer below the polarizable one can also be detected by the TEM data. Hence, our results demonstrate the applicability of TEM measurements to determine the geometry of the ice-rich layer in an active rock glacier, possible variations in ice content at the study area as well as the electrical properties of the underlying bedrock.

How to cite: Aigner, L., Roser, N., Moser, C., Maierhofer, T., Morra Di Cella, U., Hauck, C., and Flores Orozco, A.: Investigation of the induced polarization effect in transient electromagnetic soundings to characterize rock glaciers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7447, https://doi.org/10.5194/egusphere-egu22-7447, 2022.

Ten-year records of ice surface elevation changes derived from three GNSS stations placed on the interior of the Greenland ice sheet are used to assess the ability of CryoSat-2 radar altimetry to capture surface elevation changes during 2010-2021. We use GNSS interferometric reflectometry (GNSS-IR) to derive time series of continuous daily surface elevations. The footprint of GNSS-IR is about 1000 m2 and the accuracy is ±2cm, making it an excellent tool to validate ice surface height from satellite altimetry. We compare GNSS-IR derived ice surface elevations with CryoSat-2 derived surface elevations and find Cryosat-2 performs best at the GNSS site furthest north (GLS3) with a maximum difference of 12cm. The other GNSS sites have a higher residual range because of poorer data availability and local surface variations. The number of Cryosat-2 data points are roughly doubled from GLS1 and GLS2 to GLS3. GLS3 Is located in a very flat area of the ice sheet only moving 55m during 2011-2020. In contrast GLS1 moved 292m in the same period, clearly indicating a steeper slope to the ice sheet at this location, which we have difficulty correcting for because digital elevation models are associated with high uncertainty on the interior of the ice sheet. The strength of this assessment method lies in the continuous daily time series of surface elevation change derived from GNSS, as they clearly capture extreme short-term changes, which otherwise might have been perceived as errors in the radar altimetry measurements.

How to cite: Hansen, K., Larson, K. M., Willis, M. J., Colgan, W., Helm, V., and Khan, S. A.: Assessment of ESA CryoSat-2 radar altimetry data using GNSSdata at three sites on the Greenland Ice Sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7552, https://doi.org/10.5194/egusphere-egu22-7552, 2022.

The internal stratigraphy of alpine glaciers entails information about its past dynamics and accumulation rates. It further can be used for intercalibrating the age-depth scales of ice cores. The internal ice stratigraphy is often imaged using radar, but similar to polar ice sheets the deeper stratigraphy is often difficult to resolve with classical pulsed radar systems. For polar ice sheets, the introduction of phase coherent radars has illuminated this former echo-free zone (EFZ) and now patterns of folded, buckled and disrupted ice stratigraphy are clearly visible. Unfortunately, the new airborne and ground-based radar systems applied in polar regions are typically too heavy to be deployed in an alpine environment.

Here, we transfer the lightweight autonomous phase-sensitive radio-echo sounder (ApRES) to an alpine glacier targeting its echo-free zone (Colle Gnifetti, Italy/Switzerland). The ApRES is a coherent frequency modulated continuous wave radar with an integration time of 1 s per trace which we deployed in combination with a GNSS used in real time kinematic (RTK) mode. The latter allows repositioning of the antennas with sub-wavelength accuracy (approximately 5 cm) required to exploit the coherent signal. Like this, the radio-stratigraphy of the former EFZ at this site could be imaged using a matched filtering SAR method. The resulting radargrams cover former ice core sites (e.g., Ice Memory and KCC) and can be used to harmonize conflicting age-depth scales. This dataset will be analysed further in conjunction with ice-fabric measurements from ice cores to reveal how the anisotropic ice rheology imprints on the flow field of glaciers.

How to cite: Oraschewski, F., Koch, I., Ershadi, M., Hawkins, J., and Drews, R.: Illuminating the deeper radio-stratigraphy of an alpine glacier using SAR processing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7725, https://doi.org/10.5194/egusphere-egu22-7725, 2022.

As observed elsewhere on a global scale, permafrost at Mt. Zugspitze (German/Austrian Alps) is warming in response to climate change. To monitor permafrost warming and thawing, which affect the rock slope stability and thus the hazard potential, borehole temperature logging and electrical resistivity tomography (ERT) have been employed at Mt. Zugspitze for more than a decade. Furthermore, a recent study shows that the ambient seismic noise recordings of a single seismometer at the same site can be utilized to track permafrost changes over the past 15 years. This passive seismic approach is non-invasive, labour- and cost-effective and provides high temporal resolution. Together with recent advances in instrumentation allowing the measurement of seismic vibrations on a meter scale along a fiber-optic cable (known as distributed acoustic sensing), passive seismology provides unprecedented spatio-temporal resolution for monitoring applications.

Starting in July 2021, we extended the single-station deployment on Mt. Zugspitze with three small seismic arrays (six stations each, aperture ~25 m) along the permafrost-affected ridge. The stations are partly installed in a tunnel beneath the surface, which intersects a permafrost body, thus allowing in-situ observations of the frozen rock. We equipped the tunnel facilities with a fiber-optic cable, which we will interrogate on a regular basis, about once per quarter year, to resolve seasonal permafrost dynamics. A first 10-day data set of this monitoring element with seismic channel spacing of 2 m along a cable exceeding 1 km in length is already available and shows that artificial avalanche triggering explosions were successfully recorded. We present data and first results dedicated to permafrost monitoring along the fiber-optic cable and between pairs of seismic stations through cross-correlation of ambient seismic noise. In addition, the seismic arrays are designed to derive rotational ground motions, which we expect to be more sensitive to local subsurface/permafrost changes compared to the classical translational motion measurements. The experiment aims to explore the permafrost monitoring capabilities of passive seismology compared to more classical and established methods as ERT.

How to cite: Lindner, F., Smolinski, K., Igel, J., Bowden, D., Fichtner, A., and Wassermann, J.: A passive seismic approach including fiber-optic sensing for permafrost monitoring on Mt. Zugspitze (Germany), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8245, https://doi.org/10.5194/egusphere-egu22-8245, 2022.

In the Canadian Arctic, permafrost and permafrost-associated gas hydrates formed extensively during the last 1 Ma. After the last glaciation, a marine transgression followed and former terrestrially exposed shelf areas became submerged. Subaerial mean annual temperatures of -20°C or even less changed to present submarine bottom water temperatures near -1°C. The relict submarine permafrost and gas hydrates present in the Beaufort Sea still react to this ongoing thermal change which results in their continued degradation. Thawing permafrost and destabilisation of permafrost-associated gas hydrates may release previously trapped greenhouse gases and can lead to even further gas hydrate dissociation. Moreover, thawing permafrost poses a geohazard in form of landslides and ground collapses. Yet, both the extent of the submarine permafrost and the permafrost-associated gas hydrates are still not well known. Here, we present three different approaches using marine 2D multichannel seismic data to improve the current knowledge of the distribution of offshore permafrost and gas hydrates occurrences in the southern Canadian Beaufort Sea. The acoustic properties of permafrost are determined by the content of ice and unfrozen pore fluids. Changing permafrost conditions affect the elasticity of the medium making seismic methods appropriate for permafrost detection. First, we identify direct and indirect seismic reflection indicators from permafrost and gas hydrates by the presence of cross-cutting, polarity-reversed, and upward-bend reflections as well as velocity pull-ups and shallow pronounced high-amplitude reflections. Second, using diving-wave tomography provides insights into the near-surface permafrost structure by imaging the velocity structure in greater detail than achievable by standard velocity analyses.  And third, diffractions separated from the reflected wavefield yield insights into the sub-wavelength architecture of the permafrost realm on the southern Canadian Beaufort Shelf that may add information about weak phase-boundaries and small-scale heterogeneities. All methods are applied to seismic lines crossing the outer continental margin, where a maximum thermal effect of the transgression is expected, and thus a maximum lateral variation in permafrost and permafrost-associated gas hydrate phase boundaries is expected to be present. 

How to cite: Grob, H., Riedel, M., Duchesne, M. J., Krastel, S., Bustamante Restrepo, J., Fabien-Ouellet, G., Kläschen, D., Preine, J., Jin, Y. K., and Hong, J. K.: Using different seismic approaches to detect submarine permafrost and gas hydrates on the continental Beaufort shelf of the Canadian Arctic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8555, https://doi.org/10.5194/egusphere-egu22-8555, 2022.

Due to generally rising air temperatures in the European Alps in context of climate change, large areas of mountain permafrost are thawing, and subsurface pore ice is melting. Consequently, the cohesion of rock masses decreases which can constitute a threat for infrastructure like mountain huts in alpine areas. One directly affected building is the Guide Val d'Ayas al Lambronecca, a hut on a rock ledge in the Italian Alps at 3400 m above sea level. During the last decade the ground directly underneath the hut sank of about 2 m, probably due to the melting of pore ice in the subsurface below the hut. In this study, we investigate the subsurface properties beneath the hut using a 3D geophysical survey. In particular, we deploy the spectral induced polarization (SIP) method, which has emerged as a promising tool to discriminate between ice-rich and ice-poor regions in the subsurface. Our investigation is built on the hypothesis that ice can be identified in electrical images due to its high electrical resistivity and polarization (i.e., capacitive) properties at frequencies above 10 Hz. In our survey, we conducted 2D SIP measurements in summer 2020 (between 0.5 and 225 Hz) along three profiles near the hut, while real 3D SIP measurements (in the range between 1 and 240 Hz) were conducted in summer 2021. For the 3D measurements, we deployed two parallel lines, one on the southern and one on the northern rock wall of the summit where the hut is located. To improve the data quality, we used coaxial cables for the 2D measurements in 2020, while data collected in 2021 were based on the actual separation of the transmitter and receiver (i.e., instrument and cables) to reduce the contamination of the data due to parasitic electromagnetic fields. Processing of the data was based on the statistical analysis of normal and reciprocal misfits. Inversion of the data was performed in 3D using ResIPy which uses complex calculus to simultaneously resolve for the conductive and capacitive properties. Our imaging results evidence a core of ice-filled pores corresponding to high resistivity values (>10 kΩm) directly underneath the hut, this structure is overlain by lower values (<1 kΩm) in near-surface areas representing the active layer. Images of the polarization effect confirm an anomaly due to high values at frequencies above 10 Hz in the center of the rock ledge. Our study demonstrates that 3D SIP measurements can be used to differentiate between ice-rich and ice-poor areas in high mountain permafrost sites with complex topography. Moreover, 3D SIP approaches enable a detection of electrical anomalies in all three dimensions and not only along one certain direction in the case of 2D profiles. This information can be used to assess the impact of permafrost degradation on infrastructure stability in mountain regions and to support restoration actions.

How to cite: Moser, C., Maierhofer, T., Drigo, E., Morra Di Cella, U., Hauck, C., and Flores Orozco, A.: 3D Spectral Induced Polarization survey to evaluate a thawing permafrost endangered hut in the Italian Alps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8588, https://doi.org/10.5194/egusphere-egu22-8588, 2022.

High Arctic regions are experiencing an accelerated rise in temperatures, about three times more than the global average. As a result, the glacier coverage over these landscapes is reducing, uncovering soils which start their development by sustaining emergent microbial communities. These new systems will have a significant impact on the global carbon budget, thus monitoring and understanding their evolution becomes a necessity.

Geoelectrical methods have emerged as a fast, cost-effective and minimally invasive way of imaging soil moisture dynamics in the shallow subsurface. BGS PRIME technology is designed to facilitate low-power remote geoelectrical tomography by using an array of sensor electrodes. We are using such technology to monitor the year-round variability of soil electrical resistivity in 4D on a glacier forefield in the vicinity of Ny-Alesund, Svalbard. Until now, such assessment of soil properties was confined to the summer period due to harsh Arctic winter conditions making site access very difficult.

Two PRIME systems were deployed during the summer of 2021 on Midtre Lovénbreen glacier forefield, which exhibits a soil chronosequence extending from the youngest soils near the glacier snout up to soils of approximately 120 years old. The two geophysical systems are monitoring electrical resistivity within the top 2m of soil of approximately 5 and 60 years of age respectively, recording soil moisture and freeze-thaw dynamics within the active layer above the permafrost.

We present early results, a timeseries of 3D soil electrical resistivity models, that captured several precipitation events during the summer and the progression of the freezing front when soil temperatures dropped below 0 °C in October 2021. These results reveal differences in the hydrodynamic activity between the 5- and 60-year-old sites determined by soil properties and their location on the glacier forefield. In addition, soil cores were sampled from the vicinity of the PRIME systems. These were subsequently subjected to laboratory tests to describe the changes in electrical resistivity as a function of moisture content and during successive freeze-thaw cycles. Furthermore, we are working towards an integrated analysis and a more comprehensive model of soil evolution at our sites by combining geoelectrical measurements with point measurements of environmental parameters and microbiological activity.

How to cite: Cimpoiasu, M. O., Harrison, H., Meldrum, P., Wilkinson, P., Chambers, J., Bradley, J., Sommers, P., Schmidt, S. K., Irons, T., Liljestrand, D., Oroza, C., and Kuras, O.: Year-round high-resolution geoelectrical monitoring to improve the understanding of deglaciated soil evolution in the High Arctic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10159, https://doi.org/10.5194/egusphere-egu22-10159, 2022.

Several icy moons of our Solar System like Jupiter’s moon Europa have a global ocean of liquid water below their icy crust. These ocean worlds are possible targets for space missions that aim to assess their potential for habitability or even to search for life. Cryobots (or ice melting probes) are suitable tools to reach the subglacial oceans for in-situ investigations. The necessary ice shell transit provides an excellent opportunity to investigate structure and composition of the ice itself by means of geophysical and other in-situ measurements. This will allow us to better understand the evolution of icy moons and their role in our solar system.

We present current ideas as well as first results from terrestrial analogue studies. Acoustic data obtained during a field test on Langenferner Glacier, Italy was used to conduct a travel time tomography, which yields insight into heterogeneities in the local acoustic wave propagation speed through the ice. The acoustic sensor set-up was originally designed for localization of the melting probe rather than an investigation of the ice structure. However, we can still show that such opportunity data can be used to obtain a wave velocity distribution which can be further interpreted with respect to ice properties like porosity.

While we already investigated the acoustic data, we evaluate the potential of other measurements. For example, Radar measurements in combination with the acoustics can be used to identify the ice-water boundary and, in addition, cracks and inclusions in the ice. Conductivity measurements provide information on the salinity. At ice-water interface regions, the salinity is in thermochemical equilibrium with the temperature and porosity of the ice. We present our concept for on-board electrical conductivity measurements and analyze its potential, for example, to constrain ice properties and to predict ice-water interfaces based on existing terrestrial field data and process models. Furthermore, some of the cryobot’s housekeeping data might be of interest for investigating the ambiance, too. For example, the temperature and the density of the ice affect the melting velocity of the cryobot, which constitutes an inverse problem to get further information on the ice.

How to cite: Boxberg, M. S., Simson, A., Chen, Q., and Kowalski, J.: Investigation of ice with geophysical measurements during the transit of cryobots, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10195, https://doi.org/10.5194/egusphere-egu22-10195, 2022.

Geoelectrical methods are widely used for permafrost investigations by research groups, government agencies and industry. Electrical Resistivity Tomography (ERT) surveys are typically performed only once to detect the presence or absence of permafrost. Exchange of data and expertise among users is limited and usually occurs bilaterally. Neither complete information about the existence of geophysical surveys on permafrost nor the data itself is available on a global scale. Given the potential gain for identifying permafrost evidence and their spatio-temporal changes, there is a strong need for coordinated efforts regarding data, metadata, guidelines, and expertise exchange. Repetition of ERT surveys is rare, even though it could provide a quantitative spatio-temporal measure of permafrost evolution, helping to quantify the effects of climate change at local (where the ERT survey takes place) and global scales (due to the inventory).

Our International Permafrost Association (IPA) action group (2021-2023) has the main objective of bringing together the international community interested in geoelectrical measurements on permafrost and laying the foundations for an operational International Database of Geoelectrical Surveys on Permafrost (IDGSP). Our contribution presents a new international database of electrical resistivity datasets on permafrost. The core members of our action group represent more than 10 research groups, who have already contributed their own metadata (currently > 200 profiles covering 15 countries). These metadata will be fully publicly accessible in the near future whereas access to the resistivity data may be either public or restricted. Thanks to this open-access policy, we aim at increasing the level of transparency, encouraging further data providers and fostering survey repetitions by new users.

The database is set up on a virtual machine hosted by the University of Fribourg. The advanced open-source relational database system PostgreSQL is used to program the database. Homogenization and standardization of a large number of data and metadata are among the greatest challenges, yet are essential to a structured relational database. In this contribution, we present the structure of the database, statistics of the metadata uploaded, as well as first results of repetitions from legacy geoelectrical measurements on permafrost. Guidelines and strategies are developed to handle repetition challenges such as changing survey configuration, changing geometry or inaccurate/missing metadata. First steps toward transparent and reproducible automated filtering and inversion of a great number of datasets will also be presented. By archiving geoelectrical data on permafrost, the ambition of this project is the reanalysis of the full database and its climatic interpretation.

How to cite: Mollaret, C., Hilbich, C., Herring, T., Farzamian, M., Buckel, J., Dafflon, B., Draebing, D., Fossaert, H., Gugerli, R., Hauck, C., Kunz, J., Lewkowicz, A., Limbrock, J. K., Maierhofer, T., Magnin, F., Pellet, C., Pfaehler, S., Scandroglio, R., and Uhlemann, S. and the IDGSP IPA Action Group: Initiation of an international database of geoelectrical surveys on permafrost to promote data sharing, survey repetition and standardized data reprocessing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10565, https://doi.org/10.5194/egusphere-egu22-10565, 2022.

Snow on sea ice is a controlling factor for ocean-atmosphere heat flux and thus ice thickness growth, and surface albedo. Active and passive microwave remote sensing is the most promising way to estimate snow depths over large sea ice areas although improved validation is understood as a missing information to support further progress. However, severe limitations in the representative measurement of snow depth over sea ice persist, which exacerbates sea ice mass balance assessments as well as the indirect estimation of consolidated ice thickness from remotely sensed freeboard.

We have designed and flown a snow radar in combination with an electromagnetic induction device for sea ice thickness. The goal was the simultaneous measurement of both the consolidated sea ice thickness and the snow depth on top as a tool to derive snow and ice statistics for satellite validation. The snow radar was integrated into an EM-bird and flown about 15 m above the surface by suspending the instrument from a helicopter. The combination of the applied technologies hasn’t been deployed in this configuration before. The helicopter flight speed was around 70 knots, resulting in a snow measurement about every four meters. The EM instrument can detect ice thickness at 0.1m accuracy, whereas the snow radar is designed to measure snow depth at 0.05m accuracy.

Our field area was the land-fast sea ice and adjacent ice shelf in McMurdo Sound (Antarctica) in November 2021. During this time we found a relatively shallow but variable snow cover (up to about 0.3m) above sea ice of about 2m thickness. Deeper snow was only measured at the transition from the sea ice to the ice shelf, and on the ice shelf itself, where the maximum radar penetration in snow in ideal conditions is estimated to be around 2-3 meters.

We present first results of snow cover statistics in comparison to ground validation and observed snow characteristics, and we compare these results to airphotos and optical satellite imagery. We show that the measurement set-up meets the requirements for level ice and rough fast ice with patchy but dry snow cover. The system still needs to be tested over pack ice with potentially more complex snow morphology.

How to cite: Rack, W., Tan, A., Haas, C., Farooq, U., Taylor, A., Kind, A., Barnsdale, K., and Leonard, G.: Combined measurement of snow depth and sea ice thickness by helicopter EM bird in McMurdo Sound, Antarctica, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10835, https://doi.org/10.5194/egusphere-egu22-10835, 2022.

Ground surface movements and snow cover during freeze/thaw cycles of permafrost are important variables for studying climate change. GPS-IR has emerged as an effective technique to estimate the relative elevation changes of ground surface such as the thaw subsidence of frozen ground and snow depth variations. In permafrost areas, the freezing process of the ground is always accompanied by the snow accumulations, making it hard for GPS-IR to separate these two distinct signals from the estimated elevation changes. In this study, using the Signal to Noise Ratio (SNR) collected by a permafrost GPS site SG27 (Northern Alaska) in 2018, we proposed a physical model-based method to simultaneously estimate the daily snow depths and freezing-ground uplifts with GPS-IR. First, we applied GPS-IR to the SNR data to obtain the daily elevation changes of the ground surface from September 1 in 2018 to August 31 in 2019. The elevation change measurements indicate the onset of snow season on October 18 in 2018 and the end of snow-cover on June 15 in 2019. Second, we used the thermal index Accumulated Degree Days of Freezing (ADDF) calculated from the temperature records to determine the onset of the permafrost freezing season as of September 17 in 2018. Third, we fitted the Stefan function to the estimated elevation changes (i.e. freezing-ground uplifts) from September 17 to October 18 in 2018. The Stefan model agrees with the freezing uplifts with an R² of 0.65. Forth, we extended the fitted model to the time when the ground was completely frozen (November 1) to estimate daily freezing-ground uplifts up to 1.75 cm under the snowpack. Last, we extracted the snow depths from the estimated elevation changes by subtracting the corresponding freezing-ground uplifts. Our study is the first attempt to simultaneously estimate the daily freezing-ground uplifts and snow depths over the permafrost area with GPS-IR, providing the measurements to understand the coupling effects of the permafrost and snow cover.

How to cite: Hu, Y. and Wang, J.: Simultaneous estimation of snow depth and freezing-ground uplift by GPS Interferometric Reflectometry over a permafrost area, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10858, https://doi.org/10.5194/egusphere-egu22-10858, 2022.

The physical properties of the ice column are fundamental to the deformation and flow of glaciers and ice sheets. With a warming climate, surface meltwater is ever increasingly being routed and distributed throughout the ice column changing the mechanical and hence thermal properties of the ice and leading to accelerated ice flow and ice mass loss. Since the early 1990s, ice mass loss from the Greenland Ice Sheet (GrIS) has contributed ~10% of the mean global sea level rise. Seismic waves have routinely been used to study the physical characteristics of glaciers and ice sheets due to their sensitivity to both mechanical and thermal properties of ice. Traditionally, reflection seismic surveys have been chosen as the primary seismic approach but this survey method can suffer from difficult logistics in polar regions. Recent advancements in ambient noise methods and the permanent installation of a seismic network in Greenland now permit the long term study of the ice properties of the GrIS.

Rayleigh wave ellipticity measurements (the horizontal-to-vertical ratio of Rayleigh wave particle motions) are particularly sensitive to the subsurface structure beneath a seismic station. Using the polarisation properties of seismic noise, we extract Rayleigh wave ellipticity measurements from the Earth’s ambient noise for on-ice stations deployed in Greenland from 2012-- 2018. For wave periods sensitive to the ice sheet (T ≤ 3.5 s), we observe significant deviation between ellipticity measurements extracted from noise and synthetic fundamental mode calculations using a single ice column. Using a forward modelling approach we show: (1) a slow seismic shear-wave velocity at the near surface, (2) seismic attenuation, quantified as the quality factor Q, is sensitive to the temperature, water content and density of the ice and (3) the excitation of Rayleigh wave overtones plays a leading role in perturbing the ellipticity. Our results highlight how the inclusion of Q and overtone information can fill important gaps in our knowledge of ice sheet temperature, density and water content, which are important for predictions of the future evolution of the GrIS.

How to cite: Jones, G., Ferreira, A., Kulessa, B., Schimmel, M., Berbellini, A., and Morelli, A.: Characterising ice sheet properties using Rayleigh wave ellipticity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12006, https://doi.org/10.5194/egusphere-egu22-12006, 2022.

The Apennine mountain range is the backbone of the Italian peninsula, crossing it from North-West to South-East for approximately 1200 km. The main peaks are found in Central Apennines, especially in the Gran Sasso d’Italia massif, which hosts the highest Apennines peak, named Corno Grande, with its 2912 m a.s.l. During the winter season, Central Apennines are typically covered with snow, with thickness that can vary between a few centimeters to several meters. Despite the historical presence of snow in these territories, the Apennine snowpack is poorly studied and weather data coming from automatic measurement stations and manual snow measurements hardly coexist. Thus, within the SMIVIA (Snow-mantle Modeling, Inversion and Validation using multi-frequency multi-mission InSAR in Central Apennines) project, we identified the measurement sites of Pietrattina, at 1459 m a.s.l, and Campo Felice, at 1545 m a.s.l., both located in Central Apennines. There we collected automatic measurements using ad hoc installed automatic weather-snow stations (AWSS) and where we performed systematic manual measurements of the snowpack properties, from November 2020 till April 2021. The AWSS measures every 5 minutes air temperature, relative humidity, wind speed, wind direction, incoming short-wave radiation, reflected short-wave radiation, soil surface temperature, snow surface temperature and snow height. The manual part of the campaign included the digging of 10 and 8 snow pits at Pietrattina and Campo Felice sites, respectively, to measure vertical profiles of snow density, temperature, grain shape, grain size and fractional content of light absorbing impurities. Manual snow measurements provide important information on the state of the snowpack, and give the opportunity to reconstruct the history of the snowpack. Their proximity to automatic weather stations let us evaluate the impact of the very local atmospheric conditions on the snowpack evolution. These measurements were performed within the SMIVIA project to: i) evaluate the ability of the snow cover model SNOWPACK to reproduce the observed snow cover properties; ii) verify the possibility to infer snow height and snow water equivalent from the data retrieved with Earth observation satellites; iii) investigate whether the use of a combination of snow numerical models and remote sensing data may provide better results compared to using each of the aforementioned approach, separately. Nevertheless, the data collected during the SMIVIA campaign at the measurement sites of Pietrattina and Campo Felice during season 2020-2021 can also provide precious information for other fields of study, like hydrology, biology and chemistry.

How to cite: Raparelli, E., Tuccella, P., Lombardi, A., Palermo, G., Alvan Romero, N., Papa, M., Picciotti, E., Di Fabio, S., Pettinelli, E., Mattei, E., Lauro, S., Cosciotti, B., Petroselli, C., Cappelletti, D., Pecci, M., and Marzano, F. S.: Snow measurement campaign for snowpack model and satellite retrieval validation in Italian Central Apennines within SMIVIA project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12082, https://doi.org/10.5194/egusphere-egu22-12082, 2022.

Seismic 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. Seismic data can provide an alternative to remote-sensing and ground-based radar measurements for estimation of ice thickness in cases where radar techniques fail. Because of the difficult access to Alpine and Arctic environments as well as seismic sensor coupling issues in ice environments, it is of interest to optimize the use of seismic instruments in terms of sensor type, sensor numbers and layouts.

With the motivation to monitor over time the seismic activity of the lake ice and the ice properties, we conducted a series of seismic experiments on frozen lake St. Moritz in the Swiss Alps during two consecutive winters. Arrangements of sensors ranging in numbers from 96 geophones in mini-arrays to installations of 8, 2 and 1 conventional seismic sensors were used to measure the seismic wavefield generated by ice quakes (cryoseisms), artificial sources like hammer strokes, and ambient vibrations. These data provide an impressive and rich insights into the growth of the ice and variations of seismic activity with time. Even recordings with only a single station enable the determination of ice parameters and location of ice seismicity. Furthermore, we are exploring the value of recording air-coupled waves with microphones as alternative contact-free measurements related to seismic wave propagation in the ice, possibly even with sensors placed on the lake shore.

How to cite: Schmelzbach, C., May, D., Wetter, C., Stähler, S., and Clinton, J.: Monitoring lake ice with seismic and acoustic sensors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12233, https://doi.org/10.5194/egusphere-egu22-12233, 2022.

The seasonal snowpack influences the hydrology, ecology and economy of the areas where it is present. However, the real time monitoring of the seasonal snowpack is a still well known scientific challenge. In this study, we have explored the potential of muon scattering radiography (MSR) to infer the snow water equivalent (SWE) of the snowpack. We have used the energy and mass balance model Snowpack to realistically simulate the time evolution and microstructure of the snowpack. The ERA5-Land reanalysis was used as forcing of Snowpack, in a location close to the Monte Perdido massif (Central, Pyrenees) at an elevation of 2041m above sea level. The simulations cover the hydrologic year 2015/2016, approximately reaching up to 700mm of peak SWE. Then, we have coupled the Snowpack numerical simulations with the Geant4 model to simulate the propagation of the muons through the snow layers and to collect the deviation of the muon trajectories. We have measured these deviations with a virtual muon detector based in multiwire proportional chambers, replicating a real detection system designed by us. The obtained distributions of muon deviations have exhibited a strong correlation with the simulated SWE, showing a coefficient of determination of 0.99. This model presents a root-mean-square error (RMSE) of 23.9mm in the SWE estimation. In order to validate the simulation analysis results, we have replicated the numerical experiments under controlled conditions, measuring three artificial snow samples ranging from 0 to 200 mm of SWE in our laboratory. We have measured the samples with an experimental setup composed of the real muon detector whose hardware was virtually replicated for the numerical experiments. Then, we have applied the model derived from the numerical simulations to the muon deviations measured in our laboratory. We have calibrated the real measurements and we have obtained a RMSE of 38.4mm in the SWE estimation. These results show that MSR is a promising non-destructive technique that can be used for the deployment of accurate SWE monitoring networks and can eventually provide information from the internal layered structure of the snowpack.

How to cite: Orio, A., Alonso, E., Martínez, P., Díez, C., and Gómez, P.: Exploring the potential of cosmic muon scattering to measure the snow water equivalent, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12490, https://doi.org/10.5194/egusphere-egu22-12490, 2022.

The release of icebergs into the ocean through glacier calving is a major source of mass loss from tidewater glaciers across the Arctic. However, there are very few direct measurements of calving activity in Svalbard at daily to sub-daily resolution which impedes our understanding of how these processes influence ice discharge and therefore regional patterns of mass balance. Quantifying ice loss from Svalbard is important because the archipelago contains ~10% of the total Arctic glacier area and holds a sea-level equivalent of ~1.5 cm. In this contribution, we generate an 8-day time series from August 2021 of calving activity at sub-daily resolution for the Hansbreen tidewater glacier in Svalbard using a suite of state-of-the-art remote sensing instruments. Millimetre-wave radar at 94 GHz (called AVTIS2) was used to map the 3D structure of the Hansbreen frontal ice cliff, so that terminus change could be tracked and the volume of ice released through calving quantified. Millimetre-wave radar can map glacier surfaces at high angular resolution and through most weather conditions, hence is not impeded by poor weather conditions unlike instruments such as Terrestrial Laser Scanners (TLS). AVTIS2 mechanically scans across the scene of interest, measures radar backscatter along each Line of Sight (LoS) and generates 3D point clouds by calculating the range to maximum received power along each LoS. In this study, an angular area of 83° (azimuth) x 5° (elevation) was scanned which ensured the entire marine-terminating portions of the ice front were measured throughout the study period. The 3D AVTIS2 point clouds were validated using a coincident survey from a TLS (Riegl LPM-321) and a time-lapse camera deployed at the same location to provide additional validation and knowledge of environmental conditions throughout the study period. Calving events from both datasets were correlated to seismic activity recorded by two networks of geophones deployed in the vicinity of the glacier terminus. We will report on the following: (1) the calving rate of Hansbreen in August 2021, (2) the volume of ice released into the ocean through calving during the 8-day study period, (3) the capabilities of millimetre-wave radar for monitoring glacier calving fronts versus optical approaches (TLS and time-lapse camera images), and (4) calving processes at Hansbreen. This study pushes forward our understanding of frontal ablation processes in Svalbard and demonstrates new possibilities for ground-based remote sensing of ice-ocean interactions.

How to cite: Harcourt, W. D., Robertson, D. A., Macfarlane, D. G., Rea, B. R., Spagnolo, M., Benn, D. I., James, M. R., Gajek, W., Pearce, D., and How, P.: Millimetre-wave radar observations of glacier calving at Hansbreen (Svalbard) correlated with TLS, time-lapse camera images and seismic records, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-143, https://doi.org/10.5194/egusphere-egu22-143, 2022.

Climate change is affecting glaciers worldwide, leading to unprecedented melt rates. In this context, establishing systems that provide near-real-time glacier information can be of high interest. However, the effort for acquiring real-time, in situ glacier observations is large.

In a previous study, we investigated the potential for automated acquisition of real-time mass balance readings by using optical cameras installed in-situ and computer vision techniques. The setup proved to be useful for obtaining melt rates with a temporal resolution of 20 minutes. However, it is not feasible to cover an extensive portion of a glacier with such a setup.

In our contribution, we present a method to acquire glacier mass balance readings with a custom drone equipped with a camera. The principle is to acquire images of a color-coded stake, from which surface mass balance can be determined via the glaciological method. To autonomously approach and read the stake, we exploit a combination of computer vision techniques and geometrical triangulation.  The results of off-glacier test flights, as well as four flights on Rhonegletscher, Switzerland, prove that the system is successful in detecting the stake in the videos captured by the drone. The determined stake position has uncertainties of 2.4 - 4.6 m, thus being sufficient to safely approach the stake. We investigate the main factors influencing the performance of the method in more detail, and discuss potential future developments of the system.

How to cite: Cremona, A., Landmann, J., Sold, L., Borner, J., and Farinotti, D.: Testing drones and computer vision for acquiring glacier melt observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-243, https://doi.org/10.5194/egusphere-egu22-243, 2022.

The turbulent exchange of heat at the surface, including the sensible heat flux (SHF), is an important component of the surface energy balance (SEB) over glaciers and ice sheets. Yet, the turbulent heat fluxes are parameterized in all SEB models, which makes their contribution to the modelled ice ablation uncertain.

In this study, we present several years of continuous, daily, in situ observations of SHF (eddy-covariance) and ice ablation, taken at multiple contrasting sites across the ablation area of the Greenland ice sheet. We then compare these measurements to several SEB models with different settings for the surface roughness lengths.

We show that it is possible to accurately model the SHF and the daily ice ablation, provided that the prescribed surface roughness lengths, for both heat and momentum, are accurate. We propose a simple parameterization of these roughness lengths, based on both in-situ measurements and remotely sensed data (UAV, ICESat-2).  This updated parameterization can be implemented in SEB- and climate- models for improved simulations of ice sheet ablation and surface mass balance.

How to cite: van Tiggelen, M., Smeets, P. C. J. P., Reijmer, C. H., van den Broeke, M. R., van As, D., Box, J. E., and Fausto, R. S.: Momentum- & heat- flux parameterization over the Greenland Ice Sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1480, https://doi.org/10.5194/egusphere-egu22-1480, 2022.

A large amount of velocity data is now becoming available through portals, pipelines and repositories. Typically the error characterisation for these individual velocity fields or mosaics is done through sampling statistics, resulting in a proxy of precision for the whole dataset. However even within a scene pair, the appearance can change considerably, or be stable at nearby locations. For example, think of regions close to the transient snowline, or an elongated moraine band, a  crevasse train after a bump or a shear zone. Here the precision of localising an exact image match is clearly anisotropic. If such anisotropic precision estimates are taken into account, it is possible to provide a more correct error-propagation. The merit of velocity data can be found in the help for inversion for thickness estimates (as it is related to the fourth power), or shear and strain rates. Here we introduce a simple and fast methodology to generate an individual dispersion estimate, based upon the similarity surface of an image match. A linear least squares adjustment of the neighbouring similarity scores is sufficient to fit an oriented gaussian peak. This setup makes the computation fast and is easy to implement into already available processing pipelines. We demonstrate its effectiveness on two glaciers, Sermeq Kujalleq, a large outlet glacier of the Greenland icesheet, with strong shear margins and Malaspina Glacier a piedmont glacier with looped moraines. We find directionality within an image subset to be the dominant factor influencing the correlation dispersion. This stems from crevasses and moraine bands within the imagery, while a relation to differential flow, such as shear, is less pronounced. It is our hope, this methodology will narrow the integration gap between models and measurements.

How to cite: Altena, B., Kääb, A., and Wouters, B.: Precision description for remote sensing glacier velocity data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2286, https://doi.org/10.5194/egusphere-egu22-2286, 2022.

Spatially distributed winter snow accumulation over glaciers is an important information for a lot of purposes. Typically, snow depth on glaciers is measured by manual snow probing or ground penetrating radar. The point measurements of snow depth and snow density are then used to calculate the winter mass balance of the glacier.

In the last decade remote sensing techniques such as LIDAR and structure from motion (sfm) photogrammetry in combination with unmanned aerial vehicles (UAVs) have become more frequent to reconstruct snow surfaces providing a better spatial coverage and spatial resolution. Snow depth is calculated by DEM differencing of a No-Snow surface (summer surface) and the snow surface (winter surface).

However, using DEM differencing to extract snow depth over glaciers introduces the problem, that the No-Snow surface is not constant, as (1) the glacier is moving between the survey dates and (2) the surface possibly undergoes surface lowering due to melt after the summer survey.

In this study we present measurements on two small mass balance glaciers in the Austrian Alps (Goldbergkees and Kleinfleißkees). We account for the evolution of the No-Snow surface by (1) applying a simple model of the vertical ice movement and by (2) calculating the surface lowering due to melt using a distributed mass balance model.  The effect of both corrections is then validated using a dense network of manual snow depth measurements across the glacier.

How to cite: Hynek, B., Neureiter, A., Weyss, G., Ludewig, E., and Schöner, W.: Correcting UAV derived winter snow depth on glaciers by modelling the evolution of the No-Snow glacier surface, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2317, https://doi.org/10.5194/egusphere-egu22-2317, 2022.

The creation and curation of environmental data present numerous challenges and rewards. In this study, we reflect on the maturity of freely available glacier data sets (inventories and changes), as well as on related demands by data providers, data users, and data repositories in-between. The amount of glacier data has increased significantly over the last two decades, especially as remote-sensing techniques have developed quickly. The portfolio of observed parameters has increased as well, which presents new challenges for international data centers, and fosters new expectations from users.

We assess the services of the Global Terrestrial Network for Glaciers (GTN-G) as the central organization for standardized data on glacier distribution and changes. Within GTN-G, different glacier data sets are consolidated under one umbrella, and the glaciological community supports this service by actively contributing their data sets and by providing strategic guidance via an Advisory Board. To assess each GTN-G data set, we present a maturity matrix and summarize achievements, challenges, and future ambitions.

Most challenges can only be overcome in a financially secure setting for data services and with the help of international standardization. Therefore, dedicated support and long-term commitment for certified data repositories build the basis for the successful democratization of data. In the field of glacier data, this balancing act has so far been successfully achieved through joint collaboration between data repositories, data providers, and data users. However, we also note an unequal allotment of funds for data creation and projects using the data, and data curation. Considering the importance of glacier data to answering numerous key societal questions (from water availability to global sea-level rise), this imbalance needs to be adjusted. In order to guarantee the continuation and success of GTN-G in the future, basic funding schemes, flexible adaptation measures, and regular evaluations are required.

How to cite: Gärtner-Roer, I., Nussbaumer, S. U., Raup, B., Paul, F., Welty, E., Windnagel, A., Fetterer, F., and Zemp, M.: Maturity of worldwide glacier data sets – history and future ambitions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3376, https://doi.org/10.5194/egusphere-egu22-3376, 2022.

The PROMICE project runs 27 Automatic Weather Stations (AWSs) in Greenland. Most of these are located in the ablation zone of the Greenland ice sheet. From March to September 2020 a multi-frequency Global Navigation Satellite System (GNSS) antenna was installed on the AWS NUK-K at a small local glacier outside Nuuk with the purpose of testing the setup for high precision positioning. Due to the remote location, power supply is limited and the GNSS setup is constructed to minimize the power consumption. Therefore, data collection is limited to three hours each day, the antenna is passive and the data is stored on a local drive and not transmitted.

This study tests if the setup is feasible for GNSS Interferometric Reflectometry (GNSS-IR) measurements of snow depth. The method estimates the average snow depth over an area on the order 10-20 • 10³ m². GNSS-IR analysis shows good reflections during most of the covered time period. A sonic ranger is mounted on the PROMICE AWSs and used for measurement of snow depth. The GNSS reflector heights are compared to measurements from the sonic ranger. Though some differences are present, the GNSS-IR estimates capture the snow melt, as measured by the sonic ranger, well. The quality of the reflections decreases towards the end of the data series when the snow is melted. We expect that this is due to a rougher ice surface. However, useful reflections are still obtained but the uncertainty on the daily estimates increase significantly. The transition from snow to ice surface is confirmed by an albedo estimate based on measurements of shortwave radiation at the AWS.

How to cite: Dahl-Jensen, T., Khan, S. A., Citterio, M., Jakobsen, J., and Ahlstrøm, A.: GNSS-IR for snow studies at PROMICE automatic weather station in Greenland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3609, https://doi.org/10.5194/egusphere-egu22-3609, 2022.

A permanent long-range terrestrial laser scanning (TLS) system is installed at Hintereisferner, Ötztal Alps, Austria to validate snow cover dynamics such as simulated by high-resolution atmospheric models.

Snow cover dynamics include several processes such as snow fall, compaction, metamorphism, snow redistribution by wind, avalanches and melt manifested in specific magnitudes and frequencies. To be able to quantify these surface changes, the smallest possible magnitude that can be measured by the TLS needs to be known.

An uncertainty analysis of the system has been conducted acquiring its limitations. It was known before that atmospheric conditions, the scanning geometry and mechanical properties contribute to the total uncertainty, but so far, these error sources and the total uncertainty had not been quantified.

It was assumed that the position of the TLS was stationary and thus, the georeferencing of the scan was automated with an unchanged transformation matrix. A case study of 29 hourly scans during 5 and 6 November 2020, with no surface changes due to external conditions, showed vertical differences between -0.62 m and +0.47 m relative to a selected reference scan. These deviations are related to ongoing minor movements of the scanner over the scope of day and result in errors of a few decimetres due to the long range acquisition.

The accuracy of the scans improves after manual georeferencing (RiSCAN PRO), resulting in smaller deviations between -0.15 and +0.04 m relative to the selected reference scan.

The total accuracy of the TLS system is ±10 cm (vertical direction) after manual georeferencing, but strongly depends on the range between target surface and TLS. This makes it possible to detect snow fall events, snow redistribution, melt, and avalanches with changes larger than one decimeter. Snow compaction and metamorphism are processes, which are over hourly to daily time steps too small to be detected by the TLS at Hintereisferner.

Over all, the determined accuracy of the TLS shows the suitability of the system setup for validating high-resolution atmospheric models that explicitly compute snow redistribution by wind and thus significantly will improve the treatment of snow cover dynamics in future glacier mass balance research.

How to cite: Voordendag, A., Goger, B., Klug, C., Prinz, R., Rutzinger, M., and Kaser, G.: Detection of snow cover dynamics with a long range permanent TLS system at Hintereisferner (Austria) – possibilities and limitations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4008, https://doi.org/10.5194/egusphere-egu22-4008, 2022.

We have derived the glacier-specific Østrem curve to quantify the influence of a supraglacial debris cover on the mass and surface energy balance components of the Djankuat Glacier, a northwest-facing and partly debris-covered temperate valley glacier in the Caucasus region, which has been selected as a ‘reference glacier’ by the WGMS. A 2D energy balance model, in combination with meteorological data from automatic weather stations and ERA5-Land reanalysis data, are used to assess the melt-altering effect of supraglacial debris on the overall glacier runoff during 1 complete balance year. The main results show that both the surface energy balance and mass balance fluxes are modified significantly due to the presence of debris on the glacier surface. For very thin debris, a slight relative melt-enhancement occurs due to a decreased surface albedo. If debris, however, further thickens, the insulating effect becomes dominant and reduces the melt and runoff of the underlying ice significantly, as thermal conduction becomes the dominant process to induce ice melt beneath such thick debris layers. The above-mentioned effects are modelled to be increasingly pronounced with an increasing thickness of the superimposed supraglacial debris cover, and can be of great importance with respect to future glacio-hydrologic regimes and glacio-geomorphological processes.

How to cite: Verhaegen, Y., Rybak, O., Popovnin, V., and Huybrechts, P.: Deriving the Østrem curve to quantify supraglacial debris-related melt-altering effects on the Djankuat Glacier, Caucasus, Russian Federation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4217, https://doi.org/10.5194/egusphere-egu22-4217, 2022.

In recent decades, snowfalls, snow cover and duration over Central Italy have decreased and there have been some extreme snowfall events followed by extreme avalanche activities. In this regard, the Calderone Glacier (hereinafter Calderone) represents a geographical and geomorphological element of great interest and is defined as a sentinel of climate change in central Italy, as it is going through a strong phase of reduction, it is the only glacier in the Apennines,  and the southernmost in Europe, and for its position on the summit of the Italian Gran Sasso (2912 m asl), a mountain group located in the center of the Apennine belt in the Mediterranean area.

The Italian Glaciological Committee (Comitato Glaciologico Italiano (CGI) )  every year with ad hoc in-situ inspections in late spring and early autumn monitor the Calderone mass balance. The mass balance of a glacier depends on the interplay between the mass gains and losses associated with climate and those associated with the inherent flux, its monitoring is essential because it can contribute to the knowledge of the current ongoing evolution of glaciers. 

Continuation of the traditional type of monitoring, like the one performed by CGI, based on direct measurements of accumulation and ablation by means of a network of stakes, appears to be an unlikely prospect, because in-situ data gathering usually implies expensive field campaigns and with difficult access to the sites, resulting in limited spatial and temporal resolution.  In contrast, techniques based on remotely sensed data, among several techniques, those relying on Synthetic Aperture Radar (SAR) demonstrated to be very effective due to the instrument's capability of operating day and night independently of the weather conditions.

Differential interferometry or DInSAR is a tool for accurate displacement measurements, and it is useful in identifying footprints of progressing movement. DInSAR is interferometry itself, the only difference is that topographical effects are compensated by using a Digital Elevation Model (DEM) of the area of interest, creating what is referred to as a differential interferogram.

In this work we propose the mass balance for the Calderone through the DInSAR results and its comparison with CGI in-situ measurements for the winter period 2018-2020. The data used in this study consist of COSMO-SkyMed satellite X-band single-look complex images in slant geometry (SCS, level 1A product),  Stripmap Himage mode (HH polarization) at 3 m per pixel of spatial resolution, and acquisition geometry Right Descending. The processing of this satellite data was applied over the entire area covered by the images and then refined to Calderone area, it includes a pre-processing first step that include: coregistration, interferogram formation, filtering and speckle; and a second part focused on obtaining the average values, active area and total area for the calculation of the mass balance.

How to cite: Alvan Romero, N., Palermo, G., Raparelli, E., Tuccella, P., D'Aquila, P., Caira, T., Pecci, M., and Marzano, F.: Monitoring the Calderone glacieret in Central Italy from COSMO-SkyMed synthetic aperture radar at X band, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4377, https://doi.org/10.5194/egusphere-egu22-4377, 2022.

The motion of glaciers with a temperate base is highly variable in time and space as a result of glacier basal sliding being strongly modulated by subglacial hydrology. Here we investigate short term (diurnal to multi-diurnal) changes in horizontal velocity and vertical displacement caused by melt and rain water input events on the Argentière Glacier (French Alps). We use up to 13 permanent GNSS stations that have been operating continuously over three full years (since April 2019). We report observations of strong surface acceleration events occurring in response to late summer storms, during which a velocity pulse propagates from up to down glacier at a migrating speed of about 0.1 m/s, which is typical of flow drainage speeds in the distributed system. We thus suggest that transient changes in the surface velocity field during intense and short-term water input events reflect transient changes in the distributed system flow properties. Although the efficient drainage system is expected to be well developed at this time of the year, this latter does not appear to play a primary role in our observations. Using concomitant observations of subglacial flow discharge and seismic tremor amplitudes we are able to estimate the average height of cavities and the associated cavity-drainage conductivity. Examination of the vertical displacement suggests that a vertical motion of the glacier (uplift) is largely controlled by the volume increase in subglacial water cavities rather than by the vertical strain rate change. These observational constraints may be crucial to test subglacial drainage and transient friction theories.

How to cite: Togaibekov, A., Walpersdorf, A., and Gimbert, F.: Short-term surface velocity variations of the Argentière glacier monitored with a high-resolution continuous GNSS network, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4654, https://doi.org/10.5194/egusphere-egu22-4654, 2022.

Debris-covered glaciers in the Manaslu region of Nepal have been scarcely studied. Here we aim to fill this gap using new, multi-sensor, freely available 2019 Planet high-resolution (3 to 5 m) imagery, 1970 Corona declassified imagery and UAV and stake ablation data acquired in the field to characterize the surface and evolution of these glaciers over the last five decades. We report regional area changes, glacier thickness, geodetic glacier mass balance and surface velocity changes and focus on Ponkar Glacier and Thulagi Glacier and Lake for an in-depth assessment of surface geomorphology and surface feature dynamics (ponds, vegetation and ice cliffs).

Glaciers in the Manaslu region experienced a mean area loss of -0.26 ± 0.0001 % a^-1 between 1970 and 2019, with a mean surface lowering of -0.20 ± 0.02 ma^-1 over the period 1970 to 2013, corresponding to a regional geodetic mass balance of -0.17 ± 0.03 m w.e.a⁻¹. Overall, debris-covered glaciers had higher thinning rates compared to clean ice glaciers. During the period 1970 to 2013, the debris-covered Ponkar Glacier had a geodetic mass balance of -0.06 ± 0.01 m w.e.a⁻¹, with parts of the central trunk thickening and a nine-fold increase in the thinning rates over the lower parts of the glacier tongue in the recent years (2013 to 2019). Ice-surface morphology changes between 1970 and 2019 include a decrease in ogives and open crevasses, an increase in ice cliffs and ponds and the expansion of the supraglacial debris and ice-surface vegetation, suggesting reduced ice-dynamic activity.

How to cite: Racoviteanu, A. E., Glasser, N. F., Robson, B. A., Harrison, S., Millan, R., Kayastha, R. B., and Kayastha, R.: Recent evolution of debris-covered glaciers in the Manaslu region of Nepal (1970 - 2019): the case of Ponkar Glacier, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5467, https://doi.org/10.5194/egusphere-egu22-5467, 2022.

Ice cliffs are important contributors to the mass balance of debris-covered glaciers, especially in High Mountain Asia where they can account for one sixth of the melt of  debris-covered glacier tongues, despite covering less than 10% of their area. These features have been shown to evolve, appear and disappear rapidly from year to year, with high variability in relative area and number. It has been hypothesized that ice cliffs expand and melt more rapidly during the monsoon (June-September), but there are very few observations during this period. Here, we use arrays of time-lapse cameras to reconstruct the geometry of four ice cliffs at a weekly timestep over a period of four to six months at two monsoon-affected sites: Langtang Glacier in Nepal, and 24K Glacier in South-Eastern Tibet. We use Structure-from-Motion photogrammetry to derive point clouds and Digital Elevation Models (DEMs) of the glacier surface, using the stable background terrain to constrain viewing geometries and DEM errors. This technique (time-lapse photogrammetry) enables a high accuracy, quantitative measurement of processes occurring at the cliff-scale (elevation uncertainties stay below 30cm at a distance of 300m from the cameras) and at high temporal resolution over the monsoon season, when dense cloud cover and intense precipitation prevent field- or satellite-based observations. We derive the melt patterns of these cliffs from the differencing of the weekly DEMs by accounting for glacier flow. We compare the observed melt patterns with the modeled energy-balance at the cliff surface and use these observations to quantify the influence of debris slumping and redistribution, as well as supraglacial ponds and streams on the melt patterns of these cliffs. The results highlight the seasonal variations of cliff melt, which occurs at up to 8 cm/day during the monsoon, twice as high as observed in the pre- and post-monsoon period. Our energy-balance results indicate that melt rates are driven by incoming long- and shortwave radiation, and are thus highly dependent on the cliff slope and aspect, as substantiated by our photogrammetric measurements. The observations also demonstrate the competitive influence of debris, which progressively reburies the cliff by accumulating at its surface, and supraglacial streams and ponds, which maintain the cliff slope by preventing debris accumulation at the base. These results will help in understanding the surface evolution of debris-covered glaciers and enable a better representation of ice cliff melt and evolution in glacio-hydrological models.

How to cite: Kneib, M., Miles, E. S., Buri, P., Fugger, S., McCarthy, M., Zhao, C., Shaw, T. E., Truffer, M., Westoby, M., Yang, W., and Pellicciotti, F.: Sub-seasonal evolution of ice cliffs captured with time-lapse photogrammetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6163, https://doi.org/10.5194/egusphere-egu22-6163, 2022.

Glaciers are an important component of the cryosphere and are key indicators of climate change. Observations of temporal changes in glacier extent are essential for understanding the impacts of climate change, but these observations are not widely available in many parts of the world. Research indicates that climate change has had a significant impact on glacier recession, particularly in the Arctic, where glacier meltwater is an important contributor to global sea-level rise. Therefore, it is important to accurately quantify glacier recession within this sensitive region. In this study, we mapped 480 glaciers in Russian Arctic, Novaya Zemlya, using object-based image analysis (OBIA) applied to multispectral Landsat satellite imagery in Google Earth Engine (GEE) to quantify the area changes between 1986-89 to 2019-21.  Our results confirm that in 1986-89, the total glacierized area was 22958.98 km² and by 2019-21 there was an 5.56% reduction in glacier area to 21680.63 km².  Comparison between manually corrected glacier outlines taken from the Randolph Glacier Inventory (RGI) and the mapped glacier outlines derived using the OBIA method shows there is a 90.26% similarity between both datasets. This confirms that OBIA, combined with the GEE platform, is a promising method for accurately mapping glaciers, reduces the time required for manual correction, and can be applied in other glacierized regions for rapid assessment of glacier change.

How to cite: Ali, A., Dunlop, P., Coleman, S., Kerr, D., W McNabb, R., and Noormets, R.: Quantifying glacier area changes using object-based image analysis in Google Earth Engine, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6853, https://doi.org/10.5194/egusphere-egu22-6853, 2022.

High Alpine environments are rapidly changing in response to climate change, and understanding the evolution of small glaciers is a crucial step to investigate future water availability for populations that inhabits these areas. With an average loss of 1.6 km² of regional glacier area every year, Aosta Valley is predicted to lose most of its glaciers before the end of the century. With this study, we present a comprehensive analysis of a small glacier’s recent mass balance evolution (1991-2020) where no specific previous mass balance data was available. To do so, we combined historical data (topographic surveys and LiDAR DEMs of the area) with newly acquired satellite stereo imagery and aerophotogrammetric surveys to reconstruct different digital elevation models of the Thoula glacier (0.52 Km²), located on the Italian side of the Mont-Blanc Massif. The ice volume loss that occurred over this period was assessed by accomplishing two GPR surveys to investigate the ice thickness and the underlying bedrock. The Thoula glacier shows a significantly lower loss of volume in comparison to other glaciers located in the Aosta Valley region as well as most of the WGMS (Word Glacier Monitoring Service) reference glaciers for Central Europe. Particular weather-climatic conditions of the Mont Blanc Massif area, generally characterized by a greater amount of snowfall, could explain the observed differences, however, the present study shows how understanding spatio-temporal local variability of small glaciers can significantly contribute to recognizing different regional patterns developing in response to climate change.

How to cite: Mondardini, L., Perret, P., Gottardelli, S., Frasca, M., and Troilo, F.: Understanding the recent evolution of a small Alpine glacier: from geodetic mass balance reconstruction (1991-2020) to local variability of glaciers retreat., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7501, https://doi.org/10.5194/egusphere-egu22-7501, 2022.

The subglacial lake Grímsvötn, beneath Vatnajökull ice cap, has been an important study area since the first attempts to explain the physics of jökulhlaups. The lake, covered by an up to 300 m thick ice shelf, is situated within a caldera of an active central volcano. It collects meltwater produced by geothermal and volcanic activity, in addition to meltwater from the glacier surface. During most of the 20^th century the period of water accumulation was typically 4-6 years, collecting 1-3 km³ of water. The jökulhlaups, as  observed in the river at the glacier terminus ~50 km south of the lake, typically reached peak discharge of 2,000-10,000 m³s^-1 after approximately exponential increase over 2-3 weeks. In October 1996, 3.2 km³ of meltwater from an eruption north of Grímsvötn were collected in the lake. This resulted in hydrostatic uplift of the lake ice dam and sudden release of the accumulated water, reaching a peak flow of ~50,000 m³s^-1 at the glacier terminus in less than a day. Due to damage to the lake ice dam during the 1996 jökulhlaup and further undermining from geothermal activity near the dam, the water accumulation and release has been different after this event. Between 1996 and 2018, smaller jökulhlaups typically occurred at 1-2 year intervals with the largest volume of ~0.6 km³ in 2004 and 2010. The jökulhlaup discharge still resembled an exponentially rising discharge, but faster, reaching a peak discharge at the glacier front in 3-5 days after detection of flood water in the river. In 2004 and 2010 the peak discharge was ~3,000 m³s^-1. From autumn 2018 until November 2021, ~1 km³ of water accumulated in Grímsvötn. The lake level has been monitored since the 1990s, but now with increased accuracy using online GNSS stations located on the floating ice shelf and repeated glacier surface mapping using Pléiades stereo images. Around mid-November 2021 the GNSS instruments started subsiding, revealing that the lake had started draining. In 3 weeks, the discharge from the lake, estimated from the subsidence rate and the lake hypsometry, gradually increased from a few m³s^-1 to a peak discharge of ~3500 m³s^-1 on 4 December. A few days later, the lake had drained completely. We present the data showing the development at the lake prior to and during the jökulhlaup, and we report on: a) discharge measurements near the glacier front, which combined with the lake discharge allows for an estimate of the temporal subglacial floodwater storage, b) horizontal and vertical ice motion in the vicinity and above the subglacial flood route during the jökulhlaup, from ICEYE and Sentinel-1 radar images obtained with InSAR (24 hour repeat) analysis and amplitude offset tracking, showing the distribution of flood water and the widespread effect of the jökulhlaup on the horizontal ice motion, c) ice surface motion measured by a GNSS station located half-way between the lake and the glacier margin, spanning the entire jökulhlaup. All this provides new insight into the physical processes occurring during a slow, exponentially rising jökulhlaup from Grímsvötn.

How to cite: Magnússon, E., Drouin, V., Pálsson, F., Hannesdóttir, K., Belart, J. M. C., Sigurðsson, G., Wuite, J., Jóhanneson, T., Ófeigsson, B. G., Nagler, T., Gudmundsson, M. T., Högnadóttir, T., Parks, M., Roberts, M. J., and Berthier, E.: The jökulhlaup from the subglacial lake Grímsvötn, beneath Vatnajökull ice cap, in November-December 2021, revealing new insight in to slowly rising jökulhlaups, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8240, https://doi.org/10.5194/egusphere-egu22-8240, 2022.

Glaciers in High Mountain Asia have been shrinking in the recent decades. They are a valuable indicator of climate change, and their meltwater plays an important role for regional water resources. Debris-covered glaciers, which are prevalent throughout the Himalayas, exhibit complex melt processes due to their heterogeneous surface.  Previous studies have demonstrated that ice cliffs disproportionally contribute to glacier melt, but their importance at the glacier scale has been quantified for only a few sites. In this study, we exploit measurements taken since 2016 on the lake-terminating Trakarding Glacier (27.9°N, 86.5°E; 2.9 km² spanning 4,500–5,000 m a.s.l.; ~5% ice cliff cover), eastern Nepal Himalaya, to investigate the importance of cliffs for debris-covered ice melt at the glacier scale from a remote-sensing inversion and energy-balance modeling. We generated super-high-resolution (0.2 m) terrain data from aerial photographs (UAV and helicopter-borne photogrammetry) during 2018-2019 and manually delineated ~500 ice cliffs to derive surface velocity, elevation change, and specific mass balance, providing an observational estimate of ablation across the debris-covered tongue and attributable to ice cliffs. Further we employed a process-based 3D-backwasting model to estimate continuous ice cliff mass-loss over the study period. The model calculates the energy balance of ice cliff surfaces and reproduces their evolutions (cliff expansion, shrinkage, and reburial), based on the characteristics of the glacier surface and location of individual ice cliffs. This method, forced with in-situ meteorological and terrain data and evaluated against the observed changes, provides ice cliff mass-loss from the scale of individual features to the entire Trakarding Glacier.

How to cite: Sato, Y., Buri, P., Miles, E., Kneib, M., Sunako, S., Sakai, A., Pellicciotti, F., and Fujita, K.: Ice cliff mass-loss of debris-covered Trakarding Glacier, Rolwaling region, eastern Nepal Himalaya, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8327, https://doi.org/10.5194/egusphere-egu22-8327, 2022.

The intercomparison exercise by the IACS Working Group on Regional Assessments of Glacier Mass Change (RAGMAC) aims to provide an overview of good practises as well as spread of different processing approaches for assessing glacier volume changes from geodetic methods. It is composed of two experiments with mandatory and optional tasks. Participants are encouraged to contribute to all tasks.

Experiment 1 targets a comparison of spaceborne elevation changes to high-quality, high-resolution airborne data (either from laser scanning or aerial photogrammetry) as well as to in-situ surface glacier mass balance data. Test sites are Aletsch Glacier and Hintereisferner in the Alps as well as Vertisen in Norway. The expected outcome of the first experiment (with validation data) is to see how the participants account for the mismatch between DEM acquisition dates and to assess quantitatively the divergence between estimates as a function of this mismatch.

Experiment 2 is setup for regions (Northern Patagonian Icefield, Karakoram, Franz Joseph Land) where not direct validation data is available. The challenge posed here is on the intercomparison and influence of different processing steps and approaches on the elevation and volume change results. Observation periods are pre-defined and participants deliver different versions of the processing results.

The presentation will provide an overview of the exercise and the experiments and outline first results of the endeavour.

How to cite: Braun, M., Zemp, M., and Brun, F.: First results of the RAGMAC glacier elevation change intercomparison exercise, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8606, https://doi.org/10.5194/egusphere-egu22-8606, 2022.

Debris-covered glaciers accumulate supra-glacial debris on the glacier surface in the ablation zone. As long as this debris layer is not at least partly removed, it can be expected that glaciers continue to grow in length, because the thickening debris layer continuously reduces surface melt rates. Removal of the debris layer, on the other hand, is a complicated process, which depends on a number of parameters, like surface slope, debris thickness, grain size distribution and water content to name just a few. However, the way how supra-glacial debris is removed might strongly influence the dynamic reaction of the glacier itself.

A realistic study of these interactions can only be performed, if the ice flow and the debris-influenced melt is treated with a high degree of detail. In our study, we coupled a 2-D full Stokes ice dynamic and surface debris transport model with a sophisticated description of energy transfer through the debris layer. This approach ensures that ice flow and surface melt rates are simulated at high detail, including the enhanced melt rates for very thin debris cover just below the equilibrium line. We restricted our experiments to rather simple initial conditions, in order investigate the fundamental feedback mechanisms between melt rates and glacier dynamics. Therefore, we introduced rather simple, but realistic formulations of debris unloading at the glacier front. The coupled experiments show that steady-state conditions are highly unlikely for glaciers with the debris layer remaining on the glacier. However, a balance of the debris budget and the glacier mass flux is possible, when introducing debris removal from the glacier tongue. We focussed on an as realistic as possible representation of the snout geometry, in order to allow a physically sensible debris discharge. The results show that for some removal processes debris-covered glaciers have an inherent tendency to enter an oscillating state, where glacier mass balance and debris balance are out of phase. In specific experiments glacier advance periods end with the separation of the heavily debris-loaded lowermost glacier tongue, at time scales of decades to centuries, followed by an advance of the remaining clean glacier. In such cases we assume that long-term “mean-steady-state” conditions modulated by oscillations in glacier length exist and are independent from climatic variations. This makes it difficult to interpret short-term geometry observations of debris-covered glaciers in the context of climate impact.

How to cite: Mayer, C. and Licciulli, C.: What does steady state mean for debris-covered glaciers?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8742, https://doi.org/10.5194/egusphere-egu22-8742, 2022.

Intra-annual glacier velocities are key parameters to study glacier basal conditions or short-term events such as glacier surges. However, intra-annual glacier velocities remain poorly understood at a global scale, especially in mountains areas. Indeed, many ice velocity maps are now available on-line or on-demand at a temporal resolution up to 2 days and a spatial sampling up to 50 m (Millan et al., 2019) all over the world. However, these products contain gaps, noise and artefacts especially where image-matching algorithms fail because of strong surface changes, surface locking, shadow casting, clouds, or feature-less regions. Moreover, this amount of data is complex to analyse since velocities span different temporal baselines, are derived from different sensor images using different algorithms. Therefore, there is a need to fuse the available multi-temporal and multi-sensor glacier velocity observations in order to study intra-annual glacier dynamics with a high temporal resolution.

The proposed approach relies on an inversion based on the temporal closure of displacement observation networks. Because the observations have different uncertainties, not necessarily known, the inversion is solved using an Iterative Reweighted Least Square. This approach results in velocity time series which have a complete temporal coverage, a uniform temporal sampling without overlapping time intervals (i.e. without redundancy) by tacking advantage of all available velocity observations without a priori on the displacement behavior. The temporal sampling of these velocity time series can be selected. To select an optimal temporal sampling based on a compromise between temporal resolution and uncertainty, we propose to minimize Root Mean Square Error (RMSE) over stable areas and maximize Velocity Vector Coherence (VVC) over fast moving areas. The proposed approach is illustrated with both mono-sensor and multi-sensor datasets, on two different glaciers: 1) Sentinel-1 velocity observations from (Round et al., 2017) over the Kyagar glacier which is a surge glacier situated in the Karakoram range 2) Sentinel-2 and Venµs velocity observations from (Millan et al., 2019) over the Fox glacier, a temperate maritime glacier with a strong seasonality, situated in Southern Alps of New Zealand. The results reveal strong intra-annual variations of velocity with a reduced uncertainty for both glaciers: RMSE over stable areas is lower for the results than for the original velocities: 1) from 22% lower for 12-days temporal sampling to 67% lower for 36-days temporal sampling over the Kyagar glacier 2) from 78% lower for 5-days temporal sampling to 40% lower for 60-days temporal sampling over the Fox glacier.

This approach is not dataset dependent and can be applied to any available velocity observations derived from any sensors.

How to cite: Charrier, L., Yan, Y., Trouvé, E., Colin Koeniguer, E., Leinss, S., Mouginot, J., and Millan, R.: Fusion of multi-sensor, multi-temporal velocity observations to study intra-annual glacier dynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10033, https://doi.org/10.5194/egusphere-egu22-10033, 2022.

Various interdisciplinary studies have shown substantial discrepancies between modeled and remotely sensed glacier surface elevation change.It is therefore crucial to better understand and quantify uncertainties associated to both methods. We design a probabilistic framework with the aim to filter outliers, fill data voids and estimate uncertainties in glacier surface elevation changes computed from Digital Elevation Model (DEM) differentiation. The technique is based on a Bayesian formulation of the DEM difference problem and specifically targets surging and debris-covered glaciers, both at glacier and regional scales. We first define a set of physically admissible surface elevation changes as an elevation-dependent probability density function.

In a second step, terrain roughness is defined as the main descriptor for DEM uncertainty. Each surface elevation change pixel is a probability distribution. We present validation experiments in High Mountain Asia and show that the model produces results consistent with conventional DEM differencing, while avoiding the caveats of already existing methods. We further demonstrate that accounting for unstable glacier dynamics is crucial for accurate outlier filtering and robust uncertainty estimation. The technique can be applied to other types of remotely sensed glacier quantities (surface velocity etc.) and would provide more reliable characterization of uncertainty associated with changes in glacier mass and dynamics.

How to cite: Guillet, G. and Bolch, T.: Probabilistic estimation of glacier surface elevation changes from DEM differentiation: a Bayesian method for outlier filtering, gap filling and uncertainty estimation with examples from High Mountain Asia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10073, https://doi.org/10.5194/egusphere-egu22-10073, 2022.

Around 10% of glacier area in the Himalaya is debris-covered with heterogeneous distribution. Debris distribution, as a function of its thickness, induces differential melting and may lead to characteristic slope inversion. The spatial distribution of debris thickness is poorly quantified in the Himalaya with limited field-based measurements. In the present study we conducted a field expedition on the Panchi Nala Glacier (4.50 km², 60% debris-covered), western Himalaya during September 2021 and measured debris thickness at 73 points using a DGPS. Debris thickness ranges from <1 cm to 50 cm and reaches upto 1 m over extreme margins. In general, the debris is thicker (>25 cm) in the lower reaches (upto 1.5 km from snout) and decreases with increasing distance from snout. This generalization is, however, not always true as some patches of thin debris cover (<3.5 cm) in the lower portion and some patches of thick debris cover (~13 cm) at upper portion were also found. To assess the influence of debris thickness on melting, elevation difference data from Shean et al. (2020) is obtained. The correlation between debris thickness and elevation changes over corresponding pixels is negative (R = −0.58), suggesting that variation in surface elevation changes can partially be explained by the distribution of debris thickness. Spatially, the wastage is comparatively low (−0.69 m/y) around glacier snout where debris cover is thick (~15 cm) and higher (−1.14 m/y) at higher reaches (~3 km from snout) where debris cover is thin (~5 cm). Comparison of profiles derived from SRTM DEM and ASTER DEM along the central flowline for 2000 and 2020, respectively suggests that owing to differential melting, the concavity is developing on the glacier. Thus, debris thickness is playing an important role in regulating the melt and modifying the overall morphology of the Panchi Nala Glacier. 

How to cite: Garg, P. K. and Azam, M. F.: Impact of debris distribution on glacier morphology: a case study of Panchi Nala Glacier, western Himalaya using field and remote sensing measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10142, https://doi.org/10.5194/egusphere-egu22-10142, 2022.

Numerical modelling studies examining the transient behaviour of debris-covered glaciers have typically varied either the equilibrium line altitude, which is a proxy for climate, or the rate of debris deposition. Since the rate of debris production from headwall erosion is believed to be an increasing function of temperature, a more faithful representation of debris-covered glacier evolution should include a coupling between debris source strength and climate.

In this study, we examine the transient response of debris-covered glaciers to the combined effect of a warming climate and a related increasing debris source using a numerical model that couples ice flow with englacially transported debris. This allows for a debris melt-out concentration in the ablation zone that varies in time and space, depending on the evolving glacier dynamics and the debris deposition history.

We find that debris-covered glaciers in a warming climate exhibit a complex transient response with aspects of both retreat and advance, though these distinct responses occur on different timescales. This suggests that the observed present-day retreat of debris-covered glaciers may be followed by an eventual advance despite a continued increase in global mean temperature.

How to cite: Ferguson, J. C., Bolch, T., Banerjee, A., and Vieli, A.: Modelling the complex transient response of debris-covered glaciers to climate change and interaction with debris production, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10238, https://doi.org/10.5194/egusphere-egu22-10238, 2022.

High mountain environments are characterized by highly glacierized, complex, dynamic topography, often exhibiting a heterogeneous debris mantle comprising ponds and exposed ice cliffs, associated with differing ice ablation rates. These has been an increased interest in understanding these supraglacial surface features, i.e., the formation and expansion of supraglacial ponds and implications for glacier hydrology and glacier-related hazards, notably glacier lake outburst flood (GLOF) events. Until recently, supraglacial debris surfaces and their features have received less attention compared to mapping of debris-cover extents due to methodological challenges posed by their ephemeral nature. As a result, they remain poorly quantified in global glacier databases including the Global Land Ice Measurements from Space (GLIMS) and the Randolph Glacier Inventory (RGI). Furthermore, remote sensing studies used to generate these datasets generally rely on traditional “whole pixel” image classification techniques, which do not allow decomposition of a pixel into constituting materials. In this talk I summarize the state-of-art remote sensing techniques to characterize supraglacial features, such as debris material, ice cliffs, supraglacial ponds and vegetation. I particularly highlight the potential of spectral unmixing routines multi-temporal Landsat and Sentinel data combined with high-resolution multispectral imagery to quantify the composition of debris cover at multiple scales across the Himalaya with an emphasis on supraglacial ponds. I summarize the current strengths and limitations of these methods and discuss steps needed such as automation and open-source tools.

How to cite: Racoviteanu, A. E.: Remote sensing tools for monitoring supraglacial debris cover features and their fluctuations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10241, https://doi.org/10.5194/egusphere-egu22-10241, 2022.

Landslides or rockslides occur in unstable slopes around the world, of which many cases are related to changes in the cryosphere. They can result in natural hazards and are an indicator of climate change. Due to their inherent non-linearity, they are difficult to predict and often remain unobserved. Appropriate documentation allows for assessing their consequences and long-term impacts on ecosystems or the hydrological cycle.

We report on a particularly well-documented case of a supraglacial rockslide that occurred on Brenndalsbreen, an outlet glacier of Jostedalsbreen, Norway, in the period November 2009-June 2010. We assess its consequences on local glacier mass balance derived from surface elevation changes and explore potential changes in flow velocities. The rockslide occurred unobserved and did not obviously impact humans or the environment, yet, satellite imagery and aerial photogrammetrical surveys allow for a spatial and temporal quantification of the event. Furthermore, a series of digital elevation models from 2012-2021 is used to determine spatial heterogeneity in ablation rates, however, this is complicated due to the motion of the ice mass. According to the widely used Östrem curve, a debris-cover exceeding a certain threshold thickness protects the ice below from ablation, while a thin debris or dirt layer increases ablation rates. In fact, we find that during the first years after the rockslide, locally, ablation was reduced below the debris layer, while a recent high-resolution LiDAR survey that got complemented with a UAV survey a year later, clearly indicates increased ablation rates relative to the debris-free surroundings. Clear trends in surface velocities have not been found based on satellite remote sensing data. We discuss the significance of the observations on surface energy balance and geomorphological changes in the proglacial area.

How to cite: Seier, G., Abermann, J., Engen, S. H., Gjerde, M., Scheiber, T., Löffler, K., Carrivick, J. L., Andreassen, L. M., and Yde, J. C.: ‘Dancing around the Östrem curve’: High-resolution monitoring of a supraglacial rockslide on Brenndalsbreen, Norway, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11134, https://doi.org/10.5194/egusphere-egu22-11134, 2022.

It is common for temperate glaciers in mountainous regions to exhibit an extensive ablation-zone supraglacial debris cover. Although secondary reworking of surface debris and its role in modifying rates of glacier melt is receiving increasing attention, debris origin and primary distribution is poorly understood. Arguably, studies have tended to uncritically assume that debris supply is dominated by the passive transport of rockfall material that is dispersed within the ice (englacially) or is deposited onto the surface directly. We show that a substantial portion of the debris cover at Miage Glacier, Italy, can be attributed to release from medial moraine (MM) structures that can be observed englacially in debris-free ice cliffs and as supraglacial ‘melt out’ ridges containing vertically oriented clasts, occasionally supported by a fine matrix. Two MM types displaying contrasting debris characteristics were observed: one arising from the tributary confluences located near or below the equilibrium line position, and another derived from accumulation basin confluences. The former were reasonably continguous supraglacial features that in the upper- and mid-ablation area develop considerable relief that clearly acts as a primary control on debris redistribution across the glacier surface. The latter type are traceable for limited distances, and form more isolated areas of high topography in the mid-ablation area. We argue that ablation area debris cover and relief complexity in the upper- and mid-ablation area largely reflects debris entrainment at the point of medial moraine origin, though additional factors include the recent detachment of tributaries, the decline in mass contributed by specific accumulation basins, and the stochastic nature of headwall debris supply. Secondary debris redistribution processes appear to increase as glacier surface elevation declines, meaning in the lower ablation area surface relief decreases as debris distribution complexity increases.

How to cite: Swift, D., Jones, A., Westoby, M., Bryant, R., and Veness, R.: Structurally controlled englacial origin of supraglacial debris cover and relief at a debris-covered Alpine glacier, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11900, https://doi.org/10.5194/egusphere-egu22-11900, 2022.

As glaciers respond to climate change, the scientific community has dedicated increasing attention to the development of melt models for debris-covered glaciers. Here, we present an intercomparison aimed at advancing our understanding of the skills of models of different complexity to simulate ice melt under a debris layer. We compare 14 models with different degrees of complexity at nine sites in the European Alps, Caucasus, Chilean Andes, Nepalese Himalaya and the Southern Alps of New Zealand, over one melt season. We run the models with meteorological data from automatic weather stations and estimated or measured debris properties. Model performance is evaluated using on-site measurements of sub-debris melt (for all models) and surface temperature (for models based on the surface energy balance) at each site. We find that the two main categories of models considered, physically-based energy balance (EB) models and empirical temperature index (TI) models perform in a distinct manner. Temperature index models are reliably accurate when they are recalibrated, and show a range of results when parameters are uncalibrated. The most accurate energy balance models are those with the highest degree of complexity at the atmosphere-debris interface. However, we also find that additional complexity within the debris and at the debris-ice interface does not improve performance, which results from a lack of data to accurately force the models to represent these processes. The difficulty to properly estimate the physical properties of debris layers and within-the-debris processes are a likely cause. One of our main conclusions is thus that sophisticated models need high quality input data. An important data gap emerged from our experiment: the poor performance of all models at three sites was related to poor knowledge of debris properties; specifically, of thermal conductivity. Since debris properties are a major control on melt model simulations, we demonstrate that consistent data acquisition efforts are required to more rigorously evaluate existing models and support new model developments. Future work should seek to advance models by improving how they account for processes such as debris-snow interactions, moisture in the debris and refreezing. We suggest that a systematic effort of model development using a single model framework could be carried out in phase II of the Working Group.

How to cite: Pellicciotti, F., Fontrodona-Bach, A., Rounce, D. R., Fyffe, C. L., McCarthy, M., Miles, E., and Shaw, T. E.: DCG-MIP: The Debris-Covered Glacier melt Model Intercomparison exPeriment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11904, https://doi.org/10.5194/egusphere-egu22-11904, 2022.

Supraglacial debris covers the tongue of many mountain glaciers. In the course of ongoing climate change and the rapid melting of glaciers, debris extent and thickness will continue to increase. The thickness and other inherent properties of the debris layer control sub-debris melt rates and influence how glaciers respond to climate change. It is therefore essential to consider the impact of supraglacial debris on ablation in glacier surface mass balance models and glacier evolution models. However, this requires detailed knowledge on the debris thickness distribution. As debris thickness is spatially very variable, it remains a challenge to map debris thickness across the entire ablation zone of a glacier. Here we present the preliminary results of a feasibility study on the Kanderfirn in the Swiss Alps, where we deployed an Unoccupied Aerial Vehicle (UAV) with a visible and thermal infrared camera to map and analyse spatial variations in debris surface temperature, debris thickness, and sub-debris melt rates. Two independent approaches originally developed for satellite data were tested and compared to map debris thickness in high resolution. First, we used the statistical relationship between spatial UAV observations and in-situ point measurements (mapped surface temperature vs. measured debris thickness) to derive spatial debris thickness variations from mapped surface temperature variations. Second, we calculated distributed sub-debris melt rates from UAV-based elevation-change maps and quantified debris thickness through the inversion of a sub-debris ice melt model. Both methods deliver promising results. Despite the remaining challenges, the results emphasise the potential of UAVs equipped with visible and thermal infrared cameras for glacier-wide debris thickness mapping.

How to cite: Groos, A. R. and Messmer, J.: Mapping debris thickness on alpine glaciers using UAV thermography and photogrammetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12672, https://doi.org/10.5194/egusphere-egu22-12672, 2022.

In mountain regions around the world, crevasses in glacier accumulation areas undergo cycles of burial and re-exposure between one melt season and the melt season that follows. However, climate warming is extending the length of the ablation season meaning that crevasses are now exposed at the glacier surface for longer.  An analysis of air temperature inside crevasses in the accumulation area of a maritime glacier has found that air temperature inside crevasses can at times be higher than the overlying air temperature. Here we combine measurements of air temperature and wind-speed from inside crevasses with adjacent meteorological data to demonstrate that open crevasses trap incoming shortwave radiation and have complex relationships with wind shear. Results show that crevasse morphology influences warming with the effect more pronounced at wider (more open) crevasses. This highlights the potential of crevasses to enhance glacial melt by acting as heat source through positive radiative and sensible heat feedback. Therefore we hypothesis that energy balance models that treat glacier accumulation areas as smooth surfaces will be underestimating snow melt and possibly overestimating mass balance on alpine glaciers. 

How to cite: Purdie, H., Zawar-Reza, P., Schumacher, B., Katurji, M., and Bealing, P.: Air temperature variability inside crevasses in the accumulation area of a maritime glacier in the Southern Alps, New Zealand, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13196, https://doi.org/10.5194/egusphere-egu22-13196, 2022.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 D’Urville 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.

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.

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.

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.

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.

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.

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.

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.

The inter and intra-annual dynamics of seasonal snow are of key interest in the tourism-based economies of many Alpine regions as well as for millions of people in the adjacent European lowlands when it comes to freshwater supply and electricity generation. However, accurate snow observations over long periods of time and at large spatial scales are especially challenging in inaccessible mountainous areas. This can be overcome by using data from Earth Observation satellites, which have been constantly monitoring the Earth’s surface for almost 40 years. On a catchment basis, we derive the Snow Line Elevation (SLE) from Landsat data for the entire Alpine region and model the spatio-temporal dynamics in monthly time-series ranging from 1984 to today. Based on the historical observations we model future SLE dynamics comparing different uni-variate and multi-variate approaches and assess them for their ability to generate multi-year forecasts from EO-derived time series data. These forecasts can enable local and regional stakeholders to adapt to a potentially changing snow regime under climate change.

How to cite: Köhler, J., Dietz, A., and Kuenzer, C.: Modeling future Snow Line Elevation dynamics in the Alps based on long remote sensing time-series, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-993, https://doi.org/10.5194/egusphere-egu22-993, 2022.

Rationale: After the Tropical Rainfall Measuring Mission (TRMM) was successfully launched by NASA and JAXA in 1997, NASA released the first GPM-era global precipitation product (IMERG) in April 2014, aiming to obtain precipitation data with ultra-fine temporal and spatial resolution around the world. Examining the key precipitation data in different climatic areas influenced by the monsoon can effectively help users and algorithm developers maximise the accuracy and characteristics of new satellite remote sensing products. Objective: To this end, this study used the upper and middle Lancang River basin (UMLRB), a transnational river with complex climatic conditions, as the research area to explore the applicability and precipitation distribution of IMERG and TRMM, and evaluate their accuracy. Methods: In this study, various performance indexes were used to comprehensively evaluate the retrieval accuracy of IMERG and TRMM remote sensing precipitation data in UMLRB; these indexes can be divided into two categories according to the evaluation objectives. One type of indexes mainly evaluates the amplitude consistency of precipitation, and the other type of indexes is mainly used to evaluate the occurrence consistency of precipitation. Results: The results indicated that: (1) The temporal distribution of precipitation in different climatic regions was correctly detected by IMERG and TRMM in the UMLRB, and the dry and wet seasons in the climate transition zone were distinct. (2) IMERG and TRMM tended to overestimate moderate rain (1.0-20 mm/d) while underestimating heavy rain (20-50mm/d) and extreme precipitation (> 50mm/d). (3) In terms of the amplitude consistency of precipitation, the detection results of IMERG in the alpine climate zone were not completely consistent with those of TRMM, while those in the climate transition zone were consistent with TRMM. (4) The stronger the precipitation intensity, the worse the accuracy of IMERG and TRMM, especially between heavy rain (20-50 mm/d) and extreme precipitation (> 50mm/d). (5) The IMERG, which had greater application potential in complex climatic conditions, had higher accuracy than TRMM.” Conclusions/Recommendations: Therefore, before using remote sensing precipitation data to study watershed hydrometeorology in monsoon-affected areas, their seasonal distribution, precipitation intensity, and the type of remote sensing data should be carefully considered to verify their accuracy.

How to cite: Lu, C., Fang, G., Ye, J., and Yang, Z.: Accuracy assessment of IMERG and TRMM remote sensing precipitation data under the influence of monsoon over the upper and middle Lancang River basin, China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2078, https://doi.org/10.5194/egusphere-egu22-2078, 2022.

Remotely sensed MODIS (Moderate Resolution Imaging Spectroradiometer) data and the NDSI (Normalized Difference Snow Index) based approach have been applied globally for snow cover mapping. However, this method displays severe omission errors in forested areas, due to the forest canopy shading of snow. In this study, we developed a new forest snow mapping algorithm based on MODIS reflectance data, time-lapse observations of forest snow, and a random forest model. We built a time-lapse camera network in the eastern Qilian Mountains in northwestern China to monitor the forest snow processes and obtain the ground truth data. The random forest (RF) model seems to be powerful in capturing the relationships between the MODIS surface reflectance bands and the forest snow presence. The presented approach significantly improved the accuracy of binary snow cover (BSC) mapping in forests. We evaluated the performance of the proposed algorithm with the traditional NDSI-based method. The results show that the new algorithm has a superior performance in forest BSC mapping, compared to the NDSI-based BSC. The proposed RF-BSC can retrieve ~70% of all real forest snow pixels, while the NDSI-BSC can only detect 8-14%. We further investigated the geographical influence (e.g. topography, forest coverage, and solar illumination) of the algorithm performance. This study suggests that the fusion of optical remote sensing data and ground-based observations using machine learning techniques has a great potential in improving the accuracy of land cover mapping.

How to cite: Dong, C. and Luo, J.: Development of a new forest snow mapping algorithm using MODIS data, machine learning and time-lapse photography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3473, https://doi.org/10.5194/egusphere-egu22-3473, 2022.

Snow dynamics is a key parameter for the hydrological model predicting the river flow rate used in dam management. In the MORDOR model used by EDF, the information of the daily snow extent is an input to improve the flow prediction. This information is extracted from MODIS NDSI daily product. Due to cloud cover, this information can be lacking or imprecise for multiple consecutive days over one catchment, reducing the precision of the prediction.

The goal of this study is to detect the snow extent using SAR data, since it can acquire images through clouds. We focus over the Guil catchment in the French Alps. Sentinel-1 interferometric stacks from June 2018 to August 2019 are used for three different orbits.

Previous studies showed the capacity of the ratio between the current image and a reference image acquired in summer to detect wet snow [Nagler2016], or that the ratio between VH and VV could be linked to the height of snow [Lievens2019]. Interferometry has be shown capable to detect snow since the snow covered area can exhibit a lower coherence [Singh2008].

To compare these parameters using a ground truth, we projected the MODIS NDSI data on our S1-stack using a 1m DEM and considered pixels as snowy if the NDSI is above 0.4.

As pointed in other studies [Löw2002, Wang2015], it is very hard to set a threshold for these parameters, mostly because the vegetation exhibits volume scattering and changes the same way as snow. Using SVM, we investigated the capability of these parameters to detect snow in two setups:
- snow detection: the goal is to classify the pixels as snow or snow-free for all the image, using Nagler parameter in VV and VH, the ratio between VH and VV at each date and the polarimetric coherence at each date. For Nagler parameter, the reference image is the temporal average of the images over July and August 2018.
- change detection: the goal is to classify the pixels into 4 classes, snow-free to snow-free, snow to snow, snow-free to snow and snow to snow-free. Considering two consecutive images, this was done using the variation of the VV and VH ratio, the interferometric coherence between these images, and the ratio between the polarimetric coherences of the images.
For each setup, the learning and the testing were done on two samples of 20000 randomly selected pixels, equally distributed between the classes.

For the snow detection method, between 54% and 59% of the pixels are correctly classified, for the three orbits. This result is stable with the choice of the learning sample. For the change detection setup only 30% of the pixels are correctly classified. Moreover, the per-class metrics vary widely from one experience to the other. This variability as well as the low classification results underline the difficulty of the task but can also be linked to the resolution difference between MODIS used as ground truth and S1. To robustify the detection, spatial and temporal regularization seems necessary.

How to cite: Weissgerber, F., Monteil, C., and Girard, A.: Snow extend and snow change mapping with Sentinel-1 imaging using SVM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4520, https://doi.org/10.5194/egusphere-egu22-4520, 2022.

The Snow Water Equivalent (SWE) is a key variable to characterize water resource availability in mountain catchments. Despite its hydrological significance, the snow cover is poorly monitored in many regions due to a lack of in situ measurements. 

Global climate reanalysis products provide increasingly accurate data but are too coarse to be used directly in mountain regions to reconstruct snow related variables. However these reanalyses have been successfully used to generate high resolution meteorological forcing and run a snowpack model in the central Andes and the High Atlas mountain ranges (Mernild et al. 2017; Baba et al. 2018). 

The method is based on the MicroMet/SnowModel package (Liston and Elder 2006a; 2006b). MicroMet performs spatial interpolation of meteorological variables using the digital elevation model (downscaling) and the other routines of SnowModel computes the snowpack energy and mass balance. We have implemented a tool to improve the automation and scalability of this method to simulate the snow cover distribution in other regions using ERA5 or ERA5-Land. Our snow simulation tool only requires a digital elevation model as input. The land cover is extracted from the Copernicus global land cover map and the meteorological data are retrieved from either ERA5 or ERA5-Land over the period of interest.  

We used three catchments under the influence of Mediterranean climate to evaluate the performance of this tool: Tuolumne (USA), Bassies (France) and Yeso (Chile). For each catchment either the modeled SWE depth or snow depth are compared with the validation data, over periods going from 3 to 8 years.  In the Tuolumne basin, where the dataset is the most accurate with several SWE maps per year, we find a very good agreement at the basin scale (RMSE 40 mm w.e.). However, the mean RMSE in the highest elevation band (3500-4000 m asl) can exceed 500 mm w.e., which we attribute to the  lack of gravitational transport in SnowModel and errors in the spatial distribution of precipitation. To reduce these errors in particular, we are implementing a non-deterministic representation of the precipitation input data to eventually allow the assimilation of globally available remote sensing data. 

This tool will allow us to compute snow reanalyses in key mountain ranges around the Mediterranean sea over the past two decades (Pyrenees, Atlas and Mount Lebanon) and study the influence of topography and climate on the snow cover variability.

How to cite: Sourp, L., Gascoin, S., Baba, M. W., and Deschamps-Berger, C.: Development of a snow reanalysis pipeline using downscaled ERA5 data: application to Mediterranean mountains, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5117, https://doi.org/10.5194/egusphere-egu22-5117, 2022.

Satellite observations are the only means for timely and complete observations of the global snow cover. A range of different satellite snow products is available, the performance of which is of vital interest for the global user community. We provide an overview on goals and activities of the SnowPEx+ initiative, dedicated to the intercomparison of northern hemispheric and global satellite snow products, derived from data of long-term operational as well as recently launched satellites. SnowPEx+ is the continuation of SnowPEx (2014-2017), carried out as an international collaborative effort under the umbrella of Global Cryosphere Watch / WMO and funded by ESA.

SnowPEx+ focuses on two parameters of the seasonal snowpack, the snow extent (SE) from medium resolution optical satellite data (Sentinel-3, VIIRS, MODIS, AVHRR, etc.) and the snow water equivalent (SWE) from passive microwave satellite data. Overall, 15 hemispheric and global SE products (binary and fractional SE) and two SWE products are participating in the experiment. For intercomparison, daily SE products are transformed to a common map projection and standardized SnowPEx protocols are applied, elaborated by the international snow product community. The SE product evaluation applies statistical measures for quantifying the agreement between the various products, including the analysis of spatial patterns. Validation of SE products uses as benchmark high resolution snow maps from about 150 globally distributed Landsat scenes acquired in different climate zones, under different solar illumination conditions and over various land cover types. This snow reference data set, based on various retrieval algorithms, is generated and evaluated by the SnowPEx+ High Resolution Snow Products Focus Group. In-situ snow data from several organisations in Europe, North America and Asia are also used for validating the satellite SE and SWE products. SWE products are also inter-compared with gridded snow products from land surface models driven by atmospheric reanalysis data. In addition, the multi-year trends of the various SE and SWE products are evaluated. We provide an overview on the snow products, discuss the validation and intercomparison protocols, and report on preliminary results from the intercomparison and validation of various snow products.

How to cite: Nagler, T. and the SnowPEx Team: SnowPEx+: The International Snow Products Intercomparison and Evaluation Excercise 2015-2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7319, https://doi.org/10.5194/egusphere-egu22-7319, 2022.

Abstract.

Snow depths are traditionally determined by point measurements at automatic weather stations or field observations, which cannot capture the complexity of snow depth distribution in alpine terrain. Therefore, remote sensing techniques have become key tools for spatially continuous snow depth mapping. Only satellites, airborne laser scanners (ALS) or photogrammetry from piloted aircrafts are capable of covering large regions of more than 100 km². However, the accuracy of satellite data does not match/achieve the requirements for exact snow depth mapping yet. In comparison to ALS, photogrammetric methods are considerably more economic, but have the disadvantages of light and weather dependence as well as the lacking ability to penetrate high vegetation. Nevertheless, previous studies of photogrammetric snow depth mapping on a large scale have already proven the accurate implementation, but those studies were either limited to only one recording or characterized by a spatial resolution of around 2 m. These properties limit the comparison of snow depth distribution and the analysis of small-scale features.

In our study we apply airborne imagery from the current state-of-the-art survey camera Vexcel Ultracam acquired during the annual peak of winter for the five years from 2017 to 2021 in an area of approximately 300 km² around Davos, Switzerland. This enables the calculation of outstandingly improved annual snow depth maps. The high spatial resolution of the snow depth maps (0.5 m) in combination with the high-resolution orthophoto (0.25 m) enables the identification of small-scale snow depth features. Additionally, the development of masks for high-vegetated and settled areas in combination with the high accuracy of the unmasked snow depth values (root mean square error of around 0.15 m) represents a significant step forward for reliable snow depth mapping of large alpine regions with photogrammetric methods. Our study focuses on the consistent workflow used for processing the snow depth maps, demonstrates the special characteristics of the snow depth distribution and presents the potential for investigations and applications based on this unique snow depth time-series.

How to cite: Bührle, L., Marty, M., Eberhard, L., Stoffel, A., and Bühler, Y.: Snow depth mapping over large, high-alpine regions by airplane photogrammetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9744, https://doi.org/10.5194/egusphere-egu22-9744, 2022.

Precise snow cover estimations are relevant for many fields of research applications, such as for hydrological and meteorological modelling. Furthermore, snow plays an important role in hydropower management and flood prediction. Snow cover monitoring from satellite imagery has received increasing attention over the past decades. Nowadays, improvements in estimation methodologies and better availability of augmented satellite imagery provide an excellent basis for reliable estimations of fractional snow cover.

In this work we exploit the available spectral information of the Sentinel-2 MSI and Sentinel-3 OLCI for automatic estimation of fractional snow coverage. This is achieved through linear spectral unmixing with local endmembers. Similar implementation of methods that employ spectral unmixing use a reflectance model or a spectral library with pre-selected endmembers. Our approach selects the spectral endmembers from the data itself and applies them depending on the distance from the query point assuming spectral similarities of ground reflectance nearby. Endmembers are found through a pre-classification step based on conservative thresholds in combination with a similarity measure. The linear unmixing problem is solved several times for each query point using different combinations of endmembers detected in the vicinity of the query point; accounting for different illumination conditions and shaded areas. Finally, a careful selection of accurate fractional snow cover estimations is performed. This approach is globally applicable, adjusts to the local environment and illumination conditions and avoids costly endmember modelling or the provision of an external spectral library. The method was tested in different regions in the world using different satellite data including Sentinel-2, Landsat and Sentinel-3 OLCI and were inter-compared with snow information from other sources. In the presentation we will present the method, and examples of fractional snow cover maps. The performance of the method will be shown in comparison with other data and the limitations and capabilities will be discussed.

How to cite: Keuris, L., Nagler, T., Mölg, N., and Scheiblauer, S.: Fractional snow cover estimation through linear spectral unmixing of Sentinel-2 and Sentinel-3 optical satellite data using local endmembers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9932, https://doi.org/10.5194/egusphere-egu22-9932, 2022.

Global snow water equivalent (SWE) data are required for understanding the role of snow in the Earth’s water, energy and carbon cycles, and are critical for informing water resource and snow-related hazards. While exciting progress has been made in recent decades, there are currently no global SWE data at the required frequency, resolution and accuracy to address scientific and operational requirements. These data are needed to inform science and application areas and, taken as a whole, are critical to global water and food security. New higher-resolution microwave instruments can provide this information, especially when combined with modeling.  We now have the capability to put these instruments in space to monitor SWE and volume at the required resolution for improved and useful water prediction globally.  This presentation will describe the requirements of a spaceborne SWE mission, review progress in snow remote sensing technology and algorithms, and describe a potential path forward to meet identified snow data needs.

How to cite: Barros, A., Vuoyvich, C., Durand, M., Tsang, L., Houser, P., and Marshall, H.-P.: Global Snow Water Equivalent Observations from Space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10725, https://doi.org/10.5194/egusphere-egu22-10725, 2022.

Daily snow cover is an essential parameter in hydrology, climate, and environmental studies. Although remote sensing images provide valuable information on snow, they are restricted by clouds, clouds’ shadows, and temporal and spatial coverage. This study synthesizes daily snow cover maps based on climate and near clear sky Sentinel-2 and Landsat images. The motivation of this study is that snow patterns are repeatable between years with similar meteorological characteristics. Accordingly, a distance metric based on climate information is computed, including temperature and precipitation (1km resolution) as well as auxiliary data such as daily MODIS snow cover. This distance quantifies the mismatch between the days when clear sky Landsat or Sentinel-2 data is available, learning days, and days when there is no clear sky satellite data or test days. The proposed methodology is applied on a subset of the Alpine belt called the Western Swiss Alps and on the Jonschwil sub-basin, both located in Switzerland. We have synthesized daily snow cover maps for each of our regions of interest for 20 years since 2000.

To evaluate synthesized snow cover maps, we use leave-one-out cross-validation, comparison with a random selection process, and a degree-day snow model. The leave-one-out assessment shows a good agreement between the actual Landsat and the synthesized one. The synthesized snow cover maps also show that the proposed method output agrees with physical concepts as the physical features have been used along with satellite data in the proposed model. Considering physical features in synthesizing Landsat images is an innovation that allows us to use the methodology to synthesize images for the pre-satellite period. Moreover, random selection assessment shows that considering a metric based on climate and auxiliary data can synthesize snow cover as repeatable patterns depending on meteorological data.

How to cite: Zakeri, F. and Mariethoz, G.: Synthesizing Daily Snow Cover Maps Using Satellite Images and Climate Information, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11519, https://doi.org/10.5194/egusphere-egu22-11519, 2022.

Snow is an important hydrological component in Central Asia. The snowmelt contributes to about 50 % of total water formation in the region, depending on geographic conditions. Many hydro-meteorological phenomena such as floods or drought conditions can be triggered by snowmelt amounts in Central Asia. The amount of snow accumulation in the mountains of Tian-Shan and Pamir also defines the availability of water for summer months to be used for agricultural production or re-filling of reservoirs for energy production in the winter period. Thus, it is of high importance to better understand the seasonal variation of snow and if the over the global average climate warming in the region is affecting the processes related to snow accumulation and melt.

In this study, we analyze 22 years of daily Moderate Resolution Imaging Radiometer (MODIS) snow cover data that was processed using the MODSNOW-Tool, including cloud elimination. Additionally, observed snow depth data from meteorological stations were used to estimate trends related to snow cover change. We used several parameters such as snow cover duration, snow depth, snow cover extent, and snowline elevation to analyze changes.  We conducted this analysis in 18 river basins across the Central Asian domain with each river basin having different geographic conditions and the results show varying tendencies. In many river basins, a clear decrease of snow cover was found to be significant, whereas in some river basins also increase in the snow cover extent in particular months could be identified. We attributed the changes related to snow cover to available historical temperature and precipitation records from meteorological stations to better understand the driving forces. The results of this study indicate seasonal snow cover variations but also potential water shortages in particular months as well as water abundance in months where water demand is not high in Central Asia. 

How to cite: Gafurov, A., Kalashnikova, O., Niyazov, D., Mamaraimov, A., Gafurov, A., and Adkhamov, U.: Climate change-driven seasonal snow cover variations in Central Asia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12576, https://doi.org/10.5194/egusphere-egu22-12576, 2022.

Observations worldwide identify snow cover persistence together with snowfall occurrence as the most affected variables by global warming. In particular, Mediterranean mountain areas are pointed as climate warming hotspots. The characteristic snow-patched distribution shown over these areas, which result in different accumulation-ablation cycles during the cold season, usually makes spatial resolution the limiting factor for its correct representation. Remote sensing is the only feasible data source for distributed quantification of snow in mountain regions on medium to large scales, due among other to the limited access to these areas together with the lack of dense ground monitoring stations for snow variables. Among the numerous remote sensing sources, the Landsat constellation is those that better fit both basic requirements for studying snow over these areas, to cover a long period with observation and to have an high spatial resolution. However, the traditional classification algorithms for snow detection are usually based on normalized indexes that  provide a binary classification as snow and no-snow pixels throughout the study area; this simple classification may result in large error in heterogeneous and transitional areas within the snow-dominated domain. Alternatively, the spectral mixture analysis (SMA) approach provides a fraction of snow cover within each pixel and thus, constitutes a step forward to characterize heterogeneous and patchy snow areas in semiarid regions. 

This work analysed the role of mixed pixels, defined as pixels made up of different types of surfaces, in snow cover distribution over Mediterranean mountains. Sierra Nevada Mountain Range in southern Spain has been chosen as representative of a Mediterranean mountain area, which is characterized by strong altitudinal gradients with marked differences between the south (directly affected to the sea) and the north faces are found in the area. The fractional snow cover maps, at 30×30 m and 16 days spatial and temporal resolution respectively, derived from SMA of Landsat TM and ETM+ validated using as high resolution terrestrial photography (Pimentel et al., 2017) has been used for mixed pixel analysis. On the one hand, the results show the importance of mixed pixels, which can constitute more than 50% of the total pixels in some areas of the mountainous range and season of the year. On the other hand, the analysis carried out has allowed the identification of areas more prone to allocate this type of pixels, linking that fact to climatic drivers. 

This work has been funded by project MONADA - "Hydrometeorological trends in mountainous protected areas in Andalusia: examples of co-development of climatic services for strategies of adaptation to climatic change", with the economic collaboration of the European Funding for Rural Development (FEDER) and the Andalusian Ministry of Economic Transformation, Industry, Knowledge and Universities. R+D+i project 2020.

How to cite: Pimentel, R., Aparicio, J., Torralbo, P., Contreras, E., Moreno-Pérez, F., Aguilar, C., and Polo, M. J.: Quantifying the role of mixed pixels in snow cover distribution in semiarid regions: A study case in Sierra Mountain (Spain), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12801, https://doi.org/10.5194/egusphere-egu22-12801, 2022.

Estimates of mass balance across the Greenland Ice Sheet (GrIS) are commonly based on the joint interpretation of satellite radar altimetry measurements and the outputs of climate models. Conventional radar altimetry measurements, such as those produced by ESA’s CryoSat-2 platform, provide an observational constraint on the physical dimensions of the ice sheet (i.e., surface height), while climate models attempt to constrain relevant mass fluxes (i.e., precipitation, run-off, and evaporation/sublimation). However, this approach provides no direct observational insight into the large-scale state and temporal evolution of near-surface density across the ice sheet; a critical quantity through which surface deformation and mass flux estimates are linked to overall mass balance.

To date, the analysis of space-based radar altimetry measurements over the GrIS has been predominantly concerned with determining the range between the satellite and the surface as a means of quantifying changes in ice column thickness. While some studies have investigated the relative shape of the measured return echo, little attention has been paid to its actual recorded strength. Radar Statistical Reconnaissance (RSR), originally developed for use with radar reflections from the surface of Mars, provides a framework for the interpretation of backscattered surface echo powers and the quantitative estimation of near-surface properties. The RSR method relies on using the distribution of a set of observed echo strengths in order to determine their coherent and incoherent components. These decomposed reflection components are then assumed to be related to near-surface density (coherent) and wavelength-scale surface roughness (incoherent) respectively.

In this study, we present the first attempt to apply the RSR methodology to Ku-band (SIRAL; on-board ESA CryoSat-2) and Ka-band (ALtiKa; on-board ISRO/CNES SARAL) radar altimetry measurements acquired over the GrIS. In continual operation since July 2010 and March 2013 respectively, the longevity of these spacecraft along with their dense spatial coverage of the GrIS provides a tantalizing opportunity to produce long-term trends in near-surface density. Surface echo powers are extracted from recorded waveforms contained in CryoSat-2 SARin FBR data products as well as SARAL SGDR data products and organized by month. We focus on waveforms in the CryoSat-2 SARin FBR data products in lieu of those from LRM Level 1B data products in order to increase the spatial density of surface echo power measurements and therefore, the spatial resolution of the RSR results. Estimates of coherent and incoherent power are then produced on a month-by-month basis for a constant set of grid points (5 km by 5 km spacing) across the GrIS. We calibrate the coherent component of the CryoSat-2 and SARAL surface echoes to near-surface density using in situ measurements from the SUMup dataset.

This research into leveraging the radiometric information previously ignored in radar altimetry measurements to determine near-surface densities across the GrIS is a new frontier in Earth Observation. The capability to observationally determine near-surface density across the GrIS represents a fundamental contribution to refining surface mass balance estimates and understanding the evolution of the ice sheet in face of a changing climate.

How to cite: Scanlan, K. M. and Simonsen, S. B.: Inferring Near-Surface Density and Surface Roughness from Satellite-Based Radar Altimetry over Greenland, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-241, https://doi.org/10.5194/egusphere-egu22-241, 2022.

Satellite Altimetry has been continuously providing precise sea level for the last 28 years. However, the conventional altimetry is not at its best for the coast because it is hampered by mixed returns of electromagnetic waves due to disturbance from lands and inconsistencies of corrections. Since coastal regions are a vital part of human societies, improving methods to understand the coastal ocean topography, sea level, and its change is essential. In the last seven years alone, there are several specifically-designed coastal retracker that aimed to overcome the disturbance that occurred on the coasts. However, until now, there are only a few extensive studies have compared the accuracy and precision of retrackers and range corrections combination with regards to tide gauges on the coast of Indonesia. A region where the oceanographic condition and land and sea interaction is challenging, mainly due to the existence of shallow seas, narrow straits, and bays.

In this study, we compare sea level heights obtained using six processing schemes mostly dedicated to coastal areas. Three of them are for conventional altimetry (ALES, X-TRACK, and X-TRACK/ALES) and the other for SAR altimetry (STARS, SAMOSA++ in SARvatore and SINCS in TUDaBo). The first covers 20 years and corresponds to the repeat-track phase of Jason-1, Jason-2, and Jason-3. The second covers 10 years and corresponds to the SAR-mode measurements of Cryosat-2, Sentinel-3A/3B, and Sentinel-6. We apply similar state-of-the-art corrections designed for coastal areas.

On the other hand, a set of Indonesian tide gauge stations are being evaluated and selected in terms of their time series and their relationship with the GNSS station near it, identifying the effect of vertical land motion. Those tide gauges are considered as reference and used to assess which combination of retrackers and range corrections provide the sea level height which best agrees with in-situ data.

The results will have implications for understanding the goodness of altimetry processing schemes and of the corrections in the coastal zone, at less than 10 km. Moreover, the result is also will be used to determine precise MDT and in turn, gravity anomaly of Indonesian seas.

How to cite: Nadzir, Z. A., Fenoglio-Marc, L., Uebbing, B., and Kusche, J.: Exploring Coastal Altimetry Datasets for Indonesian Seas in relation to Local Tide Gauges, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-538, https://doi.org/10.5194/egusphere-egu22-538, 2022.

Runoff from the Greenland Ice Sheet has increased over recent decades affecting global sea level, regional ocean circulation, and coastal marine ecosystems. Runoff now accounts for most of Greenland’s contemporary mass imbalance, driving a decline in its net surface mass balance as the regional climate has warmed. Although automatic weather stations provide point measurements of surface mass balance components, and satellite observations have been used to monitor trends in the extent of surface melting, regional climate models have been the principal source of ice sheet wide estimates of runoff. To date however, the potential of satellite altimetry to directly monitor ice sheet surface mass balance has yet to be exploited. Here, we explore the feasibility of measuring ice sheet surface mass balance from space by using CryoSat-2 satellite altimetry to produce direct measurements of Greenland’s runoff variability, based on seasonal changes in the ice sheet’s surface elevation. Between 2011 and 2020, Greenland’s ablation zone thinned on average by 1.4 ± 0.4 m each summer and thickened by 0.9 ± 0.4 m each winter. By adjusting for the steady-state divergence of ice, we estimate that runoff was 357 ± 58 Gt/yr on average – in close agreement with regional climate model simulations (root mean square difference of 47 to 60 Gt/yr). As well as being 21 % higher between 2011 and 2020 than over the preceding three decades, runoff is now also 60 % more variable from year-to-year as a consequence of large-scale fluctuations in atmospheric circulation. In total, the ice sheet lost 3571 ± 182 Gt of ice through runoff over the 10-year survey period, with record-breaking losses of 527 ± 56 Gt/yr first in 2012 and then 496 ± 53 Gt/yr in 2019. Because this variability is not captured in global climate model simulations, our satellite record of runoff should help to refine them and improve confidence in their projections.

How to cite: Slater, T., Shepherd, A., McMillan, M., Leeson, A., Gilbert, L., Muir, A., Kuipers Munneke, P., Noël, B., Fettweis, X., van den Broeke, M., and Briggs, K.: Increased variability in Greenland Ice Sheet runoff detected by CryoSat-2 satellite altimetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2834, https://doi.org/10.5194/egusphere-egu22-2834, 2022.

Measuring river water level is essential for the global freshwater system monitoring, water resource management, hydrological model development, and climate change assessment. Despite its importance, the number of in-situ gauges has decreased over the recent decades. Moreover, many of the river systems are monitored either sparsely or not long enough to investigate their long-term evolution. Satellite altimetry is a unique technique that has enabled quantifying river levels for more than 25 years. Single mission altimetric water level time series can be obtained at the intersection of the satellite ground tracks and the river. For operational hydrology, however, single mission satellite altimetry is limited in its spatial and temporal sampling governed by the orbit configuration. This study proposes a framework to estimate the long-term sub-monthly river water level over the entire river using Least-Squares Collocation (LSC) by benefiting from multi-mission altimetric water levels (both interleaved and repeat orbit missions). The proposed method allows us to obtain dense water level observations both in time and space.  We present the results over the Mackenzie River basin, located in Canada, and validate against in-situ data.

How to cite: Saemian, P., Tourian, M. J., and Sneeuw, N.: A least-squares collocation approach to densifying river level from multi-mission satellite altimetry; Case study Mackenzie River basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3699, https://doi.org/10.5194/egusphere-egu22-3699, 2022.

Satellite altimetry is a technique of measuring height. Originally developed to observe sea level dynamics, altimetry has proven its usefulness in monitoring inland waters. Over the recent years these observations became an important supplement to the classical river gauge records. Due to the improvement of the accuracy of altimetric measurements, river water levels are being used in numerous hydrological projects, aiming to calculate water storage or to predict water levels and river discharges. Despite the improving quality of altimetric data, the accuracy of river stage measurements is still in the decimetre range, an order of magnitude lower than altimetry-based sea level observations. This is due to several factors that can lead to the deterioration of altimeter readings.

Our study is the first attempt to assess the accuracy of water levels measured by the Sentinel-3A altimetry at virtual stations (intersections of a satellite ground tracks and a river channel, hereinafter abbreviated as VS) located along Polish rivers. Further, this study aims to investigate the influence of the environmental factors on the data accuracy. The study is conducted on six biggest Polish rivers (Vistula, Odra, Warta, Bug, Narew, San) which drain predominantly lowlands, and – based on width – can be classified as small and medium rivers (40–610 m in width).

In order to assess the accuracy of measurements at virtual sites, we compare water level anomalies of these readings with stages from two adjacent gauges: one downstream and one upstream a VS. In this study we used Sentinel-3A water levels from the Hydroweb database (http://hydroweb.theia-land.fr/ – last access 09.01.2022). The time span of gauge and altimetry data ranges from April 2016 to August 2019. Since the virtual sites are located up to 73 km away from the adjacent gauges (with mean distance of 20.12 km), we decided to calculate the time shift occurring between the analysed stations. Such a unification of times is based on a two-gauge relationship, calculated for each of the satellite measurements.

We found that the root mean square error ranges from 0.12 to 0.44 m, with mean of 0.22 m. The Nash–Sutcliffe efficiency (NSE) varies between 0.40 and 0.98 (with mean of 0.84) for 67 pairs of time series, out of 68 considered. We found no correlation between the accuracy of Sentinel-3A water levels and the river width, neither for the small nor medium river sections. Likewise, land cover (determined using the Corine Land Cover 2018 data) has not been identified as an environmental factor to constrain the data accuracy. However, we found that complex river channel morphology (i.e. the occurrence of sandbars) and the unfavourable geographical setting of the VS (river channel parallel to satellite ground track or its multiple crossing) occur more often at VS with lower NSE (⩽0.8).

This study confirms the usability of the Sentinel-3A altimetry over Polish rivers and identifies factors to constrain its accuracy. The research is supported by the National Science Centre, Poland, through the project no. 2020/38/E/ST10/00295. Our results were recently published in Journal of Hydrology (https://doi.org/10.1016/j.jhydrol.2021.127355).

How to cite: Halicki, M. and Niedzielski, T.: Influence of environmental factors on the accuracy of the Sentinel-3A altimetry over Polish rivers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4221, https://doi.org/10.5194/egusphere-egu22-4221, 2022.

Almost ten years ago, the first elevation estimates based on swath processing of interferometric CryoSat-2 altimeter observations were published, mapping the surface of Devon ice cap. The new method holds a great potential to provide dense data coverage, in space and time. Indeed, ESA recently started releasing digital elevation models at a 2 by 2 km resolution for a rolling 3 month data aggregation cycle. Such spatiotemporal resolutions are especially valuable in versatile and dynamic regions as mountain glaciers. In this presentation, we describe systematic errors on the order of 10 m with about yearly periodicity that arise in the proximity of hills and valleys. One error is caused by the superposition of multiple signals, the other is caused by the Fourier-transformation in the SAR beam-forming process. Both are intrinsic to the measuring concept, but their effect can potentially be limited by data filtering strategies. We report the influence of the commonly used coherence and power threshold based filtering on derived elevation change rates. For data users, awareness of these issues is especially important to interpret the observations correctly and to understand that there is a large systematic part in the overall uncertainty.

How to cite: Haacker, J., Wouters, B., and Slobbe, C.: Systematic errors in Cryosat-2 swath elevations and their impacts on glacier mass balance estimates, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4946, https://doi.org/10.5194/egusphere-egu22-4946, 2022.

Satellite altimetry is one of the fundamental techniques for Earth observation, which provides precise measurements with frequent sampling and global coverage. However, its performance is degraded in coastal areas due to different factors, such as land contamination, erroneous tropospheric corrections or complex tidal patterns. In order to improve performance of satellite altimetry in the coastal zones, an increasing number of dedicated coastal altimetry products have been developed and validated in specific areas in later years. Those products are based on the improved analysis of backscattered signals in order to increase accuracy of altimetry observations in the coastal zones. One such product is the Adaptive Leading Edge Subwaveform (ALES) retracker, specifically aimed at the issue of land contamination. As of yet, it has not been validated along the whole, complex Norwegian coastline, with thousands of small islands, narrow fjords, and rough topography.  Thus, this study aims to validate the ALES retracker along the Norwegian coast, comparing conventional and ALES-retracked Sentinel-3 A/B observations with tide gauge observations. Altimetry-tide gauge comparison pairs are found by considering altimetry observations within optimum radii around each tide gauge, determined by minimizing the root mean square of differences (RMSD) for a range of candidate radii. It was found that the optimum radii for tide gauges located towards the open ocean are smaller than for those located inside fjords, because the observation accuracy degrades in the latter areas. Thus, it was necessary to increase radii, i.e. to include more points on the open-sea, for tide gauges inside fjords in order to minimize the RMSD. It was concluded that the ALES dataset generally gave better results (in terms of RMSD and correlation to the tide gauges) than conventional datasets, as well as giving a larger number of valid observations. The results are promising for future optimal combination of altimetry observations with other available sea-level observations in the coastal zone, e.g., from tide gauges, ships, unmanned surface vehicles (USVs) or airborne LiDAR. A prerequisite for such a combination is a reliable error description of each data type, to which the current study serves as a contribution. 

How to cite: Tomic, M., Joodaki, G., Breili, K., Gerlach, C., and Ophaug, V.: Validation of conventional and retracked Sentinel-3 observations along the Norwegian coast, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7598, https://doi.org/10.5194/egusphere-egu22-7598, 2022.

Surface water level and river discharge are key observables of the water cycle and among the most sensible indicators that integrate long-term change within a river basin. Satellite altimetry provides valuable information on water level variation in rivers, lakes and reservoirs and once combined with satellite imagery, river discharge and lake storage changes can be estimated. Over the last decade, a two-dimensional observational field is derived by merging innovative space and in-situ data. The new generation of spaceborne altimeters includes Delay Doppler since 2010 with CryoSat-2, laser technique since 2018 with ICESAT-2 and bistatic SAR altimeter techniques with SWOT planned to be launched late this year. This shows a potential for monitoring the impact of water use and to characterize climate change. The mission SWOT will provide river discharge innovatively derived from contemporaneous river slope, height and width observations.

Our hypothesis is that the new space missions provide (a) surface water levels of higher accuracy and resolution compared to previous altimetric and in-situ observations and (b) new parameters to estimate river discharge and water storage change. A better sampling of flood event detection and of the long-term evolution is expected. We discuss here methodology and applications for satellite altimetry in the fields of hydrology and consider the two open research questions: (1) How can we fully exploit the new missions to derive best estimates of water level and storage change and river discharge and (2) can we separate natural variability from human water use.

For the first goal, we derive a multi-sensor database in an automatic processing which identifies the virtual gauge location and constructs the water height and water extension time-series. Water heights of the official release and of enhanced processing in project Hydrocoastal and in-house are used. Discharge and storage change time-series are derived from hydraulic equations using water extension and slope. First river basin considered is the Rhine river basin, where we obtain at 20 virtual stations a mean accuracy of 15 cm comparing altimeter and river height data. The derived discharge agrees within 18% with the in-situ discharge estimate.

For the second goal, we study past and present discharge and storage change, which are responses to both anthropogenic (deforestation, land use change, urbanization, reservoirs) and natural (climate modes, climate variability, rainfall, glacier and snow melting) processes. We discuss potential and limitations of satellite altimetry constellations for monitor recent river extremes and long-term changes. The work is part of Collaborative Research Centre CRC1502 “Regional Climate Change: Disentangling the role of Land Use and water management” of the German Research Foundation DFG.

How to cite: Fenoglio-Marc, L., Uyanik, H., Chen, J., and Kusche, J.: Impact analysis of surface water level and discharge from the new generation of altimetry observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10723, https://doi.org/10.5194/egusphere-egu22-10723, 2022.

Multi-Frequency and multi-Satellite Approaches for enhanced snow, ice and elevation in the polar oceans: updates from Polar+ and Cryosat+ ESA projects

We propose new methods for multi-frequency snow, ice and sea surface retrievals building on the legacy of the Arctic+ Snow project where we developed two products: the dual-altimetry Snow Thickness (DuST) and the Snow on Drifting Sea Ice (SnoDSI) and on the recent ESA projects: Polar+ Snow on Sea Ice and CryoSat+ Antarctic Ocean.  

The primary objective of the Polar+ Snow of Sea Ice ESA project is to investigate multi-frequency approaches to retrieve snow thickness over all types of sea ice surfaces in the Arctic and provide a state-of-the-art snow product. Our approach follows ESA ITT recommendations to prioritise satellite-based products and will benefit from the recent "golden era in polar altimetry" with the successful launch of the laser altimeter ICESat-2 in 2018 complementing data provided by the rich fleet of radar altimeters, CryoSat-2, Sentinel-3 A/B, AltiKa. Our primary objective is to produce an optimal snow product over the recent "operational" period. This will be complemented by additional snow products covering a longer periods of climate relevance and making use of historical altimeters (Envisat, ICESat-1) and passive microwave radiometers for comparison purposes (SMOS, AMSRE, AMSR-2).

The CryoSat+ Antarctic Ocean ESA project aims at exploring alternative methods to derive sea ice thickness and sea surface height measurements over the Antarctic Ocean. The potential of CryoSat-2 to retrieve information on mesoscale features over the area is also explored.  Exciting new results include (i) a detailed inter-comparison of all processing options along-track; (ii) novel optimal interpolation techniques; (iii) dual frequency approaches tested in the SO for snow retrieval; (iv) Lagrangian drift snow products for the SO. This work is supporting the progress of the gridded product development during. Complementing this project, a new ESA project looking at tides in the Southern Ocean (ALBATROSS) started and will offer a clear pathway to impact to the new algorithms developed as part of CSAO. 

We will present exciting methods explored to validate our results against in situ, airborne and other satellite data, including from NASA’s ICESat-2.

How to cite: Tsamados, M. and the POLAR+ Snow on Sea Ice team: Multi-Frequency and multi-Satellite Approaches for enhanced snow, ice and elevation in the polar oceans: updates from Polar+ and Cryosat+ ESA projects, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12366, https://doi.org/10.5194/egusphere-egu22-12366, 2022.

Thirty years of altimetry satellite observations have provided important information that enables research discoveries and aids in the development of user-driven applications. National and international space and operational agencies have committed substantial resources to developing and continuing observations of the ocean and large water bodies (lakes, reservoirs, large rivers) through collaboration in these missions. Over the next few years, NASA and other agencies will launch new research missions with technologies that will extend, expand, and evolve observations of ocean, coastal and inland waters. New discoveries and advances in societally relevant applications can be leveraged with increased spatial, temporal and spectral resolutions. We will highlight the use of data from these existing and planned missions for operational and applied user-driven applications and their societal benefits. Topics may include the use of existing, retrospective, and expected time series that contribute to applications such as marine operations, marine biology and biodiversity, coastal studies, hurricanes and other hazards, as well as hydrologic assessments, water resources management, and other surface water applications.

How to cite: Srinivasan, M., Tsontos, V., and Hossain, F.: Applications of Satellite Altimetry Observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13067, https://doi.org/10.5194/egusphere-egu22-13067, 2022.

Sea level variations from satellite altimetry need to be consistently calibrated and monitored when used for climate studies. Here, we focus on the estimation of biases and the monitoring of precision and drifts of three SAR-altimeter missions (Sentinel-3A, Sentinel-3B and Sentinel-6MF) at eleven tide gauge stations in the German Bight (Southeastern North Sea). The corresponding operational GNSS-controlled tide gauge stations are partly located in open water, partly at the coast close to mudflats and deliver data every minute in the period 2016 to 2021. Instantaneous sea level (total water envelope) from altimetry is extracted at virtual stations in close vicinity to the gauges (2 to 24 km) and for different retrackers. The processing is optimized for the region and empirically adjusted for the comparison with the nearby tide gauges readings. The precision of the altimeters is depending on location and mission and is shown to be better than 3 cm. The relative drifts between tide gauges and altimetry are discussed.

How to cite: Esselborn, S. and Schöne, T.: Monitoring SAR-altimeter missions at non-dedicated tide gauge stations in the German Bight, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13466, https://doi.org/10.5194/egusphere-egu22-13466, 2022.

Sunlit snow is highly photochemically active and plays a key role in the exchange of gas phase species between the cryosphere and the atmosphere. Bromine (Br), Iodine (I) and Mercury (Hg) can be photoactivated by the UV radiation and, in certain circumstances, released from the snowpack into the atmosphere. Mercury is a heavy metal with a known toxicity present in the environment in several different chemical forms. Once present in the snowpack, Hg is very labile and, thanks to the UV light, it can be reduced back to elemental Hg (Hg(0)) and undergo dynamic exchange with the atmosphere. Similar to mercury, iodine can undergo photochemical activation in surface snow resulting in its presence in the surrounding atmosphere where it plays a crucial role in new particle formation. Bromine  has a central role in the mercury cycle in polar regions (through the Atmospheric mercury depletion events) as well as contributing to the tropospheric ozone cycle in the polar region causing the so-called Ozone depletion events. However, compared to Iodine and Mercury, it seems to be more stable after deposition into the snow pack. 

Here we present measurements of bromine, iodine and mercury performed by ICP-MS, on snow pit and shallow core samples taken over a 2100 km traverse in East Antarctica from the coast to the interior (Talos Dome – Dome C traverse 2016 and East Antarctic International Ice Sheet Traverse, EAIIST 2019). The shallow core and the snow pit samples at each site are estimated to cover about 10 to 20 years of snow accumulation, giving us a deposition record from approximately the late 90s, to around the early 21st century. The concentrations determined in different  sampling sites show a rather clear decrease trend from the coast with the minima as we approach the inner part of the Antarctic plateau. In addition, the analysis of surface and bulk samples from EAIIST show a decrease of concentrations toward the inland except for the sites characterised by a strong snow metamorphosis caused mainly by the wind friction. In almost all the sampling sites of the EAIIST traverse the concentrations of Br, I and Hg increase with sample depth, possibly due to snowpack photochemical activation in the upper part of the snowpack. Future studies are planned to  investigate the possible link between the determined concentration profile and the variation of the solar radiation reaching the Antarctic Plateau during spring caused by the ozone hole formation. 

How to cite: Celli, G., Cairns, W. R. L., Savarino, J., Stenni, B., Frezzotti, M., Maffezzoli, N., Turetta, C., Scarchilli, C., Delmonte, B., Traversi, R., and Spolaor, A.: Bromine, Iodine and Mercury on the East Antarctic plateau: preliminary results from sampling along a traverse., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2255, https://doi.org/10.5194/egusphere-egu22-2255, 2022.

Ice cores are valuable climate archives preserving organic compounds from atmospheric aerosols over long time ranges. Different approaches of dating are available like annual layer counting, radioactive decay and stratigraphic markers like tephra from volcanic eruption. In combination, they enable accurate dating back to 800,000 years. Secondary organic aerosols (SOA) are formed in the atmosphere by condensation of oxidized highly volatile organic compounds and their chemical profile is highly complex due to the variety of emission sources and reactions in the atmosphere.

Well-known SOA markers include pinic acid, pinonic acid or terebic acid from monoterpene oxidation. Another class of important atmospheric markers are biomass burning products. During combustion of cellulose levoglucosan, an anhydrosugar is formed while the combustion of lignin results in the formation of phenolic compounds like vanillic acid, cinnamic acid, or p-hydroxybenzoic acid. While the lignin burning products provide important information on paleo-fire history, intact polymeric lignin offers a deeper insight into the type and abundance of vegetation. By alkaline oxidation, the polymeric lignin is degraded into the lignin oxidation products (LOP) and the ratios of these products are related to wooden and non-wooden, as well as angiosperm and gymnosperm vegetation.

In this work, an analytical approach is presented covering a variety of SOA markers, biomass burning markers, and polymeric lignin, using UHPLC-HRMS and an elaborate sample preparation procedure. The method was applied to samples from Colle Gnifetti in the Swiss-Italian Alps, a part of Grenzgletscher, covering the time between 1920 and 1994. We present first data on polymeric lignin in ice core samples and an examination of correlations with known organic proxies to emphasize the relevance of lignin not only in climate archives like speleothems and sediments but also in ice core samples.

How to cite: Beschnitt, A., Schäfer, J., Schwikowski, M., and Hoffmann, T.: Trace analysis of organic aerosol markers and lignin in samples from Alpine ice core Colle Gnifetti covering the 20th century using UHPLC-HRMS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2266, https://doi.org/10.5194/egusphere-egu22-2266, 2022.

Coastal low-elevation ice caps of the (Ant-)Arctic and mountain glaciers are at the forefront of climate change. Retrieving ice core records from these regions is crucial to assess current, and predict future, environmental changes.  However, climate reconstruction from melt-affected ice cores is challenging. It requires a comprehensive understanding of the site-specific, near-surface melt conditions, which are fundamental to consequent melt-induced alteration of climate proxies.

Here, we use core-scale microfocus X-ray computer tomography to investigate melt layer microstructure in firn core sections from three (sub-)Antarctic sites (Young Island, Smyley Island, and Sherman Island) at 120-µm resolution. We present density, pore and grain cluster size of 3D melt features and discuss how the secondary imprint of the melt-refreeze process is visible in firn microstructure. We further show that the appearance of melt features varies both within profiles and from site to site. Given that local climate drives the near-surface snow properties and melt conditions, we suggest that melt layer microstructure could be further developed as a useful climate proxy itself.

How to cite: Moser, D. E., Freitag, J., and Thomas, E. R.: Insights from 3D Firn Microstructure into Near-Surface Snow Melt Conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3462, https://doi.org/10.5194/egusphere-egu22-3462, 2022.

The Beyond EPICA project for retrieving the Oldest Ice Core (1.5 Myr) in Antarctica aims at obtaining high resolution climate records and water isotopes will be one of the most important parameters investigated. Given the extremely thin nature of the annual ice core layers, as we get deep down to the core, analysis of such an ice core requires new adopted techniques on water isotopes with high accuracy and precision. Laser ablation (LA) is an established powerful technique used in various fields and it can also be applied in ice sampling serving a dual purpose: a.direct solid-gas transition and b. the smallest amount of sample possible is used for analysis and that makes LA a micro-distructive process. A new instrument which couples LA sampling with the established Cavity Ring Down Spectroscopy (CRDS) for water isotopic analysis is developed. This novel design will allow both fast gas phase sample collection directly from the ice sample and high quality water isotopic measurements. Particular focus was given in the LA system which consists of a High Energy femtosecond IR LASER and the optical elements that focus the LASER beam into the ice surface. The focusing lens system is placed inside a freezer, up above a motorized stage that accomodates the ice sample. An enclosure supplied with dry air flow was build around the optics and tested by the means of humidity experiments. Subsequent series of experiments with varying laser ablation parameters: pulse energy, repetition rate, ablation time, together with the ablated crater characterization allow the evaluation of LA efficiency in ice and thus the optimization of the parameters controlling the ablation mechanism. Understanding the LA mechanism will provide the knowledge to further develop the sampling procedure and efficiently control and guide the vaporized ice into a CRDS instrument for detection.

How to cite: Malegiannaki, E., Gkinis, V., Barbante, C., and Dahl-Jensen, D.: Optimization of ’cold’ laser ablation sampling for water isotopic analysis on ice cores, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4226, https://doi.org/10.5194/egusphere-egu22-4226, 2022.

Identifying, understanding, and constraining post-depositional processes altering the original layer sequence in ice cores is especially needed in order to avoid misinterpretation of the oldest and most highly thinned layers. The record of soluble and insoluble impurities represents an important part of the paleoclimate proxy set in ice cores but is known to be affected post-depositionally through interaction with the ice matrix, diffusion and chemical reactions. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been recognized for its micron-scale resolution and micro-destructiveness in ice core impurity analysis. Important added value comes from employing LA-ICP-MS for state-of-the-art 2D chemical imaging. The latter has already revealed a close relationship between the ice grain boundary network and impurity signals with a significant soluble component, such as Na. Here we show the latest improvements in 2D chemical imaging of ice with LA-ICP-MS, by increasing the spatial resolution from 35 to 20 and even 10 µm and extending the simultaneous analysis to cover also mostly insoluble impurity species, such as Al. The latter reveal clear signals of insoluble particle aggregates in samples of Greenland ice cores. Combining the chemical images with computer vision-based image analysis allows to separate the geochemical signals of grain boundaries and insoluble particles. Considering intensities as well as elemental ratios, this classification further highlights important differences in the geochemical signals depending on the location of the impurities in the ice matrix. Ultimately, we discuss how this refined approach may serve to investigate post-depositional changes occurring with increasing depth to the soluble and insoluble impurity components, based on grain growth and chemical reactions, respectively.

How to cite: Bohleber, P., Stoll, N., Delmonte, B., Pelillo, M., Roman, M., Siddiqi, K., Stenni, B., Vascon, S., Weikusat, I., and Barbante, C.: Glacio-chemical signature of grain boundaries and insoluble particle aggregates in ice core 2D impurity imaging, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4492, https://doi.org/10.5194/egusphere-egu22-4492, 2022.

Mineral dust archived in polar ice cores can be used to document past atmospheric circulation variability and past climate conditions over the dust source areas. Thanks to its relative immobility and stability, eolian dust concentration has been used to synchronize deep ice cores as well as ice stratigraphies and marine sediment records. However, mineral impurities in deep ice can be affected by post-depositional physical and chemical alterations, deriving from small-scale relocation of dust grains and subsequent alteration in acidic micro-environments.

By applying a set of different techniques spanning from dust concentration and grain size to iron mineralogy and elemental composition, here we provide evidence of in situ dust physical and chemical alterations observed below 1000 m depth in the 1620-m deep TALDICE ice core drilled at Talos Dome (peripheral East Antarctica, 72°49’S, 159°11’E; 2315 m a.s.l.). Results highlight significant dust aggregation and alteration of Fe-minerals with englacial precipitation of neo-formed jarosite, occurring in parallel with the decline of ferrous minerals and depletion of some major elements. Therefore, below 1000 m depth the TALDICE dust record can be considered partly affected by acidic-oxidative weathering resulting from the interaction of dust particles with highly saline acidic brines. Although limited to only one specific site, this study opens new issues concerning the interpretation of climate signals in the deepest part of ice cores.

How to cite: Delmonte, B., Baccolo, G., Maggi, V., and Frezzotti, M.: Deep ice and mineral dust: the case of the TALDICE ice core, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5031, https://doi.org/10.5194/egusphere-egu22-5031, 2022.

Over the last four decades, the Southern Ocean has been characterized by now-persistent stronger westerly winds, with consequences for the Antarctic region climate, including variations in sea ice extent and primary productivity. Here we present the first ever bromine, sodium and iodine records, tracers of sea salt aerosols, sea ice and primary productivity, from five sub-Antarctic ice cores, retrieved from Bouvet, Young, Peter I and Mount Siple Island and Mertz glacier. The aim of the study is (1) to assess if halogens deposited in sub-Antarctic regions are influenced by recent changes in wind forcing and (2) to better understand the underlying processes of halogens emission from ocean/sea ice, their transport and deposition over the Antarctic region.

The trends of sodium and bromine, emitted and transported with sea salt aerosols, suggest that wind strengthening leads to more halogens deposited in the sub-Antarctic. Also, we find that bromine is depleted with respect to the bromine-to-sodium sea-water ratio at all sites, indicating that bromine species are sustained in the marine boundary layer by halogen chemistry and are less prone to be deposited. Iodine records show a positive correlation with marginal sea ice and primary productivity variability, suggesting that iodine species emitted at the edge are deposited more efficiently than bromine species.

How to cite: Segato, D., Thomas, E. R., King, A., Tetzner, D., Moser, D. E., Turetta, C., Saiz-Lopez, A., Markle, B., Pedro, J., and Spolaor, A.: Halogens in sub-Antarctic ice cores modulated by wind forcing, sea ice and primary productivity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5683, https://doi.org/10.5194/egusphere-egu22-5683, 2022.

The historical period  (from 1850 to present) yields a window when ice core  and good historical data are both available. It thus represents an important period over which to test the relationship between ice core measurements, here stable water isotopes, and Antarctic climate.  Water stable isotopes from ice cores from Antarctica have traditionally been used to infer past surface air temperatures. Here we run an ensemble over the period 1850-2004 using the UK Met Office HadCM3 general circulation model equipped with water stable isotopes.  Simulations of water stable istopes from general circulation model can help in the interpretation of Antarctic ice cores.  This ensemble captures observed temperature and precipitation trends. Interestingly however the water isotopes exhibit little trend. This appears to be explained by compensating effect of two modes of atmospheric dynamics throughout the period. Further we use these results to examine the relationships used in Last Millienium reconstructions based on Antarctic stable water isotopes data from ice cores.  

How to cite: Goursaud Oger, S., Sime, L., and Holloway, M.: Insights from the first analysis of Antarctic water stable isotope simulations for the historical period, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5847, https://doi.org/10.5194/egusphere-egu22-5847, 2022.

The recently commenced drilling operation at the Beyond EPICA Little Dome C (BELDC) site will attempt to recover an ice core that reaches back to 1.5 Ma, a time during which the enigmatic Mid-Pleistocene Transition (MPT) took place. While the ice flow and heat flux regime at the drilling site will largely determine the age, as well as the nominal temporal resolution of the deeper parts of the BELDC core, molecular diffusion in solid ice will play a significant role in the effective temporal resolution of the δ¹⁸O signal. Here we look into the expected diffusion characteristics of the BELDC ice core by firstly addressing a previously reported problem, that of the δ¹⁸O signal loss in the deeper parts of the Dome-C ice core, particularly over Marine Isotope Stage 19 (MIS-19). By using isotope diffusion modelling in combination with high resolution δ¹⁸O data, we show the importance of the ice flow thinning function for the estimation of the diffusion length. We also comment on the large uncertainty imposed by the poor knowledge of the diffusivity coefficient. Based on recently published results on the dating of the BELDC site we provide a first order estimate of the effective resolution of the δ¹⁸O signal over the MPT transition.

How to cite: Gkinis, V., Laepple, T., Malegiannaki, E., and Shaw, F.: The problem of signal loss for the upcoming Beyond EPICA Little Dome C (BELDC) ice core., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6443, https://doi.org/10.5194/egusphere-egu22-6443, 2022.

Ice coring in blue ice areas (BIAs) serves as an alternative to deep ice core drilling, allowing collection of large-sized old ice samples in a cost-effective way because old ice samples are outcropped to the surface. However, the stratigraphy in many blue ice areas can be complicated due to complex ice flows. Based on ice layers defined by dust bands and ground penetration radar (GPR) surveys, we show that Larsen BIA has a surface transect of ice with an undisturbed horizontal stratigraphy from mid- to downstream side ice. However, the upstream ice exhibits a potential repetition of ages on scales of tens of meters. Correlating δ¹⁸O_ice, δ¹⁸O_atm, and CH­₄ records of Larsen ice with existing ice core records indicates that the analyzed gas age and ice age ranges between 9.2–23.4 ka BP and 5.6–24.7 ka BP, respectively. Radiometric ⁸¹Kr dating of one of the cores confirms the estimated gas ages within uncertainty. A tentative reconstruction based on a simple analytical framework suggests a warming of 15 ± 5 ℃ during the last deglaciation that we attribute to the retreat of the Ross Ice Shelf, and an increase in snow accumulation by a factor of 1.7–4.6 that we attribute to the increased penetration of snow-bearing storms. Exact estimation of the original deposition site and updated ice ages may enhance the tentative climate reconstructions in future studies. Our study shows that BIAs in Northern Victoria Land may contribute to obtain high-quality paleoclimate proxy records through the last deglaciation.

How to cite: Lee, G., Ahn, J., Ju, H., Ritterbusch, F., Oyabu, I., Buizert, C., Kim, S., Moon, J., Ghosh, S., Kawamura, K., Lu, Z.-T., Hong, S., Han, C. H., Hur, S. D., Jiang, W., and Yang, G.-M.: Chronostratigraphy of Larsen blue ice, East Antarctica, and a tentative reconstruction of surface temperature and accumulation rate during the last deglaciation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6818, https://doi.org/10.5194/egusphere-egu22-6818, 2022.

Observed variability in summer surface snow isotopic composition (δ^18O, δD) cannot solely be explained by precipitation events. This variability however, influences the overall summer isotope signal that is archived in ice cores. It is therefore important to explain the origin of such post-depositional modifications of the snow isotope signal to ensure an optimal interpretation of ice core isotope records. The continuous exchange of humidity between the atmospheric vapor reservoir and the snow surface through sublimation and deposition could be a key process. Yet, in the past, the surface humidity flux has been disregarded as influential for the formation of the isotope signal in snow based on the assumption of the absence of isotopic fractionation during sublimation. Here we show evidence of isotopic fractionation during snow sublimation through a combination of laboratory experiments, in-situ observations from the Greenland Ice Sheet, and snow surface modeling. We document substantial isotopic enrichment in the uppermost centimeters of snow induced by sublimation and find that the in-situ observed summer season temporal evolution of the snow surface isotopic composition (in between precipitation events) can be attributed to surface humidity fluxes. We further discuss the nature and the underlying physical process of fractionation during sublimation. Our results lead to an improved process understanding and necessitate the implementation of fractionation during the sublimation process in isotope-enabled climate models.

How to cite: Wahl, S., Steen-Larsen, H. C., Hughes, A. G., Zuhr, A., Faber, A.-K., Jones, T. R., Dietrich, L. J., Behrens, M., Reuder, J., and Hörhold, M.: Snow surface water isotope variability driven by vapor-snow exchange, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7601, https://doi.org/10.5194/egusphere-egu22-7601, 2022.

The West Antarctic Ice Sheet (WAIS) may have collapsed during the last interglacial period, between 132,000 and 116,000 years ago. The changes in topography resulting from WAIS collapse would be accompanied by significant changes in Antarctic surface climate, atmospheric circulation and ocean surface conditions. Evidence of these changes may be recorded in water-isotope ratios in precipitation archived in the ice. We conducted high-resolution simulations with an isotope-enabled version of the Weather Research and Forecasting Model over Antarctica, using boundary conditions provided by climate-model simulations with both present-day and lowered WAIS topography. The results show that while there is significant spatial variability, WAIS collapse would cause detectable isotopic changes at several locations where ice-core records have been obtained or could be obtained in the future. The most robust signals include lower δ¹⁸O at Mount Moulton Blue Ice Area and higher δ¹⁸O at SkyTrain Ice Rise in West Antarctica, and higher deuterium excess at Hercules Dome, East Antarctica. A combination of records from multiple sites would provide the strongest constraint on the timing and magnitude of past WAIS collapse.

How to cite: Duetsch, M., Blossey, P. N., Pauling, A. G., and Steig, E. J.: Response of water isotopes in precipitation to a collapse of the West Antarctic Ice Sheet in high resolution simulations with the Weather Research and Forecasting and Community Atmosphere Models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8361, https://doi.org/10.5194/egusphere-egu22-8361, 2022.

Antarctic ice cores play a central role in paleoclimatic reconstructions, as they provide a high resolution archive of past climatic and environmental processes. This study focuses on the TALDICE ice core drilled at Talos Dome (East Antarctica, 72°49’S, 159°11’E), which covers more than 343k years of climate history. A comparison is presented between samples analyzed through two different techniques: low background instrumental neutron activation analysis (INAA) and inductively coupled plasma mass spectrometry (ICP-SFMS). While the former is used to investigate only the insoluble fraction of dust, as it can only be applied to solid samples, the latter is used to assess the elemental composition of both the total and the soluble fraction of dust. We thus observe how different elements partition between soluble and insoluble phase at different depths of the ice core and link the geochemical patterns of the considered elements to the main climatic oscillations covered in the Talos Dome ice core.

We determined 45 elements through ICP-SFMS and 39 through INAA, with a good overlapping of the elements between the two techniques. Besides the determination of major elements (Na, Mg, Al, Si, K, Ca, Ti, Mn, Fe), which is important in the assessment of the impact of dust on the ecosystem (e.g. source of nutrients in the Southern Ocean), the high sensibility of both techniques also permitted the determination of trace elements. Among these, rare earth elements (REE) are of particular importance as they have been widely used as a geochemical tracer of aeolian dust sources.

We present enrichment factors and correlation matrices to assess the crustal or non-crustal origin of the considered elements. The high correlations and low enrichment factors found among insoluble elements confirm a prevalent crustal composition for mineral dust. Regarding solubility, the majority of elements exhibits a minimum in solubility during the last glacial maximum. This is the result of higher fluxes of mineral dust transported to the Antarctic continent during cold climate periods and is consistent with the crustal origin we documented for most of the elements considered.

How to cite: Di Stefano, E., Baccolo, G., Gabrielli, P., Delmonte, B., and Maggi, V.: Analysis of impurities from Talos Dome ice core to assess the solubility of different elements  using INAA and ICP-SFMS, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9900, https://doi.org/10.5194/egusphere-egu22-9900, 2022.

The isotopic composition (d¹⁸O, dD) of surface snow on an ice sheet is primarily seen as a recorder of the air temperature and is used to reconstruct past climatic conditions from ice cores. The second order parameters d-excess and ¹⁷O-excess can preserve climatic signals from the moisture origin and are indicative for kinetic fractionation processes in the water cycle between the source and the deposition site. Fractionation is especially important for the surface snow which can exposed to the atmosphere for a long time during periods without snowfall. The resulting effect of this process on the isotopic composition and the second order parameters in the interior of Antarctica is, however, unclear. In order to better understand the contribution of secondary processes on the isotopic signature and to disentangle the climate characteristics at the moisture source region and at the ice core site, we study both the isotopic composition and the second order parameters of surface and subsurface snow at Dome C on the East Antarctic Plateau. For this, we make use of an extensive data set of isotopic data (d¹⁸O, dD, dexcess and ¹⁷O-excess) from surface (0 - 1.5 cm) and subsurface snow (1.5 - 4.5 cm) covering continuously the period from 2016 to 2020. This data set is complemented with previously published isotopic data of surface snow and precipitation data back to 2011. For additional comparisons and analyses, we use data from a nearby weather station, the ERA5 reanalysis data set, a satellite-derived grain index estimate and simulations from the detailed snowpack model CROCUS.

We observe a good (weak) correlation between the ambient temperature and the surface (subsurface) layer. Most years are characterised by a strong increase in the isotopic composition towards the summer and a gentle decrease towards winter while d-excess shows a contrary behaviour. We suggest that the strength of the summer increase is related to the amount of precipitation and the magnitude of metamorphism at the surface. The degree of metamorphism can (to some degree) be approximated from observational data (e.g. the grain index) or from model output (e.g. latent heat flux from CROCUS). In addition to presenting possible mechanisms leading to strong or weak increases in the isotopic composition in summer, we developed a simple model using temperature and snowfall data from ERA5 to analyse the contribution of temperature and other parameters to the observed isotopic signals.

How to cite: Zuhr, A., Landais, A., Casado, M., Minster, B., Prié, F., Harris-Stuart, R., Picard, G., Arnaud, L., Dumont, M., Ollivier, I., and Laepple, T.: What is driving the isotopic composition of surface snow in East Antarctica? - Insights from a multi-year time series of surface snow at Dome C, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10188, https://doi.org/10.5194/egusphere-egu22-10188, 2022.

The fabric and texture evolution with depth was investigated on several firn/ice cores from ice rises on Princess Ragnhild Coast, East Antarctica. Results at 5 m sampling resolution show important variations in the crystal orientation fabrics as well as in the grain size, on centimetric to decimetric scale. Further analysis on continuous sections spanning several meters of firn core brings to light periodic variations in the fabric clustering. The fabric’s decimetre-scale periodicity displays a clear correlation with the seasonal variability of δ¹⁸O and ECM conductivity in the ice. It also reveals a previously unobserved fabric shape. This constitutes an opportunity for a close-up look of the climatic signal burial through the transition from firn to ice.

How to cite: Tsibulskaya, V. and Tison, J.-L.: Ice fabrics and textures in coastal East Antarctica reveal seasonal variations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10784, https://doi.org/10.5194/egusphere-egu22-10784, 2022.

Ice cores offer a unique opportunity to study past atmospheric composition because the trapped gases are ultimately derived from ancient atmosphere. This distinctive ability has motivated some pioneering efforts in the late 1980s to measure the elemental composition of the trapped air. The goal at that time was to reconstruct past atmospheric O₂ concentration variations (PO₂), the primary (atmospheric) signal. However, these measurements quickly revealed that oxygen, argon, and nitrogen concentrations in the ice (expressed as δO₂/N₂ and δAr/N₂) are altered by gas-trapping processes. This is the secondary (and confounding) signal formation process.

Such alterations have long thwarted the application of ice core δO₂/N₂ to reconstruct true PO₂. Empirically though, δO₂/N₂ in the trapped air has a high coherency with local summer insolation, but the cause is not well understood. Presumably, the intensity of sunlight on the surface of the ice sheet determines the extent and nature of snow metamorphism, which in turn modulates the magnitude of O₂ and Ar losses relative to N₂ at the “bubble close-off depth” (typically 70-120 m in polar regions).

Here, we discuss recent efforts to identify the secondary signals in δO₂/N₂ and extract the primary, atmospheric PO₂ signals. First, using δAr/N₂ as a proxy for insolation, we show that PO₂ remained stable prior to the Mid-Pleistocene Transition (MPT), the shift from 40- to 100-kyr glacial cycles. After the MPT, however, PO₂ began to decline, possibly linked to enhanced to weathering as a result of glacier expansion and, to a lesser degree, more exposed areas of the continental shelves.

Next, we proceed to exploit the secondary signal itself. That is, δO₂/N₂ and δAr/N₂ could be used as a direct proxy of local insolation. By examining the relationship between elemental ratios and temperature proxies, we explore the relationship between high-latitude southern hemisphere insolation and Antarctic temperature in three time slices in the Pleistocene. The implications for the nature of 40-kyr glacial cycles will be discussed.

How to cite: Yan, Y., Bender, M., and Higgins, J.: Oxygen in the trapped air: identifying primary atmospheric signals and secondary bubble close-off fractionation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12025, https://doi.org/10.5194/egusphere-egu22-12025, 2022.

Water isotope ratios of ice cores are a key source of information on past temperatures. Through fractionation within the hydrological cycle, temperature is imprinted in the water isotopic composition of snowfalls. However, this signal of climatic interest is modified after deposition when snow remains at the surface exposed to the atmosphere. Comparing time series of surface snow isotopic composition at Dome C with satellite observations of surface snow metamorphism, we found that long summer periods without precipitation favor surface snow metamorphism altering the surface snow isotopic  composition. Using excess parameters (combining dD, d¹⁷O, and d¹⁸O fractions) allow the identification of this alteration caused by sublimation and condensation of surface hoar. The combined measurement of all three isotopic compositions could help identifying ice core sections influenced by snow metamorphism in sites with very low snow accumulation.

How to cite: Casado, M., Zuhr, A., Landais, A., Picard, G., Arnaud, L., Dreossi, G., Stenni, B., and Prié, F.: Water Isotopic Signature of Surface Snow Metamorphism in Antarctica, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13161, https://doi.org/10.5194/egusphere-egu22-13161, 2022.

The vast majority of the Antarctic ice sheet is covered with snow that compacts under its own weight and transforms into ice below the surface. However, in some areas, this typically blue-colored ice is directly exposed at the surface. These so-called "blue ice areas" represent islands of negative surface mass balance through sublimation and/or melt. Moreover, blue ice areas expose old ice that is easily accessible in large quantities at the surface, and some areas contain ice that extends beyond the time scales of classic deep-drilling ice cores.

Observation and modeling efforts suggest that the location of blue ice areas is related to a specific combination of topographic and meteorological factors. In the literature, these factors are described as (i) enhanced katabatic winds that erode snow, due to an increase of the surface slope or a tunneling effect of topography, (ii) the increased albedo of blue ice (with respect to snow), which enhances ablative processes, and (iii) the presence of nunataks (mountains protruding the ice) that act as barriers to the ice flow upstream, and prevent deposition of blowing snow on the lee side of the mountain. However, it remains largely unknown which role the physical processes play in creating and/or maintaining  blue ice at the surface of the ice sheet.

Here, we study how a combination of environmental and topographic factors lead to the observation of blue ice. We also quantify the relevance of the single processes and build an interpretable model aiming at not only predicting blue ice presence, but also explaining why it is there. To do so, data is fed into a convolutional neural network, a machine learning algorithm which uses the spatial context of the data to generate a prediction on the presence of blue ice areas. More specifically, we use a U-Net architecture that through convolutions and linked up-convolutions allows to obtain a semantic segmentation (i.e., a pixel-level map) of the input data. Ground reference data is obtained from existing products of blue ice area outlines that are based on multispectral observations. These products contain considerable uncertainties, as (i) the horizontal change from snow to ice is gradual and a single threshold in this transition is not applicable uniformly over the continent, and (ii) the blue ice area extent is known to vary seasonally. Therefore, we train our deep learning model with a loss function with increasing weight towards the center of blue ice areas.

Our first results indicate that the neural network predicts the location of blue ice relatively well, and that surface elevation data plays an important role in determining the location of blue ice. In our ongoing work, we analyze both the predictions and the neural network itself to quantify which factors posses predictive capacity to explain the location of blue ice. Eventually this information may allow us to answer the simple yet important question of why blue ice areas are located where they are, with potentially important implications for their role as paleoclimate archives and for their evolution under changing climatic conditions.

How to cite: Tollenaar, V., Zekollari, H., Tuia, D., Kellenberger, B., Rußwurm, M., Lhermitte, S., and Pattyn, F.: What determines the location of Antarctic blue ice areas? A deep learning approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1294, https://doi.org/10.5194/egusphere-egu22-1294, 2022.

The temporal variability of glacier calving front positions provides essential information about the state of marine-terminating glaciers. These positions can be extracted from Synthetic Aperture Radar (SAR) images throughout the year. To automate this extraction, we apply deep learning techniques that segment the SAR images into different classes: glacier; ocean including ice-melange and sea-ice covered ocean; rock outcrop; and regions with no information like areas outside the SAR swath, layover regions and SAR shadow. The calving front position can be derived from these regions during post-processing.   
A downside of deep learning is that hyper-parameters need to be tuned manually. For this tuning, expert knowledge and experience in deep learning are required. Furthermore, the fine-tuning process takes up much time, and the researcher needs to have programming skills.
    
In the biomedical imaging domain, a deep learning framework [1] has become increasingly popular for image segmentation. The nnU-Net can be used out-of-the-box. It automatically adapts the U-Net, the state-of-the-art architecture for image segmentation, to different datasets and segmentation tasks. Hence, no more manual tuning is required. The framework outperforms specialized deep learning pipelines in a multitude of public biomedical segmentation competitions.   
We apply the nnU-Net to the task of glacier segmentation, investigating whether the framework is also beneficial in the domain of remote sensing. Therefore, we train and test the nnU-Net on CaFFe (https://github.com/Nora-Go/CaFFe), a benchmark dataset for automatic calving front detection on SAR images. CaFFe comprises geocoded, orthorectified imagery acquired by the satellite missions RADARSAT-1, ERS-1/2, ALOS PALSAR, TerraSAR-X, TanDEM-X, Envisat, and Sentinel-1, covering the period 1995 - 2020. The ground range resolution varies between 7 and 20 m². The nnU-Net learns from the multi-class "zones" labels provided with the dataset. We adopt the post-processing scheme from Gourmelon et al. [2] to extract the front from the segmented landscape regions. The test set includes images from the Mapple Glacier located on the Antarctic Peninsula and the Columbia Glacier in Alaska. The nnU-Net's calving front predictions for the Mapple Glacier lie close to the ground truth with just 125 m mean distance error. As the Columbia Glacier shows several calving front sections, its segmentation is more difficult than that of the laterally constrained Mapple Glacier. This complexity of the calving fronts is also reflected in the results: Predictions for the Columbia Glacier show a mean distance error of 635 m. Concludingly, the results demonstrate that the nnU-Net holds considerable potential for the remote sensing domain, especially for glacier segmentation.
    
[1] Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18, 203–211 (2021). https://doi.org/10.1038/s41592-020-01008-z 

[2] Gourmelon, N., Seehaus, T., Braun, M., Maier, A., Christlein, V.: Calving Fronts and Where to Find Them: A Benchmark Dataset and Methodology for Automatic Glacier Calving Front Extraction from SAR Imagery, In Prep.

How to cite: Gourmelon, N., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: Dissecting Glaciers - Can an Automated Bio-Medical Image Segmentation Tool also Segment Glaciers?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2726, https://doi.org/10.5194/egusphere-egu22-2726, 2022.

The Himalayan glacierized region has experienced a substantial rise in number and area of glacial lakes in the past two decades. These glacial lakes directly influence glacier melt, velocity, geometry, and thus overall response of the glacier to climate change. The sudden release of water from these glacial lakes poses a severe threat to downstream communities and infrastructure. Thereby, regular monitoring and modelling of these lakes bear significance in order to understand regional climate change, and mitigating the anticipated impact of glacial lake outburst flood. Here, we proposed an automated scheme for Himalayan glacial lake extent mapping using multisource remote sensing data and a state-of-the-art deep learning technique. A combination of multisource remote sensing data [Synthetic Aperture Radar (SAR) coherence, thermal, visible, near-infrared, shortwave infrared, Advanced Land Observing Satellite (ALOS) DEM, surface slope and Normalised Difference Water Index (NDWI)] is used as input to a fully connected feed-forward Convolutional Neural Network (CNN). The CNN is trained on 660 images (300×300×10) collected from 11 sites spread across Himalaya. The CNN architecture is designed for choosing optimum size, number of hidden layers, convolutional layers, filters, and other hypermeters using hit and trial method. The model performance is evaluated over 3 different sites of Eastern Himalaya, representing heterogenous landscapes. The novelty of the presented automated scheme lies in its spatio-temporal transferability over the large geographical region (~8477, 10336 and 6013 km²). The future work involves Intra-annual lake extent mapping across High-Mountain Asian region in an automated fashion.

How to cite: Kaushik, S., Singh, T., Joshi, P. K., and Dietz, A. J.: Automated mapping of Eastern Himalayan glacial lakes using deep learning and multisource remote sensing data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2904, https://doi.org/10.5194/egusphere-egu22-2904, 2022.

Artificial Intelligence for Cold Regions (AI-CORE) is a collaborative approach for applying Artificial Intelligence (AI) methods in the field of remote sensing of the cryosphere. Several research institutes (German Aerospace Center, Alfred-Wegener-Institute, Technical University Dresden) bundled their expertise to jointly develop AI-based solutions for pressing geoscientific questions in cryosphere research. The project addresses four geoscientific use cases such as the change pattern identification of outlet glaciers in Greenland, the object identification in permafrost areas, the detection of calving fronts in Antarctica and the firn-line detection on glaciers. Within this presentation, the four AI-based final approaches for each addressed use case will be presented and exemplary results will be shown. Further on, the implementation of all developed AI-methods in three different computer centers was realized and the lessons learned from implementing several ready-to-use AI-tools in different processing infrastructures will be discussed. Finally, a best-practice example for sharing AI-implementations between different institutes is provided along with opportunities and challenges faced during the present project duration.

How to cite: Dietz, A. and Baumhoer, C. and the AI-CORE Team: The AI-CORE Project - Artificial Intelligence for Cold Regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3446, https://doi.org/10.5194/egusphere-egu22-3446, 2022.

The scarcity and limited accuracy of snow and precipitation observation and estimation in high-mountain regions reduce our understanding of climatic-cryospheric processes. Thus, we compared the snow water equivalent (SWE) from winter mass balance observations of 95 glaciers distributed over the Alps, Canada, Central Asia and Scandinavia, with the cumulative gridded precipitation data from the ERA-5 and the MERRA-2 reanalysis products. We propose a machine learning model to downscale the gridded precipitation from the reanalyses to the altitude of the glaciers. The machine learning model is a gradient boosting regressor (GBR), which combines several meteorological variables from the reanalyses (air temperature and relative humidity are also downscaled to the altitude of the glaciers) and topographical parameters. Among the most important variables selected by the GBR model, are the downscaled relative humidity and the downscaled air temperature. These GBR-derived estimates are evaluated against the winter mass balance observations by means of a leave-one-glacier-out cross-validation (site-independent GBR) and a leave-one-season-out cross-validation (season-independent GBR). The estimates downscaled by the GBR show lower biases and higher correlations with the winter mass balance observations than downscaled estimates derived with a lapse-rate-based approach. Finally, the GBR estimates are used to derive SWE trends between 1981 and 2021 at high-altitudes. The trends obtained from the GBRs are more enhanced than those obtained from the gridded precipitation of the reanalyses. When the data is regrouped regionwide, significant trends are only observed for the Alps (positive) and for Scandinavia (negative), while significant positive or negative trends are observed in all the regions when looking locally at single glaciers and specific elevations. Positive (negative) SWE trends are typically observed at higher (lower) elevations, where the impact of rising temperatures is less (more) dominating.

How to cite: Guidicelli, M., Gabella, M., Huss, M., and Salzmann, N.: Snow accumulation over the world's glaciers (1981-2021) inferred from climate reanalyses and machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3701, https://doi.org/10.5194/egusphere-egu22-3701, 2022.

The last few years have seen an increasing number of studies modeling glacier evolution using deep learning. Most of these techniques have focussed on artificial neural networks (ANN) that are capable of providing a regressed value of mass balance using topographic and meteorological input features. The large number of parameters in an ANN demands a large dataset for training the parameter values. This is relatively difficult to achieve for regions with a sparse in-situ data measurement set up such as the Himalayas. For example, of the 14326 point mass balance measurements obtained from the Fluctuations of Glaciers database for the period of 1950-2020 for glaciers between 60S and 60N, a mere 362 points over four glaciers exist for the Himalayan region. These are insufficient to train complex neural network architectures over the region. We attempt to overcome this data hurdle by using transfer learning. Here, the parameters are first trained over the 9584 points in the Alps following which the weights were used for retraining for the Himalayan data points. Fourteen meteorological from the ERA5Land monthly averaged reanalysis data were used as input features for the study. A 70-30 split of the training and testing set was maintained to ensure the authenticity of the accuracy estimates via independent testing. Estimates are assessed on a glacier scale in the temporal domain to assess the feasibility of using deep learning to fill temporal gaps in data. Our method is also compared with other machine learning algorithms such as random forest-based regression and support vector-based regression and we observe that the complexity of the dataset is better represented by the neural network architecture. With an overall normalized root mean squared loss consistently less than 0.09, our results suggest the capability of deep learning to fill the temporal data gaps over the glaciers and potentially reduce the spatial gap on a regional scale.

How to cite: Anilkumar, R., Bharti, R., and Chutia, D.: Point Mass Balance Regression using Deep Neural Networks: A Transfer Learning Approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5317, https://doi.org/10.5194/egusphere-egu22-5317, 2022.

In the terrestrial cryosphere, freeze/thaw (FT) state transition plays an important and measurable role for climatic, hydrological, ecological, and biogeochemical processes in permafrost landscapes.

Satellite active and passive microwave remote sensing has shown its principal capacity to provide effective monitoring of landscape FT dynamics. Many algorithms have been developed and evaluated over time in this scope. With the advancement of data science and artificial intelligence methods, the potential of better understanding the cryosphere is emerging.

This work is dedicated to exploring an effective approach to retrieve FT state based on microwave remote sensing data using machine learning methods, which is expected to fill in some hidden blind spots in the deterministic algorithms. Time series of remote sensing data will be created as training data. In the initial stage, the work aims to test the feasibility and establish the basic neural network based on fewer training factors. In the advanced stage, we will improve the model in terms of structure, such as adding more complex dense layers and testing optimizers, and in terms of discipline, such as introducing more influencing factors for training. Related parameters, for example, land cover types, will be included in the analysis to improve the method and understanding of FT-related processes.

How to cite: Chen, Y., Wang, L., Bernier, M., and Ludwig, R.: Retrieving freeze/thaw-cycles using Machine Learning approach in Nunavik (Québec, Canada), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5612, https://doi.org/10.5194/egusphere-egu22-5612, 2022.

In this talk, we propose to use neural networks in a hybrid modelling setup to learn sub-grid-scale dynamics of sea-ice that cannot be resolved by geophysical models. The multifractal and stochastic nature of the sea-ice dynamics create significant obstacles to represent such dynamics with neural networks. Here, we will introduce and screen specific neural network architectures that might be suited for this kind of task. To prove our concept, we perform idealised twin experiments in a simplified Maxwell-Elasto-Brittle sea-ice model which includes only sea-ice dynamics within a channel-like setup. In our experiments, we use high-resolution runs as proxy for the reality, and we train neural networks to correct errors of low-resolution forecast runs.

Since we perform the two kind of runs on different grids, we need to define a projection operator from high- to low-resolution. In practice, we compare the low-resolution forecasted state at a given time to the projected state of the high resolution run at the same time. Using a catalogue of these forecasted and projected states, we will learn and screen different neural network architectures with supervised training in an offline learning setting. Together with this simplified training, the screening helps us to select appropriate architectures for the representation of multifractality and stochasticity within the sea-ice dynamics. As a next step, these screened architectures have to be scaled to larger and more complex sea-ice models like neXtSIM.

How to cite: Finn, T., Durand, C., Farchi, A., Bocquet, M., Chen, Y., Carrassi, A., and Dansereau, V.: Learning and screening of neural networks architectures for sub-grid-scale parametrizations of sea-ice dynamics from idealised twin experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5910, https://doi.org/10.5194/egusphere-egu22-5910, 2022.

The cryosphere is a highly active and dynamic environment that rapidly responds to changing climatic conditions. In particular, the physical processes behind glacial dynamics are poorly understood because they remain challenging to observe. Glacial dynamics are strongly intermittent in time and heterogeneous in space. Thus, monitoring with high spatio-temporal resolution is essential.

In course of the RESOLVE (‘High-resolution imaging in subsurface geophysics : development of a multi-instrument platform for interdisciplinary research’) project, continuous seismic observations were obtained using a dense seismic network (100 nodes, Ø 700 m) installed on Glacier d’Argentière (French Alpes) during May in 2018. This unique data set offers the chance to study targeted processes and dynamics within the cryosphere on a local scale in detail.

To identify seismic signatures of ice beds in the presence of melt-induced microseismic noise, we applied the supervised ML technique gradient tree boosting. The approach has been proven suitable to directly observe the physical state of a tectonic fault. Transferred to glacial settings, seismic surface records could therefore reveal frictional properties of the ice bed, offering completely new means to study the subglacial environment and basal sliding, which is difficult to access with conventional approaches.

We built our ML model as follows: Statistical properties of the continuous seismic records (variance, kurtosis and quantile ranges), meteorological data and a seismic source catalogue obtained using beamforming (matched field processing) serve as features which we fit to measures of the GPS displacement rate of Glacier d’Argentière (labels). Our preliminary results suggest that seismic source activity at the bottom of the glacier strongly correlates with surface displacement rates and hence, is directly linked to basal motion. By ranking the importance of our input features, we have learned that other than for reasonably long monitoring time series along tectonic faults, statistical properties of seismic observations only do not suffice in glacial environments to estimate surface displacement. Additional beamforming features however, are a rich archive that enhance the ML model performance considerably and allow to directly observe ice dynamics.

How to cite: Umlauft, J., Roux, P., Lecointre, A., Gimbert, F., Nanni, U., Walpersdorf, A., Rouet-LeDuc, B., Hulbert, C., Trugman, D., and Johnson, P.: Mapping Glacier Basal Sliding with Beamforming and Artificial Intelligence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6948, https://doi.org/10.5194/egusphere-egu22-6948, 2022.

Ice lead analysis is an essential task for evaluating climate change processes in the Arctic. Ice leads are narrow cracks in the sea-ice, which build a complex network. While detecting and modeling ice leads has been performed in numerous ways based on airborne images, the dynamics of ice leads over time remain hidden and largely unexplored. These dynamics could be analyzed by interpreting the ice leads as more than just airborne images, but as what they really are: a dynamic network. The lead’s start, end, and intersection points can be considered nodes, and the leads themselves as edges of a network. As the nodes and edges change over time, the ice lead network is constantly evolving. This new network perspective on ice leads could be of great interest for the cryospheric science community since it opens the door to new methods. For example, adapting common link prediction methods might make data-driven ice lead forecasting and tracking feasible.
To reveal the hidden dynamics of ice leads, we performed a spatio-temporal and network analysis of ice lead networks. The networks used and presented here are based on daily ice lead observations from Moderate Resolution Imaging Spectroradiometer (MODIS) between 2002 and 2020 by Hoffman et al. [1].
The spatio-temporal analysis of the ice leads exhibits seasonal, annual, and overall trends in the ice lead dynamics. We found that the number of ice leads is decreasing, and the number of width and length outliers is increasing overall. The network analysis of the ice lead graphs reveals unique network characteristics that diverge from those present in common real-world networks. Most notably, current network science methods (1) exploit the information that is embedded into the connections of the network, e.g., in connection clusters, while (2) nodes remain relatively fixed over time. Ice lead networks, however, (1) embed their relevant information spatially, e.g., in spatial clusters, and (2) shift and change drastically. These differences require improvements and modifications on common graph classification and link prediction methods such as Preferential Attachment and EvolveGCN on the domain of ice lead dynamic networks.
This work is a call for extending existing network analysis toolkits to include a new class of real-world dynamic networks. Utilizing network science techniques will hopefully further our understanding of ice leads and thus of Arctic processes that are key to climate change mitigation and adaptation.

Acknowledgments

We would like to thank Prof. Gunnar Spreen, who provided us insights into ice lead detection and possible challenges connected to the project idea. Furthermore, we would like to thank Shenyang Huang and Asst. Prof. David Rolnick for their valuable feedback and support. J.K. was supported in part by the DeepMind scholarship, the Mitacs Globalink Graduate Fellowship, and the German Academic Scholarship Foundation.

References

[1] Jay P Hoffman, Steven A Ackerman, Yinghui Liu, and Jeffrey R Key. 2019. The detection and characterization of Arctic sea ice leads with satellite imagers. Remote Sensing 11, 5 (2019), 521.

How to cite: Kaltenborn, J., Ramesh, V., and Wright, T.: Ice Lead Network Analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8945, https://doi.org/10.5194/egusphere-egu22-8945, 2022.

In recent years, the importance of dynamics in the Arctic Ocean have proven itself with respect to climate monitoring and modelling. Data used for creating models often include temperature & salinity profiles. Such profiles in the Arctic region are sparse and acquiring new data is expensive and time-consuming. Thus, efficient methods of interpolation are necessary to expand regional data. In this project, 3D temperature & salinity profiles are reconstructed using 2D surface measurements from ships, floats and satellites. The technique is based on a stacked Long Short-Term Memory (LSTM) neural network. The goal is to be able to reconstruct the profiles using remotely sensed data.

How to cite: Jensen, M., Bang-Hansen, C., Andersen, O. B., Ludwigsen, C. B., and Ehrhorn, M.: Using LSTM on surface data to reconstruct 3D Temperature & Salinity profiles in the Arctic Ocean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9753, https://doi.org/10.5194/egusphere-egu22-9753, 2022.

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors.

Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution.

Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS)  from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers.

Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term.

The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region.

How to cite: Sangiorgio, M., Bianco, E., Iovino, D., Materia, S., and Castelletti, A.: Arctic sea ice dynamics forecasting through interpretable machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10386, https://doi.org/10.5194/egusphere-egu22-10386, 2022.

Climate change intensifies glacier melt which effectively leads to the formation of numerous new glacial lakes in the overdeepenings of former glacier beds. Additionally, the area of many existing glacial lakes is increasing. More than one thousand glacial lakes have emerged in Switzerland since the Little Ice Age, and hundreds of lakes are expected to form in the 21st century. Rapid deglaciation and formation of new lakes severely affect downstream ecosystem services, hydropower production and high-alpine hazard situations. Day by day, glacier lake inventories for high-alpine terrains are increasingly becoming available to the research community. However, a high-frequency mapping and monitoring of these lakes are necessary to assess hazards and to estimate Glacial Lake Outburst Flood (GLOF) risks, especially for lakes with high seasonal variations. One way to achieve this goal is to leverage the possibilities of satellite-based remote sensing, using optical and Synthetic Aperture Radar (SAR) satellite sensors and deep learning.

There are several challenges to be tackled. Mapping glacial lakes using satellite sensors is difficult, due to the very small area of a great majority of these lakes. The inability of the optical sensors (e.g. Sentinel-2) to sense through clouds creates another bottleneck. Further challenges include cast and cloud shadows, and increased levels of lake and atmospheric turbidity. Radar sensors (e.g. Sentinel-1 SAR) are unaffected by cloud obstruction. However, handling cast shadows and natural backscattering variations from water surfaces are hurdles in SAR-based monitoring. Due to these sensor-specific limitations, optical sensors provide generally less ambiguous but temporally irregular information, while SAR data provides lower classification accuracy but without cloud gaps.

We propose a deep learning-based SAR-optical satellite data fusion pipeline that merges the complementary information from both sensors. We put forward to use Sentinel-1 SAR and Sentinel-2 L2A imagery as input to a deep network with a Convolutional Neural Network (CNN) backbone. The proposed pipeline performs a fusion of information from the two input branches that feed heterogeneous satellite data. A shared block learns embeddings (feature representation) invariant to the input satellite type, which are then fused to guide the identification of glacial lakes. Our ultimate aim is to produce geolocated maps of the target regions where the proposed bottom-up, data-driven methodology will classify each pixel either as lake or background.

This work is part of two major projects: ESA AlpGlacier project that targets mapping and monitoring of the glacial lakes in the Swiss (and European) Alps, and the UNESCO (Adaptation Fund) GLOFCA project that aims to reduce the vulnerabilities of populations in the Central Asian countries (Kazakhstan, Tajikistan, Uzbekistan, and Kyrgyzstan) from GLOFs in a changing climate. As part of the GLOFCA project, we are developing a python-based analytical toolbox for the local authorities, which incorporates the proposed deep learning-based pipeline for mapping and monitoring the glacial lakes in the target regions in Central Asia.

How to cite: Tom, M., Frey, H., and Odermatt, D.: A deep learning approach for mapping and monitoring glacial lakes from space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10637, https://doi.org/10.5194/egusphere-egu22-10637, 2022.

Over the past four decades, the inexorable growth in technology and subsequently the availability of Earth-observation and model data has been unprecedented. Hidden within these data are the fingerprints of the physical processes that govern climate variability over a wide range of spatial and temporal scales, and it is the task of the climate scientist to separate these patterns from noise. Given the wealth of data now at our disposal, machine learning methods are becoming the tools of choice in climate science for a variety of applications ranging from data assimilation, to sea ice feature detection from space. This talk summarises recent developments in the application of machine learning methods to the study of polar climate, with particular focus on Arctic sea ice. Supervised learning techniques including Gaussian process regression, and unsupervised learning techniques including cluster analysis and complex networks, are applied to various problems facing the polar climate community at present, where each application can be considered an individual component of the larger sea ice prediction problem. These applications include: seasonal sea ice forecasting, improving spatio-temporal data coverage in the presence of sparse satellite observations, and illuminating the spatio-temporal connectivity between climatological processes.

How to cite: Gregory, W.: Machine learning tools for pattern recognition in polar climate science, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12785, https://doi.org/10.5194/egusphere-egu22-12785, 2022.

We have recently applied an objective mapping type approach to merge observations from multiple altimeters, both for enhancing the temporal/spatial resolution of freeboard samples and for analyzing crossovers between satellites (Gregory et al, 2021). This mapping provides optimal interpolation of proximal observations to a location in space and time based on the covariance of the observations and a priori understanding of their spatiotemporal correlation length scales. This offers a best linear estimator and error field for the observation (radar freeboard or snow depth), which can be used to better constrain pan-Arctic uncertainties. 

In addition we will explore here a newly developed inverse modelling framework  to synchronously retrieve the snow and ice thickness from bias corrected or calibrated radar freeboards from multiple satellite retrievals. The radar equations expressed in section can be rearranged to formulate the joint forward model at gridded level relating measured radar freeboards from multiple satellites (and airborne data) to the underlying snow and ice thickness. In doing so we have also introduced a penetration factor correction term for OIB radar freeboard measurements. To solve this inverse model problem for  and  we use the following two methodologies inspired from Earth Sciences applications (i.e. seismology):  

Space ‘uncorrelated’ inverse modelling. The method is called `space uncorrelated' inverse modelling as the algorithm is applied locally, for small distinct regions in the Arctic Ocean, multiple times, until the entire Arctic ocean is covered. To sample the parameter space  we use the publicly available Neighbourhoud Algorithm (NA) developed originally for seismic tomography of Earth’s interior and recently by us to a sea ice dynamic inversion problem (Hoerton et al, 2019).   

Space ‘correlated inverse modelling. For the second method of inverse modelling, we used what we call a `space correlated' approach. Here the main algorithm is applied over the entire Arctic region, aiming to retrieve the desired parameters at once. In contrast with the previous approach, in this method we take into account positional correlations for the physical parameters when we are solving the inverse problem, the output being a map of the Arctic composed of a dynamically generated a tiling in terms of Voronoi cells. In that way, regions with less accurate observations will be more coarsely resolved while highly sampled regions will be provided on a finer grid with a smaller uncertainty. The main algorithm used here to calculate the posterior solution is called `reverse jump Monte Carlo Markov Chain' (hereafter referred to as rj-MCMC) and its concept was designed by Peter Green in 1999 (Green, 1995). Bodin and Sambridge (2009) adapted this algorithm for seismic inversion, which is the basis of the algorithm used in this study.  

How to cite: Perez Ferrer, J., Tsamados, M., Fox, M., Suciu, T., Heorton, H., and Nab, C.: Inverse modelling techniques for snow and ice thickness retrievals from satellite altimetry , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12882, https://doi.org/10.5194/egusphere-egu22-12882, 2022.

Providing suitable initial states is a long-standing problem in numerical modelling of glaciers and ice sheets, as well as in other areas of the geosciences, due to the frequent lack of observations. This is particularly acute in glaciology, where important parameters such as the underlying bed may be only very sparsely observed or even completely unobserved. Glaciological models also often require lengthy relaxation periods to dissipate incompatibilities between input datasets gathered over different timeframes, which may lead to the modelled initial state diverging significantly from the real state of the glacier, with consequent effects on the accuracy of the simulation. Sequential data assimilation using an ensemble offers one possibility for resolving both these issues: by running the model over a period for which various observational datasets are available and loading observations into the model at the time they were gathered, the model state can be brought into good agreement with the real glacier state at the end of the observational window. The mean values of the ensemble for unknown parameters, such as the bed, then also represent best guesses for the true parameter values. This assimilated model state can then be used to initialise prognostic runs without introducing model artefacts or a distorted picture of the actual glacier.

In this study, we present a framework for conducting sequential data assimilation and retrieving the bed of a glacier in a 3D setting of the open-source, finite-element glacier flow model, Elmer/Ice, and solving the Stokes equations rather than using the shallow shelf approximation. Assimilation is undertaken using the open-source PDAF library developed at the Alfred Wegener Institute. We demonstrate that the set-up allows us to accurately retrieve the bed of a synthetic glacier and present our plans to extend it to a real-world example.

How to cite: Cook, S. and Gillet-Chaulet, F.: 3D sequential data assimilation in Elmer/Ice with Stokes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-753, https://doi.org/10.5194/egusphere-egu22-753, 2022.

Mass loss from the Antarctic ice sheet is dominated by the integrity of the ice shelves that buttress it. The evolution and stability of ice shelves is dependent on a variety of parameters that cannot be directly observed, such as basal melt and ice rheology. Constraining these parameters is of great importance in making predictions of the future changes in ice shelves that have a quantifiable uncertainty. This inference task is difficult in practice as the number of unknown parameters is large, observations are often sparse, and the computational cost of ice flow models is high.

We aim to develop a framework for inferring joint distributions of mass balance and rheological parameters of ice shelves from observations such as ice geometry, surface velocities, and radar isochrones. Here, we begin by inferring a posterior distribution over basal melt parameters in along-flow sections of synthetic and real world ice shelves (Roi Baudouin). We use the technique of simulation-based inference (SBI), a machine learning framework for performing Bayesian inference when the likelihood function is intractable. The inference procedure relies on the availability of a simulator to model the dynamics of the ice shelves. For this we use the Shallow Shelf Approximation (SSA) implemented in the Python library Icepack.  First, we show that by combining these two tools we can recover the underlying parameters of synthetic 2D data with meaningful uncertainty estimates. In a second step, we apply our method to real observations and get estimates for the basal melt rates which are coherent with the data when running the forward model over a centennial timescale.

How to cite: Moss, G., Višnjević, V., Schröder, C., Macke, J., and Drews, R.: Uncertainty quantification for melt rate parameters in ice shelves using simulation-based inference, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-896, https://doi.org/10.5194/egusphere-egu22-896, 2022.

Sea ice is a key element in our climate system, and it is very sensitive to the current observed climate change. Sea ice volume is a sensitive indicator of the health of Arctic although very challenging to estimate precisely since it is a combination of sea ice area and sea ice thickness. Arctic sea ice volume has decreased by as much as 75% at the end of the summer season if compared with the conditions 40 years ago. The ongoing decline of Arctic sea ice exposes the ocean to anomalous surface heat and freshwater fluxes that can have potential implication for the Arctic region and beyond, for the general oceanic circulation itself.

For more than a decade, Mercator Ocean International develops and produces Global Ocean Reanalysis with a 1/4° resolution system. Based on the NEMO modelling platform, observations are assimilated by a reduced-order Kalman filter. In-situ CORA database, altimetric data, sea surface temperature, and sea ice concentration are jointly assimilated to constrain the ocean and sea ice model.

In previous reanalysis, long-term sea ice volume drift has been observed in the Arctic. To obtain a better constraint on the sea ice thickness, Cryosat-2 radar Freeboard data are assimilated jointly with the sea ice concentration in a multidata/multivariate sea ice analysis. The coupled ocean and ice assimilation system runs on a 7-day cycle, using IAU (Incremental Analysis Update) and a 4D increment. The “white ocean” is modelled with the multi-categories LIM3.6 sea ice numerical model. The aim of this study is to initiate the development of the future operational multi-variate and multi-data sea ice analysis system with freeboard radar assimilation.

After describing this global sea ice reanalysis system, we present results on the abilities of this configuration to reproduce sea ice extent and volume interannual variability in both hemispheres. Comparisons between experiments with and without assimilation show that the joint assimilation of CryoSat-2 radar freeboard and sea ice concentration reduces most of model biases of sea ice thickness, e.g., in the north of the Canadian Arctic Archipelago and in the Beaufort Sea in the Arctic. Moreover, radar freeboard assimilation does not hinder the good results in simulating sea ice extent previously obtained with the assimilation of only sea ice concentration. Validation with non-assimilated satellite data and in-situ data supports these findings. Lastly, snow depth significantly influences the Freeboard measurement: this study also reveals the importance of including snow information on freeboard retrieval and on the ice volume assimilation methodology.

These experiments take place in a context of increasing interest in polar regions and prepare the launch of Copernicus Sentinel expansion satellite missions.

How to cite: Chenal, A., Testut, C.-E., Garnier, F., Laurent, P., and Gilles, G.: Assimilation of CryoSat-2 radar Freeboard data in a global ocean-sea ice modelling system., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2061, https://doi.org/10.5194/egusphere-egu22-2061, 2022.

Modelling the past and future evolution of the West Antarctic Ice Sheet (WAIS) to climate and ocean forcing is challenged by the availability and quality of observed palaeo boundary conditions. Aside from point-based geochronological measurements, the only available proxy to query past ice-sheet processes on large spatial scales is Internal Reflecting Horizons (IRHs) as sounded by ice-penetrating radar. When isochronal, IRHs can be used to determine palaeo-accumulation rates and patterns, as previously demonstrated using shallow, centennially dated layers. Whilst similar efforts using deeper IRHs have previously been conducted over the East Antarctic Plateau where ice-flow is slow and ice thickness has been stable through time, much less is known of millennial-scale accumulation rates over the West Antarctic plateau due to challenging ice dynamical conditions in the downstream section of the ice sheet. Using deep and spatially extensive ice-core dated IRHs over Pine Island and Thwaites glaciers and a local layer approximation model, we quantify Holocene accumulation rates over the slow-flowing parts of these sensitive catchments. The results from the one-dimensional model are also compared with modern accumulation rates from observational and modelled datasets to investigate changes in accumulation rates and patterns between the Holocene and the present. The outcome of this work is that together with present and centennial-scale accumulation rates, our results can help determine whether a trend in accumulation rates exists between the Holocene and the present and thus test to what extent these glaciers are controlled by ice dynamics rather than changes in accumulation rates.

How to cite: Bodart, J., Bingham, R., Young, D., Blankenship, D., and Vaughan, D.: Quantifying Holocene Accumulation Rates from Ice-Core Dated Internal Layers from Ice-Penetrating Radar Data over the West Antarctic Ice Sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2535, https://doi.org/10.5194/egusphere-egu22-2535, 2022.

Accurately predicting ice crystal fabrics is key to understanding the processes and deformation in ice-sheets. Here we use SpecCAF, a continuum fabric evolution model validated against laboratory experiments, to predict the fabric evolution with an active ice stream. This is done by predicting the fabrics at the East Greenland Ice core Project (EGRIP) site. We do this using satellite data and inferred particle paths, combined with the shallow ice approximation (with basal slip) to infer a leading order approximation for the deformation through the ice sheet. We find that SpecCAF is able to predict the patterns observed at EGRIP - a girdle/horizontal maxima fabric perpendicular to the flow direction. By reducing the rate of rotational recrystallization in the model we are also able to predict the fabric strength at EGRIP. This suggests the effect of rotational recrystallization on the fabric may be primarily strain-rate/stress dependent. These results show SpecCAF can be applied to real-world conditions and provide insights into the deformation and basal-conditions of the ice sheet. As the model only considers deformation and recrystallization through dislocation creep, the results imply that - for the ice stream modelled - no other process is significantly influencing both the produced ice fabric and the deformation. We find that the model gives best results for full slip at the base of the ice sheet, implying that the level of sliding at the base of the ice sheet in the North Greenland Ice stream may be very high. The methodology used here can be extended to other ice core locations in Greenland and Antarctica.

How to cite: Richards, D., Pegler, S., and Piazolo, S.: Numerical modelling of ice stream fabrics: Implications for recrystallization processes and basal slip conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3743, https://doi.org/10.5194/egusphere-egu22-3743, 2022.

Lubrication by subglacial water or saturated subglacial sediments is crucial to controlling the movement of fast-flowing outlet glaciers and ice streams.  However, the subglacial environment is difficult to observe directly.  Here, we combine inverse modeling with ice-penetrating radar observations to characterize the ice sheet bed in the Filchner-Ronne sector of Antarctica, with a specific focus on the Recovery Glacier catchment.  First, we use the Ice Sheet System Model (ISSM; Larour et al., 2012) to assimilate satellite observations of ice sheet surface velocity (Mouginot et al., 2019) in order to solve for basal drag and ice rheology across the Filchner-Ronne sector of Antarctica.  Next, we compare these results with ice-penetrating radar observations sensitive to the presence of ponded water at the ice sheet base (Humbert et al., 2018; Langley et al., 2011), along with remotely sensed observations of active lakes (Smith et al., 2009) and putative large subglacial lakes inferred from the ice sheet surface slope (Bell et al., 2007).  We find that the main fast-flowing region of Recovery Glacier is mostly low-drag, with the exception of localized sticky spots and bands.  The boundary between rugged subglacial highlands and a deep subglacial basin near the onset of the ice stream is associated with a sharp reduction in basal drag, although surface velocity changes smoothly rather than abruptly across this transition.  An upstream shear margin, visible in satellite radar images of the ice surface, is associated with low basal drag.  The putative large lakes have low drag but are not strongly distinguished from their surroundings, and radar evidence for ponded subglacial water within them is weak.  The active lakes identified from satellite altimetry are similarly situated in areas of low basal drag, but have limited radar evidence for ponded subglacial water.  An L-curve analysis indicates that our inverse model results are robust against changes in regularization, yet the radar-identified lake candidates do not have a clear relationship with low-drag areas in the fast-flowing ice stream.  We conclude that the deep-bedded regions of Recovery Glacier are underlain by saturated subglacial sediments, but classic ponded subglacial lakes are much more rare.  Isolated sticky spots and bands within the ice stream are either due to protrusions of bedrock out of the sediments or to localized areas of frozen and/or compacted sediments.

How to cite: Wolovick, M., Höyns, L.-S., Kleiner, T., Nickel, N., Helm, V., and Humbert, A.: Basal Properties of the Filchner-Ronne Sector of Antarctica from Inverse Modeling and Comparison with Ice-Penetrating Radar Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4027, https://doi.org/10.5194/egusphere-egu22-4027, 2022.

Ongoing global glacier retreat leads to sea-level rise and changes in regional freshwater availability. For an adequate adaptation to these changes, knowledge about the ice volume and the current dynamic state of glaciers is crucial. At regional to global scales, sparse observations made the dynamic state of glaciers very difficult to assess. Thanks to recent advances in global geodetic mass-balance and velocity assessments, new ways to initialize numerical models and ice thickness estimation emerge. In this contribution, we present the COst Minimization Bed INvErsion model (COMBINE), which aims to be a cheap, flexible global data assimilation and inversion method. COMBINE uses an existing numerical model of glacier evolution (the Open Global Glacier Model, OGGM) rewritten in the machine learning framework PyTorch. This makes the model fully differentiable and allows to iteratively minimize a cost function penalizing mismatch to observations. Thanks to the flexible nature of automatic differentiation, various observational sources distributed in time can be considered (e.g. surface elevation and area changes, ice velocities). No assumption about the dynamic glacier state is needed, releasing the equilibrium assumption often required for large scale ice volume computations. In this contribution, we will demonstrate the capabilities of COMBINE in several idealized and real-world applications, and discuss its added value and upcoming challenges for operational application.

How to cite: Schmitt, P., Maussion, F., and Gregor, P.: Estimating large scale dynamic mountain glacier states with numerical modelling and data assimilation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5113, https://doi.org/10.5194/egusphere-egu22-5113, 2022.

Radar reflections from the interior of the Greenland ice sheet contain a comprehensive archive of past accumulation rates, ice dynamics, and basal melting. Combining these data with dynamic ice sheet models may greatly aid model calibration, improve past and future sea level estimates, and enable insights into past ice sheet dynamics that neither models nor data could achieve alone.

In this study, we present the first three-dimensional ice sheet model that explicitly simulates the Greenland englacial stratigraphy. Individual layers of accumulation are represented on a grid whose vertical axis is time so that they do not exchange mass with each other as the flow of ice deforms them. This isochronal advection scheme does not influence the ice dynamics and only requires modest input data from a host thermomechanical ice sheet model.

Using an ensemble of simulations, we show that direct comparison with the dated radiostratigraphy data yields notably more accurate results than calibrating simulations based on total ice thickness. We show that the isochronal scheme produces a more reliable simulation of the englacial age profile than Eulerian age tracers. Lastly, we outline how the isochronal model can be linearized as a foundation for inverse modeling and data assimilation.

How to cite: Born, A., Robinson, A., and Theofilopoulos, A.: Modeling the Greenland englacial stratigraphy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5425, https://doi.org/10.5194/egusphere-egu22-5425, 2022.

Meltwater forms at the base of the Antarctic Ice Sheet due to geothermal heat flux (GHF) and basal frictional dissipation. Despite the relatively small volume, this meltwater has a profound effect on ice-sheet stability, controlling the dynamics of the ice sheet and the interaction of the ice sheet with the ocean. However, observations of subglacial melting and hydrology in Antarctica are limited. Here we use numerical modelling to assess subglacial melt rates and hydrology beneath the Antarctic Ice Sheet. Our case study, focused on the Amery Ice Shelf catchment, shows that total subglacial melting in the catchment is 6.5 Gt yr^-1, over 50% larger than previous estimates. Uncertainty in estimates of GHF leads to a variation in total melt of ±7%. The meltwater provides an extra 8% flux of freshwater to the ocean in addition to contributions from iceberg calving and melting of the ice shelf. GHF and basal dissipation contribute equally to the total melt rate, but basal dissipation is an order of magnitude larger beneath ice streams. Remote-sensing observations, from CryoSat-2, indicating active subglacial lakes and ice-shelf basal melting constrain subglacial hydrology modelling. We observe a network of subglacial channels that link subglacial lakes and trigger isolated areas of sub-ice-shelf melting close to the grounding line. Building upon this Amery case study, we expand our analysis to quantify subglacial melt rates and hydrology beneath the entire Antarctic Ice Sheet.

How to cite: Wearing, M., Goldberg, D., Dow, C., Hogg, A., and Gourmelen, N.: Coupling modelling and satellite observations to constrain subglacial melt rates and hydrology, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8605, https://doi.org/10.5194/egusphere-egu22-8605, 2022.

Objective: Estimate Soil Freezing Characteristic Curves (SFCCs) and uncertainty bounds based on a compilation of existing measured SFCCs.

Key Findings

• Uncertainty in measured SFCCs is estimated based on measurement technique, water content, and soil disturbance
• An open-source tool for estimating and constraining SFCCs is developed for use in parameterizing freeze/thaw models

Abstract

Cold-regions landscapes are undergoing rapid change due to a warming climate. This change is impacting many elements of the landscape and is often controlled by soil freeze/thaw processes. Soil freeze/thaw is governed by the Soil Freezing Characteristic Curve (SFCC) that relates the soil temperature to its unfrozen water content. This relation is needed in all physically based numerical models including soil freeze/thaw processes. A repository of all collected SFCC data and an R package for accessing and processing this data was presented in "A Repository of 100+ Years of Measured Soil Freezing Characteristic Curves".

This rich SFCC dataset is synthesized with a focus on potential sources of error due to the combination of measurement technique, data interpretation, and physical freeze-thaw process in a specific soil. Particular attention is given to combining sources of error and working with datasets given incomplete and missing metadata. A tool is developed to extract an SFCC for a soil with specified properties alongside its uncertainty bounds. This tool is intended for use in freeze/thaw models to improve freeze/thaw estimates, and better represent the ice and liquid water content of freezing soils. As phase change accounts for a vast majority of the energy budget in freezing soils, accurately representing the process is essential for realistic predictions. In addition, SFCC type curves are provided for the common sand, silt, clay, and organic soil textures when additional data is unavailable to define the SFCC more precisely.

How to cite: Devoie, É., Gruber, S., and McKenzie, J.: Constraining Soil Freezing Models using Observed Soil Freezing Characteristic Curves, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8938, https://doi.org/10.5194/egusphere-egu22-8938, 2022.

The analysis of englacial layers using ice penetrating radar enables the characterisation and reconstruction of current and past ice sheet flow. To date, little research has been undertaken on the ice flow and englacial stratigraphy of the upper catchment of the Lambert Glacier. The Lambert Glacier catchment is one of the largest in East Antarctica, discharging ~16% of East Antarctica’s ice. Quantitative analysis of the continuity of englacial stratigraphy and ice flow has the potential to provide insight into the present-day and past flow regimes of the upper catchment of Lambert Glacier. Radar data from the British Antarctic Survey Antarctica’s Gamburtsev Province Project North (AGAP-N) aerogeophysical survey was analysed using the Internal Layer Continuity Index (ILCI). This approach identified, and characterised, a range of englacial structures and stratigraphy, including buckled layers in areas of increased ice velocity (>20ma^-1) and continuous layering across subglacial highlands with low ice velocity adjacent to the central Lambert Glacier trunk. Overall, the analysis is consistent with the present-day ice-flow velocity field and long-term stability of ice flow across the Lambert catchment. However, disrupted layer geometry at the onset of the Lambert Glacier suggests a past shift in the position of the onset of ice flow. These results have implications for the future evolution of this major ice flow catchment, and East Antarctica, under a changing climate. The ILCI method represents a valuable tool for rapidly characterising englacial stratigraphy, and the study demonstrates the transferability of the method across Antarctica. The use of quantitative tools such as ILCI for the analysis of large radar datasets will be critical for projects such as AntArchitecture (https://www.scar.org/science/antarchitecture/home/) which aims to investigate the long-term stability of the Antarctic ice sheets directly from the internal architecture of the ice sheet.

How to cite: Sanderson, R., Ross, N., Callard, L., Winter, K., Napoleoni, F., Bingham, R., and Jordan, T.: Assessing the continuity of englacial layers across the Lambert Glacier catchment., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9143, https://doi.org/10.5194/egusphere-egu22-9143, 2022.

How to cite: Sievers, I., Stenseng, L., and Rasmussen, T.: Assimilating Cyrosat2 freeboard into a coupled ice-ocean model , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9262, https://doi.org/10.5194/egusphere-egu22-9262, 2022.

No continuously updated glacier and glacial lake inventories exist for Norway. Previous inventories have been developed for the time periods of 1947-1985, 1988-1997 and 1999-2006 for glaciers and 1988-1997, 1999-2006, 2014 and 2018 for glacial lakes, by manual digitization, and semi-automated mapping. However, these methods are both time consuming and do not allow for an analysis of glacial lake behaviour on shorter timescales or on a seasonal basis. Therefore, one aim of this study is to present consistent inventories for glaciers and glacial lakes in Norway using semi-automated mapping and machine learning techniques applied on satellite imagery of different spatial and temporal resolution (Landsat 30m, 16 days, and Sentinel 10m, 5 days). An automated method that allows frequent monitoring of glacier variables can provide essential knowledge for the understanding of glacial lake dynamics in a changing climate.

In addition to glacial lake inventories, smaller ice caps with active glacial lakes are investigated more closely, aiming at following the development of glacial lakes throughout seasons. Here we are also analyzing the suitability of PlanetScope imagery compared to the Sentinel and Landsat imagery to detect the known glacial lake outburst flood events and identify currently unrecognized hazard-prone glacial lakes. Since the field-based investigations of glacial lake changes (especially of the ice-dammed lakes) are sparse in Norway, developing methods for remote-sensed, automated monitoring of glacial lake changes and glacial lake outburst floods is essential in order to develop early warning systems, detect potentially hazardous lakes and prevent human losses and damages to infrastructure and local businesses.

How to cite: Abderhalden, J. and Rogozhina, I.: Automated Tracking of Glacial Lake Outburst Floods in Norway, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9886, https://doi.org/10.5194/egusphere-egu22-9886, 2022.

Over the past few decades, polar research teams around the world have deployed long-term measurement sites to monitor changes in permafrost environments. Many of these sites include borehole sensor arrays which provide measurements of ground temperature as deep as 50 meters or more below the surface. Recent studies have attempted to leverage these borehole data from the Global Terrestrial Network of Permafrost to quantify changes in permafrost temperatures at a global scale. However, temperature measurements provide an incomplete picture of the Earth's subsurface thermal regime. It is well known that regions with warmer permafrost, i.e. where mean annual ground temperatures are close to zero, often show little to no long-term change in ground temperature due to the latent heat effect. Thus, regions where the least warming is observed  may also be the most vulnerable to rapid permafrost thaw. Since direct measurements of soil moisture in the permafrost layer are not widely available, thermal modeling of the subsurface plays a crucial role in understanding how permafrost responds to changes in the local energy balance. In this work, we explore a new probabilistic method to link observed annual temperatures in boreholes to permafrost thaw via Bayesian parameter estimation and Monte Carlo simulation with a transient heat model. We apply our approach to several sites across the Arctic and demonstrate the impact of local landscape variability on the relationship between long term changes in temperature and latent heat.

How to cite: Groenke, B., Langer, M., Gallego, G., and Boike, J.: A probabilistic analysis of permafrost temperature trends with ensemble modeling of heat transfer, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10509, https://doi.org/10.5194/egusphere-egu22-10509, 2022.

Transitions from basal no slip to basal sliding are a common feature of ice sheets, yet one that has remained difficult to observe. In this study we leverage recent advances in the processing of radar sounding data to study these transitions through their signature in englacial layers. Englacial layers encode information about strain and velocity, and the relation between their geometry and the onset of basal sliding has been demonstrated in ice flow models (the so-called "Weertman effect"). Here we leverage this relation to test the long-standing hypothesis that sliding onset takes the form of an abrupt no slip/sliding transition. By comparing the modeled signature of an abrupt sliding onset in englacial layer slopes against slope observations from the onset region of a West Antarctic ice stream (Institute Ice Stream), we conclude that observed layer geometry does not support an abrupt no slip/sliding transition. Our findings instead suggest a much smoother sliding onset, as would be consistent with temperature-dependent friction between ice and bed. Direct measurements of basal temperature at the catchment scale would allow us to confirm this hypothesis.

How to cite: Mantelli, E., Bryant, M., Seroussi, H., Raess, L., Castelletti, D., Schroeder, D., Suckale, J., and Siegert, M.: Layer geometry as a constraint on the physics of sliding onset, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11310, https://doi.org/10.5194/egusphere-egu22-11310, 2022.

Contemporary mass loss from the Antarctic ice sheet primarily originates from the discharge of
marine-terminating glaciers and ice streams. The rate of mass loss is influenced by warming ocean
and atmospheric conditions, which lead to subsequent thinning or disintegration of ice shelves and
increased outflow of upstream grounded ice. It is currently unclear how the basal thermal state of
grounded ice could evolve in the future - for example as a result of accelerated ice flow or changes
in the ice sheet geometry - but a change in the basal thermal state could impact rates of mass loss
from Antarctica. Here, we use a combination of numerical simulations and ice-penetrating radar
analysis to investigate the influence of basal thawing on 100yr simulations of the Antarctic ice
sheet’s evolution. Using the Ice-sheet and Sea-level System Model, we find that thawing patches
of frozen bed near the ice sheet margin could drive mass loss extending into the continental
interior, with the highest rates of loss coming from the George V - Adélie - Wilkes Land coast and
the Enderby - Kemp Land regions of East Antarctica. This suggests that the thawing of localized
frozen bed patches is sufficient to cause these East Antarctic regions to transition to an unstable
mass loss regime. We constrain model estimates of the basal thermal state using ice-penetrating
radar surveys and analyze radar characteristics including bed reflectivity and attenuation. In
combination, our work identifies critical regions of Antarctica where the ice-bed interface could
be close to thawing and where basal thaw could most impact mass loss.

How to cite: Dawson, E., Schroeder, D., Chu, W., Mantelli, E., and Seroussi, H.: Investigating basal thaw as a driver of mass loss from the Antarctic ice sheet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13501, https://doi.org/10.5194/egusphere-egu22-13501, 2022.

Surface loading on GNSS stations in Africa

Usifoh Saturday E^1,2,3, Nhung Le Thi^1,2, Benjamin Männel¹, Pierre Sakic¹, Dodo Joseph³, Harald Schuh^1,2
1GFZ German Research Centre for Geosciences, Potsdam, Germany, ²Institut für Geodäsie und Geoinformationstechnik Technische Universität, Berlin, Germany, ³Centre for Geodesy and Geodynamics, Toro, Bauchi State, Nigeria.

 Corresponding author: parker@gfz-potsdam.de

Abstract

The global navigation satellite systems (GNSS) have revolutionalized the ability to monitor the Earth’s system related to different types of natural processes. This includes tectonic and volcanic deformation, earthquake-related displacements, redistribution of oceanic and atmospheric mass, and changes in the continental water storage. As loading affects the GNSS cordinates, we investigated the effect and assessed the impact of applying dedicated corrections provided by the Earth System Modeling group of German Research Center for Geosciences (GFZ). However, loading caused by mass redistribution results in displacement, predominantly with seasonal periods. Significant temporal changes in mass redistribution (e.g caused by climate change) will result to further trends in the station coordinate time series.

In this contribution, we will compare the PPP coordinate time series with the loading-corrected PPP time series by looking at the amplitude and the correlation between the GNSS time series and the model corrections. Also we will compare the PPP coordinate time series with the loading time series by assessing the RMS reduction and change of amplitude.The result shows that loading-induced displacement varies considerably among GNSS stations and applying corrections to the derived time series has favourable impacts on the reduction in the non-linear motion in GNSS height time series of the African stations.

How to cite: Usifoh, S. E., Le, T. N., Männel, B., Sakic, P., Joseph, D., and Schuh, H.: Surface loading on GNSS stations in Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-64, https://doi.org/10.5194/egusphere-egu22-64, 2022.

Hydrological loading is one of the main contributors into seasonal displacements of the Earth’s crust, as derived from the Global Positioning System (GPS) permanent stations. Recent studies proved that hydrological signatures may be also observed in GPS displacements outside seasonal band. Such estimates may be, however, biased, since (1) total character of GPS displacements is generated by a set of geophysical phenomena combined with GPS-specific signals and errors and (2) the exact sensitivity of GPS for individual components has not yet been properly recognized. In this study, we propose a completely new approach to establish a set of benchmarks of GPS stations, for which sensitivity to geophysical phenomena is identified. We focus on hydrological changes within the Amazon basin, but the same approach could be employed to analyze other phenomena. Analysis is performed for vertical displacements from 63 GPS stations provided by the Nevada Geodetic Laboratory (NGL), collected between 1995 and 2021. Results are compared to data from GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow-On missions (2002-2021), provided by GFZ (GeoForschungsZentrum) as RL06 solution in a form of spherical harmonic coefficients truncated to d/o 96, filtered with DDK3 decorrelation anisotropic filter. We also utilize GLWS (Global Land Water Storage) datatset provided by University of Bonn, as a result of assimilation of GRACE Total Water Storage (TWS) anomalies into WaterGAP Global Hydrological Model (WGHM). We make also use of two hydrological models: pure WGHM and GLDAS (Global Land Data Assimilation System), for which TWS values are provided. Both GRACE and TWS data are converted to vertical displacements of Earth’s crust using load Love numbers, while GPS displacements are reduced for non-tidal atmospheric and oceanic changes. We find the largest values of trends and annual signals for GPS stations proximate to Amazon river. GRACE, GLWS and hydrological models disagree at the level of 8 mm, at maximum. This is mainly caused by the GLDAS model which lacks in the contribution of surface water. Supplementing GLDAS with surface water layer employed from WGHM reduces this difference to 1 mm. Benchmarks of GPS stations are established by using a wavelet decomposition with Meyer’s mother wavelet. We divide both the GPS, GRACE and GLWS displacement time series into 4 decomposition levels, defined by exact periods they contain. Then, we compute correlation coefficients between individual levels of details. We show that the number of 32%, 64%, 97%, 89% and 68% out of 63 GPS stations is significantly correlated to GRACE for periods, respectively, from 2 to 5 months, from 4 to 9 months, from 7 months to 1.4 years, from 1.1 to 3.0 years and from 3.0 years onwards. These numbers change into: 48%, 73%, 100%, 81% and 50% out of 63 GPS stations, when GRACE is replaced with GLWS. 12 or 21 out of 63 GPS stations correlate positively with GRACE or GLWS within entire frequency band, which means that a character of these GPS displacement time series is generated mostly by hydrological changes.

How to cite: Leszczuk, G., Klos, A., Kusche, J., Lenczuk, A., Gerdener, H., and Bogusz, J.: Benchmarking Amazonian GPS stations: an improved way to model hydrological changes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-246, https://doi.org/10.5194/egusphere-egu22-246, 2022.

Observed time-series of water transport in rivers can be perceived mathematically as a superposition of non-linear long-term trends, periodic variations, episodic events, colored instrument noise, and other components. Various statistical methods are readily available to extract and quantify both stationary and non-stationary components in order to subsequently attribute parts of the signal to underlying causal mechanisms. However, the available algorithms differ vastly in terms of computational complexity and implicit assumptions, and may thus have their own individual advantages and disadvantages. By employing a suite of time-series analysis methods for 1D (Wavelets, Singular Spectrum Analysis, Empirical Mode Decomposition) and additional statistical assessments like Pruned Exact Linear Time (PELT) tests for change point detection, we will analyze data from two virtual stations at Elbe River (Germany) and Urmia Lake (Iran) that are representative for the central European region with a rather humid climate, and the more arid conditions of Central Asia with much smaller hydrological signal variations, respectively. It is in particular the latter region with a much less developed in situ hydrometeorological observing system, where we expect that carefully processed geodetic data might contribute most to the monitoring of large-scale terrestrial water dynamics. This contribution will highlight the benefits of more advanced signal analysis methods for extracting relevant hydrometeorological information over more conventionally applied algorithms.

How to cite: Iran Pour, S., Eicker, A., Balidakis, K., Karimi, H., Amiri-Simkooei, A., and Dobslaw, H.: Efficiency of different signal processing methods to isolate signature characteristics in altimetric water level measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1449, https://doi.org/10.5194/egusphere-egu22-1449, 2022.

Global and interactively coupled climate models are important tools for projecting future climate conditions. Even though the quality and reliability of such models has increased during the most recent years, large model uncertainties still exist for various climate elements, so that it is crucial to continuously evaluate them against independent observations. Changes in the distribution and availability of terrestrial water storage (TWS), which can be measured by the satellite gravimetry missions GRACE and GRACE-FO, represent an important part of the climate system in general, and the terrestrial water cycle in particular. However, the use of satellite gravity data for the evaluation of interactively coupled climate models has only very recently become feasible. Challenges mainly arise from large model differences with respect to land water storage-related variables, from conceptual discrepancies between modeled and observed TWS, and from the still rather short time series of satellite data.

This presentation will highlight the latest results achieved from our ongoing research on climate model evaluation based on the analysis of an ensemble of models taking part in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We will focus on long-term wetting and drying conditions in TWS, by deriving several hot spot regions of common trends in GRACE/-FO observations and regions of large model consensus. However, as the observational record currently only covers about 20 years, observed trends may still be obscured by natural climate variability. Therefore, to further attribute the wetting or drying in the identified hot spot regions to either interannual/decadal variability or anthropogenic climate change, we investigate the influence of dedicated climate modes (such as ENSO, PDO, AMO etc.) on TWS variability and trends. Furthermore, we perform a numerical model investigation with 250 years of CMIP6 TWS data to quantify the degree to which trends computed over differently long time intervals can be expected to represent long-term trends, and to discriminate regions of rather robust trends from regions of large fluctuations in the trend caused by decadal climate variability.

How to cite: Jensen, L., Eicker, A., and Dobslaw, H.: Attributing land water storage trends from satellite gravimetry to long-term wetting and drying conditions with global climate models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2335, https://doi.org/10.5194/egusphere-egu22-2335, 2022.

Understanding variations in the ocean heat content is tightly linked to understanding interactions of the global energy cycle with the regional water cycle. Mass, volume, temperature and density changes of  the ocean water column can be estimated with complimentary observations of sea surface height from radar altimetry, ocean bottom pressure from Gravity Recovery and Climate Experiment (GRACE), temperature and salinity from Argo floats. These three techniques have their specific deficiencies and advantages, which can be exploited in a joint inversion framework in order to improve temporal and spatial coverage of oceanic temperature and salinity estimates as well as regionally varying sea level contributions. Solving an inverse problem for temperature and salinity, forward operators are formulated linking the satellite observations to temperature and salinity at depth. This is done by (1) parametrization of temperature and salinity profiles over the full depth of the ocean with B-splines to reduce dimensionality while keeping complexity of the data intact and (2) linearization of the integrated density from parameterized T/S curves. We apply forward operators in the East Indian Ocean to resolve for sea surface height, ocean bottom pressure, temperature and salinity, and assess the regional importance of these factors. We explore the stability of a joint inversion using these forward operators in combination with along-track radar altimetry, GRACE and temperature and salinity by exploring a closed-loop inversion.

How to cite: Yakhontova, A., Rietbroek, R., Kusche, J., Stolzenberger, S., and Uebbing, B.: Contributions of ocean bottom pressure and density changes to regional sea level change in the East Indian Ocean from GRACE, altimetry and Argo data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2586, https://doi.org/10.5194/egusphere-egu22-2586, 2022.

The German-American satellite missions GRACE (Gravity Recovery and Climate Experiment) and its successor GRACE-Follow-On (GRACE-FO) observed the unique data set of total water storage (TWS) variations over the continents since 2002. With this nearly 20 years of data, we can investigate trends in water storage beyond the strong declining trends of the ice sheets and glaciers. Unlike all other continents, Africa exhibits an overall positive trend in TWS. This contribution will take a detailed look into Africa's water storage changes and trends. Further, we attempt to explain these trends by comparison to other hydrological observations such as precipitation.

The long-term TWS increase in Africa is most pronounced in the East-African rift centred around Lake Victoria and the Niger River Basin. Other regions such as Madagaskar exhibit a (statistically significant) negative TWS trend. Furthermore, the trends are not monotonous over time. For example, the increasing trend in East Africa only started around the year 2006 and accelerated after 2012. On the other hand, South Africa wetted until 2012 and dried again since then.

This study divides the African continent into climatically similar regions and investigates the regional mean TWS signals. They are more complex than a linear trend and sinusoidal annual and semiannual seasonality; thus, we employ the STL method (Seasonal Trend decomposition based on Loess). In this way, turning points are identified in the so-called trend component to mark significant trend changes.

The observed TWS changes in Africa are caused mainly by changing precipitation patterns, as observed, for example, with the GPCP (Global Precipitation Climatology Project) data set. In some regions, such as South Africa, the correlation between precipitation and TWS change is evident, whereas other areas show a more complex relationship between these two variables.

How to cite: Boergens, E. and Güntner, A.: Trends in Africa’s Terrestrial Water Storage, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3415, https://doi.org/10.5194/egusphere-egu22-3415, 2022.

At the level of a watershed, the conservation of mass imposes that the net moisture transport through the atmospheric boundaries is balanced by the river discharge and an accumulation/depletion in terrestrial sources such as the soil, surface waters and groundwater.

There are considerable uncertainties connected with modelled water balance components, especially since most models only simulate part of the system: either the atmosphere, the surface or the subsurface. Uncertainties in boundary conditions propagate as biases in the simulated results. For example, not accounting for anthropogenic groundwater extraction potentially introduces biases in arid regions, where groundwater is a non-negligible source of moisture for the atmosphere. The use of observations is therefore an important aid to evaluate model performances and to detect possible biases in water balance components.

In this contribution, we compare total water storage changes derived from the Gravity Recovery Climate Experiment (GRACE) and its follow-on mission, with modelled components of the water balance. We use ERA5 reanalysis data to compute (net) atmospheric transports, and river discharge from GloFAS (Global Flood Awareness System). Furthermore, we use precipitation estimates (e.g. from GPCC) together with evapotranspiration from the Surface Energy Balance System (SEBS). We finally perform an accounting of the water balance components for the world’s largest watersheds and show to what extent we can find agreements, inconsistencies and biases in the data and models.

How to cite: Rietbroek, R., Penning de Vries, M., Zeng, Y., and Su, B.: Closing the water balance of large watersheds using satellite gravimetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3734, https://doi.org/10.5194/egusphere-egu22-3734, 2022.

Though machine learning (ML) methods have been around for decades, they have only more recently been adopted in the geosciences. The availability of existing long data records combined with the capability of ML algorithms to learn highly non-linear relationships between data sources means there is even more potential for the replacement or augmentation of existing scientific analyses with ML methods. Here, I give an example of how I used a convolutional neural network (CNN) for the task of pixelwise classification of the North American Land Data Assimilation System (NLDAS) Total Water Storage data into their corresponding drought levels based on the Palmer Drought Severity Index (PDSI). Promising results indicate there is much to be explored in the application of ML to drought identification and monitoring.

How to cite: Vassallo, C., Bettadpur, S., and Wilson, C.: Drought Identification in NLDAS Data using Machine Learning Methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4918, https://doi.org/10.5194/egusphere-egu22-4918, 2022.

We propose a spatial characterization of the hydrological contributions of several climate drivers that impact continental water mass storage of Australia determined by remote sensing techniques over the period 2002 - 2021. For this purpose, the Slepian functions help for recognizing the signatures of such important changes in the varying gravity field solutions provided by GRACE and GRACE-FO satellite missions such as mascon solutions of 400-km resolution. Time series of 25 Slepian coefficients that correspond to ~99.9% of the eigenvalue spectrum are used to be analyzed and compared to the profiles of climate indexes i.e. El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and South Annular Mode (SAM). The best correlations enable to extract specific Slepian coefficients, and then reconstruct the regional hydrological structures that concern each climate driver, in particular for the southeastern basins strongly influenced by the important flooding during La Niña episode of 2010.

How to cite: Ramillien, G., Seoane, L., and Darrozes, J.: Water mass impacts of the main climate drivers over Australia by satellite gravimetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5765, https://doi.org/10.5194/egusphere-egu22-5765, 2022.

In recent years, the detection and correction of the non-natural irregularities in the long climatic records, so-called homogenization, has been studied. This work is motivated by the problem of identification of origins of the breakpoints in the segmentation of difference series (difference between a candidate series and a reference series). Several segmentation methods have been developed for the difference series, but many of them assume that the reference series is homogenous. However, the homogeneity of the reference series, in reality, is uncertain and unproven. In our study, we applied the segmentation method GNSSseg (Quarello et al., 2020) on the difference between the Integrated water vapour estimates of the CODE REPRO2015 GNSS data set and the ERA5 reanalysis. About 36.5% of change points can be validated from the GPS metadata, and the origins of the remaining 64.5% are questionable (Nguyen et al., 2021). The ambiguity can be leveraged when there is at least one nearby GPS station with respect to which the candidate series can be compared. The proposed method uses weighted t-tests combining the candidate GPS and ERA series and their homologues (denoted GPS' and ERA') from each nearby station. If sufficient consistency emerges from the six tests for all the nearby stations, a decision can be made whether the breakpoint detected in the candidate GPS-ERA series is due to GPS or, alternatively, to ERA. For each quadruplet (GPS, ERA, GPS', ERA'), six t-tests are performed, and the outcomes are combined. In a set of 81 globally distributed GNSS time series spanning more than 25 years, 56 series have at least one nearby station, where 171 breakpoints are detected in segmentation, in which 136 breakpoints are attributed to the GPS. Among those, 94 breakpoints have consistent results between all the nearby stations. GPS-related breakpoints are used for the correction of the mean shift in the difference series. The impact of the breakpoint correction on the GNSS IWV trend estimates is then evaluated. 

Quarello A, Bock O, & Lebarbier E. (2020). A new segmentation method for the homogenisation of GNSS-derived IWV time-series. arXiv: Methodology.

Nguyen KN, Quarello A, Bock O, Lebarbier E. Sensitivity of Change-Point Detection and Trend Estimates to GNSS IWV Time Series Properties. Atmosphere. 2021; 12(9):1102. https://doi.org/10.3390/atmos12091102

How to cite: Nguyen, K. N., Bock, O., and Lebarbier, E.: A new method for the attribution of breakpoints in segmentation of IWV difference time series, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6390, https://doi.org/10.5194/egusphere-egu22-6390, 2022.

Drought has struck the southwest U.S. for the fourth time this millennium, reducing freshwater available to agriculture and urban centers.  We are bringing new insight by quantifying change in water in the ground using GPS elastic displacements, GRACE gravity, artificial reservoir levels, and snow models. Precipitation in Water Year 2021 was half of normal; the rise in total water in autumn and winter is 1/3 of the seasonal average (estimated using chiefly GPS); water was parched from the ground in the spring and summer, bringing water in the ground to its historic low (estimated using primarily GRACE).  In the Central Valley, soil moisture plus groundwater each year increases by 11 km3 and is maximum in April.  Only half of groundwater lost during periods of drought is replenished in subsequent years of heavy precipitation.  The Central Valley has lost groundwater at 2 km3/year from 2006 to 2021, with 2/3 of the loss coming from the southern Valley.

How to cite: Argus, D., Martens, H., Borsa, A., Wiese, D., Knappe, E., Larochelle, S., Anderson, M., Peidou, A., Khatiwada, A., Lau, N., White, A., Hoylman, Z., Swarr, M., Cao, Q., Pan, M., Chanard, K., Avouac, J.-P., Payton, G., and Landerer, F.: Intensifying hydrologic drought in California, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6800, https://doi.org/10.5194/egusphere-egu22-6800, 2022.

Earth's visible environmental changes, both natural and man-made, are influencing climate change on a global scale. For this reason, it is necessary to continuously monitor these changes and study the impact of human activities on them. One of the parameters indicating climate change is the systematic increase in temperature for the last 80 years. It causes more evaporation of water from natural and artificial water bodies. Consequently, the water content in the atmosphere is also increasing. Precipitable water is therefore one of the most important parameters when studying climate change. 

The aim of this study was to analyze long-term precipitation water data from a dense GNSS network over Poland. Twelve-year observations from over a hundred ASG-EUPOS stations were used to estimate changes in precipitation water values. These data were verified by comparison with available radio sounding data. Analysis of GPS-based PW values showed a clear increasing trend in PW values by 0.078 mm/year. The spatial-temporal distribution of mean PW values and their fluctuations over the years have been investigated. The obtained results confirm the fact that Poland lies on the border of continental and oceanic climate influence, and are in agreement with climate research concerning this region. 

How to cite: Araszkiewicz, A., Mierzwiak, M., Kiliszek, D., Nowak Da Costa, J., and Szołucha, M.: GPS-based multi-annual variation of the precipitable water over Poland territory, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7081, https://doi.org/10.5194/egusphere-egu22-7081, 2022.

To assess realistic climate change mitigation strategies, it is important to research and understand the global carbon cycle. Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases. Their atmospheric concentrations are affected by anthropogenic emissions as well as exchange fluxes with oceans and the terrestrial biosphere. For the prediction of future atmospheric CO2 and CH4 concentrations, it is critical to understand how the natural exchange fluxes respond to a changing climate. One of the factors that impact these fluxes is the changing hydrological cycle.        
In our project, we combine information about the hydrological cycle from geodetic satellites (e.g. GRACE & GRACE-FO) with carbon cycle observations from other satellites (e.g. TROPOMI & OCO-2). Specifically, we plan to investigate the impact of a changing water level in soils on CH4 emissions from wetlands and on the photosynthetic CO2 uptake of plants. Details of our approach and first results will be presented.

How to cite: Klemme, A., Warneke, T., Bovensmann, H., Weigelt, M., Müller, J., Notholt, J., and Lämmerzahl, C.: Using satellite geodesy for carbon cycle research, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7583, https://doi.org/10.5194/egusphere-egu22-7583, 2022.

Since 2002, estimates of the spatio-temporal variations of Earth’s gravity field derived from the Gravity Recovery and Climate Experiment (GRACE and now GRACE-FO) mission measurements have provided new insights into large scale water redistributions at inter-annual, seasonal and sub-seasonal timescales. It has been shown for example that for many large drainage basins the empirical relationship between aggregated Terrestrial Water Storage (TWS) and discharge at the outlet reveals an underlying dynamic that is approximately linear and time-invariant.

In this contribution, we further analyse this relationship in the case of the Amazon basin and sub-basins by investigating different physically interpretable, lumped-parameter models for the TWS-discharge dynamics. To this end, we first put forward a linear and continuous-time model using a state-space representation. We then enhance the model by introducing a non-linear term accounting for the observed saturation of the discharge. Finally, we reformulate the model by replacing the discharge by the river stage at the outlet and add a prescribed model of the rating curve to obtain the discharge. The suggested models are successively calibrated against TWS anomaly derived from GRACE data and discharge or river stage records using the prediction-error-method. It is noteworthy that one of the estimated parameters can be interpreted as the total amount of drainable water stored across the basin, a quantity that cannot be observed by GRACE alone. This quantity is estimated to be on average 1,750 km³ during the period 2004-2009. These models are eventually combined with the equation of water mass balance, in order to obtain a consistent representation of the basin-scale rainfall-runoff dynamics suited to data assimilation.

How to cite: Douch, K., Saemian, P., and Sneeuw, N.: Identification of conceptual rainfall-runoff models of large drainage basins based on GRACE and in-situ data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7903, https://doi.org/10.5194/egusphere-egu22-7903, 2022.

Continuous monitoring of the Global Terrestrial Water Storage changes (TWS) is challenging because of the large surface of continents and the variety of storage compartments (WCRP, 2018). The only observing system which provides global TWS mass change estimates so far is space gravimetry. Unfortunately, most storage compartments (lakes, groundwater, glaciers…) are too small to be resolved given the current spatial resolution of gravimetry missions. This intrinsic property makes gravimetry-based TWS changes estimates difficult to attribute and to interpret at individual basin scale.

In this context, combining gravimetry-based TWS estimates with other sources of information with higher spatial resolution is a promising strategy. In this study, we combine gravimetry data with independent observations from satellite altimetry and high resolution visible imagery to derive refined estimates of the TWS changes in hydrological basins containing lakes and glaciers (See Data used). The combination consists in including independent observations of glacier (Hugonnet et al., 2021) and lake (Cretaux et al., 2016) mass changes in the conversion process from gravity L2 data to water mass changes data. The combination is done for all regions of the world on a monthly basis.

This approach allows to split properly glacier and TWS changes at interannual to decadal time scales, and derive glacier-free estimates of TWS in the endorheic basins and the exorheic basins. We find that for the period from 2002 to 2020, the total TWS trend of 0.23±0.25 mm SLE/yr is mainly due to a mass loss in endorheic basins TWS of 0.20±0.12 mm SLE/yr. Over the same period, exorheic basins present a non-significative trend of 0.03±0.14 mm SLE/yr. On the contrary, the interannual variability in the TWS change of 4 mm SLE is mainly due to the exorheic basins TWS change.

How to cite: Blazquez, A., Berthier, E., Meyssignac, B., Longuevergne, L., and Crétaux, J.-F.: Combining space gravimetry observations with data from satellite altimetry and high resolution visible imagery to resolve mass changes of endorheic basins and exorheic basins., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8525, https://doi.org/10.5194/egusphere-egu22-8525, 2022.

Satellite gravity missions are unique observation systems to directly observe mass transport processes in the Earth system. Since 2000, CHAMP, GRACE, GOCE, and GRACE-FO have almost continuously been observing Earth’s mass changes and have improved our understanding of large-scale processes such as the global water cycle, melting of continental ice sheets and mountain glaciers, changes in ocean mass that are closely related to the mass-related component of sea-level rise, which are subtle indicators of climate change, on global to regional scale. The existing observation record of more than two decades is already closing in on the minimum time series of 30 years needed to decouple natural and anthropogenic forcing mechanisms according to the Global Climate Observing System (GCOS).

Next Generation Gravity Missions (NGGMs) are expected to be implemented in the near future to continue the observation record. The Mass-change And Geoscience International Constellation (acronym: MAGIC) is a joint investigation of ESA with NASA’s MCDO study resulting in a jointly accorded Mission Requirements Document (MRD) responding to global user community needs. These NGGM concepts have set high anticipation for enhanced monitoring capabilities of mass transports in the Earth’s system with significantly improved spatial and temporal resolution. They will allow an evaluation of long-term trends within the Terrestrial Water Storage (TWS), which was adopted as a new Essential Climate Variable in 2020.

This study is based on modeled mass transport time series of components of the TWS, obtained from future climate projections until the year 2100 following the shared socio-economic pathway scenario 5-8.5 (SSP5-8.5). It evaluates the recoverability of long-term climate trends, annual amplitude, and phase of the TWS employing closed-loop numerical simulations of different current and NGGM concepts up to a spatial resolution of 250 km (Spherical Harmonic Degree 80). The assumed satellite constellations are GRACE-type in-line single-pair missions and Bender double-pair missions with realistic noise assumptions for the key payload and ocean-tide background model errors. In the interpretation and discussion of the results, special emphasis will be given on the dependence of the length of the measurement time series and the quantification of the robustness of the derived trends, systematic changes, as well as possibilities to improve the trend parameterization.