Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5645-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-5645-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
18 Nov 2020
Model description paper |
| 18 Nov 2020
| 18 Nov 2020
COSIPY v1.3 – an open-source coupled snowpack and ice surface energy and mass balance model
Department of Geography, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 15, 91058 Erlangen, Germany
Anselm Arndt
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
Christoph Schneider
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
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EGUsphere, https://doi.org/10.5194/egusphere-2025-2166, https://doi.org/10.5194/egusphere-2025-2166, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Ecohydrological processes in heavily managed catchments are often incorrectly represented in models. We applied a tracer-aided model STARR in an ET-dominated region (the Middle Spree, NE Germany) with major management impacts. Water isotopes were useful in identifying runoff contributions and partitioning ET even at sparse resolution. Trade-offs between discharge- and isotope-based calibrations could be partially mitigated by integrating more process-based conceptualizations into the model.
Pedro Henrique Lima Alencar, Saskia Arndt, Kei Namba, Márk Somogyvári, Frederik Bart, Fabio Brill, Juan Dueñas, Peter Feindt, Daniel Johnson, Nariman Mahmoodi, Christoph Merz, Subham Mukherjee, Katrin Nissen, Eva Nora Paton, Tobias Sauter, Dörthe Tetzlaff, Franziska Tügel, Thomas Vogelpohl, Stenka Valentinova Vulova, Behnam Zamani, and Hui Hui Zhang
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As climate change escalates, the Berlin-Brandenburg region faces new challenges. Climate change-induced extreme events are expected to cause new conflicts to emerge and aggravate existing ones. To guide future research, we co-develop a list of key questions on climate and water challenges in the region. Our findings highlight the need for new research approaches. We expect this list to provide a roadmap for actionable knowledge production to address climate and water challenges in the region.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-50, https://doi.org/10.5194/egusphere-2025-50, 2025
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Glacial lake outburst floods (GLOFs) continue to threaten high-mountain communities in Nepal. We simulate potential GLOF events from five glacial lakes in the Everest region during the 21st century using a 3D flood model and several breach and SSP scenarios. Large GLOFs could extend over 100 km and inundate 80 to 100 km of roads, 735 to 1,989 houses and between 0.85 and 3.52 km2 of agricultural land. The results help to assess the changing GLOF impacts and support more accurate risk assessments.
Annelies Voordendag, Brigitta Goger, Rainer Prinz, Tobias Sauter, Thomas Mölg, Manuel Saigger, and Georg Kaser
The Cryosphere, 18, 849–868, https://doi.org/10.5194/tc-18-849-2024, https://doi.org/10.5194/tc-18-849-2024, 2024
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Wind-driven snow redistribution affects glacier mass balance. A case study of Hintereisferner glacier in Austria used high-resolution observations and simulations to model snow redistribution. Simulations matched observations, showing the potential of the model for studying snow redistribution on other mountain glaciers.
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The Cryosphere, 17, 2343–2365, https://doi.org/10.5194/tc-17-2343-2023, https://doi.org/10.5194/tc-17-2343-2023, 2023
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Calibration of surface mass balance (SMB) models on regional scales is challenging. We investigate different calibration strategies with the goal of achieving realistic simulations of the SMB in the Monte Sarmiento Massif, Tierra del Fuego. Our results show that the use of regional observations from satellite data can improve the model performance. Furthermore, we compare four melt models of different complexity to understand the benefit of increasing the processes considered in the model.
Tobias Sauter
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Patagonia is thought to be one of the wettest – if not the wettest – places on Earth. The plausibility of these numbers has never been carefully scrutinized, despite the significance of this topic to our understanding of observed environmental changes, such as glacier recession. The revised precipitation values are significantly smaller than the previously reported values, thus opening up a new perspective on the Patagonian glaciers' response to climate change.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-2166, https://doi.org/10.5194/egusphere-2025-2166, 2025
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Ecohydrological processes in heavily managed catchments are often incorrectly represented in models. We applied a tracer-aided model STARR in an ET-dominated region (the Middle Spree, NE Germany) with major management impacts. Water isotopes were useful in identifying runoff contributions and partitioning ET even at sparse resolution. Trade-offs between discharge- and isotope-based calibrations could be partially mitigated by integrating more process-based conceptualizations into the model.
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As climate change escalates, the Berlin-Brandenburg region faces new challenges. Climate change-induced extreme events are expected to cause new conflicts to emerge and aggravate existing ones. To guide future research, we co-develop a list of key questions on climate and water challenges in the region. Our findings highlight the need for new research approaches. We expect this list to provide a roadmap for actionable knowledge production to address climate and water challenges in the region.
Wilhelm Furian and Tobias Sauter
EGUsphere, https://doi.org/10.5194/egusphere-2025-50, https://doi.org/10.5194/egusphere-2025-50, 2025
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Glacial lake outburst floods (GLOFs) continue to threaten high-mountain communities in Nepal. We simulate potential GLOF events from five glacial lakes in the Everest region during the 21st century using a 3D flood model and several breach and SSP scenarios. Large GLOFs could extend over 100 km and inundate 80 to 100 km of roads, 735 to 1,989 houses and between 0.85 and 3.52 km2 of agricultural land. The results help to assess the changing GLOF impacts and support more accurate risk assessments.
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Wind-driven snow redistribution affects glacier mass balance. A case study of Hintereisferner glacier in Austria used high-resolution observations and simulations to model snow redistribution. Simulations matched observations, showing the potential of the model for studying snow redistribution on other mountain glaciers.
Franziska Temme, David Farías-Barahona, Thorsten Seehaus, Ricardo Jaña, Jorge Arigony-Neto, Inti Gonzalez, Anselm Arndt, Tobias Sauter, Christoph Schneider, and Johannes J. Fürst
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Calibration of surface mass balance (SMB) models on regional scales is challenging. We investigate different calibration strategies with the goal of achieving realistic simulations of the SMB in the Monte Sarmiento Massif, Tierra del Fuego. Our results show that the use of regional observations from satellite data can improve the model performance. Furthermore, we compare four melt models of different complexity to understand the benefit of increasing the processes considered in the model.
Mohamed H. Salim, Sebastian Schubert, Jaroslav Resler, Pavel Krč, Björn Maronga, Farah Kanani-Sühring, Matthias Sühring, and Christoph Schneider
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Guisella Gacitúa, Christoph Schneider, Jorge Arigony, Inti González, Ricardo Jaña, and Gino Casassa
Earth Syst. Sci. Data, 13, 231–236, https://doi.org/10.5194/essd-13-231-2021, https://doi.org/10.5194/essd-13-231-2021, 2021
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We performed the first successful ice thickness measurements using terrestrial ground-penetrating radar in the ablation area of Schiaparelli Glacier (Cordillera Darwin, Tierra del Fuego, Chile). Data are fundamental to understand glaciers dynamics, constrain ice dynamical modelling, and predict glacier evolution. Results show a valley-shaped bedrock below current sea level; thus further retreat of Schiaparelli Glacier will probably lead to an enlarged and strongly over-deepened proglacial lake.
Tobias Sauter
Hydrol. Earth Syst. Sci., 24, 2003–2016, https://doi.org/10.5194/hess-24-2003-2020, https://doi.org/10.5194/hess-24-2003-2020, 2020
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Patagonia is thought to be one of the wettest – if not the wettest – places on Earth. The plausibility of these numbers has never been carefully scrutinized, despite the significance of this topic to our understanding of observed environmental changes, such as glacier recession. The revised precipitation values are significantly smaller than the previously reported values, thus opening up a new perspective on the Patagonian glaciers' response to climate change.
Kirstin Hoffmann, Francisco Fernandoy, Hanno Meyer, Elizabeth R. Thomas, Marcelo Aliaga, Dieter Tetzner, Johannes Freitag, Thomas Opel, Jorge Arigony-Neto, Christian Florian Göbel, Ricardo Jaña, Delia Rodríguez Oroz, Rebecca Tuckwell, Emily Ludlow, Joseph R. McConnell, and Christoph Schneider
The Cryosphere, 14, 881–904, https://doi.org/10.5194/tc-14-881-2020, https://doi.org/10.5194/tc-14-881-2020, 2020
Rebecca Möller, Marco Möller, Peter A. Kukla, and Christoph Schneider
Earth Syst. Sci. Data, 10, 53–60, https://doi.org/10.5194/essd-10-53-2018, https://doi.org/10.5194/essd-10-53-2018, 2018
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Deposits of volcanic tephra alter the energy balance at the surface of a glacier. The effects reach from intensified melt to complete insulation, mainly depending on tephra thickness. Data from a field experiment on Iceland reveal an additional minor dependency on tephra type and suggest a substantially different behavior of tephra-covered snowpacks than of tephra-covered glacier ice. The related 50-day dataset of hourly records can readily be used for model calibration and validation purposes.
Tobias Sauter and Stephan Peter Galos
The Cryosphere, 10, 2887–2905, https://doi.org/10.5194/tc-10-2887-2016, https://doi.org/10.5194/tc-10-2887-2016, 2016
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The paper deals with the micrometeorological conditions on mountain glaciers. We use idealized large-eddy simulations to study the heat transport associated with the local wind systems and its impact on the energy exchange between atmosphere and glaciers. Our results demonstrate how the sensible heat flux variablility on glaciers is related to topographic effects and that the energy surplus is strong enough to significantly increase the local glacier melting rates.
J. Kropáček, N. Neckel, B. Tyrna, N. Holzer, A. Hovden, N. Gourmelen, C. Schneider, M. Buchroithner, and V. Hochschild
Nat. Hazards Earth Syst. Sci., 15, 2425–2437, https://doi.org/10.5194/nhess-15-2425-2015, https://doi.org/10.5194/nhess-15-2425-2015, 2015
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The supraglacial lake basin was mapped by DGPS and the SFM approach from terrestrial photographs. The maximum filling capacity of the lake was estimated, with a maximum discharge of 77.8 m3/s, calculated using an empirical relation. The flooded area in the valley was delineated by employing a raster-based hydraulic model. A coincidence of the GLOF events with high values of cumulative above-zero temperature and precipitation calculated from the HAR data set was revealed.
T. Sauter, M. Möller, R. Finkelnburg, M. Grabiec, D. Scherer, and C. Schneider
The Cryosphere, 7, 1287–1301, https://doi.org/10.5194/tc-7-1287-2013, https://doi.org/10.5194/tc-7-1287-2013, 2013
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Davor Dundovic, Joseph G. Wallwork, Stephan C. Kramer, Fabien Gillet-Chaulet, Regine Hock, and Matthew D. Piggott
Geosci. Model Dev., 18, 4023–4044, https://doi.org/10.5194/gmd-18-4023-2025, https://doi.org/10.5194/gmd-18-4023-2025, 2025
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Daniel Moreno-Parada, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
Geosci. Model Dev., 18, 3895–3919, https://doi.org/10.5194/gmd-18-3895-2025, https://doi.org/10.5194/gmd-18-3895-2025, 2025
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Geosci. Model Dev., 18, 3635–3659, https://doi.org/10.5194/gmd-18-3635-2025, https://doi.org/10.5194/gmd-18-3635-2025, 2025
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Ice-sheet models are computer programs that can simulate how the Greenland and Antarctic ice sheets will evolve in the future. The accuracy of these models depends on their resolution: how small the details are that the model can resolve. We have created a model with a variable resolution that can resolve a lot of detail in areas where lots of changes happen in the ice and less detail in areas where the ice does not move so much. This makes the model both accurate and fast.
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This study uses ice mass balance buoys – temperature- and height-measuring devices frozen into sea ice – to find how well climate models simulate (1) melt and growth of Arctic sea ice and (2) conduction of heat through Arctic sea ice. This may help understand why models produce varying amounts of sea ice in the present day. We find that models tend to show more melt, growth or conduction for a given ice thickness than the buoys, although the difference is smaller for models with more physically realistic thermodynamics.
Ole Richter, Ralph Timmermann, G. Hilmar Gudmundsson, and Jan De Rydt
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The new coupled ice sheet–ocean model addresses challenges related to horizontal resolution through advanced mesh flexibility, enabled by the use of unstructured grids. We describe the new model, verify its functioning in an idealised setting and demonstrate its advantages in a global-ocean–Antarctic ice sheet domain. The results of this study comprise an important step towards improving predictions of the Antarctic contribution to sea level rise over centennial timescales.
Niccolò Maffezzoli, Eric Rignot, Carlo Barbante, Troels Petersen, and Sebastiano Vascon
Geosci. Model Dev., 18, 2545–2568, https://doi.org/10.5194/gmd-18-2545-2025, https://doi.org/10.5194/gmd-18-2545-2025, 2025
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In this work we introduce IceBoost, a machine learning framework to model the ice thickness distribution of all the world's glaciers with greater accuracy than state-of-the-art methods. The model is trained on 3.7 million measurements globally available and provides skilful estimates across all regions. This advancement will help in better assessing future sea level changes and freshwater resources, with significance for both the scientific community and society at large.
Gong Cheng, Mansa Krishna, and Mathieu Morlighem
EGUsphere, https://doi.org/10.5194/egusphere-2025-1188, https://doi.org/10.5194/egusphere-2025-1188, 2025
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Predicting ice sheet contributions to sea level rise is challenging due to limited data and uncertainties in key processes. Traditional models require complex methods that lack flexibility. We developed PINNICLE, an open-source Python library that integrates machine learning with physical laws to improve ice sheet modeling. By combining data and physics, PINNICLE enhances predictions and adaptability, providing a powerful tool for climate research and sea level rise projections.
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025, https://doi.org/10.5194/gmd-18-1829-2025, 2025
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Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
Alek Aaron Petty, Christopher Cardinale, and Madison Smith
EGUsphere, https://doi.org/10.5194/egusphere-2025-766, https://doi.org/10.5194/egusphere-2025-766, 2025
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We put global climate models to the test against NASA’s ICESat-2 satellite to see how well they simulate global sea ice cover. By adding fancy laser data from ICESat-2, we can better assess how well the models are performing compared to the standard assessments of sea ice area. Overall the models do a good job but there’s room for improvement, especially across the Southern Ocean. We should think a bit more about sea ice density if we want more reliable freeboard comparisons.
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025, https://doi.org/10.5194/gmd-18-1673-2025, 2025
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Ice sheet models can help predict how Antarctica's ice sheets respond to environmental change, and such models benefit from comparison to geological data. Here, we use an ice sheet model output and other data to predict the erosion of debris and trace its transport to where it is deposited on the ocean floor. This allows the results of ice sheet modelling to be directly and quantitively compared to real-world data, helping to reduce uncertainty regarding Antarctic sea level contribution.
Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025, https://doi.org/10.5194/gmd-18-885-2025, 2025
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We have developed a new method to improve the accuracy of sea ice models, which predict how ice moves and deforms due to wind and ocean currents. Traditional models use parameters that are often poorly defined. The new approach uses machine learning to fine-tune these parameters by comparing simulated ice drift with satellite data. The method identifies optimal settings for the model by analysing patterns in ice deformation. This results in more accurate simulations of sea ice drift forecasting.
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev., 18, 651–670, https://doi.org/10.5194/gmd-18-651-2025, https://doi.org/10.5194/gmd-18-651-2025, 2025
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We employed the WRF-Chem model to parameterize atmospheric nitrate deposition in snow and evaluate its performance in simulating snow cover, snow depth, and concentrations of dust and nitrate using new observations from northern China. The results generally exhibit reasonable agreement with field observations in northern China, demonstrating the model's capability to simulate snow properties, including concentrations of reservoir species.
Samar Minallah, William Lipscomb, Gunter Leguy, and Harry Zekollari
EGUsphere, https://doi.org/10.5194/egusphere-2024-4152, https://doi.org/10.5194/egusphere-2024-4152, 2025
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We implemented a new modeling framework within an Earth system model to study the evolution of mountain glaciers under different climate scenarios and applied it to the European Alps. Alpine glaciers will lose a large volume fraction under current temperatures, with near complete ice loss under warmer scenarios. This is the first use of a 3D, higher-order ice flow model for regional-scale glacier simulations that will enable assessments of coupled land ice and Earth system processes.
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025, https://doi.org/10.5194/gmd-18-193-2025, 2025
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We present a method for avalanche forecasters to analyze patterns in snowpack model simulations. It uses fuzzy clustering to group small regions into larger forecast areas based on snow characteristics, locations, and temporal history. Tested in the Columbia Mountains in two winter seasons, it closely matched real forecast regions regions and identified major avalanche hazard patterns. This approach simplifies complex model outputs, helping forecasters make informed decisions.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
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We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
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The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
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Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. We conduct intercomparison analyses of Massachusetts Institute of Technology General Circulation Model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open-ocean temporal variability and Antarctic continental shelves require improvements.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
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Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
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By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
Geosci. Model Dev., 17, 7219–7244, https://doi.org/10.5194/gmd-17-7219-2024, https://doi.org/10.5194/gmd-17-7219-2024, 2024
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We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in situ observations are available. We find that when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
Therese Rieckh, Andreas Born, Alexander Robinson, Robert Law, and Gerrit Gülle
Geosci. Model Dev., 17, 6987–7000, https://doi.org/10.5194/gmd-17-6987-2024, https://doi.org/10.5194/gmd-17-6987-2024, 2024
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We present the open-source model ELSA, which simulates the internal age structure of large ice sheets. It creates layers of snow accumulation at fixed times during the simulation, which are used to model the internal stratification of the ice sheet. Together with reconstructed isochrones from radiostratigraphy data, ELSA can be used to assess ice sheet models and to improve their parameterization. ELSA can be used coupled to an ice sheet model or forced with its output.
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev., 17, 6867–6886, https://doi.org/10.5194/gmd-17-6867-2024, https://doi.org/10.5194/gmd-17-6867-2024, 2024
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In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely the Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea ice‒ocean‒ice shelf coupled model and can assimilate sea ice concentration observations. By assessing the system's performance in sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for the next 7 d and has the potential to guide ship navigation in the Antarctic sea ice zone.
Chenhui Ning, Shiming Xu, Yan Zhang, Xuantong Wang, Zhihao Fan, and Jiping Liu
Geosci. Model Dev., 17, 6847–6866, https://doi.org/10.5194/gmd-17-6847-2024, https://doi.org/10.5194/gmd-17-6847-2024, 2024
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Sea ice models are mainly based on non-moving structured grids, which is different from buoy measurements that follow the ice drift. To facilitate Lagrangian analysis, we introduce online tracking of sea ice in Community Ice CodE (CICE). We validate the sea ice tracking with buoys and evaluate the sea ice deformation in high-resolution simulations, which show multi-fractal characteristics. The source code is openly available and can be used in various scientific and operational applications.
Ulrich Strasser, Michael Warscher, Erwin Rottler, and Florian Hanzer
Geosci. Model Dev., 17, 6775–6797, https://doi.org/10.5194/gmd-17-6775-2024, https://doi.org/10.5194/gmd-17-6775-2024, 2024
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openAMUNDSEN is a fully distributed open-source snow-hydrological model for mountain catchments. It includes process representations of an empirical, semi-empirical, and physical nature. It uses temperature, precipitation, humidity, radiation, and wind speed as forcing data and is computationally efficient, of a modular nature, and easily extendible. The Python code is available on GitHub (https://github.com/openamundsen/openamundsen), including documentation (https://doc.openamundsen.org).
Matthias Rauter and Julia Kowalski
Geosci. Model Dev., 17, 6545–6569, https://doi.org/10.5194/gmd-17-6545-2024, https://doi.org/10.5194/gmd-17-6545-2024, 2024
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Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of the dense core. Only a few complex models exist to simulate this phenomenon, and the respective hazard is hard to predict. This work provides a novel flow model that focuses on simple relations while still encapsulating the significant behaviour. The model is applied to reconstruct two catastrophic powder snow avalanche events in Austria.
Till Andreas Soya Rasmussen, Jacob Poulsen, Mads Hvid Ribergaard, Ruchira Sasanka, Anthony P. Craig, Elizabeth C. Hunke, and Stefan Rethmeier
Geosci. Model Dev., 17, 6529–6544, https://doi.org/10.5194/gmd-17-6529-2024, https://doi.org/10.5194/gmd-17-6529-2024, 2024
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Earth system models (ESMs) today strive for better quality based on improved resolutions and improved physics. A limiting factor is the supercomputers at hand and how best to utilize them. This study focuses on the refactorization of one part of a sea ice model (CICE), namely the dynamics. It shows that the performance can be significantly improved, which means that one can either run the same simulations much cheaper or advance the system according to what is needed.
Iain Wheel, Douglas I. Benn, Anna J. Crawford, Joe Todd, and Thomas Zwinger
Geosci. Model Dev., 17, 5759–5777, https://doi.org/10.5194/gmd-17-5759-2024, https://doi.org/10.5194/gmd-17-5759-2024, 2024
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Calving, the detachment of large icebergs from glaciers, is one of the largest uncertainties in future sea level rise projections. This process is poorly understood, and there is an absence of detailed models capable of simulating calving. A new 3D calving model has been developed to better understand calving at glaciers where detailed modelling was previously limited. Importantly, the new model is very flexible. By allowing for unrestricted calving geometries, it can be applied at any location.
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024, https://doi.org/10.5194/gmd-17-1903-2024, 2024
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In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024, https://doi.org/10.5194/gmd-17-1297-2024, 2024
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Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024, https://doi.org/10.5194/gmd-17-1041-2024, 2024
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The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
Anjali Sandip, Ludovic Räss, and Mathieu Morlighem
Geosci. Model Dev., 17, 899–909, https://doi.org/10.5194/gmd-17-899-2024, https://doi.org/10.5194/gmd-17-899-2024, 2024
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We solve momentum balance for unstructured meshes to predict ice flow for real glaciers using a pseudo-transient method on graphics processing units (GPUs) and compare it to a standard central processing unit (CPU) implementation. We justify the GPU implementation by applying the price-to-performance metric for up to million-grid-point spatial resolutions. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions.
Julien Brondex, Kévin Fourteau, Marie Dumont, Pascal Hagenmuller, Neige Calonne, François Tuzet, and Henning Löwe
Geosci. Model Dev., 16, 7075–7106, https://doi.org/10.5194/gmd-16-7075-2023, https://doi.org/10.5194/gmd-16-7075-2023, 2023
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Vapor diffusion is one of the main processes governing snowpack evolution, and it must be accounted for in models. Recent attempts to represent vapor diffusion in numerical models have faced several difficulties regarding computational cost and mass and energy conservation. Here, we develop our own finite-element software to explore numerical approaches and enable us to overcome these difficulties. We illustrate the capability of these approaches on established numerical benchmarks.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
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Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023, https://doi.org/10.5194/gmd-16-6671-2023, 2023
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We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
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We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
Kevin Hank, Lev Tarasov, and Elisa Mantelli
Geosci. Model Dev., 16, 5627–5652, https://doi.org/10.5194/gmd-16-5627-2023, https://doi.org/10.5194/gmd-16-5627-2023, 2023
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Physically meaningful modeling of geophysical system instabilities is numerically challenging, given the potential effects of purely numerical artifacts. Here we explore the sensitivity of ice stream surge activation to numerical and physical model aspects. We find that surge characteristics exhibit a resolution dependency but converge at higher horizontal grid resolutions and are significantly affected by the incorporation of bed thermal and sub-glacial hydrology models.
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, and Angelika Humbert
Geosci. Model Dev., 16, 5305–5322, https://doi.org/10.5194/gmd-16-5305-2023, https://doi.org/10.5194/gmd-16-5305-2023, 2023
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Water underneath ice sheets affects the motion of glaciers. This study presents a newly developed code, CUAS-MPI, that simulates subglacial hydrology. It is designed for supercomputers and is hence a parallelized code. We measure the performance of this code for simulations of the entire Greenland Ice Sheet and find that the code works efficiently. Moreover, we validated the code to ensure the correctness of the solution. CUAS-MPI opens new possibilities for simulations of ice sheet hydrology.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
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Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Eric Keenan, Nander Wever, Jan T. M. Lenaerts, and Brooke Medley
Geosci. Model Dev., 16, 3203–3219, https://doi.org/10.5194/gmd-16-3203-2023, https://doi.org/10.5194/gmd-16-3203-2023, 2023
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Ice sheets gain mass via snowfall. However, snowfall is redistributed by the wind, resulting in accumulation differences of up to a factor of 5 over distances as short as 5 km. These differences complicate estimates of ice sheet contribution to sea level rise. For this reason, we have developed a new model for estimating wind-driven snow redistribution on ice sheets. We show that, over Pine Island Glacier in West Antarctica, the model improves estimates of snow accumulation variability.
Sebastian Westermann, Thomas Ingeman-Nielsen, Johanna Scheer, Kristoffer Aalstad, Juditha Aga, Nitin Chaudhary, Bernd Etzelmüller, Simon Filhol, Andreas Kääb, Cas Renette, Louise Steffensen Schmidt, Thomas Vikhamar Schuler, Robin B. Zweigel, Léo Martin, Sarah Morard, Matan Ben-Asher, Michael Angelopoulos, Julia Boike, Brian Groenke, Frederieke Miesner, Jan Nitzbon, Paul Overduin, Simone M. Stuenzi, and Moritz Langer
Geosci. Model Dev., 16, 2607–2647, https://doi.org/10.5194/gmd-16-2607-2023, https://doi.org/10.5194/gmd-16-2607-2023, 2023
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The CryoGrid community model is a new tool for simulating ground temperatures and the water and ice balance in cold regions. It is a modular design, which makes it possible to test different schemes to simulate, for example, permafrost ground in an efficient way. The model contains tools to simulate frozen and unfrozen ground, snow, glaciers, and other massive ice bodies, as well as water bodies.
Alex S. Gardner, Nicole-Jeanne Schlegel, and Eric Larour
Geosci. Model Dev., 16, 2277–2302, https://doi.org/10.5194/gmd-16-2277-2023, https://doi.org/10.5194/gmd-16-2277-2023, 2023
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This is the first description of the open-source Glacier Energy and Mass Balance (GEMB) model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. The model is evaluated against the current state of the art and in situ observations and is shown to perform well.
Yafei Nie, Chengkun Li, Martin Vancoppenolle, Bin Cheng, Fabio Boeira Dias, Xianqing Lv, and Petteri Uotila
Geosci. Model Dev., 16, 1395–1425, https://doi.org/10.5194/gmd-16-1395-2023, https://doi.org/10.5194/gmd-16-1395-2023, 2023
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State-of-the-art Earth system models simulate the observed sea ice extent relatively well, but this is often due to errors in the dynamic and other processes in the simulated sea ice changes cancelling each other out. We assessed the sensitivity of these processes simulated by the coupled ocean–sea ice model NEMO4.0-SI3 to 18 parameters. The performance of the model in simulating sea ice change processes was ultimately improved by adjusting the three identified key parameters.
Varun Sharma, Franziska Gerber, and Michael Lehning
Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, https://doi.org/10.5194/gmd-16-719-2023, 2023
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Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere.
Anne M. Felden, Daniel F. Martin, and Esmond G. Ng
Geosci. Model Dev., 16, 407–425, https://doi.org/10.5194/gmd-16-407-2023, https://doi.org/10.5194/gmd-16-407-2023, 2023
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We present and validate a novel subglacial hydrology model, SUHMO, based on an adaptive mesh refinement framework. We propose the addition of a pseudo-diffusion to recover the wall melting in channels. Computational performance analysis demonstrates the efficiency of adaptive mesh refinement on large-scale hydrologic problems. The adaptive mesh refinement approach will eventually enable better ice bed boundary conditions for ice sheet simulations at a reasonable computational cost.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Esteban Alonso-González, Kristoffer Aalstad, Mohamed Wassim Baba, Jesús Revuelto, Juan Ignacio López-Moreno, Joel Fiddes, Richard Essery, and Simon Gascoin
Geosci. Model Dev., 15, 9127–9155, https://doi.org/10.5194/gmd-15-9127-2022, https://doi.org/10.5194/gmd-15-9127-2022, 2022
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Snow cover plays an important role in many processes, but its monitoring is a challenging task. The alternative is usually to simulate the snowpack, and to improve these simulations one of the most promising options is to fuse simulations with available observations (data assimilation). In this paper we present MuSA, a data assimilation tool which facilitates the implementation of snow monitoring initiatives, allowing the assimilation of a wide variety of remotely sensed snow cover information.
Vincent Verjans, Alexander A. Robel, Helene Seroussi, Lizz Ultee, and Andrew F. Thompson
Geosci. Model Dev., 15, 8269–8293, https://doi.org/10.5194/gmd-15-8269-2022, https://doi.org/10.5194/gmd-15-8269-2022, 2022
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We describe the development of the first large-scale ice sheet model that accounts for stochasticity in a range of processes. Stochasticity allows the impacts of inherently uncertain processes on ice sheets to be represented. This includes climatic uncertainty, as the climate is inherently chaotic. Furthermore, stochastic capabilities also encompass poorly constrained glaciological processes that display strong variability at fine spatiotemporal scales. We present the model and test experiments.
Cited articles
Anderson, E. A.: Development and testing of snow pack energy balance equations,
Water Resour. Res., 4, 19–37, https://doi.org/10.1029/WR004i001p00019, 1968. a
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swis
avalanche warning: Part I: numerical model, Cold Reg. Sci. Technol., 35, 123–145, https://doi.org/10.1016/S0165-232X(02)00074-5,
2002. a, b, c, d
Bintanja, R. and Van Den Broeke, M. R.: The Surface Energy Balance of
Antarctic Snow and Blue Ice, J. Appl. Meteorol., 34,
902–926, https://doi.org/10.1175/1520-0450(1995)034<0902:TSEBOA>2.0.CO;2, 1995. a
Businger, J. A., Wyngaard, J. C., Izumi, Y., and Bradley, E. F.: Flux-profile
relationships in the atmospheric surface layer, J. Atmos. Sci., 28, 181–189,
https://doi.org/10.1175/1520-0469(1971)028<0181:FPRITA>2.0.CO;2,
1971. a
Cogley, J. G., Hock, R., Rasmussen, L. A., Arendt, A., Bauder, A.,
Braithwaite, R. J., Jansson, P., Kaser, G., Möller, M., Nicholson, L., and
Zemp, M.: Glossary of glacier mass balance and related terms, International
Association of Cryospheric Sciences, IHP-VII Technical Documents in Hydrology No. 86,
IACS Contribution No. 2,
UNESCO, Paris,
https://doi.org/10.5167/uzh-53475, 2011. a
Coléou, C. and Lesaffre, B.: Irreducible water saturation in snow:
experimental results in a cold laboratory, Ann. Glaciol., 26, 64–68,
https://doi.org/10.3189/1998AoG26-1-64-68, 1998. a
Conway, J. and Cullen, N.: Constraining turbulent heat flux parameterization
over a temperate maritime glacier in New Zealand, Ann. Glaciol.,
54, 41–51, https://doi.org/10.3189/2013AoG63A604, 2013. a, b
Dask Development Team: Dask: Library for dynamic task scheduling, DASK,
available at: https://dask.org (last access: 20 June 2020), 2016. a
Dyer, A. J.: A review of flux-profile relationships, Bound.-Lay.
Meteorol., 7, 363–372, https://doi.org/10.1007/BF00240838, 1974. a
Essery, R., Morin, S., Lejeune, Y., and Ménard, C. B.: A comparison of 1701
snow models using observations from an alpine site, Adv. Water Resour., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013. a, b
Ferziger, J. H. and Perić, M.: Computational Methods for Fluid Dynamics,
Springer Berlin Heidelberg, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-642-56026-2, 2002. a
Fletcher, R.: Practical Methods of Optimization, John Wiley & Sons,
Chichester, West Sussex England, https://doi.org/10.1002/9781118723203, 2000. a
Foken, T.: Micrometeorology, Springer, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-540-74666-9, 2008. a, b, c, d
Galos, S. P., Klug, C., Maussion, F., Covi, F., Nicholson, L., Rieg, L., Gurgiser, W., Mölg, T.,
and Kaser, G.: Reanalysis of a 10-year record (2004–2013) of seasonal mass balances at
Langenferner/Vedretta Lunga, Ortler Alps, Italy, The Cryosphere, 11, 1417–1439, https://doi.org/10.5194/tc-11-1417-2017, 2017. a
Gurgiser, W., Marzeion, B., Nicholson, L., Ortner, M., and Kaser, G.: Modeling energy and mass balance of Shallap Glacier, Peru, The Cryosphere, 7, 1787–1802, https://doi.org/10.5194/tc-7-1787-2013, 2013. a
Hantel, M., Ehrendorfer, M., and Haslinger, A.: Climate sensitivity of snow
cover duration in Austria, Int. J. Climatol., 20, 615–640,
https://doi.org/10.1002/(SICI)1097-0088(200005)20:6<615::AID-JOC489>3.0.CO;2-0,
2000. a
Hersbach, H. and Dee, D.: ERA5 reanalysis is in production, ECMWF
Newsletter, Tech. rep., European Centre for Medium-Range Weather Forecasts, available at: https://www.ecmwf.int/en/newsletter/147/news/era5-reanalysis-production (last access: 11 November 2020),
vol. 147, 2016. a
Hock, R. and Holmgren, B.: A distributed surface energy-balance model for
complex topography and its application to Storglaciären, Sweden, J. Glaciol., 51, 25–36, https://doi.org/10.3189/172756505781829566, 2005. a, b
Hoyer, S. and Hamman, J. J.: xarray: N-D labeled Arrays and Datasets in
Python, Journal of Open Research Software, 5, p. 10, https://doi.org/10.5334/jors.148, 2017. a
Huintjes, E.: Energy and mass balance modelling for glaciers on the Tibetan
Plateau – Extension, validation and application of a coupled snow and
energy balance model, Ph.D. thesis, Rheinisch-Westfälischen Technischen
Hochschule Aachen, Aachen, 2014. a
Huintjes, E., Neckel, N., Hochschild, V., and Schneider, C.: Surface energy and
mass balance at Purogangri ice cap, central Tibetan Plateau,
2001–2011, J. Glaciol., 61, 1048–1060,
https://doi.org/10.3189/2015JoG15J056, 2015a. a
Huintjes, E., Sauter, T., Schröter, B., Maussion, F., Yang, W., Kropáček,
J., Buchroithner, M., Scherer, D., Kang, S., and Schneider, C.: Evaluation of
a Coupled Snow and Energy Balance Model for Zhadang Glacier,
Tibetan Plateau, Using Glaciological Measurements and
Time-Lapse Photography, Arctic, Antarctic, and Alpine Research, 47,
573–590, https://doi.org/10.1657/AAAR0014-073, 2015b. a, b, c, d
Klok, E. and Oerlemans, J.: Model study of the spatial distribution of the
energy and mass balance of Morteratschgletscher, Switzerland, J. Glaciol., 48, 505–518, https://doi.org/10.3189/172756502781831133, 2002. a, b, c, d
Kraus, H.: An energy balance model for ablation in mountainous areas, Proceedings of the Moscow Symposium, August 1971; Actes du Colloque de Moscou, août 1971, IAHS-AISH Publ. No. 104, 1975. a
Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C. B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Ménégoz, M., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M. S., Schaedler, G., Semenov, V., Smirnova, T. G., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., and Zhu, D.: ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, 2018. a, b, c
Kuhn, M.: On the Computation of Heat Transfer Coefficients from
Energy-Balance Gradients on a Glacier, J. Glaciol., 22,
263–272, https://doi.org/10.3189/S0022143000014258, 1979. a
Kuhn, M.: Micro-Meteorological Conditions for Snow Melt, J. Glaciol., 33, 24–26, https://doi.org/10.3189/S002214300000530X, 1987. a
Machguth, H., Paul, F., Hoelzle, M., and Haeberli, W.: Distributed glacier
mass-balance modelling as an important component of modern multi-level
glacier monitoring, Ann. Glaciol., 43, 335–343,
https://doi.org/10.3189/172756406781812285, 2006. a
Machguth, H., Paul, F., Kotlarski, S., and Hoelzle, M.: Calculating distributed
glacier mass balance for the Swiss Alps from regional climate model
output: A methodical description and interpretation of the results, J.
Geophys. Res., 114, D19106, https://doi.org/10.1029/2009JD011775, 2009. a
Male, D. H. and Granger, R. J.: Snow surface energy exchange, Water Resour.
Res., 17, 609–627, https://doi.org/10.1029/WR017i003p00609, 1981. a
Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., and Finkelnburg,
R.: Precipitation Seasonality and Variability over the Tibetan
Plateau as Resolved by the High Asia Reanalysis, J.
Climate, 27, 1910–1927, https://doi.org/10.1175/JCLI-D-13-00282.1, 2014. a
Maussion, F., Gurgiser, W., Großhauser, M., Kaser, G., and Marzeion, B.: ENSO influence on surface energy and mass balance at Shallap Glacier, Cordillera Blanca, Peru, The Cryosphere, 9, 1663–1683, https://doi.org/10.5194/tc-9-1663-2015, 2015. a
Ménard, C. B. and Essery, R.: ESM-SnowMIP meteorological and evaluation
datasets at ten reference sites (in situ and bias corrected reanalysis data), dataset, PANGAEA, https://doi.org/10.1594/PANGAEA.897575,
2019. a, b
Ménard, C. B., Essery, R., Barr, A., Bartlett, P., Derry, J., Dumont, M., Fierz, C., Kim, H., Kontu, A., Lejeune, Y., Marks, D., Niwano, M., Raleigh, M., Wang, L., and Wever, N.: Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data, Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019, 2019. a
Michlmayr, G., Lehning, M., Koboltschnig, G., Holzmann, H., Zappa, M., Mott,
R., and Schöner, W.: Application of the Alpine 3D model for glacier mass balance and glacier runoff studies at Goldbergkees, Austria, Hydrol. Process, 22, 3941–3949, https://doi.org/10.1002/hyp.7102, 2008. a
Microsoft: Bing Maps, available at: https://www.bing.com/maps/, last access: 10 November 2020. a
Mölg, T. and Hardy, D. R.: Ablation and associated energy balance of a
horizontal glacier surface on Kilimanjaro, J. Geophys. Res.,
109, D16104, https://doi.org/10.1029/2003JD004338,2004. a
Mölg, T., Cullen, N. J., Hardy, D. R., Kaser, G., and Klok, L.: Mass balance
of a slope glacier on Kilimanjaro and its sensitivity to climate,
Int. J. Climatol., 28, 881–892, https://doi.org/10.1002/joc.1589, 2008. a
Mölg, T., Cullen, N. J., Hardy, D. R., Winkler, M., and Kaser, G.: Quantifying
Climate Change in the Tropical Midtroposphere over East Africa
from Glacier Shrinkage on Kilimanjaro, J. Climate, 22,
4162–4181, https://doi.org/10.1175/2009JCLI2954.1, 2009. a, b
Mölg, T., Maussion, F., Yang, W., and Scherer, D.: The footprint of Asian monsoon dynamics in the mass and energy balance of a Tibetan glacier, The Cryosphere, 6, 1445–1461, https://doi.org/10.5194/tc-6-1445-2012, 2012. a, b
Mölg, T., Maussion, F., and Scherer, D.: Mid-latitude westerlies as a driver
of glacier variability in monsoonal High Asia, Nat. Clim. Change, 4, 68–73, https://doi.org/10.1038/nclimate2055, 2014. a
Morris, E.: Turbulent transfer over snow and ice, J. Hydrol., 105,
205–223, https://doi.org/10.1016/0022-1694(89)90105-4, 1989. a
Morris, E. M.: Physics-Based Models of Snow, in: Recent Advances in the
Modeling of Hydrologic Systems, edited by: Bowles, D. S. and
O’Connell, P. E., Springer Netherlands, Dordrecht, 85–112
https://doi.org/10.1007/978-94-011-3480-4_5,
1991. a
Munro, D. S.: Surface Roughness and Bulk Heat Transfer on a Glacier: Comparison with Eddy Correlation, J. Glaciol., 35, 343–348,
https://doi.org/10.3189/S0022143000009266, 1989. a, b, c
Munro, D. S.: A surface energy exchange model of glacier melt and net mass
balance, Int. J. Climatol., 11, 689–700,
https://doi.org/10.1002/joc.3370110610, 1991. a
Nicholson, L. I., Prinz, R., Mölg, T., and Kaser, G.: Micrometeorological conditions and surface mass and energy fluxes on Lewis Glacier, Mt Kenya, in relation to other tropical glaciers, The Cryosphere, 7, 1205–1225, https://doi.org/10.5194/tc-7-1205-2013, 2013. a
Obleitner, F. and Lehning, M.: Measurement and simulation of snow and
superimposed ice at the Kongsvegen glacier, Svalbard (Spitzbergen),
J. Geophys. Res.-Atmos., 109, D04106,
https://doi.org/10.1029/2003JD003945, 2004. a
Oerlemans, J.: Glaciers and climate change, A.A. Balkema Publishers, Lisse;
Exton, (PA), 2001. a
Oerlemans, J. and Knap, W. H.: A 1 year record of global radiation and albedo
in the ablation zone of Morteratschgletscher, Switzerland, J. Glaciol., 44, 231–238, https://doi.org/10.1017/S0022143000002574, 1998. a
Østby, T. I., Schuler, T. V., Hagen, J. O., Hock, R., Kohler, J., and Reijmer, C. H.: Diagnosing the decline in climatic mass balance of glaciers in Svalbard over 1957–2014, The Cryosphere, 11, 191–215, https://doi.org/10.5194/tc-11-191-2017, 2017. a
Qu, B., Ming, J., Kang, S.-C., Zhang, G.-S., Li, Y.-W., Li, C.-D., Zhao, S.-Y., Ji, Z.-M., and Cao, J.-J.: The decreasing albedo of the Zhadang glacier on western Nyainqentanglha and the role of light-absorbing impurities, Atmos. Chem. Phys., 14, 11117–11128, https://doi.org/10.5194/acp-14-11117-2014, 2014. a
Radić, V. and Hock, R.: Modeling future glacier mass balance and volume
changes using ERA-40 reanalysis and climate models: A sensitivity study
at Storglaciären, Sweden, J. Geophys. Res.-Earth, 111, F03003, https://doi.org/10.1029/2005JF000440, 2006. a
Radić, V., Menounos, B., Shea, J., Fitzpatrick, N., Tessema, M. A., and Déry, S. J.: Evaluation of different methods to model near-surface turbulent fluxes for a mountain glacier in the Cariboo Mountains, BC, Canada, The Cryosphere, 11, 2897–2918, https://doi.org/10.5194/tc-11-2897-2017, 2017. a
Reijmer, C. H. and Hock, R.: Internal accumulation on Storglaciären,
Sweden, in a multi-layer snow model coupled to a distributed energy-and
mass-balance model, J. Glaciol., 54, 61–72,
https://doi.org/10.3189/002214308784409161, 2008. a
Rye, C. J., Willis, I. C., Arnold, N. S., and Kohler, J.: On the need for
automated multiobjective optimization and uncertainty estimation of glacier
mass balance models, J. Geophys. Res.-Earth, 117,
F02005, https://doi.org/10.1029/2011JF002184, 2012. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY v1.3 – An open-source coupled snowpack and ice surface energy and mass balance model (Version v1.3), Zenodo, https://doi.org/10.5281/zenodo.3902191, 2020a. a, b
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model, GitHub repository, https://github.com/cryotools/cosipy, last access: 20 June 2020b. a
Sauter, T. and Arndt, A.: COSIPY v1.3 – An open-source coupled snowpack and ice surface energy and mass balance model – Read the Docs documentation, available at: https://cosipy.readthedocs.io/en/latest/index.html, last access: 20 June 2020c. a
Sauter, T. and Arndt, A.: COSIPY v1.3 – An open-source coupled snowpack and ice surface energy and mass balance model – Slack comunity platform for user communication, available at: https://cosipy.slack.com, last access: 20 June 2020d. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model – General DOI pointing to the newest release, Zenodo, https://doi.org/10.5281/zenodo.2579668, 2020e. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model – Travis CI repository, available at: https://travis-ci.org/cryotools/cosipy, last access: 20 June 2020f. a
Sauter, T. and Arndt, A.: cryotools/cosipy: COSIPY – An open-source coupled snowpack and ice surface energy and mass balance model – CodeCov repository, available at: https://codecov.io/github/cryotools/cosipy/, last access: 20 June 2020g. a
Sauter, T. and Galos, S. P.: Effects of local advection on the spatial sensible heat flux variation on a mountain glacier, The Cryosphere, 10, 2887–2905, https://doi.org/10.5194/tc-10-2887-2016, 2016. a
Sauter, T. and Obleitner, F.: Assessing the uncertainty of glacier mass-balance simulations in the European Arctic based on variance decomposition, Geosci. Model Dev., 8, 3911–3928, https://doi.org/10.5194/gmd-8-3911-2015, 2015. a
Sauter, T., Möller, M., Finkelnburg, R., Grabiec, M., Scherer, D., and Schneider, C.: Snowdrift modelling for the Vestfonna ice cap, north-eastern Svalbard, The Cryosphere, 7, 1287–1301, https://doi.org/10.5194/tc-7-1287-2013, 2013. a
Schuler, T. V., Hock, R., Jackson, M., Elvehøy, H., Braun, M., Brown, I., and
Hagen, J.-O.: Distributed mass-balance and climate sensitivity modelling of
Engabreen, Norway, Ann. Glaciol., 42, 395–401,
https://doi.org/10.3189/172756405781812998,
2005. a
Sicart, J. E., Hock, R., Ribstein, P., Litt, M., and Ramirez, E.: Analysis of
seasonal variations in mass balance and meltwater discharge of the tropical
Zongo Glacier by application of a distributed energy balance model,
J. Geophys. Res., 116, D13105, https://doi.org/10.1029/2010JD015105, 2011. a, b
Siemer, A. H.: Ein eindimensionales Energie-Massenbilanzmodell einer
Schneedecke unter Berücksichtigung der Flüssigwassertransmission,
Berichte des Institutes für Meteorologie und Klimatologie der
Universität Hannover, 34, Universität Hannover, Hannover, 1988. a
Smeets, C. J. P. P. and van den Broeke, M. R.: Temporal and Spatial
Variations of the Aerodynamic Roughness Length in the Ablation
Zone of the Greenland Ice Sheet, Bound.-Lay. Meteorol., 128,
315–338, https://doi.org/10.1007/s10546-008-9291-0, 2008. a
Van Den Broeke, M., Reijmer, C., Van As, D., and Boot, W.: Daily cycle of the
surface energy balance in Antarctica and the influence of clouds,
International J. Climatol., 26, 1587–1605, https://doi.org/10.1002/joc.1323, 2006.
a
van der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A
Structure for Efficient Numerical Computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011. a
van Pelt, W. J. J., Oerlemans, J., Reijmer, C. H., Pohjola, V. A., Pettersson, R., and van Angelen, J. H.: Simulating melt, runoff and refreezing on Nordenskiöldbreen, Svalbard, using a coupled snow and energy balance model, The Cryosphere, 6, 641–659, https://doi.org/10.5194/tc-6-641-2012, 2012. a, b
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a, b, c
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J.,
van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N.,
Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, I.,
Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman,
R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and Contributors, S.: SciPy
1.0–Fundamental Algorithms for Scientific Computing in Python, Nature Methods 17, 261,
arXiv [preprint], arXiv:1907.10121, 2019. a
Wagnon, P., Ribstein, P., Kaser, G., and Berton, P.: Energy balance and runoff
seasonality of a Bolivian glacier, Global Planet. Change, 22, 49–58,
https://doi.org/10.1016/S0921-8181(99)00025-9, 1999. a
Weidemann, S. S., Sauter, T., Malz, P., Jaña, R., Arigony-Neto, J., Casassa,
G., and Schneider, C.: Glacier Mass Changes of Lake-Terminating
Grey and Tyndall Glaciers at the Southern Patagonia Icefield
Derived From Geodetic Observations and Energy and Mass Balance
Modeling, Front. Earth Sci., 6, 81, https://doi.org/10.3389/feart.2018.00081,
2018. a
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model, The Cryosphere, 8, 257–274, https://doi.org/10.5194/tc-8-257-2014, 2014. a
Whitaker, J., Khrulev, C., Huard, D., Paulik, C., Hoyer, S., and Kinoshita, B. P.: Unidata/netcdf4-python: version 1.5.1.2 release (Version v1.5.1.2rel), Zenodo, https://doi.org/10.5281/zenodo.2669496, 2019. a
Wohlfahrt, G., Hammerle, A., Niedrist, G., Scholz, K., Tomelleri, E., and Zhao,
P.: On the energy balance closure and net radiation in complex terrain,
Agr. Forest Meteorol., 226–227, 37–49,
https://doi.org/10.1016/j.agrformet.2016.05.012, 2016. a, b
Short summary
Glacial changes play a key role from a socioeconomic, political, and scientific point of view. Here, we present the open-source coupled snowpack and ice surface energy and mass balance model, which provides a lean, flexible, and user-friendly framework for modeling distributed snow and glacier mass changes. The model provides a suitable platform for sensitivity, detection, and attribution analyses for glacier changes and a tool for quantifying inherent uncertainties.
Glacial changes play a key role from a socioeconomic, political, and scientific point of view....
