Articles | Volume 10, issue 8
https://doi.org/10.5194/gmd-10-3105-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/gmd-10-3105-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
22 Aug 2017
Development and technical paper |
| 22 Aug 2017
Sea-ice evaluation of NEMO-Nordic 1.0: a NEMO–LIM3.6-based ocean–sea-ice model setup for the North Sea and Baltic Sea
Per Pemberton
CORRESPONDING AUTHOR
Swedish Meteorological and Hydrological Institute, 426 71 Gothenburg, Sweden
Ulrike Löptien
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Robinson Hordoir
Swedish Meteorological and Hydrological Institute, 601 76 Norrköping, Sweden
Anders Höglund
Swedish Meteorological and Hydrological Institute, 601 76 Norrköping, Sweden
Semjon Schimanke
Swedish Meteorological and Hydrological Institute, 601 76 Norrköping, Sweden
Lars Axell
Swedish Meteorological and Hydrological Institute, 601 76 Norrköping, Sweden
Jari Haapala
Finnish Meteorological Institute, 00101 Helsinki, Finland
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Nemo-Nordic is a regional ocean model based on a community code (NEMO). It covers the Baltic and the North Sea area and is used as a forecast model by the Swedish Meteorological and Hydrological Institute. It is also used as a research tool by scientists of several countries to study, for example, the effects of climate change on the Baltic and North seas. Using such a model permits us to understand key processes in this coastal ecosystem and how such processes will change in a future climate.
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The temperature and salinity trends at various depths in the Baltic basins from 1990 to 2020 were analyzed from a reasonable reanalysis data set. Overall, the Baltic Sea showed a clear warming trend in recent decades, the northern Baltic Sea has a slight desalination trend, and the southern Baltic Sea has a salinity increase trend. The temperature and salinity trends in the southern Baltic Sea are greater than those in the northern Baltic Sea.
Jan Åström, Fredrik Robertsen, Jari Haapala, Arttu Polojärvi, Rivo Uiboupin, and Ilja Maljutenko
The Cryosphere, 18, 2429–2442, https://doi.org/10.5194/tc-18-2429-2024, https://doi.org/10.5194/tc-18-2429-2024, 2024
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Itzel Ruvalcaba Baroni, Elin Almroth-Rosell, Lars Axell, Sam T. Fredriksson, Jenny Hieronymus, Magnus Hieronymus, Sandra-Esther Brunnabend, Matthias Gröger, Ivan Kuznetsov, Filippa Fransner, Robinson Hordoir, Saeed Falahat, and Lars Arneborg
Biogeosciences, 21, 2087–2132, https://doi.org/10.5194/bg-21-2087-2024, https://doi.org/10.5194/bg-21-2087-2024, 2024
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The health of the Baltic and North seas is threatened due to high anthropogenic pressure; thus, different methods to assess the status of these regions are urgently needed. Here, we validated a novel model simulating the ocean dynamics and biogeochemistry of the Baltic and North seas that can be used to create future climate and nutrient scenarios, contribute to European initiatives on de-eutrophication, and provide water quality advice and support on nutrient load reductions for both seas.
Thomas Spangehl, Michael Borsche, Deborah Niermann, Frank Kaspar, Semjon Schimanke, Susanne Brienen, Thomas Möller, and Maren Brast
Adv. Sci. Res., 20, 109–128, https://doi.org/10.5194/asr-20-109-2023, https://doi.org/10.5194/asr-20-109-2023, 2023
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The quality of the global reanalysis ERA5, the regional reanalysis COSMO-REA6 and a successor version (R6G2), the new Copernicus European Regional Re-Analysis (CERRA) and a regional downscaling simulation with COSMO-CLM (HoKliSim-De) is assessed for offshore wind farm planning in the German Exclusive Economic Zone (EEZ) of the North Sea. The quality is assessed using in-situ wind measurements at the research platform FINO1 and satellite-based data of the near-surface wind speed as reference.
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Geosci. Model Dev., 15, 8613–8638, https://doi.org/10.5194/gmd-15-8613-2022, https://doi.org/10.5194/gmd-15-8613-2022, 2022
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Imke Sievers, Andrea M. U. Gierisch, Till A. S. Rasmussen, Robinson Hordoir, and Lars Stenseng
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-84, https://doi.org/10.5194/tc-2022-84, 2022
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To predict Arctic sea ice models are used. Many ice models exists. They all are skill full, but give different results. Often this differences result from forcing as for example air temperature. Other differences result from the way the physical equations are solved in the model. In this study two commonly used models are compared under equal forcing, to find out how much the models differ under similar external forcing. The results are compared to observations and to eachother.
H. E. Markus Meier, Madline Kniebusch, Christian Dieterich, Matthias Gröger, Eduardo Zorita, Ragnar Elmgren, Kai Myrberg, Markus P. Ahola, Alena Bartosova, Erik Bonsdorff, Florian Börgel, Rene Capell, Ida Carlén, Thomas Carlund, Jacob Carstensen, Ole B. Christensen, Volker Dierschke, Claudia Frauen, Morten Frederiksen, Elie Gaget, Anders Galatius, Jari J. Haapala, Antti Halkka, Gustaf Hugelius, Birgit Hünicke, Jaak Jaagus, Mart Jüssi, Jukka Käyhkö, Nina Kirchner, Erik Kjellström, Karol Kulinski, Andreas Lehmann, Göran Lindström, Wilhelm May, Paul A. Miller, Volker Mohrholz, Bärbel Müller-Karulis, Diego Pavón-Jordán, Markus Quante, Marcus Reckermann, Anna Rutgersson, Oleg P. Savchuk, Martin Stendel, Laura Tuomi, Markku Viitasalo, Ralf Weisse, and Wenyan Zhang
Earth Syst. Dynam., 13, 457–593, https://doi.org/10.5194/esd-13-457-2022, https://doi.org/10.5194/esd-13-457-2022, 2022
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Anna Rutgersson, Erik Kjellström, Jari Haapala, Martin Stendel, Irina Danilovich, Martin Drews, Kirsti Jylhä, Pentti Kujala, Xiaoli Guo Larsén, Kirsten Halsnæs, Ilari Lehtonen, Anna Luomaranta, Erik Nilsson, Taru Olsson, Jani Särkkä, Laura Tuomi, and Norbert Wasmund
Earth Syst. Dynam., 13, 251–301, https://doi.org/10.5194/esd-13-251-2022, https://doi.org/10.5194/esd-13-251-2022, 2022
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Matthias Gröger, Christian Dieterich, Jari Haapala, Ha Thi Minh Ho-Hagemann, Stefan Hagemann, Jaromir Jakacki, Wilhelm May, H. E. Markus Meier, Paul A. Miller, Anna Rutgersson, and Lichuan Wu
Earth Syst. Dynam., 12, 939–973, https://doi.org/10.5194/esd-12-939-2021, https://doi.org/10.5194/esd-12-939-2021, 2021
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Regional climate studies are typically pursued by single Earth system component models (e.g., ocean models and atmosphere models). These models are driven by prescribed data which hamper the simulation of feedbacks between Earth system components. To overcome this, models were developed that interactively couple model components and allow an adequate simulation of Earth system interactions important for climate. This article reviews recent developments of such models for the Baltic Sea region.
Tuomas Kärnä, Patrik Ljungemyr, Saeed Falahat, Ida Ringgaard, Lars Axell, Vasily Korabel, Jens Murawski, Ilja Maljutenko, Anja Lindenthal, Simon Jandt-Scheelke, Svetlana Verjovkina, Ina Lorkowski, Priidik Lagemaa, Jun She, Laura Tuomi, Adam Nord, and Vibeke Huess
Geosci. Model Dev., 14, 5731–5749, https://doi.org/10.5194/gmd-14-5731-2021, https://doi.org/10.5194/gmd-14-5731-2021, 2021
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We present Nemo-Nordic 2.0, a novel operational marine model for the Baltic Sea. The model covers the Baltic Sea and the North Sea with approximately 1 nmi resolution. We validate the model's performance against sea level, water temperature, and salinity observations, as well as sea ice charts. The skill analysis demonstrates that Nemo-Nordic 2.0 can reproduce the hydrographic features of the Baltic Sea.
Heiner Dietze and Ulrike Löptien
Biogeosciences, 18, 4243–4264, https://doi.org/10.5194/bg-18-4243-2021, https://doi.org/10.5194/bg-18-4243-2021, 2021
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Britta Munkes, Ulrike Löptien, and Heiner Dietze
Biogeosciences, 18, 2347–2378, https://doi.org/10.5194/bg-18-2347-2021, https://doi.org/10.5194/bg-18-2347-2021, 2021
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Cyanobacteria blooms can strongly aggravate eutrophication problems of water bodies. Their controls are, however, not comprehensively understood, which impedes effective management and protection plans. Here we review the current understanding of cyanobacteria blooms. Juxtaposition of respective field and laboratory studies with state-of-the-art mathematical models reveals substantial uncertainty associated with nutrient demands, grazing, and death of cyanobacteria.
Thomas Krumpen, Florent Birrien, Frank Kauker, Thomas Rackow, Luisa von Albedyll, Michael Angelopoulos, H. Jakob Belter, Vladimir Bessonov, Ellen Damm, Klaus Dethloff, Jari Haapala, Christian Haas, Carolynn Harris, Stefan Hendricks, Jens Hoelemann, Mario Hoppmann, Lars Kaleschke, Michael Karcher, Nikolai Kolabutin, Ruibo Lei, Josefine Lenz, Anne Morgenstern, Marcel Nicolaus, Uwe Nixdorf, Tomash Petrovsky, Benjamin Rabe, Lasse Rabenstein, Markus Rex, Robert Ricker, Jan Rohde, Egor Shimanchuk, Suman Singha, Vasily Smolyanitsky, Vladimir Sokolov, Tim Stanton, Anna Timofeeva, Michel Tsamados, and Daniel Watkins
The Cryosphere, 14, 2173–2187, https://doi.org/10.5194/tc-14-2173-2020, https://doi.org/10.5194/tc-14-2173-2020, 2020
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Ulrike Löptien and Heiner Dietze
Biogeosciences Discuss., https://doi.org/10.5194/bg-2020-96, https://doi.org/10.5194/bg-2020-96, 2020
Manuscript not accepted for further review
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Murat Gunduz, Emin Özsoy, and Robinson Hordoir
Geosci. Model Dev., 13, 121–138, https://doi.org/10.5194/gmd-13-121-2020, https://doi.org/10.5194/gmd-13-121-2020, 2020
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The Bosphorus exchange is of critical importance for hydrodynamics and hydroclimatology of the Black Sea. In this study, we report on the development of a medium-resolution circulation model of the Black Sea, making use of surface atmospheric forcing with high space and time resolution, climatic river fluxes and strait exchange, enabled by adding elementary details of strait and coastal topography and seasonal hydrology specified in an artificial box on the Marmara Sea side.
Heiner Dietze, Ulrike Löptien, and Julia Getzlaff
Geosci. Model Dev., 13, 71–97, https://doi.org/10.5194/gmd-13-71-2020, https://doi.org/10.5194/gmd-13-71-2020, 2020
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We present a new near-global coupled biogeochemical ocean-circulation model configuration of the Southern Ocean. The configuration features both a relatively equilibrated oceanic carbon inventory and an explicit representation of mesoscale eddies. In this paper, we document the model configuration and showcase its potential to tackle research questions such as the Southern Ocean carbon uptake dynamics on decadal timescales.
Ulrike Löptien and Heiner Dietze
Biogeosciences, 16, 1865–1881, https://doi.org/10.5194/bg-16-1865-2019, https://doi.org/10.5194/bg-16-1865-2019, 2019
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Anthropogenic greenhouse gas emissions trigger complex climate feedbacks. Output form Earth system models provides a basis for related political decision-making. One challenge is to arrive at reliable model parameter estimates for the ocean biogeochemistry module. We illustrate pitfalls through which flaws in the ocean module are masked by wrongly tuning the biogeochemistry and discuss ensuing uncertainties in climate projections.
Filippa Fransner, Agneta Fransson, Christoph Humborg, Erik Gustafsson, Letizia Tedesco, Robinson Hordoir, and Jonas Nycander
Biogeosciences, 16, 863–879, https://doi.org/10.5194/bg-16-863-2019, https://doi.org/10.5194/bg-16-863-2019, 2019
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Although rivers carry large amounts of organic material to the oceans, little is known about what fate it meets when it reaches the sea. In this study we are investigating the fate of the carbon in this organic matter by the use of a numerical model in combination with ship measurements from the northern Baltic Sea. Our results suggests that there is substantial remineralization taking place, transforming the organic carbon into CO2, which is released to the atmosphere.
Robinson Hordoir, Lars Axell, Anders Höglund, Christian Dieterich, Filippa Fransner, Matthias Gröger, Ye Liu, Per Pemberton, Semjon Schimanke, Helen Andersson, Patrik Ljungemyr, Petter Nygren, Saeed Falahat, Adam Nord, Anette Jönsson, Iréne Lake, Kristofer Döös, Magnus Hieronymus, Heiner Dietze, Ulrike Löptien, Ivan Kuznetsov, Antti Westerlund, Laura Tuomi, and Jari Haapala
Geosci. Model Dev., 12, 363–386, https://doi.org/10.5194/gmd-12-363-2019, https://doi.org/10.5194/gmd-12-363-2019, 2019
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Nemo-Nordic is a regional ocean model based on a community code (NEMO). It covers the Baltic and the North Sea area and is used as a forecast model by the Swedish Meteorological and Hydrological Institute. It is also used as a research tool by scientists of several countries to study, for example, the effects of climate change on the Baltic and North seas. Using such a model permits us to understand key processes in this coastal ecosystem and how such processes will change in a future climate.
Iina Ronkainen, Jonni Lehtiranta, Mikko Lensu, Eero Rinne, Jari Haapala, and Christian Haas
The Cryosphere, 12, 3459–3476, https://doi.org/10.5194/tc-12-3459-2018, https://doi.org/10.5194/tc-12-3459-2018, 2018
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We quantify the sea ice thickness variability in the Bay of Bothnia using various observational data sets. For the first time we use helicopter and shipborne electromagnetic soundings to study changes in drift ice of the Bay of Bothnia. Our results show that the interannual variability of ice thickness is larger in the drift ice zone than in the fast ice zone. Furthermore, the mean thickness of heavily ridged ice near the coast can be several times larger than that of fast ice.
Volkmar Sauerland, Ulrike Löptien, Claudine Leonhard, Andreas Oschlies, and Anand Srivastav
Geosci. Model Dev., 11, 1181–1198, https://doi.org/10.5194/gmd-11-1181-2018, https://doi.org/10.5194/gmd-11-1181-2018, 2018
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We present a concept to prove that a parametric model is well calibrated, i.e., that changes of its free parameters cannot lead to a much better model–data misfit anymore. The intention is motivated by the fact that calibrating global biogeochemical ocean models is important for assessment and inter-model comparison but computationally expensive.
Sofia Saraiva, H. E. Markus Meier, Helén Andersson, Anders Höglund, Christian Dieterich, Robinson Hordoir, and Kari Eilola
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2018-16, https://doi.org/10.5194/esd-2018-16, 2018
Revised manuscript not accepted
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Uncertainties are estimated in Baltic Sea climate projections by performing scenarios combining 4 Global Climate Models, 2 future gas emissions (RCP4.5, RCP8.5) and 3 nutrient load scenarios. Results on primary production, nitrogen fixation, and hypoxic areas show that uncertainties caused by the nutrients loads are greater than uncertainties due to GCMs and RCPs. In all scenarios, nutrient load abatement strategy, Baltic Sea Action Plan, will lead to an improvement in the environmental state.
Markus Schartau, Philip Wallhead, John Hemmings, Ulrike Löptien, Iris Kriest, Shubham Krishna, Ben A. Ward, Thomas Slawig, and Andreas Oschlies
Biogeosciences, 14, 1647–1701, https://doi.org/10.5194/bg-14-1647-2017, https://doi.org/10.5194/bg-14-1647-2017, 2017
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Plankton models have become an integral part in marine ecosystem and biogeochemical research. These models differ in complexity and in their number of parameters. How values are assigned to parameters is essential. An overview of major methodologies of parameter estimation is provided. Aspects of parameter identification in the literature are diverse. Individual findings could be better synthesized if notation and expertise of the different scientific communities would be reasonably merged.
Heiner Dietze, Julia Getzlaff, and Ulrike Löptien
Biogeosciences, 14, 1561–1576, https://doi.org/10.5194/bg-14-1561-2017, https://doi.org/10.5194/bg-14-1561-2017, 2017
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The Southern Ocean is a sink for anthropogenic carbon. Projections of how this sink will evolve in an ever-warming climate are based on coupled ocean-circulation–biogeochemical models. This study compares uncertainties of simulated oceanic carbon uptake associated to physical (eddy) parameterizations with those associated wtih (unconstrained) supply of bioavailable iron supply to the surface ocean.
Heiner Dietze and Ulrike Löptien
Ocean Sci., 12, 977–986, https://doi.org/10.5194/os-12-977-2016, https://doi.org/10.5194/os-12-977-2016, 2016
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Winds blowing over the ocean drive ocean currents. The oceanic response to winds is, in turn, influenced by ocean currents. Theoretical considerations suggest that the latter effect is especially pronounced in the Baltic Sea. The study presented here puts theses theoretical considerations in a high-resolution ocean circulation model of the Baltic Sea to the test.
U. Löptien and H. Dietze
Ocean Sci., 11, 573–590, https://doi.org/10.5194/os-11-573-2015, https://doi.org/10.5194/os-11-573-2015, 2015
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Marine biogeochemical ocean models are embedded into earth system models - which are, to an increasing degree, applied to project the fate of our warming world. These biogeochemical models generally depend on poorly constrained model parameters. In this study we investigate the the demands on observations for an objective estimation of such parameters. A major result is that even modest noise (10%) inherent to observations can hinder the assignment of reasonable parameters.
U. Löptien and L. Axell
The Cryosphere, 8, 2409–2418, https://doi.org/10.5194/tc-8-2409-2014, https://doi.org/10.5194/tc-8-2409-2014, 2014
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The Baltic Sea is a seasonally ice-covered marginal sea in central northern Europe. In wintertime, on-time shipping depends crucially on sea ice forecasts. Among the forecasting tools heavily applied are numerical models, which suffer from a lack of calibration data because relevant ice properties are difficult (and costly) to monitor. We developed an innovative and inexpensive approach, by using ship speed observations obtained by the automatic identification system (AIS) to asses such models.
U. Löptien and H. Dietze
Earth Syst. Sci. Data, 6, 367–374, https://doi.org/10.5194/essd-6-367-2014, https://doi.org/10.5194/essd-6-367-2014, 2014
H. Dietze, U. Löptien, and K. Getzlaff
Geosci. Model Dev., 7, 1713–1731, https://doi.org/10.5194/gmd-7-1713-2014, https://doi.org/10.5194/gmd-7-1713-2014, 2014
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SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)
Graphics-processing-unit-accelerated ice flow solver for unstructured meshes using the Shallow-Shelf Approximation (FastIceFlo v1.0.1)
A finite-element framework to explore the numerical solution of the coupled problem of heat conduction, water vapor diffusion, and settlement in dry snow (IvoriFEM v0.1.0)
AvaFrame com1DFA (v1.3): a thickness-integrated computational avalanche module – theory, numerics, and testing
Universal differential equations for glacier ice flow modelling
A new model for supraglacial hydrology evolution and drainage for the Greenland Ice Sheet (SHED v1.0)
Modeling sensitivities of thermally and hydraulically driven ice stream surge cycling
A parallel implementation of the confined–unconfined aquifer system model for subglacial hydrology: design, verification, and performance analysis (CUAS-MPI v0.1.0)
Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms
An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0
A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance
The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere
Glacier Energy and Mass Balance (GEMB): a model of firn processes for cryosphere research
Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean
Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling
SUHMO: an adaptive mesh refinement SUbglacial Hydrology MOdel v1.0
Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau
The Multiple Snow Data Assimilation System (MuSA v1.0)
The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)
Tim Hill, Derek Bingham, Gwenn E. Flowers, and Matthew J. Hoffman
Geosci. Model Dev., 18, 4045–4074, https://doi.org/10.5194/gmd-18-4045-2025, https://doi.org/10.5194/gmd-18-4045-2025, 2025
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Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian process (GP) emulators to accelerate subglacial drainage modelling by ~ 1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for > 90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.
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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Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform-resolution simulations.
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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We introduce Nix, an ice-sheet model designed for understanding how large masses of ice behave. Nix is a computer programme that simulates the movement and temperature evolution in ice sheets. It helps us study how ice sheets respond to changes in the atmosphere and ocean. We found that ice temperatures play an essential role in determining the motion and stability of ice sheets. Nix is a useful tool for learning how climate change affects polar ice sheets.
Constantijn J. Berends, Victor Azizi, Jorge A. Bernales, and Roderik S. W. van de Wal
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.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
Geosci. Model Dev., 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, https://doi.org/10.5194/gmd-18-3583-2025, 2025
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How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
Alex E. West and Edward W. Blockley
Geosci. Model Dev., 18, 3041–3064, https://doi.org/10.5194/gmd-18-3041-2025, https://doi.org/10.5194/gmd-18-3041-2025, 2025
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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
Geosci. Model Dev., 18, 2945–2960, https://doi.org/10.5194/gmd-18-2945-2025, https://doi.org/10.5194/gmd-18-2945-2025, 2025
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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.
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Short summary
The Baltic Sea is seasonally ice covered with intense wintertime ship traffic and a sensitive ecosystem. Understanding the sea-ice pack is important for climate effect studies and forecasting. A NEMO-LIM3.6-based model setup for the North Sea/Baltic Sea is introduced, including a method for ice in the coastal zone. We evaluate different sea-ice parameters and overall find that the model agrees well with the observation though deformed ice is more challenging to capture.
The Baltic Sea is seasonally ice covered with intense wintertime ship traffic and a sensitive...
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