Articles | Volume 18, issue 19
https://doi.org/10.5194/gmd-18-6885-2025
© Author(s) 2025. 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-18-6885-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
07 Oct 2025
Development and technical paper |
| 07 Oct 2025
| 07 Oct 2025
Implementation of an intermediate-complexity snow-physics scheme (ISBA-Explicit Snow) into a sea ice model (SI3): 1D thermodynamic coupling and validation
Théo Brivoal
CORRESPONDING AUTHOR
Centre National de Recherches Météorologiques (CNRM), Météo-France, CNRS, Université de Toulouse, Toulouse, France
Virginie Guemas
Centre National de Recherches Météorologiques (CNRM), Météo-France, CNRS, Université de Toulouse, Toulouse, France
Martin Vancoppenolle
LOCEAN-IPSL, CNRS, Sorbonne Université, Paris, France
Clément Rousset
LOCEAN-IPSL, CNRS, Sorbonne Université, Paris, France
Bertrand Decharme
Centre National de Recherches Météorologiques (CNRM), Météo-France, CNRS, Université de Toulouse, Toulouse, France
Related authors
No articles found.
Bertrand Decharme, Diane Tzanos, Lucas Hardouin, Aaron Boone, Marie Minvielle, Patrick Le Moigne, and Rémi Gaillard
EGUsphere, https://doi.org/10.5194/egusphere-2026-860, https://doi.org/10.5194/egusphere-2026-860, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We developed a new method to represent how organic matter in soils, together with a mineral soil compaction adjustment, influences the movement of water and heat in land models. We implemented this approach in a global model and performed long-term simulations driven by weather data and global soil maps. Compared with an older empirical method, it produces more consistent soil moisture, runoff, evaporation, and ground temperature and shows closer agreement with observations.
Letizia Tedesco, Giulia Castellani, Pedro Duarte, Meibing Jin, Sebastien Moreau, Eric Mortenson, Benjamin Tobey Saenz, Nadja Steiner, and Martin Vancoppenolle
The Cryosphere, 20, 723–736, https://doi.org/10.5194/tc-20-723-2026, https://doi.org/10.5194/tc-20-723-2026, 2026
Short summary
Short summary
Sea ice hosts tiny algae that support polar marine life, yet their growth remains challenging to simulate. We tested six computer models using data from a 2015 Arctic drifting ice expedition to see how well they reproduced spring algae blooms and nutrient changes. While tuning helped models better match algae growth, nutrients remained difficult to capture. Our results highlight key challenges in representing fragile sea‑ice habitats that are expected to become more common as the Arctic warms.
Silvana Ramos Buarque, Bertrand Decharme, Alina L. Barbu, and Laurent Franchisteguy
Earth Syst. Sci. Data, 17, 7227–7249, https://doi.org/10.5194/essd-17-7227-2025, https://doi.org/10.5194/essd-17-7227-2025, 2025
Short summary
Short summary
The Crocus-ERA5 snow dataset supports Arctic snow monitoring and contributes to the Arctic Report Card. It improves on its predecessor with higher spatial resolution (0.25° vs. 0.75°), enhancing topographic and land cover detail. The product’s performance is assessed in terms of snow depth and cover compared to in situ observations and satellite data. The findings show a notable improvement, though remaining biases appear in boreal forests, where snow–forest interactions are not captured.
Bertrand Decharme
Geosci. Model Dev., 18, 9349–9384, https://doi.org/10.5194/gmd-18-9349-2025, https://doi.org/10.5194/gmd-18-9349-2025, 2025
Short summary
Short summary
This study resolves a key inconsistency in how Earth system models represent the physical properties of soil organic matter in land surface models. It introduces a new method to compute its volumetric fraction and physical effects using standard input data and soil mixture theory. Validated with experimental mixtures and field observations, the proposed framework improves the physical realism of soil property estimates.
Raphael Garisoain, Christine Delire, Bertrand Decharme, and Laure Gandois
EGUsphere, https://doi.org/10.5194/egusphere-2025-5248, https://doi.org/10.5194/egusphere-2025-5248, 2025
Short summary
Short summary
Peatlands store vast amounts of carbon, helping to slow climate change. We studied a mountain peatland in the Pyrenees to understand how warming and drought affect its ability to retain carbon. Using land surface modeling and field data from the past 70 years, we found that higher temperatures increase plant growth, but frequent and intense droughts cause large carbon losses, threatening peatlands’ role as long-term carbon sinks.
Hugues Goosse, Stephy Libera, Alberto C. Naveira Garabato, Benjamin Richaud, Alessandro Silvano, and Martin Vancoppenolle
The Cryosphere, 19, 5763–5779, https://doi.org/10.5194/tc-19-5763-2025, https://doi.org/10.5194/tc-19-5763-2025, 2025
Short summary
Short summary
The position of the winter sea ice edge in the Southern Ocean is strongly linked to the one of the Antarctic Circumpolar Current and thus to ocean bathymetry. This is due to the influence of the Antarctic Circumpolar Current on the southward heat flux that limits sea ice expansion, directly through oceanic processes and indirectly through its influence on atmospheric heat transport.
Léna L. Champiot-Bayard, Lester L. Kwiatkowski, and Martin M. Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2025-4296, https://doi.org/10.5194/egusphere-2025-4296, 2025
Short summary
Short summary
To investigate the future of primary production, we analyzed outputs from several climate models. Changes vary greatly from one region to another: some areas show large increases, while others experience little change or even declines. These differences reflect the balance between water temperature, light availability, and nutrient supply. We also found that the newer generation of climate models projects a much stronger increase than previous models, but with greater uncertainty.
Rubaya Pervin, Scott Robeson, Mallory Barnes, Stephen Sitch, Anthony Walker, Ben Poulter, Fabienne Maignan, Qing Sun, Thomas Colligan, Sönke Zaehle, Kashif Mahmud, Peter Anthoni, Almut Arneth, Vivek Arora, Vladislav Bastrikov, Liam Bogucki, Bertrand Decharme, Christine Delire, Stefanie Falk, Akihiko Ito, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Michael O’Sullivan, Wenping Yuan, and Natasha MacBean
EGUsphere, https://doi.org/10.5194/egusphere-2025-2841, https://doi.org/10.5194/egusphere-2025-2841, 2025
Short summary
Short summary
Drylands contribute more than a third of the global vegetation productivity. Yet, these regions are not well represented in global vegetation models. Here, we tested how well 15 global models capture annual changes in dryland vegetation productivity. Models that didn’t have vegetation change over time or fire have lower variability in vegetation productivity. Models need better representation of grass cover types and their coverage. Our work highlights where and how these models need to improve.
Baylor Fox-Kemper, Patricia DeRepentigny, Anne Marie Treguier, Christian Stepanek, Eleanor O’Rourke, Chloe Mackallah, Alberto Meucci, Yevgeny Aksenov, Paul J. Durack, Nicole Feldl, Vanessa Hernaman, Céline Heuzé, Doroteaciro Iovino, Gaurav Madan, André L. Marquez, François Massonnet, Jenny Mecking, Dhrubajyoti Samanta, Patrick C. Taylor, Wan-Ling Tseng, and Martin Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2025-3083, https://doi.org/10.5194/egusphere-2025-3083, 2025
Short summary
Short summary
The earth system model variables needed for studies of the ocean and sea ice are prioritized and requested.
Bertrand Decharme and Jeanne Colin
Earth Syst. Dynam., 16, 729–752, https://doi.org/10.5194/esd-16-729-2025, https://doi.org/10.5194/esd-16-729-2025, 2025
Short summary
Short summary
Our study uses a global climate model to investigate how groundwater and floodplains influence today's climate. We found that these continental water sources, often overlooked in climate models, can influence precipitation, temperature, and land surface hydrology. This research contributes to a better understanding of the dynamics of the Earth system and highlights the importance of considering interactions between hydrology and the atmosphere.
Yushi Morioka, Eric Maisonnave, Sébastien Masson, Clement Rousset, Luis Kornblueh, Marco Giorgetta, Masami Nonaka, and Swadhin K. Behera
EGUsphere, https://doi.org/10.5194/egusphere-2025-2258, https://doi.org/10.5194/egusphere-2025-2258, 2025
Preprint archived
Short summary
Short summary
Ocean mesoscale eddies, which have a horizontal scale with an order of 100 km, play a prominent role in global ocean heat transport that regulates Earth climate. Here we newly develop an eddy-permitting climate model to demonstrate that the increased ocean model resolution improves representation of air-sea interaction in the western and eastern boundary current regions, while the improved sea ice model physics benefit realistic simulation of sea ice variability.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Julia Pfeffer, Anny Cazenave, Alejandro Blazquez, Bertrand Decharme, Simon Munier, and Anne Barnoud
Hydrol. Earth Syst. Sci., 27, 3743–3768, https://doi.org/10.5194/hess-27-3743-2023, https://doi.org/10.5194/hess-27-3743-2023, 2023
Short summary
Short summary
The GRACE (Gravity Recovery And Climate Experiment) satellite mission enabled the quantification of water mass redistributions from 2002 to 2017. The analysis of GRACE satellite data shows here that slow changes in terrestrial water storage occurring over a few years to a decade are severely underestimated by global hydrological models. Several sources of errors may explain such biases, likely including the inaccurate representation of groundwater storage changes.
Max Thomas, Briana Cate, Jack Garnett, Inga J. Smith, Martin Vancoppenolle, and Crispin Halsall
The Cryosphere, 17, 3193–3201, https://doi.org/10.5194/tc-17-3193-2023, https://doi.org/10.5194/tc-17-3193-2023, 2023
Short summary
Short summary
A recent study showed that pollutants can be enriched in growing sea ice beyond what we would expect from a perfectly dissolved chemical. We hypothesise that this effect is caused by the specific properties of the pollutants working in combination with fluid moving through the sea ice. To test our hypothesis, we replicate this behaviour in a sea-ice model and show that this type of modelling can be applied to predicting the transport of chemicals with complex behaviour in sea ice.
Antoine Sobaga, Bertrand Decharme, Florence Habets, Christine Delire, Noële Enjelvin, Paul-Olivier Redon, Pierre Faure-Catteloin, and Patrick Le Moigne
Hydrol. Earth Syst. Sci., 27, 2437–2461, https://doi.org/10.5194/hess-27-2437-2023, https://doi.org/10.5194/hess-27-2437-2023, 2023
Short summary
Short summary
Seven instrumented lysimeters are used to assess the simulation of the soil water dynamic in one land surface model. Four water potential and hydraulic conductivity closed-form equations, including one mixed form, are evaluated. One form is more relevant for simulating drainage, especially during intense drainage events. The soil profile heterogeneity of one parameter of the closed-form equations is shown to be important.
Katherine Hutchinson, Julie Deshayes, Christian Éthé, Clément Rousset, Casimir de Lavergne, Martin Vancoppenolle, Nicolas C. Jourdain, and Pierre Mathiot
Geosci. Model Dev., 16, 3629–3650, https://doi.org/10.5194/gmd-16-3629-2023, https://doi.org/10.5194/gmd-16-3629-2023, 2023
Short summary
Short summary
Bottom Water constitutes the lower half of the ocean’s overturning system and is primarily formed in the Weddell and Ross Sea in the Antarctic due to interactions between the atmosphere, ocean, sea ice and ice shelves. Here we use a global ocean 1° resolution model with explicit representation of the three large ice shelves important for the formation of the parent waters of Bottom Water. We find doing so reduces salt biases, improves water mass realism and gives realistic ice shelf melt rates.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
The Cryosphere, 17, 1935–1965, https://doi.org/10.5194/tc-17-1935-2023, https://doi.org/10.5194/tc-17-1935-2023, 2023
Short summary
Short summary
This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations in ocean–sea ice models. The summer ice concentration simulation in both hemispheres can be improved with changed surface heat fluxes. The winter Antarctic ice concentration and the Arctic drift speed near the ice edge and the ice velocity direction simulations are improved with changed wind stress. The radiation fluxes and winds in atmospheric reanalyses are crucial for sea ice simulations.
Jeff Polton, James Harle, Jason Holt, Anna Katavouta, Dale Partridge, Jenny Jardine, Sarah Wakelin, Julia Rulent, Anthony Wise, Katherine Hutchinson, David Byrne, Diego Bruciaferri, Enda O'Dea, Michela De Dominicis, Pierre Mathiot, Andrew Coward, Andrew Yool, Julien Palmiéri, Gennadi Lessin, Claudia Gabriela Mayorga-Adame, Valérie Le Guennec, Alex Arnold, and Clément Rousset
Geosci. Model Dev., 16, 1481–1510, https://doi.org/10.5194/gmd-16-1481-2023, https://doi.org/10.5194/gmd-16-1481-2023, 2023
Short summary
Short summary
The aim is to increase the capacity of the modelling community to respond to societally important questions that require ocean modelling. The concept of reproducibility for regional ocean modelling is developed: advocating methods for reproducible workflows and standardised methods of assessment. Then, targeting the NEMO framework, we give practical advice and worked examples, highlighting key considerations that will the expedite development cycle and upskill the user community.
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
Short summary
Short summary
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.
Hugues Goosse, Sofia Allende Contador, Cecilia M. Bitz, Edward Blanchard-Wrigglesworth, Clare Eayrs, Thierry Fichefet, Kenza Himmich, Pierre-Vincent Huot, François Klein, Sylvain Marchi, François Massonnet, Bianca Mezzina, Charles Pelletier, Lettie Roach, Martin Vancoppenolle, and Nicole P. M. van Lipzig
The Cryosphere, 17, 407–425, https://doi.org/10.5194/tc-17-407-2023, https://doi.org/10.5194/tc-17-407-2023, 2023
Short summary
Short summary
Using idealized sensitivity experiments with a regional atmosphere–ocean–sea ice model, we show that sea ice advance is constrained by initial conditions in March and the retreat season is influenced by the magnitude of several physical processes, in particular by the ice–albedo feedback and ice transport. Atmospheric feedbacks amplify the response of the winter ice extent to perturbations, while some negative feedbacks related to heat conduction fluxes act on the ice volume.
Yona Silvy, Clément Rousset, Eric Guilyardi, Jean-Baptiste Sallée, Juliette Mignot, Christian Ethé, and Gurvan Madec
Geosci. Model Dev., 15, 7683–7713, https://doi.org/10.5194/gmd-15-7683-2022, https://doi.org/10.5194/gmd-15-7683-2022, 2022
Short summary
Short summary
A modeling framework is introduced to understand and decompose the mechanisms causing the ocean temperature, salinity and circulation to change since the pre-industrial period and into 21st century scenarios of global warming. This framework aims to look at the response to changes in the winds and in heat and freshwater exchanges at the ocean interface in global climate models, throughout the 1850–2100 period, to unravel their individual effects on the changing physical structure of the ocean.
Antoine Sobaga, Bertrand Decharme, Florence Habets, Christine Delire, Noële Enjelvin, Paul-Olivier Redon, Pierre Faure-Catteloin, and Patrick Le Moigne
EGUsphere, https://doi.org/10.5194/egusphere-2022-274, https://doi.org/10.5194/egusphere-2022-274, 2022
Preprint archived
Short summary
Short summary
Seven instrumented lysimeters are used to assess the simulation of the soil water dynamic in one land surface model. Three water potential and hydraulic conductivity closed-form equations including one mixed form are evaluated. The mixed form is more relevant to simulate drainage especially during intense drainage events. Soil profile heterogeneity of one parameter of the closed-form equations is shown to be important.
Simon Munier and Bertrand Decharme
Earth Syst. Sci. Data, 14, 2239–2258, https://doi.org/10.5194/essd-14-2239-2022, https://doi.org/10.5194/essd-14-2239-2022, 2022
Short summary
Short summary
This paper presents a new global-scale river network at 1/12°, generated automatically and assessed over the 69 largest basins of the world. A set of hydro-geomorphological parameters are derived at the same spatial resolution, including a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The dataset may be useful for hydrology modelling or climate studies.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
Short summary
Short summary
The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Xia Lin, François Massonnet, Thierry Fichefet, and Martin Vancoppenolle
Geosci. Model Dev., 14, 6331–6354, https://doi.org/10.5194/gmd-14-6331-2021, https://doi.org/10.5194/gmd-14-6331-2021, 2021
Short summary
Short summary
This study introduces a new Sea Ice Evaluation Tool (SITool) to evaluate the model skills on the bipolar sea ice simulations by providing performance metrics and diagnostics. SITool is applied to evaluate the CMIP6 OMIP simulations. By changing the atmospheric forcing from CORE-II to JRA55-do data, many aspects of sea ice simulations are improved. SITool will be useful for helping teams managing various versions of a sea ice model or tracking the time evolution of model performance.
Cited articles
Anderson, E. A.: A Point Energy and Mass Balance Model of a Snow Cover, U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, Office of Hydrology, 172 pp., 1976.
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic model of sea ice, J. Geophys. Res.-Oceans, 104, 15669–15677, https://doi.org/10.1029/1999JC900100, 1999.
Bitz, C. M., Holland, M. M., Weaver, A. J., and Eby, M.: Simulating the ice-thickness distribution in a coupled climate model, J. Geophys. Res.-Oceans, 106, 2441–2463, https://doi.org/10.1029/1999JC000113, 2001.
Bohren, C. F. and Barkstrom, B. R.: Theory of the optical properties of snow, J. Geophys. Res., 79, 4527–4535, https://doi.org/10.1029/JC079i030p04527, 1974.
Bohren, C. F. and Beschta, R. L.: Snowpack albedo and snow density, Cold Reg. Sci. Technol., 1, 47–50, https://doi.org/10.1016/0165-232X(79)90018-1, 1979.
Boone, A.: Description du Schema de Neige ISBA-ES (Explicit Snow), 2002.
Boone, A. and Etchevers, P.: An Intercomparison of Three Snow Schemes of Varying Complexity Coupled to the Same Land Surface Model: Local-Scale Evaluation at an Alpine Site, J. Hydrometeor., 2, 374–394, https://doi.org/10.1175/1525-7541(2001)002<0374:AIOTSS>2.0.CO;2, 2001.
Brivoal, T.: NEMO_SI3_ISBAES_fortran_source_files_and_namelists, Zenodo [code], https://doi.org/10.5281/zenodo.14719233, 2024a.
Brivoal, T.: FORCINGS_SI3_ISBAES_SHEBA, Zenodo [data set], https://doi.org/10.5281/zenodo.14720364, 2024b.
Brivoal, T.: DATA_SI3_ISBAES_VALIDATION_AT_SHEBA, Zenodo [data set], https://doi.org/10.5281/zenodo.14720517, 2025.
Brun, E., Martin, E., Simon, V., Gendre, C., and Coleou, C.: An Energy and Mass Model of Snow Cover Suitable for Operational Avalanche Forecasting, J. Glaciol., 35, 333–342, https://doi.org/10.3189/S0022143000009254, 1989.
Brun, E., Martin, E., and Spiridonov, V.: Coupling a multi-layered snow model with a GCM, Ann. Glaciol., 25, 66–72, https://doi.org/10.3189/S0260305500013811, 1997.
Calonne, N., Milliancourt, L., Burr, A., Philip, A., Martin, C. L., Flin, F., and Geindreau, C.: Thermal Conductivity of Snow, Firn, and Porous Ice From 3-D Image-Based Computations, Geophys. Res. Lett., 46, 13079–13089, https://doi.org/10.1029/2019GL085228, 2019.
Decharme, B., Brun, E., Boone, A., Delire, C., Le Moigne, P., and Morin, S.: Impacts of snow and organic soils parameterization on northern Eurasian soil temperature profiles simulated by the ISBA land surface model, The Cryosphere, 10, 853–877, https://doi.org/10.5194/tc-10-853-2016, 2016.
Domine, F., Barrere, M., and Sarrazin, D.: Seasonal evolution of the effective thermal conductivity of the snow and the soil in high Arctic herb tundra at Bylot Island, Canada, The Cryosphere, 10, 2573–2588, https://doi.org/10.5194/tc-10-2573-2016, 2016.
Domine, F., Gauthier, G., Vionnet, V., Fauteux, D., Dumont, M., and Barrere, M.: Snow physical properties may be a significant determinant of lemming population dynamics in the high Arctic, Arct. Sci., 4, 813–826, https://doi.org/10.1139/as-2018-0008, 2018.
Douville, H.: La neige et sa paramétrisation dans les simulations climatiques, PhD thesis, Université Toulouse III, https://theses.fr/1995TOU30253 (last access: 30 September 2025), 1995.
Fichefet, T., Tartinville, B., and Goosse, H.: Sensitivity of the Antarctic sea ice to the thermal conductivity of snow, Geophys. Res. Lett., 27, 401–404, https://doi.org/10.1029/1999GL002397, 2000.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gurvan, M., Bourdallé-Badie, R., Chanut, J., Clementi, E., Coward, A., Ethé, C., Iovino, D., Lea, D., Lévy, C., Lovato, T., Martin, N., Masson, S., Mocavero, S., Rousset, C., Storkey, D., Müeller, S., Nurser, G., Bell, M., Samson, G., Mathiot, P., Mele, F., and Moulin, A.: NEMO ocean engine, Zenodo [code], https://doi.org/10.5281/zenodo.6334656, 2022.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. R. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Holland, M. M., Clemens-Sewall, D., Landrum, L., Light, B., Perovich, D., Polashenski, C., Smith, M., and Webster, M.: The influence of snow on sea ice as assessed from simulations of CESM2, The Cryosphere, 15, 4981–4998, https://doi.org/10.5194/tc-15-4981-2021, 2021.
Hunke, E., Lipscomb, W., Jones, P., Turner, A., Jeffery, N., and Elliott, S.: CICE, The Los Alamos Sea Ice Model, Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2017.
Itkin, P., Hendricks, S., Webster, M., von Albedyll, L., Arndt, S., Divine, D., Jaggi, M., Oggier, M., Raphael, I., Ricker, R., Rohde, J., Schneebeli, M., and Liston, G. E.: Sea ice and snow characteristics from year-long transects at the MOSAiC Central Observatory, Elem. Sci. Anthr., 11, 00048, https://doi.org/10.1525/elementa.2022.00048, 2023.
Jafari, M. and Lehning, M.: Convection of snow: when and why does it happen?, Front. Earth Sci., 11, https://doi.org/10.3389/feart.2023.1167760, 2023.
Jutras, M., Vancoppenolle, M., Lourenço, A., Vivier, F., Carnat, G., Madec, G., Rousset, C., and Tison, J.-L.: Thermodynamics of slush and snow–ice formation in the Antarctic sea-ice zone, Deep Sea Res. Part II Top. Stud. Oceanogr., 131, 75–83, https://doi.org/10.1016/j.dsr2.2016.03.008, 2016.
Kojima, K.: Densification of Seasonal Snow Cover, Phys. Snow Ice Proc., 1, 929–952, 1967.
Kondo, J. and Yamazaki, T.: A Prediction Model for Snowmelt, Snow Surface Temperature and Freezing Depth Using a Heat Balance Method, Appl. Meteor. Climatol., 29, 375–384, https://doi.org/10.1175/1520-0450(1990)029<0375:APMFSS>2.0.CO;2, 1990.
Large, W. and Yeager, S.: Diurnal to decadal global forcing for ocean and sea-ice models: The data sets and flux climatologies, UCAR/NCAR, https://doi.org/10.5065/D6KK98Q6, 2004.
Lecomte, O., Fichefet, T., Vancoppenolle, M., Domine, F., Massonnet, F., Mathiot, P., Morin, S., and Barriat, P. y.: On the formulation of snow thermal conductivity in large-scale sea ice models, J. Adv. Model. Earth Syst., 5, 542–557, https://doi.org/10.1002/jame.20039, 2013.
Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C., Drevillon, M., Benkiran, M., Testut, C.-E., Bourdalle-Badie, R., Gasparin, F., Hernandez, O., Levier, B., Drillet, Y., Remy, E., and Le Traon, P.-Y.: Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time
Leonard, K. C. and Maksym, T.: The importance of wind-blown snow redistribution to snow accumulation on Bellingshausen Sea ice, Ann. Glaciol., 52, 271–278, https://doi.org/10.3189/172756411795931651, 2011.
Liston, G. E., Itkin, P., Stroeve, J., Tschudi, M., Stewart, J. S., Pedersen, S. H., Reinking, A. K., and Elder, K.: A Lagrangian Snow-Evolution System for Sea-Ice Applications (SnowModel-LG): Part I—Model Description, J. Geophys. Res.-Oceans, 125, e2019JC015913, https://doi.org/10.1029/2019JC015913, 2020.
Liston, G. E., Stroeve, J., and Itkin, P.: Lagrangian Snow Distributions for Sea-Ice Applications, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/27A0P5M6LZBI, 2021.
Loth, B., Graf, H.-F., and Oberhuber, J. M.: Snow cover model for global climate simulations, J. Geophys. Res.-Atmos., 98, 10451–10464, https://doi.org/10.1029/93JD00324, 1993.
Lynch-Stieglitz, M.: The Development and Validation of a Simple Snow Model for the GISS GCM, https://doi.org/10.1175/1520-0442(1994)007<1842:TDAVOA>2.0.CO;2, 1994.
Macfarlane, A. R., Löwe, H., Gimenes, L., Wagner, D. N., Dadic, R., Ottersberg, R., Hämmerle, S., and Schneebeli, M.: Temporospatial variability of snow's thermal conductivity on Arctic sea ice, The Cryosphere, 17, 5417–5434, https://doi.org/10.5194/tc-17-5417-2023, 2023.
Madec, G. and the NEMO System Team: NEMO Ocean Engine Reference Manual, Zenodo, https://doi.org/10.5281/zenodo.1464816, 2024.
Mellor, M.: Properties of snow, https://erdc-library.erdc.dren.mil/items/81b728f7-5e0b-4ef8-e053-411ac80adeb3 (last access: 29 September 2025), 1964.
Nicolaus, M., Haas, C., and Bareiss, J.: Observations of superimposed ice formation at melt-onset on fast ice on Kongsfjorden, Svalbard, Phys. Chem. Earth Parts ABC, 28, 1241–1248, https://doi.org/10.1016/j.pce.2003.08.048, 2003.
Pahaut, E.: Les cristaux de neige et leurs métamorphoses (), Centre d'Étude de la Neige, 58 pp., 1975.
Perovich, D. K.: On the aggregate-scale partitioning of solar radiation in Arctic sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment, J. Geophys. Res.-Oceans, 110, https://doi.org/10.1029/2004JC002512, 2005.
Perovich, D. K. and Polashenski, C.: Albedo evolution of seasonal Arctic sea ice, Geophys. Res. Lett., 39, https://doi.org/10.1029/2012GL051432, 2012.
Perovich, D. K., Grenfell, T. C., Light, B., Richter-Menge, J. A., Sturm, M., Tucker III, W. B., Eicken, H., Maykut, G. A., and Elder, B.: SHEBA: Snow and Ice Studies CD-ROM, https://psl.noaa.gov/arctic/sheba/netcdf/datasets/Ice_snow_data/ICEWEB/INDEX.HTM (last access: 29 September 2025), 1999.
Persson, P. O. G., Fairall, C. W., Andreas, E. L., Guest, P. S., and Perovich, D. K.: Measurements near the Atmospheric Surface Flux Group tower at SHEBA: Near-surface conditions and surface energy budget, J. Geophys. Res.-Oceans, 107, SHE 21-1-SHE 21-35, https://doi.org/10.1029/2000JC000705, 2002.
Petty, A. A., Webster, M., Boisvert, L., and Markus, T.: The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis, Geosci. Model Dev., 11, 4577–4602, https://doi.org/10.5194/gmd-11-4577-2018, 2018.
Riche, F. and Schneebeli, M.: Thermal conductivity of snow measured by three independent methods and anisotropy considerations, The Cryosphere, 7, 217–227, https://doi.org/10.5194/tc-7-217-2013, 2013.
Rousset, C., Vancoppenolle, M., Madec, G., Fichefet, T., Flavoni, S., Barthélemy, A., Benshila, R., Chanut, J., Levy, C., Masson, S., and Vivier, F.: The Louvain-La-Neuve sea ice model LIM3.6: global and regional capabilities, Geosci. Model Dev., 8, 2991–3005, https://doi.org/10.5194/gmd-8-2991-2015, 2015.
Royer, A., Picard, G., Vargel, C., Langlois, A., Gouttevin, I., and Dumont, M.: Improved Simulation of Arctic Circumpolar Land Area Snow Properties and Soil Temperatures, Front. Earth Sci., 9, https://doi.org/10.3389/feart.2021.685140, 2021.
Shapero, D. R., Badgeley, J. A., Hoffman, A. O., and Joughin, I. R.: icepack: a new glacier flow modeling package in Python, version 1.0, Geosci. Model Dev., 14, 4593–4616, https://doi.org/10.5194/gmd-14-4593-2021, 2021.
Sommer, C. G., Lehning, M., and Fierz, C.: Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow, Front. Earth Sci., 6, https://doi.org/10.3389/feart.2018.00004, 2018.
Stroeve, J., Liston, G. E., Buzzard, S., Zhou, L., Mallett, R., Barrett, A., Tschudi, M., Tsamados, M., Itkin, P., and Stewart, J. S.: A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel-LG): Part II—Analyses, J. Geophys. Res.-Oceans, 125, e2019JC015900, https://doi.org/10.1029/2019JC015900, 2020.
Sturm, M. and Benson, C. S.: Vapor transport, grain growth and depth-hoar development in the subarctic snow, J. Glaciol., 43, 42–59, https://doi.org/10.3189/S0022143000002793, 1997.
Sturm, M. and Massom, R. A.: Snow in the sea ice system: friend or foe?, in: Sea Ice, John Wiley & Sons, Ltd, 65–109, https://doi.org/10.1002/9781118778371.ch3, 2017.
Sturm, M., Holmgren, J., König, M., and Morris, K.: The thermal conductivity of seasonal snow, J. Glaciol., 43, 26–41, https://doi.org/10.3189/S0022143000002781, 1997.
Sturm, M., Holmgren, J., and Perovich, D. K.: Winter snow cover on the sea ice of the Arctic Ocean at the Surface Heat Budget of the Arctic Ocean (SHEBA): Temporal evolution and spatial variability, J. Geophys. Res.-Oceans, 107, SHE 23-1–SHE 23-17, https://doi.org/10.1029/2000JC000400, 2002.
Sun, S., Jin, J., and Xue, Y.: A simple snow-atmosphere-soil transfer model, J. Geophys. Res.-Atmos., 104, 19587–19597, https://doi.org/10.1029/1999JD900305, 1999.
Thorndike, A. S., Rothrock, D. A., Maykut, G. A., and Colony, R.: The thickness distribution of sea ice, J. Geophys. Res., 80, 4501–4513, https://doi.org/10.1029/JC080i033p04501, 1975.
Vancoppenolle, M., Fichefet, T., Goosse, H., Bouillon, S., Madec, G., and Maqueda, M. A. M.: Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 1. Model description and validation, Ocean Model., 27, 33–53, https://doi.org/10.1016/j.ocemod.2008.10.005, 2009.
Vancoppenolle, M., Rousset, C., Blockley, E., Aksenov, Y., Feltham, D., Fichefet, T., Garric, G., Guémas, V., Iovino, D., Keeley, S., Madec, G., Massonnet, F., Ridley, J., Schroeder, D., and Tietsche, S.: SI3, the NEMO Sea Ice Engine, Zenodo [code], https://doi.org/10.5281/zenodo.7534900, 2023.
Verseghy, D. L.: Class-A Canadian land surface scheme for GCMS. I. Soil model, Int. J. Climatol., 11, 111–133, https://doi.org/10.1002/joc.3370110202, 1991.
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.
Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M.-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L., and Chauvin, F.: The CNRM-CM5.1 global climate model: description and basic evaluation, Clim. Dynam., 40, 2091–2121, https://doi.org/10.1007/s00382-011-1259-y, 2013.
Wagner, D. N., Shupe, M. D., Cox, C., Persson, O. G., Uttal, T., Frey, M. M., Kirchgaessner, A., Schneebeli, M., Jaggi, M., Macfarlane, A. R., Itkin, P., Arndt, S., Hendricks, S., Krampe, D., Nicolaus, M., Ricker, R., Regnery, J., Kolabutin, N., Shimanshuck, E., Oggier, M., Raphael, I., Stroeve, J., and Lehning, M.: Snowfall and snow accumulation during the MOSAiC winter and spring seasons, The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, 2022.
Webster, M., Gerland, S., Holland, M., Hunke, E., Kwok, R., Lecomte, O., Massom, R., Perovich, D., and Sturm, M.: Snow in the changing sea-ice systems, Nat. Clim. Change, 8, 946–953, https://doi.org/10.1038/s41558-018-0286-7, 2018.
Wever, N., Rossmann, L., Maaß, N., Leonard, K. C., Kaleschke, L., Nicolaus, M., and Lehning, M.: Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model, Geosci. Model Dev., 13, 99–119, https://doi.org/10.5194/gmd-13-99-2020, 2020.
Xiong, C. and Shi, J.: Snow specific surface area remote sensing retrieval using a microstructure based reflectance model, Remote Sens. Environ., 204, 838–849, https://doi.org/10.1016/j.rse.2017.09.017, 2018.
Short summary
Snow in polar regions is key to sea ice formation and the Earth's climate, but current climate models simplify snow cover on sea ice. This study integrates an intermediate-complexity snow-physics scheme into a sea ice model designed for climate applications. We show that modeling the temporal changes in properties such as the density and thermal conductivity of the snow layers leads to a more accurate representation of heat transfer between the underlying sea ice and the atmosphere.
Snow in polar regions is key to sea ice formation and the Earth's climate, but current climate...
