Articles | Volume 17, issue 12
https://doi.org/10.5194/essd-17-7227-2025
© 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/essd-17-7227-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Insights into the North Hemisphere daily snowpack at high resolution from the new Crocus–ERA5 product
Silvana Ramos Buarque
CORRESPONDING AUTHOR
Météo-France, CNRS, Univ. Toulouse, CNRM, Toulouse, France
Bertrand Decharme
Météo-France, CNRS, Univ. Toulouse, CNRM, Toulouse, France
Alina L. Barbu
Météo-France, CNRS, Univ. Toulouse, CNRM, Toulouse, France
Laurent Franchisteguy
Météo-France, Direction des Systèmes d'Observation, Toulouse, France
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Louise C. Sime, Rachel Diamond, Christian Stepanek, Chris Brierley, David Schroeder, Masa Kageyama, Irene Malmierca-Vallet, Ed Blockley, Alex West, Danny Feltham, Jeff Ridley, Pascale Braconnot, Charles J. R. Williams, Xiaoxu Shi, Bette L. Otto-Bliesner, Sophia I. Macarewich, Silvana Ramos Buarque, Qiong Zhang, Allegra LeGrande, Weipeng Zheng, Dabang Jiang, Polina Morozova, Chuncheng Guo, Zhongshi Zhang, Nicholas Yeung, Laurie Menviel, Sandeep Narayanasetti, Olivia Reeves, Matthew Pollock, and Anni Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2025-3531, https://doi.org/10.5194/egusphere-2025-3531, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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The Arctic may have lost its summer sea ice 127,000 years ago during a naturally warm period in Earth’s past. Climate models can be tested by recreating those conditions, with similar sunlight and greenhouse gas levels. Analysing the large sea ice changes in these simulations helps us understand how the Arctic might respond in the near future and improves how we test and trust our climate models.
Masa Kageyama, Louise C. Sime, Marie Sicard, Maria-Vittoria Guarino, Anne de Vernal, Ruediger Stein, David Schroeder, Irene Malmierca-Vallet, Ayako Abe-Ouchi, Cecilia Bitz, Pascale Braconnot, Esther C. Brady, Jian Cao, Matthew A. Chamberlain, Danny Feltham, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina Morozova, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Ryouta O'ishi, Silvana Ramos Buarque, David Salas y Melia, Sam Sherriff-Tadano, Julienne Stroeve, Xiaoxu Shi, Bo Sun, Robert A. Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, Weipeng Zheng, and Tilo Ziehn
Clim. Past, 17, 37–62, https://doi.org/10.5194/cp-17-37-2021, https://doi.org/10.5194/cp-17-37-2021, 2021
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The Last interglacial (ca. 127 000 years ago) is a period with increased summer insolation at high northern latitudes, resulting in a strong reduction in Arctic sea ice. The latest PMIP4-CMIP6 models all simulate this decrease, consistent with reconstructions. However, neither the models nor the reconstructions agree on the possibility of a seasonally ice-free Arctic. Work to clarify the reasons for this model divergence and the conflicting interpretations of the records will thus be needed.
Bette L. Otto-Bliesner, Esther C. Brady, Anni Zhao, Chris M. Brierley, Yarrow Axford, Emilie Capron, Aline Govin, Jeremy S. Hoffman, Elizabeth Isaacs, Masa Kageyama, Paolo Scussolini, Polychronis C. Tzedakis, Charles J. R. Williams, Eric Wolff, Ayako Abe-Ouchi, Pascale Braconnot, Silvana Ramos Buarque, Jian Cao, Anne de Vernal, Maria Vittoria Guarino, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina A. Morozova, Kerim H. Nisancioglu, Ryouta O'ishi, David Salas y Mélia, Xiaoxu Shi, Marie Sicard, Louise Sime, Christian Stepanek, Robert Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, and Weipeng Zheng
Clim. Past, 17, 63–94, https://doi.org/10.5194/cp-17-63-2021, https://doi.org/10.5194/cp-17-63-2021, 2021
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The CMIP6–PMIP4 Tier 1 lig127k experiment was designed to address the climate responses to strong orbital forcing. We present a multi-model ensemble of 17 climate models, most of which have also completed the CMIP6 DECK experiments and are thus important for assessing future projections. The lig127ksimulations show strong summer warming over the NH continents. More than half of the models simulate a retreat of the Arctic minimum summer ice edge similar to the average for 2000–2018.
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
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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
This preprint is open for discussion and under review for Biogeosciences (BG).
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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.
Théo Brivoal, Virginie Guemas, Martin Vancoppenolle, Clément Rousset, and Bertrand Decharme
Geosci. Model Dev., 18, 6885–6902, https://doi.org/10.5194/gmd-18-6885-2025, https://doi.org/10.5194/gmd-18-6885-2025, 2025
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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.
Louise C. Sime, Rachel Diamond, Christian Stepanek, Chris Brierley, David Schroeder, Masa Kageyama, Irene Malmierca-Vallet, Ed Blockley, Alex West, Danny Feltham, Jeff Ridley, Pascale Braconnot, Charles J. R. Williams, Xiaoxu Shi, Bette L. Otto-Bliesner, Sophia I. Macarewich, Silvana Ramos Buarque, Qiong Zhang, Allegra LeGrande, Weipeng Zheng, Dabang Jiang, Polina Morozova, Chuncheng Guo, Zhongshi Zhang, Nicholas Yeung, Laurie Menviel, Sandeep Narayanasetti, Olivia Reeves, Matthew Pollock, and Anni Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2025-3531, https://doi.org/10.5194/egusphere-2025-3531, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
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The Arctic may have lost its summer sea ice 127,000 years ago during a naturally warm period in Earth’s past. Climate models can be tested by recreating those conditions, with similar sunlight and greenhouse gas levels. Analysing the large sea ice changes in these simulations helps us understand how the Arctic might respond in the near future and improves how we test and trust our climate models.
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
This preprint is open for discussion and under review for Biogeosciences (BG).
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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.
Amali A. Amali, Clemens Schwingshackl, Akihiko Ito, Alina Barbu, Christine Delire, Daniele Peano, David M. Lawrence, David Wårlind, Eddy Robertson, Edouard L. Davin, Elena Shevliakova, Ian N. Harman, Nicolas Vuichard, Paul A. Miller, Peter J. Lawrence, Tilo Ziehn, Tomohiro Hajima, Victor Brovkin, Yanwu Zhang, Vivek K. Arora, and Julia Pongratz
Earth Syst. Dynam., 16, 803–840, https://doi.org/10.5194/esd-16-803-2025, https://doi.org/10.5194/esd-16-803-2025, 2025
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Our study explored the impact of anthropogenic land-use change (LUC) on climate dynamics, focusing on biogeophysical (BGP) and biogeochemical (BGC) effects using data from the Land Use Model Intercomparison Project (LUMIP) and the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that LUC-induced carbon emissions contribute to a BGC warming of 0.21 °C, with BGC effects dominating globally over BGP effects, which show regional variability. Our findings highlight discrepancies in model simulations and emphasize the need for improved representations of LUC processes.
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
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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.
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
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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
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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.
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
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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.
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
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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
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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
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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.
Pascal Marquet, Pauline Martinet, Jean-François Mahfouf, Alina Lavinia Barbu, and Benjamin Ménétrier
Atmos. Meas. Tech., 15, 2021–2035, https://doi.org/10.5194/amt-15-2021-2022, https://doi.org/10.5194/amt-15-2021-2022, 2022
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Two conservative thermodynamic variables (moist-air entropy potential temperature and total water content) are introduced into a one-dimensional EnVar data assimilation system to demonstrate their benefit for future operational assimilation schemes, with the use of microwave brightness temperatures from a ground-based radiometer installed during the field campaign SOFGO3D. Results show that the brightness temperatures analysed with the new variables are improved, including the liquid water.
Thibault Guinaldo, Simon Munier, Patrick Le Moigne, Aaron Boone, Bertrand Decharme, Margarita Choulga, and Delphine J. Leroux
Geosci. Model Dev., 14, 1309–1344, https://doi.org/10.5194/gmd-14-1309-2021, https://doi.org/10.5194/gmd-14-1309-2021, 2021
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Lakes are of fundamental importance in the Earth system as they support essential environmental and economic services such as freshwater supply. Despite the impact of lakes on the water cycle, they are generally not considered in global hydrological studies. Based on a model called MLake, we assessed both the importance of lakes in simulating river flows at global scale and the value of their level variations for water resource management.
Ruth Petrie, Sébastien Denvil, Sasha Ames, Guillaume Levavasseur, Sandro Fiore, Chris Allen, Fabrizio Antonio, Katharina Berger, Pierre-Antoine Bretonnière, Luca Cinquini, Eli Dart, Prashanth Dwarakanath, Kelsey Druken, Ben Evans, Laurent Franchistéguy, Sébastien Gardoll, Eric Gerbier, Mark Greenslade, David Hassell, Alan Iwi, Martin Juckes, Stephan Kindermann, Lukasz Lacinski, Maria Mirto, Atef Ben Nasser, Paola Nassisi, Eric Nienhouse, Sergey Nikonov, Alessandra Nuzzo, Clare Richards, Syazwan Ridzwan, Michel Rixen, Kim Serradell, Kate Snow, Ag Stephens, Martina Stockhause, Hans Vahlenkamp, and Rick Wagner
Geosci. Model Dev., 14, 629–644, https://doi.org/10.5194/gmd-14-629-2021, https://doi.org/10.5194/gmd-14-629-2021, 2021
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This paper describes the infrastructure that is used to distribute Coupled Model Intercomparison Project Phase 6 (CMIP6) data around the world for analysis by the climate research community. It is expected that there will be ~20 PB (petabytes) of data available for analysis. The operations team performed a series of preparation "data challenges" to ensure all components of the infrastructure were operational for when the data became available for timely data distribution and subsequent analysis.
Masa Kageyama, Louise C. Sime, Marie Sicard, Maria-Vittoria Guarino, Anne de Vernal, Ruediger Stein, David Schroeder, Irene Malmierca-Vallet, Ayako Abe-Ouchi, Cecilia Bitz, Pascale Braconnot, Esther C. Brady, Jian Cao, Matthew A. Chamberlain, Danny Feltham, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina Morozova, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Ryouta O'ishi, Silvana Ramos Buarque, David Salas y Melia, Sam Sherriff-Tadano, Julienne Stroeve, Xiaoxu Shi, Bo Sun, Robert A. Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, Weipeng Zheng, and Tilo Ziehn
Clim. Past, 17, 37–62, https://doi.org/10.5194/cp-17-37-2021, https://doi.org/10.5194/cp-17-37-2021, 2021
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The Last interglacial (ca. 127 000 years ago) is a period with increased summer insolation at high northern latitudes, resulting in a strong reduction in Arctic sea ice. The latest PMIP4-CMIP6 models all simulate this decrease, consistent with reconstructions. However, neither the models nor the reconstructions agree on the possibility of a seasonally ice-free Arctic. Work to clarify the reasons for this model divergence and the conflicting interpretations of the records will thus be needed.
Bette L. Otto-Bliesner, Esther C. Brady, Anni Zhao, Chris M. Brierley, Yarrow Axford, Emilie Capron, Aline Govin, Jeremy S. Hoffman, Elizabeth Isaacs, Masa Kageyama, Paolo Scussolini, Polychronis C. Tzedakis, Charles J. R. Williams, Eric Wolff, Ayako Abe-Ouchi, Pascale Braconnot, Silvana Ramos Buarque, Jian Cao, Anne de Vernal, Maria Vittoria Guarino, Chuncheng Guo, Allegra N. LeGrande, Gerrit Lohmann, Katrin J. Meissner, Laurie Menviel, Polina A. Morozova, Kerim H. Nisancioglu, Ryouta O'ishi, David Salas y Mélia, Xiaoxu Shi, Marie Sicard, Louise Sime, Christian Stepanek, Robert Tomas, Evgeny Volodin, Nicholas K. H. Yeung, Qiong Zhang, Zhongshi Zhang, and Weipeng Zheng
Clim. Past, 17, 63–94, https://doi.org/10.5194/cp-17-63-2021, https://doi.org/10.5194/cp-17-63-2021, 2021
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The CMIP6–PMIP4 Tier 1 lig127k experiment was designed to address the climate responses to strong orbital forcing. We present a multi-model ensemble of 17 climate models, most of which have also completed the CMIP6 DECK experiments and are thus important for assessing future projections. The lig127ksimulations show strong summer warming over the NH continents. More than half of the models simulate a retreat of the Arctic minimum summer ice edge similar to the average for 2000–2018.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Cited articles
Biskaborn, B. K., Smith, S. L., Noetzli, J., Matthes, H., Vieira, G., Streletskiy, D. A., Schoeneich, P., Romanovsky, V. E., Lewkowicz, A. G., Abramov, A., Allard, M., Boike, J., Cable, W. L., Christiansen, H. H., Delaloye, R., Diekmann, B., Drozdov, D., Etzelmüller, B., Grosse, G., Guglielmin, M., Ingeman-Nielsen, T., Isaksen, K., Ishikawa, M., Johansson, M., Johannsson, H., Joo, A., Kaverin, D., Kholodov, A., Konstantinov, P., Kröger, T., Lambiel, C., Lanckman, J.-P., Luo, D., Malkova, G., Meiklejohn, I., Moskalenko, N., Oliva, M., Phillips, M., Ramos, M., Sannel, A. B. K., Sergeev, D., Seybold, C., Skryabin, P., Vasiliev, A., Wu, Q., Yoshikawa, K., Zheleznyak, M., and Lantuit, H.: Permafrost is warming at a global scale, Nat. Commun., 10, 264, https://doi.org/10.1038/s41467-018-08240-4, 2019. a, b
Boone, A., Samuelsson, P., Gollvik, S., Napoly, A., Jarlan, L., Brun, E., and Decharme, B.: The interactions between soil–biosphere–atmosphere land surface model with a multi-energy balance (ISBA-MEB) option in SURFEXv8 – Part 1: Model description, Geosci. Model Dev., 10, 843–872, https://doi.org/10.5194/gmd-10-843-2017, 2017. a
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. a, b
Brun, E., David, P., Sudul, M., and Brunot, G.: A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting, J. Glaciol., 38, 13–22, https://doi.org/10.3189/S0022143000009552, 1992. a, b
Brun, E., Vionnet, V., Boone, A., Decharme, B., Peings, Y., Valette, R., Karbou, F., and Morin, S.: Simulation of northern Eurasian local snow depth, mass, and density using a detailed snowpack model and meteorological reanalyses, J. Hydrometeorol., 14, 203–219, https://doi.org/10.1175/JHM-D-12-012.1, 2013. a, b, c, d, e, f, g, h, i
Cai, Z., You, Q., Chen, H. W., Zhang, R., Zuo, Z., Chen, D., Cohen, J., and Screen, J. A.: Assessing Arctic wetting: Performances of CMIP6 models and projections of precipitation changes, Atmos. Res., 297, 107124, https://doi.org/10.1016/j.atmosres.2023.107124, 2024. a
Callaghan, T. V., Johansson, M., Brown, R. D., Groisman, P. Y., Labba, N., Radionov, V., Barry, R. G., Bulygina, O. N., Essery, R. L. H., Frolov, D. M., Golubev, V. N., Grenfell, T. C., Petrushina, M. N., Razuvaev, V. N., Robinson, D. A., Romanov, P., Shindell, D., Shmakin, A. B., Sokratov, S. A., Warren, S., and Yang, D.: The changing face of Arctic snow cover: A synthesis of observed and projected changes, Ambio, 40, 17–31, 2011. a, b
Cohen, J., Screen, J. A., Furtado, J. C., Barlow, M., Whittleston, D., Coumou, D., Francis, J., Dethloff, K., Entekhabi, D., Overland, J., and Jones, J.: Recent Arctic amplification and extreme mid-latitude weather, Nat. Gosci., 7, 627–637, https://doi.org/https://doi.org/10.1038/ngeo2234, 2014. a
Decharme, B.: Crocus-ERA-Interim daily snow product over the Northern Hemisphere at 0.5° resolution [Data set], Zenodo [data set], https://doi.org/10.5281/zenodo.14513040, 2024. a
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. a, b
Decharme, B., Delire, C., Minvielle, M., Colin, J., Vergnes, J.-P., Alias, A., Saint-Martin, D., Séférian, R., Sénési, S., and Voldoire, A.: Recent changes in the ISBA-CTRIP land surface system for use in the CNRM-CM6 climate model and in global off-line hydrological applications, J. Adv. Model. Earth Syst., 11, 1207–1252, https://doi.org/10.1029/2018MS001545, 2019. a, b, c, d
Decharme, B., Barbu, A., and Ramos Buarque, S.: Crocus-ERA5 daily snow product over the Northern Hemisphere at 0.25° resolution [Data set], Zenodo [data set], https://doi.org/10.5281/zenodo.14513248, 2024. a, b, c
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Mteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a, b
Derksen, C. and Mudryk, L.: Assessment of Arctic seasonal snow cover rates of change, The Cryosphere, 17, 1431–1443, https://doi.org/10.5194/tc-17-1431-2023, 2023. a
Derksen, C., Walker, A., and Goodison, B.: Evaluation of passive microwave snow water equivalent retrievals across the boreal forest/tundra transition of western Canada, Remote Sens. Environ., 96, 315–327, https://doi.org/10.1016/j.rse.2005.02.014, 2005. a
Déry, S. J. and Yau, M.: Simulation of an Arctic ground blizzard using a coupled blowing snow–atmosphere model, J. Hydrometeorol., 2, 579–598, 2001. a
Dozier, J., Bair, E. H., and Davis, R. E.: Estimating the spatial distribution of snow water equivalent in the world's mountains, WIREs Water, 3, 461–474, https://doi.org/10.1002/wat2.1140, 2016. a
Druckenmiller, M. L., Thoman, R. L., Moon, T. A., Andreassen, L. M., Ballinger, T. J., Berner, L. T., Bernhard, G. H., Bhatt, U. S., Bigalke, S., Bjerke, J. W., Box, J. E., Brettschneider, B., Brubaker, M., Burgess, D., Butler, A. H., Christiansen, H. H., Decharme, B., Derksen, C., Divine, D., Jensen, C. D., Chereque, A. E., Epstein, H. E., Farrell, S., Fausto, R. S., Fettweis, X., Fioletov, V. E., Florentine, C., Forbes, B. C., Frost, G. V. J., Gerland, S., Grooß, J.-U., Hanna, E., Hanssen-Bauer, I., Heatta, M. J., Hendricks, S., Ialongo, I., Isaksen, K., Jeuring, J., Jia, G., Johnsen, B., Kaleschke, L., Kim, S.-J., Kohler, J., Labe, Z., Lader, R., Lakkala, K., Lara, M. J., Lee, S. H., Loomis, B. D., Luks, B., Luojus, K., Macander, M. J., Magnússon, R. I., Mankoff, K. D., Manney, G., Medley, B., Meier, W. N., Montesano, P. M., Mote, T. L., Mudryk, L., Müller, R., Neigh, C. S. R., Nyland, K. E., Overland, J. E., Pálsson, F., Poinar, K., Perovich, D. K., Petty, A., Phoenix, G. K., Ricker, R., Romanovsky, V. E., Sass, L., Scheller, J. H., Serreze, M. C., Shiklomanov, N. I., Smith, B. E., Smith, S. L., Streletskiy, D. A., Svendby, T., Tedesco, M., Thomson, L., Thorsteinsson, T., Tian-Kunze, X., Timmermans, M.-L., Tømmervik, H., Waigl, C. F., Walker, D. S. A., Walsh, J. E., Wang, M., Webster, M., Wehrlé, A., Wolken, G. J., Wouters, B., and Yang, D.: The Arctic, B. Am. Meteorol. Soc., 105, S277–S330, https://doi.org/10.1175/BAMS-D-24-0101.1, 2024. a, b, c, d
Dunn, R. J. H., Miller, J. B., Willett, K. M., Gobron, N., Ades, M., Adler, R., Alexe, M., Allan, R. P., Anderson, J., Anneville, O., Aono, Y., Arguez, A., Arosio, C., Augustine, J. A., Azorin-Molina, C., Barichivich, J., Barnes, J. E., Beck, H. E., Bellouin, N., Benedetti, A., Blagrave, K., Blenkinsop, S., Bock, O., Bodin, X., Bosilovich, M., Boucher, O., Buechler, D., Buehler, S. A., Campos, D., Carrea, L., Chang, K.-L., Christiansen, H. H., Christy, J. R., Chung, E.-S., Ciasto, L. M., Clingan, S., Coldewey-Egbers, M., Cooper, O. R., Cornes, R. C., Covey, C., Créatux, J.-F., Crimmins, T., Cropper, T., Crotwell, M., Culpepper, J., Cusicanqui, D., Davis, S. M., de Jeu, R. A. M., Degenstein, D., Delaloye, R., Dokulil, M. T., Donat, M. G., Dorigo, W. A., Dugan, H. A., Durre, I., Dutton, G., Duveiller, G., Estilow, T. W., Estrella, N., Fereday, D., Fioletov, V. E., Flemming, J., Foster, M. J., Franz, B., Frith, S. M., Froidevaux, L., Füllekrug, M., Garforth, J., Garg, J., Gibbes, B., Goodman, S., Goto, A., Gruber, A., Gu, G., Hahn, S., Haimberger, L., Hall, B. D., Harris, I., Hemming, D. L., Hirschi, M., Ho, S., Holzworth, R., Hrbáček, F., Hu, G., Hurst, D. F., Inness, A., Isaksen, K., John, V. O., Jones, P. D., Junod, R., Kääb, A., Kaiser, J. W., Kaufmann, V., Kellerer-Pirklbauer, A., Kent, E. C., Kidd, R., Kipling, Z., Koppa, A., Kraemer, B. M., Kramarova, N., Kruger, A., Fuente, S. L., Laas, A., Lan, X., Lang, T., Lantz, K. O., Lavers, D. A., Leblanc, T., Leibensperger, E. M., Lennard, C., Liu, Y., Loeb, N. G., Loyola, D., Maberly, S. C., Madelon, R., Magnin, F., Matsuzaki, S.-I., May, L., Mayer, M., McCabe, M. F., McVicar, T. R., Mears, C. A., Menzel, A., Merchant, C. J., Meyer, M. F., Miralles, D. G., Moesinger, L., Monet, G., Montzka, S. A., Morice, C., Mrekaj, I., Mühle, J., Nance, D., Nicolas, J. P., Noetzli, J., Noll, B., O'Keefe, J., Osborn, T. J., Park, T., Parrington, M., Pellet, C., Pelto, M. S., Petersen, K., Phillips, C., Pierson, D., Pinto, I., Po-Chedley, S., Pogliotti, P., Polvani, L., Preimesberger, W., Price, C., Pulkkanen, M., Randel, W. J., Rémy, S., Ricciardulli, L., Richardson, A. D., Robinson, D. A., Rocha, W., Rodell, M., Rodriguez-Fernandez, N., Rosenlof, K. H., Rozanov, A., Rozkošný, J., Rusanovskaya, O. O., Rutishauser, T., Sabeerali, C. T., Sánchez-Lugo, A., Sawaengphokhai, P., Schenzinger, V., Schlegel, R. W., Schmid, M., Schneider, U., Sezaki, F., Sharma, S., Shi, L., Shimaraeva, S. V., Silow, E. A., Simmons, A. J., Smith, S. L., Soden, B. J., Sofieva, V., Sparks, T. H., Sreejith, O., Stackhouse, P. W., Stauffer, R., Steinbrecht, W., Steiner, A. K., Stradiotti, P., Streletskiy, D. A., Surendran, D. E., Thackeray, S. J., Thibert, E., Timofeyev, M. A., Tourpali, K., Tye, M. R., van der A, R., van der Schalie, R., van der Schrier, G., van Vliet, A. J., Verburg, P., Vernier, J.-P., Vimont, I. J., Virts, K., Vivero, S., Vömel, H., Vose, R. S., Wang, R. H. J., Wang, X., Warnock, T., Weber, M., Wiese, D. N., Wild, J. D., Williams, E., Wong, T., Woolway, R. I., Yin, X., Zeng, Z., Zhao, L., Zhou, X., Ziemke, J. R., Ziese, M., Zotta, R. M., Zou, C.-Z., Allen, J., Camper, A. V., Haley, B. O., Hammer, G., Love-Brotak, S. E., Ohlmann, L., Noguchi, L., Riddle, D. B., and Veasey, S. W.: Global Climate, B. Am. Meteorol. Soc., 104, S11–S145, https://doi.org/10.1175/BAMS-D-23-0090.1, 2023. a
Essery, R., Mazzotti, G., Barr, S., Jonas, T., Quaife, T., and Rutter, N.: A Flexible Snow Model (FSM 2.1.1) including a forest canopy, Geosc, Model Dev,, 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, 2025. a, b
Estilow, T. W., Young, A. H., and Robinson, D. A.: A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring, Earth Syst. Sci. Data, 7, 137–142, https://doi.org/10.5194/essd-7-137-2015, 2015. a, b, c
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fontrodona-Bach, A., Schaefli, B., Woods, R., Teuling, A. J., and Larsen, J. R.: NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in situ snow depth time series, Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023, 2023. a
Helfrich, S. R., McNamara, D., Ramsay, B. H., Baldwin, T., and Kasheta, T.: Enhancements to, and forthcoming developments in the Interactive Multisensor Snow and Ice Mapping System (IMS), Hydrol. Process., 21, 1576–1586, https://doi.org/10.1002/hyp.6720, 2007. a, b, c
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. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c, d
Ingleby, B., Arduini, G., Balsamo, G., Boussetta, S., Ochi, K., Pinnington, E., and de Rosnay, P.: Improved two-metre temperature forecasts in the 2024 upgrade, ECMWF Newsletter, ECMWF, https://doi.org//10.21957/bi49s20qa8, 2024. a
Juckes, M., Taylor, K. E., Durack, P. J., Lawrence, B., Mizielinski, M. S., Pamment, A., Peterschmitt, J.-Y., Rixen, M., and Sénési, S.: The CMIP6 Data Request (DREQ, version 01.00.31), Geosci. Model Dev., 13, 201–224, https://doi.org/10.5194/gmd-13-201-2020, 2020. a
Kouki, K., Luojus, K., and Riihelä, A.: Evaluation of snow cover properties in ERA5 and ERA5-Land with several satellite-based datasets in the Northern Hemisphere in spring 1982–2018, The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, 2023. a
Landrum, L. L. and Holland, M. M.: Influences of changing sea ice and snow thicknesses on simulated Arctic winter heat fluxes, The Cryosphere, 16, 1483–1495, https://doi.org/10.5194/tc-16-1483-2022, 2022. a
Larue, F., Royer, A., De Sève, D., Langlois, A., Roy, A., and Brucker, L.: Validation of GlobSnow-2 snow water equivalent over Eastern Canada, Remote Sens. Environ., 194, 264–277, https://doi.org/10.1016/j.rse.2017.03.027, 2017. a
Letterly, A., Key, J., and Liu, Y.: Arctic climate: changes in sea ice extent outweigh changes in snow cover, The Cryosphere, 12, 3373–3382, https://doi.org/10.5194/tc-12-3373-2018, 2018. a
Luo, D., Wu, Q., Jin, H., Marchenko, S. S., Lü, L., and Gao, S.: Recent changes in the active layer thickness across the northern hemisphere, Environ. Earth Sci., 75, 555, https://doi.org/10.1007/s12665-015-5229-2, 2016. a
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013. a
Matthes, H., Damseaux, A., Westermann, S., Beer, C., Boone, A., Burke, E., Decharme, B., Genet, H., Jafarov, E., Langer, M., Parmentier, F.-J., Porada, P., Gagne-Landmann, A., Huntzinger, D., Rogers, B. M., Schädel, C., Stacke, T., Wells, J., and Wieder, W. R.: Advances in Permafrost Representation: Biophysical Processes in Earth System Models and the Role of Offline Models, Permafrost Periglac. Process., 36, 302–318, https://doi.org/10.1002/ppp.2269, 2025. a
Meredith, M., Sommerkorn, M., Cassota, S., Derksen, C., Ekaykin, A., Hollowed, A., and Kofinas, G.: Polar Regions, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge University Press, 203–320, https://doi.org/10.1017/9781009157964.005, 2022. a
Monteiro, D., Caillaud, C., Lafaysse, M., Napoly, A., Fructus, M., Alias, A., and Morin, S.: Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model, Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, 2024. a
Mortimer, C., Mudryk, L., Derksen, C., Luojus, K., Brown, R., Kelly, R., and Tedesco, M.: Evaluation of long-term Northern Hemisphere snow water equivalent products, The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, 2020. a, b
Mudryk, L., Derksen, C., Kushner, P. J., and Brown, R.: Characterization of Northern Hemisphere Snow Water Equivalent Datasets, 1981–2010, J. Climate, 28, 8037–8051, https://doi.org/10.1175/JCLI-D-15-0229.1, 2015. a, b
Mudryk, L., Santolaria-Otín, M., Krinner, G., Ménégoz, M., Derksen, C., Brutel-Vuilmet, C., Brady, M., and Essery, R.: Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble, The Cryosphere, 14, 2495–2514, https://doi.org/10.5194/tc-14-2495-2020, 2020. a
Mudryk, L. R., Elias Chereque, A., Derksen, C., Luojus, K., and Decharme, B.: NOAA Arctic Report Card 2024: Terrestrial Snow Cover, Tech. Rep.. NOAA Technical Report 2024-04, NOAA, https://doi.org/10.25923/4bb3-3f87, 2024. a, b, c, d
Napoly, A., Boone, A., and Welfringer, T.: ISBA-MEB (SURFEX v8.1): model snow evaluation for local-scale forest sites, Geosci. Model Dev., 13, 6523–6545, https://doi.org/10.5194/gmd-13-6523-2020, 2020. a, b
Natali, S. M., Watts, J. D., Rogers, B. M., Potter, S., Ludwig, S. M., Selbmann, A.-K., Sullivan, P. F., Abbott, B. W., Arndt, K. A., Birch, L., Björkman, M. P., Bloom, A. A., Celis, G., Christensen, T. R., Christiansen, C. T., Commane, R., Cooper, E. J., Crill, P., Czimczik, C., Davydov, S., Du, J., Egan, J. E., Elberling, B., Euskirchen, E. S., Friborg, T., Genet, H., Göckede, M., Goodrich, J. P., Grogan, P., Helbig, M., Jafarov, E. E., Jastrow, J. D., Kalhori, A. A. M., Kim, Y., Kimball, J. S., Kutzbach, L., Lara, M. J., Larsen, K. S., Lee, B.-Y., Liu, Z., Loranty, M. M., Lund, M., Lupascu, M., Madani, N., Malhotra, A., Matamala, R., McFarland, J., McGuire, A. D., Michelsen, A., Minions, C., Oechel, W. C., Olefeldt, D., Parmentier, F.-J. W., Pirk, N., Poulter, B., Quinton, W., Rezanezhad, F., Risk, D., Sachs, T., Schaefer, K., Schmidt, N. M., Schuur, E. A. G., Semenchuk, P. R., Shaver, G., Sonnentag, O., Starr, G., Treat, C. C., Waldrop, M. P., Wang, Y., Welker, J., Wille, C., Xu, X., Zhang, Z., Zhuang, Q., and Zona, D.: Large loss of CO2 in winter observed across the northern permafrost region, Nat. Clim. Change, 9, 852–857, https://doi.org/10.1038/s41558-019-0592-8, 2019. a
NOAA Arctic Program: Arctic Report Card (Annual Publication), https://arctic.noaa.gov/report-card/ (last access: 29 July 2025), 2025. a
Overland, J., Dunlea, E., Box, J. E., Corell, R., Forsius, M., Kattsov, V., Olsen, M. S., Pawlak, J., Reiersen, L.-O., and Wang, M.: The urgency of Arctic change, Polar Sci., 21, 6–13, https://doi.org/10.1016/j.polar.2018.11.008, 2019. a
Park, H., Kim, Y., and Kimball, J. S.: Widespread permafrost vulnerability and soil active layer increases over the high northern latitudes inferred from satellite remote sensing and process model assessments, Remote Sens. Environ., 175, 349–358, https://doi.org/10.1016/j.rse.2015.12.046, 2016. a, b
Peings, Y. and Magnusdottir, G.: Role of sea surface temperature, Arctic sea ice and Siberian snow in forcing the atmospheric circulation in winter of 2012–2013, Clim. Dynam., 45, 1181–1206, 2015. a
Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Weyer, N., Alegría, A., Nicolai, M., Okem, A., Petzold, J., and Rama, B.: The ocean and cryosphere in a changing climate, IPCC special report on the ocean and cryosphere in a changing climate, Cambridge University Press, https://doi.org/10.1017/9781009157964, 2019. a
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., and Norberg, J.: Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018, Nature, 581, 294–298, https://doi.org/10.1038/s41586-020-2258-0, 2020. a, b, c
Ramos Buarque, S. and Salas y Melia, D.: Link between the North Atlantic Oscillation and the surface mass balance components of the Greenland Ice Sheet under preindustrial and last interglacial climates: a study with a coupled global circulation model, Clim. Past, 14, 1707–1725, https://doi.org/10.5194/cp-14-1707-2018, 2018. a
Romanovsky, V. E., Smith, S. L., and Christiansen, H. H.: Permafrost thermal state in the polar Northern Hemisphere during the international polar year 2007–2009: a synthesis, Permafrost Periglac. Process., 21, 106–116, 2010. a
Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B., Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P.: A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data, 9, 389–413, https://doi.org/10.5194/essd-9-389-2017, 2017. a, b
See, C. R., Virkkala, A.-M., Natali, S. M., Rogers, B. M., Mauritz, M., Biasi, C., Bokhorst, S., Boike, J., Bret-Harte, M. S., Celis, G., Chae, N., Christensen, T. R., Murner, S. J., Dengel, S., Dolman, H., Edgar, C. W., Elberling, B., Emmerton, C. A., Euskirchen, E. S., Göckede, M., Grelle, A., Heffernan, L., Helbig, M., Holl, D., Humphreys, E., Iwata, H., Järveoja, J., Kobayashi, H., Kochendorfer, J., Kolari, P., Kotani, A., Kutzbach, L., Kwon, M. J., Lathrop, E. R., López-Blanco, E., Mammarella, I., Marushchak, M. E., Mastepanov, M., Matsuura, Y., Merbold, L., Meyer, G., Minions, C., Nilsson, M. B., Nojeim, J., Oberbauer, S. F., Olefeldt, D., Park, S.-J., Parmentier, F.-J. W., Peichl, M., Peter, D., Petrov, R., Poyatos, R., Prokushkin, A. S., Quinton, W., Rodenhizer, H., Sachs, T., Savage, K., Schulze, C., Sjögersten, S., Sonnentag, O., Louis, V. L. S., Torn, M. S., Tuittila, E.-S., Ueyama, M., Varlagin, A., Voigt, C., Watts, J. D., Zona, D., Zyryanov, V. I., and Schuur, E. A. G.: Decadal increases in carbon uptake offset by respiratory losses across northern permafrost ecosystems, Nat. Clim. Change, 14, 853–862, https://doi.org/10.1038/s41558-024-02057-4, 2024. a
Shao, D., Li, H., Wang, J., Hao, X., Che, T., and Ji, W.: Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach, Earth Syst. Sci. Data, 14, 795–809, https://doi.org/10.5194/essd-14-795-2022, 2022. a
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.: Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes, J. Hydrometeorol., 11, 1380–1394, https://doi.org/10.1175/2010JHM1202.1, 2010. a
US National Ice Center: IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1 [data set], https://doi.org/10.7265/N52R3PMC, 2008. a, b
Vihma, T., Screen, J., Tjernström, M., Newton, B., Zhang, X., Popova, V., Deser, C., Holland, M., and Prowse, T.: The atmospheric role in the Arctic water cycle: A review on processes, past and future changes, and their impacts, J. Geophys. Res.-Biogeo., 121, 586–620, 2016. a
Wang, C., Graham, R. M., Wang, K., Gerland, S., and Granskog, M. A.: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, 2019. a
Wang, K., Zhang, T., and Yang, D.: Permafrost dynamics and their hydrologic impacts over the Russian Arctic drainage basin, Adv. Clim. Change Res., 12, 482–498, 2021. a
Winkler, M., Schellander, H., and Gruber, S.: Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model, Hydrol. Earth Syst. Sci., 25, 1165–1187, https://doi.org/10.5194/hess-25-1165-2021, 2021. a
Zhang, T.: Influence of the seasonal snow cover on the ground thermal regime: An overview, Rev. Geophys., 43, https://doi.org/10.1029/2004RG000157, 2005. a, b, c
Zhong, X., Zhang, T., Kang, S., Wang, K., Zheng, L., Hu, Y., and Wang, H.: Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012, The Cryosphere, 12, 227–245, https://doi.org/10.5194/tc-12-227-2018, 2018. a, b, c
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.
The Crocus-ERA5 snow dataset supports Arctic snow monitoring and contributes to the Arctic...
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