Articles | Volume 19, issue 12
https://doi.org/10.5194/tc-19-6547-2025
© 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/tc-19-6547-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Dec 2025
Research article |
| 05 Dec 2025
| 05 Dec 2025
Assessing uncertainties in modeling the climate of the Siberian frozen soils by contrasting CMIP6 and LS3MIP
Zhicheng Luo
Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt, Frankfurt a.M., Germany
Danny Risto
Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt, Frankfurt a.M., Germany
Bodo Ahrens
CORRESPONDING AUTHOR
Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt, Frankfurt a.M., Germany
Related authors
Mittal Parmar, Kristina Fröhlich, Antonella Sanna, Tobias Stacke, Marianna Benassi, Zhicheng Luo, Daniele Peano, and Bodo Ahrens
EGUsphere, https://doi.org/10.5194/egusphere-2026-381, https://doi.org/10.5194/egusphere-2026-381, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Seasonally frozen ground strongly influences land–atmosphere interactions, yet its simulation remains uncertain in land surface models. This study evaluates 2 standalone LSM simulations forced by reanalysis data (1986–2022) and validates results against reference observations at 26 Russian sites. Results show contrasting biases linked to snow insulation and freeze–thaw processes, highlighting the need for improved snow and soil parameterizations.
Mittal Parmar, Kristina Fröhlich, Antonella Sanna, Tobias Stacke, Marianna Benassi, Zhicheng Luo, Daniele Peano, and Bodo Ahrens
EGUsphere, https://doi.org/10.5194/egusphere-2026-381, https://doi.org/10.5194/egusphere-2026-381, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Seasonally frozen ground strongly influences land–atmosphere interactions, yet its simulation remains uncertain in land surface models. This study evaluates 2 standalone LSM simulations forced by reanalysis data (1986–2022) and validates results against reference observations at 26 Russian sites. Results show contrasting biases linked to snow insulation and freeze–thaw processes, highlighting the need for improved snow and soil parameterizations.
Fanni Dóra Kelemen, Richard Lohmann, Jiang Zhu, and Bodo Ahrens
Clim. Past, 22, 505–516, https://doi.org/10.5194/cp-22-505-2026, https://doi.org/10.5194/cp-22-505-2026, 2026
Short summary
Short summary
The arrangement of continents and oceans strongly affects climate by shaping large-scale circulation patterns. We study, how early Eocene geography (53.5 Ma) influenced mid-latitude storms and persistent high-pressure systems, focusing on the West Siberian Sea and absent Antarctic Circumpolar Current. The climate model simulation of the early Eocene, shows a more balanced hemispheric distribution, through increased northern and decreased southern mid-latitude storm activity compared to today.
Prashant Singh and Bodo Ahrens
Atmos. Chem. Phys., 25, 17869–17888, https://doi.org/10.5194/acp-25-17869-2025, https://doi.org/10.5194/acp-25-17869-2025, 2025
Short summary
Short summary
Intense deep convective clouds (e.g. lightning events) can rapidly move water vapour and other gases into the upper troposphere. The Third Pole region, especially the Himalayas, frequently experiences such storms. ICON (Icosahedral Nonhydrostatic )-CLM (climate limited-area mode) (3.3 km) and ERA5 reanalysis data (30 km), these convective events can lift water vapour into the upper troposphere but rarely into the lower stratosphere in the Third Pole. After reaching the upper troposphere, the water vapour tends to move horizontally away from the region.
Christian Czakay, Larisa Tarasova, and Bodo Ahrens
EGUsphere, https://doi.org/10.5194/egusphere-2025-3532, https://doi.org/10.5194/egusphere-2025-3532, 2025
Short summary
Short summary
In this study, we simulated streamflow in German river catchments for climate projections using a deep learning model. Flood-generating processes were identified using explainable artificial intelligence. In the median, the models project mostly less rain-on-snow floods in Germany in the future and an overall lower importance of snowmelt. The average and strongest rain-on-snow floods will have a higher magnitude. The trends found for the individual climate models can vary considerably.
Richard Lohmann, Christopher Purr, and Bodo Ahrens
EGUsphere, https://doi.org/10.5194/egusphere-2025-3670, https://doi.org/10.5194/egusphere-2025-3670, 2025
Short summary
Short summary
This study investigates the relationship between atmospheric blocking and the extreme events heatwaves, heavy rainfall and calm events in Germany in atmospheric reanalyses and CMIP6 climate simulations. In the reanalyses, the statistical relationship is more pronounced between blocking and calms than between blocking and heavy precipitation. In the simulated future climate, the frequency of the three extreme event types increases with nearly unchanged relationship of blocking with the extremes.
Praveen Kumar Pothapakula, Amelie Hoff, Anika Obermann-Hellhund, Timo Keber, and Bodo Ahrens
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2022-24, https://doi.org/10.5194/esd-2022-24, 2022
Preprint withdrawn
Short summary
Short summary
The Vb-cyclones simulated with a coupled regional climate model with two different driving data sets are compared against each other in historical period, thereafter the future climate predictions were analyzed. The Vb-cyclones in two simulations agree well in terms of their occurrence, intensity and track in two simulations, though there are discrepancies in seasonal cycles and their process linking Mediterranean Sea in historical period. So significant changes were observed in the future.
Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
Short summary
Short summary
We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Cited articles
Abbott, B. W. and Jones, J. B.: Permafrost collapse alters soil carbon stocks, respiration, CH4, and N2O in upland tundra, Global Change Biology, 21, 4570–4587, https://doi.org/10.1111/gcb.13069, 2015. a
Åkerman, H. J. and Johansson, M.: Thawing permafrost and thicker active layers in sub-arctic Sweden, Permafrost and Periglacial Processes, 19, 279–292, https://doi.org/10.1002/ppp.626, 2008. a
Andresen, C. G., Lawrence, D. M., Wilson, C. J., McGuire, A. D., Koven, C., Schaefer, K., Jafarov, E., Peng, S., Chen, X., Gouttevin, I., Burke, E., Chadburn, S., Ji, D., Chen, G., Hayes, D., and Zhang, W.: Soil moisture and hydrology projections of the permafrost region – a model intercomparison, The Cryosphere, 14, 445–459, https://doi.org/10.5194/tc-14-445-2020, 2020. a
Beringer, J., Lynch, A. H., Chapin, F. S., Mack, M., and Bonan, G. B.: The Representation of Arctic Soils in the Land Surface Model: The Importance of Mosses, Journal of Climate, 14, 3324–3335, https://doi.org/10.1175/1520-0442(2001)014<3324:TROASI>2.0.CO;2, 2001. a
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, Nature Communications, 10, 264, https://doi.org/10.1038/s41467-018-08240-4, 2019. a
Boike, J., Kattenstroth, B., Abramova, K., Bornemann, N., Chetverova, A., Fedorova, I., Fröb, K., Grigoriev, M., Grüber, M., Kutzbach, L., Langer, M., Minke, M., Muster, S., Piel, K., Pfeiffer, E.-M., Stoof, G., Westermann, S., Wischnewski, K., Wille, C., and Hubberten, H.-W.: Baseline characteristics of climate, permafrost and land cover from a new permafrost observatory in the Lena River Delta, Siberia (1998–2011), Biogeosciences, 10, 2105–2128, https://doi.org/10.5194/bg-10-2105-2013, 2013. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical, Version 20180711, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5195, 2018. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Ghattas, J., Cadule, P., Ducharne, A., Vuichard, N., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 LS3MIP land-hist, Version 20191107, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5205, 2019. a
Bowring, S. P. K., Lauerwald, R., Guenet, B., Zhu, D., Guimberteau, M., Tootchi, A., Ducharne, A., and Ciais, P.: ORCHIDEE MICT-LEAK (r5459), a global model for the production, transport, and transformation of dissolved organic carbon from Arctic permafrost regions – Part 1: Rationale, model description, and simulation protocol, Geosci. Model Dev., 12, 3503–3521, https://doi.org/10.5194/gmd-12-3503-2019, 2019. a
Brown, J., Sidlauskas, F. J., and Delinski, G.: International Permafrost Association circum-Arctic map of permafrost and ground ice conditions, The Survey, Information Services, Reston, Va., Denver, Colo., oCLC: 38148545, 1997. a
Brunke, M. A., Broxton, P., Pelletier, J., Gochis, D., Hazenberg, P., Lawrence, D. M., Leung, L. R., Niu, G.-Y., Troch, P. A., and Zeng, X.: Implementing and Evaluating Variable Soil Thickness in the Community Land Model, Version 4.5 (CLM4.5), Journal of Climate, 29, 3441–3461, https://doi.org/10.1175/JCLI-D-15-0307.1, 2016. a
Bulygina, O., Razuvaev, V., and Aleksandrova, T.: Description of the dataset of snow characteristics at meteorological stations in Russia and the former USSR, All-Russian Research Institute of Hydrometeorological Information – World Data Center, Obninsk, http://aisori-m.meteo.ru/waisori/index.xhtml?idata=7 (last access: 13 October 2022), 2014a. a
Bulygina, O., Razuvaev, V., and Alexandrova, T.: Description of the data array of daily air temperature and precipitation at meteorological stations in Russia and the former USSR (TTTR), All-Russian Research Institute of Hydrometeorological Information – World Data Center, Obninsk, http://aisori-m.meteo.ru/waisori/index.xhtml?idata=5 (last access: 13 October 2022), 2014b. a
Burke, E. J., Zhang, Y., and Krinner, G.: Evaluating permafrost physics in the Coupled Model Intercomparison Project 6 (CMIP6) models and their sensitivity to climate change, The Cryosphere, 14, 3155–3174, https://doi.org/10.5194/tc-14-3155-2020, 2020. a, b, c
Cai, L., Lee, H., Aas, K. S., and Westermann, S.: Projecting circum-Arctic excess-ground-ice melt with a sub-grid representation in the Community Land Model, The Cryosphere, 14, 4611–4626, https://doi.org/10.5194/tc-14-4611-2020, 2020. a
Cai, Z., You, Q., Wu, F., Chen, H. W., Chen, D., and Cohen, J.: Arctic Warming Revealed by Multiple CMIP6 Models: Evaluation of Historical Simulations and Quantification of Future Projection Uncertainties, Journal of Climate, 34, 4871–4892, https://doi.org/10.1175/JCLI-D-20-0791.1, 2021. a
Chadburn, S., Burke, E., Essery, R., Boike, J., Langer, M., Heikenfeld, M., Cox, P., and Friedlingstein, P.: An improved representation of physical permafrost dynamics in the JULES land-surface model, Geosci. Model Dev., 8, 1493–1508, https://doi.org/10.5194/gmd-8-1493-2015, 2015. a, b
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011. a
Cuntz, M. and Haverd, V.: Physically Accurate Soil Freeze-Thaw Processes in a Global Land Surface Scheme, Journal of Advances in Modeling Earth Systems, 10, 54–77, https://doi.org/10.1002/2017MS001100, 2018. a
Damseaux, A., Matthes, H., Dutch, V. R., Wake, L., and Rutter, N.: Impact of snow thermal conductivity schemes on pan-Arctic permafrost dynamics in the Community Land Model version 5.0, The Cryosphere, 19, 1539–1558, https://doi.org/10.5194/tc-19-1539-2025, 2025. a, b, c
Danabasoglu, G.: NCAR CESM2 model output prepared for CMIP6 LS3MIP land-hist, Version 20190128, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.7650, 2019a. a
Danabasoglu, G.: NCAR CESM2 model output prepared for CMIP6 CMIP historical, Version 20190116, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.7627, 2019b. 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
Decharme, B., Delire, C., Minvielle, M., Colin, J., Vergnes, J., 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, Journal of Advances in Modeling Earth Systems, 11, 1207–1252, https://doi.org/10.1029/2018MS001545, 2019. a, b
Deng, M., Meng, X., Lu, Y., Li, Z., Zhao, L., Hu, Z., Chen, H., Shang, L., Wang, S., and Li, Q.: Impact and Sensitivity Analysis of Soil Water and Heat Transfer Parameterizations in Community Land Surface Model on the Tibetan Plateau, Journal of Advances in Modeling Earth Systems, 13, e2021MS002670, https://doi.org/10.1029/2021MS002670, 2021. a
de Vrese, P., Georgievski, G., Gonzalez Rouco, J. F., Notz, D., Stacke, T., Steinert, N. J., Wilkenskjeld, S., and Brovkin, V.: Representation of soil hydrology in permafrost regions may explain large part of inter-model spread in simulated Arctic and subarctic climate, The Cryosphere, 17, 2095–2118, https://doi.org/10.5194/tc-17-2095-2023, 2023. a
Du, R., Peng, X., Frauenfeld, O. W., Jin, H., Wang, K., Zhao, Y., Luo, D., and Mu, C.: Quantitative Impact of Organic Matter and Soil Moisture on Permafrost, Journal of Geophysical Research-Atmospheres, 128, e2022JD037686, https://doi.org/10.1029/2022JD037686, 2023. a
Dutch, V. R., Rutter, N., Wake, L., Sandells, M., Derksen, C., Walker, B., Hould Gosselin, G., Sonnentag, O., Essery, R., Kelly, R., Marsh, P., King, J., and Boike, J.: Impact of measured and simulated tundra snowpack properties on heat transfer, The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, 2022. a
Ekici, A., Beer, C., Hagemann, S., Boike, J., Langer, M., and Hauck, C.: Simulating high-latitude permafrost regions by the JSBACH terrestrial ecosystem model, Geosci. Model Dev., 7, 631–647, https://doi.org/10.5194/gmd-7-631-2014, 2014. a, b
Ekici, A., Chadburn, S., Chaudhary, N., Hajdu, L. H., Marmy, A., Peng, S., Boike, J., Burke, E., Friend, A. D., Hauck, C., Krinner, G., Langer, M., Miller, P. A., and Beer, C.: Site-level model intercomparison of high latitude and high altitude soil thermal dynamics in tundra and barren landscapes, The Cryosphere, 9, 1343–1361, https://doi.org/10.5194/tc-9-1343-2015, 2015. a
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, b
Frauenfeld, O. W. and Zhang, T.: An observational 71-year history of seasonally frozen ground changes in the Eurasian high latitudes, Environmental Research Letters, 6, 044024, https://doi.org/10.1088/1748-9326/6/4/044024, 2011. a
Fuchs, M., Grosse, G., Strauss, J., Günther, F., Grigoriev, M., Maximov, G. M., and Hugelius, G.: Carbon and nitrogen pools in thermokarst-affected permafrost landscapes in Arctic Siberia, Biogeosciences, 15, 953–971, https://doi.org/10.5194/bg-15-953-2018, 2018. a
Groenke, B., Langer, M., Nitzbon, J., Westermann, S., Gallego, G., and Boike, J.: Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer, The Cryosphere, 17, 3505–3533, https://doi.org/10.5194/tc-17-3505-2023, 2023. a
Groisman, P. Y. and Bartalev, S. A.: Northern Eurasia earth science partnership initiative (NEESPI), science plan overview, Global and Planetary Change, 56, 215–234, https://doi.org/10.1016/j.gloplacha.2006.07.027, 2007. a
Guimberteau, M., Zhu, D., Maignan, F., Huang, Y., Yue, C., Dantec-Nédélec, S., Ottlé, C., Jornet-Puig, A., Bastos, A., Laurent, P., Goll, D., Bowring, S., Chang, J., Guenet, B., Tifafi, M., Peng, S., Krinner, G., Ducharne, A., Wang, F., Wang, T., Wang, X., Wang, Y., Yin, Z., Lauerwald, R., Joetzjer, E., Qiu, C., Kim, H., and Ciais, P.: ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation, Geosci. Model Dev., 11, 121–163, https://doi.org/10.5194/gmd-11-121-2018, 2018. a
Guo, Q., Kino, K., Li, S., Nitta, T., Takeshima, A., Nitta, T., Onuma, Y., Satoh, Y., Suzuki, T., Takata, K., Yoshida, N., and Yoshimura, K.: Description of MATSIRO6, Division of Climate System Research, Atmosphere and Ocean Research Institute, The University of Tokyo, https://doi.org/10.15083/0002000181, 2021. a
Heijmans, M. M. P. D., Magnússon, R. Í., Lara, M. J., Frost, G. V., Myers-Smith, I. H., Van Huissteden, J., Jorgenson, M. T., Fedorov, A. N., Epstein, H. E., Lawrence, D. M., and Limpens, J.: Tundra vegetation change and impacts on permafrost, Nature Reviews Earth & Environment, 3, 68–84, https://doi.org/10.1038/s43017-021-00233-0, 2022. a
Jafarov, E. and Schaefer, K.: The importance of a surface organic layer in simulating permafrost thermal and carbon dynamics, The Cryosphere, 10, 465–475, https://doi.org/10.5194/tc-10-465-2016, 2016. a
Jafarov, E. E., Harp, D. R., Coon, E. T., Dafflon, B., Tran, A. P., Atchley, A. L., Lin, Y., and Wilson, C. J.: Estimation of subsurface porosities and thermal conductivities of polygonal tundra by coupled inversion of electrical resistivity, temperature, and moisture content data, The Cryosphere, 14, 77–91, https://doi.org/10.5194/tc-14-77-2020, 2020. a
Jain, S., Scaife, A. A., Shepherd, T. G., Deser, C., Dunstone, N., Schmidt, G. A., Trenberth, K. E., and Turkington, T.: Importance of internal variability for climate model assessment, npj Climate and Atmospheric Science, 6, 68, https://doi.org/10.1038/s41612-023-00389-0, 2023. a
Koven, C. D., Riley, W. J., and Stern, A.: Analysis of Permafrost Thermal Dynamics and Response to Climate Change in the CMIP5 Earth System Models, Journal of Climate, 26, 1877–1900, https://doi.org/10.1175/JCLI-D-12-00228.1, 2013. a, b
Koven, C. D., Lawrence, D. M., and Riley, W. J.: Permafrost carbon–climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics, Proceedings of the National Academy of Sciences, 112, 3752–3757, https://doi.org/10.1073/pnas.1415123112, 2015. a
Kudryavtsev, V. A., Garagulya, L. S., Kondrat yeva, K. A., and Melamed, V. G.: Fundamentals of frost forecasting in geological engineering investigations, Cold Regions Research and Engineering Laboratory, 606, 489 pp., 1977. a
Kuma, P., Bender, F. A., and Jönsson, A. R.: Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity, Journal of Advances in Modeling Earth Systems, 15, e2022MS003588, https://doi.org/10.1029/2022MS003588, 2023. a, b, c
Langer, M., Westermann, S., Muster, S., Piel, K., and Boike, J.: The surface energy balance of a polygonal tundra site in northern Siberia – Part 2: Winter, The Cryosphere, 5, 509–524, https://doi.org/10.5194/tc-5-509-2011, 2011a. a, b
Langer, M., Westermann, S., Muster, S., Piel, K., and Boike, J.: The surface energy balance of a polygonal tundra site in northern Siberia – Part 1: Spring to fall, The Cryosphere, 5, 151–171, https://doi.org/10.5194/tc-5-151-2011, 2011b. a
Lawrence, D. M. and Slater, A. G.: A projection of severe near-surface permafrost degradation during the 21st century, Geophysical Research Letters, 32, L24401, https://doi.org/10.1029/2005GL025080, 2005. a, b
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., Van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., Van Den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, Journal of Advances in Modeling Earth Systems, 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a
Li, Q., Sun, S., and Xue, Y.: Analyses and development of a hierarchy of frozen soil models for cold region study, Journal of Geophysical Research-Atmospheres, 115, 2009JD012530, https://doi.org/10.1029/2009JD012530, 2010. a
Li, X., Wu, T., Wu, X., Chen, J., Zhu, X., Hu, G., Li, R., Qiao, Y., Yang, C., Hao, J., Ni, J., and Ma, W.: Assessing the simulated soil hydrothermal regime of the active layer from the Noah-MP land surface model (v1.1) in the permafrost regions of the Qinghai–Tibet Plateau, Geosci. Model Dev., 14, 1753–1771, https://doi.org/10.5194/gmd-14-1753-2021, 2021. a
Li, Y., Zhao, M., Motesharrei, S., Mu, Q., Kalnay, E., and Li, S.: Local cooling and warming effects of forests based on satellite observations, Nature Communications, 6, 6603, https://doi.org/10.1038/ncomms7603, 2015. a
Liljedahl, A. K., Boike, J., Daanen, R. P., Fedorov, A. N., Frost, G. V., Grosse, G., Hinzman, L. D., Iijma, Y., Jorgenson, J. C., Matveyeva, N., Necsoiu, M., Raynolds, M. K., Romanovsky, V. E., Schulla, J., Tape, K. D., Walker, D. A., Wilson, C. J., Yabuki, H., and Zona, D.: Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology, Nature Geoscience, 9, 312–318, https://doi.org/10.1038/ngeo2674, 2016. a
Luo, Z., Ahrens, B., and Risto, D.: Code and scripts for: [Assessing Uncertainties in Modeling the Climate of the Siberian Frozen Soils by Contrasting CMIP6 and LS3MIP], Zenodo [code], https://doi.org/10.5281/zenodo.17157176, 2025. a
Matthes, H., Rinke, A., Zhou, X., and Dethloff, K.: Uncertainties in coupled regional Arctic climate simulations associated with the used land surface model, Journal of Geophysical Research-Atmospheres, 122, 7755–7771, https://doi.org/10.1002/2016JD026213, 2017. a
Matthes, H., Damseaux, A., Westermann, S., Beer, C., Boone, A., Burke, E., Decharme, B., Genet, H., Jafarov, E., Langer, M., Parmentier, F., Porada, P., Gagne-Landmann, A., Huntzinger, D., Rogers, B., Schädel, C., Stacke, T., Wells, J., and Wieder, W.: Advances in Permafrost Representation: Biophysical Processes in Earth System Models and the Role of Offline Models, Permafrost and Periglacial Processes, 36, 302–318, https://doi.org/10.1002/ppp.2269, 2025. a
McNeall, D., Robertson, E., and Wiltshire, A.: Constraining the carbon cycle in JULES-ES-1.0, Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, 2024. a
Menard, C. B., Essery, R., Krinner, G., Arduini, G., Bartlett, P., Boone, A., Brutel-Vuilmet, C., Burke, E., Cuntz, M., Dai, Y., Decharme, B., Dutra, E., Fang, X., Fierz, C., Gusev, Y., Hagemann, S., Haverd, V., Kim, H., Lafaysse, M., Marke, T., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Schädler, G., Semenov, V. A., Smirnova, T., Strasser, U., Swenson, S., Turkov, D., Wever, N., and Yuan, H.: Scientific and Human Errors in a Snow Model Intercomparison, Bulletin of the American Meteorological Society, 102, E61–E79, https://doi.org/10.1175/BAMS-D-19-0329.1, 2021. a
Muñoz-Sabater, J.: ERA5-Land monthly averaged data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.68d2bb30, 2019. a, b
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
Nitzbon, J., Westermann, S., Langer, M., Martin, L. C. P., Strauss, J., Laboor, S., and Boike, J.: Fast response of cold ice-rich permafrost in northeast Siberia to a warming climate, Nature Communications, 11, 2201, https://doi.org/10.1038/s41467-020-15725-8, 2020. a
Niu, G.-Y. and Yang, Z.-L.: Effects of Frozen Soil on Snowmelt Runoff and Soil Water Storage at a Continental Scale, Journal of Hydrometeorology, 7, 937–952, https://doi.org/10.1175/JHM538.1, 2006. a
Obu, J., Westermann, S., Bartsch, A., Berdnikov, N., Christiansen, H. H., Dashtseren, A., Delaloye, R., Elberling, B., Etzelmüller, B., Kholodov, A., Khomutov, A., Kääb, A., Leibman, M. O., Lewkowicz, A. G., Panda, S. K., Romanovsky, V., Way, R. G., Westergaard-Nielsen, A., Wu, T., Yamkhin, J., and Zou, D.: Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale, Earth-Science Reviews, 193, 299–316, https://doi.org/10.1016/j.earscirev.2019.04.023, 2019. a
Onuma, Y. and Kim, H.: MIROC MIROC6 model output prepared for CMIP6 LS3MIP land-hist, Version 20200406, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5622, 2020. a
Park, H., Sherstyukov, A. B., Fedorov, A. N., Polyakov, I. V., and Walsh, J. E.: An observation-based assessment of the influences of air temperature and snow depth on soil temperature in Russia, Environmental Research Letters, 9, 064026, https://doi.org/10.1088/1748-9326/9/6/064026, 2014. a
Park, H., Fedorov, A. N., Zheleznyak, M. N., Konstantinov, P. Y., and Walsh, J. E.: Effect of snow cover on pan-Arctic permafrost thermal regimes, Climate Dynamics, 44, 2873–2895, https://doi.org/10.1007/s00382-014-2356-5, 2015. a
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Communications Earth & Environment, 3, 168, https://doi.org/10.1038/s43247-022-00498-3, 2022. a
Rashid, H. A.: Diverse Responses of Global-Mean Surface Temperature to External Forcings and Internal Climate Variability in Observations and CMIP6 Models, Geophysical Research Letters, 48, e2021GL093194, https://doi.org/10.1029/2021GL093194, 2021. a
Ridley, J., Menary, M., Kuhlbrodt, T., Andrews, M., and Andrews, T.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP historical, Version 20190619, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6109, 2019. a
Risto, D., Fröhlich, K., and Ahrens, B.: Snow Representation over Siberia in Operational Seasonal Forecasting Systems, Atmosphere, 13, 1002, https://doi.org/10.3390/atmos13071002, 2022. a
Romanovsky, V., Sazonova, T., Balobaev, V., Shender, N., and Sergueev, D.: Past and recent changes in air and permafrost temperatures in eastern Siberia, Global and Planetary Change, 56, 399–413, https://doi.org/10.1016/j.gloplacha.2006.07.022, 2007. a
Rößger, N., Sachs, T., Wille, C., Boike, J., and Kutzbach, L.: Seasonal increase of methane emissions linked to warming in Siberian tundra, Nature Climate Change, 12, 1031–1036, https://doi.org/10.1038/s41558-022-01512-4, 2022. a
Schuur, E. A. G., McGuire, A. D., Schädel, C., Grosse, G., Harden, J. W., Hayes, D. J., Hugelius, G., Koven, C. D., Kuhry, P., Lawrence, D. M., Natali, S. M., Olefeldt, D., Romanovsky, V. E., Schaefer, K., Turetsky, M. R., Treat, C. C., and Vonk, J. E.: Climate change and the permafrost carbon feedback, Nature, 520, 171–179, https://doi.org/10.1038/nature14338, 2015. a
Schwarzwald, K. and Lenssen, N.: The importance of internal climate variability in climate impact projections, Proceedings of the National Academy of Sciences, 119, e2208095119, https://doi.org/10.1073/pnas.2208095119, 2022. a
Schädel, C., Koven, C. D., Lawrence, D. M., Celis, G., Garnello, A. J., Hutchings, J., Mauritz, M., Natali, S. M., Pegoraro, E., Rodenhizer, H., Salmon, V. G., Taylor, M. A., Webb, E. E., Wieder, W. R., and Schuur, E. A.: Divergent patterns of experimental and model-derived permafrost ecosystem carbon dynamics in response to Arctic warming, Environmental Research Letters, 13, 105002, https://doi.org/10.1088/1748-9326/aae0ff, 2018. a
Seferian, R.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP historical, Version 20180915, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4068, 2018. a
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., De Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1: Description and Evaluation of the U.K. Earth System Model, Journal of Advances in Modeling Earth Systems, 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019. a
Sherstyukov, A.: Statistical quality control of a daily soil temperature dataset, All-Russian Research Institute of Hydrometeorological Information – World Data Center, Obninsk, 2012b. a
Smith, S. L., O'Neill, H. B., Isaksen, K., Noetzli, J., and Romanovsky, V. E.: The changing thermal state of permafrost, Nature Reviews Earth & Environment, 3, 10–23, https://doi.org/10.1038/s43017-021-00240-1, 2022. a
Streletskiy, D. A., Maslakov, A., Grosse, G., Shiklomanov, N. I., Farquharson, L., Zwieback, S., Iwahana, G., Bartsch, A., Liu, L., Strozzi, T., Lee, H., and Debolskiy, M. V.: Thawing permafrost is subsiding in the Northern Hemisphere—review and perspectives, Environmental Research Letters, 20, 013006, https://doi.org/10.1088/1748-9326/ada2ff, 2025. a
Sturm, M., Holmgren, J., König, M., and Morris, K.: The thermal conductivity of seasonal snow, Journal of Glaciology, 43, 26–41, https://doi.org/10.3189/S0022143000002781, 1997. a, b
Swenson, S. C., Lawrence, D. M., and Lee, H.: Improved simulation of the terrestrial hydrological cycle in permafrost regions by the Community Land Model, Journal of Advances in Modeling Earth Systems, 4, 2012MS000165, https://doi.org/10.1029/2012MS000165, 2012. a
Takata, K., Emori, S., and Watanabe, T.: Development of the minimal advanced treatments of surface interaction and runoff, Global and Planetary Change, 38, 209–222, https://doi.org/10.1016/S0921-8181(03)00030-4, 2003. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical, Version 20190405, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6113, 2019. a
Tarnocai, C., Canadell, J. G., Schuur, E. A. G., Kuhry, P., Mazhitova, G., and Zimov, S.: Soil organic carbon pools in the northern circumpolar permafrost region, Global Biogeochemical Cycles, 23, 2008GB003327, https://doi.org/10.1029/2008GB003327, 2009. a
Tatebe, H. and Watanabe, M.: MIROC MIROC6 model output prepared for CMIP6 CMIP historical, Version 20181130, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5603, 2018. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, Journal of Geophysical Research-Atmospheres, 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001. a
Turetsky, M. R., Abbott, B. W., Jones, M. C., Walter Anthony, K., Olefeldt, D., Schuur, E. A. G., Koven, C., McGuire, A. D., Grosse, G., Kuhry, P., Hugelius, G., Lawrence, D. M., Gibson, C., and Sannel, A. B. K.: Permafrost collapse is accelerating carbon release, Nature, 569, 32–34, https://doi.org/10.1038/d41586-019-01313-4, 2019. a
van den Hurk, B., Kim, H., Krinner, G., Seneviratne, S. I., Derksen, C., Oki, T., Douville, H., Colin, J., Ducharne, A., Cheruy, F., Viovy, N., Puma, M. J., Wada, Y., Li, W., Jia, B., Alessandri, A., Lawrence, D. M., Weedon, G. P., Ellis, R., Hagemann, S., Mao, J., Flanner, M. G., Zampieri, M., Materia, S., Law, R. M., and Sheffield, J.: LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project – aims, setup and expected outcome, Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, 2016. a, b, c
Van Kampenhout, L., Lenaerts, J. T. M., Lipscomb, W. H., Sacks, W. J., Lawrence, D. M., Slater, A. G., and Van Den Broeke, M. R.: Improving the Representation of Polar Snow and Firn in the Community Earth System Model, Journal of Advances in Modeling Earth Systems, 9, 2583–2600, https://doi.org/10.1002/2017MS000988, 2017. a
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a
Voldoire, A.: CMIP6 simulations of the CNRM-CERFACS based on CNRM-CM6-1 model for CMIP experiment historical, Version 20180620, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4066, 2018. a
Voldoire, A.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 LS3MIP land-hist. Version 20190812, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.9599, 2019a. a
Voldoire, A.: CNRM-CERFACS CNRM-CM6-1 model output prepared for CMIP6 LS3MIP land-hist, Version 20190722, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4095, 2019b. a
Walter Anthony, K., Schneider Von Deimling, T., Nitze, I., Frolking, S., Emond, A., Daanen, R., Anthony, P., Lindgren, P., Jones, B., and Grosse, G.: 21st-century modeled permafrost carbon emissions accelerated by abrupt thaw beneath lakes, Nature Communications, 9, 3262, https://doi.org/10.1038/s41467-018-05738-9, 2018. a
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019. a
Wang, T., Ottlé, C., Boone, A., Ciais, P., Brun, E., Morin, S., Krinner, G., Piao, S., and Peng, S.: Evaluation of an improved intermediate complexity snow scheme in the ORCHIDEE land surface model, Journal of Geophysical Research-Atmospheres, 118, 6064–6079, https://doi.org/10.1002/jgrd.50395, 2013. a, b
Wang, W., Rinke, A., Moore, J. C., Ji, D., Cui, X., Peng, S., Lawrence, D. M., McGuire, A. D., Burke, E. J., Chen, X., Decharme, B., Koven, C., MacDougall, A., Saito, K., Zhang, W., Alkama, R., Bohn, T. J., Ciais, P., Delire, C., Gouttevin, I., Hajima, T., Krinner, G., Lettenmaier, D. P., Miller, P. A., Smith, B., Sueyoshi, T., and Sherstiukov, A. B.: Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region, The Cryosphere, 10, 1721–1737, https://doi.org/10.5194/tc-10-1721-2016, 2016. a, b, c, d, e, f, g
Wang, X., Ran, Y., Pang, G., Chen, D., Su, B., Chen, R., Li, X., Chen, H. W., Yang, M., Gou, X., Jorgenson, M. T., Aalto, J., Li, R., Peng, X., Wu, T., Clow, G. D., Wan, G., Wu, X., and Luo, D.: Contrasting characteristics, changes, and linkages of permafrost between the Arctic and the Third Pole, Earth-Science Reviews, 230, 104042, https://doi.org/10.1016/j.earscirev.2022.104042, 2022. a
Wilks, D. S.: Statistical methods in the atmospheric sciences, fourth edition edn., Elsevier, Amsterdam, Netherlands; Cambridge, MA, ISBN 978-0-12-815823-4, 2019. a
Wiltshire, A., Robertson, E., Burke, E., and Liddicoat, S.: MOHC UKESM1.0-LL model output prepared for CMIP6 LS3MIP, Version 20200803, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.14462, 2020a. a
Wiltshire, A., Robertson, E., Burke, E., and Liddicoat, S.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 LS3MIP, Version 20200803, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.14460, 2020b. a
Wiltshire, A. J., Duran Rojas, M. C., Edwards, J. M., Gedney, N., Harper, A. B., Hartley, A. J., Hendry, M. A., Robertson, E., and Smout-Day, K.: JULES-GL7: the Global Land configuration of the Joint UK Land Environment Simulator version 7.0 and 7.2, Geosci. Model Dev., 13, 483–505, https://doi.org/10.5194/gmd-13-483-2020, 2020c. a
Woo, M.-K.: Permafrost Hydrology, Springer Berlin Heidelberg, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-23462-0, 2012. a
Yang, S., Li, R., Zhao, L., Wu, T., Wu, X., Zhang, Y., Shi, J., and Qiao, Y.: Evaluation of the Performance of CLM5.0 in Soil Hydrothermal Dynamics in Permafrost Regions on the Qinghai–Tibet Plateau, Remote Sensing, 14, 6228, https://doi.org/10.3390/rs14246228, 2022. a
Ye, K. and Messori, G.: Inter-model spread in the wintertime Arctic amplification in the CMIP6 models and the important role of internal climate variability, Global and Planetary Change, 204, 103543, https://doi.org/10.1016/j.gloplacha.2021.103543, 2021. a
Yokohata, T., Saito, K., Takata, K., Nitta, T., Satoh, Y., Hajima, T., Sueyoshi, T., and Iwahana, G.: Model improvement and future projection of permafrost processes in a global land surface model, Progress in Earth and Planetary Science, 7, 69, https://doi.org/10.1186/s40645-020-00380-w, 2020. a, b
You, Q., Cai, Z., Pepin, N., Chen, D., Ahrens, B., Jiang, Z., Wu, F., Kang, S., Zhang, R., Wu, T., Wang, P., Li, M., Zuo, Z., Gao, Y., Zhai, P., and Zhang, Y.: Warming amplification over the Arctic Pole and Third Pole: Trends, mechanisms and consequences, Earth-Science Reviews, 217, 103625, https://doi.org/10.1016/j.earscirev.2021.103625, 2021. a
Zhang, T.: Influence of the seasonal snow cover on the ground thermal regime: An overview, Reviews of Geophysics, 43, 2004RG000157, https://doi.org/10.1029/2004RG000157, 2005. a
Zhang, Y., Sherstyukov, A. B., Qian, B., Kokelj, S. V., and Lantz, T. C.: Impacts of snow on soil temperature observed across the circumpolar north, Environmental Research Letters, 13, 044012, https://doi.org/10.1088/1748-9326/aab1e7, 2018. a, b, c
Zhu, D., Ciais, P., Krinner, G., Maignan, F., Jornet Puig, A., and Hugelius, G.: Controls of soil organic matter on soil thermal dynamics in the northern high latitudes, Nature Communications, 10, 3172, https://doi.org/10.1038/s41467-019-11103-1, 2019. a
Short summary
Climate models face challenges in accurately simulating cold regions' soil temperatures and snow conditions. By comparing different models, we found that the land surface models have a strong impact on simulation errors. Additionally, they struggle to account for snow’s insulating effect on the ground properly. Our findings highlight the need for improving frozen soil simulation, which is crucial for understanding the climate impacts of frozen soil.
Climate models face challenges in accurately simulating cold regions' soil temperatures and snow...
