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Constrained Earth system models show a stronger reduction in future Northern Hemisphere snowmelt water

Nature Climate Change volume 15, pages 514–520 (2025)Cite this article

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Abstract

Although Earth system models (ESMs) tend to overestimate historical land surface warming, they also overestimate snow amounts in the Northern Hemisphere. By combining ground-based datasets and ESMs, we find that this paradoxical phenomenon is predominantly driven by an overestimation of light snowfall frequency. Using spatially distributed emergent constraints, we show that this paradox persists in mid- (2041–2060) and long-term (2081–2100) projections, affecting more than half of the Northern Hemisphere’s land surface. ESMs underestimate the frequency of freezing days by 12–19% and overestimate snow water equivalent by 28–34%. Constrained projections indicate that the raw ESM outputs overestimate future Northern Hemisphere snowmelt water by 12–16% across 53–60% of the Northern Hemisphere’s land surface. This snowmelt water overprediction implies that the amount of water available in the future for agriculture, industry, ecosystems and domestic use may be lower than unadjusted ESM projections suggest.

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Fig. 1: Characteristics and mechanisms of the snow water resources paradox in the extended NH cold season for the period 1982–2014.
Fig. 2: Observationally constrained projections of mean freezing day frequency and mean SWE in the extended NH cold season for the period 2041–2060 under SSP245 at the grid level.
Fig. 3: Implications for future annual mean snowmelt water in the NH under SSP245 for the period 2041–2060 at the grid level.

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Data availability

The daily precipitation products from GPCC, CPC and MSWEP were derived from https://opendata.dwd.de/climate_environment/GPCC/full_data_daily_v2022/, https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html and https://www.gloh2o.org/mswep/, respectively. The daily temperature records from Berkeley Earth, ERA5-Land, MERRA-2 and the Japanese 55-year Reanalysis (JRA-55) were derived from https://berkeleyearth.org/data/, https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview, https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/ and https://rda.ucar.edu/datasets/d628000/, respectively. The CMIP6 model simulations, including SWE, snowmelt water, precipitation and temperature, were acquired from https://esgf-node.llnl.gov/projects/cmip6/. The CMIP5 model simulations were acquired from https://esgf-node.llnl.gov/projects/cmip5/. The observation-based SWE datasets were acquired from GlobSnow-v3 (https://doi.org/10.1594/PANGAEA.911944)79, SnowCCI-v2 (https://catalogue.ceda.ac.uk/uuid/93cf539bc3004cc8b98006e69078d86b/), MERRA-2 (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/), ERA5-Land (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview), GLDAS-v2 (https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM10_M_2.0/summary?keywords=snow%20water%20equivalent), NCEP2 (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html), FLDAS (https://disc.gsfc.nasa.gov/datasets/FLDAS_NOAH01_C_GL_M_001/summary?keywords=snow%20water%20equivalent), CFSR (https://esgf-node.llnl.gov/search/create-ip/) and ERA5 (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-monthly-means?tab=overview). The Offline Land Model Experiment (LMIP) and the Prescribed Land Surface States (LFMIP) CMIP6 experiments, including SWE and snowmelt water, were derived from https://aims2.llnl.gov/search/cmip6/.

Code availability

Codes to reproduce the study are available via GitHub at https://github.com/alanchai/Overcoming-the-Northern-Hemisphere-snow-water-resources-paradox.git (ref. 80).

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Acknowledgements

C.M. acknowledges support from the National Key Research and Development Program of China (2024YFF0809301) and the National Natural Science Foundation of China (U24A20572). Y.C. acknowledges support from the National Natural Science Foundation of China (42301018). P.G. acknowledges National Science Foundation (NSF) LEAP Science and Technology Center award no. 2019625. L.S. acknowledges the UKRI FLF scheme (MR/V022008/1).

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Authors and Affiliations

  1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Yuanfang Chai, Chiyuan Miao, Yi Wu & Xuewei Fan

  2. Earth and Environmental Engineering, Climate School and Center for Learning the Earth with Artificial intelligence and Physics (LEAP), Columbia University, New York, NY, USA

    Pierre Gentine

  3. Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada

    Lawrence Mudryk

  4. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA

    Chad W. Thackeray

  5. Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

    Wouter R. Berghuijs

  6. School of Geography and the Environment, University of Oxford, Oxford, UK

    Louise Slater

  7. Key Laboratory of Meteorological Disaster of Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing, China

    Qiaohong Sun & Francis Zwiers

  8. Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada

    Francis Zwiers

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Contributions

Y.C., C.M. and F.Z. led the writing, designed the research and performed the data analysis. P.G., L.M., C.W.T., W.R.B., Y.W., X.F., L.S. and Q.S. revised the paper and provided valuable comments.

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Correspondence to Chiyuan Miao.

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Chai, Y., Miao, C., Gentine, P. et al. Constrained Earth system models show a stronger reduction in future Northern Hemisphere snowmelt water. Nat. Clim. Chang. 15, 514–520 (2025). https://doi.org/10.1038/s41558-025-02308-y

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