Abstract
Surface albedo feedback (SAF) amplifies warming in northern high latitudes, affecting the Arctic climate system, ecosystems, infrastructure, and global trade routes. However, Earth system models (ESMs) exhibit large uncertainties in SAF projections, complicating future Arctic warming estimates. Here, we develop a machine-learning method based on emergent constraints (ECs) and use in-situ observations to constrain SAF projections over Arctic land regions. Our approach leverages a physical relationship between historical albedo-temperature dynamics (1985–2014) and future SAF (2070–2099) across ESM ensembles. The constrained SAF is reduced by 0.29–0.52 W m-2 K-1 across emission scenarios, with uncertainties decreased by 45–55% compared to unconstrained projections. These findings enhance confidence in regional climate projections, offering more precise insights for climate adaptation and policy in vulnerable high-latitude communities.
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Data availability
The CMIP6 and CMIP5 earth system model outputs are available at https://esgf-node.llnl.gov/search/cmip6/. The FLUXNET 2015 dataset is available at https://fluxnet.org/data/download-data/. The AmeriFlux dataset is available at https://ameriflux.lbl.gov/sites/site-search/. The ICOS final fully quality controlled observational data (Level 2) dataset is available at https://www.icos-cp.eu/data-products/. The in-situ air temperature observation dataset is available at https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/. The GLASS albedo product is available at https://glass.hku.hk/download.html. The CLARA-A3 albedo product is available at https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=CLARA_AVHRR_V003. The ERA5 albedo daily product is available at https://cds.climate.copernicus.eu/datasets/derived-era5-single-levels-daily-statistics?tab=download/. The MERRA2 albedo hourly product is available at https://disc.gsfc.nasa.gov/datasets/M2T1NXRAD_5.12.4/summary/. The NOAA LAI product is available at https://noaa-cdr-leaf-area-index-fapar-pds.s3.amazonaws.com/index.html#data/. The GIMMS LAI4g LAI product is available at https://doi.org/10.5281/zenodo.7649107. The GLASS LAI product is available at https://glass-product.bnu.edu.cn/introduction1/LAI.html. The GLOBMAP LAI product is available at https://zenodo.org/records/4700264.
The source data for figures are publicly available at https://doi.org/10.6084/m9.figshare.30664439
Code availability
The R scripts used in this study are accessible at Figshare: https://doi.org/10.6084/m9.figshare.30664439
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (No. 42225707, 42471025, 42501031), the National Key Research and Development Program of China (No. 2020YFA0608502), the International Partnership Program of Chinese Academy of Sciences (177GJHZ2023084FN) and AgriWATER (ID 95338) in the framework of the ESA-MOST Dragon 6 program. MW acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 268020496 – TRR 172. MW is grateful for funding of project grant no. 316646266 by DFG within the framework of Priority Programme SPP 1294 to promote research with HALO.
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G.L. and X.L. conceived the study, L.Yu and G.L. performed the experiments and conducted the analysis. L.Yao, Q.T., W.M., J.Q., S.H., X.L., J.P. aided in interpreting and improving the analysis. L. Yu and G.L. wrote the paper with inputs and edits from all co-authors.
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Yu, L., Leng, G., Yao, L. et al. Machine-learning emergent constraints on surface albedo feedback over Arctic land regions. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71779-0
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DOI: https://doi.org/10.1038/s41467-026-71779-0
