Machine-learning emergent constraints on surface albedo feedback over Arctic land regions
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.
Authors
- Shengzhi Huang (ORCID: https://orcid.org/0000-0001-7592-5268)
- Qiuhong Tang (ORCID: https://orcid.org/0000-0002-0886-6699)
- Jian Peng (ORCID: https://orcid.org/0000-0002-4071-0512)
- Linfei Yu (ORCID: https://orcid.org/0000-0002-8255-0860)
- Manfred Wendisch (ORCID: https://orcid.org/0000-0002-4652-5561)
- J. F. Qiu
- G. Leng
- Lei Yao (ORCID: https://orcid.org/0009-0003-4286-638X)
- Xiaoyong Liao (ORCID: https://orcid.org/0000-0001-9287-3498)
Institutions
- Helmholtz Centre for Environmental Research (DE)
- Chinese Academy of Sciences (CN)
- Xi'an University of Technology (CN)
- Institute of Geographic Sciences and Natural Resources Research (CN)
- University of Chinese Academy of Sciences (CN)
- Leipzig University (DE)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-04-10
- DOI
- https://doi.org/10.1038/s41467-026-71779-0
- Primary Topic
- Climate change and permafrost
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China