Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models
Abstract Accurate snow depth datasets are crucial for water resource management, comprehensive climate change evaluations, and the sustainable advancement of the ice-and-snow economy in the context of rapid climate change. To create a high-resolution monthly snow depth dataset tailored for the North...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04925-w |
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| author | Shiqiu Lin Xiaona Chen Shunlin Liang Yangxiaoyue Liu Yu Li Huan Li |
| author_facet | Shiqiu Lin Xiaona Chen Shunlin Liang Yangxiaoyue Liu Yu Li Huan Li |
| author_sort | Shiqiu Lin |
| collection | DOAJ |
| description | Abstract Accurate snow depth datasets are crucial for water resource management, comprehensive climate change evaluations, and the sustainable advancement of the ice-and-snow economy in the context of rapid climate change. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world’s pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023. This dataset facilitates the exploration of potential alterations in snow depth under future scenarios in the Northern Hemisphere. |
| format | Article |
| id | doaj-art-d3d7e10c486a40e6bda5c8b93aebf23c |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-d3d7e10c486a40e6bda5c8b93aebf23c2025-08-20T02:17:09ZengNature PortfolioScientific Data2052-44632025-04-0112111710.1038/s41597-025-04925-wMonthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 modelsShiqiu Lin0Xiaona Chen1Shunlin Liang2Yangxiaoyue Liu3Yu Li4Huan Li5State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesJockey Club Laboratory of Quantitative Remote Sensing, Department of Geography, University of Hong KongState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesHUN-REN Balaton Limnological Research InstituteAbstract Accurate snow depth datasets are crucial for water resource management, comprehensive climate change evaluations, and the sustainable advancement of the ice-and-snow economy in the context of rapid climate change. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world’s pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023. This dataset facilitates the exploration of potential alterations in snow depth under future scenarios in the Northern Hemisphere.https://doi.org/10.1038/s41597-025-04925-w |
| spellingShingle | Shiqiu Lin Xiaona Chen Shunlin Liang Yangxiaoyue Liu Yu Li Huan Li Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models Scientific Data |
| title | Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models |
| title_full | Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models |
| title_fullStr | Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models |
| title_full_unstemmed | Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models |
| title_short | Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models |
| title_sort | monthly 0 05° winter months snow depth dataset for the northern hemisphere from 21 cmip6 models |
| url | https://doi.org/10.1038/s41597-025-04925-w |
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