GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties
Abstract This article introduces the Generalized Analog Regression Downscaling method Large Ensemble (GARD-LENS) dataset, comprised of daily precipitation, mean temperature, and temperature range over the Contiguous U.S., Alaska, and Hawaii at 12-km, 4-km, and 1-km resolutions, respectively. GARD-LE...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04205-z |
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| author | Samantha H. Hartke Andrew J. Newman Ethan Gutmann Rachel McCrary Nicholas D. Lybarger Flavio Lehner |
| author_facet | Samantha H. Hartke Andrew J. Newman Ethan Gutmann Rachel McCrary Nicholas D. Lybarger Flavio Lehner |
| author_sort | Samantha H. Hartke |
| collection | DOAJ |
| description | Abstract This article introduces the Generalized Analog Regression Downscaling method Large Ensemble (GARD-LENS) dataset, comprised of daily precipitation, mean temperature, and temperature range over the Contiguous U.S., Alaska, and Hawaii at 12-km, 4-km, and 1-km resolutions, respectively. GARD-LENS statistically downscales three CMIP6 global climate model large ensembles, CESM2, CanESM5, and EC-Earth3, totaling 200 ensemble members. GARD-LENS is the first downscaled SMILE (single model initial-condition large ensemble), providing information about the role of internal climate variability at high resolutions. The 150-year record of this large ensemble dataset provides ample data for assessing trends and extremes and allows users to robustly assess internal variability, forced climate signals, and time of emergence at high resolutions. As the need for high resolution, robust climate datasets continues to grow, GARD-LENS will be a valuable tool for scientists and practitioners who wish to account for internal variability in their future climate analyses and adaptation plans. |
| format | Article |
| id | doaj-art-29abec6ce4894069a8939a67cd88da5c |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-29abec6ce4894069a8939a67cd88da5c2025-08-20T02:31:37ZengNature PortfolioScientific Data2052-44632024-12-0111111510.1038/s41597-024-04205-zGARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertaintiesSamantha H. Hartke0Andrew J. Newman1Ethan Gutmann2Rachel McCrary3Nicholas D. Lybarger4Flavio Lehner5National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) Research Applications Lab (RAL)National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) Research Applications Lab (RAL)National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) Research Applications Lab (RAL)National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) Research Applications Lab (RAL)National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) Research Applications Lab (RAL)National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) Climate and Global Dynamics (CGD)Abstract This article introduces the Generalized Analog Regression Downscaling method Large Ensemble (GARD-LENS) dataset, comprised of daily precipitation, mean temperature, and temperature range over the Contiguous U.S., Alaska, and Hawaii at 12-km, 4-km, and 1-km resolutions, respectively. GARD-LENS statistically downscales three CMIP6 global climate model large ensembles, CESM2, CanESM5, and EC-Earth3, totaling 200 ensemble members. GARD-LENS is the first downscaled SMILE (single model initial-condition large ensemble), providing information about the role of internal climate variability at high resolutions. The 150-year record of this large ensemble dataset provides ample data for assessing trends and extremes and allows users to robustly assess internal variability, forced climate signals, and time of emergence at high resolutions. As the need for high resolution, robust climate datasets continues to grow, GARD-LENS will be a valuable tool for scientists and practitioners who wish to account for internal variability in their future climate analyses and adaptation plans.https://doi.org/10.1038/s41597-024-04205-z |
| spellingShingle | Samantha H. Hartke Andrew J. Newman Ethan Gutmann Rachel McCrary Nicholas D. Lybarger Flavio Lehner GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties Scientific Data |
| title | GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties |
| title_full | GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties |
| title_fullStr | GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties |
| title_full_unstemmed | GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties |
| title_short | GARD-LENS: A downscaled large ensemble dataset for understanding future climate and its uncertainties |
| title_sort | gard lens a downscaled large ensemble dataset for understanding future climate and its uncertainties |
| url | https://doi.org/10.1038/s41597-024-04205-z |
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