Process‐Based Machine Learning Observationally Constrains Future Regional Warming Projections
Abstract We present the results of a novel process‐based machine learning method to constrain climate model uncertainty in future regional temperature projections. Ridge‐ERA5—a ridge regression model—learns coefficients to represent observed relationships between daily near‐surface temperature anoma...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Online Access: | https://doi.org/10.1029/2025JH000698 |
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| _version_ | 1849431507832143872 |
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| author | Sophie Wilkinson Peer Nowack Manoj Joshi |
| author_facet | Sophie Wilkinson Peer Nowack Manoj Joshi |
| author_sort | Sophie Wilkinson |
| collection | DOAJ |
| description | Abstract We present the results of a novel process‐based machine learning method to constrain climate model uncertainty in future regional temperature projections. Ridge‐ERA5—a ridge regression model—learns coefficients to represent observed relationships between daily near‐surface temperature anomalies and predictor variables from ERA5 reanalysis in Northern Hemisphere land regions. Combining the historically constrained Ridge‐ERA5 coefficients with inputs from CMIP6 future projections enables a derivation of observational constraints on regional warming. Although the multi‐model mean falls within the constrained range of temperatures in all tested regions, a subset of models which predict the greatest degree of warming tend to be excluded and decomposition of the constraint into predictor variable contributions suggests error‐cancellation of feedbacks in some models and regions. |
| format | Article |
| id | doaj-art-da86ab39c9c14566bf75ac7dca5bba0d |
| institution | Kabale University |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-da86ab39c9c14566bf75ac7dca5bba0d2025-08-20T03:27:37ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-06-0122n/an/a10.1029/2025JH000698Process‐Based Machine Learning Observationally Constrains Future Regional Warming ProjectionsSophie Wilkinson0Peer Nowack1Manoj Joshi2Climatic Research Unit School of Environmental Sciences University of East Anglia Norwich UKInstitute of Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe GermanyClimatic Research Unit School of Environmental Sciences University of East Anglia Norwich UKAbstract We present the results of a novel process‐based machine learning method to constrain climate model uncertainty in future regional temperature projections. Ridge‐ERA5—a ridge regression model—learns coefficients to represent observed relationships between daily near‐surface temperature anomalies and predictor variables from ERA5 reanalysis in Northern Hemisphere land regions. Combining the historically constrained Ridge‐ERA5 coefficients with inputs from CMIP6 future projections enables a derivation of observational constraints on regional warming. Although the multi‐model mean falls within the constrained range of temperatures in all tested regions, a subset of models which predict the greatest degree of warming tend to be excluded and decomposition of the constraint into predictor variable contributions suggests error‐cancellation of feedbacks in some models and regions.https://doi.org/10.1029/2025JH000698 |
| spellingShingle | Sophie Wilkinson Peer Nowack Manoj Joshi Process‐Based Machine Learning Observationally Constrains Future Regional Warming Projections Journal of Geophysical Research: Machine Learning and Computation |
| title | Process‐Based Machine Learning Observationally Constrains Future Regional Warming Projections |
| title_full | Process‐Based Machine Learning Observationally Constrains Future Regional Warming Projections |
| title_fullStr | Process‐Based Machine Learning Observationally Constrains Future Regional Warming Projections |
| title_full_unstemmed | Process‐Based Machine Learning Observationally Constrains Future Regional Warming Projections |
| title_short | Process‐Based Machine Learning Observationally Constrains Future Regional Warming Projections |
| title_sort | process based machine learning observationally constrains future regional warming projections |
| url | https://doi.org/10.1029/2025JH000698 |
| work_keys_str_mv | AT sophiewilkinson processbasedmachinelearningobservationallyconstrainsfutureregionalwarmingprojections AT peernowack processbasedmachinelearningobservationallyconstrainsfutureregionalwarmingprojections AT manojjoshi processbasedmachinelearningobservationallyconstrainsfutureregionalwarmingprojections |