Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866911538478055424 |
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| author | Schmidt, Luca Effenberger, Nina |
| author_facet | Schmidt, Luca Effenberger, Nina |
| contents | While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine learning (ML) emulators offer a way to bypass the high computational costs, their effective use remains challenging. The hurdles are diverse, ranging from limited accessibility and a lack of specialized knowledge to a general mistrust of ML methods that are perceived as insufficiently physical. Here, we introduce a framework to overcome these barriers by integrating both climate science and machine learning perspectives. We find that designing easy-to-adopt emulators that address a clearly defined task and demonstrating their reliability offers a promising path for bridging the gap between our two fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22320 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation Schmidt, Luca Effenberger, Nina Machine Learning Applications While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine learning (ML) emulators offer a way to bypass the high computational costs, their effective use remains challenging. The hurdles are diverse, ranging from limited accessibility and a lack of specialized knowledge to a general mistrust of ML methods that are perceived as insufficiently physical. Here, we introduce a framework to overcome these barriers by integrating both climate science and machine learning perspectives. We find that designing easy-to-adopt emulators that address a clearly defined task and demonstrating their reliability offers a promising path for bridging the gap between our two fields. |
| title | Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2603.22320 |