Transferring climate change physical knowledge
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866915453225402368 |
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| author | Immorlano, Francesco Eyring, Veronika de Gouville, Thomas le Monnier Accarino, Gabriele Elia, Donatello Mandt, Stephan Aloisio, Giovanni Gentine, Pierre |
| author_facet | Immorlano, Francesco Eyring, Veronika de Gouville, Thomas le Monnier Accarino, Gabriele Elia, Donatello Mandt, Stephan Aloisio, Giovanni Gentine, Pierre |
| contents | Precise and reliable climate projections are required for climate adaptation and mitigation, but Earth system models still exhibit great uncertainties. Several approaches have been developed to reduce the spread of climate projections and feedbacks, yet those methods cannot capture the nonlinear complexity inherent in the climate system. Using a Transfer Learning approach, we show that Machine Learning can be used to optimally leverage and merge the knowledge gained from global temperature maps simulated by Earth system models and observed in the historical period to reduce the spread of global surface air temperature fields projected in the 21st century. We reach an uncertainty reduction of more than 50% with respect to state-of-the-art approaches while giving evidence that our method provides improved regional temperature patterns together with narrower projections uncertainty, urgently required for climate adaptation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_14780 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Transferring climate change physical knowledge Immorlano, Francesco Eyring, Veronika de Gouville, Thomas le Monnier Accarino, Gabriele Elia, Donatello Mandt, Stephan Aloisio, Giovanni Gentine, Pierre Atmospheric and Oceanic Physics Artificial Intelligence Machine Learning Precise and reliable climate projections are required for climate adaptation and mitigation, but Earth system models still exhibit great uncertainties. Several approaches have been developed to reduce the spread of climate projections and feedbacks, yet those methods cannot capture the nonlinear complexity inherent in the climate system. Using a Transfer Learning approach, we show that Machine Learning can be used to optimally leverage and merge the knowledge gained from global temperature maps simulated by Earth system models and observed in the historical period to reduce the spread of global surface air temperature fields projected in the 21st century. We reach an uncertainty reduction of more than 50% with respect to state-of-the-art approaches while giving evidence that our method provides improved regional temperature patterns together with narrower projections uncertainty, urgently required for climate adaptation. |
| title | Transferring climate change physical knowledge |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2309.14780 |