A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909858128723968 |
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| author | Cresswell-Clay, Nathaniel Liu, Bowen Durran, Dale Liu, Zihui Espinosa, Zachary I. Moreno, Raul Karlbauer, Matthias |
| author_facet | Cresswell-Clay, Nathaniel Liu, Bowen Durran, Dale Liu, Zihui Espinosa, Zachary I. Moreno, Raul Karlbauer, Matthias |
| contents | A key challenge for computationally intensive state-of-the-art Earth System models is to distinguish global warming signals from interannual variability. Here we introduce DLESyM, a parsimonious deep learning model that accurately simulates the Earth's current climate over 1000-year periods with no smoothing or drift. DLESyM simulations equal or exceed key metrics of seasonal and interannual variability--such as tropical cyclogenesis over the range of observed intensities, the cycle of the Indian Summer monsoon, and the climatology of mid-latitude blocking events--when compared to historical simulations from four leading models from the 6th Climate Model Intercomparison Project. DLESyM, trained on both historical reanalysis data and satellite observations, is an accurate, highly efficient model of the coupled Earth system, empowering long-range sub-seasonal and seasonal forecasts while using a fraction of the energy and computational time required by traditional models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16247 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate Cresswell-Clay, Nathaniel Liu, Bowen Durran, Dale Liu, Zihui Espinosa, Zachary I. Moreno, Raul Karlbauer, Matthias Atmospheric and Oceanic Physics A key challenge for computationally intensive state-of-the-art Earth System models is to distinguish global warming signals from interannual variability. Here we introduce DLESyM, a parsimonious deep learning model that accurately simulates the Earth's current climate over 1000-year periods with no smoothing or drift. DLESyM simulations equal or exceed key metrics of seasonal and interannual variability--such as tropical cyclogenesis over the range of observed intensities, the cycle of the Indian Summer monsoon, and the climatology of mid-latitude blocking events--when compared to historical simulations from four leading models from the 6th Climate Model Intercomparison Project. DLESyM, trained on both historical reanalysis data and satellite observations, is an accurate, highly efficient model of the coupled Earth system, empowering long-range sub-seasonal and seasonal forecasts while using a fraction of the energy and computational time required by traditional models. |
| title | A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2409.16247 |