A dynamical geography of observed trends in the global ocean
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912354024816640 |
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| author | Nardelli, Bruno Buongiorno Iudicone, Daniele |
| author_facet | Nardelli, Bruno Buongiorno Iudicone, Daniele |
| contents | Revealing the ongoing changes in ocean dynamics and their impact on marine ecosystems requires the joint analysis of multiple variables. Yet, global observational records only cover a few decades, posing a challenge in the separation of climatic trends from internal dynamical modes. Here, we apply an empirical stochastic model to identify the emergent patterns of trends in six fundamental components of upper ocean physics. We analyze a data-driven reconstruction of the ocean state covering the 1993-2018 period. We found that including temporal derivatives into the state vector enhances the description of the ocean's dynamical system. Once Pacific oscillations are properly accounted for, averaged surface warming appears >60% faster, and a deep reshaping of the seascape is revealed. A clustering of the trend patterns identifies the main factors that drive observed trends in chlorophyll-a concentration. This data-driven approach opens new perspectives in empirical climate modelling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18981 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A dynamical geography of observed trends in the global ocean Nardelli, Bruno Buongiorno Iudicone, Daniele Atmospheric and Oceanic Physics Revealing the ongoing changes in ocean dynamics and their impact on marine ecosystems requires the joint analysis of multiple variables. Yet, global observational records only cover a few decades, posing a challenge in the separation of climatic trends from internal dynamical modes. Here, we apply an empirical stochastic model to identify the emergent patterns of trends in six fundamental components of upper ocean physics. We analyze a data-driven reconstruction of the ocean state covering the 1993-2018 period. We found that including temporal derivatives into the state vector enhances the description of the ocean's dynamical system. Once Pacific oscillations are properly accounted for, averaged surface warming appears >60% faster, and a deep reshaping of the seascape is revealed. A clustering of the trend patterns identifies the main factors that drive observed trends in chlorophyll-a concentration. This data-driven approach opens new perspectives in empirical climate modelling. |
| title | A dynamical geography of observed trends in the global ocean |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2405.18981 |