| Autor: |
Qinxue Gu, Liping Zhang, Liwei Jia, Thomas L. Delworth, Xiaosong Yang, Fanrong Zeng, William F. Cooke, Shouwei Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
npj Climate and Atmospheric Science, Vol 7, Iss 1, Pp 1-15 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2397-3722 |
| DOI: |
10.1038/s41612-024-00802-2 |
| Popis: |
Abstract Coastal communities face substantial risks from long-term sea level rise and decadal sea level variations, with the North Atlantic and U.S. East Coast being particularly vulnerable under changing climates. Employing a self-organizing map-based framework, we assess the North Atlantic sea level variability and predictability using 5000-year sea level anomalies (SLA) from two preindustrial control model simulations. Preferred transitions among patterns of variability are identified, revealing long-term predictability on decadal timescales related to shifts in Atlantic meridional overturning circulation phases. Combining this framework with model-analog techniques, we demonstrate prediction skill of large-scale SLA patterns and low-frequency coastal SLA variations comparable to that from initialized hindcasts. Moreover, additional short-term predictability is identified after the exclusion of low-frequency signals, which arises from slow gyre circulation adjustment triggered by the North Atlantic Oscillation-like stochastic variability. This study highlights the potential of machine learning to assess sources of predictability and to enable long-term climate prediction. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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