Bridging large-scale and coastal variability to improve seasonal sea level predictions along the U.S. and Canadian West Coast
Abstract Coastal communities are increasingly vulnerable to long-term sea level rise and fluctuations driven by climate variability. While recent advances in coupled climate models enable sea level predictions several months in advance, further efforts are needed to assess and enhance seasonal predi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-025-01182-x |
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| _version_ | 1849345267730481152 |
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| author | Qinxue Gu Liwei Jia Liping Zhang Thomas L. Delworth Xiaosong Yang Nathaniel C. Johnson Feiyu Lu Colleen E. McHugh William F. Cooke |
| author_facet | Qinxue Gu Liwei Jia Liping Zhang Thomas L. Delworth Xiaosong Yang Nathaniel C. Johnson Feiyu Lu Colleen E. McHugh William F. Cooke |
| author_sort | Qinxue Gu |
| collection | DOAJ |
| description | Abstract Coastal communities are increasingly vulnerable to long-term sea level rise and fluctuations driven by climate variability. While recent advances in coupled climate models enable sea level predictions several months in advance, further efforts are needed to assess and enhance seasonal prediction of coastal sea level. In this study, we evaluate seasonal prediction skill for large-scale and coastal sea level along the U.S. and Canadian West Coast using multiple forecast systems. Prediction skill peaks in the tropical Indo-Pacific and extends into the eastern North Pacific, declining from south to north along the coast. Using self-organizing maps (SOMs), a machine learning technique, we identify sources of large-scale sea level variability and predictability in the eastern tropical and North Pacific, closely linked to the El Niño–Southern Oscillation. Finally, we improve coastal sea level predictions from dynamical models by leveraging the connection between large-scale and coastal sea level through SOM-reconstructed and model-analog approaches. |
| format | Article |
| id | doaj-art-4443fdca2fb14bc8a493c355b6e37cc3 |
| institution | Kabale University |
| issn | 2397-3722 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Climate and Atmospheric Science |
| spelling | doaj-art-4443fdca2fb14bc8a493c355b6e37cc32025-08-20T03:42:30ZengNature Portfolionpj Climate and Atmospheric Science2397-37222025-08-018111410.1038/s41612-025-01182-xBridging large-scale and coastal variability to improve seasonal sea level predictions along the U.S. and Canadian West CoastQinxue Gu0Liwei Jia1Liping Zhang2Thomas L. Delworth3Xiaosong Yang4Nathaniel C. Johnson5Feiyu Lu6Colleen E. McHugh7William F. Cooke8The Program in Atmospheric and Oceanic Sciences, Princeton UniversityNOAA/Geophysical Fluid Dynamics LaboratoryNOAA/Geophysical Fluid Dynamics LaboratoryNOAA/Geophysical Fluid Dynamics LaboratoryNOAA/Geophysical Fluid Dynamics LaboratoryNOAA/Geophysical Fluid Dynamics LaboratoryNOAA/Geophysical Fluid Dynamics LaboratoryNOAA/Geophysical Fluid Dynamics LaboratoryNOAA/Geophysical Fluid Dynamics LaboratoryAbstract Coastal communities are increasingly vulnerable to long-term sea level rise and fluctuations driven by climate variability. While recent advances in coupled climate models enable sea level predictions several months in advance, further efforts are needed to assess and enhance seasonal prediction of coastal sea level. In this study, we evaluate seasonal prediction skill for large-scale and coastal sea level along the U.S. and Canadian West Coast using multiple forecast systems. Prediction skill peaks in the tropical Indo-Pacific and extends into the eastern North Pacific, declining from south to north along the coast. Using self-organizing maps (SOMs), a machine learning technique, we identify sources of large-scale sea level variability and predictability in the eastern tropical and North Pacific, closely linked to the El Niño–Southern Oscillation. Finally, we improve coastal sea level predictions from dynamical models by leveraging the connection between large-scale and coastal sea level through SOM-reconstructed and model-analog approaches.https://doi.org/10.1038/s41612-025-01182-x |
| spellingShingle | Qinxue Gu Liwei Jia Liping Zhang Thomas L. Delworth Xiaosong Yang Nathaniel C. Johnson Feiyu Lu Colleen E. McHugh William F. Cooke Bridging large-scale and coastal variability to improve seasonal sea level predictions along the U.S. and Canadian West Coast npj Climate and Atmospheric Science |
| title | Bridging large-scale and coastal variability to improve seasonal sea level predictions along the U.S. and Canadian West Coast |
| title_full | Bridging large-scale and coastal variability to improve seasonal sea level predictions along the U.S. and Canadian West Coast |
| title_fullStr | Bridging large-scale and coastal variability to improve seasonal sea level predictions along the U.S. and Canadian West Coast |
| title_full_unstemmed | Bridging large-scale and coastal variability to improve seasonal sea level predictions along the U.S. and Canadian West Coast |
| title_short | Bridging large-scale and coastal variability to improve seasonal sea level predictions along the U.S. and Canadian West Coast |
| title_sort | bridging large scale and coastal variability to improve seasonal sea level predictions along the u s and canadian west coast |
| url | https://doi.org/10.1038/s41612-025-01182-x |
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