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Sea surface temperature predictability in the North Pacific from multi-model seasonal forecast
Title: | Sea surface temperature predictability in the North Pacific from multi-model seasonal forecast |
Authors: | Yati, Emi Browse this author | Minobe, Shoshiro Browse this author →KAKEN DB |
Keywords: | SST | Seasonal forecast | Multi-model ensemble | Predictability | Prediction skill | C3S |
Issue Date: | Dec-2021 |
Publisher: | Springer |
Journal Title: | Journal of oceanography |
Volume: | 77 |
Issue: | 6 |
Start Page: | 897 |
End Page: | 906 |
Publisher DOI: | 10.1007/s10872-021-00618-1 |
Abstract: | Sea surface temperature (SST) prediction based on the multi-model seasonal forecast with numerous ensemble members have more useful skills to estimate the possibility of climate events than individual models. Hence, we assessed SST predictability in the North Pacific (NP) from multi-model seasonal forecasts. We used 23 years of hindcast data from three seasonal forecasting systems in the Copernicus Climate Change Service to estimate the prediction skill based on temporal correlation. We evaluated the predictability of the SST from the ensemble members' width spread, and co-variability between the ensemble mean and observation. Our analysis revealed that areas with low prediction skills were related to either the large spread of ensemble members or the ensemble members not capturing the observation within their spread. The large spread of ensemble members reflected the high forecast uncertainty, as exemplified in the Kuroshio-Oyashio Extension region in July. The ensemble members not capturing the observation indicates the model bias; thus, there is room for improvements in model prediction. On the other hand, the high prediction skills of the multi-model were related to the small spread of ensemble members that captures the observation, as in the central NP in January. Such high predictability is linked to El Nino Southern Oscillation (ENSO) via teleconnection. |
Rights: | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10872-021-00618-1 |
Type: | article (author version) |
URI: | http://hdl.handle.net/2115/87348 |
Appears in Collections: | 理学院・理学研究院 (Graduate School of Science / Faculty of Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 見延 庄士郎
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