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 Niño Southern Oscillation (ENSO) via teleconnection.
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Acknowledgements
EY was supported by the doctoral program scholarship from the Research and Innovation Science and Technology Project, Ministry of Research and Technology/National Research and Innovation Agency of the Republic of Indonesia. SM was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP18H04129, 19H05704). We thank C3S, ECMWF, DWD, and CMCC center for provided the seasonal forecast data and NOAA for the OISST Version 2 dataset. We also thank the reviewers and editors for their constructive comments.
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EY and SM designed this study, collected the data, analyzed the results, wrote, and discussed the manuscript.
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Yati, E., Minobe, S. Sea surface temperature predictability in the North Pacific from multi-model seasonal forecast. J Oceanogr 77, 897–906 (2021). https://doi.org/10.1007/s10872-021-00618-1
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DOI: https://doi.org/10.1007/s10872-021-00618-1