Seasonal prediction skills of FIO-ESM for North Pacific sea surface temperature and precipitation
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Seasonal prediction skills of FIO-ESM for North Pacific sea surface temperature and precipitation Yiding Zhao1, 2, Xunqiang Yin2, 3, 4, Yajuan Song2, 3, 4, Fangli Qiao2, 3, 4* 1 College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China 2 First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China 3 Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and
Technology (Qingdao), Qingdao 266071, China 4 Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China
Received 10 September 2017; accepted 6 February 2018 © Chinese Society for Oceanography and Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract
The seasonal prediction of sea surface temperature (SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model (FIO-ESM) is assessed in this study. The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions. The average skill of the North Pacific variability (NPV) index from 1 to 6 months lead is as high as 0.72 (0.55) when El Niño-Southern Oscillation and NPV are in phase (out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6% (23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction. Key words: seasonal prediction, North Pacific, sea surface temperature, precipitation, FIO-ESM climate model Citation: Zhao Yiding, Yin Xunqiang, Song Yajuan, Qiao Fangli. 2019. Seasonal prediction skills of FIO-ESM for North Pacific sea surface temperature and precipitation. Acta Oceanologica Sinica, 38(1): 5–12, doi: 10.1007/s13131-019-1366-x
1 Introduction The North Pacific climate variability significantly affects the physical, biological and human environment over the ocean and its adjacent continents (Latif and Barnett, 1994; Mantua et al., 1997; Overland et al., 2010). Accurate predictions of climate variability are crucial for social management, such as for disaster prevention in the affected regions. The temporal characteristics of the North Pacific sea surface temperature (SST) exhibit variability on different time scales extending from weather forecasting scale to decadal or even longer scales. On the interannual and interdecadal time scales, the North Pacific SST can strongly influence the El Niño-Southern
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