| 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 |
| Seasonal prediction skills of FIO-ESM for North Pacific sea surface temperature and precipitation |
| FIO-ESM气候模式北太平洋海表温度和降水的季节性预报技巧评估 |
| Received:September 10, 2017 |
| DOI：10.1007/s13131-019-1366-x |
| Key words:seasonal prediction North Pacific sea surface temperature precipitation FIO-ESM climate model |
| 中文关键词: 季节性预测 北太平洋 海表温度 降水 FIO-ESM气候模式 |
| 基金项目:The National Natural Science Foundation of China (NSFC)-Shandong Joint Fund for Marine Science Research Centers under contract No. U1606405; the National Programme on Global Change and Air-Sea Interaction under contract Nos GASI-IPOVAI-05 and GASI-IPOVAI-06; the International Cooperation Project on the China-Australia Research Centre for Maritime Engineering of Ministry of Science and Technology, China under contract No. 2016YFE0101400... |
| Author Name | Affiliation | E-mail | | ZHAO Yiding | College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China | | | YIN Xunqiang | First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266071, China
Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China | | | SONG Yajuan | First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266071, China
Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China | | | QIAO Fangli | First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266071, China
Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China | qiaofl@fio.org.cn |
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| 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 mid-latitudes. 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. |
| 中文摘要: |
| 本文利用FIO-ESM（First Institute of Oceanography Earth System Model）气候模式后报结果，对北太平洋海表温度（SST）和降水的季节性预报技巧进行评估。该气候模式同化系统采用了集合调整卡尔曼滤波（EAKF）同化方法，模式同化结果与观测数据对比，表现出较高的可靠性，可为预测提供合理的初始场。在此基础上，开展了1993-2013年期间逐月的6个月集合预测。分析发现，FIO-ESM气候模式在北太平洋大部分海域表现出较高的SST预报技巧，尤其是在中纬度海域改善最为明显。通过海-气相互作用，改进的海洋预测要素传递到大气中，使得降水在中纬度地区的预报技巧也有较明显的改进。接下来计算了NPV（North Pacific variability）指数的预报技巧，在ENSO与NPV同位相（反位相）时，NPV指数的平均预报技巧可达0.72（0.55），相比CFSv2的结果，FIO-ESM气候模式NPV指数的预报技巧提高了11.6%（23.6%）。通过对不同季节NPV指数预报技巧的分析发现，FIO-ESM气候模式在预测的后期预报技巧明显高于持续性预测的技巧。 |
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