| 南京信息工程大学气候预测系统1.0版简介 |
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| 引用本文： | 贺嘉樱,伍继业,罗京佳.南京信息工程大学气候预测系统1.0版简介[J].大气科学学报,2020,43(1):128-143. |
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| 作者姓名： | 贺嘉樱 伍继业 罗京佳 |
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| 作者单位： | 南京信息工程大学大气科学学院-气候与应用前沿研究院/气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心,江苏南京210044;南京信息工程大学大气科学学院-气候与应用前沿研究院/气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心,江苏南京210044;南京信息工程大学大气科学学院-气候与应用前沿研究院/气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心,江苏南京210044 |
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| 基金项目： | 南京信息工程大学人才启动经费 |
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| 摘 要： | 南京信息工程大学气候预测系统1.0版(NUIST CFS1.0)是基于日本海洋科学技术开发机构(JAMSTEC)的SINTEX-F模式发展而来,可以实现对全球气候异常的季节-年际预测。对过去近40 a的集合历史回报预测试验结果的评估发现,该预测系统对热带太平洋和印度洋海温异常具有良好的预测技巧,并且该系统能提前1.5~2 a对ENSO(Nino3.4指数)做出有技巧的预测(即相关系数达0.5),同时也可以提前1~2个季节对印度洋偶极子(IOD)做出有较高技巧的预测,展现了对主要热带气候信号的良好预测技巧。但是与国内外所有动力模式预测系统类似,该系统对东亚地区的气候异常预测还存在较大的不足。考虑到ENSO对东亚地区气候异常的强烈影响,本文尝试去除与ENSO预测相关的系统偏差来初步订正东亚地区夏季温度异常和降水距平百分率的预测结果。对比订正前后的结果表明,这一简单的订正方法有助于提高我国气候异常的预测准确率。同时选取2019年夏季气温异常和降水距平百分率的实时预测结果作为个例进行分析,发现订正能够提供一定的技巧改善,但与观测结果相比仍存在较大偏差,需要在今后的工作中不断改进完善。此外,本文也初步评估了NUIST CFS1.0对我国冬春季的气候预测技巧,并提供了经简单订正后的2019/2020年冬季和2020年春季的实时预测结果。
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| 关 键 词： | 南信大气候预测系统 气候模式 气候预测 预测结果订正 |
| 收稿时间： | 2019/11/10 0:00:00 |
| 修稿时间： | 2019/11/25 0:00:00 |
Introduction to climate forecast system version 1.0 of Nanjing University of Information Science and Technology |
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| HE Jiaying,WU Jiye,LUO Jingjia.Introduction to climate forecast system version 1.0 of Nanjing University of Information Science and Technology[J].大气科学学报,2020,43(1):128-143. |
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| Authors: | HE Jiaying WU Jiye LUO Jingjia |
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| Institution: | Institute for Climate and Application Research(ICAR)-School of Atmospheric Sciences/Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China,Institute for Climate and Application Research(ICAR)-School of Atmospheric Sciences/Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China and Institute for Climate and Application Research(ICAR)-School of Atmospheric Sciences/Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China |
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| Abstract: | Based on the JAMSTEC SINTEX-F model,this paper developed NUIST climate forecast system 1.0 version (NUIST CFS1.0) for seasonal-to-multiyear forecasts of global climate anomalies.The 9-member ensemble hindcast experiments during 1982-2018 were conducted using the NUIST super-computer.The assessment of the hindcasts shows that NUIST CFS1.0 displays useful skills in predicting sea surface temperature anomalies in the tropical Pacific and Indian Ocean.In particular,ENSO (Niño3.4 index) is skillfully predicted up to 1.5 to 2 years in advance.And the Indian Ocean Dipole (IOD) can be predicted 1-2 seasons in advance.It shows good prediction skills for major tropical climate signals.However,prediction of the East Asia climate is rather poor,which is a long-standing problem in almost all current and past dynamical prediction systems.Considering the large impact of ENSO on East Asia climate,this paper adopted a simple method to improve prediction of NUIST CFS1.0 by correcting the systematic biases of model in predicting the impact of ENSO on climate in East Asia in summer.The results suggest that this simple correction method can improve both the hindcast and real time forecasts of surface air temperature and precipitation anomaly percentage in China.The spatial pattern correlations of the two variables in China are increased to some degree.Further improvement of the forecast system and correction methods are under development.In addition,a preliminary assessment of the climate prediction in winter and spring is performed.Real time forecasts of the temperature anomalies and precipitation anomaly percentages in China during winter 2019/2020 and spring 2020 are provided. |
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| Keywords: | NUIST CFS1 0 climate model climate forecast bias correction |
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