Improving the prediction skill for China summer rainfall through correcting leading modes in Beijing Climate Center's Climate System Model
Improving the prediction skill for China summer rainfall through correcting leading modes in Beijing Climate Center's Climate System Model
复制标题
通过修正BCC_CSM中的主导模态提高中国夏季降雨的预报技巧
DOI：
10.1002/joc.6076
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Feng Guolin
中科院分区：
文献类型：
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作者：
Wang Xiaojuan;Liu Li;Hu Po;Gong Zhiqiang;Feng Guolin
本研究分析了北京气候中心气候系统模型（BCC_CSM）在中国夏季降水预测方面的性能，特别是其再现经验正交函数分解主导模态的空间结构和时间变化的能力。结果表明，BCC_CSM对夏季平均降水格局具有预测性，即再现华南和海域降水偏多，华北和内陆地区降水偏少。该模型可以再现观测到的夏季降雨的前两种主要模态的空间格局；但它未能正确显示前两个主成分的年际变化，这可能导致我国大部分地区夏季降水异常预报技术水平较低。然后将领先的基于模式的校正（LMC）方法应用于后处理过程以校正预测误差。交叉验证证实LMC方法可以将观测到的夏季降水的历史空间格局信息与原始模型预测相结合，使得修正后的模型输出更好地反映主导模态的空间结构及其年际变化。与原模型输出相比，修正后的中国夏季降水异常的正时间相关系数面积增大，1991—2015年平均空间相关系数由修正前的-0.01提高到0.17。 LMC方法显示了其提高BCC_CSM预测技能的潜力，可用于操作预测。
In this study, the performance of Beijing Climate Center's Climate System Model (BCC_CSM) is analysed in terms of China summer rainfall prediction, especially its capability to reproduce the spatial structure and temporal variation of the leading modes of empirical orthogonal function decomposition. Results show that the BCC_CSM has predictability for the summer mean rainfall pattern, namely, reproducing more rainfall in South China and maritime area, and less rainfall in North China and inland area. The model can reproduce the spatial pattern of the first two leading modes of observed summer rainfall; however, it fails to show correct interannual variability of the first two principle components, which may lead to a low skill in summer rainfall anomaly prediction over most parts of China. The leading mode‐based correction (LMC) method is then applied for the post processing procedure to correct prediction errors. Cross validation confirms that the LMC method can integrate the historical spatial pattern information of observed summer rainfall with the original model prediction, making the corrected model output better reflect spatial structure of the leading modes and their interannual variation. Compared with the original model output, the positive temporal correlation coefficient area for the corrected China summer rainfall anomalies is thus increased, and the average spatial correlation coefficient during 1991–2015 is improved from −0.01 before the correction to 0.17. The LMC method shows its potential for improving the prediction skill of BCC_CSM, which can be used in operational prediction.