基于BCC_CSM模式的中国东部夏季降水预测检验及订正

Verification and Correction of East China Summer Rainfall Prediction Based on BCC_CSM Model

基于国家气候中心第二代季节预测模式的历史回报试验数据,检验了模式对我国东部夏季降水的预测能力,探讨了预测误差形成的可能原因,并应用降尺度方法提高了模式的降水预测技巧。分析表明:(1)模式能在一定程度上把握我国东部夏季降水时空变率的两个主要模态(偶极子型模态和全区一致型模态),但是不同超前时间的预测在刻画模态方差贡献、异常空间分布特征、时间系数的年际变化等方面存在明显误差;(2)模式能够合理预测大尺度环流和海表温度(SST)的变化特征,但是对中国东部夏季降水的总体预测技巧有限,这与模式不能准确刻画西太平洋副热带高压、大陆高压、中高纬阻塞高压等环流系统以及热带太平洋、印度洋SST变率对中国东部降水模态的影响有关;(3)针对1991~2003年回报试验数据中的500 h Pa位势高度、850 h Pa纬向风和经向风、SST变量,在全球范围内寻找并定位与中国东部站点降水关系最密切的预报因子,进而建立针对降水预测的单因子线性回归、多因子逐步和多元回归模型。采用2004~2013年回报试验对所建立的降水预测模型进行了独立检验,结果表明:所建立的降尺度预测模型能显著提高中国东部地区夏季降水的预报技巧。以6月1日起报试验为例,预测的第一模态(第二模态)与观测的空间相关系数由原始的0.12(0.48)提高到了0.58(0.80),时间相关系数则从0.47(0.15)提高到0.80(0.67);其它超前时间的预测试验中,降尺度预测模型的降水预测技巧相比模式原始预测技巧也同样明显提高。

Based on the re-forecast data from the second-generation seasonal prediction model of National Climate Center, the model＇s capability to predict summer rainfall over East China and possible reasons for the forecast errors are investigated. Furthermore, the rainfall forecast skill is improved by the application of downscaling approaches. Results indicate that the model is able to capture the two major modes of spatiotemporal variability of summer rainfall over East China to some extent（i.e. the dipole mode and the uniform-distribution mode）. However, forecasts at various lead times show obvious errors in variance contributions of these modes and spatial distributions of anomalies and interannual variations of time coefficients, etc. In addition, although the model can reasonably reproduce variations of large-scale circulation and sea surface temperature（SST）, it shows limited skills in forecasting summer rainfall over East China. This is partially due to the model＇s inability to realistically depict the impacts of circulation systems such as the West Pacific subtropical high, the continental high and the middle–high-latitude blocking high. Influences of SST in the tropical Pacific and Indian Ocean on major rainfall modes over East China are also not well described in the model. Furthermore, in terms of the 500-h Pa geopotential height, 850-h Pa zonal and meridional winds, and SST in reforecasts for 1991–2003, predictors with the closest relationship with East China rainfall are identified on global scale and used to establish the single-factor linear regression, multi-factor stepwise regression, and multiple regression downscaling models for rainfall prediction. These downscaling rainfall prediction models are tested independently using reforecasts for 2004–2013, and significant improvements in the forecast of East China summer rainfall are obtained. For the forecast initialized on June 1, for example, the spatial correlation coefficient between predicted and observed EOF1（EOF2） modes increases from 0.12（0.48） for the original prediction to 0.58（0.80） for the downscale prediction, and the corresponding temporal correlation coefficient rises from 0.47（0.15） to 0.80（0.67）. Compared to the original forecasts by the model at other lead times, the downscaling forecast models also significantly enhance the prediction skill of rainfall.