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High-resolution Model for Seasonal Prediction of Surface Shortwave Radiation in China
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摘要: 基于中国气象科学研究院T255全球高分辨率气候系统模式(CAMS-CSM)2011—2020年多样本集合回报试验，评估模式在中国及3个典型区域地表短波辐射(downward short-wave radiation flux，DSWRF)的季节预测能力。结果表明：CAMS-CSM模式能较好预测DSWRF的季节变化特征，但春季、夏季预测强度偏弱，秋季、冬季偏强。不同季节、不同地区DSWRF异常场的预报技巧差异明显。由DSWRF异常的空间相关系数和时间相关系数可以看到，内蒙古和西北地区秋季、冬季预报技巧较高，京津冀部分地区夏季、秋季节预报技巧较低。从趋势异常综合评分指数看，中国区域超前1个月预报各季节评分均超过70分，对西北地区夏季、秋季的评分指数最高，超过80分。整体而言，高分辨率气候模式对中国区域DSWRF超前0~1个月预报有一定预测能力，尤其是太阳能资源丰富的西北地区，可为未来DSWRF短期预测及太阳能清洁能源选址等提供参考。除模式系统性偏差外，春季、夏季DSWRF预报偏差主要来源于总云量预报的模拟偏差，改进模式云微物理过程是提高DSWRF季节预测能力的重要途径。Abstract: Based on the global high-resolution climate model CAMS-CSM developed by Chinese Academy of Meteorological Sciences, the seasonal prediction skill of downward short-wave radiation flux (DSWRF) in China and three key regions is evaluated during the period of 2011-2020. The results show that the high-resolution version of CAMS-CSM can well predict the seasonal and interannual variability of DSWRF, but the predicted intensity is relatively weaker in spring and summer, while slightly stronger in autumn and winter compared to the observation. The prediction of the climate mean state doesn't change much with the lead time, indicating the systematic bias of the DSWRF is formed steadily in the early stage of model integration. However, there are obvious diversities in the prediction skill of the DSWRF anomalies in different seasons and different regions. From the anomalous spatial and temporal correlation coefficients, it can be noted that the prediction skill is higher in Inner Mongolia and Northwest China in autumn and winter, while lower in some areas of Beijing-Tianjin-Hebei in summer and autumn. From the perspective of comprehensive assessment of trend anomalies (P index), the model can score more than 70 points for all seasons in China at 0-month lead time, and the best performance can be close to 80 points for summer and autumn in Northwest China. Overall, the high-resolution version of CAMS-CSM climate model has certain prediction capability for DSWRF at 0-1 month ahead in China, especially in northwest regions where the solar-radiation is rich all year, which can provide specific scientific guidance for the future DSWRF short-term prediction and the solar energy site selection. In addition to the systematic bias of the model, there is a significant negative correlation between the predicted DSWRF bias and the total cloud cover bias, indicating that the bias of DSWRF prediction mainly comes from the simulation bias of total cloud cover, especially in spring and summer, as well as in autumn and winter in South China. In order to improve the prediction accuracy of DSWRF, it is an effective way to reduce the uncertainty of the model cloud microphysical processes. However, it is difficult to meet the demand of practical application with only high-resolution climate model, and its results still need to be processed with methods such as dynamic downscaling and bias revision to further improve the prediction skills.
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图 3 各季节DSWRF LM0预报与观测气候态差异
Fig. 3 Difference of seasonal DSWRF between the prediction at LM0 and the observation
图 5 各季节DSWRF LM0预报与观测标准差的差异
Fig. 5 Differences of seasonal DSWRF standard deviation between the prediction at LM0 and the observation
图 6 DSWRF LM0预报异常与观测时间相关系数
(黑色打点表示达到0.1显著性水平)
Fig. 6 TCC of DSWRF between the prediction at LM0 and the observation
(black dots denote passing the test of 0.1 level)
图 7 各季节DSWRF不同超前时间预报区域平均与观测的时间相关系数
Fig. 7 TCC of DSWRF between regional averaged prediction at different lead months and the observation in different seasons
图 8 各季节DSWRF不同超前时间预报区域平均与观测的距平相关系数
Fig. 8 ACC of DSWRF between regional averaged prediction at different lead months and observation in different seasons
图 9 各季节DSWRF不同超前时间预报区域平均的P指数
Fig. 9 Regional averaged P index of DSWRF predicted at different lead months in different seasons
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