期刊
CLIMATE DYNAMICS
卷 56, 期 7-8, 页码 2057-2069出版社
SPRINGER
DOI: 10.1007/s00382-020-05574-y
关键词
Summer rainfall in eastern China; Subseasonal prediction skill and predictability; Western Pacific subtropical high; Pacific-Japan teleconnection pattern; Internal variability and SST forcing; Perfect model skill
资金
- National Key R&D Program of China [2018YFC1505806, 2018YFB1502803]
- China Meteorological Administration Forecaster Program [CMAYBY2020-164]
- National Natural Science Foundation of China [41875100]
The study examines the subseasonal prediction skills and biases of summer rainfall over eastern China in the Beijing Climate Center (BCC) Atmospheric General Circulation Model (BCC_AGCM2.2), finding that the model shows some skill in predicting summer rainfall within certain lead-times but overall has low subseasonal prediction skill due to biases in the model. Additionally, the Pacific-Japan pattern is not well predicted for both its spatial pattern and subseasonal evolution.
The present study examines subseasonal prediction skills and biases of the summer rainfall over eastern China in the Beijing Climate Center (BCC) Atmospheric General Circulation Model (BCC_AGCM2.2) and assesses the predictability of eastern China summer rainfall based on the multi-member forecasts. The BCC_AGCM2.2 model shows some skill in predicting summer rainfall over eastern China within the lead-times of 0-9 days. However, the subseasonal prediction skill is low on average, which is linked to the ability of the model in predicting the western Pacific subtropical high (WPSH). The low prediction skills may partially be attributed to biases in the model, including a wider meridional span and a weaker intensity of rainbelt in Yangtze River valley, earlier meridional movement, and a further northward shift of WPSH compared to that in the observations. These biases result in an obvious dry bias along the monsoon rainbelt, and a wet bias to both the north and south sides. Moreover, as a major contributor to the predictability, the Pacific-Japan pattern is not well predicted for both its spatial pattern and subseasonal evolution. The low forecast skill of summer rainfall in eastern China seems due to a dominant role of the atmospheric internal variability and a minor influence of the sea surface temperature in extratropical climate variability. Nevertheless, enhanced prediction skills under the assumption of a perfect model imply the potential to improve the prediction skill of the summer rainfall over eastern China through reducing model biases.
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