Skilful two-month-leading hybrid climate prediction for winter temperature over China

摘要

Two skilful 2-month-leading hybrid downscaling prediction schemes in October for winter surface air temperature (SAT) over China are proposed in this paper. The schemes are based on the year-to-year increment approach and the coupled climate patterns between the winter SAT over China and its predictors. Observed North Pacific sea surface temperature (SST) from the preceding July to September, Arctic sea ice concentration (SIC) in the preceding August and winter sea level pressure (SLP) over pan Eurasia from version 2 of the Climate Forecast System (CFSv2) are selected as the predictors based on the fundamental physics. Individual-predictor schemes (IP-schemes), that is, SLP-scheme, SST-scheme, SIC-scheme, indicate that these predictors exhibit prediction skills in different regions. Multi-predictor scheme I (MP-schemeI) is developed by combining three predictors. However, MP-schemeI shows limited skill in predicting SAT over Northeast China (NECTA), due to the limited skill of CFSv2 over the extratropics. Thus, MP-schemeII is established, in which a hybrid downscaling model for NECTA is constructed. These two MP-schemes have comparable prediction skill over China, but MP-schemeII outperforms MP-schemeI over NEC. The temporal (spatial) anomaly correlation coefficient (ACC) increases from 0.23 (0.15) in MP-schemeI to 0.36 (0.21) in MP-schemeII, and the ratio of the same sign of anomalous years (Anomalous-RSS) improves from 39.1% to 56.5% over NEC. For the winter SAT over China, the MP-schemes greatly enhance the prediction compared with CFSv2 outputs and the IP-schemes. The temporal (spatial) ACC increases from 0.29 (0.05) in CFSv2 to 0.52 (0.29) in the MP-schemes, and the station-average root-mean-square error decreases by about 48.0% compared with CFSv2. Moreover, the RSS (Anomalous-RSS) of the winter SAT over China is 55.9% (47.4%) in CFSv2 and 64.7% (63.2%) in the MP-schemes. This indicates that the MP-schemes perform better in predicting anomalous winters.