It is widely recognized that rainfall over the Yangtze River valley （YRV） strengthens considerably during the decaying summer of E1 Nifio, as demonstrated by the catastrophic flooding suffered in the summer of 1998....It is widely recognized that rainfall over the Yangtze River valley （YRV） strengthens considerably during the decaying summer of E1 Nifio, as demonstrated by the catastrophic flooding suffered in the summer of 1998. Nevertheless, the rainfall over the YRV in the summer of 2016 was much weaker than that in 1998, despite the intensity of the 2016 E1 Nifio having been as strong as that in 1998. A thorough comparison of the YRV summer rainfall anomaly between 2016 and 1998 suggests that the difference was caused by the sub-seasonal variation in the YRV rainfall anomaly between these two years, principally in August. The precipitation anomaly was negative in August 2016--different to the positive anomaly of 1998.展开更多
Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier mu...Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble（MME） prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function（PDF） prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos.41320104007,U1502233,41675078 and 41461164005)
文摘It is widely recognized that rainfall over the Yangtze River valley （YRV） strengthens considerably during the decaying summer of E1 Nifio, as demonstrated by the catastrophic flooding suffered in the summer of 1998. Nevertheless, the rainfall over the YRV in the summer of 2016 was much weaker than that in 1998, despite the intensity of the 2016 E1 Nifio having been as strong as that in 1998. A thorough comparison of the YRV summer rainfall anomaly between 2016 and 1998 suggests that the difference was caused by the sub-seasonal variation in the YRV rainfall anomaly between these two years, principally in August. The precipitation anomaly was negative in August 2016--different to the positive anomaly of 1998.
基金co-supported by the National Natural Science Foundation (Grant Nos. 41005052 and 41375086)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA05110201)the National Basic Research Program of China (Grant No. 2010CB950403)
文摘Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble（MME） prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function（PDF） prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.
