本文利用国家气候中心气候系统模式(Beijing climate center climate System Model,BCC_CSM1.1m)提供的1991—2014年海表温度回报数据,将逐步回归模态投影方法(stepwise Pattern Projection Method,SPPM)应用到改进BCC_CSM1.1m模式El N...本文利用国家气候中心气候系统模式(Beijing climate center climate System Model,BCC_CSM1.1m)提供的1991—2014年海表温度回报数据,将逐步回归模态投影方法(stepwise Pattern Projection Method,SPPM)应用到改进BCC_CSM1.1m模式El Nino和南方涛动(ENSO)预报研究。SPPM是一种经验性模式误差订正方法,其主要思路是在大尺度模式预报因子场中找寻出与格点观测预报变量相关性高的信号,通过投影将这种信号反演出来,然后建立回归方程得到订正后的预报结果。本文交叉检验和滚动独立样本检验的结果表明,利用SPPM可以有效地提高BCC_CSM1.1m气候系统模式的预报技巧,尤其是在热带太平洋地区以及印度洋海区,24年交叉检验Nino3.4指数提前6个月预报的相关系数技巧可以提高8%~10%,预报误差得到显著降低。不同季节SPPM订正效果略有不同,其中对秋季的预报技巧提升最为显著。与此同时,交叉检验结果还显示,SPPM对El Nino中心纬向位置的预报也有一定程度的改进。展开更多
基于国家气候中心气候系统模式(Beijing Climate Center Climate System Model,BCC_CSM1.1m)和美国NCEP/NCAR的气候预测模式(The NCEP Climate Forecast System Version 2,CFSv2)分别建立针对长江流域汛期降水的动力与统计相结合的降尺...基于国家气候中心气候系统模式(Beijing Climate Center Climate System Model,BCC_CSM1.1m)和美国NCEP/NCAR的气候预测模式(The NCEP Climate Forecast System Version 2,CFSv2)分别建立针对长江流域汛期降水的动力与统计相结合的降尺度预测模型,并比较两模式对应模型的预报技巧和差异来源。分别选择两模式2月起报的500 hPa及200 hPa全球位势高度场为预报因子,结合年际增量及经验正交分解(EOF)迭代法建立降尺度模型(分别简称DY_CSM1.1m和DY_CFSv2),研究发现:(1) EOF迭代法中截断解释方差的递增增加了预报因子的协同性和稳定性,从而显著提高预报技巧,并由此确定98%的截断解释方差为模型的最优参数。(2)两模型基于最优参数的预测效果均优于模式原始的降水预测,其中DY_CSM1.1m预测技巧更高,对应29 a距平相关系数(ACC)平均评分可达0.43,尤其在长江干流区域预报效果显著提高。将两模型预测的降水年际增量百分率转换为降水距平百分率时,ACC多年平均评分降为0.27和0.22,仍高于模式原始预测。(3) DY_CSM1.1m的ACC历年评分和长江流域汛期降水年际增量均与西太平洋副热带高压的一系列指数具有高相关性(以西太平洋副高脊线位置指数为例,DY_CFSv2则无此关系),因此BCC_CSM1.1m在西太平洋地区模拟性能优于CFSv2是导致该模式降尺度后预报技巧更高的重要原因,这一点在典型洪涝年1998和2020年中得以佐证。展开更多
During winter of 2021/2022,the temperature in China is characterized by a warm-to-cold transition,and the average temperature anomaly in February 2022 is−1.6℃,the coldest February in 2013-2022.We revealed the circula...During winter of 2021/2022,the temperature in China is characterized by a warm-to-cold transition,and the average temperature anomaly in February 2022 is−1.6℃,the coldest February in 2013-2022.We revealed the circulation regimes and physical mechanisms associated with this reversal event and demonstrated the advantage of a regional model downscaling over the use of the global model alone in predicting.In early winter,the warm anomalies are mainly related to an anomalous anticyclonic system downstream of a PNA-like(Pacific-North America)Rossby-wave train induced by La Niña.In late winter,due to the circulation response to the central Pacific warming and negative tropical Indian Ocean Dipole(TIOD),two‘−+−’Rossby-wave trains from high latitudes and the tropical Indian Ocean jointly lead to an anomalous cyclonic system in China.Meanwhile,an anticyclonic blocking system on the northern side of Baikal brings strong and cold air to China.These two systems together cause a significant drop in surface air temperature anomaly in China during the late winter.The Beijing Climate Center climate system model(BCC_CSM1.1 m)can essentially predict this temperature reversal in China about five months in advance.However,the reversal amplitude is weaker due to warm deviations over the tropical Pacific Ocean and equatorial Indian Ocean.Using dynamic downscaling,a regional Climate-Weather Research and Forecasting(CWRF)model correctly predicts the cold SAT anomalies in late winter 2021/2022.The regional model depicts more realistic circulation patterns in East Asia;the anomalous cyclonic system in Inner Mongolia accompanied by the northerly anomalies contribute to a lower-than-normal SAT over China.This study reveals the cooperative effect of wave trains from high latitudes and the tropics on the subseasonal temperature reversal and demonstrates a possible solution to improve the forecast skill by dynamic downscaling according to precise characterization of local surface information.展开更多
文摘基于国家气候中心气候系统模式(Beijing Climate Center Climate System Model,BCC_CSM1.1m)和美国NCEP/NCAR的气候预测模式(The NCEP Climate Forecast System Version 2,CFSv2)分别建立针对长江流域汛期降水的动力与统计相结合的降尺度预测模型,并比较两模式对应模型的预报技巧和差异来源。分别选择两模式2月起报的500 hPa及200 hPa全球位势高度场为预报因子,结合年际增量及经验正交分解(EOF)迭代法建立降尺度模型(分别简称DY_CSM1.1m和DY_CFSv2),研究发现:(1) EOF迭代法中截断解释方差的递增增加了预报因子的协同性和稳定性,从而显著提高预报技巧,并由此确定98%的截断解释方差为模型的最优参数。(2)两模型基于最优参数的预测效果均优于模式原始的降水预测,其中DY_CSM1.1m预测技巧更高,对应29 a距平相关系数(ACC)平均评分可达0.43,尤其在长江干流区域预报效果显著提高。将两模型预测的降水年际增量百分率转换为降水距平百分率时,ACC多年平均评分降为0.27和0.22,仍高于模式原始预测。(3) DY_CSM1.1m的ACC历年评分和长江流域汛期降水年际增量均与西太平洋副热带高压的一系列指数具有高相关性(以西太平洋副高脊线位置指数为例,DY_CFSv2则无此关系),因此BCC_CSM1.1m在西太平洋地区模拟性能优于CFSv2是导致该模式降尺度后预报技巧更高的重要原因,这一点在典型洪涝年1998和2020年中得以佐证。
基金supported by the National Natural Science Foundation of China(U2242207)the National Key Research and Development Program of China(2022YFE0136000)+1 种基金the National Natural Science Foundation of China(41790471,42105037,41965005)the Innovative Development Special Project of China Meteorological Administration(CXFZ2023J003,CXFZ2023P025).
文摘During winter of 2021/2022,the temperature in China is characterized by a warm-to-cold transition,and the average temperature anomaly in February 2022 is−1.6℃,the coldest February in 2013-2022.We revealed the circulation regimes and physical mechanisms associated with this reversal event and demonstrated the advantage of a regional model downscaling over the use of the global model alone in predicting.In early winter,the warm anomalies are mainly related to an anomalous anticyclonic system downstream of a PNA-like(Pacific-North America)Rossby-wave train induced by La Niña.In late winter,due to the circulation response to the central Pacific warming and negative tropical Indian Ocean Dipole(TIOD),two‘−+−’Rossby-wave trains from high latitudes and the tropical Indian Ocean jointly lead to an anomalous cyclonic system in China.Meanwhile,an anticyclonic blocking system on the northern side of Baikal brings strong and cold air to China.These two systems together cause a significant drop in surface air temperature anomaly in China during the late winter.The Beijing Climate Center climate system model(BCC_CSM1.1 m)can essentially predict this temperature reversal in China about five months in advance.However,the reversal amplitude is weaker due to warm deviations over the tropical Pacific Ocean and equatorial Indian Ocean.Using dynamic downscaling,a regional Climate-Weather Research and Forecasting(CWRF)model correctly predicts the cold SAT anomalies in late winter 2021/2022.The regional model depicts more realistic circulation patterns in East Asia;the anomalous cyclonic system in Inner Mongolia accompanied by the northerly anomalies contribute to a lower-than-normal SAT over China.This study reveals the cooperative effect of wave trains from high latitudes and the tropics on the subseasonal temperature reversal and demonstrates a possible solution to improve the forecast skill by dynamic downscaling according to precise characterization of local surface information.
