Three deterministic prediction evaluation methods,including the standard deviation,root-mean-square error,and time correlation coefficient,and three extreme temperature indices were used to assess the performance of t...Three deterministic prediction evaluation methods,including the standard deviation,root-mean-square error,and time correlation coefficient,and three extreme temperature indices were used to assess the performance of the BCC_CSM2_MR model from CMIP6 in simulating the climate of Northwest China based on monthly grid air temperature data from ground stations.The model performance was evaluated using the daily mean temperature,daily minimum temperature,and daily maximum temperature from 1961 to 2014 and future temperature changes in Northwest China under different radiative forcing scenarios.The BCC_CSM2_MR model reproduces well the seasonal changes,spatial distribution,and other characteristics of the daily mean temperature in Northwest China,especially in the Tarim Basin,the Kunlun and Qilian mountains,and Shaanxi.There is still some deviation in the simulation of the daily mean temperature in the high terrains of the Tianshan,Kunlun,and Altai mountains.The model better simulates the daily minimum temperature than the daily maximum temperature.The simulation error is smallest in summer,followed by autumn and winter,and largest in spring.In terms of extreme temperature indices,the deviations are smaller for cold nights,warm nights,and the annual maximum daily minimum temperatures.Furthermore,the model can capture the increase in warm events and the decrease in cold events.Under different forcing scenarios,there is a general warming trend in Northwest China,with the greatest warming in Xinjiang.展开更多
This study focuses on model predictive skill with respect to stratospheric sudden warming(SSW) events by comparing the hindcast results of BCC_CSM1.1(m) with those of the ECMWF's model under the sub-seasonal to se...This study focuses on model predictive skill with respect to stratospheric sudden warming(SSW) events by comparing the hindcast results of BCC_CSM1.1(m) with those of the ECMWF's model under the sub-seasonal to seasonal prediction project of the World Weather Research Program and World Climate Research Program. When the hindcasts are initiated less than two weeks before SSW onset, BCC_CSM and ECMWF show comparable predictive skill in terms of the temporal evolution of the stratospheric circumpolar westerlies and polar temperature up to 30 days after SSW onset. However, with earlier hindcast initialization, the predictive skill of BCC_CSM gradually decreases, and the reproduced maximum circulation anomalies in the hindcasts initiated four weeks before SSW onset replicate only 10% of the circulation anomaly intensities in observations. The earliest successful prediction of the breakdown of the stratospheric polar vortex accompanying SSW onset for BCC_CSM(ECMWF) is the hindcast initiated two(three) weeks earlier. The predictive skills of both models during SSW winters are always higher than that during non-SSW winters, in relation to the successfully captured tropospheric precursors and the associated upward propagation of planetary waves by the model initializations. To narrow the gap in SSW predictive skill between BCC_CSM and ECMWF, ensemble forecasts and error corrections are performed with BCC_CSM. The SSW predictive skill in the ensemble hindcasts and the error corrections are improved compared with the previous control forecasts.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
基金supported by the Numerical model development project of China Meteorological Administration(QHMS2018018,QHMS2019016)Research Fund Project of Chengdu University of Information Technology(KYTZ201721)
文摘Three deterministic prediction evaluation methods,including the standard deviation,root-mean-square error,and time correlation coefficient,and three extreme temperature indices were used to assess the performance of the BCC_CSM2_MR model from CMIP6 in simulating the climate of Northwest China based on monthly grid air temperature data from ground stations.The model performance was evaluated using the daily mean temperature,daily minimum temperature,and daily maximum temperature from 1961 to 2014 and future temperature changes in Northwest China under different radiative forcing scenarios.The BCC_CSM2_MR model reproduces well the seasonal changes,spatial distribution,and other characteristics of the daily mean temperature in Northwest China,especially in the Tarim Basin,the Kunlun and Qilian mountains,and Shaanxi.There is still some deviation in the simulation of the daily mean temperature in the high terrains of the Tianshan,Kunlun,and Altai mountains.The model better simulates the daily minimum temperature than the daily maximum temperature.The simulation error is smallest in summer,followed by autumn and winter,and largest in spring.In terms of extreme temperature indices,the deviations are smaller for cold nights,warm nights,and the annual maximum daily minimum temperatures.Furthermore,the model can capture the increase in warm events and the decrease in cold events.Under different forcing scenarios,there is a general warming trend in Northwest China,with the greatest warming in Xinjiang.
基金supported by the National Key R&D Program of China (Grant Nos. 2016YFA0602104 and 2016YFA0602102)the National Natural Science Foundation of China (Grant Nos. 41705024, 41575041, 41705039 and 41705076)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA17010105)the Startup Foundation for Introducing Talent of NUIST (Grant No. 2016r060)the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘This study focuses on model predictive skill with respect to stratospheric sudden warming(SSW) events by comparing the hindcast results of BCC_CSM1.1(m) with those of the ECMWF's model under the sub-seasonal to seasonal prediction project of the World Weather Research Program and World Climate Research Program. When the hindcasts are initiated less than two weeks before SSW onset, BCC_CSM and ECMWF show comparable predictive skill in terms of the temporal evolution of the stratospheric circumpolar westerlies and polar temperature up to 30 days after SSW onset. However, with earlier hindcast initialization, the predictive skill of BCC_CSM gradually decreases, and the reproduced maximum circulation anomalies in the hindcasts initiated four weeks before SSW onset replicate only 10% of the circulation anomaly intensities in observations. The earliest successful prediction of the breakdown of the stratospheric polar vortex accompanying SSW onset for BCC_CSM(ECMWF) is the hindcast initiated two(three) weeks earlier. The predictive skills of both models during SSW winters are always higher than that during non-SSW winters, in relation to the successfully captured tropospheric precursors and the associated upward propagation of planetary waves by the model initializations. To narrow the gap in SSW predictive skill between BCC_CSM and ECMWF, ensemble forecasts and error corrections are performed with BCC_CSM. The SSW predictive skill in the ensemble hindcasts and the error corrections are improved compared with the previous control forecasts.
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。
