To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in p...To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in predicting the autumn SIC and its interannual variability over the Barents–East Siberian Seas(BES).It is found that CFSv2 presents much better prediction skill for the September SIC over BES than the Arctic as a whole at 1–6-month leads,and high prediction skill for the interannual variability of the SIC over BES is displayed at 1–2-month leads after removing the linear trend.CFSv2 can reasonably reproduce the relationship between the SIC over BES in September and such factors as the surface air temperature(SAT),200-hPa geopotential height,sea surface temperature(SST),and North Atlantic Oscillation.In addition,it is found that the prescribed SIC initial condition in August as an input to CFSv2 is also essential.Therefore,the above atmospheric and oceanic factors,as well as an accurate initial condition of SIC,all contribute to a high prediction skill for SIC over BES in September.Based on a statistical prediction method,the contributions from individual predictability sources are further identified.The high prediction skill of CFSv2 for the interannual variability of SIC over BES is largely attributable to its accurate predictions of the SAT and SST,as well as a better initial condition of SIC.展开更多
再分析风场资料已广泛应用于我国舟山群岛海域可再生能源评估、海洋灾害预防决策以及港口运维和船舶运输等涉海发展领域,然而不同业务机构所提供的再分析数据在舟山近海的性能表现不一,严重阻碍了此类数据的有效应用。基于2018年全年单...再分析风场资料已广泛应用于我国舟山群岛海域可再生能源评估、海洋灾害预防决策以及港口运维和船舶运输等涉海发展领域,然而不同业务机构所提供的再分析数据在舟山近海的性能表现不一,严重阻碍了此类数据的有效应用。基于2018年全年单点浮标观测资料,综合评价了舟山群岛近海面(10 m)风场的长期变化趋势,并利用误差分析和风玫瑰图等统计工具对6种主流海表风场再分析资料,包括:ECMWF第五代全球大气再分析数据(the 5th generation ECMWF atmospheric reanalysis,ERA5)、NECP第二版全球高分辨率再分析数据(climate forecast system version 2,CFSv2)、美国宇航局物理海洋学分布存档中心的多卫星融合资料(cross-calibrated multi-platform,CCMP)、日本55年再分析数据(Japanese 55-year reanalysis,JRA-55)、第二版现代研究与应用回顾性分析数据(modern-era retrospective analysis for research and applications version 2,MERRA-2)和ECMWF哥白尼大气监测服务再分析数据(the Copernicus Atmosphere Monitoring Service,CAMS)在时间变化特征上进行了对比与评估。研究表明:在综合性能方面,ERA5对风场的再现能力最优,其次为JRA-55;在要素可信度方面,ERA5对风速的再现情况相对较优,而CFSv2的风向再现情况较好;风场产品在不同季节的模拟能力有所差异;不同风场产品在不同风速区间的重构能力也有所不同;在全年风向分布方面,各再分析资料都存在显著的东向偏差。研究结果为不同应用场景下风场资料的选取提供评估依据,并为进一步开发适用于舟山群岛近海的高精度长周期风场数据产品奠定基础。展开更多
Two prediction models are developed to predict the number of landfalling tropical cyclones(LTCs) in China during June–August(JJA). One is a statistical model using preceding predictors from the observation, and the o...Two prediction models are developed to predict the number of landfalling tropical cyclones(LTCs) in China during June–August(JJA). One is a statistical model using preceding predictors from the observation, and the other is a hybrid model using both the aforementioned preceding predictors and concurrent summer large-scale environmental conditions from the NCEP Climate Forecast System version 2(CFSv2).(1) For the statistical model, the year-to-year increment method is adopted to analyze the predictors and their physical processes, and the JJA number of LTCs in China is then predicted by using the previous boreal summer sea surface temperature(SST) in Southwest Indonesia,preceding October South Australia sea level pressure, and winter SST in the Sea of Japan. The temporal correlation coefficient between the observed and predicted number of LTCs during 1983–2017 is 0.63.(2) For the hybrid prediction model, the prediction skill of CFSv2 initiated each month from February to May in capturing the relationships between summer environmental conditions(denoted by seven potential factors: three steering factors and four genesis factors) and the JJA number of LTCs is firstly evaluated. For the 2-and 1-month leads, CFSv2 has successfully reproduced these relationships. For the 4-, 3-, and 2-month leads, the predictor of geopotential height at 500 h Pa over the western North Pacific(WNP) shows the worst forecasting skill among these factors. In general, the summer relative vorticity at 850 h Pa over the WNP is a modest predictor, with stable and good forecasting skills at all lead times.展开更多
基金Supported by the National Key Research and Development Program of China(2022YFE0106800)National Natural Science Foundation of China(42230603)Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(311021001)。
文摘To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in predicting the autumn SIC and its interannual variability over the Barents–East Siberian Seas(BES).It is found that CFSv2 presents much better prediction skill for the September SIC over BES than the Arctic as a whole at 1–6-month leads,and high prediction skill for the interannual variability of the SIC over BES is displayed at 1–2-month leads after removing the linear trend.CFSv2 can reasonably reproduce the relationship between the SIC over BES in September and such factors as the surface air temperature(SAT),200-hPa geopotential height,sea surface temperature(SST),and North Atlantic Oscillation.In addition,it is found that the prescribed SIC initial condition in August as an input to CFSv2 is also essential.Therefore,the above atmospheric and oceanic factors,as well as an accurate initial condition of SIC,all contribute to a high prediction skill for SIC over BES in September.Based on a statistical prediction method,the contributions from individual predictability sources are further identified.The high prediction skill of CFSv2 for the interannual variability of SIC over BES is largely attributable to its accurate predictions of the SAT and SST,as well as a better initial condition of SIC.
文摘再分析风场资料已广泛应用于我国舟山群岛海域可再生能源评估、海洋灾害预防决策以及港口运维和船舶运输等涉海发展领域,然而不同业务机构所提供的再分析数据在舟山近海的性能表现不一,严重阻碍了此类数据的有效应用。基于2018年全年单点浮标观测资料,综合评价了舟山群岛近海面(10 m)风场的长期变化趋势,并利用误差分析和风玫瑰图等统计工具对6种主流海表风场再分析资料,包括:ECMWF第五代全球大气再分析数据(the 5th generation ECMWF atmospheric reanalysis,ERA5)、NECP第二版全球高分辨率再分析数据(climate forecast system version 2,CFSv2)、美国宇航局物理海洋学分布存档中心的多卫星融合资料(cross-calibrated multi-platform,CCMP)、日本55年再分析数据(Japanese 55-year reanalysis,JRA-55)、第二版现代研究与应用回顾性分析数据(modern-era retrospective analysis for research and applications version 2,MERRA-2)和ECMWF哥白尼大气监测服务再分析数据(the Copernicus Atmosphere Monitoring Service,CAMS)在时间变化特征上进行了对比与评估。研究表明:在综合性能方面,ERA5对风场的再现能力最优,其次为JRA-55;在要素可信度方面,ERA5对风速的再现情况相对较优,而CFSv2的风向再现情况较好;风场产品在不同季节的模拟能力有所差异;不同风场产品在不同风速区间的重构能力也有所不同;在全年风向分布方面,各再分析资料都存在显著的东向偏差。研究结果为不同应用场景下风场资料的选取提供评估依据,并为进一步开发适用于舟山群岛近海的高精度长周期风场数据产品奠定基础。
基金Supported by the National Natural Science Foundation of China(41421004 and 41325018)National Key Research and Development Program of China(2017YFA0603802)
文摘Two prediction models are developed to predict the number of landfalling tropical cyclones(LTCs) in China during June–August(JJA). One is a statistical model using preceding predictors from the observation, and the other is a hybrid model using both the aforementioned preceding predictors and concurrent summer large-scale environmental conditions from the NCEP Climate Forecast System version 2(CFSv2).(1) For the statistical model, the year-to-year increment method is adopted to analyze the predictors and their physical processes, and the JJA number of LTCs in China is then predicted by using the previous boreal summer sea surface temperature(SST) in Southwest Indonesia,preceding October South Australia sea level pressure, and winter SST in the Sea of Japan. The temporal correlation coefficient between the observed and predicted number of LTCs during 1983–2017 is 0.63.(2) For the hybrid prediction model, the prediction skill of CFSv2 initiated each month from February to May in capturing the relationships between summer environmental conditions(denoted by seven potential factors: three steering factors and four genesis factors) and the JJA number of LTCs is firstly evaluated. For the 2-and 1-month leads, CFSv2 has successfully reproduced these relationships. For the 4-, 3-, and 2-month leads, the predictor of geopotential height at 500 h Pa over the western North Pacific(WNP) shows the worst forecasting skill among these factors. In general, the summer relative vorticity at 850 h Pa over the WNP is a modest predictor, with stable and good forecasting skills at all lead times.
