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基于NCEP-GEFS回算资料的我国极端温度变化特征研究
引用本文:高丽,任宏利,郑嘉雯,陈权亮.基于NCEP-GEFS回算资料的我国极端温度变化特征研究[J].大气科学学报,2019,42(1):58-67.
作者姓名:高丽  任宏利  郑嘉雯  陈权亮
作者单位:国家气象中心中国气象局数值预报中心;国家气候中心中国气象局气候研究开放实验室;成都信息工程大学大气科学学院/高原大气与环境四川省重点实验室
基金项目:国家自然科学基金资助项目(41875138);国家科技支撑计划项目(2015BAC03B01);气象预报业务关键技术发展专项(YBGJXM2018-04)
摘    要:利用美国NCEP全球集合预报系统(GEFS)历史回算资料和中国均一化格点观测数据,分析了我国近30 a来极端温度变化特征,重点考察了该模式预报系统对这一变化特征的刻画性能。通过估算格点观测和模式资料中2 m温度的历史气候百分位,分析了我国冬夏两季极端温度的气候特征以及极端温度日数的气候分布和多年变化趋势。结果表明,我国冬季极端低温和夏季极端高温的空间分布表现出较强区域性特征:东北、华北和青藏高原区域冬季极端低温的百分位阈值对应的温度较低,而华南、西北和长江流域夏季极端高温的阈值温度则较高;近30 a来我国夏季平均温度和极端高温日数几乎都呈现上升趋势,冬季平均温度则在我国大部分区域呈上升趋势、西北和东北部分地区呈下降趋势,相应地冬季极端低温日数在大部分区域呈下降趋势、仅在西北、东北和华南部分地区略有上升。NCEP-GEFS回算资料能较好地再现我国冬夏两季平均气温、冬季极端低温和夏季极端高温日数的气候趋势和年际变化,但在各区域都有不同程度的冷偏差,冬季偏差明显大于夏季,并随着预报时长的增加,冬季冷偏差逐渐增强,而夏季冷偏差则逐渐减弱。因此,本文建议采用基于百分位阈值的相对极端性定义,可自动修正模式分析场和预报场中的系统性偏差。

关 键 词:NCEP-GEFS  集合预报  极端温度  回报  变化特征
收稿时间:2018/9/11 0:00:00
修稿时间:2018/11/20 0:00:00

Diagnosis features of extreme temperature variations in China based on the NCEP-GEFS reforecasts
GAO Li,REN Hongli,ZHENG Jiawen and CHEN Quanliang.Diagnosis features of extreme temperature variations in China based on the NCEP-GEFS reforecasts[J].大气科学学报,2019,42(1):58-67.
Authors:GAO Li  REN Hongli  ZHENG Jiawen and CHEN Quanliang
Institution:CMA Numerical Prediction Center, National Meteorological Center, Beijing 100081, China,Laboratory for Climate Studies, National Climate Center, Beijing 100081, China,CMA Numerical Prediction Center, National Meteorological Center, Beijing 100081, China;Laboratory for Climate Studies, National Climate Center, Beijing 100081, China;College of Atmospheric Science/Plateau Atmosphere and Environment Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225, China and College of Atmospheric Science/Plateau Atmosphere and Environment Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225, China
Abstract:In this study,based on the reforecast data of NCEP Global Ensemble Forecast System(GEFS) and China homogeneous grid-point observational data,features of extreme temperature variations in the past 30 years are analyzed,and the performance of the NCEP-GEFS in representing these kinds of features is thoroughly investigated.By estimating the historical climatic percentile of 2 m temperature in the observational and model data,the characteristics of extreme temperature in winter and summer,along with the spatial distribution and multi-year trend of extreme temperature days,are analyzed.The results show that some strong regional features exist in the spatial distributions of winter extreme low temperature(ELT) and summer extreme high temperature(EHT) in China,i.e.there are relatively lower temperatures corresponding to the percentile thresholds of the winter ELT in northeastern China,northern China and the Qinghai-Tibet Plateau,with higher temperatures corresponding to the percentile thresholds of the summer EHT in southern China,northwestern China and the Yangtze River Basin.Both the summer mean temperature and EHT days throughout China show increasing trends in the past 30 years,and the winter mean temperatures are also increasing throughout most of China,yet decreasing in northwestern and northeastern China.Correspondingly,the numbers of days of the winter ELT are decreasing in most areas,and only slightly increasing in small parts of northwestern,northeastern and southern China.The NCEP-GEFS reforecasts are able to accurately reproduce the climatic trends and interannual variations of the seasonal mean temperature and extreme temperature days in the winter and summer of China,yet varying degrees of cold biases exist in the different regions.The biases in winter are significantly larger than those in summer,and as the forecast length increases,these cold biases are gradually strengthened in winter,while gradually weakened in summer.Therefore,it is suggested to adopt the relative definition of extreme temperature based on the percentile threshold,which can automatically correct these systematic biases in the model analysis and prediction products.
Keywords:NCEP-GEFS  ensemble prediction  extreme temperature  reforecasts  variation features
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