Study On The Risk Assessment And Mapping Of High Temperature In China And Typical Regions

The daily maximum air temperature(T_max)is a key variable in climate change,disaster warning,and resource and environmental analysis,as well as a key parameter for global and regional high temperature analysis.Therefore,it is of great practical significance to accurately obtain the spatial information of T_max for grasping the law of high temperature variation in different climate regions in China under the background of global warming and predicting extreme weather.However,due to the sparse distribution of ground meteorological stations and large error of spatial interpolation method,it is difficult to obtain high-precision spatial information of T_max.In order to obtain spatially continuous T_max data,this study proposes an estimation framework for obtaining high-precision T_max.Firstly,we build a near surface air temperature(T_a)diurnal variation model to estimate T_max for China from 1979 to 2018 based on multi-source data.Then,in order to further improve the estimation accuracy,we divided China into six regions according to climate conditions and topography,and established calibration models for different regions.The analysis shows that the mean absolute error(MAE)of the dataset is about 1.07?and the root mean square error(RMSE)is 1.52?,which improves the accuracy of the traditional method by nearly 1°C.Based on the constructed T_maxdataset,this study analyzes the temporal and spatial variation characteristics of T_max and extreme temperature indices in China in the past 40 years,and further explores the impact of ocean on temperature change in China combined with the ocean climate modal indices.The generalized extreme value(GEV)distribution was used to calculate the recurrence level of extreme high temperature once in 10 years,once in 20 years,once in 50 years and once in 100years,and generated the high temperature risk map in China.The main results are as follows:(1)From 1979 to 2018,the annual average T_max in most regions of China showed an increasing trend,but during the global warming hiatus(1998-2012),T_max in all regions of China exhibited a downward trend.T_max in spring,summer,autumn and winter increased at a rate of 0.48?/10a,0.31?/10a,0.23?/10a and 0.27?/10a,respectively.In summer and autumn,T_max in northeast China increased the fastest among the six regions.In spring,T_max in92.73%of the regions in China showed an upward trend,among which,T_max of the whole north China had an increasing trend in spring.(2)The spatial distribution of extreme high temperature recurrence levels in China has obvious seasonal characteristics.In winter,the extreme high temperature is the lowest in northeast China due to the influence of latitude.In spring,summer and autumn,the extreme high temperature over the Qinghai-Tibet Plateau is the lowest due to the influence of plateau climate.The recurrence level of extreme high temperature from January to March and October to December showed a decreasing trend from south to north.From May to August,the maximum value of extreme high temperature once in 10 years and once in 20 years was distributed in Turpan Basin,Xinjiang.In June,the recurrence level of extreme high temperature once in 50 years and once in 100 years in north China was the highest among the six regions.(3)The number of summer days(SU),warm days(TX90p)and maximum T_max(TXx)showed an increasing trend in all regions,while the number of icing days(ID)and cold days(TX10p)showed a decreasing trend.Except for the northwest region,the minimum T_max(TXn)showed an upward trend in other regions.The areas with a significant decreasing trend of SU,TX90p,TXx and TXn mainly distributed in western China.The occurrence time of maximum and minimum values of SU,TXn,TXx and ID during 1979-2018 was consistent with previous research results.The abnormal changes of the extreme temperature indices mainly occurred in El Ni(?)o years or La Ni(?)a years.(4)We found that the influence of the Indian Ocean Basin Warming(IOBW)on temperature in China were generally greater than those of the North Atlantic Oscillation(NAO)and the NINO3.4 area sea surface temperature(NINO3.4)after making analysis of ocean climate modal indices with temperature.The warming of the Indian Ocean has a strong positive influence on the warm events in most parts of China,and the regions with a significant negative impact on warm events mainly distributed in western China.The response of temperature in different regions of China to the changes of three ocean climate modal indices can provide a reference for extreme weather early warning.