Possible Impact of Spatial and Temporal Non-Uniformity in Land Surface Temperature Data on Trend Estimation

作     者：Zhiyu LI Wenjun ZHANG Haiming XU 

作者机构：Joint International Research Laboratory of Climate and Environment Change Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science &Technology College of Atmospheric Sciences Nanjing University of Information Science & Technology 

出 版 物：《Journal of Meteorological Research》 (气象学报（英文版）)

年 卷 期：2018年第32卷第5期

页      面：819-828页

核心收录：

学科分类：07[理学] 0707[理学-海洋科学] 070601[理学-气象学] 0815[工学-水利工程] 0706[理学-大气科学] 0816[工学-测绘科学与技术] 0824[工学-船舶与海洋工程] 0825[工学-航空宇航科学与技术] 

基　　金：Supported by the National Natural Science Foundation of China(41490643 and 41675073) Jiangsu Provincial "333 Talents" Project "Six Talents Highlands" Project Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD) Innovation Project of Jiangsu Province(KYLX16_0927) 

主　　题：temperature trend gridded data non-uniformity 

摘      要：The present work investigates possible impact of the non-uniformity in observed land surface temperature on trend estimation, based on Climatic Research Unit （CRU） Temperature Version 4 （CRUTEM4） monthly temperature data-sets from 1900 to 2012. The CRU land temperature data exhibit remarkable non-uniformity in spatial and temporal features. The data are characterized by an uneven spatial distribution of missing records and station density, and dis-play a significant increase of available sites around 1950. Considering the impact of missing data, the trends seem to be more stable and reliable when estimated based on data with 〈 40% missing percent, compared to the data with above 40% missing percent. Mean absolute error （MAE） between data with 〈 40% missing percent and global data is only 0.011℃ （0.014℃） for 1900-50 （1951-2012）. The associated trend estimated by reliable data is 0.087℃ decade^-1 （0.186℃ decade^-l） for 1900-50 （1951-2012）, almost the same as the trend of the global data. However, due to non-uniform spatial distribution of missing data, the global signal seems mainly coming from the regions with good data coverage, especially for the period 1900-50. This is also confirmed by an extreme test conducted with the records in the United States and Africa. In addition, the influences of spatial and temporal non-uniform features in observation data on trend estimation are significant for the areas with poor data coverage, such as Africa, while insig-nificant for the countries with good data coverage, such as the United States.