Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1175/JCLI-D-18-0275.1 |
Homogenization of Monthly Ground Surface Temperature in China during 1961-2016 and Performances of GLDAS Reanalysis Products | |
Xu, Wenhui1; Sun, Chenghu1,2; Zuo, Jingqing2,3; Ma, Zhuguo4,5; Li, Weijing2,3; Yang, Song6,7 | |
2019-02-01 | |
发表期刊 | JOURNAL OF CLIMATE
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ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2019 |
卷号 | 32期号:4页码:1121-1135 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
英文摘要 | Maps of observed ground surface temperature (GST) in China generally contain inhomogeneities due to relocation of the observation site, changes in observation method, transition to automatic instruments, and so on. By using the observations of collocated manual and automatic weather stations in China, bias in daily GST caused by the transition to automatic observation systems is corrected for the first time in the present work. Then, the inhomogeneities caused by nonclimatic factors (e.g., relocation of the station and change of observation time) in the historical records of monthly GST are further reduced by using the penalized maximal F-test method. Analysis based on this new homogenized dataset reveals that the trend of annual-mean GST in China is approximately 0.273 degrees C decade(-1) during 1961-2016. The warming trend is stronger in winter (0.321 degrees C decade(-1)) and spring (0.312 degrees C decade(-1)) and weakest in summer (0.173 degrees C decade(-1)). Spatially, all the stations in China, except for a few stations in southern China, present warming trends in the annual mean and in spring, fall, and winter seasons. In summer, cooling trends are observed in central and southern China. Moreover, we assess the monthly GST from five reanalysis products of the Global Land Data Assimilation System (GLDAS) during 1980-2016. The warming trends of Noah and the Catchment Land Surface Model (CLSM) from GLDAS-V2.0 are the closest to those of the homogenized observation, while the linear trends in the other three products (Noah, CLM, and MOS) from GLDAS-V1 are obviously different from those of the homogenized observation. Also, it is found that the spatial distribution of the warming trend is substantially overestimated in central China but underestimated in the other regions of China in these five GLDAS reanalysis products. |
英文关键词 | Climate change Automatic weather stations Data quality control Surface observations Changepoint analysis |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000457325500003 |
WOS关键词 | SOIL-TEMPERATURE ; MEMORY |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/20858 |
专题 | 气候变化 |
作者单位 | 1.China Meteorol Adm, Natl Meteorol Informat Ctr, Beijing, Peoples R China; 2.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing, Peoples R China; 3.CMA Nanjing Univ Joint Lab Climate Predict Studie, China Meteorol Adm, Natl Climate Ctr, Beijing, Peoples R China; 4.Chinese Acad Sci, Inst Atmospher Phys, CAS Key Lab Reg Climate Environm Temperate East A, Beijing, Peoples R China; 5.Univ Chinese Acad Sci, Beijing, Peoples R China; 6.Sun Yat Sen Univ, Sch Atmospher Sci, Guangzhou, Guangdong, Peoples R China; 7.Sun Yat Sen Univ, Guangdong Prov Key Lab Climate Change & Nat Disas, Guangzhou, Guangdong, Peoples R China |
推荐引用方式 GB/T 7714 | Xu, Wenhui,Sun, Chenghu,Zuo, Jingqing,et al. Homogenization of Monthly Ground Surface Temperature in China during 1961-2016 and Performances of GLDAS Reanalysis Products[J]. JOURNAL OF CLIMATE,2019,32(4):1121-1135. |
APA | Xu, Wenhui,Sun, Chenghu,Zuo, Jingqing,Ma, Zhuguo,Li, Weijing,&Yang, Song.(2019).Homogenization of Monthly Ground Surface Temperature in China during 1961-2016 and Performances of GLDAS Reanalysis Products.JOURNAL OF CLIMATE,32(4),1121-1135. |
MLA | Xu, Wenhui,et al."Homogenization of Monthly Ground Surface Temperature in China during 1961-2016 and Performances of GLDAS Reanalysis Products".JOURNAL OF CLIMATE 32.4(2019):1121-1135. |
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