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Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 04 Jun 2019

Submitted as: data description paper | 04 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

A new merge of global surface temperature datasets since the start of the 20th Century

Xiang Yun1,2, Boyin Huang3, Jiayi Cheng1,a, Wenhui Xu4, Shaobo Qiao1,a, and Qingxiang Li1,a Xiang Yun et al.
  • 1School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disasters, SUN Yat-Sen University, Guangzhou, China
  • 2Chinese Academy of Meteorological Sciences, CMA, Beijing, China
  • 3National Centers of Environmental Information, NOAA, Asheville, USA
  • 4National Meteorological Information Center, CMA, Beijing, China
  • aalso at: Southern Laboratory of Ocean Science and Engineering (Guangdong Zhuhai), Zhuhai, China

Abstract. Global surface temperature (ST) datasets are the foundation for global climate change research. There are several global ST datasets developed by different groups in NOAA/NCEI,NASA/GISS and UKMO Hadley Centre & UEA/CRU. This study presents a new global ST dataset, the China Merged Surface Temperature (CMST) dataset. CMST is created by merging the China-Land Surface Air Temperature (C-LSAT1.3) with the sea surface temperature (SST) data from the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5). The merge of C-LSAT and ERSSTv5 shows a high spatial coverage extended to the high latitudes and is more consistent with a reference of multi-datasets average in Polar Regions. Comparisons indicate that CMST is consistent with other existing global ST datasets in interannual-decadal variations and long-term trends at global, hemispheric, and regional scales from 1900 to 2017. Therefore CMST dataset can be used for global climate change assessment, monitoring, and detection. CMST dataset presented in this article is publicly available at: (Yun et al., 2019) and has been published on the Climate Explorer website of the Royal Netherlands Meteorological Institute (KNMI) at:

Xiang Yun et al.
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Xiang Yun et al.
Data sets

China Merged Surface Temperature (CMST) X. Yun, B. Huang, J. Cheng, W. Xu, S. Qiao, and Q. Li

Xiang Yun et al.
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Short summary
Global ST dataset have been blamed for underestimating the recent warming trend. This study merged ERSSTv5 with our newly developed C-LSAT, producing a global land and marine surface temperature dataset - CMST. Comparing with existing datasets, the statistical significance of the GMST warming trend during the past century remains unchanged, while the recent warming trend since 1998 increases slightly and is statistically significant.
Global ST dataset have been blamed for underestimating the recent warming trend. This study...