School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disasters, SUN Yat-Sen University, Guangzhou, China
Chinese Academy of Meteorological Sciences, CMA, Beijing, China
National Centers of Environmental Information, NOAA, Asheville, USA
Jiayi Cheng
School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disasters, SUN Yat-Sen University, Guangzhou, China
Southern Laboratory of Ocean Science and Engineering (Guangdong Zhuhai), Zhuhai, China
Wenhui Xu
National Meteorological Information Center, CMA, Beijing, China
Shaobo Qiao
School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disasters, SUN Yat-Sen University, Guangzhou, China
Southern Laboratory of Ocean Science and Engineering (Guangdong Zhuhai), Zhuhai, China
School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disasters, SUN Yat-Sen University, Guangzhou, China
Southern Laboratory of Ocean Science and Engineering (Guangdong Zhuhai), Zhuhai, China
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Total article views: 6,546 (including HTML, PDF, and XML)
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Total article views: 4,954 (including HTML, PDF, and XML)
Thereof 4,468 with geography defined
and 486 with unknown origin.
Total article views: 1,592 (including HTML, PDF, and XML)
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Global ST datasets 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 datasets have been blamed for underestimating the recent warming trend. This study...
