Consistency of global warming trends strengthened since 1880s

• 作者: Boyin Huang Panmao Zhai Phil Jones Qingxiang Li Wenbin Sun Wenjie Dong Xiaolan Wang
• 来源期刊: 科学通报（英文版）
• 年，卷（期）: 2020,（20）
• 所属期刊栏目: SHORT COMMUNICATIONS
• 页码范围: 1709-1712
• 页数: 4页
• 语种: 英文

摘要

Global mean surface temperature (GMST) is one of the most important large-scale indicators for characterizing climate change on Earth, and Surface Temperature (ST) is also the most accurate key climate element currently understood by scientists and the public.Even so, there have been extensive discussions about the accuracy of global (regional) surface temperature (air temperature)changes [1].From the perspective of climatic data acquisition and data reliability, the current GMST series and the evaluation of global warming rates are all based on several observation-based datasets produced by combining anomalies of Land Surface Air Temperatures (LSAT) and Sea Surface Temperatures (SST).Compared with the larger uncertainties evaluated from regional climate change observation studies [2-4], the theory and methodology of global ST change observations are more mature,and the conclusion of global warming trend since the last century is beyond doubt [5].However, due to the diversities in the station data collection and statistical methods in processing these data,the differences between the GMST series lead to slight differences in warming trends at different time periods.In particular, discussions of short-term climate trends in recent years [6-8] have stimulated many studies about how to accurately detect past changes in climate.It is of great scientific importance for global climate change responses to accurately understand or to reduce the uncertainties in assessing GMST trends.These issues have been extensively discussed in previous IPCC assessment reports.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	Qingxiang Li		1	0	0.0	0.0
2	Wenbin Sun		1	0	0.0	0.0
3	Boyin Huang		1	0	0.0	0.0
4	Wenjie Dong		1	0	0.0	0.0
5	Xiaolan Wang		1	0	0.0	0.0
6	Panmao Zhai		1	0	0.0	0.0
7	Phil Jones		1	0	0.0	0.0