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[lj.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).
升级了新的1850年以来的全球陆地气温数据集(C-LSAT2.0),结合美国NOAA/NCEI研发的ERSSTv5,将全球表面温度(CMST)观测数据集延长至1854~2019年,为全球气候变化研究提供了一个新的基准数据.对比发现,基于CMST的全球温度变化序列在1880年以前略高于其他几个全球序列,差异主要来源于采用不同海温数据所致,各个序列之间存在结构性不确定性;1880年之后, 5个全球表面温度观测序列的一致性非常高,并有显著一致的变暖趋势,具有高可靠性.基于C-LSAT2.0和CMST,对1880~2019年全球变暖趋势进行了估计,结果表明:近140年, 120年, 60年和40年陆地平均气温增暖趋势分别为:0.103±0.016, 0.115±0.020, 0.252±0.035和0.293±0.055°C/10 a;全球表面温度增暖趋势分别为:0.072±0.010, 0.084±0.011, 0.150±0.019和0.185±0.032°C/10 a.对1900~2018年全球年均温度EOF分析表明,前两个特征向量明显地反映了全球温度变化的主要模态:即全球一致升温模态和与太平洋年代际振荡(IPO)密切相关的模态.说明近120年全球温度变化主要由外部强迫(人类活动)和自然变率(IPO)控制.
作者
Qjngxiang Li
Wenbin Sun
Boyin Huang
Wenjie Dong
Xiaolan Wang
Panmao Zhai
Phil Jones
李庆祥;孙文彬;Boyin Huang;董文杰;Xiaolan Wang;翟盘茂;Phil Jones(School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disasters,Sun Yat-sen University,Guangzhou 510275,China;Key Laboratory of Tropical Atmosphere-Ocean System(Sun Yat-sen University),Ministry of Education,and Southern Laboratory of Ocean Science and Engineering(Zhuhai),Zhuhai 519082,China;National Centers for Environmental Information,National Oceanic and Atmospheric Administration,Asheville NC 28801,USA;Climate Research Division.Environment and Climate Change Canada,Toronto M3H5T4,Canada;Chinese Academy of Meteorological Sciences,China Meteorological Administration,Beijing 100081,China;Climatic Research Unit,School of Environmental Sciences,University of East Anglia,Norwich NR47TJ,UK)
基金
supported by the National Natural Science Foundation of China (41975105)
the National Key Research & Development Program of China (2018YFC1507705 and 2017YFC1502301)。
