globalchange  > 影响、适应和脆弱性
DOI: 10.5194/cp-14-139-2018
Scopus记录号: 2-s2.0-85041651047
论文题名:
Signal detection in global mean temperatures after "Paris": An uncertainty and sensitivity analysis
作者: Visser H.; Dangendorf S.; Van Vuuren D.P.; Bregman B.; Petersen A.C.
刊名: Climate of the Past
ISSN: 18149324
出版年: 2018
卷: 14, 期:2
起始页码: 139
结束页码: 155
语种: 英语
Scopus关键词: anthropogenic effect ; climate modeling ; data set ; detection method ; general circulation model ; sensitivity analysis ; simulation ; surface temperature ; temperature effect ; uncertainty analysis ; France ; Ile de France ; Paris ; Ville de Paris
英文摘要: In December 2015, 195 countries agreed in Paris to "hold the increase in global mean surface temperature (GMST) well below 2.0 °C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 °C. Since large financial flows will be needed to keep GMSTs below these targets, it is important to know how GMST has progressed since pre-industrial times. However, the Paris Agreement is not conclusive as regards methods to calculate it. Should trend progression be deduced from GCM simulations or from instrumental records by (statistical) trend methods? Which simulations or GMST datasets should be chosen, and which trend models? What is "pre-industrial" and, finally, are the Paris targets formulated for total warming, originating from both natural and anthropogenic forcing, or do they refer to anthropogenic warming only? To find answers to these questions we performed an uncertainty and sensitivity analysis where datasets and model choices have been varied. For all cases we evaluated trend progression along with uncertainty information. To do so, we analysed four trend approaches and applied these to the five leading observational GMST products. We find GMST progression to be largely independent of various trend model approaches. However, GMST progression is significantly influenced by the choice of GMST datasets. Uncertainties due to natural variability are largest in size. As a parallel path, we calculated GMST progression from an ensemble of 42 GCM simulations. Mean progression derived from GCM-based GMSTs appears to lie in the range of trend-dataset combinations. A difference between both approaches appears to be the width of uncertainty bands: GCM simulations show a much wider spread. Finally, we discuss various choices for pre-industrial baselines and the role of warming definitions. Based on these findings we propose an estimate for signal progression in GMSTs since pre-industrial. © Author(s) 2018.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/109595
Appears in Collections:影响、适应和脆弱性
气候变化事实与影响

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作者单位: PBL Netherlands Environmental Assessment Agency, Bilthoven, Netherlands; Research Institute for Water and Environment, University Siegen, Siegen, Germany; Faculty of Geosciences, University Utrecht, Utrecht, Netherlands; Institute for Science Innovation and Society, Radboud University, Nijmegen, Netherlands; 5STEaPP, University College London, London, United Kingdom

Recommended Citation:
Visser H.,Dangendorf S.,Van Vuuren D.P.,et al. Signal detection in global mean temperatures after "Paris": An uncertainty and sensitivity analysis[J]. Climate of the Past,2018-01-01,14(2)
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