Literature DB >> 33523939

Making climate projections conditional on historical observations.

Aurélien Ribes1, Saïd Qasmi2, Nathan P Gillett3.   

Abstract

Many studies have sought to constrain climate projections based on recent observations. Until recently, these constraints had limited impact, and projected warming ranges were driven primarily by model outputs. Here, we use the newest climate model ensemble, improved observations, and a new statistical method to narrow uncertainty on estimates of past and future human-induced warming. Cross-validation suggests that our method produces robust results and is not overconfident. We derive consistent observationally constrained estimates of attributable warming to date and warming rate, the response to a range of future scenarios, and metrics of climate sensitivity. We find that historical observations narrow uncertainty on projected future warming by about 50%. Our results suggest that using an unconstrained multimodel ensemble is no longer the best choice for global mean temperature projections and that the lower end of previous estimates of 21st century warming can now be excluded.
Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Entities:  

Year:  2021        PMID: 33523939     DOI: 10.1126/sciadv.abc0671

Source DB:  PubMed          Journal:  Sci Adv        ISSN: 2375-2548            Impact factor:   14.136


  4 in total

1.  Climate simulations: recognize the 'hot model' problem.

Authors:  Zeke Hausfather; Kate Marvel; Gavin A Schmidt; John W Nielsen-Gammon; Mark Zelinka
Journal:  Nature       Date:  2022-05       Impact factor: 49.962

2.  How well have CMIP3, CMIP5 and CMIP6 future climate projections portrayed the recently observed warming.

Authors:  D Carvalho; S Rafael; A Monteiro; V Rodrigues; M Lopes; A Rocha
Journal:  Sci Rep       Date:  2022-07-14       Impact factor: 4.996

3.  Significant impact of forcing uncertainty in a large ensemble of climate model simulations.

Authors:  John C Fyfe; Viatcheslav V Kharin; Benjamin D Santer; Jason N S Cole; Nathan P Gillett
Journal:  Proc Natl Acad Sci U S A       Date:  2021-06-08       Impact factor: 11.205

4.  Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets.

Authors:  Gavin D Madakumbura; Chad W Thackeray; Jesse Norris; Naomi Goldenson; Alex Hall
Journal:  Nat Commun       Date:  2021-07-06       Impact factor: 14.919

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.