Literature DB >> 33542219

More accurate quantification of model-to-model agreement in externally forced climatic responses over the coming century.

Nicola Maher1, Scott B Power2,3,4, Jochem Marotzke5.   

Abstract

Separating how model-to-model differences in the forced response (UMD) and internal variability (UIV) contribute to the uncertainty in climate projections is important, but challenging. Reducing UMD increases confidence in projections, while UIV characterises the range of possible futures that might occur purely by chance. Separating these uncertainties is limited in traditional multi-model ensembles because most models have only a small number of realisations; furthermore, some models are not independent. Here, we use six largely independent single model initial-condition large ensembles to separate the contributions of UMD and UIV in projecting 21st-century changes of temperature, precipitation, and their temporal variability under strong forcing (RCP8.5). We provide a method that produces similar results using traditional multi-model archives. While UMD is larger than UIV for both temperature and precipitation changes, UIV is larger than UMD for the changes in temporal variability of both temperature and precipitation, between 20° and 80° latitude in both hemispheres. Over large regions and for all variables considered here except temporal temperature variability, models agree on the sign of the forced response whereas they disagree widely on the magnitude. Our separation method can readily be extended to other climate variables.

Entities:  

Year:  2021        PMID: 33542219      PMCID: PMC7862648          DOI: 10.1038/s41467-020-20635-w

Source DB:  PubMed          Journal:  Nat Commun        ISSN: 2041-1723            Impact factor:   14.919


  7 in total

1.  Robust twenty-first-century projections of El Niño and related precipitation variability.

Authors:  Scott Power; François Delage; Christine Chung; Greg Kociuba; Kevin Keay
Journal:  Nature       Date:  2013-10-13       Impact factor: 49.962

2.  Increased variability of eastern Pacific El Niño under greenhouse warming.

Authors:  Wenju Cai; Guojian Wang; Boris Dewitte; Lixin Wu; Agus Santoso; Ken Takahashi; Yun Yang; Aude Carréric; Michael J McPhaden
Journal:  Nature       Date:  2018-12-12       Impact factor: 49.962

3.  Climate models predict increasing temperature variability in poor countries.

Authors:  Sebastian Bathiany; Vasilis Dakos; Marten Scheffer; Timothy M Lenton
Journal:  Sci Adv       Date:  2018-05-02       Impact factor: 14.136

4.  Precipitation Biases in CMIP5 Models over the South Asian Region.

Authors:  Raju Pathak; Sandeep Sahany; Saroj Kanta Mishra; S K Dash
Journal:  Sci Rep       Date:  2019-07-03       Impact factor: 4.379

5.  How well must climate models agree with observations?

Authors:  Dirk Notz
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2015-10-13       Impact factor: 4.226

6.  Change in the magnitude and mechanisms of global temperature variability with warming.

Authors:  Patrick T Brown; Yi Ming; Wenhong Li; Spencer A Hill
Journal:  Nat Clim Chang       Date:  2017-09-04

7.  Precipitation variability increases in a warmer climate.

Authors:  Angeline G Pendergrass; Reto Knutti; Flavio Lehner; Clara Deser; Benjamin M Sanderson
Journal:  Sci Rep       Date:  2017-12-21       Impact factor: 4.379

  7 in total
  1 in total

1.  Large-scale emergence of regional changes in year-to-year temperature variability by the end of the 21st century.

Authors:  Dirk Olonscheck; Andrew P Schurer; Lucie Lücke; Gabriele C Hegerl
Journal:  Nat Commun       Date:  2021-12-13       Impact factor: 14.919

  1 in total

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