Cited by 918Climate variability and modelsMeteorological Phenomena and SimulationsClimate Change Policy and Economics

The DOI matches its Crossref record; 11 of the 17 references it cites were found in a registry or an index, and 6 are not checked yet; none of the 9 sources its claims cite is checked for support yet.

Climate model genealogy: Generation CMIP5 and how we got there

Open access
Knutti, Reto, Masson, David, Gettelman, Andrew·2013·Geophysical Research Letters

Abstract

A new ensemble of climate models is becoming available and provides the basis for climate change projections. Here, we show a first analysis indicating that the models in the new ensemble agree better with observations than those in older ones and that the poorest models have been eliminated. Most models are strongly tied to their predecessors, and some also exchange ideas and code with other models, thus supporting an earlier hypothesis that the models in the new ensemble are neither independent of each other nor independent of the earlier generation. On the basis of one atmosphere model, we show how statistical methods can identify similarities between model versions and complement process understanding in characterizing how and why a model has changed. We argue that the interdependence of models complicates the interpretation of multimodel ensembles but largely goes unnoticed.

Checked 2026-10-06 · 6 references not checked yet

Overview

Integrity signals

Registry

Crossref · 10.1002/grl.50256

Cites

11 of 17 references verified0 citation errors · 0 not checkable

Statistics

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Claims the paper makes

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Show all 21 claims

  • It may state two separate claims as one. 10
  • The quote only partly states this claim. 1
  • Probably not a substantial claim. 7
15 of 15
  • The authors state this as an assumption.

    The models in an ensemble are approximately a representative sample of uncertainty in how to best describe the climate system given limited observations, imperfect understanding, and finite computational resources.

    “This assumes that the models are approximately a representative sample of our uncertainty in how to best describe the climate system given limited observations, imperfect understanding, and finite computational resources [Knutti, 2008;Yokohata et al., 2012].”
  • The authors state this as an assumption.

    There are not too many similarities among models that would bias the results of an ensemble.

    “It also assumes that there are not too many similarities that would bias the results.”
2 of 2

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Each contradiction between two passages listed here was confirmed by an independent check.

  • Overall distance metric is described two different ways

    Minor

    The methods section says the combined temperature-precipitation distance used for Figure 3 is a mean square, while the Figure 3 caption calls it a root mean square. Taking the square root changes the reported distance values.

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Metadata

Authors
Knutti, R., Masson, D., & Gettelman, A.
Affiliations
  • ETH Zurich (CH)
  • Federal Office of Meteorology and Climatology MeteoSwiss (CH)
  • NSF National Center for Atmospheric Research (US)
Corresponding author
Knutti, Reto
Venue
Geophysical Research Letters
Published
2013-03-26
Volume
40
Issue
6
Pages
1194-1199
ISSN
  • 0094-8276 (ISSN-L)
  • 1944-8007
OpenAlex
W1796961055
Cited by
918
References
17
Citations by year
2026: 37 · 2025: 42 · 2024: 54 · 2023: 73 · 2022: 114 · 2021: 126 · 2020: 109 · 2019: 64 · 2018: 76 · 2017: 67 · 2016: 51 · 2015: 55 · 2014: 42 · 2013: 7
FWCI
39.41
Type
journal article
Language
en
Open access
Locations
  • Geophysical Research Letters (publishedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
  • ETH Zürich Research Collection
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
Keywords
CMIP5, multi-model ensemble, model validation against observations, climate change projections, statistical model comparison

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