Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 308

The DOI matches its Crossref record.

Constraining the Ratio of Global Warming to Cumulative CO2 Emissions Using CMIP5 Simulations*

Gillett, Nathan P., Arora, Vivek K., Matthews, Damon, Allen, Myles R.·2013·Journal of Climate

Abstract

The ratio of warming to cumulative emissions of carbon dioxide has been shown to be approximately independent of time and emissions scenarios and directly relates emissions to temperature. It is therefore a potentially important tool for climate mitigation policy. The transient climate response to cumulative carbon emissions (TCRE), defined as the ratio of global-mean warming to cumulative emissions at CO2 doubling in a 1% yr−1 CO2 increase experiment, ranges from 0.8 to 2.4 K EgC−1 in 15 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5)—a somewhat broader range than that found in a previous generation of carbon–climate models. Using newly available simulations and a new observational temperature dataset to 2010, TCRE is estimated from observations by dividing an observationally constrained estimate of CO2-attributable warming by an estimate of cumulative carbon emissions to date, yielding an observationally constrained 5%–95% range of 0.7–2.0 K EgC−1.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1175/jcli-d-12-00476.1

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 308

Metadata

Authors
Gillett, N. P., Arora, V. K., Matthews, D., & Allen, M. R.
Venue
Journal of Climate
Published
2013-09-09
Volume
26
Issue
18
Pages
6844-6858
ISSN
  • 0894-8755 (ISSN-L)
  • 1520-0442
OpenAlex
W2077042386
Cited by
308
References
0
FWCI
22.41
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Constraining the Ratio of Global Warming to Cumulative CO2 Emissions Using CMIP5 Simulations*. https://app.cochat.ai/papers/10.1175/jcli-d-12-00476.1
The paper
Gillett, N. P., Arora, V. K., Matthews, D., & Allen, M. R. (2013). Constraining the Ratio of Global Warming to Cumulative CO2 Emissions Using CMIP5 Simulations*. Journal of Climate. https://doi.org/10.1175/jcli-d-12-00476.1

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.