The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Quantifying Uncertainties in Climate System Properties with the Use of Recent Climate Observations
Abstract
We derive joint probability density distributions for three key uncertain properties of the climate system, using an optimal fingerprinting approach to compare simulations of an intermediate complexity climate model with three distinct diagnostics of recent climate observations. On the basis of the marginal probability distributions, the 5 to 95% confidence intervals are 1.4 to 7.7 kelvin for climate sensitivity and −0.30 to −0.95 watt per square meter for the net aerosol forcing. The oceanic heat uptake is not well constrained, but ocean temperature observations do help to constrain climate sensitivity. The uncertainty in the net aerosol forcing is much smaller than the uncertainty range for the indirect aerosol forcing alone given in the Intergovernmental Panel on Climate Change Third Assessment Report.
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Cites
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Self-citationAnthropogenic Warming of Earth's Climate System
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Cited by
Constraints on future changes in climate and the hydrologic cycle
Allen, M. R., & Ingram, W. J. (2002) · Nature · doi:10.1038/nature01092 · Cited by 3,009
Self-citationGreenhouse-gas emission targets for limiting global warming to 2 °C
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Metadata
- Authors
- Forest, C. E., Stone, P. H., Sokolov, A. P., Allen, M. R., & Webster, M. D.
- Venue
- Science
- Published
- 2002-01-04
- Volume
- 295
- Issue
- 5552
- Pages
- 113-117
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2028732731
- Cited by
- 477
- References
- 22
- FWCI
- 33.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- Forest, C. E., Stone, P. H., Sokolov, A. P., Allen, M. R., & Webster, M. D. (2002). Quantifying Uncertainties in Climate System Properties with the Use of Recent Climate Observations. Science. https://doi.org/10.1126/science.1064419
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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.
