The use of the multi-model ensemble in probabilistic climate projections
Abstract
Recent coordinated efforts, in which numerous climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate in a probabilistic way. This paper outlines the motivation for using multi-model ensembles, reviews the methodologies published so far and compares their results for regional temperature projections. The challenges in interpreting multi-model results, caused by the lack of verification of climate projections, the problem of model dependence, bias and tuning as well as the difficulty in making sense of an ‘ensemble of opportunity’, are discussed in detail.
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The best climate predictions are synthesized from ensembles of 20+ climate models.
“The best climate predictions are synthesized from ensembles of 20+ climate models [782].”
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Self-citationReduced global warming from CMIP6 projections when weighting models by performance and independence
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Beyond equilibrium climate sensitivity
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CMIP5 Scientific Gaps and Recommendations for CMIP6
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Storyline approach to the construction of regional climate change information
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Multi-site evaluation of terrestrial evaporation models using FLUXNET data
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Emission pathways consistent with a 2 °C global temperature limit
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Temporal ecology in the Anthropocene
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Toward a Quantitative Estimate of Future Heat Wave Mortality under Global Climate Change
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Self-citationA Simple, Coherent Framework for Partitioning Uncertainty in Climate Predictions
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Characterizing Uncertainty of the Hydrologic Impacts of Climate Change
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Global evaluation of runoff from 10 state-of-the-art hydrological models
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Mapping model agreement on future climate projections
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Design Criteria of Urban Drainage Infrastructures under Climate Change
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Skill and independence weighting for multi-model assessments
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On the Effective Number of Climate Models
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A Review of Uncertainties in Global Temperature Projections over the Twenty-First Century
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Climate Change and Water Resources Management: A Federal Perspective
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Spatial Analysis to Quantify Numerical Model Bias and Dependence: How Many Climate Models Are There?
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- Authors
- Tebaldi, C., & Knutti, R.
- Affiliations
- NSF National Center for Atmospheric Research (US)
- ETH Zurich (CH)
- Venue
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Publisher
- The Royal Society
- Published
- 2007-06-14
- Volume
- 365
- Issue
- 1857
- Pages
- 2053-2075
- ISSN
- 1364-503X (ISSN-L)
- 1471-2962
- OpenAlex
- W2100956194
- Cited by
- 1,862
- References
- 69
- Citations by year
- 2026: 124 · 2025: 115 · 2024: 112 · 2023: 110 · 2022: 132 · 2021: 117 · 2020: 122 · 2019: 121 · 2018: 107 · 2017: 111 · 2016: 116 · 2015: 112 · 2014: 102 · 2013: 82 · 2012: 65
- FWCI
- 35.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (publishedVersion)
- PubMed (publishedVersion)
- 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
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Geoengineering
- Keywords
- multi-model ensemble, probabilistic climate projections, uncertainty quantification, model dependence, model bias, model calibration, climate model intercomparison
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