The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
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 [782].”
From Tackling climate change with machine learning (2023) · cites this paper as [782]
Citations
Cites
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Cited by
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The art of modelling range-shifting species: The art of modelling range-shifting species
Elith, J., Kearney, M., & Phillips, S. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2010.00036.x · Cited by 2,813
Changes in Climate Extremes and their Impacts on the Natural Physical Environment
Seneviratne, S. I., Nicholls, N., Easterling, D. R., Goodess, C. M., Kanae, S., Kossin, J. P., Luo, Y., Marengo, J. A., McInnes, K. L., Rahimi, M., et al. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9781139177245.006 · Cited by 2,609
Uncertainty in climate change projections: the role of internal variability
Deser, C., Phillips, A. S., Bourdette, V., & Teng, H. (2010) · Climate Dynamics · doi:10.1007/s00382-010-0977-x · Cited by 1,773
Robustness and uncertainties in the new CMIP5 climate model projections
Knutti, R., & Sedláček, J. (2013) · Nature Climate Change · doi:10.1038/nclimate1716 · Cited by 1,569
Self-citationRobust negative impacts of climate change on African agriculture
Schlenker, W., & Lobell, D. B. (2010) · Environmental Research Letters · doi:10.1088/1748-9326/5/1/014010 · Cited by 1,492
Metadata
- 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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