Challenges in combining projections from multiple climate models.
Open accessAbstract
Recent coordinated efforts, in which numerous general circulation climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multimodel ensembles sample initial conditions, parameters, and structural uncertainties in the model design, and they have prompted a variety of approaches to quantifying uncertainty in future climate change. International climate change assessments also rely heavily on these models. These assessments often provide equal-weighted averages as best-guess results, assuming that individual model biases will at least partly cancel and that a model average prediction is more likely to be correct than a prediction from a single model based on the result that a multimodel average of present-day climate generally outperforms any individual model. This study outlines the motivation for using multimodel ensembles and discusses various challenges in interpreting them. Among these challenges are that the number of models in these ensembles is usually small, their distribution in the model or parameter space is unclear, and that extreme behavior is often not sampled. Model skill in simulating present-day climate conditions is shown to relate only weakly to the magnitude of predicted change. It is thus unclear by how much the confidence in future projections should increase based on improvements in simulating present-day conditions, a reduction of intermodel spread, or a larger number of models. Averaging model output may further lead to a loss of signal—for example, for precipitation change where the predicted changes are spatially heterogeneous, such that the true expected change is very likely to be larger than suggested by a model average. Last, there is little agreement on metrics to separate “good” and “bad” models, and there is concern that model development, evaluation, and posterior weighting or ranking are all using the same datasets. While the multimodel average appears to still be useful in some situations, these results show that more quantitative methods to evaluate model performance are critical to maximize the value of climate change projections from global models.
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The full error cancellation limit is inconsistent with what has been observed in reality.
“However, this limit of full error cancellation is not consistent with what has been observed in reality.”
From Risks of Model Weighting in Multimodel Climate Projections (2010) · cites this paper as Knutti et al. (2010)
Knutti et al. (2010) found that half of typical surface temperature biases of climate models would remain even if an infinite number of models of the same quality were combined.
“For example, Knutti et al. (2010) have shown that half of the typical surface temperature biases of climate models would remain, even if an infinite number of models of the same quality were combined.”
From Risks of Model Weighting in Multimodel Climate Projections (2010) · cites this paper as Knutti et al. (2010)
Correlations between present-day model performance measured by alternative variables and future climate changes are weak.
“Whetton et al. (2007), Jun et al. (2008), Knutti et al. (2010), and other studies, for example, show that the correlations between presentday model performance (in terms of such alternative variables) and future changes are in fact weak”
From Risks of Model Weighting in Multimodel Climate Projections (2010) · cites this paper as Knutti et al. (2010)
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Self-citationPeña, M., & van den Dool, H. (2008) · Journal of Climate · doi:10.1175/2008jcli2226.1 · Cited by 61 · see citing works
Harris, G. R., Sexton, D. M. H., Booth, B. B. B., Collins, M. D., Murphy, J. M., & Webb, M. J. (2006) · Climate Dynamics · doi:10.1007/s00382-006-0142-8 · Cited by 61 · see citing works
Meinshausen, M. A., Raper, S. C. B., & Wigley, T. M. L. (2008) · doi:10.5194/acpd-8-6153-2008 · Cited by 55 · see citing works
Alternatives to stabilization scenarios
Frame, D. J., Stone, D. A., Stott, P. A., & Allen, M. R. (2006) · Geophysical Research Letters · doi:10.1029/2006gl025801 · Cited by 54 · see citing works
Constraints on Climate Sensitivity from Radiation Patterns in Climate Models
Huber, M., Mahlstein, I., Wild, M., Fasullo, J., & Knutti, R. (2010) · Journal of Climate · doi:10.1175/2010jcli3403.1 · Cited by 50 · see citing works
Self-citationPerkins-Kirkpatrick, S. E., Pitman, A. J., & Sisson, S. A. (2009) · Geophysical Research Letters · doi:10.1029/2009gl037293 · Cited by 46 · see citing works
Oracle-based optimization applied to climate model calibration
Beltran‐Royo, C., Edwards, N. R., Haurie, A. B., Vial, J. ‐., & Zachary, D. S. (2005) · Environmental Modeling & Assessment · doi:10.1007/s10666-005-9024-4 · Cited by 14 · see citing works
Uncertainties in the Prediction of Regional Climate Change
Giorgi, F., & Francisco, R. (2001) · Advances in global change research · doi:10.1007/0-306-48051-4_14 · Cited by 8 · see citing works
Towards the Probabilistic Earth-System ModelPreprint
Palmer, T. N., Doblas-Reyes, F. J., Weisheimer, A., Shutts, G. J., Berner, J., & Murphy, J. M. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0812.1074 · Cited by 8 · see citing works
Constraints on climate sensitivity from radiation patterns in climate models
Huber, M., Knutti, R., Mahlstein, I., et al. (2009) · EGU General Assembly Conference Abstracts · Cited by 1 · see citing works
Self-citationCarlin (2002) · doi:10.1175/1520-0442(2002)015
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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 · see citing works
Self-citationClimate model genealogy: Generation CMIP5 and how we got there
Knutti, R., Masson, D., & Gettelman, A. (2013) · Geophysical Research Letters · doi:10.1002/grl.50256 · Cited by 918 · see citing works
Self-citationRisks of Model Weighting in Multimodel Climate Projections
Weigel, A. P., Knutti, R., Liniger, M. A., & Appenzeller, C. (2010) · Journal of Climate · doi:10.1175/2010jcli3594.1 · Cited by 386 · see citing works
Self-citationDiffenbaugh, N. S., & Scherer, M. (2011) · Climatic Change · doi:10.1007/s10584-011-0112-y · Cited by 282 · see citing works
Model and parameter uncertainty in IDF relationships under climate change
Chandra, R., Saha, U., & Mujumdar, P. P. (2015) · Advances in Water Resources · doi:10.1016/j.advwatres.2015.02.011 · Cited by 96 · see citing works
- Authors
- Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., & Meehl, G. A.
- Affiliations
- ETH Zurich (CH)
- Colorado School of Mines (US)
- Climate Central (US)
- Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
- NSF National Center for Atmospheric Research (US)
- Venue
- Journal of Climate
- Publisher
- American Meteorological Society
- Published
- 2010-05-15
- Volume
- 23
- Issue
- 10
- Pages
- 2739-2758
- ISSN
- 0894-8755 (ISSN-L)
- 1520-0442
- OpenAlex
- W2004053254
- Cited by
- 1,270
- References
- 105
- Citations by year
- 2026: 96 · 2025: 73 · 2024: 80 · 2023: 78 · 2022: 79 · 2021: 79 · 2020: 105 · 2019: 68 · 2018: 82 · 2017: 83 · 2016: 73 · 2015: 83 · 2014: 76 · 2013: 72 · 2012: 69
- FWCI
- 40.39
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Climate (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Science Foundation
- U.S. Department of Energy (DEFC0297ER62402, FC02-97ER62402)
- National Center for Atmospheric Research
- National Oceanic and Atmospheric Administration
- Office of Science (Cooperative Agreement DE-FC02-97ER62402, DEFC0297ER62402)
- Climate Program Office
- 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
- multi-model ensemble, climate change projections, model averaging, uncertainty quantification, inter-model spread, model skill, precipitation change, spatial heterogeneity, model ranking, model evaluation metrics
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 ›
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