References
- Allen, M. R., and Ingram, W. J. (2002), “Constraints on Future Changes in Climate and the Hydrologic Cycle,” Nature, 419, 224–232. DOI: https://doi.org/10.1038/nature01092.
- Annan, J. D., and Hargreaves, J. C. (2010), “Reliability of the CMIP3 Ensemble,” Geophysical Research Letters, 37, L02703. DOI: https://doi.org/10.1029/2009GL041994.
- Annan, J. D. (2011), “Understanding the CMIP3 Multimodel Ensemble,” Journal of Climate, 24, 4529–4538.
- Bhat, K. S., Haran, M., Terando, A., and Keller, K. (2011), “Climate Projections Using Bayesian Model Averaging and Space–Time Dependence,” Journal of Agricultural, Biological, and Environmental Statistics, 16, 606–628. DOI: https://doi.org/10.1007/s13253-011-0069-3.
- Bishop, C. H., and Abramowitz, G. (2013), “Climate Model Dependence and the Replicate Earth Paradigm,” Climate Dynamics, 41, 885–900. DOI: https://doi.org/10.1007/s00382-012-1610-y.
- Bowman, K. W., Cressie, N., Qu, X., and Hall, A. D. (2018), “A Hierarchical Statistical Framework for Emergent Constraints: Application to Snow-Albedo Feedback,” Geophysical Research Letters, 45, 13050–13059. DOI: https://doi.org/10.1029/2018GL080082.
- Bracegirdle, T. J., and Stephenson, D. B. (2012), “Higher Precision Estimates of Regional Polar Warming by Ensemble Regression of Climate Model Projections,” Climate Dynamics, 39, 2805–2821. DOI: https://doi.org/10.1007/s00382-012-1330-3.
- Bracegirdle, T. J. (2013), “On the Robustness of Emergent Constraints Used in Multimodel Climate Change Projections of Arctic Warming,” Journal of Climate, 26, 669–678.
- Brient, F. (2020), “Reducing Uncertainties in Climate Projections With Emergent Constraints: Concepts, Examples and Prospects,” Advances in Atmospheric Sciences, 37, 1–15. DOI: https://doi.org/10.1007/s00376-019-9140-8.
- Burke, E. J., Jones, C. D., and Koven, C. D. (2013), “Estimating the Permafrost-Carbon Climate Response in the CMIP5 Climate Models Using a Simplified Approach,” Journal of Climate, 26, 4897–4909. DOI: https://doi.org/10.1175/JCLI-D-12-00550.1.
- Buser, C. M., Künsch, H. R., Lüthi, D., Wild, M., and Schär, C. (2009), “Bayesian Multi-Model Projection of Climate: Bias Assumptions and Interannual Variability,” Climate Dynamics, 33, 849–868. DOI: https://doi.org/10.1007/s00382-009-0588-6.
- Chandler, R. E. (2013), “Exploiting Strength, Discounting Weakness: Combining Information From Multiple Climate Simulators,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371, 20120388. DOI: https://doi.org/10.1098/rsta.2012.0388.
- Collins, M. (2007), “Ensembles and Probabilities: A New Era in the Prediction of Climate Change,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365, 1957–1970. DOI: https://doi.org/10.1098/rsta.2007.2068.
- Collins, M., Chandler, R. E., Cox, P. M., Huthnance, J. M., Rougier, J., and Stephenson, D. B. (2012), “Quantifying Future Climate Change,” Nature Climate Change, 2, 403–409. DOI: https://doi.org/10.1038/nclimate1414.
- Cox, P. M., Huntingford, C., and Williamson, M. S. (2018), “Emergent Constraint on Equilibrium Climate Sensitivity From Global Temperature Variability,” Nature, 553, 319–322. DOI: https://doi.org/10.1038/nature25450.
- Craig, P. S., Goldstein, M., Rougier, J. C., and Seheult, A. H. (2001), “Bayesian Forecasting for Complex Systems Using Computer Simulations,” Journal of the American Statistical Association, 96, 717–729. DOI: https://doi.org/10.1198/016214501753168370.
- Cubasch, U., Meehl, G. A., Boer, G. J., Stouffer, R. J., Dix, M., Noda, A., Senior, C. A., Raper, S., and Yap, K. S. (2001), Projections of Future Climate Change, in Climate Change 2001: The Scientific Bases. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, eds. J. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, K. Dai, X. Maskell, and C. A. Johnson, Cambridge: Cambridge University Press, p. 881.
- Deser, C., Phillips, A. S., Bourdette, V., and Teng, H. (2012), “Uncertainty in Climate Change Projections: The Role of Internal Variability,” Climate Dynamics, 38, 527–546. DOI: https://doi.org/10.1007/s00382-010-0977-x.
- Frost, C., and Thompson, S. G. (2000), “Correcting for Regression Dilution Bias: Comparison of Methods for a Single Predictor Variable,” Journal of the Royal Statistical Society, Series A, 163, 173–189. DOI: https://doi.org/10.1111/1467-985X.00164.
- Furrer, R., Sain, S. R., Nychka, D. W., and Meehl, G. A. (2007), “Multivariate Bayesian Analysis of Atmosphere-Ocean General Circulation Models,” Environmental and Ecological Statistics, 14, 249–266. DOI: https://doi.org/10.1007/s10651-007-0018-z.
- Greene, A. M., Goddard, L., and Lall, U. (2006), “Probabilistic Multimodel Regional Temperature Change Projections,” Journal of Climate, 19, 4326–4343. DOI: https://doi.org/10.1175/JCLI3864.1.
- Hall, A., Cox, P., Huntingford, C., and Klein, S. (2019), “Progressing Emergent Constraints on Future Climate Change,” Nature Climate Change, 9, 269–278. DOI: https://doi.org/10.1038/s41558-019-0436-6.
- Hall, A. D. and Qu, X. (2006), “Using the Current Seasonal Cycle to Constrain Snow Albedo Feedback in Future Climate Change,” Geophysical Research Letters, 33, 1–4. DOI: https://doi.org/10.1029/2005GL025127.
- Hawkins, E., and Sutton, R. T. (2009), “The Potential to Narrow Uncertainty in Regional Climate Predictions,” Bulletin of the American Meteorological Society, 90, 1095–1107. DOI: https://doi.org/10.1175/2009BAMS2607.1.
- Hawkins, E. (2011), “The Potential to Narrow Uncertainty in Projections of Regional Precipitation Change,” Climate Dynamics, 37, 407–418.
- Holland, M. M., and Bitz, C. M. (2003), “Polar Amplification of Climate Change in Coupled Models,” Climate Dynamics, 21, 221–232. DOI: https://doi.org/10.1007/s00382-003-0332-6.
- Jun, M., Knutti, R., and Nychka, D. W. (2008), “Spatial Analysis to Quantify Numerical Model Bias and Dependence,” Journal of the American Statistical Association, 103, 934–947. DOI: https://doi.org/10.1198/016214507000001265.
- Kennedy, M. C., and O’Hagan, A. (2001), “Bayesian Calibration of Computer Models,” Journal of the Royal Statistical Society, Series B, 63, 425–464. DOI: https://doi.org/10.1111/1467-9868.00294.
- Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. A. (2010), “Challenges in Combining Projections From Multiple Climate Models,” Journal of Climate, 23, 2739–2758. DOI: https://doi.org/10.1175/2009JCLI3361.1.
- Knutti, R., Masson, D., and Gettelman, A. (2013), “Climate Model Genealogy: Generation CMIP5 and How We Got There,” Geophysical Research Letters, 40, 1194–1199. DOI: https://doi.org/10.1002/grl.50256.
- Knutti, R., Sedláček, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., and Eyring, V. (2017), “A Climate Model Projection Weighting Scheme Accounting for Performance and Interdependence,” Geophysical Research Letters, 44, 1909–1918. DOI: https://doi.org/10.1002/2016GL072012.
- Koven, C. D., Riley, W. J., and Stern, A. (2013), “Analysis of Permafrost Thermal Dynamics and Response to Climate Change in the CMIP5 Earth System Models,” Journal of Climate, 26, 1877–1900. DOI: https://doi.org/10.1175/JCLI-D-12-00228.1.
- Lambert, S. J., and Boer, G. J. (2001), “CMIP1 Evaluation and Intercomparison of Coupled Climate Models,” Climate Dynamics, 17, 83–106. DOI: https://doi.org/10.1007/PL00013736.
- Mahlstein, I., and Knutti, R. (2011), “Ocean Heat Transport as a Cause for Model Uncertainty in Projected Arctic Warming,” Journal of Climate, 24, 1451–1460. DOI: https://doi.org/10.1175/2010JCLI3713.1.
- Masson, D., and Knutti, R. (2011), “Climate Model Genealogy,” Geophysical Research Letters, 38, L08703. DOI: https://doi.org/10.1029/2011GL046864.
- McKinnon, K. A., and Deser, C. (2018), “Internal Variability and Regional Climate Trends in an Observational Large Ensemble,” Journal of Climate, 31, 6783–6802. DOI: https://doi.org/10.1175/JCLI-D-17-0901.1.
- Min, S. K., and Hense, A. (2006), “A Bayesian Approach to Climate Model Evaluation and Multi-Model Averaging With an Application to Global Mean Surface Temperatures From IPCC AR4 Coupled Climate Models,” Geophysical Research Letters, 33, L08708. DOI: https://doi.org/10.1029/2006GL025779.
- Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J. (2010), “The Next Generation of Scenarios for Climate Change Research and Assessment,” Nature, 463, 747–756. DOI: https://doi.org/10.1038/nature08823.
- Northrop, P. J., and Chandler, R. E. (2014), “Quantifying Sources of Uncertainty in Projections of Future Climate,” Journal of Climate, 27, 8793–8808. DOI: https://doi.org/10.1175/JCLI-D-14-00265.1.
- Oreskes, N., Shrader-Frechette, K., and Belitz, K. (1994), “Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences,” Science, 263, 641–646. DOI: https://doi.org/10.1126/science.263.5147.641.
- Parker, W. S. (2006), “Understanding Pluralism in Climate Modeling,” Foundations of Science, 11, 349–368. DOI: https://doi.org/10.1007/s10699-005-3196-x.
- Pennell, C., and Reichler, T. (2011), “On the Effective Number of Climate Models,” Journal of Climate, 24, 2358–2367. DOI: https://doi.org/10.1175/2010JCLI3814.1.
- Poppick, A., McInerney, D. J., Moyer, E. J., and Stein, M. L. (2016), “Temperatures in Transient Climates: Improved Methods for Simulations With Evolving Temporal Covariances,” The Annals of Applied Statistics, 10, 477–505.
- Qu, X., and Hall, A. D. (2014), “On the Persistent Spread in Snow-Albedo Feedback,” Climate Dynamics, 42, 69–81. DOI: https://doi.org/10.1007/s00382-013-1774-0.
- Räisänen, J., and Palmer, T. N. (2001), “A Probability and Decision-Model Analysis of a Multimodel Ensemble of Climate Change Simulations,” Journal of Climate, 14, 3212–3226. DOI: .
- Rougier, J. C., Goldstein, M., and House, L. (2013), “Second-Order Exchangeability Analysis for Multimodel Ensembles,” Journal of the American Statistical Association, 108, 852–863. DOI: https://doi.org/10.1080/01621459.2013.802963.
- Sanderson, B. M., Knutti, R., and Caldwell, P. M. (2015a), “A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble,” Journal of Climate, 28, 5171–5194. DOI: https://doi.org/10.1175/JCLI-D-14-00362.1.
- Sanderson, B. M. (2015b), “Addressing Interdependency in a Multimodel Ensemble by Interpolation of Model Properties,” Journal of Climate, 28, 5150–5170.
- Shiogama, H., Emori, S., Hanasaki, N., Abe, M., Masutomi, Y., Takahashi, K., and Nozawa, T. (2011), “Observational Constraints Indicate Risk of Drying in the Amazon Basin,” Nature Communications, 2, 253. DOI: https://doi.org/10.1038/ncomms1252.
- Slater, A. G., and Lawrence, D. M. (2013), “Diagnosing Present and Future Permafrost From Climate Models,” Journal of Climate, 26, 5608–5623. DOI: https://doi.org/10.1175/JCLI-D-12-00341.1.
- Smith, R. L., Tebaldi, C., Nychka, D. W., and Mearns, L. O. (2009), “Bayesian Modeling of Uncertainty in Ensembles of Climate Models,” Journal of the American Statistical Association, 104, 97–116. DOI: https://doi.org/10.1198/jasa.2009.0007.
- Stainforth, D. A., Allen, M. R., Tredger, E. R., and Smith, L. A. (2007), “Confidence, Uncertainty and Decision-Support Relevance in Climate Predictions,” Philosophical Transactions of the Royal Society A, 365, 2145–2161. DOI: https://doi.org/10.1098/rsta.2007.2074.
- Stephenson, D. B., Collins, M., Rougier, J. C., and Chandler, R. E. (2012), “Statistical Problems in the Probabilistic Prediction of Climate Change,” Environmetrics, 23, 364–372. DOI: https://doi.org/10.1002/env.2153.
- Taylor, K. E., Stouffer, R. J., and Meehl, G. A. (2012), “An Overview of CMIP5 and the Experiment Design,” Bulletin of the American Meteorological Society, 93, 485–498. DOI: https://doi.org/10.1175/BAMS-D-11-00094.1.
- Tebaldi, C., and Knutti, R. (2007), “The Use of the Multi-Model Ensemble in Probabilistic Climate Projections,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365, 2053–2075. DOI: https://doi.org/10.1098/rsta.2007.2076.
- Tebaldi, C., and Sansó, B. (2009), “Joint Projections of Temperature and Precipitation Change From Multiple Climate Models: A Hierarchical Bayesian Approach,” Journal of the Royal Statistical Society, Series A, 172, 83–106. DOI: https://doi.org/10.1111/j.1467-985X.2008.00545.x.
- Tebaldi, C., Smith, R. L., Nychka, D. W., and Mearns, L. O. (2005), “Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles,” Journal of Climate, 18, 1524–1540. DOI: https://doi.org/10.1175/JCLI3363.1.
- Thompson, D. W. J., Barnes, E. A., Deser, C., Foust, W. E., and Phillips, A. S. (2015), “Quantifying the Role of Internal Climate Variability in Future Climate Trends,” Journal of Climate, 28, 6443–6456. DOI: https://doi.org/10.1175/JCLI-D-14-00830.1.
- Watterson, I. G., and Whetton, P. H. (2011), “Distributions of Decadal Means of Temperature and Precipitation Change Under Global Warming,” Journal of Geophysical Research: Atmospheres, 116, 1–13. DOI: https://doi.org/10.1029/2010JD014502.
- Weigel, A. P., Knutti, R., Liniger, M. A., and Appenzeller, C. (2010), “Risks of Model Weighting in Multimodel Climate Projections,” Journal of Climate, 23, 4175–4191. DOI: https://doi.org/10.1175/2010JCLI3594.1.
- Yip, S., Ferro, C. A. T., and Stephenson, D. B. (2011), “A Simple, Coherent Framework for Partitioning Uncertainty in Climate Predictions,” Journal of Climate, 24, 4634–4643. DOI: https://doi.org/10.1175/2011JCLI4085.1.