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Taking climate model evaluation to the next level

Nature Climate Change volume 9, pages 102–110 (2019)Cite this article

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Abstract

Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.

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Fig. 1: Annual mean SST error from the CMIP5 multi-model ensemble.
Fig. 2: Schematic diagram of the workflow for CMIP Evaluation Tools running alongside the ESGF.
Fig. 3: Examples of newly developed physical and biogeochemical emergent constraints since the AR5.
Fig. 4: Model skill and independence weights for CMIP5 models evaluated over the contiguous United States/Canada domain.

References

  1. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  2. Meehl, G. A., Boer, G. J., Covey, C., Latif, M. & Stouffer, R. J. The Coupled Model Intercomparison Project (CMIP). Bull. Am. Meteorol. Soc. 81, 313–318 (2000).

    Google Scholar 

  3. Meehl, G. A. et al. THE WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull. Am. Meteorol. Soc. 88, 1383–1394 (2007).

    Google Scholar 

  4. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Google Scholar 

  5. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Google Scholar 

  6. Stouffer, R. J. et al. CMIP5 scientific gaps and recommendations for CMIP6. Bull. Am. Meteorol. Soc. 98, 95–105 (2017).

    Google Scholar 

  7. Flato, G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 9 (IPCC, Cambridge Univ. Press, 2013).

  8. Maraun, D. et al. Towards process-informed bias correction of climate change simulations. Nat. Clim. Change 7, 764–773 (2017).

    Google Scholar 

  9. Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 12 (IPCC, Cambridge Univ. Press, 2013).

  10. Knutti, R. et al. A climate model projection weighting scheme accounting for performance and interdependence. Geophys. Res. Lett. 44, 1909–1918 (2017).

    Google Scholar 

  11. Wenzel, S., Eyring, V., Gerber, E. P. & Karpechko, A. Y. Constraining future summer austral jet stream positions in the CMIP5 ensemble by process-oriented multiple diagnostic regression. J. Clim. 29, 673–687 (2016).

    Google Scholar 

  12. Sanderson, B. M., Wehner, M. & Knutti, R. Skill and independence weighting for multi-model assessments. Geosci. Model Dev. 10, 2379–2395 (2017).

    Google Scholar 

  13. Sanderson, B. M., Knutti, R. & Caldwell, P. Addressing interdependency in a multimodel ensemble by interpolation of model properties. J. Clim. 28, 5150–5170 (2015).

    Google Scholar 

  14. Bishop, C. H. & Abramowitz, G. Climate model dependence and the replicate Earth paradigm. Clim. Dynam. 41, 885–900 (2013).

    Google Scholar 

  15. Abramowitz, G. & Bishop, C. H. Climate model dependence and the ensemble dependence transformation of CMIP projections. J. Clim. 28, 2332–2348 (2015).

    Google Scholar 

  16. Alexander, K. & Easterbrook, S. M. The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations. Geosci. Model Dev. 8, 1221–1232 (2015).

    Google Scholar 

  17. Knutti, R. The end of model democracy? Climatic Change 102, 395–404 (2010).

    Google Scholar 

  18. Eyring, V. et al. ESMValTool (v1.0) — a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP. Geosci. Model Dev. 9, 1747–1802 (2016).

    CAS  Google Scholar 

  19. Gleckler, P. et al. A more powerful reality test for climate models. Eos https://doi.org/10.1029/2016eo051663 (2016).

  20. Lauer, A. et al. Process-level improvements in CMIP5 models and their impact on tropical variability, the Southern Ocean, and monsoons. Earth Syst. Dynam. 9, 33–67 (2018).

    Google Scholar 

  21. Ma, C.-C., Mechoso, C. R., Robertson, A. W. & Arakawa, A. Peruvian stratus clouds and the tropical pacific circulation: a coupled ocean-atmosphere GCM study. J. Clim. 9, 1635–1645 (1996).

    Google Scholar 

  22. Hourdin, F. et al. Parameterization of convective transport in the boundary layer and its impact on the representation of the diurnal cycle of wind and dust emissions. Atmos. Chem. Phys. 15, 6775–6788 (2015).

    CAS  Google Scholar 

  23. Richter, I. Climate model biases in the eastern tropical oceans: causes, impacts and ways forward. WIREs Clim. Change 6, 345–358 (2015).

    Google Scholar 

  24. Wang, C. Z., Zhang, L. P., Lee, S. K., Wu, L. X. & Mechoso, C. R. A global perspective on CMIP5 climate model biases. Nat. Clim. Change 4, 201–205 (2014).

    Google Scholar 

  25. Fyfe, J. C., Gillett, N. P. & Zwiers, F. W. Overestimated global warming over the past 20 years. Nat. Clim. Change 3, 767–769 (2013).

    Google Scholar 

  26. Meehl, G. A., Hu, A., Arblaster, J. M., Fasullo, J. & Trenberth, K. E. Externally forced and internally generated decadal climate variability associated with the Interdecadal Pacific Oscillation. J. Clim. 26, 7298–7310 (2013).

    Google Scholar 

  27. Fyfe, J. C. et al. Making sense of the early-2000s warming slowdown. Nat. Clim. Change 6, 224–228 (2016).

    Google Scholar 

  28. Santer, B. D. et al. Volcanic contribution to decadal changes in tropospheric temperature. Nat. Geosci. 7, 185–189 (2014).

    CAS  Google Scholar 

  29. Meehl, G. A., Teng, H. & Arblaster, J. M. Climate model simulations of the observed early-2000s hiatus of global warming. Nat. Clim. Change 4, 898–902 (2014).

    Google Scholar 

  30. Thoma, M., Greatbatch, R. J., Kadow, C. & Gerdes, R. Decadal hindcasts initialized using observed surface wind stress: evaluation and prediction out to 2024. Geophys. Res. Lett. 42, 6454–6461 (2015).

    Google Scholar 

  31. Meehl, G. A., Hu, A. & Teng, H. Initialized decadal prediction for transition to positive phase of the Interdecadal Pacific Oscillation. Nat. Commun. 7, 11718 (2016).

    CAS  Google Scholar 

  32. Mears, C. A., Santer, B. D., Wentz, F. J., Taylor, K. E. & Wehner, M. F. Relationship between temperature and precipitable water changes over tropical oceans. Geophys. Res. Lett. 34, L24709 (2007).

    Google Scholar 

  33. Karl, T. R. et al. Possible artifacts of data biases in the recent global surface warming hiatus. Science 348, 1469–1472 (2015).

    CAS  Google Scholar 

  34. Mears, C. A. & Wentz, F. J. The effect of diurnal correction on satellite-derived lower tropospheric temperature. Science 309, 1548–1551 (2005).

    CAS  Google Scholar 

  35. Mauritsen, T. et al. Tuning the climate of a global model. J. Adv. Model. Earth Syst. 4, M00A01 (2012).

    Google Scholar 

  36. Hourdin, F. et al. The art and science of climate model tuning. Bull. Am. Meteorol. Soc. 98, 589–602 (2017).

  37. Bodas-Salcedo, A. et al. COSP Satellite simulation software for model assessment. Bull. Am. Meteorol. Soc. 92, 1023–1043 (2011).

    Google Scholar 

  38. Eyring, V. et al. Towards improved and more routine Earth system model evaluation in CMIP. Earth Syst. Dynam. 7, 813–830 (2016).

    Google Scholar 

  39. Phillips, A. S., Deser, C. & Fasullo, J. Evaluating modes of variability in climate models. Eos 95, 453–455 (2014).

    Google Scholar 

  40. Luo, Y. Q. et al. A framework for benchmarking land models. Biogeosciences 9, 3857–3874 (2012).

    Google Scholar 

  41. Prabhat. et al. TECA: a parallel toolkit for extreme climate analysis. Proc. Comput. Sci. 9, 866–876 (2012).

    Google Scholar 

  42. Ferraro, R., Waliser, D. E., Gleckler, P., Taylor, K. E. & Eyring, V. Evolving Obs4MIPs to support phase 6 of the Coupled Model Intercomparison Project (CMIP6). Bull. Am. Meteorol. Soc. 96, ES131–ES133 (2015).

  43. Hall, A. & Qu, X. Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys. Res. Lett. 33, L03502 (2006).

    Google Scholar 

  44. Allen, M. R. & Ingram, W. J. Constraints on future changes in climate and the hydrologic cycle. Nature 419, 224–232 (2002).

    CAS  Google Scholar 

  45. Massonnet, F. et al. Constraining projections of summer Arctic sea ice. Cryosphere 6, 1383–1394 (2012).

    Google Scholar 

  46. O’Gorman, P. A. Sensitivity of tropical precipitation extremes to climate change. Nat. Geosci. 5, 697–700 (2012).

    Google Scholar 

  47. Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).

    CAS  Google Scholar 

  48. Kidston, J. & Gerber, E. P. Intermodel variability of the poleward shift of the austral jet stream in the CMIP3 integrations linked to biases in 20th century climatology. Geophys. Res. Lett. 37, L09708 (2010).

    Google Scholar 

  49. Tsushima, Y. et al. Robustness, uncertainties, and emergent constraints in the radiative responses of stratocumulus cloud regimes to future warming. Clim. Dynam. 46, 3025–3039 (2016).

    Google Scholar 

  50. Brient, F. & Bony, S. Interpretation of the positive low-cloud feedback predicted by a climate model under global warming. Clim. Dynam. 40, 2415–2431 (2013).

    Google Scholar 

  51. Brient, F. & Schneider, T. Constraints on climate sensitivity from space-based measurements of low-cloud reflection. J. Clim. 29, 5821–5835 (2016).

    Google Scholar 

  52. Lipat, B. R., Tselioudis, G., Grise, K. M. & Polvani, L. M. CMIP5 models’ shortwave cloud radiative response and climate sensitivity linked to the climatological Hadley cell extent. Geophys. Res. Lett. 44, 5739–5748 (2017).

    Google Scholar 

  53. Sherwood, S. C., Bony, S. & Dufresne, J. L. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505, 37–42 (2014).

    Google Scholar 

  54. Tian, B. Spread of model climate sensitivity linked to double-Intertropical Convergence Zone bias. Geophys. Res. Lett. 42, 4133–4141 (2015).

    Google Scholar 

  55. Cox, P. M., Huntingford, C. & Williamson, M. S. Emergent constraint on equilibrium climate sensitivity from global temperature variability. Nature 553, 319–322 (2018).

    CAS  Google Scholar 

  56. Klein, S. A. & Hall, A. Emergent constraints for cloud feedbacks. Curr. Clim. Change Rep. 1, 276–287 (2015).

    Google Scholar 

  57. Dessler, A. E. & Forster, P. M. An estimate of equilibrium climate sensitivity from interannual variability. J. Geophys. Res. Atmos. 123, 8634–8645 (2018).

    Google Scholar 

  58. DeAngelis, A. M., Qu, X., Zelinka, M. D. & Hall, A. An observational radiative constraint on hydrologic cycle intensification. Nature 528, 249–253 (2015).

    CAS  Google Scholar 

  59. Li, G., Xie, S.-P., He, C. & Chen, Z. Western Pacific emergent constraint lowers projected increase in Indian summer monsoon rainfall. Nat. Clim. Change 7, 708–712 (2017).

    Google Scholar 

  60. Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Emergent constraints on climate-carbon cycle feedbacks in the CMIP5 Earth system models. J. Geophys. Res. Biogeosci. 119, 794–807 (2014).

    CAS  Google Scholar 

  61. Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).

    Google Scholar 

  62. Kwiatkowski, L. et al. Emergent constraints on projections of declining primary production in the tropical oceans. Nat. Clim. Change 7, 355–358 (2017).

    CAS  Google Scholar 

  63. Chadburn, S. E. et al. An observation-based constraint on permafrost loss as a function of global warming. Nat. Clim. Change 7, 340–344 (2017).

    Google Scholar 

  64. Hoffman, F. M. et al. Causes and implications of persistent atmospheric carbon dioxide biases in Earth system models. J. Geophys. Res. Biogeosci. 119, 141–162 (2014).

    CAS  Google Scholar 

  65. Donat, M. G., Pitman, A. J. & Angélil, O. Understanding and reducing future uncertainty in mid-latitude daily heat extremes via land surface feedback constraints. Geophys. Res. Lett. 45, 10627–10636 (2018).

    Google Scholar 

  66. Qu, X. & Hall, A. What controls the strength of snow-albedo feedback? J. Clim. 20, 3971–3981 (2007).

    Google Scholar 

  67. Waugh, D. W. & Eyring, V. Quantitative performance metrics for stratospheric-resolving chemistry-climate models. Atmos. Chem. Phys. 8, 5699–5713 (2008).

    CAS  Google Scholar 

  68. Karpechko, A. Y., Maraun, D. & Eyring, V. Improving Antarctic total ozone projections by a process-oriented multiple diagnostic ensemble regression. J. Atmos. Sci. 70, 3959–3976 (2013).

    Google Scholar 

  69. Räisänen, J., Ruokolainen, L. & Ylhäisi, J. Weighting of model results for improving best estimates of climate change. Clim. Dynam. 35, 407–422 (2010).

    Google Scholar 

  70. Lorenz, R. et al. Prospects and caveats of weighting climate models for summer maximum temperature projections over North America. J. Geophys. Res. Atmos. 123, 4509–4526 (2018).

    Google Scholar 

  71. Abramowitz, G. et al. Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing. Earth Syst. Dynam. Discuss. https://doi.org/10.5194/esd-2018-51 (2018).

  72. Herger, N. et al. Selecting a climate model subset to optimise key ensemble properties. Earth Syst. Dynam. 9, 135–151 (2018).

    Google Scholar 

  73. Herger, N. et al. Calibrating climate model ensembles for assessing extremes in a changing climate. J. Geophys. Res. Atmos. 123, 5988–6004 (2018).

    Google Scholar 

  74. Santer, B. D. et al. Incorporating model quality information in climate change detection and attribution studies. Proc. Natl Acad. Sci. USA 106, 14778–14783 (2009).

    CAS  Google Scholar 

  75. Caldwell, P. M. et al. Statistical significance of climate sensitivity predictors obtained by data mining. Geophys. Res. Lett. 41, 1803–1808 (2014).

    Google Scholar 

  76. Masson, D. & Knutti, R. Climate model genealogy. Geophys. Res. Lett. 38, L08703 (2011).

    Google Scholar 

  77. Pennell, C. & Reichler, T. On the effective number of climate models. J. Clim. 24, 2358–2367 (2011).

    Google Scholar 

  78. Sunyer, M. A., Madsen, H., Rosbjerg, D. & Arnbjerg-Nielsen, K. A Bayesian approach for uncertainty quantification of extreme precipitation projections including climate model interdependency and nonstationary bias. J. Clim. 27, 7113–7132 (2014).

    Google Scholar 

  79. Sanderson, B. M., Knutti, R. & Caldwell, P. A representative democracy to reduce interdependency in a multimodel ensemble. J. Clim. 28, 5171–5194 (2015).

    Google Scholar 

  80. Zhang, X. et al. Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Clim. Change 2, 851–870 (2011).

  81. Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W. & Bronaugh, D. Climate extremes indices in the CMIP5 multimodel ensemble. Part 1. Model evaluation in the present climate. J. Geophys. Res. Atmos. 118, 1716–1733 (2013).

    Google Scholar 

  82. Pendergrass, A. G. & Hartmann, D. L. The atmospheric energy constraint on global-mean precipitation change. J. Clim. 27, 757–768 (2014).

    Google Scholar 

  83. Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).

    Google Scholar 

  84. Gutowski, W. J. Jr et al. WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6. Geosci. Model Dev. 9, 4087–4095 (2016).

    Google Scholar 

  85. van Oldenborgh, G. J., Otto, F. E. L., Haustein, K. & Cullen, H. Climate change increases the probability of heavy rains like those of storm Desmond in the UK—an event attribution study in near-real time. Hydrol. Earth Syst. Sci. Discuss. 12, 13197–13216 (2015).

    Google Scholar 

  86. Ruane, A. C., Goldberg, R. & Chryssanthacopoulos, J. Climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation. Agric. For. Meteorol. 200, 233–248 (2015).

    Google Scholar 

  87. Elliott, J. et al. The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261–277 (2015).

    Google Scholar 

  88. Ruane, A. C. et al. The Vulnerability, Impacts, Adaptation and Climate Services Advisory Board (VIACS AB v1.0) contribution to CMIP6. Geosci. Model Dev. 9, 3493–3515 (2016).

    Google Scholar 

  89. Warszawski, L. et al. The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).

    CAS  Google Scholar 

  90. Ruane, A. C. & McDermid, S. P. Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment. Earth Perspect. 4, 1 (2017).

    Google Scholar 

  91. Stevens, B. & Bony, S. What are climate models missing? Science 340, 1053–1054 (2013).

    CAS  Google Scholar 

  92. Webb, M. J. et al. The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6. Geosci. Model Dev. 10, 359–384 (2017).

    CAS  Google Scholar 

  93. Mears, C. A., Wentz, F. J., Thorne, P. & Bernie, D. Assessing uncertainty in estimates of atmospheric temperature changes from MSU and AMSU using a Monte-Carlo estimation technique. J. Geophys. Res. Atmos. 116, D08112 (2011).

    Google Scholar 

  94. Argo Argo Float Data and Metadata from Global Data Assembly Centre (Argo GDAC) (SEANOE, 2000); https://doi.org/10.17882/42182

  95. Malavelle, F. F. et al. Strong constraints on aerosol–cloud interactions from volcanic eruptions. Nature 546, 485–491 (2017).

    CAS  Google Scholar 

  96. Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature (in the press).

  97. Fountalis, I., Bracco, A. & Dovrolis, C. ENSO in CMIP5 simulations: network connectivity from the recent past to the twenty-third century. Clim. Dynam. 45, 511–538 (2015).

    Google Scholar 

  98. Barz, B., Rodner, E., Guanche Garcia, Y. & Denzler, J. Detecting regions of maximal divergence for spatio-temporal anomaly detection. IEEE Trans. Pattern Anal. Mach. Intell. https://doi.org/10.1109/TPAMI.2018.2823766 (2018).

  99. Runge, J. et al. Identifying causal gateways and mediators in complex spatio-temporal systems. Nat. Commun. 6, 8502 (2015).

    CAS  Google Scholar 

  100. Kageyama, M. et al. The PMIP4 contribution to CMIP6 – Part 1: Overview and over-arching analysis plan. Geosci. Model Dev. 11, 1033–1057 (2018).

  101. Haarsma, R. J. et al. High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6. Geosci. Model Dev. 9, 4185–4208 (2016).

    Google Scholar 

  102. IPCC Special Report on Global Warming of 1.5 °C (Cambridge Univ. Press, 2018).

Download references

Acknowledgements

The authors acknowledge the Aspen Global Change Institute (AGCI) for hosting a workshop on Earth System Model Evaluation to Improve Process Understanding in August 2017 as part of its traditionally landmark summer interdisciplinary sessions (http://www.agci.org/event/17s2). NASA, the Heising-Simons Foundation, Horizon 2020 European Union’s Framework Programme for Research and Innovation under Grant Agreement No 641816, the Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO) project, the ESA Climate Change Initiative (CCI) Climate Model User Group (CMUG), WCRP and the Department of Energy (DOE) all provided support for the workshop. The viewpoint presented here substantially draws on conclusions from that workshop. Portions of this study were supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the US DOE Office of Biological & Environmental Research (BER) Cooperative Agreement DE-FC02-97ER62402 and Contract No. DE-AC05-00OR22725 and the National Science Foundation. NCAR is sponsored by the National Science Foundation.

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Authors and Affiliations

  1. Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany

    Veronika Eyring & Bettina K. Gier

  2. University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany

    Veronika Eyring & Bettina K. Gier

  3. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK

    Peter M. Cox & Mark S. Williamson

  4. Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada

    Gregory M. Flato

  5. Program for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA, USA

    Peter J. Gleckler, Peter Caldwell, Stephen A. Klein & Benjamin D. Santer

  6. Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, New South Wales, Australia

    Gab Abramowitz & Steven C. Sherwood

  7. Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    William D. Collins

  8. Department of Earth and Planetary Science, University of California, Berkeley, CA, USA

    William D. Collins

  9. University of California, Los Angeles, CA, USA

    Alex D. Hall

  10. Computational Earth Sciences Group and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Forrest M. Hoffman

  11. Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA

    Forrest M. Hoffman

  12. Department of Geographical Sciences, University of Maryland, College Park, MD, USA

    George C. Hurtt

  13. Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA

    Alexandra Jahn

  14. Met Office Hadley Centre, Exeter, UK

    Chris D. Jones

  15. Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA

    John P. Krasting

  16. Laboratoire de Météorologie Dynamique (LMD), IPSL, Paris, France

    Lester Kwiatkowski

  17. Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

    Ruth Lorenz

  18. Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA

    Eric Maloney & Robert Pincus

  19. National Center for Atmospheric Research (NCAR), Boulder, CO, USA

    Gerald A. Meehl, Angeline G. Pendergrass, Benjamin M. Sanderson & Isla R. Simpson

  20. NASA Goddard Institute for Space Studies, New York, NY, USA

    Alex C. Ruane

  21. University of Arizona, Tucson, AZ, USA

    Joellen L. Russell & Ronald J. Stouffer

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  1. Veronika Eyring
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  2. Peter M. Cox
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  17. Ruth Lorenz
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  19. Gerald A. Meehl
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V.E., P.M.C., G.M.F. and P.J.G. were the co-chairs of the AGCI workshop and led the writing of the paper. All authors participated in the AGCI workshop and contributed to discussions and writing of the text.

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Correspondence to Veronika Eyring.

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Eyring, V., Cox, P.M., Flato, G.M. et al. Taking climate model evaluation to the next level. Nature Clim Change 9, 102–110 (2019). https://doi.org/10.1038/s41558-018-0355-y

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