Perspective | Published:

Taking climate model evaluation to the next level


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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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 ( 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.

Author information

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.

Competing interests

The authors declare no competing interests.

Correspondence to Veronika Eyring.

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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.