Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate

• Published in: Journal of geophysical research. Atmospheres 2013-02, Vol.118 (4), p.1716-1733
• Main Authors: Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W., Bronaugh, D.
• Format: Article
• Language: English
• Subjects: Climate; Climate change; climate model evaluation; Climate models; Climatology; Computer simulation; Earth, ocean, space; Exact sciences and technology; External geophysics; extreme events; Geophysics; Global climate; Mathematical models; Meteorology; observations; Precipitation; reanalysis; Representations; temperature; Uncertainty
• ISSN: 2169-897X; 2169-8996
• DOI: 10.1002/jgrd.50203

This paper provides a first overview of the performance of state‐of‐the‐art global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), and compares it to that in the previous model generation (CMIP3). For the first time, the indices based on daily temperature and precipitation are calculated with a consistent methodology across multimodel simulations and four reanalysis data sets (ERA40, ERA‐Interim, NCEP/NCAR, and NCEP‐DOE) and are made available at the ETCCDI indices archive website. Our analyses show that the CMIP5 models are generally able to simulate climate extremes and their trend patterns as represented by the indices in comparison to a gridded observational indices data set (HadEX2). The spread amongst CMIP5 models for several temperature indices is reduced compared to CMIP3 models, despite the larger number of models participating in CMIP5. Some improvements in the CMIP5 ensemble relative to CMIP3 are also found in the representation of the magnitude of precipitation indices. We find substantial discrepancies between the reanalyses, indicating considerable uncertainties regarding their simulation of extremes. The overall performance of individual models is summarized by a “portrait” diagram based on root‐mean‐square errors of model climatologies for each index and model relative to four reanalyses. This metric analysis shows that the median model climatology outperforms individual models for all indices, but the uncertainties related to the underlying reference data sets are reflected in the individual model performance metrics. Key PointsWe calculate indices in a consistent manner across models and reanalysesMulti‐model ensembles compare reasonably well with observation‐based indicesThere are large uncertainties in the representation of extremes in reanalyses