Planton, Yann Y., Guilyardi, Eric, Wittenberg, Andrew T., Lee, Jimwoo, Gleckler, Peter J., Bayr, Tobias , McGregor, Shayne, McPhaden, Michael J., Roehrig, Romain, Vialard, Jerome and Voldoire, Aurore (2021) Evaluating climate models with the CLIVAR 2020 ENSO metrics package. Bulletin of the American Meteorological Society, 102 (2). E193-E217. DOI 10.1175/BAMS-D-19-0337.1.

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Abstract

The El Niño-Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean - Variability, Predictability, and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to (1) highlight aspects that need improvement; (2) monitor progress across model generations; (3) help in selecting models that are well suited for particular analyses; (4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to (5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multi-petabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.

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