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Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool

Lauer, Axel, Eyring, Veronika, Righi, Mattia, Buchwitz, Michael, Defourny, Pierre, Evaldsson, Martin, Friedlingstein, Pierre, de Jeu, Richard, de Leeuw, Gerrit, Loew, Alexander, Merchant, Christopher J., Müller, Benjamin, Popp, Thomas, Reuter, Maximilian, Sandven, Stein, Senftleben, Daniel, Stengel, Martin, Van Roozendael, Michel, Wenzel, Sabrina, Willén, Ulrika
Remote sensing of environment 2017
aerosols, climate change, climate models, climatic factors, computer software, data collection, diagnostic techniques, greenhouse gases, ice, land cover, ozone, remote sensing, satellites, soil water, space and time, surface water temperature, time series analysis, uncertainty
The Coupled Model Intercomparison Project (CMIP) is now moving into its sixth phase and aims at a more routine evaluation of the models as soon as the model output is published to the Earth System Grid Federation (ESGF). To meet this goal the Earth System Model Evaluation Tool (ESMValTool), a community diagnostics and performance metrics tool for the systematic evaluation of Earth system models (ESMs) in CMIP, has been developed and a first version (1.0) released as open source software in 2015. Here, an enhanced version of the ESMValTool is presented that exploits a subset of Essential Climate Variables (ECVs) from the European Space Agency's Climate Change Initiative (ESA CCI) Phase 2 and this version is used to demonstrate the value of the data for model evaluation. This subset includes consistent, long-term time series of ECVs obtained from harmonized, reprocessed products from different satellite instruments for sea surface temperature, sea ice, cloud, soil moisture, land cover, aerosol, ozone, and greenhouse gases. The ESA CCI data allow extending the calculation of performance metrics as summary statistics for some variables and add an important alternative data set in other cases where observations are already available. The provision of uncertainty estimates on a per grid basis for the ESA CCI data sets is used in a new extended version of the Taylor diagram and provides important additional information for a more objective evaluation of the models. In our analysis we place a specific focus on the comparability of model and satellite data both in time and space. The ESA CCI data are well suited for an evaluation of results from global climate models across ESM compartments as well as an analysis of long-term trends, variability and change in the context of a changing climate. The enhanced version of the ESMValTool is released as open source software and ready to support routine model evaluation in CMIP6 and at individual modeling centers.