ESMValTool: Assessing, visualising and analysing CCI data


ESMValTool has been used to assess, visualise and analyise CCI data

ESMValTool allows routine comparison of single or multiple models, against predecessor versions and/or observations.

The priority is to assess essential climate variables for a range of known systematic biases common to earth modelling systems such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases and soil hydrology-climate interactions, as well as atmospheric CO2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols.

An enhanced version of the Earth System Model evaluation tool (ESMValTool, Eyring et al., 2016) has been developed that exploits a subset of Essential Climate Variables (ECVs) from the European Space Agency’s Climate Change Initiative (ESA CCI) Phase 2.

This version of the ESMValTool has been used to demonstrate the value of the data for model evaluation (Lauer et al., 2017). The subset of ESA CCI data used consists of 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 (see figure). 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.

Figure 1: Relative space-time root-mean-square deviation (RMSD) calculated from the climatological seasonal cycle of CMIP5 simulations (from Lauer et al., 2017). A relative performance is displayed, with blue shading indicating better and red shading indicating worse performance than the median of all model results. A diagonal split of a grid square shows the relative error with respect to the reference data set (lower right triangle) and the alternative data set (upper left triangle). White boxes are used when data are not available for a given model and variable.

The enhanced version of the ESMValTool is released as open source software and ready to support routine model evaluation in CMIP6 and at individual modelling centers.

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About the authors

Axel Lauer, a senior researcher with DLR, is leading the development of data benchmarking using the ESMValTool for the cross-assessment of aerosol, cloud and radiation essential climate variables.

Veronika Eyring, senior scientist at DLR, focuses on Earth system modelling and model evaluation with observations to better understand chemistry‑climate interactions and climate change, and to improve the models.