Towards improved and more routine Earth system model evaluation in CMIP

Kurzfassung

The Coupled Model Intercomparison Project (CMIP) is now moving into its 6th phase. Given the diversity and complexity of Earth system models (ESMs), we argue that the CMIP community has reached a critical juncture at which many baseline aspects of model evaluation need to be performed much more efficiently to enable a systematic and rapid performance assessment of the large number of models participating in CMIP. To support routine evaluation in CMIP and at individual modeling centers, community tools such as the Earth system model evaluation tool (ESMValTool, http://www.esmvaltool.org/) are developed that allow for comparison of single or multiple models, either against predecessor versions or against observations. The ESMValTool has been developed in such a way that additional diagnostics and performance metrics can be easily added. The priority of the effort so far has been to target specific scientific themes focusing on selected Essential Climate Variables (ECVs) and a range of known systematic biases. The tool also integrates other packages, such as the NCAR Climate Variability Diagnostics Package or the cloud regime error metric, and can reproduce most of the analysis of the IPCC AR5 evaluation chapter. The ESMValTool together with other efforts such as the PCMDI metrics package can be used to broadly and comprehensively characterize the performance of the wide variety of models and model versions that will contribute to CMIP6. Ultimately, we envisage running these tools alongside the Earth System Grid Federation as soon as the CMIP model output is submitted utilizing observations available in standard formats (obs4MIPs) or provided by the user. This presentation provides a perspective of how a more routine model evaluation can be achieved in CMIP while at the same time highlighting the need for further research to, for instance, identify the processes that contribute most to systematic biases and the magnitude and uncertainty of future climate projections.