Evaluation of CMIP6 models toward dynamical downscaling over 14 CORDEX domains

Both reliability and independence of global climate model (GCM) simulation are essential for model selection to generate a reasonable uncertainty range of dynamical downscaling simulations. In this study, we evaluate the performance and interdependency of 37 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in terms of seven key large-scale driving fields over 14 CORDEX domains. A multivariable integrated evaluation method is used to evaluate and rank the models' ability to simulate multiple variables in terms of their climatological mean and interannual variability. The results suggest that the model performance varies considerably with seasons, domains, and variables evaluated, and no model outperforms in all aspects. However, the multi-model ensemble mean performs much better than almost all models. Among 37 CMIP6 models, the MPI-ESM1-2-HR and FIO-ESM-2-0 rank top two due to their overall good performance across all domains. To measure the model interdependency in terms of multiple fields, we define the similarity of multivariate error fields between pairwise models. Our results indicate that the dependence exists between most of the CMIP6 models, and the models sharing the same idea or/and concept generally show less independence. Furthermore, we hierarchically cluster the top 15 models with good performance based on the similarity of multivariate error fields to identify relatively independent models. Our evaluation can provide useful guidance on the selection of CMIP6 models based on their performance and relative independence, which helps to generate a more reliable ensemble of dynamical downscaling simulations with reasonable inter-model spread.

Automated summary

This study evaluates the performance and interdependency of 37 CMIP6 models in terms of large-scale driving fields across 14 CORDEX domains. The results show that model performance varies with seasons, domains, and evaluated variables, and the multi-model ensemble mean performs better than individual models. The study also finds dependence between most CMIP6 models and that models sharing the same idea or concept tend to show less independence. By hierarchically clustering the top 15 models based on multivariate error field similarity, relatively independent models are identified. This evaluation can guide the selection of CMIP6 models based on performance and relative independence, improving the reliability of dynamical downscaling simulations.