Abstract
Despite the necessity of Global Climate Models (GCMs) sub-selection in
the dynamical downscaling experiments, an objective approach for their
selection is currently lacking. Building on the previously established
concepts in GCMs evaluation frameworks, we relatively rank 37 GCMs from
the 6th phase of Coupled Models Intercomparison Project (CMIP6) over
four regions representing the contiguous United States (CONUS). The
ranking is based on their performance across 60 evaluation metrics in
the historical period (1981–2014). To ensure that the outcome is not
method-dependent, we employ two distinct approaches to remove the
redundancy in the evaluation criteria. The first approach is a simple
weighted averaging technique. Each GCM is ranked based on its weighted
average performance across evaluation measures, after each metric is
weighted between zero and one depending on its uniqueness. The second
approach applies empirical orthogonal function analysis in which each
GCM is ranked based on its sum of distances from the reference in the
principal component space. The two methodologies work in contrasting
ways to remove the metrics redundancy but eventually develop similar
GCMs rankings. While the models from the same institute tend to display
comparable skills, the high-resolution model versions distinctively
perform better than their lower-resolution counterparts. The results
from this study should be helpful in the selection of models for
dynamical downscaling efforts, such as the COordinated Regional
Downscaling Experiment (CORDEX), and in understanding the strengths and
deficiencies of CMIP6 GCMs in the representation of various background
climate characteristics across CONUS.