Abstract
Despite the necessity of Global Climate Models (GCMs) sub-selection in downscaling studies, an objective approach for their selection is currently lacking. Building on the previously established concepts in GCMs evaluation frameworks, we develop a weighted averaging technique to remove the redundancy in the evaluation criteria and rank 37 GCMs from the sixth phase of the Coupled Models Intercomparison Project over the contiguous United States. GCMs are rated based on their average performance across 66 evaluation measures in the historical period (1981–2014) after each metric is weighted between zero and one, depending on its uniqueness. The robustness of the outcome is tested by repeating the process with the 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. A disparity in GCMs' behavior related to their sensitivity to the size of the evaluation suite is observed, highlighting the need for comprehensive multi-variable GCMs evaluation at varying timescales for determining their skillfulness over a region. The sub-selection goal is to use a representative set of skillful models over the region of interest without substantial overlap in their future climate responses and modeling errors in representing historical climate. Additional analyses of GCMs' independence and spread in their future projections provide the necessary information to objectively select GCMs while keeping all aspects of necessity in view.
| Original language | English |
|---|---|
| Article number | e2022JD036659 |
| Journal | Journal of Geophysical Research: Atmospheres |
| Volume | 127 |
| Issue number | 21 |
| DOIs | |
| State | Published - Nov 16 2022 |
Funding
The authors thank three anonymous reviewers for their helpful and insightful comments. This research is supported by the US Department of Energy (DOE) Water Power Technologies Office as a part of the SECURE Water Act Section 9505 Assessment. The authors thank the Earth System Grid Federation (ESGF) for archiving and providing free access to the CMIP6 data set. This research used the Oak Ridge Leadership Computing Facility resources at Oak Ridge National Laboratory, which is a DOE Office of Science User Facility. MA, DR, and SCK are employees of UT-Battelle LLC under contract DE-AC05-00OR22725 with the US DOE. Accordingly, the US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript or allow others to do so, for US Government purposes. The authors thank three anonymous reviewers for their helpful and insightful comments. This research is supported by the US Department of Energy (DOE) Water Power Technologies Office as a part of the SECURE Water Act Section 9505 Assessment. The authors thank the Earth System Grid Federation (ESGF) for archiving and providing free access to the CMIP6 data set. This research used the Oak Ridge Leadership Computing Facility resources at Oak Ridge National Laboratory, which is a DOE Office of Science User Facility. MA, DR, and SCK are employees of UT‐Battelle LLC under contract DE‐AC05‐00OR22725 with the US DOE. Accordingly, the US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid‐up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript or allow others to do so, for US Government purposes.
Keywords
- CONUS
- CORDEX
- downscaling
- global climate models
- model ranking