1. Evaluation of CMIP6 GCMs Over the CONUS for Downscaling Studies.
- Author
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Ashfaq, Moetasim, Rastogi, Deeksha, Kitson, Joy, Abid, Muhammad Adnan, and Kao, Shih‐Chieh
- Subjects
DOWNSCALING (Climatology) ,BIAS correction (Topology) ,CONUS ,ATMOSPHERIC models ,ORTHOGONAL functions ,HISTORICAL errors - 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. Plain Language Summary: Global Climate Models (GCMs) provide climate change projections at spatial scales much coarser than the scales at which regional and local planning decisions are made. Therefore, GCMs projections are spatially refined through various downscaling procedures. A sub‐selection of GCMs is often needed before their downscaling due to issues related to their performance, data availability, and resources required for spatial refinement. Here we evaluate GCMs from the sixth phase of Coupled Models Intercomparison Project over four regions representing the contiguous United States to objectively guide the GCMs' sub‐selection decision‐making. We use two distinct approaches to rank the models using their performance across 66 evaluation metrics in the historical period. The two methodologies work in contrasting ways to remove the metrics redundancy but eventually develop similar GCMs rankings. The sub‐selection of GCMs requires selected GCMs to be reasonably skillful 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. Key Points: A sub‐selection of global climate models from the large Coupled Model Intercomparison Projects ensemble is often necessary before downscaling due to several unavoidable constraintsWe evaluate models for their objective sub‐selection using two distinct approaches that remove the redundancy in 66 evaluation metricsTwo methods develop a similar ranking, placing the high‐resolution models distinctively higher than their lower‐resolution counterparts [ABSTRACT FROM AUTHOR]
- Published
- 2022
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