Use-Inspired, Process-Oriented GCM Selection: Prioritizing Models for Regional Dynamical Downscaling

• DOI: 10.1175/bams-d-23-0100.1
• Publication Date: 2023-07-25T16:19:48Z
• Authors: Naomi Goldenson; L. Ruby Leung; Linda O. Mearns; David W. Pierce; Kevin A. Reed; Isla R. Simpson; Paul Ullrich; Will Krantz; Alex Hall; Andrew Jones; Stefan Rahimi
• Keywords: Atmospheric sciences; Climate change science; Regional models; Model comparison; 01 natural sciences; Physical Geography and Environmental Geoscience; Atmospheric Sciences; Climate models; Decision support; Climate Action; Earth Sciences; Downscaling; Meteorology & Atmospheric Sciences; Astronomical and Space Sciences; 0105 earth and related environmental sciences

Abstract Dynamical downscaling is a crucial process for providing regional climate information for broad uses, using coarser-resolution global models to drive higher-resolution regional climate simulations. The pool of global climate models (GCMs) providing the fields needed for dynamical downscaling has increased from the previous generations of the Coupled Model Intercomparison Project (CMIP). However, with limited computational resources, the need for prioritizing the GCMs for subsequent downscaling studies remains. GCM selection for dynamical downscaling should focus on evaluating processes relevant for providing boundary conditions to the regional models and be inspired by regional uses such as the response of extremes to changes in the boundary conditions. This leads to the need for metrics representing processes of relevance to diverse stakeholders and subregions of a domain. Procedures to account for metric redundancy and the statistical distinguishability of GCM rankings are required. Further, procedures for selecting realizations from ensembles of top-performing GCM simulations can be used to span the range of climate change signals in multiple ways. As a result, distinct weighting of metrics and prioritization of particular realizations may depend on user needs. We provide high-level guidelines for such region-specific evaluations and address how CMIP7 might enable dynamical downscaling of a representative sample of high-quality models across representative shared socioeconomic pathways (SSPs).