Selecting CMIP6 GCMs for CORDEX Dynamical Downscaling: Model Performance, Independence, and Climate Change Signals. Issue 4 (4th April 2022)

• Record Type: Journal Article
• Authors: Di Virgilio, Giovanni; Ji, Fei; Tam, Eugene; Nishant, Nidhi; Evans, Jason P.; Thomas, Chris; Riley, Matthew L.; Beyer, Kathleen; Grose, Michael R.; Narsey, Sugata; Delage, Francois
• Is Part Of: Earth's future. Volume 10:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-04
• Subjects: climate change adaptation -- CORDEX‐Australasia -- ENSO -- IOD -- regional climate -- SAM; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2021EF002625
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract: Global climate models (GCMs) are essential for investigating climate change, but their coarse scale limits their efficacy for climate adaptation planning at the regional scales where climate impacts manifest. Dynamical downscaling of GCM outputs better resolves regional climate and thus provides improved guidance for climate policy at regional scales. Being expensive to run, downscaling uses a subset of GCMs, necessitating careful GCM selection. This evaluation identifies a suitable subset of CMIP6 GCMs for downscaling over Australia by assessing individual GCMs against three criteria: (a) performance simulating daily climate variable distributions, climate means, extremes, and modes; (b) model independence; and (c) climate change signal diversity. Over Australia, GCMs are generally biased cold (warm) for maximum (minimum) temperature, with larger biases for minimum temperature. GCMs are generally wet biased, especially over the monsoonal north, but dry biased over eastern regions. Most GCMs show larger biases for temperature and precipitation over geographically complex, heavily populated eastern regions, relative to other regions. Evaluations identify a distinct group of 11 GCMs that perform consistently poorly across climate variables, statistics, and timescales with widespread, statistically significant biases, versus 13 GCMs that show consistent adequate‐to‐good performance with substantially reduced errors. Assessment of model independence highlights the lack of independence between several high‐performing GCMs, particularly from allied modeling groups, demonstrating the importance of careful ensemble selection when making selective samples of climate space. Once GCM climate signal diversity is considered, 6–8 mid‐to‐high‐performing, independent GCMs occupy the full range of the future climate space and, thus, are suitable for dynamical downscaling over CORDEX‐Australasia. Key Points: Per‐global climate model (GCM) performances varied markedly, though the best‐performing GCMs simulated consistently well across temperature variables and statistics Assessment of model independence highlights the lack of independence between several high‐performing GCMs Six to eight mid‐to‐high‐performing, independent GCMs capturing a broad range of the future climate space are identified as suitable for downscaling