Selecting CMIP6 GCMs for CORDEX Dynamical Downscaling: Model Performance, Independence, and Climate Change Signals

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.

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

“…Notably, all models behaved differently in capturing the ECDF of different indices and for different continents. These inconsistencies could be attributed to the various land surface schemes and simulation of features such as vegetation 36 and orography 47 , unrealistic large-scale variability 35 , 48 , and contrasting internal variability between climate models and observations 23 , 49 .…”

“…These performance metrics were combined for a universal rank Eqs. ( 1 ) and ( 2 ) in which all metrics were assigned an equal weight 35 , 36 . where is the error of metric j and particular model i in space and time.…”

“…For risk management purposes, climate projections should include a wide range of feasible spectrums of anticipated future climate change 36 . The climate change signals (CCS) in extreme events were calculated as the difference between (i) the far future and the historical period, (ii) the near future and the historical period, and (iii) the far future and the near future.…”

ACCESS datasets for CMIP6: methodology and idealised experiments

“…ACCESS-CMIP6 data are also now being used as forcing inputs for regional downscaling activities, including CORDEX-CMIP6 23 (Gutowski et al 2016), COWCLIP 24 and Australian national and state climate projections (e.g. Di Virgilio et al 2022). Further use of the ACCESS models and their CMIP6 datasets is welcomed, with information on the availability of ACCESS software and data provided in the next section.…”

The Impact of Climate Change on Operational Probable Maximum Precipitation Estimates

“…These two models were selected based on shared nominal resolution, variables, frequency, and experiments to ACCESS‐CM2. Over the Australia continent, ACCESS‐CM2 outperforms MPI‐ESM1.2‐LR and MIROC6 against criteria such as simulating daily climate variable distributions, climate means, extremes, and modes and climate change signal diversity (Di Virgilio et al., 2022). ACCESS‐CM2 also shows higher skill in simulating extreme precipitation and temperature indices over global land area compared to MPI‐ESM1.2‐LR and MIROC6 (Kim, Min, et al., 2020).…”