No 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.

Global climate models (GCMs) are an essential tool for simulating past and present climates and projecting future climate change. However, current GCMs, such as those submitted to the Coupled Model Inter-comparison Project Phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6, respectively), represent atmospheric and land processes at spatial resolutions of 70-500 km (Eyring et al., 2016;Flato et al., 2013;Randall et al., 2007). Their coarse resolution lacks the fine scale details required for local and regional impact assessments and adaptation planning and limiting their direct use on regional scales (

In convective clouds, satellite‐observed deepening or increased amount of clouds with increasing aerosol concentration has been reported and is sometimes interpreted as aerosol‐induced invigoration of the clouds. However, such correlations can be affected by meteorological factors that affect both aerosol and clouds, as well as observational issues. In this study, we examine the behavior in a 660 × 660 km2 region of the South Pacific during June 2007, previously found by Koren et al. (2014) to show strong correlation between cloud fraction, cloud top pressure, and aerosols, using a cloud‐resolving model with meteorological boundary conditions specified from a reanalysis. The model assumes constant aerosol loading, yet reproduces vigorous clouds at times of high real‐world aerosol concentrations. Days with high‐ and low‐aerosol loading exhibit deep‐convective and shallow clouds, respectively, in both observations and the simulation. Synoptic analysis shows that vigorous clouds occur at times of strong surface troughs, which are associated with high winds and advection of boundary layer air from the Southern Ocean where sea‐salt aerosol is abundant, thus accounting for the high correlation. Our model results show that aerosol‐cloud relationships can be explained by coexisting but independent wind‐aerosol and wind‐cloud relationships and that no cloud condensation nuclei effect is required.