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The Community Atmosphere Biosphere Land Exchange (CABLE) model is a third‐generation land surface model (LSM). CABLE is commonly used as a stand‐alone LSM, coupled to the Australian Community Climate and Earth Systems Simulator global climate model and coupled to the Weather Research and Forecasting (WRF) model for regional applications. Here, we evaluate an updated version of CABLE within a WRF physics ensemble over the COordinated Regional Downscaling EXperiment (CORDEX) AustralAsia domain. The ensemble consists of different cumulus, radiation and planetary boundary layer (PBL) schemes. Simulations are carried out within the NASA Unified WRF modeling framework, NU‐WRF. Our analysis did not identify one configuration that consistently performed the best for all diagnostics and regions. Of the cumulus parameterizations the Grell‐Freitas cumulus scheme consistently overpredicted precipitation, while the new Tiedtke scheme was the best in simulating the timing of precipitation events. For the radiation schemes, the RRTMG radiation scheme had a general warm bias. For the PBL schemes, the YSU scheme had a warm bias, and the MYJ PBL scheme a cool bias. Results are strongly dependent on the region of interest, with the northern tropics and southwest Western Australia being more sensitive to the choice of physics options compared to southeastern Australia which showed less overall variation and overall better performance across the ensemble. Comparisons with simulations using the Unified Noah LSM showed that CABLE in NU‐WRF has a more realistic simulation of evapotranspiration when compared to GLEAM estimates. Key Points: We identify WRF physics configurations that perform well with CABLESimulation of latent heat fluxes over Australia are more skillful with WRF‐LIS‐CABLE than WRF‐Noah [ABSTRACT FROM AUTHOR]

A two-layer steady-state resistance model is compared with routine meteorological data collected from the Western Australian wheat belt during 2000–06. Major difficulties in implementing such a model are the correct parameterization for the incoming longwave radiation and estimation of daily soil moisture, neither of which are routinely measured. These difficulties are addressed by testing parameterizations for incoming longwave radiation calibrated to local conditions and incorporating a soil–water balance model based on routine weather data. The modified model has RMSE and biases ranging from 2.4° to 3.1°C and -0.2° to 0.8°C, respectively, across the wheat belt when comparing all minimum nocturnal temperatures. The model is shown to predict frost events approximately 55% of the time and illustrates that frost damage to foliage may occur when screen temperatures are < 2°C. [ABSTRACT FROM AUTHOR]

ABSTRACT A high resolution (5 km), single initialization, 30 year (1970-1999) Weather Research and Forecast regional climate model ( RCM) ensemble for southwest Western Australia ( SWWA) is evaluated. The article focuses on the ability of the RCM to simulate winter cold fronts, which are the main source of rainfall for the region, and assesses the spatial and temporal characteristics of climate extremes within the region's cereal crop growing season. To explore uncertainty, a four-member ensemble was run, using lateral boundary conditions from general circulation models ( GCMs) of the Coupled Model Intercomparison Project Phase 3; ECHAM5, Model for Interdisciplinary Research on Climate 3.2 ( MIROC 3.2), Community Climate System Model version 3 ( CCSM3) and Commonwealth Scientific and Industrial Research Organisation ( CSIRO) mk3.5. Simulations are evaluated against gridded observations of temperature and precipitation and atmospheric conditions are compared to a simulation using ERA-Interim reanalysis boundary conditions, which is used as a surrogate truth. Results show that generally, the RCM simulations were able to represent the climatology of SWWA well, however differences in the positioning of the subtropical high pressure belt were apparent which influenced the number of fronts traversing the region and hence winter precipitation biases. Systematic temperature biases were present in some ensemble members and the RCM was found to be colder than the driving GCM in all simulations. Biases impacted model skill in representing temperature extremes and this was particularly apparent in the MIROC-forced simulation, which was the worst performing RCM for both temperature and precipitation. The dynamical causes of the biases are explored and findings show that nonetheless, the RCM provides added value, particularly in the spatio-temporal representation of wet season rainfall. [ABSTRACT FROM AUTHOR]