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The Weather Research and Forecasting (WRF) Model is evaluated as a regional climate model for the simulation of climate indices that are relevant to viticulture in Western Australia's wine regions at a 5-km resolution under current and future climate. WRF is driven with ERA-Interim reanalysis for the current climate and three global climate models (GCMs) for both current and future climate. The focus of the analysis is on a selection of climate indices that are commonly used in climate-viticulture research. Simulations of current climate are evaluated against an observational dataset to quantify model errors over the 1981-2010 period. Changes to the indices under future climate based on the SRES A2 emissions scenario are then assessed through an analysis of future (2030-59) minus present (1970-99) climate. Results show that when WRF is driven with ERA-Interim there is generally good agreement with observations for all of the indices although there is a noticeable negative bias for the simulation of precipitation. The results for the GCM-forced simulations were less consistent. Namely, while the GCM-forced simulations performed reasonably well for the temperature indices, all simulations performed inconsistently for the precipitation index. Climate projections showed significant warming for both of the temperature indices and indicated potential risks to Western Australia's wine growing regions under future climate, particularly in the north. There was disagreement between simulations with regard to the projections of the precipitation indices and hence greater uncertainty as to how these will be characterized under future climate. [ABSTRACT FROM AUTHOR]

The authors evaluate a 30-yr (1981-2010) Weather Research and Forecast (WRF) Model regional climate simulation over the southwest of Western Australia (SWWA), a region with a Mediterranean climate, using ERA-Interim boundary conditions. The analysis assesses the spatial and temporal characteristics of climate extremes, using a selection of climate indices, with an emphasis on metrics that are relevant for forestry and agricultural applications. Two nested domains at 10- and 5-km resolution are examined, with the higher-resolution simulation resolving convection explicitly. Simulation results are compared with a high-resolution, gridded observational dataset that provides daily rainfall, minimum temperatures, and maximum temperatures. Results show that, at both resolutions, the model is able to simulate the daily, seasonal, and annual variation of temperature and precipitation well, including extreme events. The higher-resolution domain displayed significant performance gains in simulating dry-season convective precipitation, rainfall around complex terrain, and the spatial distribution of frost conditions. The high-resolution domain was, however, influenced by grid-edge effects in the southwestern margin, which reduced the ability of the domain to represent frontal rainfall along the coastal region. On the basis of these results, the authors feel confident in using the WRF Model for regional climate simulations for the SWWA, including studies that focus on the spatial and temporal representation of climate extremes. This study provides a baseline climatological description at a high resolution that can be used for impact studies and will also provide a benchmark for climate simulations driven by general circulation models. [ABSTRACT FROM AUTHOR]

The authors use a sophisticated coupled land-atmosphere modeling system for a Southern Hemisphere subdomain centered over southeastern Australia to evaluate differences in simulation skill from two different land surface initialization approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, and the second uses land surface states obtained from reanalyses. The authors find that land surface initialization using prior offline simulations contribute to relative gains in subseasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10%-20% within the first 30 days of the forecast can be attributed to the land surface initialization method using offline states. For precipitation there is no distinct preference for the land surface initialization method, with limited gains in forecast skill irrespective of the lead time. The authors evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill, exceeding 20% in some regions. These results were statistically significant at the 98% confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialize the land surface contributed to relative gains in forecast skill, reaching 40% in parts of the domain that were statistically significant at the 98% confidence level. The contrasting impact of the land surface initialization method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms. Therefore, land surface initialization from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over southeastern Australia. [ABSTRACT FROM AUTHOR]