Search

Regional climate models (RCMs) are used to dynamically downscale global climate models (GCMs) to provide high-resolution projections of future climate change to better inform policy and decision making at the regional scale. However, biases from GCMs are transferred to RCMs and this can limit the usefulness of the regional climate projections. This paper investigates the influence of bias correcting 4 GCMs from the Coupled Model Intercomparison Project 3, for regional climate simulations over the CORDEX-Australasia domain using Weather Research and Forecasting. The GCM outputs are bias corrected against ERA-Interim reanalysis as a surrogate truth. Results show that over decadal time scales bias correction removes large systematic precipitation and temperature biases. However, bias correction also introduced biases where there were none, introduced biases of the opposite sign, or enhanced existing biases in other regions in some instances. The dynamical mechanisms driving the changes in the biases are explored. [ABSTRACT FROM AUTHOR]

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]

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]

Regional climate models are sensitive to the forcing data used, as well as different model physics options. Additionally, the behaviour of physics parameterisations may vary depending on the location of the domain due to different climatic regimes. In this study, we carry out a sensitivity analysis of the weather research and forecasting model to different driving data and model physics options over a 10-km resolution domain in the southwest of Western Australia, a region with Mediterranean climate. Simulations are carried out on a seasonal time-scale, in order to better inform future long-term regional climate simulations for this region. We show that the choice of radiation scheme had a strong influence on both temperature and precipitation; the choice of planetary boundary layer scheme has a particularly large influence on minimum temperatures; and, the choice of cumulus scheme or more complex micro-physics did not strongly influence precipitation simulations. More importantly, we show that the same radiation scheme, when used with different driving data, can lead to different results. [ABSTRACT FROM AUTHOR]

NARCliM2.0 comprises two Weather Research and Forecasting (WRF) regional climate models (RCMs) downscaling five CMIP6 global climate models contributing to the Coordinated Regional Downscaling Experiment over Australasia at 20 km resolution, and south-east Australia at 4 km convection-permitting resolution. We first describe NARCliM2.0's design, including selecting two, definitive RCMs via testing seventy-eight RCMs using different parameterisations for planetary boundary layer, microphysics, cumulus, radiation, and land surface model (LSM). We then assess NARCliM2.0's skill in simulating the historical climate versus CMIP3-forced NARCliM1.0 and CMIP5-forced NARCliM1.5 RCMs and compare differences in future climate projections. RCMs using the new Noah-MP LSM in WRF with default settings confer substantial improvements in simulating temperature variables versus RCMs using Noah-Unified. Noah-MP confers smaller improvements in simulating precipitation, except for large improvements over Australia's southeast coast. Activating Noah-MP's dynamic vegetation cover and/or runoff options primarily improve simulation of minimum temperature. NARCliM2.0 confers large reductions in maximum temperature bias versus NARCliM1.0 and 1.5 (1.x), with small absolute biases of ~0.5 K over many regions versus over ~2 K for NARCliM1.x. NARCliM2.0 reduces wet biases versus NARCliM1.x by as much as 50 %, but retains dry biases over Australia's north. NARCliM2.0 is biased warmer for minimum temperature versus NARCliM1.5 which is partly inherited from stronger warm biases in CMIP6 versus CMIP5 GCMs. Under shared socioeconomic pathway (SSP)3-7.0, NARCliM2.0 projects ~3 K warming by 2060–79 over inland regions versus ~2.5 K over coastal regions. NARCliM2.0-SSP3-7.0 projects dry futures over most of Australia, except for wet futures over Australia's north and parts of western Australia which are largest in summer. NARCliM2.0-SSP1-2.6 projects dry changes over Australia with only few exceptions. NARCliM2.0 is a valuable resource for assessing climate change impacts on societies and natural systems and informing resilience planning by reducing model biases versus earlier NARCliM generations and providing more up-to-date future climate projections utilising CMIP6. [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]

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]