A regional climate modelling projection ensemble experiment – NARCliM

Abstract

Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensembles members that can be simulated such that choices must be made concerning which Global Climate Models (GCMs) to downscale from, and which Regional Climate Models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCMs and RCMs, as well as spanning the range of future climate projections present in the full GCM ensemble. The created ensemble provides a more robust view of future regional climate changes

Statistical testing of dynamically downscaled rainfall data for the Upper Hunter region, New South Wales, Australia

“…The low correlations ranging from 0.3 to 0.6 are similar to the correlations between NARCliM reanalysis, gauge and AWAP (Figure 8 and 9), and confirm that the RCMs are not highly correlated, thus, conditionally independent from each other. This is consistent with the design of the NARCliM project where Evans et al (2014) chose three RCM configurations that were as independent as possible from each other.…”

“…The R3 reanalysis better reproduces the autocorrelations of the gauge and AWAP data than R1 and R2, especially at grid points P1 and P2. These differences in the autocorrelations of R1, R2 and R3, are unsurprising as Evans et al (2014) selected three configurations of the WRF model that were as independent as possible. At grid point P3 all three NARCliM reanalyses overestimate the autocorrelations of observed gauge and AWAP rainfall.…”

Extreme precipitation in WRF during the Newcastle East Coast Low of 2007

“…With the number of regional climate simulations being performed at ∼ 10 km resolution increasing (e.g. Evans et al 2014;Jacob et al 2014) and growing interest in the representation of precipitation extremes within these models (particularly for specific event studies Ji et al 2015), a better understanding of the sensitivity of extreme precipitation to physics schemes is required.…”

Pricing the urban cooling benefits of solar panel deployment in Sydney, Australia

“…clouds) therefore affect the amount of energy received at the surface, and changes in the surface energy balance (including changes in the partitioning of net radiation between sensible heat and latent heat) affects the atmospheric temperature and humidity. We use a configuration of WRF that has been extensively tested over Australia3435. WRF simulations use triple-nesting with three domains at 50 km, 10 km and 2 km resolution and we focus on the 2 km simulations (Supplementary Figure S1).…”