Comparison of various climate change projections of eastern Australian rainfall

Abstract

The Australian eastern seaboard is a distinct climate entity from the interior of the continent, with different climatic influences on each side of the Great Dividing Range. Therefore, it is plausible that downscaling of global climate models could reveal meaningful regional detail, or 'added value', in the climate change signal of mean rainfall change in eastern Australia under future scenarios. However, because downscaling is typically done using a limited set of global climate models and downscaling methods, the results from a downscaling study may not represent the range of uncertainty in plausible projected change for a region suggested by the ensemble of host global climate models. A complete and unbiased representation of the plausible changes in the climate is essential in producing climate projections useful for future planning. As part of this aim it is important to quantify any differences in the change signal between global climate models and downscaling, and understand the cause of these differences in terms of plausible added regional detail in the climate change signal, the impact of sub-sampling global climate models and the effect of the downscaling models themselves. Here we examine rainfall projections in eastern Australia under a high emissions scenario by late in the century from ensembles of global climate models, two dynamical downscaling models and one statistical downscaling model. We find no cases where all three downscaling methods show the same clear regional spatial detail in the change signal that is distinct from the host models. However, some downscaled projections suggest that the eastern seaboard could see little change in spring rainfall, in contrast to the substantial rainfall decrease inland. The change signal in the downscaled outputs is broadly similar at the large scale in the various model outputs, with a few notable exceptions. For example, the model median from dynamical downscaling projects a rainfall increase over the entirety of eastern Australia in autumn that is greater than the global models. Also, there are some instances where a downscaling method produces changes outside the range of host models over eastern Australia as a whole, thus expanding the projected range of uncertainty. Results are particularly uncertain for summer, where no two downscaling studies clearly agree. There are also some confounding factors from the model configuration used in downscaling, where the particular zones used for statistical models and the model components used in dynamical models have an influence on results and produce additional uncertainty.

Rainfall in Australia's eastern seaboard: a review of confidence in projections based on observations and physical processes

“…The NARCliM projections of future rainfall for northeast NSW were shown by Grose et al (2015a) to be broadly consistent with the other modelling methods examined here in that there is considerable variation between different modelling configurations, while noting some indication of a small increase for summer and autumn, little change or a small decrease for winter and little change for spring.…”

“…The NRM projections of rainfall in Australia's central eastern seaboard region show a high degree of variation between different modelling methods (including between different GCMs as well as between different modelling methods (Dowdy et al 2015;Grose et al 2015a)), such that there is relatively high uncertainty in the rainfall projections for this region as compared to other regions throughout Australia. A wide range of factors relating to the uncertainty in these projections are examined here in order to estimate the degree of confidence that could be attached to them.…”

The impact of climate change and urban growth on urban climate and heat stress in a subtropical city

“…For many parts of Australia, including the Brisbane area, there is uncertainty regarding future rainfall with climate change (Walsh et al, 2002;IPCC, 2014;CSIRO and Bureau of Meteorology, 2015;Grose et al, 2015). Rainfall may decrease around Brisbane during winter; however, these results are uncertain (CSIRO and Bureau of Meteorology, 2015).…”

A Bayesian posterior predictive framework for weighting ensemble regional climate models

“…The NARCliM ensemble downscales four GCMs (MIROC3.2, ECHAM5, CCCMA3.1, and CSIRO-Mk3.0) with three versions of the WRF modelling framework (which we call R1, R2, and R3, Skamarock et al, 2008) that differ in parameterisations of radiation, cumulus physics, surface physics, and planetary boundary layer physics. NARCliM output has been evaluated in terms of its ability to reproduce the observed mean climate Olson et al, 2016b;Grose et al, 2015), climate extremes (Cortés-Hernández et al, 2015;PerkinsKirkpatrick et al, 2016;Walsh et al, 2016;Kiem et al, 2016;Sharples et al, 2016), and important regional climate phenomena Pepler et al, 2016). These studies demonstrate that while the downscaling has provided added value , a range of model errors are present within the ensemble.…”