Refining rainfall projections for the Murray Darling Basin of south-east Australia—the effect of sampling model results based on performance

Abstract

One of the aims of developing new climate projections is to better address the requirements of stakeholders—particularly those who require less uncertainty and/or probabilistic information to work with. Projections are continually updated over time as more, and newer, climate model simulations of the future become available but this can introduce problems when it comes to interpreting large samples with differing results. Regional projections of rainfall are characterised by a high level of uncertainty, partly because of different sensitivities of the different models. Some models can be demonstrated to perform relatively poorly when assessed by their ability to simulate present-day means and variability and here we show that the uncertainty in model projections can potentially be reduced when the projection from these models are either discounted or ignored entirely. When applied to the Murray Darling Basin of south east Australia, it is possible to demonstrate a clustering of the results from the better performing models. These indicate that the rainfall changes to be expected as a result of increased greenhouse gas concentrations into the future are more likely to be at the drier end of the full set of model results. This occurs because the better performing models indicate decreases in winter and spring which are significantly different to the changes indicated by the other models. These results suggest that there are compelling reasons for discounting, if not entirely dismissing, some model results based on their failure to satisfy some basic performance criteria.