The method of producing climate change datasets impacts the resulting policy guidance and chance of mal-adaptation

Abstract

Impact, adaptation and vulnerability (IAV) research underpin strategies for adaptation to climate change and help to conceptualise what life may look like in decades to come. Research draws on information from global climate models (GCMs) though typically post-processed into a secondary product with finer resolution through methods of downscaling. Through worked examples set in an Australian context we assess the influence of GCM sub-setting, geographic area sub-setting and downscaling method on the regional change signal. Examples demonstrate that choices impact on the final results differently depending on factors such as application needs, range of uncertainty of the projected variable, amplitude of natural variability, and size of study region. For heat extremes, the choice of emissions scenario is of prime importance, but for a given scenario the method of preparing data can affect the magnitude of the projection by a factor of two or more, strongly affecting the indicated adaptation decision. For catchment level runoff projections, the choice of emission scenario is less dominant. Rather the method of selecting and producing application-ready datasets is crucial as demonstrated by results with opposing sign of change, raising the real possibility of mal-adaptive decisions. This work illustrates the potential pitfalls of GCM sub-sampling or the use of a single downscaled product when conducting IAV research. Using the broad range of change from all available model sources, whilst making the application more complex, avoids the larger problem of over-confidence in climate projections and lessens the chance of mal-adaptation.