Are estimates of anthropogenic and natural influences on Australia’s extreme 2010–2012 rainfall model-dependent?
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Australia experienced much above average rainfall in association with strong, extended La Niña conditions during 2010–2012. Was the heavy Australian rainfall influenced by La Niña conditions and/or anthropogenic greenhouse gases? We investigate the relative contributions of anthropogenic climate change and natural climatic variability to the likelihood of heavy Australian rainfall using three distinct model datasets. Area-average rainfall anomalies for model simulations with natural forcings only were compared to simulations with both anthropogenic and natural forcings using 16 models participating in the Coupled Model Intercomparison Project Phase 5. Using fraction of attributable risk to compare the likelihood of unusual rainfall between the parallel experiments, we find attribution statements are uncertain, with FAR values sensitive to the attribution parameters considered, including thresholds, regions and seasons. When heavy rainfall probabilities were next investigated in ensembles of two atmospheric general circulation models, run with and without anthropogenically-induced sea surface temperature changes, results were model-dependent. Overall, the attribution of seasonal-scale heavy Australia rainfall to a particular cause is likely more complicated than for temperature extremes. As estimates of the greenhouse gas attributable change in rainfall risk may depend on the model datasets considered, it is also useful to consider model outputs from several datasets and using various estimates of counterfactual surface conditions to establish robust attribution statements for extreme rainfall events. In contrast, comparing the likelihoods of heavy rainfall during simulated La Niña years with El Niño/neutral years reveals a substantial La Niña influence on Australian rainfall during 2010–2012 that is robust to changes in the attribution framework.
KeywordsAttribution Climate change Extreme rainfall ENSO Australia
This research was supported by funding from the Australian Research Council Centre of Excellence for Climate System Science (Grant CE 110001028). This work was also supported by the NCI National Facility at the ANU. We also thank the Dáithí Stone at the Lawrence Berkeley National Laboratory and Nikos Christidis and Peter Stott at the Met Office Hadley Centre for generously providing access to data. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank climate modelling groups for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Finally, we thank the two reviewers for their useful comments on the manuscript.
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