![]() 2010), reflecting inherent uncertainties arising from natural variability in climate, the numerical equations underlying climate models (including representations of physical processes), and future human-induced emissions of greenhouse gases.Īlthough making decisions under conditions of uncertainty can often be complex, there is little doubt that better decisions can be made with a knowledge of uncertainty. It is now widely recognized that such predictions must be framed in probabilistic language (e.g., Jenkins et al. Providing society with reliable regional predictions of climate change is becoming more and more pressing, not least so that individuals, businesses, and national infrastructure can become well adapted to anticipated changes in climate. Given that seasonal forecasts are performed operationally already at several centers around the world, in a seamless forecast system they provide a resource that can be used without cost to help calibrate climate change projections and make them more reliable for users. Results show that the calibrated climate change probabilities are closer to the proxy truth than the uncalibrated probabilities. Quantitative assessments of reliability of the low-resolution model, run in seasonal hindcast mode, are used to calibrate climate change time-slice projections made with the same low-resolution model. The reason for using this approach is simply that the twenty-first-century climate change signal is not yet known and, hence, no climate change projections can be verified using observations. Here output from the high-resolution version of the model is treated as a proxy for truth. This proposal is tested for fast atmospheric processes (such as clouds and convection) by considering output from versions of the same atmospheric general circulation model run at two different resolutions and forced with prescribed sea surface temperatures and sea ice. In earlier work, it was proposed that the reliability of climate change projections, particularly of regional rainfall, could be improved if such projections were calibrated using quantitative measures of reliability obtained by running the same model in seasonal forecast mode. ![]()
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