Probabilistic projections of regional temperature and precipitation extending from observed time series

Probabilistic projections of change in regional temperature and precipitation previously derived allow for the range of sensitivities to global warming simulated by CMIP3 models. However, the changes were relative to an idealized base climate for 1980–1999, disregarding observed trends, such as those in rainfall in some Australian regions. Here we propose a method that represents projections for both forced change and decadal means as time series that extend from the observed series, illustrated using data for central Victoria. The main idea is to estimate the time-evolving underlying (or forced) past climate then convert this to a series of absolute values, by using the mean of the full observational record. We again use the pattern scaling assumption, and combine the CMIP3 sensitivities used for future change with a global warming series beginning at 1900. Like the confidence interval of regression theory, the analysis gives an estimate of the range of the underlying climate at each decade. This range can be augmented to allow for natural variability. A Bayesian theory can be applied to combine the model-based sensitivity with that estimated from observations. The time series are modified and the persistence of current observed anomalies considered, ultimately merging the probabilistic projections with the observed record. For some other cases, such as rainfall in southwest and north Australia and temperature in the state of Iowa, the two sensitivity estimates appear less compatible, and possible additional forcings are considered. Examples of the potential use of such time series are presented. Copyright Springer Science+Business Media Dordrecht 2013