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

Abstract

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.

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Acknowledgements

This work contributes to the Australian Climate Change Science Program and CSIRO’s Climate Adaptation Flagship. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the World Climate Research Programme’s Working Group on Coupled Modelling for their roles in making available the CMIP3 data set. Further analysis and advice from CSIRO team members, in particular Jonas Bhend, is gratefully acknowledged. Comments by Tom Wigley have been very helpful.

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Correspondence to I. G. Watterson.

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Watterson, I.G., Whetton, P.H. Probabilistic projections of regional temperature and precipitation extending from observed time series. Climatic Change 119, 677–691 (2013). https://doi.org/10.1007/s10584-013-0755-y

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  • DOI: https://doi.org/10.1007/s10584-013-0755-y

Keywords

  • Probability Density Function
  • Base Period
  • CMIP3 Model
  • Forced Change
  • Forced Climate