Journals Proceedings

Abstract

Bridging models called statistical downscaling models are required to connect the global climate model output and the local weather variables for climate change impact prediction. The uncertainty associated with the model should be quantified for reliable climate change impact studies. The sources of uncertainty include natural variability, uncertainty in the climate model(s), downscaling model, model inadequacy and in the predicted results. In this work, a new approach developed by the authors and called BUASCSDSEC (Bayesian uncertainty analysis for stochastic classification and statistical downscaling with stochastic dependent error coupling) is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools for combined classification and statistical downscaling. It is based on coupling dependent modeling error with classification and statistical downscaling models in a way that the dependency among modeling errors will impact the result of both classification and statistical downscaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. For the validation data set, it is observed that the CDFs of the daily predicted samples are consistent with the observed CDF of precipitation. From the results obtained, directions of research for improvement are briefly presented.