Assessing the influence of ocean-atmosphere forcing on precipitation and drought characteristics at global and regional scales

Previous studies have demonstrated that ocean-atmosphere forcing by anomalous sea surface temperatures (SST) significantly influences large-scale hydroclimatic variability. However, the influence of SST anomalies on the stochastic characteristics of regional precipitation and drought is not well understood. This dissertation investigates the influence of SST forcing on hydroclimatic variability at global and regional scales.An ensemble of 20th century atmospheric general circulation model (AGCM) simulations is used to evaluate the SST-forced signal and chaotic noise components of simulated seasonal precipitation. Temporal variability and trends in signal and potential predictability (signal-to-noise ratio) are shown to be significant over much of the globe, while variability in noise is not significant over most regions. Results suggest that ocean-atmosphere forcing of seasonal precipitation is not stationary results are discussed in the contexts of seasonal climate prediction and global climate change.The influence of SST anomalies on stochastic characteristics of precipitation and drought is subsequently evaluated using two ensembles of AGCM simulations forced with observed (interannually varying) SST and their climatological annual cycle, respectively. SST anomalies are shown to significantly increase interannual variability of precipitation and persistence of precipitation anomalies throughout the tropics and some midlatitude regions. With respect to drought, SST anomalies are shown to increase the duration and magnitude of drought events over much of the tropics outside of the tropics SST anomalies contribute a small increase in likelihood of persistent drought events.Coupled (atmosphere-ocean-land) general circulation models (CGCMs) pose a number of applications to drought research. The final portion of this dissertation evaluates precipitation characteristics and ocean-atmosphere coupling in nine state-of-the-art CGCMs. CGCMs are shown to generally reproduce the large-scale distributions of mean precipitation and its interannual variability, as well as the serial correlation of seasonal precipitation anomalies. Models exhibit significant coupling between global precipitation and SST variability similar to observations. However, biases in simulated precipitation characteristics are significant at the regional scale. Results suggest that while direct applications of CGCMs to regional drought analysis are limited, CGCMs can provide insight into the dominant mechanisms of large-scale hydroclimatic variability.