Influences of Climate Variability and Change on Precipitation Characteristics and Extremes.

摘要

This study focuses on two main broad areas of active research on climate: climate variability and climate change and their implications on regional precipitation characteristics. All the analysis is carried out for a climate change-sensitive region, the state of Florida, USA. The focus of the climate variability analysis is to evaluate the influence of individual and coupled phases (cool and warm) of Atlantic multidecadal oscillation (AMO) and El Nino southern oscillation (ENSO) on regional precipitation characteristics. The two oscillations in cool and warm phases modulate each other which have implications on flood control and water supply in the region. Extreme precipitation indices, temporal distribution of rainfall within extreme storm events, dry and wet spell transitions and antecedent conditions preceding extremes are evaluated. Kernel density estimates using Gaussian kernel for distribution-free comparative analysis and bootstrap sampling-based confidence intervals are used to compare warm and cool phases of different lengths. Depth-duration-frequency (DDF) curves are also developed using generalized extreme value (GEV) distributions characterizing the extremes. Parametric and nonparametric hypothesis tests confirm statistically significant changes in the characteristics from one phase to another of individual and coupled oscillations. A comprehensive analysis is carried out providing several new insights about the influences of oscillations on precipitation.;Under climate change research, this dissertation addresses two key components: (1) downscaling and (2) bias corrections. This dissertation presents new variants and compares different statistical downscaling techniques for estimation of regional precipitation along with providing methods to link general circulation model (GCM) and National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) variables. This study also introduces new approaches to optimally select the predictor variables which help in modeling regional precipitation and further provides a mechanism to select an optimum spatial resolution to downscale the precipitation projections. New methods for correcting the biases in monthly downscaled precipitation projections are proposed, developed and evaluated in this study. The methods include bias corrections in an optimization framework using various objective functions, hybrid methods based on universal function approximation and new variants.