Evaluations of historical and projected high-resolution dynamically downscaled ensemble over the continental United States
There has been extensive research in the science community to quantify the impact that anthropogenic greenhouse gas emissions will have on regional climate change. Earth system models (ESM) have been shown to accurately project changes in the climate system at a continental scale, but lack the spatial resolution needed to represent mesoscale processes that affect regional climate extremes. To rectify this limitation, a technique known as dynamically downscaling was introduced to better resolve these small-scale processes and provide society with quantitative evaluation of regional climate risks associated with a warming climate. This dissertation uses an ensemble of dynamically downscaled model simulations with varying boundary conditions. The Weather Research and Forecast (WRF) model is used to evaluate the performance of five 12-km spatial resolution decadal historical and future simulations with a domain that covers most of North America. The initial and boundary conditions are from three ESMs (GFDL-ESM2G, CCSM4, and HadGEM2) with varying climate sensitivities. The future projections will use two greenhouse gas (GHG) concentration scenarios and two decadal-length time slices (2045-2054 and 2085-2094), which is compared to a historical decade (1995-2004). Chapter 2 quantifies the uncertainty associated with bias correction, spectral nudging, and the lateral boundary conditions when comparing historical simulations to observations. In addition to showing the “added value” of the dynamical downscaling technique over the ESM data, this section evaluates the model performance for the ensemble. The results indicate that the simulation’s performance depends on both location and the features/variable being tested. The use of an ensemble mean and median leads to a better performance in measuring the climatology, but is significantly biased for the extremes when compared to the individual RCM simulations. Chapter 3 of this dissertation examines projections of extreme temperatures. Probability density functions of daily maximum/minimum temperatures are analyzed. The uncertainties associated with using different boundary conditions as well as future GHG concentrations on extreme events, such as heat waves and days with temperature higher than 95°F, are investigated. The distribution of summer daily maximum temperature experiences a significant warm-side shift and increased variability, while the distribution of winter daily minimum temperature is projected to have a less significant warm-side shift with decreased variability. Chapter 4 examines the projections of extreme daily precipitation over the U.S. to quantify the effects a warming climate on precipitation distribution and intensity. There is a large increase in the projected frequency of extreme precipitation events over the entire CONUS and a decrease in median precipitation days. Moreover, most regions show an increase in the number of dry days for the future scenarios. The magnitude of extreme precipitation events is projected to increase at all temperatures above freezing in the CONUS. The strongest precipitation events are increasing mostly as a result of a shift in the precipitation distribution due to the increase of temperature (Clausius-Claperyon relationship), but are also affected by changes in some dynamical factors. Chapter 5 points to some of the important additions to the climate change literature this dissertation has made. By quantifying the uncertainties associated with climate extremes using this ensemble, there is a better understanding of how model setup, emission scenarios, and climate sensitivity in the ESM data will affect climate projections when considering extreme temperature and precipitation events.
climate change, climate modeling, extreme temperature, extreme precipitation
University of Illinois - Urbana-Champaign
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University of Illinois at Urbana-Champaign