Evaluations of historical and projected high-resolution dynamically downscaled ensemble over the continental United States
type: CreativeWork
description: 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 tempe…
abstract: 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 tempe…
author: Zobel, Zachary
language: en
keywords: climate change, climate modeling, extreme temperature, extreme precipitation
date published: 2019-02-06T19:32:41Z
date modified: 2026-07-22T22:24:40Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/102399