Date of Award

1-1-2012

Document Type

Campus Access Dissertation

Department

Geography

First Advisor

Greg J Carbone

Abstract

Global climate models (GCMs) provide most climate change projections, but their coarse resolution must be downscaled to more local scales in order to conduct meaningful climate impact assessments. This dissertation investigates dynamically downscaled regional climate model (RCM) output from the North American Regional Climate Change Assessment Program (NARCCAP) in the Southeast United States. Analysis uses a suite of statistical measures to assess model skill in hindcasting minimum and maximum temperature and mean precipitation during an historical reference period, 1970-1999. It identifies model biases and sheds light on their causes. Most models demonstrated high skill for temperature during the historical period. The outlier models included two RCMs run with the Geophysical Fluids Dynamics Lab (GFDL) as their lateral boundary conditions; these models suffered from a cold maximum temperature bias, attributed to erroneously high soil moisture. Precipitation skill showed mixed skill - relatively high when measured using a probability density function overlap measure or the index of agreement, but relatively low when measured with root-mean square error or mean absolute error, because several models overestimate the frequency of extreme precipitation events. Downscaling generally improves projections of minimum temperature and mean precipitation at local scales for RCMs run with the Community Climate Model (CCSM) and Candian Global Climate Model version 3 (CGCM3), while adding value for CCSM-based runs with respect to maximum temperature.

The historical analysis set the stage for interpreting future projections (2040-2069) of minimum and maximum temperature and mean precipitation change, and helps to quantify associated uncertainties in these scenarios. Projected minimum temperatures show an ensemble mean increase between 1° and 2°C in the winter and early spring, and an increase between 2° and 3°C for all other months. Maximum temperatures show an ensemble mean increase between 1° and 2°C in winter and early spring with increases between 2° and 4°C from mid spring through fall. Precipitation increases up to 10% in the eastern part of the region from late summer through early spring. Ensemble mean decreases of up to 10% occurred in January, April, June, and July. In western portions, precipitation increases up to 10% in January through March, May, August, September, and November with an up to 12% decrease in precipitation in March, May through July, and October. This work provides users of NARCCAP data with indepth validation of commonly used climate variables from several ensemble members against observations, determines the "value added" by RCMs in the downscaling process, and assesses atmospheric processes internal to each RCM which feed back into the climate system. Additionally, recommendations are made for selecting NARCCAP members based on the intended assessment by stakeholders of climate information. Lastly, this work serves as a template for the type of indepth analysis needed for climate models to provide added confidence in a models' ability to simulate all aspects of the climate system.

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