Kara Smith

Global Change Fellow Alumna | Meteorology | North Carolina State University

2013 - 2014 Global Change Fellow

Where are they now?

Since November 2017, Kara is working as a Post Doc at NC State University on a project with the World Bank, which is an extension of her graduate research.

Statement of purpose:

I am a second year Ph.D. student at NC State University working with Professor Semazzi. My research is on the evaluation of climate models and development of climate information products for use in adaptation, conservation, and other planning strategies. After graduation, I plan to continue working on applied climate research. The focus of my research will be to assist local and government agencies for regional planning purposes.

Description of research:

Downscaled climate datasets provide guidance to ecologists and conservation scientists and practitioners as they evaluate adaptation and conservation strategies. Our regionally focused research project addresses the SECSC Mission and the SECSC Draft Science Plan Science Theme 1, task 4: “Characterize how Global Projections May be Downscaled and Bias Corrected” and the SECSC FY2012 Annual Science Work Plan Priority 1A: “Synthesis of the state of the science on regional downscaling of global climate models” by providing an estimate of error in downscaled model data for the metrics and variables identified by our LCC and USGS partners as critical for the Southeastern United States. The datasets to be evaluated include, but are not limited to the North American Regional Climate Change Assessment Program (NARCCAP), COAPS Land-Atmosphere Regional Ensemble Climate Change Experiment for the SE US (CLAREnCE10) and 15 km Decadal, Monthly and Daily Downscaled Climate Projections by Steve Hostetler. The analysis will include evaluation of the relative merits of statistical and dynamical models in reproducing past changes of derived climate variables of interest. Empirical Orthogonal Function (EOF) analysis will be done on the data to determine if each model predicts the significant modes of variability for each metric or variable correctly. This will be done to assess the confidence of the model projections for the next few decades, which are the most relevant for planning purposes.

Contact Information:

Email: kasmith5@ncsu.edu