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: “Developing Regional Modeling Techniques Applicable for Simulating Future Climate Conditions in the Carolinas”
Citation:
Mallard, M., Chris Nolte, T. Spero, R. Bullock, J. Herwehe, AND Kiran Alapaty. : “Developing Regional Modeling Techniques Applicable for Simulating Future Climate Conditions in the Carolinas”. Carolinas Climate resilience Conference, Charolotte, NC, April 28 - 29, 2014.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
Global climate models (GCMs) are currently used to obtain information about future changes in the large-scale climate. However, such simulations are typically done at coarse spatial resolutions, with model grid boxes on the order of 100 km on a horizontal side. Therefore, techniques have been developed to “downscale” GCM projections to smaller regional scales,so that meaningful information can be provided to policy-makers and other stakeholders, by using regional climate models (RCMs). Efforts are ongoing at the U.S. EPA to further develop simulations from large-scale GCM data, producing RCM-generated climate information at county scales with grid boxes that are 12 km on each horizontal side. The effectiveness of thedownscaling methods is demonstrated by simulating past climate states and evaluatingaccuracy and variability. In this study, such multi-year RCM simulations are evaluated over a model domain covering the eastern U.S., where the analysis focuses on the Carolinas. A global observationally-based dataset serves as a stand-in for the GCM and its coarse information is used to drive the higher-resolution regional model. The simulated rainfall and other surface variables from the RCM will be evaluated and compared against the information that could be obtained from the GCM-proxy in order to demonstrate the added value of downscaling with an RCM. The goal is to apply these modeling techniques for future climate projections that can beused by partners in the Carolinas for environmental and ecosystem research as well asmanagement.