Improving Representation of Extreme Precipitation Events in Regional Climate Models

Funding Amount and Duration:

$83,398 from October 1, 2013 - July 31, 2014

Funding Source:

  • U.S. Geological Survey, University of Oklahoma

Principal Investigators:

  • Ming Xue, University of Oklahoma

About:

Publication: An evaluation of dynamical downscaling of Central Plains summer precipitation using a WRF-based regional climate model at a convection-permitting 4 km resolution

Final Report

The South Central U.S. encompasses a wide range of ecosystem types and precipitation patterns. Average annual precipitation is less than 10 inches in northwest New Mexico but can exceed 60 inches further east in Louisiana. Much of the region relies on warm-season convective precipitation – that is, highly localized brief but intense periods of rainfall that are common in the summer. This type of precipitation is a significant driver of climate and ecosystem function in the region, but it is also notoriously difficult to predict since it occurs at such small spatial and temporal scales. While global climate models are helpful for understanding and predicting large-scale precipitation trends, they often do not capture many of the smaller atmospheric and earth surface processes that influence local and regional precipitation trends, like convective precipitation.

To address this gap in climate modeling capabilities, researchers developed regional climate models that are better able to project small-scale precipitation patterns and localized extreme precipitation events. Researchers combined information about land surface and water conditions with weather and climate models in order to quantify the local-scale impacts of climate on water resources. This highly localized information will assist regional decision-makers in addressing the challenge of predicting precipitation in the South Central U.S., leading to a better understanding of potential future impacts on agriculture, fish and wildlife, water quality and availability, and cultural resources.