An effective downscaling technique for winter precipitation and snowpack in the western United States
Location
Eccles Conference Center
Event Website
http://water.usu.edu
Start Date
4-1-2014 6:25 PM
End Date
4-1-2014 6:30 PM
Description
There are often large biases associated with climate predictions and these are problematic when it comes to their application in the future assessments of water resources and ecosystems. This study applies a combination of statistical and dynamical techniques to improve regional climate projections. A statistical technique was employed to first correct biases in the forcing lateral boundary condition data from the Community Climate System Model (CCSM), which are subsequently used to drive the Weather Research and Forecasting (WRF) model in climate simulations for the western United States (U.S.). The WRF simulations were initially performed for the years 1969-1999 using three datasets: a) a global reanalysis (NCEP), b) original CCSM, and c) bias-corrected CCSM data. The 1969-1999 simulations revealed that the bias-corrected CCSM data led to considerably improved WRF downscaling in precipitation, snow water equivalent (SWE), and associated atmospheric dynamics in comparison to simulations forced with the original CCSM data. We also carried out mid-twenty-first-century predictions, which show marked climate change differences in spatial pattern, magnitude, and long-term trend of precipitation and SWE between simulations forced with the original and corrected CCSM data. The implication of such differences in the future climate will also be discussed.
An effective downscaling technique for winter precipitation and snowpack in the western United States
Eccles Conference Center
There are often large biases associated with climate predictions and these are problematic when it comes to their application in the future assessments of water resources and ecosystems. This study applies a combination of statistical and dynamical techniques to improve regional climate projections. A statistical technique was employed to first correct biases in the forcing lateral boundary condition data from the Community Climate System Model (CCSM), which are subsequently used to drive the Weather Research and Forecasting (WRF) model in climate simulations for the western United States (U.S.). The WRF simulations were initially performed for the years 1969-1999 using three datasets: a) a global reanalysis (NCEP), b) original CCSM, and c) bias-corrected CCSM data. The 1969-1999 simulations revealed that the bias-corrected CCSM data led to considerably improved WRF downscaling in precipitation, snow water equivalent (SWE), and associated atmospheric dynamics in comparison to simulations forced with the original CCSM data. We also carried out mid-twenty-first-century predictions, which show marked climate change differences in spatial pattern, magnitude, and long-term trend of precipitation and SWE between simulations forced with the original and corrected CCSM data. The implication of such differences in the future climate will also be discussed.
https://digitalcommons.usu.edu/runoff/2014/2014Posters/9