• Number 361  |
• April 23, 2012

Taming uncertainty in climate prediction

Uncertainty just became more certain, thanks to atmospheric and computational researchers at DOE’s Pacific Northwest National Laboratory using a scientific approach called "uncertainty quantification," or UQ, to assess and reduce uncertainties to better simulate precipitation. Their study is the first to apply a stochastic sampling method to select model inputs for precipitation models and improve atmospheric simulations within a regional weather research and forecasting model. Their approach marks a significant advance in representing precipitation, one of the most difficult climate components to simulate.

The word "uncertain" always seems to appear when describing Earth and atmospheric systems in numerical models. Trying to represent complexity through computer simulations has limitations, not the least of which is a lack of sufficient computing power. Getting a handle on uncertainties to effectively and efficiently represent current weather and climate systems in a computer model paves the way for scientists to apply those same techniques to predict the future climate changes. Sound predictions will give planners the tools to forecast the probability of extreme weather and climate events.

This work is supported by the DOE Office of Science's Advanced Scientific Computing Research Applied Mathematics. Computations were done at the National Center for Computational Sciences, EMSL, and the National Energy Research Scientific Computing Center.

[Kristin Manke, 509.372.6011,
kristin.manke@pnnl.gov]