The Intermediate Complexity Atmospheric Research Model

Tuesday, 16 December 2014: 5:15 PM
Ethan D Gutmann, National Center for Atmospheric Research, Boulder, CO, United States, Martyn P Clark, NCAR, Boulder, CO, United States, Roy Rasmussen, NCAR/RAL, Boulder, CO, United States, J R Arnold, US Army Corps of Engineers, Jacksonville, FL, United States and Levi D Brekke, U.S. Bureau of Reclamation, Denver, CO, United States
High-resolution, non-hydrostatic atmospheric models are extremely computationally expensive, and, for a certain class of problems, their complexity hinders our ability to ask key scientific questions, particularly those related to hydrology and climate change. For changes in precipitation in particular, an atmospheric model grid spacing capable of resolving the structure of mountain ranges is of critical importance, yet such simulations can not currently be performed with an advanced regional climate model for long time periods, large areas, and many climate models. Here we present a newly developed Intermediate Complexity Atmospheric Research model (ICAR) capable of simulating critical atmospheric processes two to three orders of magnitude faster than a state of the art regional climate model. ICAR uses a simplified dynamical formulation based off of linear theory, combined with the circulation field from a low-resolution climate model. The resulting three-dimensional wind field is used to advect heat and moisture across the domain, while sub-grid physics are processed by standard and simplified physics schemes (e.g. microphysics) from the Weather Research and Forecasting (WRF) model. ICAR is tested in comparison to WRF by downscaling a climate change scenario over the Colorado Rockies. Both atmospheric models predict increases in precipitation across the domain with a greater increase on the western half. In contrast, statistically downscaled precipitation using multiple common statistical methods predict decreases in precipitation over the western half of the domain. ICAR is a useful tool for climate change and weather forecast downscaling, particularly for orographic precipitation or model uncertainty tests in which large ensembles are required. In addition, ICAR may be useful to fields that have not traditionally been able to use an atmospheric model, but require transient simulations of precipitation with respect to, e.g., glacial or landscape evolution.