Xiaoqing Wu

Professor
Dept: Geological and Atmospheric Sciences
Office:3011 Agronomy
Phone:515-294-9872
Website:http://www.public.iastate.edu/~wuxq/homepage.html

My research interests include numerical modeling, diagnostic,and theoretical studies to understand convection, cloud, radiation and precipitation processes and to improve their representation in general circulation models (GCMs) for predicting future climate. Studying and modeling of cloud systems are motivated by their profound effects on the global circulation, radiation budget and surface temperature, and the need for improved climate models and data for policy makers to determine safe levels of greenhouse gases for the Earth system. Cloud, radiation and precipitation processes are key components of the global water and energy cycle and operate on a wide range of time and space scales. Convection and clouds affect atmospheric temperature, moisture and wind fields through the release of latent heat; the redistributions of heat, moisture and momentum; and the precipitation. Clouds strongly affect the planetary energy budget and surface temperature through the reflection of sunlight, the absorption of infrared radiation from the surface and the emission of radiation to the surface as part of greenhouse effect. Since individual clouds have a spatial scale of less than 10 km that is much smaller than the conventional grid size of several hundred kilometers in climate and weather prediction models, they must be quantitatively formulated in terms of resolved variables in the prognostic equations of temperature, moisture and wind. Deriving such formulations for convection and clouds has been a major challenge for the climate modeling community due to the lack of observations of cloud and microphysical properties. To address this problem, my collaborators at NCAR and I have developed a cloud-resolving model (CRM) which resolves individual clouds but covers a large horizontal domain to generate cloud-scale datasets. The diagnostic and theoretical studies applying the high-resolution datasets generated by the CRM have lead to the improved representation of convection and cloud-radiation interaction in the climate models, and the improved simulations of global climate mean state and variability.