Towards Stochastic Deep Convective Parameterization

By Johnny Lin

Presented at: Northeastern Illinois University, Mathematical Sciences Seminar, 2 Feb 2007, Chicago, IL.


Convective parameterizations used in general circulation models (GCMs) generally only simulate the mean or first-order moment of convective ensembles and do not explicitly include higher-order moments. The influence of including unresolved higher-order moments is investigated using stochastic deep convective parameterization. Impacts are tested in an tropical atmospheric model of intermediate complexity as well as a comprehensive GCM. Adding convective noise noticeably affects tropical intraseasonal variability, suggesting inclusion of such noise in GCMs might be beneficial. Model response to the noise is sensitive not only to the noise amplitude, but also to such particulars of the stochastic parameterization as autocorrelation time and interaction with model dynamics.

Updated: February 8, 2007. Author: Johnny Lin <email address>. This work is licensed under a Creative Commons Attribution-ShareAlike 2.0 License.