Thursday, 13 January 2000: 1:00 PM
Generally, convective parameterizations used in general circulation
models (GCMs) aim only to simulate the mean or first-order moment
of convection. Higher-order moment sub-grid convective variability is
not explicitly considered in these parameterizations. In this study, a
convective parameterization is developed that attempts to include
higher-order moments by representing convection as a stochastic process.
The parameterization uses a traditional GCM convective scheme
(i.e. the Betts-Miller scheme) to calculate an expected value of
convective precipitation (and thus the vertical integral of convective
heating) at each time step. This expected value is then used to set
the parameters of a probability distribution function that has been
pre-calculated based upon observations. The convective heating is
chosen as a random number from this distribution, which has been
constrained by large-scale variables, but now also includes
higher-moment variability of convection. This stochastic convective
parameterization is implemented in an intermediate-complexity tropical
atmospheric model.
Preliminary results indicate that the inclusion of the stochastic convective parameterization noticeably affects tropical variability. However, the quantitative effects, including those on intraseasonal variability, do appear to have a fairly strong dependence on the details of how the stochastic effects are parameterized. For instance, increased autocorrelation time in the stochastic process tends to increase the influence of stochastic effects.
![](images/flag.png)
![](images/blue_ribbon.png)