The effects of stochastic convective processes on Tropical intraseasonal variability

Abstract

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. It is proposed that the influence of including unresolved higher-order moments can be investigated using stochastic convective parameterizations. Two approaches are identified: a modeling approach and a statistical empirical approach. These approaches are tested using the the Neelin-Zeng Quasi-Equilibrium Tropical Circulation Model (QTCM1), an intermediate-level atmospheric model that includes primitive equation nonlinearity.

As an example of the first approach, a simple stochastic convective parameterization is implemented that includes a random contribution to the convective available potential energy (CAPE). 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.

As an example of the second approach, an empirically-based stochastic convective parameterization is developed that uses an assumed mixed lognormal distribution of rainfall, tuned with parameter values derived from observations, to control selected non-mean statistical properties of convection. Inclusion of the unresolved variance using this scheme is also found to have a noticeable impact upon atmospheric intraseasonal variability in the tropics. Testing of this stochastic convective parameterization reveals that large-scale model dynamics interacts heavily with the convective parameterization, in ways such that the resulting output is fundamentally different from the input. This suggests stochastic parameterizations cannot be calibrated outside of a model's dynamical framework.