Mechanisms involved in the maintenance of intraseasonal variability
are investigated using various versions of the
Neelin-Zeng Quasi-Equilibrium Tropical Circulation Model (QTCM1).
This model is an intermediate-level atmospheric model that
includes primitive equation nonlinearity.
Mechanisms investigated include evaporation-wind feedback,
mid-latitude storms, and stochastic convective processes.

Experiments indicate that evaporation-wind feedback
partially organizes model intraseasonal variability by reducing damping,
but is not by itself sufficient to sustain this oscillation for the most
realistic parameters. Excitation by extratropical variability is a
major source of energy for the intraseasonal variability in this
model. When mid-latitude storms are suppressed, tropical intraseasonal
variability is nearly eliminated. However, the eastward propagating
intraseasonal signal appears most clearly when mid-latitude excitation
is aided by the evaporation-wind feedback.

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.

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.

More information is available at http://www.johnny-lin.com/.