The aim for a more accurate representation of tropical convection in global circulation models is a longstanding issue. Here, the relationships between large and convective scales in observations and a stochastic multicloud model (SMCM) to ultimately support the design of a novel convection parameterization with stochastic elements are investigated. Observations of tropical convection obtained at Darwin and Kwajalein are used here. It is found that the variability of observed tropical convection generally decreases with increasing large-scale forcing, implying a transition from stochastic to more deterministic behavior with increasing forcing. Convection shows a more systematic relationship with measures related to large-scale convergence compared to measures related to energetics (e.g., CAPE). Using the observations, the parameters in the SMCM are adjusted. Then, the SMCM is forced with the time series of the observed large-scale state and the simulated convective behavior is compared to that observed. It is found that the SMCM cloud fields compare better with observations when using predictors related to convergence rather than energetics. Furthermore, the underlying framework of the SMCM is able to reproduce the observed functional dependencies of convective variability on the imposed large-scale state-an encouraging result on the road toward a novel convection parameterization approach. However, establishing sound cause-and-effect relationships between tropical convection and the large-scale environment remains problematic and warrants further research.

[1] A persistent problem for numerical weather and climate models is the representation of tropical convective precipitation which for the most part occurs on spatial and temporal scales too small and too short to be explicitly resolved. Given that model parameterizations represent this subgrid convection as a function of the large-scale atmospheric state, an understanding of the strongest relationships between the two scales is needed. This study introduces a method to create two concurrent long-term data sets that describe both the large-scale atmosphere and the characteristics of the small-scale convection. Important relationships between these two scales are then investigated. It is found that convective precipitation, through convective precipitation area, has the strongest relationship with dynamical variables such as moisture convergence and vertical velocity at midlevels. The magnitude of the fluctuations of convective strength about the mean is found to be anticorrelated with the strength of the large-scale variables, indicating a more stochastic behavior of tropical convection in weakly than strongly forced regimes, respectively. Atmospheric stability related variables are not found to be positively related to either convective precipitation area or convective precipitation intensity, which is often an assumption made in convective parameterization. On the contrary, in a more unstable atmosphere, there is lower convective precipitation.Citation: Davies, L., C. Jakob, P. May, V. V. Kumar, and S. Xie (2013), Relationships between the large-scale atmosphere and the small-scale convective state for Darwin, Australia,

[1] There is no objective definition to separate cumulus congestus clouds from the shallow cumulus and deep clouds. This has generated misinterpretation about the role of congestus clouds to promote deep convection through the potential of moistening the middle troposphere. In this study, an objective identification for the different tropical cumulus modes is found by examining the occurrence frequency of the cloud cell top heights (CTHs) and near-ground (at 2.5 km height) rainfall properties of these cells using a three-season database of the Darwin C-band polarimetric radar. Four cumulus modes were identified, namely a shallow cumulus mode with CTH in the trade inversion layer (1-3 km), a congestus mode with tops in the highly stable middle troposphere (3-6.5 km), a deep convective mode with tops in the region of free convection (6.5-15 km), and an overshooting convection mode with tops in the tropical tropopause layer (CTH >15 km). The study also investigates the connections between these cumulus modes during heavy rainfall events. The congestus mode occurs predominantly from~10 h prior to the peak rainfall event to~2 h past the event. The deep cloud populations (Modes 3 and 4) have their maxima at and shortly after the time of the rainfall peak, with maximum occurrence just below the tropical tropopause layer. A comparison of the heavy rainfall events occurring in morning (oceanic) conditions against the afternoon (continental) conditions revealed a higher ratio of the shallow to the deep cloud population and a shorter transition time from the shallow to the onset of deep population in the morning-oceanic conditions than the afternoon-land conditions. It is also found through the analysis of the large-scale moisture budget data set that for both the morning and afternoon events, the moistening peaked before the peak in the congestus populations.Citation: Kumar, V. V., C. Jakob, A. Protat, P. T. May, and L. Davies (2013), The four cumulus cloud modes and their progression during rainfall events: A C-band polarimetric radar perspective,

Two seasons of Darwin, Australia, C-band polarimetric (CPOL) research radar, radiosoundings, and lightning data are examined to study the relative influence of the large-scale atmospheric regimes and the underlying surface types on tropical convective cloud properties and their diurnal evolution. The authors find that in the “deep westerly” regime, which corresponds to the monsoon period, the convective cloud occurrence rate is highest, consistent with its highest relative humidity. However, these convective clouds have relatively low cloud-top heights, smaller-than-average cell volumes, and are electrically least active. In this regime, the cloud cell volume does not vary significantly across different underlying surfaces and afternoon convective activity is suppressed. Thus, the picture emerging is that the convective cloud activity in the deep westerly regime is primarily regulated by the large-scale conditions. The remaining regimes (“easterly,” “shallow westerly,” and “moist easterly”) also demonstrate strong dependence on the large-scale forcing and a secondary dependence on the underlying surface type. The easterly regime has a small convective cloud occurrence rate and low cloud heights but higher lightning counts per convective cloud. The other two regimes have moderate convective cloud occurrence rates and larger cloud sizes. The easterly, shallow westerly, and moist easterly regimes exhibit a strong, clearly defined semidiurnal convective cloud occurrence pattern, with peaks in the early morning and afternoon periods. The cell onset times in these three regimes depend on the combination of local time and the underlying surface.

Observations of tropical convection from precipitation radar and the concurring large-scale atmospheric state at two locations (Darwin and Kwajalein) are used to establish effective stochastic models to parameterise subgrid-scale tropical convective activity. Two approaches are presented which rely on the assumption that tropical convection induces a stationary equilibrium distribution. In the first approach we parameterise convection variables such as convective area fraction as an instantaneous random realisation conditioned on the large-scale vertical velocities according to a probability density function estimated from the observations. In the second approach convection variables are generated in a Markov process conditioned on the large-scale vertical velocity, allowing for non-trivial temporal correlations. Despite the different prevalent atmospheric and oceanic regimes at the two locations, with Kwajalein being exposed to a purely oceanic weather regime and Darwin exhibiting land-sea interaction, we establish that the empirical measure for the convective variables conditioned on large-scale mid-level vertical velocities for the two locations are close. This allows us to train the stochastic models at one location and then generate time series of convective activity at the other location. The proposed stochastic subgrid-scale models adequately reproduce the statistics of the observed convective variables and we discuss how they may be used in future scale-independent mass-flux convection parameterisations.