The influence of shallow cloud populations on transitions to deep convection in the Amazon

In this study, a pair of convection-permitting (2-km grid spacing), month-long, wet season Weather Research and Forecasting (WRF) simulations with and without the Eddy-Diffusivity Mass-Flux (EDMF) scheme are performed for a portion of the Green Ocean Amazon (GoAmazon) 2014/5 field campaign period. EDMF produces an ensemble of subgrid-scale convective plumes that evolve in response to the boundary layer meteorology and can develop into shallow clouds. The objective of this study is to determine how different treatments of shallow cumulus clouds (i.e., with and without EDMF) impact the total cloud population and precipitation across the Amazonian rainforest, with emphasis on impacts on the likelihood of shallow-to-deep convection transitions. Results indicate that the large-scale synoptic conditions in the EDMF and control simulations are nearly identical, however, on the local scale their rainfall patterns diverge drastically and the biases decrease in EDMF. The EDMF scheme significantly increases the frequency of shallow clouds, but the frequencies of deep clouds are similar between the simulations. Deep convective clouds (DCC) are tracked using a cloud tracking algorithm to examine the impact of shallow cumulus on the surrounding ambient environment where deep convective clouds initiate. Results suggest that a rapid increase of low-level cloudiness acts to cool and moisten the low-to-mid troposphere during the day, favoring the transition to deep convection.

Sensitivity of mixed-phase moderately deep convective clouds to parameterizations of ice formation – an ensemble perspective

The formation of ice in clouds is an important processes in mixed-phase and ice-phase clouds. Yet, the representation of ice formation in numerical models is highly uncertain. In the last decade, several new parameterizations for heterogeneous freezing have been proposed. However, it is currently unclear what the effect of choosing one parameterization over another is in the context of numerical weather prediction. We conducted high-resolution simulations (Δx=250 m) of moderately deep convective clouds (cloud top ∼-18 ∘C) over the southwestern United Kingdom using several formulations of ice formation and compared the resulting changes in cloud field properties to the spread of an initial condition ensemble for the same case. The strongest impact of altering the ice formation representation is found in the hydrometeor number concentration and mass mixing ratio profiles. While changes in accumulated precipitation are around 10 %, high precipitation rates (95th percentile) vary by 20 %. Using different ice formation representations changes the outgoing short-wave radiation by about 2.9 W m−2 averaged over daylight hours. The choice of a particular representation for ice formation always has a smaller impact then omitting heterogeneous ice formation completely. Excluding the representation of the Hallett–Mossop process or altering the heterogeneous freezing parameterization has an impact of similar magnitude on most cloud macro- and microphysical variables with the exception of the frozen hydrometeor mass mixing ratios and number concentrations. A comparison to the spread of cloud properties in a 10-member high-resolution initial condition ensemble shows that the sensitivity of hydrometeor profiles to the formulation of ice formation processes is larger than sensitivity to initial conditions. In particular, excluding the Hallett–Mossop representation results in profiles clearly different from any in the ensemble. In contrast, the ensemble spread clearly exceeds the changes introduced by using different ice formation representations in accumulated precipitation, precipitation rates, condensed water path, cloud fraction, and outgoing radiation fluxes.

Transport of isoprene and its oxidation products by deep convective clouds in the Amazon

<p>Transport of organic trace gases by deep convective clouds plays an important role for new particle formation (NPF) and particle growth in the upper atmosphere. Isoprene accounts for a major fraction of the global volatile organic vapor emissions and a significant fraction is emitted in the Amazon. We examined transport and chemical processing of isoprene and its oxidation products in a deep convective cloud over the Amazon using a box model. Trajectories of individual air parcels of the cloud derived from a large eddy simulation are used as input to the model. Our results show that there exist two main pathways for NPF from isoprene associated with deep convection. The first one is when the gas transport occurs through a cloud with low lightning activity and with efficient gas uptake of low-volatile oxidation products by ice particles. Some of the isoprene will reach the cloud outflow where it is further aged and produces low volatile species capable of forming and growing new particles. The second way is via transport through clouds with high lightning activity and with low gas uptake by ice. For this case, low volatile oxidation products will reach the immediate outflow in concentrations close to the values observed in the boundary layer. The efficiency of gas condensation on ice particles is still uncertain and further research in this direction is needed.</p>