Impacts of varying concentrations of cloud condensation nuclei on deep convective cloud updrafts: A multimodel assessment
Journal of the Atmospheric Sciences American Meteorological Society 78:4 (2021) 1147-1172
Abstract:
This study presents results from a model intercomparison project, focusing on the range of responses in deep convective cloud updrafts to varying cloud condensation nuclei (CCN) concentrations among seven state-of-the-art cloud-resolving models. Simulations of scattered convective clouds near Houston, Texas, are conducted, after being initialized with both relatively low and high CCN concentrations. Deep convective updrafts are identified, and trends in the updraft intensity and frequency are assessed. The factors contributing to the vertical velocity tendencies are examined to identify the physical processes associated with the CCN-induced updraft changes. The models show several consistent trends. In general, the changes between the High-CCN and Low-CCN simulations in updraft magnitudes throughout the depth of the troposphere are within 15% for all of the models. All models produce stronger (~+5%–15%) mean updrafts from ~4–7 km above ground level (AGL) in the High-CCN simulations, followed by a waning response up to ~8 km AGL in most of the models. Thermal buoyancy was more sensitive than condensate loading to varying CCN concentrations in most of the models and more impactful in the mean updraft responses. However, there are also differences between the models. The change in the amount of deep convective updrafts varies significantly. Furthermore, approximately half the models demonstrate neutral-to-weaker (~−5% to 0%) updrafts above ~8 km AGL, while the other models show stronger (~+10%) updrafts in the High-CCN simulations. The combination of the CCN-induced impacts on the buoyancy and vertical perturbation pressure gradient terms better explains these middle- and upper-tropospheric updraft trends than the buoyancy terms alone.Anthropogenic aerosols modulated twentieth-century Sahel rainfall variability via impacts on North Atlantic sea surface temperature
Copernicus Publications (2021)
Using the learnings of machine learning to distill cloud controlling environmental regimes from satellite observations
Copernicus Publications (2021)
A large-scale analysis of pockets of open cells and their radiative impact
Geophysical Research Letters American Geophysical Union 48:6 (2021) e2020GL092213
Abstract:
Pockets of open cells sometimes form within closed‐cell stratocumulus cloud decks but little is known about their statistical properties or prevalence. A convolutional neural network was used to detect occurrences of pockets of open cells (POCs). Trained on a small hand‐logged dataset and applied to 13 years of satellite imagery the neural network is able to classify 8,491 POCs. This extensive database allows the first robust analysis of the spatial and temporal prevalence of these phenomena, as well as a detailed analysis of their micro‐physical properties. We find a large (30%) increase in cloud effective radius inside POCs as compared to their surroundings and similarly large (20%) decrease in cloud fraction. This also allows their global radiative effect to be determined. Using simple radiative approximations we find that the instantaneous global annual mean top‐of‐atmosphere perturbation by all POCs is only 0.01 W/m2.An overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project: aerosol-cloud-radiation interactions in the Southeast Atlantic basin
Atmospheric Chemistry and Physics Copernicus Publications 21:3 (2021) 1507-1563