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Abstract. Deep convective updraft invigoration via indirect effects of increased aerosol number concentration on cloud microphysics is frequently cited as a driver of correlations between aerosol and deep convection properties. Here, we critically evaluate the theoretical, modeling, and observational evidence for warm- and cold-phase invigoration pathways. Though warm-phase invigoration is plausible and theoretically supported via lowering of the supersaturation with increased cloud droplet concentration in polluted conditions, the significance of this effect depends on substantial supersaturation changes in real-world convective clouds that have not been observed. Much of the theoretical support for cold-phase invigoration depends on unrealistic assumptions of instantaneous freezing and unloading of condensate in growing, isolated updrafts. When applying more realistic assumptions, impacts on buoyancy from enhanced latent heating via fusion in polluted conditions are largely canceled by greater condensate loading. Many foundational observational studies supporting invigoration have several fundamental methodological flaws that render their findings incorrect or highly questionable. Thus, much of the evidence for invigoration has come from numerical modeling, but different models and setups have produced a vast range of results. Furthermore, modeled aerosol impacts on deep convection are rarely tested for robustness, and microphysical biases relative to observations persist, rendering many results unreliable for application to the real world. Without clear theoretical, modeling, or observational support, and given that enervation rather than invigoration may occur for some deep convective regimes and environments, it is entirely possible that the overall impact of cold-phase invigoration is negligible. Substantial mesoscale variability of dominant thermodynamic controls on convective updraft strength coupled with substantial updraft and aerosol variability in any given event are poorly quantified by observations and present further challenges to isolating aerosol effects. Observational isolation and quantification of convective invigoration by aerosols is also complicated by limitations of available cloud condensation nuclei and updraft speed proxies, aerosol correlations with meteorological conditions, and cloud impacts on aerosols. Furthermore, many cloud processes, such as entrainment and condensate fallout, modulate updraft strength and aerosol–cloud interactions, varying with cloud life cycle and organization, but these processes remain poorly characterized. Considering these challenges, recommendations for future observational and modeling research related to aerosol invigoration of deep convection are provided.

Bin and bulk schemes are the two primary methods to parameterize cloud microphysical processes. This study attempts to reveal how their structural differences (size‐resolved vs. moment‐resolved) manifest in terms of cloud and precipitation properties. We use a bulk scheme, the Arbitrary Moment Predictor (AMP), which uses process parameterizations identical to those in a bin scheme but predicts only moments of the size distribution like a bulk scheme. As such, differences between simulations using AMP's bin scheme and simulations using AMP itself must come from their structural differences. In one‐dimensional kinematic simulations, the overall difference between AMP (bulk) and bin schemes is found to be small. Full‐microphysics AMP and bin simulations have similar mean liquid water path (mean percent difference <4%), but AMP simulates significantly lower mean precipitation rate (−35%) than the bin scheme due to slower precipitation onset. Individual processes are also tested. Condensation is represented almost perfectly with AMP, and only small AMP‐bin differences emerge due to nucleation, evaporation, and sedimentation. Collision‐coalescence is the single biggest reason for AMP‐bin divergence. Closer inspection shows that this divergence is primarily a result of autoconversion and not of accretion. In full microphysics simulations, lowering the diameter threshold separating cloud and rain category in AMP fromtoreduces the largest AMP‐bin difference to ∼10%, making the effect of structural differences between AMP (and perhaps triple‐moment bulk schemes generally) and bin even smaller than the parameterization differences between the two bin schemes.

Increases in aerosol concentration are well known to influence the microphysical processes and radiative properties of clouds. By reducing droplet size, an increase in aerosol can lessen collision efficiency and increase liquid water path (LWP) in precipitating clouds or enhance evaporation rate and decrease LWP in non‐precipitating clouds. We utilize large eddy simulations to further investigate these aerosol indirect effects in Arctic mixed‐phase clouds and find, in agreement with previous studies, precipitating clouds to experience an increase in LWP and non‐precipitating clouds a decrease in LWP. Most importantly however, our results reveal a different explanation for why such an LWP decrease occurs in decoupled, non‐precipitating clouds. We find enhanced evaporation near cloud top to be driven primarily by a strengthening of maximum radiative cooling rate with aerosol concentration which drives stronger entrainment, an effect that holds true even in clouds that are optically thick.

Rainfall prediction by weather forecasting models is strongly dependent on the microphysical parametrization being utilized within the model. As forecasting models have become more advanced, they are more commonly using double‐moment bulk microphysical parametrizations. While these double‐moment schemes are more sophisticated and require fewera prioriparameters than single‐moment parametrizations, a number of parameter values must still be fixed for quantities that are not Prognosed or diagnosed. Two such parameters, the width of the rain drop size distribution and the choice of collection efficiencies between liquid hydrometeors, are examined here. Simulations of deep convective storms were performed in which the collection efficiency dataset and thea prioriwidth of the rain drop size distribution (RSD) were individually and simultaneously modified. Analysis of the results show that thea prioriwidth of the RSD was a larger control on the total accumulated precipitation (a change of up to 75% over the typical values tested in this article) than the choice of collection efficiency dataset used (a change of up to 10%). Changing the collection efficiency dataset produces most of the impacts on precipitation rates through changes in the warm rain process rates. On the other hand, the decrease in precipitation with narrowing RSDs occurs in association with the following processes: (a) decreased rain production due to increased evaporation, (b) decreased rain production due to decreased ice melting, and (c) slower raindrop fall speed which leads to longer residency times and changes in rain self‐collection. These results add to the growing body of work showing that the representation of hydrometeor size distributions is critically important, and suggests that more work should be done to better represent the width of the RSD in models, including further development of triple‐moment and bin schemes.