Search

Aerosol‐cloud interactions (ACI) pose the largest uncertainty for climate projection. Among many challenges of understanding ACI, the question of whether ACI can be deterministically predicted has not been explicitly answered. Here we attempt to answer this question by predicting cloud droplet number concentration Nc ${N}_{c}$ from aerosol number concentration Na ${N}_{a}$ and ambient conditions using a data‐driven framework. We use aerosol properties, vertical velocity fluctuations, and meteorological states from the ACTIVATE field observations (2020–2022) as predictors to estimate Nc ${N}_{c}$. We show that the campaign‐wide Nc ${N}_{c}$ can be successfully predicted using machine learning models despite the strongly nonlinear and multi‐scale nature of ACI. However, the observation‐trained machine learning model fails to predict Nc ${N}_{c}$ in individual cases while it successfully predicts Nc ${N}_{c}$ of randomly selected data points that cover a broad spatiotemporal scale. This suggests that, within a data‐driven framework, the Nc ${N}_{c}$ prediction is uncertain at fine spatiotemporal scales. Plain Language Summary: Ambient aerosol particles act as seeds for ice crystals and cloud droplets that form clouds. Both aerosols and clouds regulate the energy and water budgets of the Earth via radiative and cloud micro/macro‐processes. This is the so‐called aerosol‐cloud interactions (ACI). ACI remains the source of the largest uncertainty for accurate climate projections, due to incomplete understanding of nonlinear multi‐scale processes, limited observations across various cloud regimes, and insufficient computational power to resolve them in models. Quantifying the relation between the cloud droplet Nc $\left({N}_{c}\right)$ and aerosol Na $\left({N}_{a}\right)$ number concentration has been a central challenge of understanding and representing ACI. In this work, we tackle this challenge by predicting Nc ${N}_{c}$ from observations made during the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) using machine learning models. We show that the climatological Nc ${N}_{c}$ can be successfully predicted despite the strongly nonlinear and multi‐scale nature of ACI. However, the observation‐trained machine learning model fails to predict Nc ${N}_{c}$ at fine spatiotemporal scales. Key Points: Three‐year in situ measurements (179 flights) provide adequate data to train and validate a random forest model (RFM) to study aerosol‐cloud interactionsThe RFM can successfully predict cloud droplet number concentration Nc ${N}_{c}$ and identify importance of key predictorsData‐driven Nc ${N}_{c}$ prediction in individual cases shows strong dependency on sampling strategy [ABSTRACT FROM AUTHOR]

Marine boundary layer clouds play a critical role in Earth's energy balance. Their microphysical and radiative properties are highly impacted by ambient aerosols and dynamic forcings. In this study, we evaluate the representation of these clouds and related aerosol–cloud interaction processes in the single-column version of the E3SM climate model (SCM) against field measurements collected during the NASA ACTIVATE campaign over the western North Atlantic, as well as intercompare results with high-resolution process level models. We show that E3SM SCM reproduces the macrophysical properties of post-frontal boundary layer clouds in a cold-air outbreak (CAO) case well. However, it generates fewer but larger cloud droplets compared to aircraft measurements. Further sensitivity tests show that the underestimation of both aerosol number concentration and vertical velocity variance contributes to this bias. Aerosol–cloud interactions are examined by perturbing prescribed aerosol properties in E3SM SCM with fixed dynamics. Higher aerosol number concentration or hygroscopicity leads to more numerous but smaller cloud droplets, resulting in a stronger cooling via shortwave cloud forcing. This apparent Twomey effect is consistent with prior climate model studies. The cloud liquid water path shows a weakly positive relation with cloud droplet number concentration due to precipitation suppression. This weak aerosol effect on cloud macrophysics may be attributed to the dominant impact of strong dynamical forcing associated with the CAO. Our findings indicate that the SCM framework is a key tool to bridge the gap between climate models, process level models, and field observations to facilitate process level understanding. [ABSTRACT FROM AUTHOR]

Process modeling of Aerosol‐cloud interaction (ACI) is essential to bridging gaps between observational analysis and climate modeling of aerosol effects in the Earth system and eventually reducing climate projection uncertainties. In this study, we examine ACI in summertime precipitating shallow cumuli observed during the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). Aerosols and precipitating shallow cumuli were extensively observed with in‐situ and remote‐sensing instruments during two research flight cases on 02 June and 07 June, respectively, during the ACTIVATE summer 2021 deployment phase. We perform observational analysis and large‐eddy simulation (LES) of aerosol effect on precipitating cumulus in these two cases. Given the measured aerosol size distributions and meteorological conditions, LES is able to reproduce the observed cloud properties by aircraft such as liquid water content (LWC), cloud droplet number concentration (Nc) and effective radius reff. However, it produces smaller liquid water path (LWP) and larger Nc compared to the satellite retrievals. Both 02 and 07 June cases are over warm waters of the Gulf Stream and have a cloud top height over 3 km, but the 07 June case is more polluted and has larger LWC. We find that the Na‐induced LWP adjustment is dominated by precipitation feedback for the 2 June precipitating case and there is no clear entrainment feedback in both cases. An increase of cloud fraction due to a decrease of aerosol number concentration is also shown in the simulations for the 02 June case. Plain Language Summary: Aerosol‐cloud‐interaction (ACI) regulates the energy budget of the Earth and poses the largest uncertainty in climate projection. Particularly, ACI of low clouds is poorly understood and causes the spread of Earth System Models (ESMs) in predicting cloud and climate responses to aerosol changes. Process studies have shown a nonlinear cloud water amount and cloud fraction adjustments due to aerosol changes via precipitation and cloud top entrainment, which are not often captured correctly in ESMs. This study explores the physical mechanisms of ACI in marine low clouds with a focus on precipitating low clouds using a cloud process model and unprecedented field campaign measurements of meteorology states, cloud properties, and aerosols collected during the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment. We show that the aerosol‐induced cloud water amount adjustment is dominated by changes in precipitation and there is no clear entrainment feedback in both cases. Our findings can help improve the representation of ACI within precipitating marine low clouds in ESMs. Key Points: Aerosol‐cloud interactions in precipitating shallow cumuli are investigated using large‐eddy simulations (LES) and observationsLES show that aerosol‐induced cumulus cloud water adjustment is dominated by precipitation with no clear entrainment feedbackAn increase in cloud fraction in response to aerosol number concentration decrease is shown in the precipitating cumuli [ABSTRACT FROM AUTHOR]

Marine boundary-layer clouds play a critical role in the Earth's energy balance. Their microphysical and radiative properties are highly impacted by ambient aerosols and dynamical forcings. In this study, we evaluate the representation of these clouds and related aerosol-cloud interactions processes in the single-column version of E3SM climate model (SCM), against field measurements collected during the NASA ACTIVATE campaign over the western North Atlantic, as well as intercompare with high-resolution process-level models. Results show that E3SM-SCM, driven by the ERA5 reanalysis, reproduces the cloud properties as good as the high-resolution WRF simulations. For stronger surface forcings combined with a weaker subsidence taken from a WRF cloud-resolving simulation, both E3SM-SCM and WRF large-eddy simulation produce thicker clouds. This indicates that a proper combination of large-scale dynamics, sub-grid scale parameterizations, and model configurations is needed to obtain optimal performance of cloud simulations. In the E3SM-SCM sensitivity tests with fixed dynamics but perturbed aerosol properties, higher aerosol number concentration leads to more numerous but smaller cloud droplets, resulting in a stronger shortwave cloud forcing (i.e., stronger radiative cooling). This apparent Twomey effect is consistent with prior climate model studies. Cloud liquid water path shows a weakly positive relation with cloud droplet number concentration associated with precipitation suppression, which is different from the nonlinear relation approximated from prior observations and E3SM studies, warranting future investigation. Our findings indicate that the SCM framework is a key tool to bridge the gap between climate models, high-resolution models, and field observations to facilitate process-level understanding. [ABSTRACT FROM AUTHOR]

The winter synoptic evolution of the western North Atlantic and its influence on the atmospheric boundary layer is described by means of a regime classification based on Self‐Organizing Maps applied to 12 years of data (2009–2020). The regimes are classified into categories according to daily 600‐hPa geopotential height: dominant ridge, trough to ridge eastward transition (trough‐ridge), dominant trough, and ridge to trough eastward transition (ridge–trough). A fifth synoptic regime resembles the winter climatological mean. Coherent changes in sea‐level pressure and large‐scale winds are in concert with the synoptic regimes: (a) the ridge regime is associated with a well‐developed anticyclone; (b) the trough‐ridge gives rise to a low‐pressure center over the ocean, ascents, and northerly winds over the coastal zone; (c) trough is associated with the eastward displacement of a cyclone, coastal subsidence, and northerly winds, all representative characteristics of cold‐air outbreaks; and (d) the ridge–trough regime features the development of an anticyclone and weak coastal winds. Low clouds are characteristic of the trough regime, with both trough and trough–ridge featuring synoptic maxima in cloud droplet number concentration (Nd). The Nd increase is primarily observed near the coast, concomitant with strong surface heat fluxes exceeding by more than 400 W m−2 compared to fluxes further east. Five consecutive days of aircraft observations collected during the ACTIVATE campaign corroborates the climatological characterization, confirming the occurrence of high Nd for days identified as trough. This study emphasizes the role of boundary‐layer dynamics and aerosol activation and their roles in modulating cloud microphysics. Plain Language Summary: The synoptic evolution of boundary layer clouds over the western North Atlantic is described by means of a regime classification based on Self‐Organizing Maps. The analysis is able to capture events with low and high low‐cloud coverage. High‐cloud coverage days are associated with cold‐air outbreaks (CAOs). The combination of cold and dry conditions gives rise to an enhancement of surface heat fluxes during CAO, consistent with an increase in cloud fraction. In addition, prevailing winds during CAO days explain the occurrence of a synoptic maximum in cloud droplet number concentration, linked to transport of continental aerosol over the ocean. Overall, the dynamics of midlatitude low clouds substantially differ from archetypal stratocumulus clouds regimes. Key Points: Winter synoptic evolution is well described by a clustering method applied to 600 hPa geopotential heightMarine low clouds are characteristic of the trough regime, associated with strong surface heat fluxesCold‐air outbreaks are associated with trough and ridge–trough regimes, and witness peaks in cloud droplet number and aerosol concentrations [ABSTRACT FROM AUTHOR]

Aerosol effects on micro/macrophysical properties of marine stratocumulus clouds over the western North Atlantic Ocean (WNAO) are investigated using in situ measurements and large-eddy simulations (LES) for two cold-air outbreak (CAO) cases (28 February and 1 March 2020) during the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE). The LES is able to reproduce the vertical profiles of liquid water content (LWC), effective radius reff and cloud droplet number concentration Nc from fast cloud droplet probe (FCDP) in situ measurements for both cases. Furthermore, we show that aerosols affect cloud properties (Nc, reff, and LWC) via the prescribed bulk hygroscopicity of aerosols (κ ¯) and aerosol size distribution characteristics. Nc, reff, and liquid water path (LWP) are positively correlated to κ ¯ and aerosol number concentration (Na) while cloud fractional cover (CFC) is insensitive to κ ¯ and aerosol size distributions for the two cases. The realistic changes to aerosol size distribution (number concentration, width, and the geometrical diameter) with the same meteorology state allow us to investigate aerosol effects on cloud properties without meteorological feedback. We also use the LES results to evaluate cloud properties from two reanalysis products, ERA5 and MERRA-2. Compared to LES, the ERA5 is able to capture the time evolution of LWP and total cloud coverage within the study domain during both CAO cases while MERRA-2 underestimates them. [ABSTRACT FROM AUTHOR]

We investigate the effect of turbulence on the combined condensational and collisional growth of cloud droplets by means of high-resolution direct numerical simulations of turbulence and a superparticle approximation for droplet dynamics and collisions. The droplets are subject to turbulence as well as gravity, and their collision and coalescence efficiencies are taken to be unity. We solve the thermodynamic equations governing temperature, water vapor mixing ratio, and the resulting supersaturation fields together with the Navier–Stokes equation. We find that the droplet size distribution broadens with increasing Reynolds number and/or mean energy dissipation rate. Turbulence affects the condensational growth directly through supersaturation fluctuations, and it influences collisional growth indirectly through condensation. Our simulations show for the first time that, in the absence of the mean updraft cooling, supersaturation-fluctuation-induced broadening of droplet size distributions enhances the collisional growth. This is contrary to classical (nonturbulent) condensational growth, which leads to a growing mean droplet size, but a narrower droplet size distribution. Our findings, instead, show that condensational growth facilitates collisional growth by broadening the size distribution in the tails at an early stage of rain formation. With increasing Reynolds numbers, evaporation becomes stronger. This counteracts the broadening effect due to condensation at late stages of rain formation. Our conclusions are consistent with results of laboratory experiments and field observations, and show that supersaturation fluctuations are important for precipitation. [ABSTRACT FROM AUTHOR]

Condensational growth of cloud droplets due to supersaturation fluctuations is investigated by solving the hydrodynamic and thermodynamic equations using direct numerical simulations (DNS) with droplets being modeled as Lagrangian particles. The supersaturation field is calculated directly by simulating the temperature and water vapor fields instead of being treated as a passive scalar. Thermodynamic feedbacks to the fields due to condensation are also included for completeness. We find that the width of droplet size distributions increases with time, which is contrary to the classical theory without supersaturation fluctuations, where condensational growth leads to progressively narrower size distributions. Nevertheless, in agreement with earlier Lagrangian stochastic models of the condensational growth, the standard deviation of the surface area of droplets increases as t1/2. Also, for the first time, we explicitly demonstrate that the time evolution of the size distribution is sensitive to the Reynolds number, but insensitive to the mean energy dissipation rate. This is shown to be due to the fact that temperature fluctuations and water vapor mixing ratio fluctuations increase with increasing Reynolds number; therefore the resulting supersaturation fluctuations are enhanced with increasing Reynolds number. Our simulations may explain the broadening of the size distribution in stratiform clouds qualitatively, where the mean updraft velocity is almost zero. [ABSTRACT FROM AUTHOR]

We investigate the effect of turbulence on the collisional growth of micrometer-sized droplets through high-resolution numerical simulations with well-resolved Kolmogorov scales, assuming a collision and coalescence efficiency of unity. The droplet dynamics and collisions are approximated using a superparticle approach. In the absence of gravity, we show that the time evolution of the shape of the droplet-size distribution due to turbulence-induced collisions depends strongly on the turbulent energy-dissipation rate , but only weakly on the Reynolds number. This can be explained through the dependence of the mean collision rate described by the Saffman–Turner collision model. Consistent with the Saffman–Turner collision model and its extensions, the collision rate increases as even when coalescence is invoked. The size distribution exhibits power-law behavior with a slope of −3.7 from a maximum at approximately 10 up to about 40 μm. When gravity is invoked, turbulence is found to dominate the time evolution of an initially monodisperse droplet distribution at early times. At later times, however, gravity takes over and dominates the collisional growth. We find that the formation of large droplets is very sensitive to the turbulent energy dissipation rate. This is because turbulence enhances the collisional growth between similar-sized droplets at the early stage of raindrop formation. The mean collision rate grows exponentially, which is consistent with the theoretical prediction of the continuous collisional growth even when turbulence-generated collisions are invoked. This consistency only reflects the mean effect of turbulence on collisional growth. [ABSTRACT FROM AUTHOR]

Turbulence is argued to play a crucial role in cloud droplet growth. The combined problem of turbulence and cloud droplet growth is numerically challenging. Here an Eulerian scheme based on the Smoluchowski equation is compared with two Lagrangian superparticle (or superdroplet) schemes in the presence of condensation and collection. The growth processes are studied either separately or in combination using either two-dimensional turbulence, a steady flow or just gravitational acceleration without gas flow. Good agreement between the different schemes for the time evolution of the size spectra is observed in the presence of gravity or turbulence. The Lagrangian superparticle schemes are found to be superior over the Eulerian one in terms of computational performance. However, it is shown that the use of interpolation schemes such as the cloud-in-cell algorithm is detrimental in connection with superparticle or superdroplet approaches. Furthermore, the use of symmetric over asymmetric collection schemes is shown to reduce the amount of scatter in the results. For the Eulerian scheme, gravitational collection is rather sensitive to the mass bin resolution, but not so in the case with turbulence. [ABSTRACT FROM AUTHOR]