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Marine stratocumulus cloud properties over the Southeast Atlantic Ocean are impacted by contact between above-cloud biomass burning aerosols and cloud tops. Different vertical separations (0 to 2000 m) between the aerosol layer and cloud tops were observed on six research flights in September 2016 during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign. There were 30 contact profiles, where an aerosol layer with aerosol concentration (Na) > 500 cm−3 was within 100 m of cloud tops, and 41 separated profiles, where the aerosol layer with Na > 500 cm−3 was located more than 100 m above cloud tops. For contact profiles, the average cloud droplet concentration (Nc) in the cloud layer was up to 68 cm−3 higher, the effective radius (Re) up to 1.3 µm lower, and the liquid water content (LWC) within 0.01 g m−3 compared to separated profiles. Free-tropospheric humidity was higher in the presence of biomass burning aerosols, and contact profiles had a smaller decrease in humidity (and positive buoyancy) across cloud tops with higher median above-cloud Na (895 cm−3) compared to separated profiles (30 cm−3). Due to droplet evaporation from entrainment mixing of warm, dry free-tropospheric air into the clouds, the median Nc and LWC for contact profiles decreased with height by 21 and 9 % in the top 20 % of the cloud layer. The impact of droplet evaporation was stronger during separated profiles as a greater decrease in humidity (and negative buoyancy) across cloud tops led to greater decreases in median Nc (30 %) and LWC (16 %) near cloud tops. Below-cloud Na was sampled during 61 profiles, and most contact profiles (20 out of 28) were within high-Na (> 350 cm−3) boundary layers, while most separated profiles (22 out of 33) were within low-Na (< 350 cm−3) boundary layers. Although the differences in below-cloud Na were statistically insignificant, contact profiles within low-Na boundary layers had up to 34.9 cm−3 higher Nc compared to separated profiles. This is consistent with a weaker impact of droplet evaporation in the presence of biomass burning aerosols within 100 m above cloud tops. For contact profiles within high-Na boundary layers, the presence of biomass burning aerosols led to higher below-cloud Na (up to 70.5 cm−3) and additional droplet nucleation above the cloud base along with weaker droplet evaporation. Consequently, the contact profiles in high-Na boundary layers had up to 88.4 cm−3 higher Nc compared to separated profiles. These results motivate investigations of aerosol–cloud–precipitation interactions over the Southeast Atlantic since the changes in Nc and Re induced by the presence of above-cloud biomass burning aerosols are likely to impact precipitation rates, liquid water path, and cloud fraction, and modulate closed-to-open-cell transitions.

Open cloud cells can be described in ideal form as connected clouds that surround spots of isolated clear skies in their centers. This cloud pattern is typically associated with marine stratocumulus (MSc) that form in the oceanic boundary layer. However, it can form in deeper convective clouds as well. Here, we focus on deep-open-cells (with tops reaching up to ~5–7 km) that form in the post-frontal regions of winter Mediterranean cyclones, and examine their properties and evolution. Using a Lagrangian analysis of satellite data, we show that deep-open-cells have a larger equivalent diameter (~58 ± 18 km) and oscillate with a longer periodicity (~3.5 ± 1 h) compared to shallow MSc. A numerical simulation of one Cyprus low event reveals that precipitation-generated convergence and divergence dynamic patterns are the main driver of the open cells’ organization and oscillations. Thus, our findings generalize the mechanism attributed to the behavior of shallow marine cells to deeper convective systems.

The retrieval of aerosol parameters from passive satellite instruments in cloudy scenes is challenging, partly because clouds and cloud-related processes may significantly modify aerosol optical depth (AOD) and particle size, a problem that is further compounded by 3D radiative processes. Recent advances in retrieval algorithms such as the “color ratio” method, which utilizes the measurements at a shorter (470 nm) and a longer (860 nm) wavelength, have demonstrated the simultaneous derivation of AOD and cloud optical depth (COD) for scenes in which absorbing aerosols are found to overlay low-level cloud decks. This study shows simultaneous retrievals of above-cloud aerosol optical depth (ACAOD) and aerosol-corrected cloud optical depth (COD) from airborne measurements of cloud-reflected and sky radiances using the color ratio method. These airborne measurements were taken over marine stratocumulus clouds with NASA's Cloud Absorption Radiometer (CAR) during the SAFARI 2000 field campaign offshore of Namibia. The ACAOD is partitioned between the AOD below-aircraft (AOD_cloudtop) and above-aircraft AOD (AOD_sky). The results show good agreement between AOD_sky and sun-photometer measurements of the above-aircraft AOD. The results also show that the use of aircraft-based sun-photometer measurements to validate satellite retrievals of the ACAOD is complicated by the lack of information on AOD below aircraft. Specifically, the CAR-retrieved AOD_cloudtop captures this “missing” aerosol layer caught between the aircraft and cloud top, which is required to quantify above-cloud aerosol loading and effectively validate satellite retrievals. In addition, the study finds a strong anticorrelation between the AOD_cloudtop and COD for cases in which COD < 10 and a weaker anticorrelation for COD > 10, which may be associated with the uncertainties in the color ratio method at lower AODs and CODs. The influence of 3D radiative effects on the retrievals is examined, and the results show that at cloud troughs, 3D effects increase retrieved ACAOD by about 3 %–11 % and retrieved COD by about 25 %. The results show that the color ratio method has little sensitivity to 3D effects at overcast stratocumulus cloud decks. These results demonstrate a novel airborne measurement approach for assessing satellite retrievals of aerosols above clouds, thereby filling a major gap in global aerosol observations.

This paper presents a process-oriented evaluation of precipitating stratocumulus and its transition to cumulus in version 1 of the Energy Exascale Earth System Model (E3SMv1) using comprehensive case-study observations from a field campaign of the Atmospheric Radiation Measurement program (ARM). The E3SMv1 single-column model (SCM) of the marine boundary layer and its low clouds and precipitation are compared to observations including subcloud drizzle retrievals from a combination of Doppler radar and lidar backscatter measurements. The SCM is also compared to a large-eddy simulation (LES) of the same case. The combination of advanced remote sensing observations and LES is a powerful framework to evaluate the physical parameterizations of large-scale models. Given the observed large-scale environment, the E3SMv1 SCM realistically represents the evolution of clouds and boundary layer structure during the stratocumulus-to-cumulus transition. The model well simulates the liquid water path and its diurnal cycle in the stratocumulus period as well as the two-layer vertical thermodynamic structure and lower cloud fraction in the transition period. E3SMv1’s success in simulating the cloud in the stratocumulus period permitted examination of its precipitation processes. Here problems were identified with E3SMv1 producing an unrealistically small subcloud precipitation fraction, an unrealistic double peak in the vertical profiles of precipitation mass, and drizzle that evaporates too close to the surface. Further model diagnostics determined that these unrealistic characteristics resulted from an overly long microphysics time step and an unrealistic parameterization of the precipitation fraction. These results imply that careful consideration of these issues is needed in order to better simulate precipitation processes in marine stratocumulus.

A large parameter-perturbed ensemble (PPE) of the Met Office climate model is used to explore the relationship between radiative feedbacks and the present-day simulation of the associated physical processes. We highlight three tropical regimes (deep convection over ocean and land, and marine stratocumulus) in which the same set of processes drives the present-day simulation of clouds and their feedbacks. In each case, the amount of the dominant cloud types reduces in response to warming and the reduction is approximately proportional to the amount simulated in the present day. In deep convective regions, convective process parameters lead the spread among multiple contributing processes, with vegetation processes contributing as well for the land regions. Multiple parameters, such as boundary layer processes, drive stratocumulus regions. However, the low-thick clouds are systematically overestimated, suggesting a structural error in their process representations which would limit the efficacy of the constraint. The influence of convection is largely confined to the tropical deep convective regions in the present day but extends to mid-latitudes under warming. Because of this, contributing processes to the spread in the present-day and the response are different in the extra-tropics, making it much more difficult to establish links between the present-day and the feedback within the region. This suggests that identifying a constraint on convective processes in the tropics for the present-day simulations could constrain both the tropical feedbacks and feedbacks in the extra-tropics. A parameter representing deep-convective entrainment links the present-day tropical mean high cloud and clear-sky longwave flux to their feedbacks in our model, suggesting a potential process constraint from the observations. Understanding and improving the detailed processes controlling feedbacks is ultimately only possible in individual models. Different process-based constraints might be inferred for different models. The approach described here could usefully extended to other single model ensembles and the collective understandings could be valuable for improving model process and feedbacks more generally.

Marine boundary layer (MBL) cloud morphology associated with two summertime cold fronts over the eastern North Atlantic (ENA) is investigated using high-resolution simulations from the Weather Research and Forecasting (WRF) Model and observations from the Atmospheric Radiation Measurement (ARM) ENA Climate Research Facility. Lagrangian trajectories are used to study the evolution of post-cold-frontal MBL clouds from solid stratocumulus to broken cumulus. Clouds within specified domains in the vicinity of transitions are classified according to their degree of decoupling, and cloud-base and cloud-top breakup processes are evaluated. The Lagrangian derivative of the surface latent heat flux is found to be strongly correlated with that of the cloud fraction at cloud base in the simulations. Cloud-top entrainment instability (CTEI) is shown to operate only in the decoupled MBL. A new indicator of inversion strength at cloud top that employs the vertical gradients of equivalent potential temperature and saturation equivalent potential temperature, which can be computed directly from soundings, is proposed as an alternative to CTEI. Overall, results suggest that the deepening–warming hypothesis suggested by Bretherton and Wyant explains many of the characteristics of the summertime postfrontal MBL evolution of cloud structure over the ENA, thereby widening the phase space over which the hypothesis may be applied. A subset of the deepening–warming hypothesis involving warming initially dominating over moistening is proposed. It is postulated that changes in climate change–induced modifications in cold-frontal structure over the ENA may be accompanied by coincident changes in the location and timing of MBL cloud transitions in the post-cold-frontal environment.

The role of nonlocal transport on the development and maintenance of marine stratocumulus (Sc) clouds in coarse-resolution models is investigated, with a special emphasis on the downdraft contribution. A new parameterization of cloud-top-triggered downdrafts is proposed and validated against large-eddy simulation (LES) for two Sc cases. The applied nonlocal mass-flux scheme is part of the stochastic multiplume eddy-diffusivity/mass-flux (EDMF) framework decomposing the turbulent transport into local and nonlocal contributions. The complementary local turbulent transport is represented with the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme. This EDMF version has been implemented in the Weather Research and Forecasting (WRF) single-column model (SCM) and tested for three model versions: without mass flux, with updrafts only, and with both updrafts and downdrafts. In the LES, the downdraft and updraft contributions to the total heat and moisture transport are comparable and significant. The WRF SCM results show a good agreement between the parameterized downdraft turbulent transport and LES. While including updrafts greatly improves the modeling of Sc clouds over the simulation without mass flux, the addition of downdrafts is less significant, although it helps improve the moisture profile in the planetary boundary layer.

The southeast Atlantic (SEA) region is host to a climatologically significant biomass burning aerosol layer overlying marine stratocumulus. We present the first results of the directly measured above-cloud aerosol optical depth (ACAOD) from the recent ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) airborne field campaign during August and September 2016. In our analysis, we use data from the Spectrometers for Sky-Scanning Sun-Tracking Atmospheric Research (4STAR) instrument and found an average ACAOD of 0.32 at 501 nm (range of 0.02 to 1.04), with an average Ångström exponent (AE) above clouds of 1.71. The AE is much lower at 1.25 for the full column (including below-cloud-level aerosol, with an average of 0.36 at 501 nm and a range of 0.02 to 0.74), indicating the presence of large aerosol particles, likely marine aerosol, in the lower atmospheric column. The ACAOD is observed from 4STAR to be highest near the coast at about 12∘ S, whereas its variability is largest at the southern edge of the average aerosol plume, as indicated by 12 years of MODIS observations. In comparison to MODIS-derived ACAOD and long-term fine-mode plume-average AOD along a diagonal routine track extending out from the coast of Namibia, the directly measured ACAOD from 4STAR is slightly lower than the ACAOD product from MODIS. The peak ACAOD expected from MODIS AOD retrievals averaged over a long term along the routine diagonal flight track (peak of 0.5) was measured to be closer to coast in 2016 at about 1.5–4∘ E, with 4STAR ACAOD averages showing a peak of 0.42. When considering the full observation set over the SEA, by spatially binning each sampled AOD, we obtain a geographically representative mean ACAOD of 0.37. Vertical profiles of AOD showcase the variability in the altitude of the aerosol plume and its separation from the cloud top. We measured larger AOD at a high altitude near the coast than farther from the coast, while generally observing a larger vertical gap farther from the coast. Changes in AOD with altitude are correlated with carbon monoxide, a gas tracer of the biomass burning aerosol plume. Vertical extent of gaps between aerosol and cloud show a wide distribution, with a near-zero gap being most frequent. The gap distribution with longitude is observed to be largest at about 7∘ E, farther from coast than expected from previous studies.

The evaluation of radiative forcing associated with aerosol-cloud interactions remains a significant source of uncertainty in future climate projections. The problem is confounded by the fact that aerosol particles influence clouds locally, and that averaging to larger spatial and/or temporal scales carries biases that depend on the heterogeneity and spatial correlation of the interacting fields and the non-linearity of the responses. Mimicking commonly applied satellite data analyses for calculation of albedo susceptibility So, we quantify So aggregation biases using an ensemble of 127 large eddy simulations of marine stratocumulus. We explore the cloud field properties that control this spatial aggregation bias, and quantify the bias for a large range of shallow stratocumulus cloud conditions manifesting a variety of morphologies and range of cloud fractions. We show that So spatial aggregation biases can be on the order of 100s of percent, depending on methodology. Key uncertainties emanate from the typically applied adiabatic drop concentration Nd retrieval, the correlation between aerosol and cloud fields, and the extent to which averaging reduces the variance in cloud albedo Ac and Nd. Biases are more often positive than negative. So biases are highly correlated to biases in the adjustment. Temporal aggregation biases are shown to offset spatial averaging biases. Both spatial and temporal biases have significant implications for observationally based assessments of aerosol indirect effects and our inferences of underlying aerosol-cloud-radiation effects.

This research was partly supported by European Research Council Project constRaining the EffeCts of Aerosols on Precipitation under the European Union’s Horizon 2020 Research and Innovation Program Grant 724602. M.W.C., P.-L.M., and J.M. were supported by the “Enabling Aerosol-cloud interactions at GLobal convection-permitting scalES (EAGLES)” project (74358), funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Earth System Model Development program. The Pacific Northwest National Laboratory is operated for the U.S. Department of Energy by Battelle Memorial Institute under contract DEAC05-76RL01830. M.Y., M.W.C, D.W.P, and P.S. were supported by the Natural Environment Research Council (UK) project ACRUISE (grant number: NE/S005390/1). V.T. acknowledges support from the Estonian Research Council grant PSG202. M.S.D. was supported in part by NASA headquarters under the NASA Earth and Space Science Fellowship Program, grant number NNX-80NSSC17K0404, and in part by the CIRES Visiting Fellows Program that is funded by the NOAA Cooperative Agreement with CIRES, grant number NA17OAR4320101. A.Sorooshian was supported by ONR grant N00014-21-1-2115 and NASA grant 80NSSC19K0442 in support of ACTIVATE, a NASA Earth Venture Suborbital-3 (EVS-3) investigation funded by NASA’s Earth Science Division and managed through the Earth System Science Pathfinder Program Office. A. Schmidt acknowledges funding from NERC grants NE/S00436X/1 (V-PLUS), NE/T006897/1 (ADVANCE), and NE/P013406/1 (A-CURE). Z.L. is funded by the US National Science Foundation (AGS1837811) and NASA (80NSSC20K0131). A.Possner is funded by the Federal Ministry of Education and Research (BMBF) under the "Make our Planet Great Again - German Research Initiative", grant number 57429624, implemented by the German Academic Exchange Service (DAAD). E.G. was supported by a Royal Society University Research Fellowship (URF/R1/191602). F.G. acknowledges support from The Branco Weiss Fellowship - Society in Science, administered by the ETH Zurich, and from a Veni grant of the Dutch Research Council (NWO). J.Q. acknowledges support from the EU Horizon2020 projects ACACIA (GA 875036) and FORCES (GA 821205). P.S. acknowledges support by the European Research Council (ERC) project constRaining the EffeCts of Aerosols on Precipitation (RECAP) under the European Union’s Horizon 2020 research and innovation program with grant agreement 724602 and from the FORCeS project under the European Union’s Horizon 2020 research program with grant agreement 821205. R.W. acknowledges support from the US National Oceanographic and Atmospheric Administration (NOAA Award NA20OAR4320271). G.F. acknowledges funding from a NOAA Earth’s Radiation Budget grant, NOAA CPO Climate & CI #03-01-07-001. The National Center for Atmospheric Research is funded by the U.S. National Science Foundation. A.Povey is funded as part of the Natural Environment Research Council’s support of the National Centre for Earth Observation, contract number PR140015.

Low clouds cover over a quarter of the planet’s surface in the annual mean and exert a significant cooling effect on global climate. Despite their importance, the representation of such clouds in global circulation models remains a major source of uncertainty in projections of future climate. The lifetime of such clouds can be modified by precipitation, which is observed to occur 20-40% of the time in stratocumulus over the ocean. The processes by which precipitation forms are also highly uncertain and accurate quantitative precipitation forecasting remains a grand challenge in meteorology. The goal of this research is to explore and evaluate the representation of clouds in observations and models of the atmosphere, with a focus on improving our understanding of the primary process responsible for liquid precipitation formation, collision-coalescence.The first goal of this research is to develop a metric for the dynamical "age" of small cumulus for use in contexting in situ observations. The lifetime of shallow cumulus is typically under an hour hence the metric must be accurate for a single measurement of the cloud. It is found that the deviation of cloud total water mixing ratio from the mean surface mixed layer total water mixing ratio is an effective measure because the mechanism by which total water mixing ratio is diluted, entrainment, is irreversible.Collision-coalescence is notoriously difficult to observe in situ and its implementation in models of the atmosphere is highly uncertain because it is an inherently local process, dependent on interactions of drops on length scales as small as micrometers. The second goal of this dissertation is to assess the ability of theoretically derived collision-coalescence rates to explain observations of cloud drops in marine stratocumulus off the coast of Monterey, California. Drop size spectra averaged over length scales of 1.5 and 30 kilometers are found to require enhancements of collision rates that vary as a function of the mode of the drop size distribution from 0.1 for the largest mode sizes to over 20 for the smallest sizes. Unresolvable small-scale variability in the DSD is likely a major factor in determining the spread in required enhancements.The third goal of the research presented herein is to implement a high spectral resolution bin microphysics scheme in a large eddy simulation (LES) model. The high resolution scheme is validated by comparison with simulations using the default spectral resolution. Considerable differences in microphysical structure and planetary boundary layer turbulence are found when collision coalescence is active due to artificial acceleration of the process at the default resolution, which causes higher concentrations at large drop sizes than in the high resolution configuration.Finally, the fourth goal of this research is to evaluate the ability of a turbulent collision kernel to improve the representation of precipitation formation. The net effect of the turbulent kernel is to deplete the cloud top of large drops, but the effect of this varies depending on total drop concentration such that precipitating and non-precipitating cases have opposite responses. Precipitating cases experience an enhanced sedimentation flux throughout the boundary layer at default resolution, while nonprecipitating cases and high spectral resolution simulations experience further suppression of precipitation. We conclude that the effects of changing spectral resolution dominate the effects of the turbulent kernel. This finding is contingent on the assumption that microphysical boundary conditions (i.e. cloud condensation nuclei concentrations) should be constant across spectral resolution configurations.

Quantification of the radiative adjustment of marine low-clouds to aerosol perturbations, regionally and globally, remains the largest source of uncertainty in assessing current and future climate. An important step towards quantifying the role of aerosol in modifying cloud radiative properties is to quantify the susceptibility of cloud albedo and liquid water path (LWP) to perturbations in cloud droplet number concentration (Nd). We use 10 years of space-borne observations from the polar-orbiting Aqua satellite, to quantify the albedo susceptibility of marine low-clouds over the northeast (NE) Pacific stratocumulus region to Nd perturbations. Overall, we find a low-cloud brightening potential of 20.8 ± 0.96 W m−2 ln(Nd)−1, despite an overall negative LWP adjustment for non-precipitating marine stratocumulus, owing to the high occurrence (37% of the time) of thin non-precipitating clouds (LWP −2) that exhibit brightening. In addition, we identify two more susceptibility regimes, the entrainment-darkening regime (36% of the time), corresponding to negative LWP adjustment, and the precipitating-brightening regime (22% of the time), corresponding to precipitation suppression. The influence of large-scale meteorological conditions, obtained from the ERA5 reanalysis, on the albedo susceptibility is also examined. Over the NE Pacific, clear seasonal covariabilities among meteorological factors related to the large-scale circulation are found to play an important role in grouping favorable conditions for each susceptibility regime. Our results indicate that, for the NE Pacific stratocumulus deck, the strongest positively susceptible cloud states occur most frequently for low cloud top height (CTH), the highest lower-tropospheric stability (LTS), low sea-surface temperature (SST), and the lowest free-tropospheric relative humidity (RHft) conditions, whereas cloud states that exhibit negative LWP adjustment occur most frequently under high CTH and intermediate LTS, SST, and RHft conditions. The warm rain suppression driven cloud brightening is found to preferably occur either under unstable atmospheric conditions (low LTS) or high RHft conditions that co-occur with warm SST. Mutual information analyses reveal a dominating control of LWP, Nd and CTH (cloud state indicators) on low-cloud albedo susceptibility, rather than of the meteorological factors that drive these cloud states.

The number of cloud droplets formed at the cloud base depends both on the properties of aerosol particles and the updraft velocity of an air parcel at the cloud base. As the spatial scale of updrafts is too small to be resolved in global atmospheric models, the updraft velocity is commonly parameterised based on the available turbulent kinetic energy. Here we present alternative methods through parameterising updraft velocity based on high-resolution large eddy simulation (LES) runs in the case of marine stratocumulus clouds. First we use our simulations to assess the accuracy of a simple linear parametrisation where the updraft velocity depends only on cloud top radiative cooling. In addition, we present two different machine learning methods (Gaussian process emulation and random forest) that account for different boundary layer conditions and cloud properties. We conclude that both machine learning parameterisations reproduce the LES-based updraft velocities at about the same accuracy, while the simple approach employing radiative cooling only produce on average lower coefficient of determination and higher root mean square error values. Finally, we apply these machine learning methods to find the key parameters affecting cloud base updraft velocities.

Aerosol-cloud-precipitation interactions (ACIs) provide the greatest source of uncertainties in predicting changes in Earth’s energy budget due to poor representation of marine stratocumulus and the associated ACIs in climate models. Using in situ data from 329 cloud profiles across 24 research flights from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign in September 2016, August 2017, and October 2018, it is shown that contact between above-cloud biomass-burning aerosols and marine stratocumulus over the southeast Atlantic Ocean was associated with precipitation suppression and a decrease in the precipitation susceptibility (So) to aerosols. The 173 “contact” profiles with aerosol concentration (Na) greater than 500 cm−3 within 100 m above cloud tops had 50 % lower precipitation rate (Rp) and 20 % lower So, on average, compared to 156 “separated” profiles with Na less than 500 cm−3 up to at least 100 m above cloud tops. Contact and separated profiles had statistically significant differences in droplet concentration (Nc) and effective radius (Re) (95 % confidence intervals from a two-sample t-test are reported). Contact profiles had 84 to 90 cm−3 higher Nc and 1.4 to 1.6 μm lower Re compared to separated profiles. In clean boundary layers (below-cloud Na less than 350 cm−3), contact profiles had 25 to 31 cm−3 higher Nc and 0.2 to 0.5 μm lower Re. In polluted boundary layers (below-cloud Na exceeding 350 cm−3), contact profiles had 98 to 108 cm−3 higher Nc and 1.6 to 1.8 μm lower Re. On the other hand, contact and separated profiles had statistically insignificant differences between the average liquid water path, cloud thickness, and meteorological parameters like surface temperature, lower tropospheric stability, and estimated inversion strength. These results suggest the changes in cloud properties were driven by ACIs rather than meteorological effects, and the existing relationships between Rp and Nc must be adjusted to account for the role of ACIs.

Data collected with a holographic instrument [Holographic Detector for Clouds (HOLODEC)] on board the High-Performance Instrumented Airborne Platform for Environmental Research Gulfstream-V (HIAPER GV) aircraft from marine stratocumulus clouds during the Cloud System Evolution in the Trades (CSET) field project are examined for spatial uniformity. During one flight leg at 1190 m altitude, 1816 consecutive holograms were taken, which were approximately 40 m apart with individual hologram dimensions of 1.16 cm × 0.68 cm × 12.0 cm and with droplet concentrations of up to 500 cm−3. Unlike earlier studies, minimally intrusive data processing (e.g., bypassing calculation of number concentrations, binning, and parametric fitting) is used to test for spatial uniformity of clouds on intra- and interhologram spatial scales (a few centimeters and 40 m, respectively). As a means to test this, measured droplet count fluctuations are normalized with the expected standard deviation from theoretical Poisson distributions, which signifies randomness. Despite the absence of trends in the mean concentration, it is found that the null hypothesis of spatial uniformity on both spatial scales can be rejected with compelling statistical confidence. Monte Carlo simulations suggest that weak clustering explains this signature. These findings also hold for size-resolved analysis but with less certainty. Clustering of droplets caused by, for example, entrainment and turbulence, is size dependent and is likely to influence key processes such as droplet growth and thus cloud lifetime.

Satellite measurements from Terra's Moderate Resolution Imaging Spectroradiometer (MODIS) represent our longest, single‐platform, global record of the effective radius (Re) of the cloud drop size distribution. Quantifying its error characteristics has been challenging because systematic errors in retrieved Re covary with the structural characteristics of the cloud and the Sun‐view geometry. Recently, it has been shown that the bias in MODIS Re can be estimated by fusing MODIS data with data from Terra's Multi‐angle Imaging SpectroRadiometer (MISR). Here, we relate the bias to the observed underlying conditions to derive regional‐scale, bias‐corrected, monthly‐mean Re 1.6, Re 2.1, and Re 3.7 values retrieved from the 1.6, 2.1, and 3.7 μm MODIS spectral channels. Our results reveal that monthly‐mean bias in Re 2.1 exhibits large regional dependency, ranging from at least ~1 to 10 μm (15 to 60%) varying with scene heterogeneity, optical depth, and solar zenith angle. Regional bias‐corrected monthly‐mean Re 2.1 ranges from 4 to 17 μm, compared to 10 to 25 μm for uncorrected Re 2.1, with estimated uncertainties of 0.1 to 1.8 μm. The bias‐corrected monthly‐mean Re 3.7 and Re 2.1 show difference of approximately +0.6 μm in the coastal marine stratocumulus regions and down to approximately −2 μm in the cumuliform cloud regions, compared to uncorrected values of about −1 to −6 μm, respectively. Bias‐corrected Re values compare favorably to other independent data sources, including field observations, global model simulations, and satellite retrievals that do not use retrieval techniques similar to MODIS. This work changes the interpretation of global Re distributions from MODIS Re products and may further impact studies, which use the original MODIS Re products to study, for example, aerosol‐cloud interactions and cloud microphysical parameterization., Key Points Regional monthly mean MODIS Re is biased high by at least 1 to 10 μm and depends on cloud horizontal heterogeneity and Sun angleBias‐corrected values of Re compare favorably to other independent satellite retrievals, field observations, and global model simulationsSpectral channel differences in bias‐corrected Re is reduced but still suggests that vertical variability of Re is cloud‐type dependent

A new anelastic large-eddy simulation (LES) model with an Eulerian dynamical core and Lagrangian particle-based microphysics is presented. The dynamical core uses the multidimensional positive-definite advection transport algorithm (MPDATA) advection scheme and the generalized conjugate residual pressure solver, whereas the microphysics scheme is based on the super-droplet method. Algorithms for coupling of Lagrangian microphysics with Eulerian dynamics are presented, including spatial and temporal discretizations and a condensation substepping algorithm. The model is free of numerical diffusion in the droplet size spectrum. Activation of droplets is modeled explicitly, making the model less sensitive to local supersaturation maxima than models in which activation is parameterized. Simulations of a drizzling marine stratocumulus give results in agreement with other LES models. It is shown that in the super-droplet method a relatively low number of computational particles is sufficient to obtain correct averaged properties of a cloud, but condensation and collision–coalescence have to be modeled with a time step of the order of 0.1 s. Such short time steps are achieved by substepping, as the model time step is typically around 1 s. Simulations with and without an explicit subgrid-scale turbulence model are compared. Effects of modeling subgrid-scale motion of super-droplets are investigated. The model achieves high computational performance by using graphics processing unit (GPU) accelerators.

Climate models generally simulate a unidirectional, positive liquid water path (LWP) response to increasing aerosol number concentration. However, satellite observations and large-eddy simulations ...

This paper introduces the OCO2CLD-LIDAR-AUX product, which uses the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar and the Orbiting Carbon Observatory-2 (OCO-2) hyperspectral A-band spectrometer. CALIPSO provides a prior cloud top pressure (Ptop) for an OCO-2-based retrieval of cloud optical depth, Ptop and cloud geometric thickness expressed in hPa. Measurements are of single-layer liquid clouds over oceans from September 2014 to December 2016 when collocated data are available. Retrieval performance is best for solar zenith angles <45∘ and when the cloud phase classification, which also uses OCO-2's weak CO2 band, is more confident. The highest quality optical depth retrievals agree with those from the Moderate Resolution Imaging Spectroradiometer (MODIS) with discrepancies smaller than the MODIS-reported uncertainty. Retrieved thicknesses are consistent with a substantially subadiabatic structure over marine stratocumulus regions, in which extinction is weighted towards the cloud top. Cloud top pressure in these clouds shows a 4 hPa bias compared with CALIPSO which we attribute mainly to the assumed vertical structure of cloud extinction after showing little sensitivity to the presence of CALIPSO-identified aerosol layers or assumed cloud droplet effective radius. This is the first case of success in obtaining internal cloud structure from hyperspectral A-band measurements and exploits otherwise unused OCO-2 data. This retrieval approach should provide additional constraints on satellite-based estimates of cloud droplet number concentration from visible imagery, which rely on parameterization of the cloud thickness.

The states of coupling between clouds and surface or boundary-layer have been investigated much more extensively for marine stratocumulus clouds than for continental low clouds, partly due to more complex thermodynamic structures over land. A manifestation is a lack of robust remote sensing methods to identify coupled and decoupled clouds over land. Here, we have generalized the concept of coupling and decoupling to low clouds over land, based on potential temperature profiles. Furthermore, by using ample measurements from a lidar and a suite of surface meteorological instruments at the U.S. Department of Energy’s Atmospheric Radiation Measurement Program’s Southern Great Plains site from 1998 to 2019, we have developed a method to simultaneously retrieve the planetary boundary layer (PBL) height (PBLH) and coupled states under cloudy conditions during the daytime. The coupled states derived from lidar show strong consistency with those derived from radiosondes. Retrieving the PBLH under cloudy conditions that has been a persistent problem in lidar remote sensing, is resolved in this study. Our method can lead to high-quality retrievals of the PBLH under cloudy conditions and the determination of cloud coupling states. With the new method, we find that coupled clouds are sensitive to changes in the PBL with a strong diurnal cycle, whereas decoupled clouds and the PBL are weakly related. Since coupled and decoupled clouds have distinct features, our new method offers an advanced tool to separately investigate them in climate systems.

The cloud albedo in the marine subtropical stratocumulus regions plays a key role in regulating the regional energy budget. Based on 12 years of monthly data from multiple satellite datasets, the long-term, monthly and seasonal cycle of averaged cloud albedo in five stratocumulus regions were investigated to intercompare the atmosphere-only simulations between phases 5 and 6 of the Coupled Model Intercomparison Project (AMIP5 and AMIP6). Statistical results showed that the long-term regressed cloud albedos were underestimated in most AMIP6 models compared with the satellite-driven cloud albedos, and the AMIP6 models produced a similar spread as AMIP5 over all regions. The monthly averaged values and seasonal cycle of cloud albedo of AMIP6 ensemble mean showed a better correlation with the satellite-driven observations than that of the AMIP5 ensemble mean. However, the AMIP6 model still failed to reproduce the values and amplitude in some regions. By employing the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) data, this study estimated the relative contributions of different aerosols and meteorological factors on the long-term variation of marine stratocumulus cloud albedo under different cloud liquid water path (LWP) conditions. The multiple regression models can explain ∼ 65 % of the changes in the cloud albedo. Under the monthly mean LWP ≤ 65 g m−2, dust and black carbon dominantly contributed to the changes in the cloud albedo, while dust and sulfur dioxide aerosol contributed the most under the condition of 65 g m−2 < LWP ≤ 120 g m−2. These results suggest that the parameterization of cloud–aerosol interactions is crucial for accurately simulating the cloud albedo in climate models.

openaire: EC/H2020/646857/EU//ECLAIR acp-2019-1167 Artificial enhancement of precipitation via hygroscopic cloud seeding is investigated with a numerical large-eddy simulation model coupled with a spectral aerosol-cloud microphysics module. We focus our investigation on marine stratocumulus clouds and evaluate our model results by comparing them with recently published results from field observations. Creating multiple realizations of a single cloud event with the model provides a robust method to detect and attribute the seeding effects, which reinforces the analysis based on experimental data. Owing to the detailed representation of aerosol-cloud interactions, our model successfully reproduces the microphysical signatures attributed to the seeding, that were also seen in the observations. Moreover, the model simulations show up to a 2–3 fold increase in the precipitation flux due to the seeding, depending on the seeding rate and injection strategy. However, our simulations suggest that a relatively high seeding particle emission rate is needed for a substantial increase in the precipitation yield, as compared with the estimated seeding concentrations from the field campaign. In practical applications, the seeding aerosol is often produced by flare burning. It is speculated, that the required amount of large seeding particles suggested by our results could pose a technical challenge to the flare-based approach.

This study assesses the relative importance of time integration error in present-day climate simulations conducted with the atmosphere component of the Energy Exascale Earth System Model version 1 (EAMv1) at 1∘ horizontal resolution. We show that a factor-of-6 reduction of time step size in all major parts of the model leads to significant changes in the long-term mean climate. Examples of changes in 10-year mean zonal averages include the following: up to 0.5 K of warming in the lower troposphere and cooling in the tropical and subtropical upper troposphere, 1 %–10 % decreases in relative humidity throughout the troposphere, and 10 %–20 % decreases in cloud fraction in the upper troposphere and decreases exceeding 20 % in the subtropical lower troposphere. In terms of the 10-year mean geographical distribution, systematic decreases of 20 %–50 % are seen in total cloud cover and cloud radiative effects in the subtropics. These changes imply that the reduction of temporal truncation errors leads to a notable although unsurprising degradation of agreement between the simulated and observed present-day climate; to regain optimal climate fidelity in the absence of those truncation errors, the model would require retuning. A coarse-grained attribution of the time step sensitivities is carried out by shortening time steps used in various components of EAM or by revising the numerical coupling between some processes. Our analysis leads to the finding that the marked decreases in the subtropical low-cloud fraction and total cloud radiative effect are caused not by the step size used for the collectively subcycled turbulence, shallow convection, and stratiform cloud macrophysics and microphysics parameterizations but rather by the step sizes used outside those subcycles. Further analysis suggests that the coupling frequency between the subcycles and the rest of EAM significantly affects the subtropical marine stratocumulus decks, while deep convection has significant impacts on trade cumulus. The step size of the cloud macrophysics and microphysics subcycle itself appears to have a primary impact on cloud fraction in the upper troposphere and also in the midlatitude near-surface layers. Impacts of step sizes used by the dynamical core and the radiation parameterization appear to be relatively small. These results provide useful clues for future studies aiming at understanding and addressing the root causes of sensitivities to time step sizes and process coupling frequencies in EAM. While this study focuses on EAMv1 and the conclusions are likely model-specific, the presented experimentation strategy has general value for weather and climate model development, as the methodology can help researchers identify and understand sources of time integration error in sophisticated multi-component models.

Low clouds play a key role in the Earth-atmosphere energy balance and influence agricultural production and solar-power generation. Smoke aloft has been found to enhance marine stratocumulus through aerosol-cloud interactions, but its role in regions with strong human activities and complex monsoon circulation remains unclear. Here we show that biomass burning aerosols aloft strongly increase the low cloud coverage over both land and ocean in subtropical southeastern Asia. The degree of this enhancement and its spatial extent are comparable to that in the Southeast Atlantic, even though the total biomass burning emissions in Southeast Asia are only one-fifth of those in Southern Africa. We find that a synergetic effect of aerosol-cloud-boundary layer interaction with the monsoon is the main reason for the strong semi-direct effect and enhanced low cloud formation in southeastern Asia., Biomass burning emissions have been shown to influence clouds in the Atlantic, but its influence in other regions is not well known. Here, the authors show that biomass burning aerosols increase the low-cloud cover over subtropical southeastern Asia by a similar magnitude than over the Atlantic.

Assimilation of satellite precipitation data into numerical models presents several difficulties, with two of the most important being the non-Gaussian error distributions associated with precipitation, and large model and observation errors. As a result, improving the model forecast beyond a few hours by assimilating precipitation has been found to be difficult. To identify the challenges and propose practical solutions to assimilation of precipitation, statistics are calculated for global precipitation in a low-resolution NCEP Global Forecast System (GFS) model and the TRMM Multisatellite Precipitation Analysis (TMPA). The samples are constructed using the same model with the same forecast period, observation variables, and resolution as in the follow-on GFS/TMPA precipitation assimilation experiments presented in the companion paper. The statistical results indicate that the T62 and T126 GFS models generally have positive bias in precipitation compared to the TMPA observations, and that the simulation of the marine stratocumulus precipitation is not realistic in the T62 GFS model. It is necessary to apply to precipitation either the commonly used logarithm transformation or the newly proposed Gaussian transformation to obtain a better relationship between the model and observational precipitation. When the Gaussian transformations are separately applied to the model and observational precipitation, they serve as a bias correction that corrects the amplitude-dependent biases. In addition, using a spatially and/or temporally averaged precipitation variable, such as the 6-h accumulated precipitation, should be advantageous for precipitation assimilation.

During the Marine Aerosol Cloud and Wildfire Study (MACAWS) in June and July of 2018, aerosol composition and cloud condensation nuclei (CCN) properties were measured over the N.E. Pacific to characterize the influence of aerosol hygroscopicity on predictions of ambient CCN and stratocumulus cloud droplet number concentrations (CDNC). Three vertical regions were characterized, corresponding to the marine boundary layer (MBL), an above‐cloud organic aerosol layer (AC‐OAL), and the free troposphere (FT) above the AC‐OAL. The aerosol hygroscopicity parameter (κ) was calculated from CCN measurements (κCCN) and bulk aerosol mass spectrometer (AMS) measurements (κAMS). Within the MBL, measured hygroscopicities varied between values typical of both continental environments (~0.2) and remote marine locations (~0.7). For most flights, CCN closure was achieved within 20% in the MBL. For five of the seven flights, assuming a constant aerosol size distribution produced similar or better CCN closure than assuming a constant “marine” hygroscopicity (κ = 0.72). An aerosol‐cloud parcel model was used to characterize the sensitivity of predicted stratocumulus CDNC to aerosol hygroscopicity, size distribution properties, and updraft velocity. Average CDNC sensitivity to accumulation mode aerosol hygroscopicity is 39% as large as the sensitivity to the geometric median diameter in this environment. Simulations suggest CDNC sensitivity to hygroscopicity is largest in marine stratocumulus with low updraft velocities (0.6 m s−1), where hygroscopic properties of the Aitken mode dominate hygroscopicity sensitivity.