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Organic matter can initiate heterogeneous ice nucleation in supercooled water droplets, thereby influencing atmospheric cloud glaciation. Predicting the ice nucleation ability of organic matter-containing cloud droplets is challenging due to the unknown mechanism for templating ice. Here, we observed the presence of nanoparticles in aqueous samples of known ice-nucleating biopolymers cellulose and lignin, as well as in newly identified ice-nucleating biopolymers xylan and laminarin. Using our drop Freezing Ice Nuclei Counter (FINC), we measured the median ice nucleation temperature (T50) of xylan and of laminarin droplets of 2 μl to be −14.2 and −20.0 °C, respectively. Next, we characterized these samples using nanoparticle tracking analysis, and we detected and quantified nanoparticles with mean diameters between 132 and 267 nm. Xylan contained the largest nanoparticles and froze at higher temperatures. Xylan also dictated the freezing in a 1:1:1:1 mixture with cellulose, lignin, laminarin, and xylan. Filtration experiments down to 300 kDa with the xylan sample indicated that the presence of nanoparticles triggered freezing. Overall, only samples with mean diameters above 150 nm froze above −20 °C. Furthermore, we determined the ice-active site densities normalized to particle concentrations, surface area, and mass of the nanoparticles to show that the samples' nucleation site densities are similar to sea spray aerosols and nanometer-sized dust. The identification and characterization of xylan and laminarin as nanometer-sized ice-nucleating substances expands the growing list of organic matter capable of impacting cloud formation and thus climate. [ABSTRACT FROM AUTHOR]

Previous studies have shown that low‐level mixed‐phase clouds that form during idealized moist intrusions into the Arctic can exist in either a stable (stratus) or a convective (stratocumulus) state. Here, we examine the conditions that promote a transition from the stable to the convective state through idealized simulations using a three‐dimensional large‐eddy simulation model coupled with a one‐dimensional multilayer sea ice model. We find that the vertical distribution of the initial dew point temperature (Td) profile fundamentally influences whether a transition between the two states occurs or not. If the initial moisture content of the advected airmass decreases rapidly with height, then a turbulent transition is likely to occur and a stratocumulus cloud can form. However, the availability and properties of aerosols as well as the cloud ice content can delay or even prevent stratocumulus formation, regardless if the conditions in terms of the initial Td profile are favorable. A low cloud ice water content promotes a stably stratified cloud layer and delays the transition. Furthermore, if no cloud condensation nuclei are available at the base of the cloud when a cloud‐layer instability forms, then there is no new droplet formation, the buoyancy remains low and the cloud remains as a stratus. Our results suggest that the low‐level mixed‐phase cloud evolution and the thermodynamic transition of an airmass during a moist intrusion into the Arctic are closely linked to the aerosol processing by the cloud, that is, a chemical transformation, and that the two processes should be considered simultaneously. Plain Language Summary: Clouds strongly influence the energy budget of the central Arctic and tend to warm the surface, in particular during winter. We have used a detailed numerical model to study low‐altitude clouds that form when warm, moist air is transported over the Arctic pack ice. The clouds can exist as two types, either as stratus (thin clouds with little turbulent motion) or stratocumulus (thicker clouds with substantial turbulence). The thicker stratocumulus clouds warm the surface more than the thinner stratus clouds and it is therefore important to understand why some clouds transition into stratocumulus while others stay as stratus. We find that the vertical profile of atmospheric moisture plays a key role for the transition; if the air is moist at the surface but relatively dry aloft, then the transition occurs faster than if the air is relatively moist throughout the atmospheric column. Aerosol particles also play an important role for the transition; in the clean central Arctic, a lack of aerosol particles can prevent cloud droplet formation and thereby also the formation of stratocumulus clouds. Aerosol particles can also influence the formation of ice crystals in the cloud, which we show can affect if and when a stratocumulus cloud forms. Key Points: Mixed‐phase clouds that form during Arctic moist intrusions can transform from a stable (stratus) to a convective state (stratocumulus)The initial moisture profile fundamentally influences whether the transition between the two states occurs or notThe presence or absence of aerosols acting as cloud condensation or ice nuclei can delay or even prevent the transition [ABSTRACT FROM AUTHOR]

The study on the characteristic and the parameterization of cloud droplet effective radius (re) is important for the optimization of warm cloud microphysical schemes in numerical models. Based on numerical simulations combined with the four aircraft observations of stratiform warm clouds in Jiangxi, China, this work investigated their microphysical properties, focusing on the evolutionary pattern and the parameterization of re, as well as the correlation between re and the number concentration (Nc) of cloud droplet and liquid water path (LWP). It is found that there is a negative correlation between re and Nc, and the rate of the decrease could be increased by 2–4 times with a strengthening of collision‐coalescence process. What's more, the direct introduction of collision‐coalescence is conducted based on the existing re parameterization schemes, and the results show that it can perform well in most of collision‐coalescence intensities. It is also found that LWP is positively correlated with re when re is less than 12 μm, after which the relationship changes to a negative correlation due to the impact of rainfall intensity. Plain Language Summary: The bin microphysical scheme can reproduce the microphysical structures of warm cloud, combined with aircraft observations to investigate the microphysics within warm clouds. This work analyzes the evolution characteristics and the parameterization of effective radius in warm clouds based on bin microphysics simulations. Thereafter, the impact of these cloud microphysical quantities to liquid water path (LWP) are also researched. The results show that the rate of effective radius decrease with number concentration can be intensified by collision‐coalescence intensity, and an effective radius parameterization scheme related to collision‐coalescence is proposed based on the existing parameterization schemes. The variation pattern of LWP with effective radius is linked to the critical radius for rainfall intensity. Above all, the effective radius variation pattern depends largely on collision‐coalescence intensity and can affect LWP a lot. These findings are important for understanding warm cloud microphysical parameterization and improving climate assessment. Key Points: The relation between the effective radius and cloud droplet number concentration greatly influenced by collision‐coalescence intensityAn effective radius parameterization scheme which is directly related to collision‐coalescence is considered based on the existing schemesLiquid water path has different relationship with number and effective radius of cloud droplets, also affected by the rainfall intensity [ABSTRACT FROM AUTHOR]

Cloud seeding involves boosting precipitation by releasing substances into the air that act as cloud condensation or ice nuclei. These substances encourage the development of clouds and precipitation. It's like giving Mother Nature a gentle push to assist with rainfall in specific areas. The current work aimed to suggest Al2O3 as an alternate compound in cloud seeding rather than silver iodide. In this research, a unique approach is used to identify condensation nuclei, which play a crucial role in cloud formation and droplet growth. Various samples and four sources were included in the current study; refrigerated helfa powder, Himalayan salt, generator powder, and pollen, were analyzed using different physicochemical instruments. The proportions of chemical compounds in the samples show that there is 1.392% of Al2O3 in Refrigerated helfa which is the highest than in the other 3 sources, while the proportions of elements in the samples indicate that refrigerated helfa contains the lowest toxic compound, and although Al2O3 is insoluble in water, it is hygroscopic and can absorb 6.4% of humidity within 24 hours. As for the surface tension, refrigerated helfa shows lower density and surface tension than the other three sources with values of 0.9480 and 47.89 respectively. Al2O3 shows high humid absorptivity and refrigerated helfa can be used as a main source for Al2O3 which has a low effect on biota and is recommended for use in cloud seeding. However further work is recommended to be carried out in using Al2O3 as an alternative compound to silver iodide in cloud seeding. [ABSTRACT FROM AUTHOR]

The article presents the correction for a article titled "Towards reliable retrievals of cloud droplet number for non-precipitating planetary boundary layer clouds and their susceptibility to aerosol" published on August 12, 2024.

Remote sensing has been a key resource for developing extensive and detailed datasets for studying and constraining aerosol-cloud-climate interactions. However, aerosol-cloud collocation challenges, algorithm limitations, as well as difficulties in unraveling dynamic from aerosol-related effects on cloud microphysics, have long challenged precise retrievals of cloud droplet number concentrations. By combining a series of remote sensing techniques and in situ measurements at ground level, we developed a semiautomated approach that can address several retrieval issues for a robust estimation of cloud droplet number for non-precipitating Planetary Boundary Layer (PBL) clouds. The approach is based on satellite retrievals of the PBL cloud droplet number (Ndsat) using the geostationary meteorological satellite data of the Optimal Cloud Analysis (OCA) product, which is obtained by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The parameters of the retrieval are optimized through closure with droplet number obtained from a combination of ground-based remote sensing data and in situ observations at ground level. More specifically, the remote sensing data are used to retrieve cloud-scale vertical velocity, and the in situ aerosol measurements at ground level were used constrain as input to a state-of-the-art droplet activation parameterization to predict the respective Cloud Condensation Nuclei (CCN) spectra, cloud maximum supersaturation and droplet number concentration (Nd), accounting for the effects of vertical velocity distribution and lateral entrainment. Closure studies between collocated Nd and Nd sat are then used to evaluate exising droplet spectral width parameters used for the retrieval of droplet number, and determine the optimal values for retrieval. This methodology, used to study aerosol-cloud interactions for non-precipitating clouds formed over the Athens Metropolitan Area (AMA), Greece, during the springtime period from March to May 2020, shows that droplet closure can be achieved to within ±33.4%, comparable to the level of closure obtained in many in situ studies. Given this, the ease of applying this approach with satellite data obtained from SEVIRI with high temporal (15 min) and spatial resolution (3.6 km Ã--4.6 km), opens the possibility of continuous and reliable Ndsat, giving rise to high value datasets for aerosol-cloud-climate interaction studies. [ABSTRACT FROM AUTHOR]

Aerosol effective radiative forcing (ERF) has persisted as the most uncertain aspect of anthropogenic forcing over the industrial period, limiting our ability to constrain estimates of climate sensitivity and to confidently predict 21st century climate change. Aerosol–cloud interactions are the most uncertain component of aerosol ERF. The 2014–2015 Holuhraun volcanic eruption acted as a large source of sulfur dioxide, providing an opportunistic experiment for studying aerosol–cloud interactions at a climatically relevant scale. We evaluate the observed aerosol-induced perturbation to marine liquid cloud properties inside the volcanic plume in the first month of the eruption and compare the results to those from UKESM1 (UK Earth System Model). In the first 2 weeks, as expected, we find an in-plume shift to smaller and more numerous cloud droplets in both the observations and the simulations. We find an observed increase in liquid water path (LWP) values inside the plume that is not captured in UKESM1. However, in the third week, the in-plume shift to smaller and more numerous cloud droplets is neither observed nor modelled, and there are discrepancies between the observed and modelled response in the fourth week. An analysis of the model simulations and trajectory modelling reveals that air mass history and background meteorological factors can strongly influence aerosol–cloud interactions between the weeks of our analysis. Overall, our study supports the findings of many previous studies: the aerosol impact on cloud effective radius is significant, with differences in the observed and modelled response for in-cloud LWP. [ABSTRACT FROM AUTHOR]

In this study we explore aerosol–cloud interactions in liquid-phase clouds over eastern China (EC) and its adjacent ocean (ECO) using the WRF-Chem–SBM model with four-dimensional assimilation. The results show that our simulations and analyses based on each vertical layer provide a more detailed representation of the aerosol–cloud relationship compared to the column-based analyses which have been widely conducted previously. For aerosol activation, cloud droplet number concentration (Nd) generally increases with aerosol number concentration (Naero) at low Naero and decreases with Naero at high Naero. The main difference between EC and ECO is that Nd increases faster in ECO than EC at low Naero due to abundant water vapor, whereas at high Naero , when aerosol activation in ECO is suppressed, Nd in EC shows significant fluctuation due to strong surface effects (longwave radiation cooling and terrain uplift) and intense updrafts. Cloud liquid water content (CLWC) increases with Nd , but the increase rate gradually slows down for precipitating clouds, while CLWC increases and then decreases in non-precipitating clouds. Higher Nd and CLWC can be found in EC than in ECO, and the transition-point Nd value at which CLWC in non-precipitating clouds changes from increasing to decreasing is also higher in EC. Aerosol activation is strongest at moderate Naero , but CLWC increases relatively fast at low Naero. ECO cloud processes are more limited by cooling and humidification, whereas strong and diverse surface and atmospheric processes in EC allow intense cloud processes to occur under significant warming or drying conditions. [ABSTRACT FROM AUTHOR]

Wind reversals resulting in southerly flow along the California coast are not well understood in terms of how aerosol and cloud characteristics change. This gap is addressed using airborne field measurements enhanced with data from spaceborne remote sensing (Moderate Resolution Imaging Spectroradiometer), surface stations (Interagency Monitoring of Protected Visual Environments), and models (Navy Aerosol Analysis and Prediction System and Coupled Ocean–Atmosphere Mesoscale Prediction System), with a focus on submicron and supermicron aerosol, as well as cloud microphysical variables: cloud droplet number concentration (Nd), cloud optical thickness (COT), and cloud droplet effective radius (re). Southerly flow coincided with higher values of submicron aerosol concentration (Na) and mass concentrations of species representative of fine-aerosol pollution (NO 3- and nss-SO 42-) as well as shipping and continental emissions (V, oxalate, NH 4+ , Ni, OC, and EC). Supermicron Na did not change; however, heightened levels of acidic species in southerly flow coincided with reduced Cl - : Na + , suggestive of Cl - depletion in salt particles. Clouds responded correspondingly in southerly flow, with more acidic cloud water and higher levels of similar species as in the aerosol phase (e.g., NO 3- , nss-SO 42- , NH 4+ , V), along with elevated values of Nd and COT and reduced re during campaigns with similar cloud liquid water paths. Case study flights help to visualize offshore pollution gradients and highlight the sensitivity of the results to the presence of widespread smoke coverage including how associated plumes have enhanced supermicron Na. These results have implications for aerosol–cloud interactions during wind reversals and have relevance for weather, public welfare, and aviation. [ABSTRACT FROM AUTHOR]

The "23.7" event, an extreme rainstorm that affected North China from July 29 to 2 August 2023, was simulated using the Weather Research and Forecasting (WRF) model, version 4.2. We focus on dynamically diagnosing and analyzing the mass and latent heat budgets of rainwater during the extreme precipitation event on July 31 in the Beijing area, where the hourly rainfall reached an extraordinary 111.8 mm. Generally, the model effectively simulated the rainstorm, enabling further assessment of the extreme precipitation. Results indicated that under the combined influence of three major weather systems -- the residual circulation of Typhoon Doksuri (a low-pressure system after typhoon landfall), the embryonic stage of Typhoon Khanun, and the North China high-pressure dam -- a continuous influx of moisture and energy was transported to the North China region, promoting heavy precipitation. Application of vorticity equation diagnostics indicates that the horizontal transport term is the primary source term. Mass balance analysis reveals that the primary source of rainwater is the accretion of cloud droplets by rain, and the condensation of water vapor into cloud droplets is the main contributor to the latent heat. [ABSTRACT FROM AUTHOR]

Here, we compared the concentrations of cloud condensation nuclei (CCN) and particle number size distributions (PNSDs) measured during the transient period from the winter to the summer East Asian monsoon in 2021 with those in 2014 to explore possible responses to how CCN responds to upwind continental air pollutant mitigation and marine traffic fuel sulfur content (FSC) regulation over the northwest Pacific Ocean (NWPO). We also employed the Positive Matrix Factorization (PMF) analysis to apportion concentrations of CCN (Nccn) to different sources in order to quantify its source-specified responses to mitigation of air pollution during the transient period. Our results showed that (1) upwind continental mitigation likely reduced Nccn by approximately 200 cm−3 and 400 cm−3 at 0.2% and 0.4% supersaturation (SS), respectively, in the marine background atmosphere over the NWPO; (2) FSC regulation resulted in a decrease in Nccn at 0.4% SS by about 50 cm−3 and was nearly negligible at 0.2% SS over the NWPO. Additionally, a PMF-resolved factor, characterized by a dominant nucleation mode, was present only in 2014 and disappeared in 2021, likely due to the reduction. This estimation, however, suffered from uncertainties since seasonal changes were hard to be deducted accurately. PMF-resolved factors accurately represented Nccn in 80–90% of cases, but this accuracy was not observed in the remaining cases. Finally, an integrated analysis of satellite-derived cloud parameters and ship-based measurements indicated that the reduced Nccn over the NWPO might be co-limited with meteorological factors in forming cloud droplets during the transient period. [ABSTRACT FROM AUTHOR]

Different turbulent entrainment‐mixing mechanisms between clouds and environment are essential to cloud‐related processes; however, accurate representation of entrainment‐mixing in weather/climate models still poses a challenge. This study exploits the use of machine learning (ML) to address this challenge. Four ML (Light Gradient Boosting Machine [LGB], eXtreme Gradient Boosting, Random Forest, and Support Vector Regression) are examined and compared. It is found that LGB performs best, and thus is selected to understand the impact of entrainment‐mixing on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, the trained LGB provides more accurate microphysical properties (number concentration and cloud droplet spectral dispersion). The partial dependences of predicted microphysics on features exhibit a strong alignment with physical mechanisms and expectations, as determined by the interpreting method, thus overcoming the limitations of the "black box" scheme. The underlying mechanisms are that the smaller number concentration and larger spectral dispersion correspond to more inhomogeneous entrainment‐mixing. Specifically, number concentration after entrainment‐mixing is positively correlated with adiabatic number concentration and liquid water content affected by entrainment‐mixing, and inversely correlated with adiabatic volume mean radius. Spectral dispersion after entrainment‐mixing is negatively correlated with liquid water content affected by entrainment‐mixing, turbulent dissipation rate and relative humidity of entrained air. Sensitivity analysis further suggests that number concentration is mainly determined by cloud microphysical properties whereas spectral dispersion is influenced by both cloud microphysical properties and environmental variables. The results indicate that the LGB scheme has the potential to enhance the representation of entrainment‐mixing in weather/climate models. Plain Language Summary: Entrainment‐mixing processes occurring between clouds and environmental air have significant effects on cloud‐climate feedback, precipitation, and radiative transfer. Accurately representing these processes has been challenging with previously proposed parameterizations. Machine learning (ML) excels at identifying complex nonlinear relationships and avoids the inherent limitations of conventional parameterizations. Thus, we explore the use of ML to acquire the number concentration and cloud droplet spectral dispersion affected by entrainment‐mixing processes. Simulation data from Explicit Mixing Parcel Model are employed to train, validate, and test the four MLs, including Light Gradient Boosting Machine (LGB), eXtreme Gradient Boosting, Random Forest and Support Vector Regression. After evaluation, the LGB is shown to obtain the most accurate microphysics among the four ML schemes and traditional parameterizations. Additionally, the interpreting method for peeking inside the "black box" reveals that a smaller number concentration and a larger spectral dispersion are indicative of the more inhomogeneous entrainment‐mixing. The relative correlations between predicted microphysics and various features align with our expectations and the underlying physical principles. Sensitivity tests further confirm that incorporating more features produces a more robust and efficient prediction. Overall, this study affirms the reliability and applicability of ML to develop/replace subgrid parameterizations in actual weather/climate models. Key Points: Machine learning schemes are trained to predict cloud microphysical properties affected by turbulent entrainment‐mixing processesThe proposed Light Gradient Boosting Machine provides more accurate microphysical properties compared with traditional parameterization schemesThe partial dependencies of microphysics on features prove a robust alignment with physical mechanisms through the interpreting method [ABSTRACT FROM AUTHOR]

Understanding the direct initiation of detonation is crucial for developing effective detonation inhibition strategies. We numerically investigate the direct initiation of hydrogen/air detonation in a circular water cloud using the Eulerian–Lagrangian method with two-way gas–droplet coupling. Parametric analyses demonstrate that both droplet concentration and cloud radius have significant effects on peak pressure trajectories of the detonation wave. Three direct initiation modes of detonation in fine water sprays are observed: supercritical, critical, and subcritical. These modes are identified based on the triple point trajectories and the evolutions of local shock speeds. In critical initiation, the characteristic scales for the gas–droplet two-phase detonation are plotted. Analyzing the gas temperature and chemical timescale reveals that critical initiation involves three phases: overdriven detonation due to strong blast wave, detonation decoupling resulting from fine droplets with high evaporation rates, and detonation re-initiation induced by focusing of transverse detonation waves, as well as its decaying to approach the Chapman–Jouguet speed. Furthermore, subcritical initiation generally has overdriven detonation followed by detonation decoupling, featured by quickly fading peak pressure trajectories. This is because the triple points and transverse waves are weakened by two-phase exchanges and cannot directly initiate gas reactions. The influence of water cloud characteristics, including droplet evaporation rate, temperature, diameter, and Weber number, is explored. Results show four distinctive zones of the water cloud in the two-phase detonation initiation: pre-evaporation, low evaporation rate, high evaporation rate, and central evaporation. It is also demonstrated that the shocked cloud initially expands outwardly and then shrinks. The low-evaporation-rate zone first vanishes, followed by the outer cloud, while the inner cloud persists until the final stage. The droplet behaviors follow Pilch and Erdman model. [ABSTRACT FROM AUTHOR]

Clouds can be classified into regimes based on their appearance or meteorological controlling factors. The cloud appearance regimes inherently include adjustments to aerosol effects, such as transitions between closed and open cells. Therefore, calculating cloud susceptibilities to aerosols for each cloud‐appearance regime individually and then aggregating them excludes much of the cloud adjustment component of the susceptibilities. In contrast, aggregating susceptibilities over regimes defined by cloud‐controlling factors includes the full effects of cloud adjustments. Here we compared the susceptibilities of the two kinds of cloud regimes and demonstrated this effect. Overall, increasing cloud droplet number concentration (Nd) consistently correlates to weaker precipitation, higher cloud fraction (CF), and reduced liquid water path, regardless of how the regime is defined. However, their susceptibilities to Nd aggregated over cloud‐appearance regimes are significantly lower than those aggregated over cloud‐controlling factors regimes, with lower‐tropospheric stability (LTS) serving as an example to define cloud‐controlling factors regimes. This underestimation is more pronounced for CF susceptibility, where the susceptibility for cloud appearance regimes is only 1/4 of the susceptibility for cloud controlling regimes. These findings imply that relying solely on cloud‐appearance regimes may underestimate the effective radiative forcing produced by cloud adjustment (ERFaci). Nevertheless, the substantial variability in the magnitude of cloud adjustment across appearance regimes at similar LTS also suggests that a single cloud‐controlling factor is not sufficient to fully separate cloud regimes to quantify cloud adjustment. Therefore, identifying a comprehensive set of cloud‐controlling factors is essential for accurately quantifying cloud adjustments in future studies. Plain Language Summary: Understanding the impact of aerosols on marine low clouds is vital for accurately predicting changes in Earth's climate. Their interactions, known as aerosol‐cloud interactions (ACI), introduce significant uncertainty into our estimates of how much human activities contribute to climate change. Clouds exhibit varying responses to changes in cloud droplet number concentration (Nd), a proxy for aerosols, depending on specific cloud regimes representing distinct atmospheric conditions. However, the definition of cloud regime is often vague, with some defined based on the appearance of clouds and others on their meteorological controlling factors. In our study, we compared the sensitivity of cloud properties to Nd between these two different definitions of cloud regimes. We found that cloud susceptibilities to Nd aggregated over cloud‐appearance regimes are notably lower than those aggregated over cloud‐controlling factors regimes. Cloud fraction susceptibility to Nd aggregated over cloud appearance regimes is only one‐fourth of that over cloud controlling regimes. These results suggest that quantifying cloud adjustment to Nd simply within cloud appearance regimes will likely underestimate the effective radiative forcing of ACI (ERFaci) without accounting for the effect of aerosols on cloud‐appearance regimes. Key Points: Cloud‐appearance regimes are unsuitable for quantifying cloud adjustment to cloud droplet number concentration (Nd)The susceptibility of cloud fraction to Nd for cloud‐appearance regimes is only 1/4 of that based on cloud‐controlling regimesQuantifying cloud adjustment simply within cloud‐appearance regimes is likely to underestimate ERFaci [ABSTRACT FROM AUTHOR]

Weekly cycles (WCs) in cloud properties have been reported and linked to aerosol effects. Yet the extent to which human activities contribute to their occurrence remains unclear. Here, we revisit aerosol–cloud interactions from the WCs over central Europe using long‐term satellite and reanalysis data. Significant WCs in aerosol and cloud droplet number concentration (Nd) are detected with minima/maxima on Monday/Friday, indicating a clear signal of the Twomey effect. Notably, Nd–to–aerosol sensitivity from WCs is found to decrease at larger aerosol concentrations, confirming the nonlinear behavior of the aerosol–Nd relation (in log–log space) reported previously, but from a distinct perspective. Nevertheless, no discernible WCs in liquid water path are found. The pronounced WCs in cloud cover are demonstrated to be driven by natural variability. Our results indicate that the WCs offer a useful pathway for investigating the Twomey effect, but are not as effective for detecting cloud adjustments. Plain Language Summary: Aerosol–cloud interactions are the largest uncertainty in the anthropogenic forcing of climate. Specifically, an increase in aerosols increases cloud droplet number concentration (the Twomey effect), which further changes liquid water path and cloud cover (cloud adjustments), ultimately alters radiations. Weekly cycles would be a useful tool for such study, assuming no 7‐day periodicity in meteorological dynamics. In this study, we revisit aerosol–cloud interactions from weekly cycles using long‐term satellite observations and reanalysis data. The analysis is restricted to central Europe—a region with strong weekly cycles in anthropogenic emissions. We find significant weekly cycles in aerosol and cloud droplet number concentration with minima/maxima on Monday/Friday. Importantly, the sensitivity of cloud droplet number concentration to aerosol is found to decrease in polluted conditions, confirming the behavior of the nonlinear cloud response to aerosol as reported previously. It is demonstrated that the weekly cycle in liquid water path is negligible; though a pronounced cycle in cloud cover detected, it is predominately caused by natural variability. Hence, caution is warranted when attributing observed weekly cycle in cloud cover to aerosol effects. Conclusively, weekly cycles are useful for detecting the Twomey effect but less effective for cloud adjustments. Key Points: The analysis of weekly cycles reveals a reduced sensitivity of cloud droplet number concentration to aerosol at larger aerosol loadingThe strong weekly cycle in cloud cover is mainly a result of natural variability rather than being attributed to aerosol effectsWeekly cycles offer a useful pathway to investigating the Twomey effect, but are not as effective for detecting cloud adjustments [ABSTRACT FROM AUTHOR]

Liquid water in cloud droplets and aqueous aerosols serves as an important reaction medium for the formation of secondary aerosol through aqueous-phase reactions (aqSA). Large uncertainties remain in estimates of the production and chemical evolution of aqSA in the dilute solutions found in cloud droplets and the concentrated solutions found in aerosol liquid water, which is partly due to the lack of available measurement tools and techniques. A new oxidation flow reactor (OFR), the Accelerated Production and Processing of Aerosols (APPA) reactor, was developed to measure secondary aerosol formed through gas- and aqueous-phase reactions, both for laboratory gas mixtures containing one or more precursors and for ambient air. For simulating in-cloud processes, ∼ 3.3 µ m diameter droplets formed on monodisperse seed particles are introduced into the top of the reactor, and the relative humidity (RH) inside it is controlled to 100 %. Similar measurements made with the RH in the reactor < 100 % provide contrasts for aerosol formation with no liquid water and with varying amounts of aerosol liquid water. The reactor was characterized through a series of experiments and used to form secondary aerosol from known concentrations of an organic precursor and from ambient air. The residence time distributions of both gases and particles are narrow relative to other OFRs and lack the tails at long residence time expected with laminar flow. Initial cloud processing experiments focused on the well-studied oxidation of dissolved SO 2 by O 3 , with the observed growth of seed particles resulting from the added sulfuric acid agreeing well with estimates based on the relevant set of aqueous-phase reactions. The OH exposure (OH exp⁡) for low RH, high RH, and in-cloud conditions was determined experimentally from the loss of SO 2 and benzene and simulated from the KinSim chemical kinetics solver with inputs of the measured 254 nm UV intensity profile through the reactor and loss of O 3 due to photolysis. The aerosol yield for toluene at high OH exp⁡ ranged from 21.4 % at low RH with dry seed particles present in the reactor to 78.1 % with cloud droplets present. Measurement of the composition of the secondary aerosol formed from ambient air using an aerosol mass spectrometer showed that the oxygen-to-carbon ratio (O : C) of the organic component increased with increasing RH (and liquid water content). [ABSTRACT FROM AUTHOR]

This study proposed an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. The matching characteristics between aerosol and cloud particle size distributions and gamma distributions were studied using aircraft observation data. The feasibility of the retrieval of the particle effective radius from lidar ratios and backscatter ratios was simulated and studied. A method for inverting the effective radius and number concentration of atmospheric aerosols or small cloud droplets using the backscatter ratio was proposed, and the error sources and applicability of the algorithm were analyzed. This algorithm was suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmospheric particles and has good robustness. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 µm. For cloud droplets, it is 1.0–10 µm. An atmospheric-observation experiment was conducted using the multi-wavelength lidar developed by Xi'an University of Technology, and a thin cloud layer was captured. The microphysical parameters of aerosol and clouds during this process were retrieved. The results clearly demonstrate the growth of the effective radius and number concentration. [ABSTRACT FROM AUTHOR]

In this study, we investigated the feasibility of rain enhancement by cloud seeding over a target area (the Sameura Dam catchment area, Kochi Prefecture) in early summer. The effects of salt micro-powder (MP) and hygroscopic flare (HF) seeding on the initial cloud microphysical structures were investigated using a detailed bin microphysics parcel model with background atmospheric aerosol data collected from ground-based observations conducted on the windward side of the target area and seeding aerosol data collected from the coordinated flights of seeding helicopter and in-situ measurement aircraft. Numerical seeding experiments showed that the size distributions of cloud droplets were broadened, and the onset of raindrop formation was accelerated by MP and HF seeding, although MP seeding showed more notable seeding effects than did HF seeding. MP seeding increased the mean droplet size and decreased the total number concentration of cloud droplets, whereas HF seeding had the opposite effect. Based on the relationship between the increase/decrease ratio of the cloud droplet number concentration and increase/decrease ratio of the surface precipitation by hygroscopic seeding obtained in previous studies, MP seeding had a positive seeding effect, whereas HF seeding had a negative effect. In the numerical seeding experiments, a range of variations in the number concentration and hygroscopicity of background aerosol particles, updraft velocity near the cloud base, the amount of seeding material applied, and the change in the physicochemical properties of the seeding aerosols to improve seeding effects were also considered. However, the outline of the results described above remained unchanged. These results demonstrate the possibility of increasing surface precipitation by MP seeding over the catchment. However, seeding a large amount of MP (NaCl) is necessary to enhance precipitation substantially. Simultaneously, considering the environmental impact is essential, as shown in our study. [ABSTRACT FROM AUTHOR]

Numerical simulations of turbulent moist Rayleigh–Bénard convection driving CCN activation and droplet growth in the laboratory Pi chamber are discussed. Supersaturation fluctuations come from isobaric mixing of warm and humid air rising from the lower boundary with colder air featuring lower water vapor concentrations descending from the upper boundary. Lagrangian particle–based microphysics is used to represent the growth of haze CCN and cloud droplets with kinetic, solute, and surface tension effects included. Dry CCN spectra in the range between 2- and 200-nm radii from field observations are considered. Increasing the total CCN concentration from pristine to polluted conditions results in an increase in the droplet concentration and reduction in the mean droplet radius and spectral width. These are in agreement with Pi chamber observations and numerical simulations, as well as with numerous past studies of CCN cloud-base activation in natural clouds. The key result is that a relatively small fraction of the available CCN is activated in the Pi chamber fluctuating supersaturations, from about a half in the pristine case to only a 10th in the polluted case. The activation fraction as a function of the dry CCN radius is similar in all simulations, close to zero at the CCN small end, increasing to a maximum at CCN radius around 50 nm, and decreasing to close to zero at the large CCN end. This is explained as too small supersaturations to activate small CCN as in natural clouds and insufficient time to allow large CCN reaching the critical radius. Significance Statement: Impact of turbulence on the formation and growth of cloud droplets is an important cloud physics problem. Laboratory experiments in the Michigan Technological University cloud chamber provide key insights into this problem. Numerical simulations of cloud chamber processes discussed in this paper complement laboratory experiments by providing insights difficult or impossible to obtain in the laboratory. The study contrasts the formation and growth of cloud droplets in the laboratory cloud chamber with processes taking place in natural clouds. The differences documented in this paper pose questions concerning the impact of turbulence on the formation and growth of cloud droplets as a result of interactions of clouds with their environment. [ABSTRACT FROM AUTHOR]

Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data‐driven reduced‐order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision‐coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision‐coalescence in models. We demonstrate how deep learning based reduced‐order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision‐coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment‐based representations. Plain Language Summary: Clouds are made from many ice particles and liquid droplets, with a variety of sizes, shapes, and chemical compositions. Traditional approaches for representing small‐scale (microphysical) cloud processes start by defining some set of variables, which is expected to contain key information about cloud properties. These variables are "moments" corresponding to important statistics of the population of cloud particles, such as total mass of cloud water, or total number of particles. One can use physical arguments to develop a set of equations that describe how these bulk properties evolve in time. Traditional moment‐based models require much less computational power than more sophisticated microphysical models, but have limited accuracy. In this study, we explore an alternative approach, where a model "learns" how to represent the state of a cloud using a small number of variables. By investigating the geometry of this compressed representation, we find that we can represent the merging of cloud droplets (coalescence) using only three variables, so this approach may lead to more accurate models without requiring excessive computational resources. We can use this approach to design new models with favorable features, such as representing droplet coalescence using linear functions. Key Points: We use deep learning to find reduced‐order representations of the droplet size distribution from spectral bin microphysics simulationsDroplet coalescence has an intrinsic dimension of three, providing a lower limit on the number of degrees of freedom needed for bulk modelsWe obtain reduced‐order representations of the droplet size distribution where droplet coalescence can be represented as a linear process [ABSTRACT FROM AUTHOR]

In this study a convective precipitation event in southwestern Iran was examined using the aerosolaware bulk microphysical scheme implemented in the Weather Research and Forecasting (WRF) model. Two simulations were conducted for this event, which included the control and polluted simulations. In the control simulation, the concentration of aerosols in the current climate was not considrable. In contrast, the aerosol concentration was increased by a factor of 5 at all grid points in the polluted simulation. The main aim was to study the effects of aerosols on cloud microphysics and precipitation. The simulated vertical temperature and wind speed profiles were compared with the radiosonde data, and the model well simulated temperature and wind speed. During the convective event, southerly to southwesterly warm and dry winds dominated, causing a substantial transport of aerosols and humidity. The reflection of shortwave radiation by clouds in the innermost domain was increased in the polluted experiment, indicating that the first indirect effect of aerosols had a significant impact on the radiative balance of the atmosphere. In contrast to the effect of clouds on shortwave radiation, the effect of clouds on longwave radiation was positive at the top of the atmosphere (TOA) because clouds reflect longwave radiation emitted by the earth's surface. The impact of an increase in concentration of aerosols on cloud development was substantial in this simulation, which contained a high convergence of vertical moisture flux and strong winds over the region. The convergence of the vertical moisture flux indicates that more water vapor is available to be condensed on aerosols, which increases the cloud water content. Thus, the number density of cloud droplets is higher in the polluted compared to the control simulation. The altitude of the maximum mass density of cloud droplets is between 3 and 6 km; due to higher specific humidity at these altitudes, higher water vapor can be condensed on condensation nuclei. Also, the mass density of rain drops is higher in the polluted compared to the control simulation up to the altitude of 3 km, which is due to a higher collision of cloud droplets in the polluted simulation. An increase in ice and snow, which indicates a higher lifting of droplets to the freezing level, is seen in this simulation with the negative convergence of vertical moisture flux. This indicates that these regions may help the large-scale collection of moisture and its lifting. On the other hand, with a divergence of moisture in the northern and the whole domain, the cloud water content decreases. In addition, with a high moisture difference, there is higher precipitation in the polluted compared to control simulations because in the humid atmosphere, there is enough water vapor to be condensed on aerosols, that leads to the formation of larger cloud droplets. Thus, the collision of cloud droplets is more efficient, and precipitation increases. In addition, due to a lower cloud base, there is less chance for the evaporation and melting of precipitation. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols not only cause severe haze pollution, but also affect climate through changes in cloud properties. However, during the haze pollution, aerosol‐cloud interactions are not well understood due to a lack of in situ observations. In this study, we conducted simultaneous observations of cloud droplet and particle number size distribution, together with supporting atmospheric parameters, from ground to cloud base in East China using a high‐payload tethered airship. We found that high concentrations of aerosols and cloud condensation nuclei were constrained below cloud, leading to the pronounced "Twomey effect" near the cloud base. The cloud inhibited the pollutants dispersion by reducing surface heat flux and thus deteriorated the near‐surface haze pollution. Satellite retrievals matched well with the in situ observations for low stratus clouds, while were insufficient to quantify aerosol‐cloud interactions for other cases. Our results highlight the importance to combine in situ vertical and satellite observations to quantify the aerosol‐cloud interactions. Plain Language Summary: Atmospheric aerosols, one of the major pollutants contributing to air pollution, also play an important role in climate through their interactions with clouds. The impact of aerosols on cloud properties remains the largest uncertainty in climate projections, partly due to a lack of in situ observations. Here, we conducted simultaneous observations on atmospheric aerosols and clouds from ground to 1,200 m above ground level in East China using a high‐payload tethered airship. We found aerosols number concentration was high below the clouds, which increased the cloud droplet concentration and decreased the cloud droplet diameter near cloud base. The clouds deteriorated the near‐surface air pollution, thus increasing exposure to hazardous levels. For low stratiform clouds, the satellite retrievals matched well with the observations, suggesting the satellite observation is a powerful tool to investigate clouds. However, the aerosol‐cloud interactions can still be underestimated by satellite measurements as the satellites record cloud properties near cloud top. We emphasize the need for direct in situ observations from the ground to high altitudes to quantify the effects of aerosols on cloud properties. Key Points: The pronounced Twomey effect near the cloud base was directly observed during the haze pollution by the tethered airship measurementThe observed Twomey effect at the cloud base in East China contradicts the satellite‐detected anti‐Twomey effect at the top of cloudsSatellite retrieved cloud effective radius is comparable to observation near cloud base of low stratus clouds, while is biased for others [ABSTRACT FROM AUTHOR]

The cloud properties and governing processes in Southern Ocean marine boundary layer clouds have emerged as a central issue in understanding the Earth's climate sensitivity. While our understanding of Southern Ocean cloud feedbacks have evolved in the most recent climate model intercomparison, the background properties of simulated summertime clouds in the Southern Ocean are not consistent with measurements due to known biases in simulating cloud condensation nuclei concentrations. This paper presents several case studies collected during the Capricorn 2 and Marcus campaigns held aboard Australian research vessels in the Austral Summer of 2018. Combining the surface‐observed cases with MODIS data along forward and backward air mass trajectories, we demonstrate the evolution of cloud properties with time. These cases are consistent with multi‐year statistics showing that long trajectories of air masses over the Antarctic ice sheet are critical to creating high droplet number clouds in the high latitude summer Southern Ocean. We speculate that secondary aerosol production via the oxidation of biogenically derived aerosol precursor gasses over the high actinic flux region of the high latitude ice sheets is fundamental to maintaining relatively high droplet numbers in Southern Ocean clouds during Summer. Plain Language Summary: The amount of warming the Earth will experience because of increasing carbon dioxide levels in the atmosphere is sensitive to the properties of clouds that occur over the Southern Ocean. The atmosphere over the circumpolar Southern Ocean is poorly understood and presents significant challenges to climate models. Here we document the properties of the ubiquitous Southern Ocean low‐level clouds that exert a strong influence on the albedo of this region. We find that high cloud droplet number concentrations are associated with air masses that have taken paths over the high‐altitude ice sheets. The chemistry of the aerosol on which the cloud droplets form suggests that aerosols that have recently condensed from gasses emitted by phytoplankton in the highly productive waters near Antarctica are an important component of the cloud properties that must be correctly simulated in models. Key Points: High cloud droplet number concentrations are associated with air masses that have recently passed over continental AntarcticaCloud droplet number concentrations decrease with time as clouds evolve in over‐water trajectories due to scavenging by precipitationKatabatic flows bring high concentrations of cloud condensation nuclei to the marine boundary layer where they influence cloud properties [ABSTRACT FROM AUTHOR]

Given the importance of constraining cloud droplet number concentrations (Nd) in low-level clouds, we explore two methods for retrieving Nd from surface-based remote sensing that emphasize the information content in lidar measurements. Because Nd is the zeroth moment of the droplet size distribution (DSD), and all remote sensing approaches respond to DSD moments that are at least 2 orders of magnitude greater than the zeroth moment, deriving Nd from remote sensing measurements has significant uncertainty. At minimum, such algorithms require the extrapolation of information from two other measurements that respond to different moments of the DSD. Lidar, for instance, is sensitive to the second moment (cross-sectional area) of the DSD, while other measures from microwave sensors respond to higher-order moments. We develop methods using a simple lidar forward model that demonstrates that the depth to the maximum in lidar-attenuated backscatter (Rmax⁡) is strongly sensitive to Nd when some measure of the liquid water content vertical profile is given or assumed. Knowledge of Rmax⁡ to within 5 m can constrain Nd to within several tens of percent. However, operational lidar networks provide vertical resolutions of > 15 m, making a direct calculation of Nd from Rmax⁡ very uncertain. Therefore, we develop a Bayesian optimal estimation algorithm that brings additional information to the inversion such as lidar-derived extinction and radar reflectivity near the cloud top. This statistical approach provides reasonable characterizations of Nd and effective radius (re) to within approximately a factor of 2 and 30 %, respectively. By comparing surface-derived cloud properties with MODIS satellite and aircraft data collected during the MARCUS and CAPRICORN II campaigns, we demonstrate the utility of the methodology. [ABSTRACT FROM AUTHOR]

General circulation models' (GCMs) estimates of the liquid water path adjustment to anthropogenic aerosol emissions differ in sign from other lines of evidence. This reduces confidence in estimates of the effective radiative forcing of the climate by aerosol–cloud interactions (ERFaci). The discrepancy is thought to stem in part from GCMs' inability to represent the turbulence–microphysics interactions in cloud-top entrainment, a mechanism that leads to a reduction in liquid water in response to an anthropogenic increase in aerosols. In the real atmosphere, enhanced cloud-top entrainment is thought to be the dominant adjustment mechanism for liquid water path, weakening the overall ERFaci. We show that the latest generation of GCMs includes models that produce a negative correlation between the present-day cloud droplet number and liquid water path, a key piece of observational evidence supporting liquid water path reduction by anthropogenic aerosols and one that earlier-generation GCMs could not reproduce. However, even in GCMs with this negative correlation, the increase in anthropogenic aerosols from preindustrial to present-day values still leads to an increase in the simulated liquid water path due to the parameterized precipitation suppression mechanism. This adds to the evidence that correlations in the present-day climate are not necessarily causal. We investigate sources of confounding to explain the noncausal correlation between liquid water path and droplet number. These results are a reminder that assessments of climate parameters based on multiple lines of evidence must carefully consider the complementary strengths of different lines when the lines disagree. [ABSTRACT FROM AUTHOR]

The representation of Arctic clouds and their phase distributions, i.e., the amount of ice and supercooled water, influences predictions of future Arctic warming. Therefore, it is essential that cloud phase is correctly captured by models in order to accurately predict the future Arctic climate. Ice crystal formation in clouds happens through ice nucleation (primary ice production) and ice multiplication (secondary ice production). In common weather and climate models, rime splintering is the only secondary ice production process included. In addition, prescribed number concentrations of cloud condensation nuclei or cloud droplets and ice-nucleating particles are often overestimated in Arctic environments by standard model configurations. This can lead to a misrepresentation of the phase distribution and precipitation formation in Arctic mixed-phase clouds, with important implications for the Arctic surface energy budget. During the Ny-Ålesund Aerosol Cloud Experiment (NASCENT), a holographic probe mounted on a tethered balloon took in situ measurements of number and mass concentrations of ice crystals and cloud droplets in Svalbard, Norway, during fall 2019 and spring 2020. In this study, we choose one case study from this campaign that shows evidence of strong secondary ice production and use the Weather Research and Forecasting (WRF) model to simulate it at a high vertical and spatial resolution. We test the performance of different microphysical parametrizations and apply a new state-of-the-art secondary ice parametrization. We find that agreement with observations highly depends on the prescribed cloud condensation nuclei/cloud droplet and ice-nucleating particle concentrations and requires an enhancement of secondary ice production processes. Lowering mass mixing ratio thresholds for rime splintering inside the Morrison microphysics scheme is crucial to enable secondary ice production and thereby match observations for the right reasons. In our case, rime splintering is required to initiate collisional breakup. The simulated contribution from collisional breakup is larger than that from droplet shattering. Simulating ice production correctly for the right reasons is a prerequisite for reliable simulations of Arctic mixed-phase cloud responses to future temperature or aerosol perturbations. [ABSTRACT FROM AUTHOR]

Cloud/fog droplets comprise a myriad of chemical compounds and are living environments in which microorganisms are present and active. These chemical and biological elements can evolve in various ways within the cloud system, and the aqueous transformation of chemicals contributes to atmospheric chemistry. In situ cloud studies are fundamental in this sense, because they enable us to study the variability in cloud chemical composition as a function of environmental conditions and assess their potential for transforming chemical compounds. To achieve this objective, cloud water collectors have been developed in recent decades to recover water from clouds and fogs using different designs and collection methods. In this study, a new active ground-based cloud collector was developed and tested for sampling cloud water to assess the cloud microbiology and chemistry. This new instrument, BOOGIE, is an easy mobile sampler for cloud water collection with the objective of being cleanable and sterilisable, respecting chemical and microbial cloud integrity, and presenting an efficient collection rate of cloud water. Computational fluid dynamics simulations were performed to theoretically assess the capture of cloud droplets by this new sampler. Few turbulences have been observed inside the collector and a 50 % collection efficiency cutoff of 10 µm has been estimated. The collector was deployed at Puy de Dôme station under cloudy conditions for evaluation. The water collection rates were measured at 156 ± 52 mL h-1 for a collection of 17 cloud events; considering the measured liquid water content, the sampling efficiency of this new collector has been estimated at 87.2 ± 8.6 % over the same set of cloud events. BOOGIE was compared with other active cloud collectors commonly used by the scientific community (Cloud Water Sampler and Caltech Active Strand Cloud Collector version 2). Four cloud events were collected; the three samplers presented similar collection efficiencies (between 79 % and 88 % on average). The measured ionic composition was comparable even if differences were highlighted between collectors, the consequence of different designs, and the intrinsic homogeneity in the chemical composition within the cloud system. [ABSTRACT FROM AUTHOR]

The relationship between precipitation and cloud properties in Southwest China are investigated by using the CLARA‐A2 cloud parameters data and TRMM‐3B43 precipitation data from 1998 to 2015. Ice water path (IWP) and cloud top height (CTH) are significantly and positively correlated with precipitation in all regions, indicating that ice‐phase processes and cloud development processes are the critical processes influencing precipitation. Precipitation is also directly associated with cloud fractional coverage (CFC) due to the significant positive correlation between CFC and precipitation in all regions except the Sichuan Basin (SCB). A positive correlation between liquid water path (LWP) and precipitation is found in the Eastern Tibetan Plateau (ETP) and Yunnan‐Kweichow Plateau (YKP), but not in the Western Tibetan Plateau (WTP) and SCB. Notably, the response of precipitation to LWP is not as good as that to IWP in SCB. Precipitation is significantly negatively correlated with ice effective radius (IREF) in WTP and ETP and positively correlated with liquid effective radius (LREF) in ETP, YKP and SCB. IREF and LREF are closely related to cloud microphysical processes. Specifically, small IREF could accelerate the Bergeron process and thus increase precipitation, while large LREF is closely related to the cloud droplets coalescence process. Results indicate that the difference in precipitation between the cold and warm seasons is related to convective available potential energy (CAPE) and low troposphere relative humidity (RH). High CAPE and RH favour the precipitation occurrence in Southwest China. The influence of CAPE and RH on precipitation is more significant in the ETP than that in the WTP, owing to the orographic lifting and moisture transport from the Indian Ocean. Thermodynamic and humidity conditions have a greater impact on precipitation by influencing LREF, LWP and IWP in YKP. In SCB, precipitation shows a strong dependence on CAPE, IWP and LREF, but not on RH. [ABSTRACT FROM AUTHOR]

In cloudy situations, infrared (IR) and microwave (MW) observations are complementary, with infrared observations being sensitive to small cloud droplets and ice particles and with microwave observations being sensitive to precipitation. This complementarity can lead to fruitful synergies in precipitation science (e.g.,). However, several sources of errors do exist in the treatment of infrared and microwave data that could prevent such synergy. This paper studies several of these sources to estimate their impact on retrievals. To do so, simulations from the radiative transfer (RT) for TIROS Operational Vertical Sounder (RTTOV v13) are used to build simulated observations. Indeed, we make use of a fully simulated framework to explain the impacts of the identified errors. A combination of infrared and microwave frequencies is built within a Bayesian inversion framework. Synergy is studied using different experiments: (i) with several sources of errors eliminated, (ii) with only one source of errors considered at a time and (iii) with all sources of errors together. The derived retrievals of frozen hydrometeors for each experiment are examined in a statistical study of 15 d in summer and 15 d in winter over the Atlantic Ocean. One of the main outcomes of the study is that the combination of infrared and microwave frequencies takes advantage of the strengths of both spectral ranges, leading to more accurate retrievals. Each source of error has more or less impact depending on the type of hydrometeor. Another outcome of the study is that, in all cases explored, even though the radiative transfer and numerical modeling errors may decrease the magnitude of benefits generated by the combination of infrared and microwave frequencies, the compromise remains positive. [ABSTRACT FROM AUTHOR]

We present our ongoing work aimed at accelerating a particle‐resolved direct numerical simulation model designed to study aerosol–cloud–turbulence interactions. The dynamical model consists of two main components—a set of fluid dynamics equations for air velocity, temperature, and humidity, coupled with a set of equations for particle (i.e., cloud droplet) tracing. Rather than attempting to replace the original numerical solution method in its entirety with a machine learning (ML) method, we consider developing a hybrid approach. We exploit the potential of neural operator learning to yield fast and accurate surrogate models and, in this study, develop such surrogates for the velocity and vorticity fields. We discuss results from numerical experiments designed to assess the performance of ML architectures under consideration as well as their suitability for capturing the behavior of relevant dynamical systems. [ABSTRACT FROM AUTHOR]

An unresolved problem of the current Global Climate Models (GCM) is the unrealistic distribution of rainfall over the Indian Summer Monsoon (ISM) region, which is also related to the persistent dry bias over the Indian landmass. Therefore, quantitative prediction of the intensity of rainfall events has remained a challenge for state-of-the-art GCMs. Based on observations, it is hypothesized that the insufficient growth of cloud droplets and the processes responsible for the cloud-to-rainwater conversion are the key components in distinguishing between shallow and convective clouds. The Eulerian–Lagrangian particle-by-particle-based small-scale model provides a path for reassessing the 'autoconversion' parameterization schemes and suggests the relative dispersion-based 'autoconversion' parameterization scheme for the climate model. The realistic information on cloud drop size distribution is incorporated into the microphysical parameterization scheme of the climate model. Two sensitivity simulations are conducted using the climate forecast system (CFSv2) model. The coupled climate model incorporates a relative dispersion-based Liu–Daum-type autoconversion parameterization scheme in place of the traditional Sundqvist-type autoconversion, which, based on small-scale model analysis, makes the model more accurate in simulating the probability distribution (PDF) of rainfall with accompanying specific humidity, liquid water content, and outgoing long-wave radiation (OLR). The improved simulation of rainfall PDF appears to have been aided by a significantly improved simulation of OLR, which led to a more accurate simulation of the ISM rainfall. [ABSTRACT FROM AUTHOR]

Monomethylamine (MMA), dimethylamine (DMA), and trimethylamine (TMA) are important compounds for atmospheric key processes, e.g., new particle formation (NPF). A description of their multiphase chemical processing within atmospheric models is incomplete, but mandatory to describe their atmospheric budgets. In this study, a detailed multiphase chemistry mechanism was developed and first process model investigations were performed. The simulations focused on pristine marine conditions, where open research questions exist regarding ambient gas-phase concentrations of methylamines, particularly with regards to unexpectedly high DMA levels. The simulations reveal that TMA oxidation in cloud droplets results into DMA formation with a yield of around 87%, a missing DMA production pathway in current models. Also, it is demonstrated that about 21% and 69% of the respective DMA and TMA gas-phase oxidation proceed via autoxidation yielding HOOCH2NHCHO and HOOCH2N(CHO)CH2OOH, respectively. The discussed processes should be included into atmospheric models for advanced predictions of NPF and climate impacts. [ABSTRACT FROM AUTHOR]

Near real-time aerosol fields from the Copernicus Atmospheric Monitoring Services (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are configured for use in the HARMONIE-AROME Numerical Weather Prediction model. Aerosol mass mixing ratios from CAMS are introduced in the model through the first guess and lateral boundary conditions and are advected by the model dynamics. The cloud droplet number concentration is obtained from the aerosol fields and used by the microphysics and radiation schemes in the model. The results show an improvement in radiation, especially during desert dust events (differences of nearly 100 W/m2 are obtained). There is also a change in precipitation patterns, with an increase in precipitation, mainly during heavy precipitation events. A reduction in spurious fog is also found. In addition, the use of the CAMS near real-time aerosols results in an improvement in global shortwave radiation forecasts when the clouds are thick due to an improved estimation of the cloud droplet number concentration. [ABSTRACT FROM AUTHOR]

The ice phase in clouds is essential for precipitation formation over continents. The underlying processes for ice growth are still poorly understood, leading to large uncertainties in precipitation forecasts and climate simulations. One crucial aspect is the Wegener–Bergeron–Findeisen (WBF) process, which describes the growth of ice crystals at the expense of cloud droplets, leading to a partial or full glaciation of the cloud. In the CLOUDLAB project, we employ glaciogenic cloud seeding to initiate the ice phase in supercooled low-level clouds in Switzerland using uncrewed aerial vehicles with the goal of investigating the WBF process. An extensive setup of ground-based remote-sensing and balloon-borne in situ instrumentation allows us to observe the formation and subsequent growth of ice crystals in great detail. In this study, we compare the seeding signals observed in the field to those simulated using a numerical weather model in large-eddy mode (ICON-LEM). We first demonstrate the capability of the model to accurately simulate and reproduce the seeding experiments across different environmental conditions. Second, we investigate the WBF process in the model by comparing the simulated cloud droplet and ice crystal number concentration changes to in situ measurements. In the field experiments, simultaneous reductions in cloud droplet number concentrations with increased ice crystal number concentrations were observed, with periods showing a full depletion of cloud droplets. The model can reproduce the observed ice crystal number concentrations most of the time; however, it cannot reproduce the observed fast reductions in cloud droplet number concentrations. Our detailed analysis shows that the WBF process appears to be less efficient in the model than in the field. In the model, exaggerated ice crystal number concentrations are required to produce comparable changes in cloud droplet number concentrations, highlighting the inefficiency of the WBF process in the numerical weather model ICON. [ABSTRACT FROM AUTHOR]

Winter fog and severe aerosol loading in the boundary layer over northern India, particularly in the Indo-Gangetic Plain (IGP), disrupt the daily lives of millions of people in the region. To better understand the role of aerosol–radiation (AR) feedback on the occurrence, spatial extent, and persistence of winter fog, as well as the associated aqueous chemistry in fog in the IGP, several model simulations have been performed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). While WRF-Chem was able to represent the fog formation for the 23–24 December 2017 fog event over the central IGP in comparison to station and satellite observations, the model underestimated PM 2.5 concentrations compared to the Central Pollution Control Board (CPCB) of India monitoring network. While evaluating aerosol composition for fog events in the IGP, we found that the WRF-Chem aerosol composition was quite different from measurements obtained during the Winter Fog Experiment (WiFEX) in Delhi, with secondary aerosols, particularly the chloride aerosol fraction, being strongly underpredicted (∼ 66.6 %). Missing emission sources (e.g., industry and residential burning of cow dung and trash) and aerosol and chemistry processes need to be investigated to improve model–observation agreement. By investigating a fog event on 23–24 December 2017 over the central IGP, we found that the aerosol–radiation feedback weakens turbulence, lowers the boundary layer height, and increases PM 2.5 concentrations and relative humidity (RH) within the boundary layer. Factors affecting the feedback include loss of aerosols through deposition of cloud droplets and internal mixing of absorbing and scattering aerosols. Aqueous-phase chemistry increases the PM 2.5 concentrations, which subsequently affect the aerosol–radiation feedback by both increased mass concentrations and aerosol sizes. With aerosol–radiation interaction and aqueous-phase chemistry, fog formation began 1–2 h earlier and caused a longer fog duration than when these processes were not included in the WRF-Chem simulation. The increase in RH in both experiments was found to be important for fog formation as it promoted the growth of aerosol size through water uptake, increasing the fog water content over the IGP. The results from this study suggest that the aerosol–radiation feedback and secondary aerosol formation play an important role in the air quality and the intensity and lifetime of fog over the IGP, yet other feedbacks, such as aerosol–cloud interactions, need to be quantified. [ABSTRACT FROM AUTHOR]

Current operational satellite retrievals of cloud optical and microphysical properties go back to the Nakajima-King technique developed at the end of the 1980 s. This technique is based on library calculations for plane-parallel homogeneous clouds. It often works well for overcast skies but leads to substantial errors for inhomogeneous and broken cloud fields where the plane-parallel-geometry assumption is no longer valid. The basic concept of a new technique was first introduced in nuclear physics to quantify the critical conditions of reactors. Based on the eigenvalues of the radiative transfer equation, their approach provides a powerful means to parameterize the structure of 3D media. This parameterization was later successfully applied to relate surface reflectance spectra to 3D canopy structure and is known as the spectrally-invariant approximation. The proposed approach adapts this technique to the remote sensing of cloud properties such as droplet single scattering albedo and the average number of scatterings (which are the fundamental parameters in radiative transfer theory), with an emphasis on quantifying the associated errors and uncertainties. This retrieval is free from the plane-parallel homogeneous cloud assumption. [ABSTRACT FROM AUTHOR]

Understanding how entrainment and mixing shape the cloud droplet size distribution (DSD) is crucial for understanding the optical properties and precipitation efficiency of clouds. Different mixing scenarios, mainly homogeneous and inhomogeneous, shape the DSD in a distinct way and alter the cloud's impact on climate. However, the prevalence of these mixing scenarios and how they vary in space and time is still uncertain, as underlying processes are commonly unresolved by conventional numerical models. To overcome this challenge, we employ the L3 model, which considers supersaturation fluctuations and turbulent mixing down to the finest relevant lengthscales, making it possible to represent different mixing scenarios realistically. We investigate the spatial and temporal evolution of mixing scenarios over the life cycle of shallow cumulus clouds for varying boundary layer humidities and aerosol concentrations. Our findings suggest homogeneous mixing is generally predominant in cumulus clouds, while different mixing scenarios occur concurrently in the same cloud. Notably, inhomogeneous mixing increases over the cloud life cycle across all analyzed cases. The mean and standard deviation of supersaturation are found to be the most capable indicators of this evolution, providing a comprehensive insight into the characteristics of mixing scenarios. Finally, we show inhomogeneous mixing is more prevalent in drier boundary layers and for higher aerosol concentrations, underscoring the need for a more comprehensive investigation of how these mixing dynamics evolve in a changing climate. Plain Language Summary: Clouds play a crucial role in Earth's climate system by influencing the radiation balance and moisture transfer. When clouds mix with their environment, which occurs during a process known as entrainment and mixing, the number and size of cloud droplets change, affecting cloud optical properties and their ability to precipitate. This mixing can happen in two major ways: either the cloud and environmental air are rapidly mixed (homogeneous mixing) or slowly mixed (inhomogeneous mixing). However, accurately representing these effects, particularly inhomogeneous mixing, is challenging in nearly all atmospheric models, including weather or climate models, as essential scales and processes are unresolved. By applying a high‐resolution model that resolves all essential scales and processes, we investigate how mixing scenarios evolve over the cloud life cycle. We show that while homogeneous mixing prevails, older (mature or dissipating) clouds tend to mix more inhomogeneously. Moreover, clouds in drier and more polluted environments are shown to favor inhomogeneous mixing. In these environments, inhomogeneous mixing becomes the predominant mixing scenario for older clouds, emphasizing the need for further investigations on the impact of different mixing scenarios in a changing climate. Key Points: The evolution of predominant mixing scenarios during the shallow cumulus life cycle is investigatedWhile homogeneous mixing is prevalent, inhomogeneous mixing gradually increases as the cloud agesInhomogeneous mixing is more favored for higher aerosol concentrations and in drier boundary layers [ABSTRACT FROM AUTHOR]

Accurately simulating and forecasting supercooled water has been crucial for addressing the threat of aircraft icing. This study utilized the Weather Research and Forecast model and Aerosol‐Aware Thompson‐Eidhammer microphysics (TE14) scheme to simulate supercooled water properties. Three high‐resolution experiments were conducted to explore the impact of increased model resolution and different aerosol initial conditions. The synoptic‐scale characteristics and cloud extent were well‐matched between observations and the model simulation, which established a background for supercooled water comparisons. Microphysical characteristics of supercooled droplets and the formation of supercooled large drops demonstrated the TE14 scheme's reasonable simulation of supercooled water microphysics under different aerosol loadings. In situ aircraft measurements were used to validate the simulated supercooled water properties. Results highlighted the sensitivity of supercooled water simulation to aerosol number concentration via droplet activation in the scheme. The choice for the aerosol initial condition could result in a significant underestimation or overestimation of supercooled water number concentration, consequently leading to biased estimations of droplet size. Incorporating real‐time forecast aerosol data improved the simulation by increasing cloud droplet number concentration and reducing droplet size. These findings underscored the importance of aerosols via droplet activation in accurately simulating cloud water properties and emphasized their role in forecasting aircraft icing hazards. Plain Language Summary: The study examined the simulation of supercooled water during a frontal event in the arid region of northwestern China, where high concentrations of suspended particulate matter could increase the threat of aircraft icing. With the use of the Aerosol‐Aware Thompson‐Eidhammer microphysics scheme and a regional weather prediction model, the simulation results indicated the performance of droplet activation parameterization was reliable under realistic aerosol conditions. Underestimating environmental aerosol in the cloud process resulted in a likely underestimation of supercooled cloud droplet number concentration, leading to an overestimation to their size. Incorporating real‐time aerosol data improved the droplet number, resulting in increased supercooled water content and more accurate droplet sizes. These findings underscored the importance of aerosols via droplet activation in accurately simulating cloud water properties and emphasized their role in forecasting aircraft icing hazards. Key Points: The simulation of supercooled water is sensitive to the initial condition of aerosol number concentrationA pronounced amount of supercooled large drops is simulated in the polluted scenarios and analyzed statisticallyUsing real‐time forecast aerosol data improves the simulation of supercooled droplet number concentration and mean particle size [ABSTRACT FROM AUTHOR]

In situ and spaceborne studies reveal the prevalence of thin clouds in the major Stratocumulus‐to‐Cumulus Transition (SCT) regions. Using instantaneous satellite and reanalysis data, this study investigates the properties of thin clouds in the Southeast Pacific Ocean and their impact on the cloud radiative effect (CRE). Our findings demonstrate that thin clouds are intrinsic to the SCT. The overcast stratocumulus‐dominated regime exhibits a minimal presence of thin clouds, which become notably prominent after the clouds breakup into the cumulus‐dominated regime. The regime dependence of the occurrence of thin clouds is also observed in terms of the marine cold‐air outbreak parameter and the sea surface temperature. Thin clouds at a given cloud cover significantly modulate the shortwave (SW) and longwave (LW) components of CRE. SW CRE decreases by 46 %–65 % with increasing thin cloud cover. They account for a larger variance in cloud albedo than the combined influence of the liquid water path and effective radius. Furthermore, LW CRE decreases by about 12 %–52 % with thin cloud cover. An increase in the fraction of thin clouds also leads to a larger fraction of negative SW CRE offset by positive LW CRE at a given cloud cover. This LW compensation ranges from approximately 8 % at overcast cloud cover to as much as 19 % at about 50 % cloud cover. These findings elucidate the crucial role of thin clouds, and thus cloud morphology, in modulating CRE and underscore the necessity of their accurate representation in climate models. Plain Language Summary: The eastern regions of major global oceans are primarily covered by low‐altitude clouds. These bright clouds overlay the dark ocean surface, reflecting incoming solar radiation and cooling the Earth. Concurrently, they act as a barrier to the energy emitted by the ocean surface, preventing it from escaping the Earth and thereby contributing to the Earth's warming. Satellite observations reveal the prevalence of thin clouds in these oceanic regions. In contrast to their thicker counterparts, thin clouds are less effective in reflecting solar radiation and blocking surface emissions. This study investigates the impact of thin clouds on radiation using data from various sources. We show that the presence of thin clouds significantly reduces cloud brightness by approximately 46 %–65 %, even when the cloud cover remains constant. Thin clouds have a more substantial effect on a cloud's ability to reflect solar radiation compared to the combined influence of cloud water and cloud droplet size. Furthermore, since thin clouds allow some surface emissions to pass through, their presence at a constant cloud cover reduces their warming effect by up to 52 %. These findings emphasize the crucial role of thin clouds in shaping the contribution of clouds to Earth's energy balance. Key Points: Thin clouds dim cloud brightness by up to 65 percent at a fixed cloud coverThin clouds exert a greater influence on cloud albedo than the combined effects of cloud water and droplet size under constant cloud coverThin clouds reduce longwave warming by up to 52 percent and modulate the balance between longwave warming and shortwave cooling [ABSTRACT FROM AUTHOR]

Ice nucleating particles (INPs) can be important for deep convective storms through affecting microphysical, thermodynamical, and radiative properties and precipitation. Since the mass of a hailstone is predominantly contributed by supercooled cloud droplets, INPs in supercooled cloud droplets during deep convective storms can be indicated from INPs in hailstones. In this study, hailstones are collected from eight deep convective cases in three regions of China, and immersion freezing INPs in hailstones are measured using droplet freezing experiments over a wide temperature range from −27.5°C to −4.0°C. INP concentrations can vary by 2–3 orders of magnitude, which is consistent with the result in a previous study on hailstones for the same eight cases that insoluble aerosol number concentrations in hailstones also have a variability of 2–3 orders of magnitude. Using the fitting method, three INP modes are suggested to be included for most hailstone samples, while one to two INP modes can be good enough for a couple of hailstone samples. Furthermore, the INPs for warmer mode are more heat‐sensitive, indicating possible existence of proteinaceous biological species. Plain Language Summary: Ice nucleating particles (INPs) are a small portion of aerosols that can initiate ice crystals at temperatures between −38°C and 0°C in the atmosphere. The knowledge of INPs immersed in hailstones is helpful for understanding the heterogeneous ice production process during deep convective storms. This study measures immersion freezing INPs in hailstones collected from eight deep convective storm cases using droplet freezing experiments. Over the experiment temperature range from −27.5°C to −4.0°C, the variability of INP concentrations can be 2–3 orders of magnitude. Through further investigation of freezing nucleus spectra, three INP modes are suggested to be the best description of INP concentrations for most hailstone samples. Indications are found that INPs for warmer mode are more likely to be proteinaceous biological species. Key Points: Over a wide temperature range from −27.5°C to −4.0°C, ice nucleating particle concentrations for hailstones in China are measuredBest fitting of ice nucleating particle concentrations over the experiment temperature range suggests three modes for most hailstonesIce nucleating particles are heat‐sensitive at high temperatures, indicating the existence of proteinaceous biological species [ABSTRACT FROM AUTHOR]

Understanding aerosol‐cloud interactions under pristine atmospheric conditions is crucial for meaningful global climate change predictions. However, ability of aerosol particles to act as cloud condensation nuclei (CCN) activity of aerosols in pristine atmosphere remains limited, mainly because of the difficulty of measuring it in pristine environments. Given the extremely low CCN number concentration under low supersaturations (SSs) and the complex impact of the kinetic limitation in CCN measurements, size‐resolved CCN activity measurements at low SSs cannot be achieved with traditional methods using differential mobility analyzers and a CCN counter (CCNC) because of the low sample efficiency and the impact of particles with multiple charges. To overcome this difficulty, we propose a new method for measuring size‐resolved CCN activity at low aerosol number concentrations. The new method is based on a combined system comprising an aerodynamic aerosol classifier (AAC) and a CCNC (AAC‐CCNC system). A control program was developed to achieve high‐resolution scans of the SS and particle size within a reasonable time frame. A data inversion scheme, including corrections for the transfer function and kinetic limitation, was developed to obtain accurate size‐resolved CCN activity. In July‐August 2022, the new method was used to measure the size‐resolved CCN activity on the Tibetan Plateau. This new method can advance the size‐resolved CCN measurements, thereby enhancing our understanding of aerosol‐cloud interactions in pristine atmosphere. Plain Language Summary: Cloud condensation nuclei (CCN) are the aerosols which can form cloud droplets. Assessing the ability of aerosols to serve as CCN (CCN activity) is fundamental for understanding the interactions between aerosol and clouds and their impacts on climate and precipitation. Particularly in regions with minimal human influence, such as polar areas and the Tibetan Plateau, the low aerosol concentration makes aerosol‐cloud interaction and its climate impact highly sensitive to aerosol CCN activity. However, traditional CCN activity measurement techniques are not applicable at conditions of extremely low aerosol concentrations, restricting the understanding of aerosol‐cloud interactions and their impacts in those regions. In this study, we developed a new system for CCN activity measurements at low aerosol concentration and corresponding data processing algorithm based on a particle size selection technique distinct from that used in traditional systems. The new system was successfully deployed in a field experiment conducted in Qinghai‐Tibet Plateau in summer 2022. Comparison with CCN concentration measurement indicates that the uncertainty of the CCN activity measurement is within a reasonable range. Notably, this marks the first instance of online measurement of particle size‐resolved CCN activity in the hinterland of the Qinghai‐Tibet Plateau. Key Points: A new aerodynamic aerosol classifier‐cloud condensation nuclei counter (AAC‐CCNC) system is developed for measureing size‐resolved cloud condensation nuclei (CCN) activity under low supersaturated conditions in pristine atmospheresUncertainty correction relating to the impact of kinetic limitations in the CCN counter and the transfer function of the AAC is developedThe AAC‐CCNC system is successfully deployed for size‐resolved CCN activity measurements in the Tibetan Plateau [ABSTRACT FROM AUTHOR]

Aerosol-cloud interactions (ACIs) are vital for regulating Earth's climate by influencing energy and water cycles. Yet, effects of ACI bear large uncertainties, evidenced by systematic discrepancies between observed and modeled estimates. This study quantifies a major bias in ACI determinations, stemming from conventional surface or space measurements that fail to capture aerosol at the cloud level unless the cloud is coupled with land surface. We introduce an advanced approach to determine radiative forcing of ACI by accounting for cloud-surface coupling. By integrating field observations, satellite data, and model simulations, this approach reveals a drastic alteration in aerosol vertical transport and ACI effects caused by cloud coupling. In coupled regimes, aerosols enhance cloud droplet number concentration across the boundary layer more homogeneously than in decoupled conditions, under which aerosols from the free atmosphere predominantly affect cloud properties, leading to marked cooling effects. Our findings spotlight cloud-surface coupling as a key factor for ACI quantification, hinting at potential underassessments in traditional estimates. [ABSTRACT FROM AUTHOR]

Observations of marine stratus clouds in clean air off the Californian coast reveal a functional relationship between the number of cloud condensation nuclei (CCN) and supersaturation. Satellite‐derived liquid droplet density estimates the number density of CCN. Combining the estimated supersaturation using Köhler theory, global maps of supersaturation and the critical activation size of CCN are estimated. Here, we show that high supersaturation >0.5% persists over the oceans with a critical CCN size of 25–30 nm, which is smaller than the conventional wisdom of 60 nm. Independent support for such high supersaturation in the marine cloud is obtained from CCN measurements provided by the "Atmospheric Tomography Mission." Higher supersaturation implies smaller activation size for CCN making cloud formation more sensitive to changes in aerosol nucleation. Plain Language Summary: Clouds in Earth's atmosphere are of fundamental importance for the climate by regulating the reflection of sunlight into space and interacting with thermal radiation from Earth. Clouds form when moist air ascends and gets supersaturated with water vapor that condenses on aerosol particles of sufficient sizes, which then grow into cloud droplets. The aerosol number‐density and size spectrum influence the resulting cloud properties, and the supersaturation determines which aerosols can be activated into cloud drops. Here, we show that the supersaturation in marine liquid clouds is significantly higher than in the conventional view. As a consequence, much smaller aerosols can serve as cloud condensation nuclei. This can make cloud formation more sensitive to changes in aerosol properties than previously thought. Such a result should be of general interest and lead to a better understanding of aerosol‐cloud interactions, which presently constitute the largest uncertainty in our understanding of climate. Key Points: On average, supersaturation in marine clouds is significantly higher than the conventional view of 0.2%–0.3%Due to the higher supersaturation, much smaller aerosols get activated into cloud dropletsThe results are essential for better understanding aerosols‐cloud interactions [ABSTRACT FROM AUTHOR]

Meteorological processes are investigated during the development of deep convective clouds over the Yellow Sea inside a quasi‐stationary rainy front in the East Asian region. WRF‐Chem simulations were conducted to assess the effects of aqueous‐phase chemistry on the concentrations of PM2.5 and hydrometers in deep convective clouds and the associated precipitation. A reduction in the south‐north air temperature gradient induced an inactive quasi‐stationary rainy front with nonlinear zonal clouds during the Changma period in the East Asian region in 2017, causing intensified surface heating due to solar radiation. A mesoscale cyclone was formed near the Shantung Peninsula inside the inactive quasi‐stationary rainy front, extending the eastward tongue of the troughs across the Yellow Sea. Deep convective clouds (CC16 on July 16, CC17 on July 17, and CC18 on July 18) formed in a severe mode on the east side of the mesoscale cyclone over the Yellow Sea at midnight as the warm type, as the warm and humid south‐southwesterly winds present at that time produced convection energy. PM2.5 from eastern China flowed into warm‐type CC16, CC17, and CC18 areas, producing more cloud droplets below 500 hPa via activation. In contrast, the release of additional condensation heat amplifies updrafts within convective clouds during efficient nighttime environmental cooling, thereby enhancing the production of cold‐type ice, reaching altitudes of up to 15 km. The WRF‐Chem PM2.5 outcomes indicate a significant increase in heavy rainfall, exceeding 10 mm hr−1, particularly in the context of the development of cold‐type convective clouds. Plain Language Summary: The regional phenomenon of summer rain, known as Changma in Korea, usually occurs with a quasi‐stationary rainy front that forms between the continental polar air mass over the East Asian region and the maritime tropical air mass over the northwest Pacific. An inactive quasi‐stationary rainy front with nonlinear zonal clouds induced by a reduction in the south‐north air temperature gradient during the Changma period led to the efficient absorption of solar radiation on the surface of the East Asian region. In addition, convective clouds have frequently developed over the Yellow Sea inside the inactive quasi‐stationary rainy front, resulting in extreme events with large amounts of short‐term precipitation in inland Korea. We found that aqueous‐phase chemistry in convective clouds led to the production of PM2.5 and nucleated cloud droplets, enhancing precipitation on 16–18 July 2017. While warm‐type convective clouds developed due to warm and humid south‐southwesterly flows over the Yellow Sea, WRF‐Chem‐simulated PM2.5 promoted cold‐type ice production to a top convection height of 15 km. The entrainment of near‐surface PM2.5 aerosols in the south‐southwesterly flows from eastern China and the secondary activations over the boundary layer clouds enhanced heavy rainfall to more than 10 mm hr−1 in cold‐type convective clouds. Key Points: A reduction in the air temperature gradient induced an inactive rainy front, forming a mesoscale cyclone over the Yellow SeaPM2.5 aerosols in eastern China flowed out to the Yellow Sea, wherein the PM2.5 inflows contributed to the development of cold‐type cloudsCold‐type clouds promoted heavy rainfall exceeding 10 mm hr−1 over the Yellow Sea and Korea [ABSTRACT FROM AUTHOR]

A better understanding of vertical distributions of aerosol and cloud droplet number concentrations is important for the study of aerosol‐cloud interaction which is one of the key uncertainties in climate change projection. In this study, we compared the aerosol mass and number concentrations simulated by a size‐resolved advanced particle microphysics (APM) package coupled with a global 3‐D chemical transport model (GEOS‐Chem) with the NASA's Atmospheric Tomography Mission (ATom) measurements at the Pacific Ocean and the Atlantic Ocean during ATom‐1 and ATom‐2 campaign periods. Generally, the model captured the spatial pattern and seasonal variation of aircraft observed aerosol mass and number concentrations. The model simulated cloud droplet number concentrations (CDNC) were compared with CloudSat retrievals whose orbits were nearby ATom's flight tracks on the same days. Correlation coefficients of simulated and retrieved CDNC at ATom‐1 Pacific Ocean, ATom‐1 Atlantic Ocean, ATom‐2 Pacific Ocean, and ATom‐2 Atlantic Ocean are 0.83, 0.97, 0.87, and 0.73, respectively. Both satellite retrievals and model simulations indicated that the averaged values of CDNC at the Pacific Ocean and the Atlantic Ocean during ATom‐1 and ATom‐2 periods decreased with altitudes. The ratio of CDNC in the lower troposphere (1,000–800 hPa) to those in the middle troposphere (600–400 hPa) was 1.9 (1.6–2.3) based on CloudSat retrievals and 2.2 (1.2–3.0) based on the model simulations. Model analysis indicated that secondary particles dominate CDNC in the atmosphere and primary organic aerosols have important contributions to CDNC, especially at the Atlantic Ocean during ATom‐1. Plain Language Summary: Climatic effects of aerosol and cloud are strongly influenced by aerosol vertical distributions. In order to better understand the relationship between aerosols and cloud droplets, measurement and model simulation of vertical distributions of aerosol and cloud droplet number concentrations at the same regions are needed. Although satellite retrieval has been applied to obtain vertical distribution of cloud droplet number concentration, it is challenging to retrieve vertical distribution of aerosol number concentration. In this study, we used ATom aircraft campaign measured aerosol mass and number concentrations and CloudSat retrieved CDNC along orbits nearby ATom's flight tracks to validate the model simulations at the same places during the same periods. Our study indicated that the model can capture the major characteristics shown in aircraft measurements and CloudSat retrievals and could be a useful tool for the estimation of aerosol‐cloud interaction's impacts on climate change. Based on process analysis in the model, we found secondary particles dominate CDNC at the Pacific and Atlantic Oceans during ATom‐1 and ATom‐2 periods and primary organic aerosols, which are associated with transport of open fire, have important contributions to CDNC, especially at the Atlantic Ocean during ATom‐1. Key Points: Using ATom measurements and CloudSat retrievals nearby flight tracks to represent vertical aerosol and droplet numbers at the same placesModel captured major characteristics of vertical distribution of aerosol and droplet shown in aircraft measurements and CloudSat retrievalsSecondary particles dominate CDNC, while open fire organic aerosols contribute remarkably to CDNC over the Atlantic Ocean during ATom‐1 [ABSTRACT FROM AUTHOR]

Aerosol particles influence cloud formation and properties. Hence climate models that aim for a physical representation of the climate system include aerosol modules. In order to represent more and more processes and aerosol species, their representation has grown increasingly detailed. However, depending on one's modelling purpose, the increased model complexity may not be beneficial, for example because it hinders understanding of model behaviour. Hence we develop a simplification in the form of a climatology of aerosol concentrations. In one approach, the climatology prescribes properties important for cloud droplet and ice crystal formation, the gateways for aerosols to enter the model cloud microphysics scheme. Another approach prescribes aerosol mass and number concentrations in general. Both climatologies are derived from full ECHAM-HAM simulations and can serve to replace the HAM aerosol module and thus drastically simplify the aerosol treatment. The first simplification reduces computational model time by roughly 65 %. However, the naive mean climatological treatment needs improvement to give results that are satisfyingly close to the full model. We find that mean cloud condensation nuclei (CCN) concentrations yield an underestimation of cloud droplet number concentration (CDNC) in the Southern Ocean, which we can reduce by allowing only CCN at cloud base (which have experienced hygroscopic growth in these conditions) to enter the climatology. This highlights the value of the simplification approach in pointing to unexpected model behaviour and providing a new perspective for its study and model development. [ABSTRACT FROM AUTHOR]

The present study assesses the simulated precipitation and cloud properties using three microphysics schemes (Morrison, Thompson and MY) implemented in the Weather Research and Forecasting model. The precipitation, differential reflectivity (ZDR), specific differential phase (KDP) and mass-weighted mean diameter of raindrops (Dm) are compared with measurements from a heavy rainfall event that occurred on 27 June 2020 during the Integrative Monsoon Frontal Rainfall Experiment (IMFRE). The results indicate that all three microphysics schemes generally capture the characteristics of rainfall, ZDR, KDP and Dm, but tend to overestimate their intensity. To enhance the model performance, adjustments are made based on the MY scheme, which exhibited the best performance. Specifically, the overall coalescence and collision parameter (Ec) is reduced, which effectively decreases Dm and makes it more consistent with observations. Generally, reducing Ec leads to an increase in the simulated content (Qr) and number concentration (Nr) of raindrops across most time steps and altitudes. With a smaller Ec, the impact of microphysical processes on Nr and Qr varies with time and altitude. Generally, the autoconversion of droplets to raindrops primarily contributes to Nr, while the accretion of cloud droplets by raindrops plays a more significant role in increasing Qr. In this study, it is emphasized that even if the precipitation characteristics could be adequately reproduced, accurately simulating microphysical characteristics remains challenging and it still needs adjustments in the most physically based parameterizations to achieve more accurate simulation. [ABSTRACT FROM AUTHOR]

Aerosol–cloud–precipitation interactions (ACI) are a known major cause of uncertainties in simulations of the future climate. An improved understanding of the in-cloud processes accompanying ACI could help in advancing their implementation in global climate models. This is especially the case for marine stratocumulus clouds, which constitute the most common cloud type globally. In this work, a dataset composed of satellite observations and reanalysis data is used in explainable machine learning models to analyze the relationship between the cloud droplet number concentration ( N d ), cloud liquid water path (LWP), and the fraction of precipitating clouds (PF) in five distinct marine stratocumulus regions. This framework makes use of Shapley additive explanation (SHAP) values, allowing to isolate the impact of N d from other confounding factors, which proved to be very difficult in previous satellite-based studies. All regions display a decrease of PF and an increase in LWP with increasing N d , despite marked inter-regional differences in the distribution of N d . Polluted (high N d ) conditions are characterized by an increase of 12 gm−2 in LWP and a decrease of 0.13 in PF on average when compared to pristine (low N d ) conditions. The negative N d –PF relationship is stronger in high LWP conditions, while the positive N d –LWP relationship is amplified in precipitating clouds. These findings indicate that precipitation suppression plays an important role in MSC adjusting to aerosol-driven perturbations in N d . [ABSTRACT FROM AUTHOR]

A relatively simple 1D RANS model of the time evolution of the planetary boundary layer is extended to include water vapor and cloud droplets plus transfers between them. Radiative fluxes and flux divergence are also included. An underlying ocean surface is treated as a source of water vapor and as a sink for cloud or fog droplets. With a constant sea surface temperature and a steady wind, initially dry or relatively dry air will moisten, starting at the surface. Turbulent boundary layer mixing will then lead towards a layer with a well-mixed potential temperature (and so temperature decreasing with height) and well-mixed water vapor mixing ratio. As a result, the air will, sooner or later, become saturated at some level, and a stratus cloud will form. [ABSTRACT FROM AUTHOR]

Various cloud systems responsible for snowfall along the western coast of Japan are formed over the Sea of Japan. In the present study, stratiform snow clouds associated with a mid-level trough were investigated using an instrumented aircraft and dual Doppler radars. The snow clouds exhibited a double-layer structure with thermodynamically and kinematically different characteristics. The top height and base height of the clouds were 4.5 km and 0.9 km at temperatures of -29 °C and -5 °C, respectively. The layer below 2 km mean sea level (MSL) had turbulent air, which reflected its convectively unstable stratification. The maximum updraft exceeded 4 m s-1 at approximately 1 km MSL, and the maximum cloud water content was 0.6 g m-3. The layer above 2 km was less turbulent and characterized by a weak updraft of < 2 m s-1 and maximum cloud water content of 0.1 g m-3. These values were considerably lower than those in the lower layer. The weak updraft was likely caused by an approaching mid-level trough. Ice crystal and precipitation particle concentrations, measured using two-dimensional cloud and precipitation optical array probes, respectively, were almost constant with height and measured several tens of particles L-1 and several particles L-1, respectively. Precipitation particles grew by the seeder-feeder mechanism in the two-layer stratiform snow cloud. In the upper layer (2 - 4.5 km), precipitation particles increased in size by vapor deposition and showed a remarkable broadening of size distributions toward large sizes. In the lower layer (0.9 - 2 km MSL), the precipitation particles grew further via accretion of supercooled cloud droplets and produced denser particles like graupel with no substantial change in the size distribution. Below 0.9 km MSL, particle concentrations decreased at all sizes due to sublimation and melting. [ABSTRACT FROM AUTHOR]