Liquid Cloud Research Articles

Glaciation of liquid clouds, snowfall, and reduced cloud cover at industrial aerosol hot spots.

The ability of anthropogenic aerosols to freeze supercooled cloud droplets remains debated. In this work, we present observational evidence for the glaciation of supercooled liquid-water clouds at industrial aerosol hot spots at temperatures between -10° and -24°C. Compared with the nearby liquid-water clouds, shortwave reflectance was reduced by 14% and longwave radiance was increased by 4% in the glaciation-affected regions. There was an 8% reduction in cloud cover and an 18% reduction in cloud optical thickness. Additionally, daily glaciation-induced snowfall accumulations reached 15 millimeters. Glaciation events downwind of industrial aerosol hot spots indicate that anthropogenic aerosols likely serve as ice-nucleating particles. However, rare glaciation events downwind of nuclear power plants indicate that factors other than aerosol emissions may also play a role in the observed glaciation events.

PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations

Abstract. Cloud radar Doppler spectra are of particular interest for investigating cloud microphysical processes, such as ice formation, riming and ice multiplication. When hydrometeor types within a cloud radar observation volume have different terminal fall velocities, they can produce individual Doppler spectrum peaks. The peaks of different particle types can overlap and be further broadened and blended by turbulence and other dynamical effects. If these (sub-)peaks can be separated, properties of the underlying hydrometeor populations can potentially be estimated, such as their fall velocity, number, size and to some extent their shape. However, this task is complex and dependent on the operation settings of the specific cloud radar, as well as atmospheric dynamics and hydrometeor characteristics. As a consequence, there is a need for adjustable tools that are able to detect peaks in cloud radar Doppler spectra to extract the valuable information contained in them. This paper presents the synergistic use of two algorithms used for analyzing the peaks in Doppler spectra: PEAKO and peakTree. PEAKO is a supervised machine learning tool that can be trained to obtain the optimal parameters for detecting peaks in Doppler spectra for specific cloud radar instrument settings. The learned parameters can then be applied by peakTree, which is used to detect, organize and interpret Doppler spectrum peaks. The application of the improved PEAKO–peakTree toolkit is demonstrated in two case studies. The interpretation is supported by forward-simulated cloud radar Doppler spectra by the Passive and Active Microwave TRAnsfer tool (PAMTRA), which are also used to explore the limitations of the algorithm toolkit posed by turbulence and the number of spectral averages chosen in the radar settings. From the PAMTRA simulations, we can conclude that a minimum number of n = 20–40 spectral averages is desirable for Doppler spectrum peak discrimination. Furthermore, small liquid peaks can only be reliably separated for eddy dissipation rate values up to approximately 0.0002 m2 s−3 in the simulation setup which we tested here. The first case study demonstrates that the methods work for different radar systems and settings by comparing the results for two cloud radar systems which were operated simultaneously at a site in Punta Arenas, Chile. Detected peaks which can be attributed to liquid droplets agree well between the two systems, as well as with an independent liquid-predicting neural network. The second case study compares PEAKO–peakTree-detected cloud radar Doppler spectrum peaks to in situ observations collected by a balloon-based holographic imager during a campaign in Ny-Ålesund, Svalbard. This case demonstrates the algorithm toolkit's ability to identify different hydrometeor types but also reveals its limitations posed by strong turbulence and a low n. Despite these challenges, the algorithm toolkit offers a powerful means of extracting comprehensive information from cloud radar observations. In the future, we envision PEAKO–peakTree applications on the one hand for interpreting cloud microphysics in case studies. The identification of liquid cloud peaks emerges as a valuable asset, e.g., in studies on cloud radiative effects, in seeder–feeder processes, or for tracing vertical air motions. Furthermore, the computation of the moments for each subpeak enables the tracking of hydrometeor populations and the observation of growth processes along fallstreaks. On the other hand, PEAKO–peakTree applications could be extended to statistical evaluations of longer data sets. Both algorithms are openly available on GitHub, offering accessibility for the scientific community.

The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators

Abstract. Supercooled liquid clouds are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We take advantage of the Macquarie Island Cloud and Radiation Experiment (MICRE) to perform an analysis of observed and simulated cloud processes over the Southern Ocean in a region and season dominated by supercooled liquid clouds. Using a single-column version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), we compare two different cloud microphysical schemes to ground-based observations of cloud, precipitation, and radiation over a 2.5-month period (1 January–17 March 2017). Both schemes are able to reproduce aspects of the cloud and radiation observations during MICRE to within the uncertainty of the data when the thermodynamic profile is prescribed with relaxation. There are differences in water mass and representation of reflectivity between the schemes. A sensitivity study of the cloud microphysics schemes, one a bulk one-moment scheme and the other a two-moment scheme with prediction of mass and number, indicates that several key processes create differences between the schemes. Surface radiative fluxes and total water path are highly sensitive to the formation and fall speed of precipitation. The prediction of hydrometeor number with the two-moment scheme yields a better comparison with observed reflectivity and radiative fluxes, despite predicting higher liquid water contents than observed. With the two-moment scheme, we are also able to test the sensitivity of the results to the input of liquid cloud condensation nuclei (CCN) and ice nuclei (IN). The cloud properties and resulting radiative effects are found to be sensitive to the CCN and IN concentrations. More CCN and IN increase liquid and ice water paths, respectively. Thus, both the dynamic environment and aerosols, integrated through the cloud microphysics, are important for properly representing Southern Ocean cloud radiative effects.

The Cycle 46 Configuration of the HARMONIE-AROME Forecast Model

The aim of this technical note is to describe the Cycle 46 reference configuration of the HARMONIE-AROME convection-permitting numerical weather prediction model. HARMONIE-AROME is one of the canonical system configurations that is developed, maintained, and validated in the ACCORD consortium, a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale numerical weather prediction. This technical note describes updates to the physical parametrizations, both upper-air and surface, configuration choices such as lateral boundary conditions, model levels, horizontal resolution, model time step, and databases associated with the model, such as for physiography and aerosols. Much of the physics developments are related to improving the representation of clouds in the model, including developments in the turbulence, shallow convection, and statistical cloud scheme, as well as changes in radiation and cloud microphysics concerning cloud droplet number concentration and longwave cloud liquid optical properties. Near real-time aerosols and the ICE-T microphysics scheme, which improves the representation of supercooled liquid, and a wind farm parametrization have been added as options. Surface-wise, one of the main advances is the implementation of the lake model FLake. An outlook on upcoming developments is also included.

Resolution dependence of southern Atlantic Ocean stratocumulus decks

AbstractOceanic stratocumulus decks of clouds are among the largest contributors to the Earth's radiation budget, covering around a fifth of the planet's surface and reflecting a large part of the incoming solar radiation. Unfortunately, these clouds are not well represented in modern climate models, resulting in one of the leading causes of uncertainty in climate change projections. This contribution analyses this issue from a novel perspective and sheds light on the mechanisms behind the misrepresentation and resolution dependence of these large clouds. The analysis is based on realistic week‐long simulations performed over a km oceanic domain. Four horizontal resolutions, between 4.4 and 0.55 km, are employed, resulting in a timely investigation, especially in light of the high resolutions employed by present and near‐future climate projections. Results show that the liquid cloud water, the main contributor to the simulated grid‐scale clouds, decreases with a power‐law decay as the resolution increases, whereas the water vapour, responsible for subgrid‐scale clouds, is much less affected by the grid spacing. The leading cause is identified as an imbalance between the rates of change of the advection and turbulence parametrisation terms. In order to verify this observation and provide a possible mitigation to the issue, a second set of simulations is performed where the turbulence parametrisation is tuned. The strategy proves to be successful, confirming the hypotheses and resulting not only in a resolution‐independent radiation budget but also cloud coverage.

Supercooled Water Cloud Detection From Polarized Multi‐Angle Imager Data Using 1.37 μm Water Vapor Polarized Channel

AbstractDetecting supercooled water clouds (SWCs) is essential for enhancing artificial rainfall, preventing aircraft ice accretion, and developing a better understanding of radiative energy balance. The 1.37 μm channel, known as strong water vapor absorbing, was made polarized in the polarized multi‐angle imager (PMAI) onboard FengYun‐3G satellite. The infight data shown that the new 1.37 μm polarized channel could be used to detect SWCs. The cloudbow is observed around the 140° scattering angle in the 1.37 μm polarization image, with a maximum polarization reflectance of approximately 0.04–0.06. The indicated water clouds with spherical particles in the high‐level altitude could be SWCs. Then, the SWCs detected by 1.37 μm polarized channel is verified using polarized reflectance of other channels, the reflectance difference of channels, and thermal infrared bright temperature. The presence of cloudbow in 1.03 and 1.64 μm channels indicate liquid water cloud. The reflectance difference between 1.03 and 1.64 μm of SWCs agree with characteristic of water cloud. The thermal infrared channels from the imager on the same platform indicate cold cloud with the brightness temperature far below 273.16 K. Therefore, the only use of 1.37 μm polarized channel could perform the identification of SWCs. PMAI provides a powerful tool for monitoring supercooled water clouds.

Multi‐Layer Cloud Detection and Distributions Over the Asia–Pacific Region Based on Geostationary Satellite Imagers

AbstractA large portion of cloud scenes over the globe shows multiple layers composed of different phases, in general with ice clouds on the top and liquid water clouds beneath. Such multi‐layer (ML) clouds constitute major challenges in cloud observations and weather and climate modeling. This study improved a threshold algorithm for detecting ice‐over‐water ML clouds using geostationary satellites. Optimal thresholds were established for the spectral characteristics of the Advanced Himawari Imager (AHI) and the Advanced Geostationary Radiation Imager (AGRI), accounting for differences between land and ocean surfaces. Validation with collocated space radar and lidar measurements indicated the identification accuracies of approximately 82% over the land and 76% over the ocean. Annual distributions of ML clouds inferred by AHI and AGRI exhibited strong similarity. Furthermore, 6 years of hourly observations revealed distinct monthly and daily variations in ice‐over‐water clouds over the Asia–Pacific region. The ML cloud monthly variations were similar to those of the seasonal convection cycle, with occurrence frequencies over the typical regions higher in summer (maximum ∼27%) and lower (minimum 6%–10%) in winter. Regarding daily variations, ice‐over‐water clouds occurred more frequently around local noon over most of the six time zones (from UTC + 06 to UTC + 11) throughout all seasons. The refined spatiotemporal distribution of ML clouds, particularly the daily variations, is possible to improve our understanding of cloud vertical distributions and radiative effects, and has the potential to promote subsequent validation and parameterization of cloud overlapping in global climate modeling.

Glaciation of mixed-phase clouds: insights from bulk model and bin-microphysics large-eddy simulation informed by laboratory experiment

Abstract. Mixed-phase clouds affect precipitation and radiation differently from liquid and ice clouds, posing greater challenges to their representation in numerical simulations. Recent laboratory experiments using the Pi Cloud Chamber explored cloud glaciation conditions based on increased injection of ice-nucleating particles. In this study, we use two approaches to reproduce the results of the laboratory experiments: a bulk scalar mixing model and large-eddy simulation (LES) with bin microphysics. The first approach assumes a well-mixed domain to provide an efficient assessment of the mean cloud properties for a wide range of conditions. The second approach resolves the energy-carrying turbulence, the particle size distribution, and their spatial distribution to provide more details. These modeling approaches enable a separate and detailed examination of liquid and ice properties, which is challenging in the laboratory. Both approaches demonstrate that, with an increased ice number concentration, the flow and microphysical properties exhibit the same changes in trends. Additionally, both approaches show that the ice integral radius reaches the theoretical glaciation threshold when the cloud is subsaturated with respect to liquid water. The main difference between the results of the two approaches is that the bulk model allows for the complete glaciation of the cloud. However, LES reveals that, in a dynamic system, the cloud is not completely glaciated as liquid water droplets are continuously produced near the warm lower boundary and subsequently mixed into the chamber interior. These results highlight the importance of the ice mass fraction in distinguishing the mixed-phase clouds and ice clouds.

Novel method for inversion of microphysical properties of clouds using Raman lidar data

Aerosol–cloud–precipitation interactions are important in the balance of Earth’s radiation budget. To further explore the relationship between clouds and precipitation, and to improve operational weather modification, it is necessary to study the microphysical parameters of liquid water clouds. Here, an inversion method that uses a back propagation (BP) neural network based on a genetic algorithm (GA), namely a GABP, is proposed to invert cloud microphysical parameters using ground-based dual-field-of-view (FOV) Raman lidar data. To verify the feasibility of the method, long-term continuous observations were conducted in the Liupan Mountains (China). Results revealed that the proposed inversion method using the GABP is feasible for retrieving the liquid water content (LWC) and the cloud droplet effective radius after training a large number of data measured simultaneously by the Raman lidar and a microwave radiometer. When inverting LWC, the root mean square error (RMSE) of the GABP algorithm was found in the range 0–0.005, whereas the RMSE of the BP algorithm fluctuated in the range 0–0.01. It was evident that the GABP algorithm yields better inversion results and finer detail. When maintaining other variables and comparing the inversion results of signals in the inner and outer FOVs, the RMSE of the inner FOV signal was within 0.005 at near-ground heights (i.e., <2 km), whereas the outer FOV signal exceeded 0.005 at certain heights. This study developed a feasible solution for detecting characteristic cloud microphysical parameters using a Raman lidar, which could be used to study aerosol–cloud–precipitation interactions, and thereby have considerable practical importance for improving artificial rainfall operations.

In situ observations of supercooled liquid water clouds over Dome C, Antarctica, by balloon-borne sondes

Abstract. Clouds in Antarctica are key elements that affect radiative forcing and thus Antarctic climate evolution. Although the vast majority of clouds are composed of ice crystals, a non-negligible fraction constitutes supercooled liquid water (SLW; water held in liquid form below 0 °C). Numerical weather prediction models have great difficulty in forecasting SLW clouds over Antarctica, favouring ice at the expense of liquid water and therefore incorrectly estimating the cloud radiative forcing. Remote-sensing observations of SLW clouds have been carried out for several years at Concordia Station (75° S, 123° E; 3233 m above mean sea level), combining active lidar measurements (SLW cloud detection) and passive HAMSTRAD microwave measurements (liquid water path, LWP). The present project aimed at in situ observations of SLW clouds using sondes developed by the company Anasphere, specifically designed for SLW content (SLWC) measurements. These SLWC sondes were coupled to standard meteorological pressure–temperature–humidity sondes from Vaisala and released under meteorological balloons. During the 2021–2022 summer campaign, 15 launches were made, of which 7 were scientifically exploitable above a height of 400 m above ground level, a threshold height imposed by the time the SLWC sonde takes to stabilize after launch. The three main outcomes from our analyses are as follows: (a) the first in situ observations so far of SLW clouds in Antarctica with SLWC sondes; (b) on average, the consistency of SLW cloud heights as observed by in situ sondes and remote-sensing lidar; and (c) the liquid water path (vertically integrated SLWC) deduced by the sondes being generally equal to or greater than the LWP remotely sensed by HAMSTRAD. In general, the SLW clouds were observed in a layer close to saturation (U &gt; 80 %) or saturated (U ∼ 100 %–105 %) just below or at the lowermost part of the entrainment zone, or capping inversion zone, which exists at the top of the planetary boundary layer and is characterized by an inflection point in the potential temperature vertical profile. Our results are consistent with the theoretical view that SLW clouds form and remain at the top of the planetary boundary layer.

Evaluation and Improvement of a CALIPSO-Based Algorithm for Cloud Base Height in China

Clouds are crucial in regulating the Earth’s energy budget. Global cloud top heights have been easily retrieved from satellite measurements, but there are few methods for determining cloud base height (CBH) from satellite measurements. The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm was proposed to derive the height of the lower-troposphere liquid cloud base by using the Cloud-Aerosol Lidar with Orthogonal polarization cloud aerosol LiDAR (CALIOP) profiles and weather observations at airports from aviation routine and special weather report (METARs and SPECIs, called METAR) observation data in the United States. A modification to the CBASE algorithm over China (CNMETAR-CBASE) is presented in this paper. In this paper, the ability of the CBASE algorithm to calculate CBH in China is evaluated, and METAR observations over China (CNMETAR) were then used to modify the CBASE algorithm. The results including CNMETAR observation data in China can better retrieve CBH over China compared with the results using the original CBASE algorithm, and the accuracy of the global CBH results has been improved. Overestimations of CBH with the original algorithm range from 500 to 800 m in China, which have been reduced to about 300 m with an improved algorithm. The deviations calculated by the algorithm also have a significant reduction, from 480 m (CBASE) to 420 m (CNMETAR-CBASE). In conclusion, the modified CBASE algorithm not only calculates the CBH more accurately in China but also improves the results of the global CBH retrieved from satellites.

Significance of anthropogenic black carbon in modulating atmospheric and cloud properties through aerosol-radiation interaction during a winter-time fog-haze

The present study investigated the aerosol-radiation-cloud interaction of anthropogenic black carbon (BC) during a severe fog-haze event by utilizing WRF-Chem, multi-satellite and in-situ observations, reanalyses datasets, and HYSPLIT model. The WRF-Chem model adequately captured the regional distribution of aerosol, cloud, and meteorological variables over the study domain. The maximum BC surface concentration (≥7 μg m−3) was predominantly evident over densely populated central and lower Indo-Gangetic Plain (IGP) regions. Although smoke aerosols usually persisted within 4 km above the surface at daylight hours, they reached as high as 6 km during nighttime, specifically over the central IGP regions. The heavily influenced areas by BC aerosols expanded to almost the entire landmass and the continental outflow region upon doubling the anthropogenic emission. The geographical distribution of maximum perturbations in net shortwave radiation flux at the surface closely matched the spatial patterns of the highest BC concentration, indicating a crucial role of BC in directly preventing the incoming sunlight from reaching the surface. Consequently, the reduced solar radiation at the ground might result in substantial surface cooling (between −0.3 and −0.5 °C), eventually prohibiting surface evapotranspiration and diminishing the outgoing sensible and latent heat fluxes. Also, more amplified cooling in the polluted atmosphere can prevent further development of the planetary boundary layer (PBL), increasing particle entrapment near the surface via an ‘aerosol-radiation-PBL’ feedback loop. However, the most striking contrast between the two scenarios (typical and polluted) was the prolonged and intensified warming (0.5–0.8 °C) in the mid-troposphere, causing a remarkable drop in moisture (more than two to three times) and hence burn-off of liquid cloud droplets due to the semi-direct effect of increased BC aerosols. The intense mid-tropospheric heating could remarkably enhance the updraft velocity, supporting the vertical transport of the additional moisture to higher altitudes, forming more ice clouds at night. Therefore, the current study highlighted the urgency of mitigating anthropogenic BC emissions in highly polluted regions since increased emissions could considerably worsen severe fog-haze conditions and influence both liquid water and ice clouds, depending on the atmospheric conditions.

An oxidation flow reactor for simulating and accelerating secondary aerosol formation in aerosol liquid water and cloud droplets

Abstract. 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 µm 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 &lt; 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 SO2 by O3, 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 (OHexp) for low RH, high RH, and in-cloud conditions was determined experimentally from the loss of SO2 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 O3 due to photolysis. The aerosol yield for toluene at high OHexp 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).

Bayesian cloud-top phase determination for Meteosat Second Generation

Abstract. A comprehensive understanding of the cloud thermodynamic phase is crucial for assessing the cloud radiative effect and is a prerequisite for remote sensing retrievals of microphysical cloud properties. While previous algorithms mainly detected ice and liquid phases, there is now a growing awareness for the need to further distinguish between warm liquid, supercooled and mixed-phase clouds. To address this need, we introduce a novel method named ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI), which enables cloud detection and the determination of cloud-top phase using SEVIRI (Spinning Enhanced Visible and Infrared Imager), the geostationary passive imager aboard Meteosat Second Generation. ProPS discriminates between clear sky, optically thin ice (TI) cloud, optically thick ice (IC) cloud, mixed-phase (MP) cloud, supercooled liquid (SC) cloud and warm liquid (LQ) cloud. Our method uses a Bayesian approach based on the cloud mask and cloud phase from the lidar–radar cloud product DARDAR (liDAR/raDAR). The validation of ProPS using 6 months of independent DARDAR data shows promising results: the daytime algorithm successfully detects 93 % of clouds and 86 % of clear-sky pixels. In addition, for phase determination, ProPS accurately classifies 91 % of IC, 78 % of TI, 52 % of MP, 58 % of SC and 86 % of LQ clouds, providing a significant improvement in accurate cloud-top phase discrimination compared to traditional retrieval methods.

Technical note: Retrieval of the supercooled liquid fraction in mixed-phase clouds from Himawari-8 observations

Abstract. The supercooled liquid fraction (SLF) in mixed-phase clouds (MPCs) is an essential variable of cloud microphysical processes and climate sensitivity. However, the SLF is currently calculated in spaceborne remote sensing only as the cloud phase–frequency ratio of adjacent pixels, which results in a loss of the original resolution in observations of cloud liquid or ice content within MPCs. Here, we present a novel method for retrieving the SLF in MPCs based on the differences in radiative properties of supercooled liquid droplets and ice particles at visible (VIS) and shortwave infrared (SWI) channels of the geostationary Himawari-8. Liquid and ice water paths are inferred by assuming that clouds are composed of only liquid or ice, with the real cloud water path (CWP) expressed as a combination of these two water paths (SLF and 1-SLF as coefficients), and the SLF is determined by referring to the CWP from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The statistically relatively small cloud phase spatial inhomogeneity at a Himawari-8 pixel level indicates an optimal scene for cloud retrieval. The SLF results are comparable to global SLF distributions observed by active instruments, particularly for single-layered cloud systems. While accessing the method's feasibility, SLF averages are estimated between 74 % and 78 % in Southern Ocean (SO) stratocumulus across seasons, contrasting with a range of 29 % to 32 % in northeastern Asia. The former exhibits a minimum SLF around midday in summer and a maximum in winter, while the latter trend differs. This novel algorithm will be valuable for research to track the evolution of MPCs and constrain the related climate impact.

Aerosol-induced closure of marine cloud cells: enhanced effects in the presence of precipitation

Abstract. The Weather Research Forecasting (WRF) version 4.3 model is configured within a Lagrangian framework to quantify the impact of aerosols on evolving cloud fields. Kilometer-scale simulations utilizing meteorological boundary conditions are based on 10 case study days offering diverse meteorology during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA). Measurements from aircraft, the ground-based Atmosphere Radiation Measurement (ARM) site at Graciosa Island in the Azores, and A-Train and geostationary satellites are utilized for validation, demonstrating good agreement with the WRF-simulated cloud and aerosol properties. Higher aerosol concentration leads to suppressed drizzle and increased cloud water content in all case study days. These changes lead to larger radiative cooling rates at cloud top, enhanced vertical velocity variance, and increased vertical and horizontal wind speed near the base of the lower-tropospheric inversion. As a result, marine cloud cell area expands, narrowing the gap between shallow clouds and increasing cloud optical thickness, liquid water content, and the top-of-atmosphere outgoing shortwave flux. While similar aerosol effects are observed in lightly to non-raining clouds, they tend to be smaller by comparison. These simulations show a relationship between cloud cell area expansion and the radiative adjustments caused by liquid water path and cloud fraction changes. The adjustments are positive and scale as 74 % and 51 %, respectively, relative to the Twomey effect. While higher-resolution large-eddy simulations may provide improved representation of cloud-top mixing processes, these results emphasize the importance of addressing mesoscale cloud-state transitions in the quantification of aerosol radiative forcing that cannot be attained from traditional climate models.

On the retrieval of cloud optical thickness from spectral radiances - A sensitivity study with high albedo surfaces

Measurements of spectral zenith radiance in the 320–950 nm wavelength range have been carried out since 2021 at the Thule High Arctic Atmospheric Observatory (THAAO, https://www.thuleatmos-it.it/, 76.5° N, 68.8° W, 225 m a.s.l) located in Pituffik, northern Greenland. This study evaluates whether such observations could provide, in principle, scientifically meaningful cloud optical depth (τ) estimates, what would be the limitations, which are the necessary auxiliary measurements, and which radiance wavelengths would be better suited for the goal. Although clouds play a critical role in the Arctic, a climatology of τ in high albedo conditions is particularly difficult to obtain. THAAO might have the instrument capabilities to provide such a long-term dataset. We use a radiative transfer package to simulate visible spectra with different cloud and surface conditions, assuming homogeneous overcast sky and liquid water clouds of fixed geometrical thickness. Simulations are run to reproduce typical conditions encountered at THAAO when measurements are carried out. We find that the assumption of a broadband albedo instead of a spectrally-resolved one is the source of the largest uncertainties. Tests on size and phase of cloud particles showed that a 50% uncertainty in reff leads to a ∼10% error in τ, and that a 10% contamination of ice crystals in a low-level liquid water cloud leads to an error in τ estimates of less than 5%. All the tests showed that the most critical τ range is between the thin and thick cloud regimes (τ ∼ 7–15), where the retrievals can be less reliable. Otherwise, tests suggest that in the environmental conditions that characterize late spring and summer at THAAO, and given the observatory measurements capabilities, estimates of τ for low-level clouds could be accurately retrieved both in high and low surface albedo conditions by means of zenith radiance measurements in the UV-Vis-NIR range.

The Cloud Indicator: A novel algorithm for automatic detection and classification of clouds using airborne in situ observations

Airborne in situ observations of aerosols and clouds provide relevant data sets crucial to improving the understanding of cloud microphysical processes and reducing uncertainties connected to future climate predictions. However, the selection and classification of cloud sequences in such data sets can be very time-consuming if done manually, and criteria used by the community are numerous and prone to misclassification, especially when coarse aerosol particles (> 1 μm diameter) are present.In this study, we present the Cloud Indicator, a novel algorithm that automatically detects and classifies measurement periods inside clouds. The Cloud Indicator was developed using data from three international airborne field campaigns, including ATom (Atmospheric Tomography; 2016–2018), A-LIFE (Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics; 2017), and FIREX-AQ (Fire Influence on Regional to Global Environments Experiment and Air Quality; 2019). The algorithm utilizes size distribution measurements, combined with measurements of relative humidity and temperature, to automatically detect flight sequences in clouds and classify the cloud type. As an additional criterion for the Cloud Indicator, we established the cloud-aerosol volume factor fCA to ensure a precise and robust distinction between clouds and aerosol layers such as mineral dust or biomass burning thereby reducing misclassifications. The Cloud Indicator algorithm was developed with data from a second-generation Cloud, Aerosol, and Precipitation Spectrometer (CAPS) which was calibrated with a novel calibration procedure – introduced in this study - that results in a refractive index independent calibration and works also in the field. However, the Cloud Indicator algorithm is not restricted specifically to the CAPS and thus allows its application to a variety of in situ instruments that measure coarse aerosol and cloud particle size.Case studies from ATom and A-LIFE demonstrate the ability of the Cloud Indicator to precisely screen (airborne) in situ data sets for clouds. The algorithm separates the data set into cloud-free periods, Aerosol-Cloud Transition Regime (ACTR), liquid clouds, clouds in the Mixed-Phase Temperature Regime (MPTR), and cirrus clouds. The unique ability of the Cloud Indicator to successfully differentiate between layers of enhanced coarse-mode aerosol concentrations and clouds is demonstrated by measurements in a complex mixture of clouds embedded into a mineral dust layer during the A-LIFE. The empirically derived parameter thresholds of the Cloud Indicator are in good agreement with values in the literature.

Impact of Aerosols on the Macrophysical and Microphysical Characteristics of Ice-Phase and Mixed-Phase Clouds over the Tibetan Plateau

Using CloudSat/CALIPSO satellite data and ERA5 reanalysis data from 2006 to 2010, the effects of aerosols on ice- and mixed-phase, single-layer, non-precipitating clouds over the Tibetan Plateau during nighttime in the MAM (March to May), JJA (June to August), SON (September to November), and DJF (December to February) seasons were examined. The results indicated the following: (1) The macrophysical and microphysical characteristics of ice- and mixed-phase clouds exhibit a nonlinear trend with increasing aerosol optical depth (AOD). When the logarithm of AOD (lnAOD) was ≤−4.0, with increasing AOD during MAM and JJA nights, the cloud thickness and ice particle effective radius of ice-phase clouds and mixed-phase clouds, the ice water path and ice particle number concentration of ice-phase clouds, and the liquid water path and cloud fraction of mixed-phase clouds all decreased; during SON and DJF nights, the cloud thickness of ice-phase clouds, cloud top height, liquid droplet number concentration, and liquid water path of mixed-phase clouds all decreased. When the lnAOD was &gt;−4.0, with increasing AOD during MAM and JJA nights, the cloud top height, cloud base height, cloud fraction, and ice particle number concentration of ice-phase clouds, and the ice water path of mixed-phase clouds all increased; during SON and DJF nights, the cloud fraction of mixed-phase clouds and the ice water path of ice-phase clouds all increased. (2) Under the condition of excluding meteorological factors, including the U-component of wind, V-component of wind, pressure vertical velocity, temperature, and relative humidity at the atmospheric pressure heights near the average cloud top height, within the cloud, and the average cloud base height, as well as precipitable water vapor, convective available potential energy, and surface pressure. During MAM and JJA nights. When the lnAOD was ≤−4.0, an increase in aerosols may have led to a decrease in the thickness of ice and mixed-phase cloud layers, as well as a reduction in cloud water path values. In contrast, when the lnAOD was &gt;−4.0, an increase in aerosols may contribute to elevated cloud base and cloud top heights for ice-phase clouds. During SON and DJF nights, changes in various cloud characteristics may be influenced by both aerosols and meteorological factors.

Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol–cloud interactions

Abstract. Process-oriented observational constraints for the anthropogenic effective radiative forcing due to aerosol–cloud interactions (ERFaci) are highly desirable because the uncertainty associated with ERFaci poses a significant challenge to climate prediction. The contoured frequency by optical depth diagram (CFODD) analysis supports the evaluation of model representation of cloud liquid-to-rain conversion processes because the slope of a CFODD, generated from joint MODerate Resolution Imaging Spectroradiometer (MODIS)-CloudSat cloud retrievals, provides an estimate of cloud droplet collection efficiency in single-layer warm liquid clouds. Here, we present an updated CFODD analysis as an observational constraint on the ERFaci due to warm rain processes and apply it to the U.S. Department of Energy's Energy Exascale Earth System Model version 2 (E3SMv2). A series of sensitivity experiments shows that E3SMv2 droplet collection efficiencies and ERFaci are highly sensitive to autoconversion, i.e., the rate of mass transfer from cloud liquid to rain, yielding a strong correlation between the CFODD slope and the shortwave component of ERFaci (ERFaciSW; Pearson's R=-0.91). E3SMv2's CFODD slope (0.20 ± 0.04) is in agreement with observations (0.20 ± 0.03). The strong sensitivity of ERFaciSW to the CFODD slope provides a useful constraint on highly uncertain warm rain processes, whereby ERFaciSW, constrained by MODIS-CloudSat, is estimated by calculating the intercept of the linear association between the ERFaciSW and the CFODD slopes, using the MODIS-CloudSat CFODD slope as a reference.