Cloud Droplet Number Concentration Research Articles

Impact of Cloud Condensation Nuclei Reduction on Cloud Characteristics and Solar Radiation during COVID-19 Lockdown 2020 in Moscow

We used MODIS observations to retrieve number concentration of cloud droplets (Nd) at cloud lower boundary during spring 2018–2020 for the Moscow region. Looking through the similar synoptic situations of the northern clear air advection, we obtained Nd within the limits of 200–300 cm−3. During the lockdown period, with similar northern advection conditions, the reduction of Nd on 40–50 cm−3 (or 14–16%), with the increase in droplet effective radius by 8 ± 1% and cloud optical thickness reduction by 5 ± 2%, was observed in contrast to the values in typical conditions in 2018–2019. We used these values for setting up corresponding parameters in numerical simulations with the COSMO-Ru model. According to the numerical experiments, we showed that the observed reduction in cloud droplet concentration by 50 cm−3 provides a 5–9 W/m2 (or 9–11%) increase in global irradiance at ground in overcast cloud conditions with LWP = 200–400 g/m2.

Validation of the dual field-of-view polarization LIDAR technique for the retrieval of homogeneous water cloud microphysical properties: a study based on a polarimetric Monte Carlo simulation.

This article highlights the validation of the dual fields-of-view (FOVs) polarization lidar technique for the retrieval of cloud droplet effective radius in conjunction with cloud extinction coefficient of homogeneous water cloud via simulation approach. The simulation is based on polarimetric Monte Carlo method incorporated with semianalytic features under multiple-scattering conditions. The simulation results show that the depolarization ratio measured at dual-FOVs is a function of the cloud droplet effective radius and cloud extinction coefficient. Using the method of standard deviation on extensive simulation results and then by applying the polynomial regression, two polynomial relationships are obtained expressing the retrieval of the cloud droplet effective radius and cloud extinction coefficient from the layer integrated depolarization ratio at low optical depths close to the cloud bottom. Eventually, the results those presented by Ref.[1] are validated. The water cloud microphysical properties, liquid water content and cloud droplet number concentration are the functions of these two parameters and thus can be found numerically.

Detrainment Dominates CCN Concentrations Around Non‐Precipitating Convective Clouds Over the Amazon

AbstractWe investigated the relationship between the number concentration of cloud droplets (Nd) in ice‐free convective clouds and of particles large enough to act as cloud condensation nuclei (CCN) measured at the lateral boundaries of cloud elements. The data were collected during the ACRIDICON‐CHUVA aircraft campaign over the Amazon Basin. The results indicate that the CCN particles at the lateral cloud boundaries are dominated by detrainment from the cloud. The CCN concentrations detrained from non‐precipitating convective clouds are smaller compared to below cloud bases. The detrained CCN particles from precipitating cloud volumes have relatively larger sizes, but lower concentrations. Our findings indicate that CCN particles ingested from below cloud bases are activated into cloud droplets, which evaporate at the lateral boundaries and above cloud base and release the CCN again to ambient cloud‐free air, after some cloud processing. These results support the hypothesis that the CCN around the cloud are cloud‐processed.

Large‐eddy simulations of drizzling shallow cumuli using a turbulence‐aware autoconversion parametrization

AbstractTurbulence in clouds enhances collision between cloud droplets, which is critical to drizzle formation in shallow cumulus clouds. In this study, we develop an autoconversion parametrization that considers the turbulence‐induced collision enhancement (TICE) obtained from a particle trajectory model. We implement the developed parametrization to a large‐eddy simulation model and simulate the Rain In Cumulus over the Ocean (RICO) case to investigate the effects of TICE on shallow cumulus clouds and their sensitivities to the cloud droplet number concentration and horizontal grid resolution. TICE significantly increases the autoconversion rate, leading to large increases in the accretion rate, rainwater path and surface rain rate. The simulations of shallow cumulus clouds are highly sensitive to the cloud droplet number concentration and horizontal grid resolution, and the inclusion of TICE overall reduces the sensitivities. For example, the large rainwater path and surface rain rate that appear when cloud droplet number concentration is relatively low are reduced by TICE, whereas the very small rainwater path and surface rain rate that appear when cloud droplet number concentration is relatively high are increased by TICE. Comparisons with two other TICE‐aware parametrizations based on direct numerical simulation results of droplet collisions in turbulence show that there are substantial differences in TICE effects for different parametrizations, which implies a large uncertainty in simulating turbulent clouds. Among the three parametrizations, the parametrization developed in this study shows intermediate TICE‐induced increases in rainwater path and surface rain rate.

Apportionment of the Pre‐Industrial to Present‐Day Climate Forcing by Methane Using UKESM1: The Role of the Cloud Radiative Effect

AbstractThe Year 1850 to 2014 increase in methane from 808 to 1831 ppb leads to an effective radiative forcing (ERF) of 0.97 ± 0.04 W m−2 in the United Kingdom's Earth System Model, UKESM1. The direct methane contribution is 0.54 ± 0.04 W m−2. It is better represented in UKESM1 than in its predecessor model HadGEM2 due to shortwave and longwave absorption improvements and the absence of an anomalous dust response in the UKESM1 simulations. An indirect ozone ERF of 0.13–0.20 W m−2 is due to the tropospheric ozone increase outweighing that of the stratospheric decrease. The indirect water vapor ERF of 0.02–0.07 W m−2 is consistent with previous estimates. The methane increase also leads to a cloud radiative effect of 0.12 ± 0.02 W m−2 from thermodynamic adjustments and aerosol‐cloud interactions (aci). Shortwave and longwave contributions of 0.23 and −0.35 W m−2 to the cloud forcing arise from radiative heating and stabilization of the upper troposphere, reducing convection and global cloud cover. The aerosol‐mediated contribution (0.28–0.30 W m−2) is due to changes in oxidants reducing new particle formation (−8%), shifting the aerosol size distribution toward fewer but larger particles. Cloud droplet number concentration decreases and cloud droplet effective radius increases. This reduction in the Twomey effect switches the cloud forcing sign (−0.14 to 0.12 W m−2) and is due to chemistry‐aerosol‐cloud coupling in UKESM1. Despite uncertainties in rapid adjustments and process representation in models, these results highlight the potential importance of chemistry‐aerosol‐cloud interactions and dynamical adjustments in climate forcing.

Aerosol–stratocumulus interactions: towards a better process understanding using closures between observations and large eddy simulations

Abstract. We carried out a closure study of aerosol–cloud interactions during stratocumulus formation using a large eddy simulation model UCLALES–SALSA (University of California Los Angeles large eddy simulation model–sectional aerosol module for large applications) and observations from the 2020 cloud sampling campaign at Puijo SMEAR IV (Station for Measuring Ecosystem–Atmosphere Relations) in Kuopio, Finland. The unique observational setup combining in situ and cloud remote sensing measurements allowed a closer look into the aerosol size–composition dependence of droplet activation and droplet growth in turbulent boundary layer driven by surface forcing and radiative cooling. UCLALES–SALSA uses spectral bin microphysics for aerosols and hydrometeors, and incorporates a full description of their interactions into the turbulent-convective radiation-dynamical model of stratocumulus. Based on our results, the model successfully described the probability distribution of updraught velocities and consequently the size dependency of aerosol activation into cloud droplets, and further recreated the size distributions for both interstitial aerosol and cloud droplets. This is the first time such a detailed closure is achieved not only accounting for activation of cloud droplets in different updraughts, but also accounting for processes evaporating droplets and drizzle production through coagulation–coalescence. We studied two cases of cloud formation, one diurnal (24 September 2020) and one nocturnal (31 October 2020), with high and low aerosol loadings, respectively. Aerosol number concentrations differ more than 1 order of magnitude between cases and therefore, lead to cloud droplet number concentration (CDNC) values which range from less than 100 cm−3 up to 1000 cm−3. Different aerosol loadings affected supersaturation at the cloud base, and thus the size of aerosol particles activating to cloud droplets. Due to higher CDNC, the mean size of cloud droplets in the diurnal high aerosol case was lower. Thus, droplet evaporation in downdraughts affected more the observed CDNC at Puijo altitude compared to the low aerosol case. In addition, in the low aerosol case, the presence of large aerosol particles in the accumulation mode played a significant role in the droplet spectrum evolution as it promoted the drizzle formation through collision and coalescence processes. Also, during the event, the formation of ice particles was observed due to subzero temperature at the cloud top. Although the modelled number concentration of ice hydrometeors was too low to be directly measured, the retrieval of hydrometeor sedimentation velocities with cloud radar allowed us to assess the realism of modelled ice particles. The studied cases are presented in detail and can be further used by the cloud modellers to test and validate their models in a well-characterized modelling setup. We also provide recommendations on how increasing amount of information on aerosol properties could improve the understanding of processes affecting cloud droplet number and liquid water content in stratiform clouds.

Cloud adjustments from large-scale smoke–circulation interactions strongly modulate the southeastern Atlantic stratocumulus-to-cumulus transition

Abstract. Smoke from southern Africa blankets the southeastern Atlantic Ocean from June to October, producing strong and competing aerosol radiative effects. Smoke effects on the transition between overcast stratocumulus and scattered cumulus clouds are investigated along a Lagrangian (air-mass-following) trajectory in regional climate and large eddy simulation models. Results are compared with observations from three recent field campaigns that took place in August 2017: ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES), CLouds and Aerosol Radiative Impacts and Forcing: Year 2017 (CLARIFY), and Layered Atlantic Smoke Interactions with Clouds (LASIC). The case study is set up around the joint ORACLES–CLARIFY flight that took place near Ascension Island on 18 August 2017. Smoke sampled upstream on an ORACLES flight on 15 August 2017 likely entrained into the marine boundary layer later sampled during the joint flight. The case is first simulated with the WRF-CAM5 regional climate model in three distinct setups: (1) FireOn, in which smoke emissions and any resulting smoke–cloud–radiation interactions are included; (2) FireOff, in which no smoke emissions are included; (3) RadOff, in which smoke emissions and their microphysical effects are included but aerosol does not interact directly with radiation. Over the course of the Lagrangian trajectory, differences in free tropospheric thermodynamic properties between FireOn and FireOff are nearly identical to those between FireOn and RadOff, showing that aerosol–radiation interactions are primarily responsible for the free tropospheric effects. These effects are non-intuitive: in addition to the expected heating within the core of the smoke plume, there is also a “banding” effect of cooler temperature (∼1–2 K) and greatly enhanced moisture (>2 g kg−1) at the plume top. This banding effect is caused by a vertical displacement of the former continental boundary layer in the free troposphere in the FireOn simulation resulting from anomalous diabatic heating due to smoke absorption of sunlight that manifests primarily as a few hundred meters per day reduction in large-scale subsidence over the ocean. A large eddy simulation (LES) is then forced with free tropospheric fields taken from the outputs for the WRF-CAM5 FireOn and FireOff runs. Cases are run by selectively perturbing one variable (e.g., aerosol number concentration, temperature, moisture, vertical velocity) at a time to better understand the contributions from different indirect (microphysical), “large-scale” semi-direct (above-cloud thermodynamic and subsidence changes), and “local” semi-direct (below-cloud smoke absorption) effects. Despite a more than 5-fold increase in cloud droplet number concentration when including smoke aerosol concentrations, minimal differences in cloud fraction evolution are simulated by the LES when comparing the base case with a perturbed aerosol case with identical thermodynamic and dynamic forcings. A factor of 2 decrease in background free tropospheric aerosol concentrations from the FireOff simulation shifts the cloud evolution from a classical entrainment-driven “deepening–warming” transition to trade cumulus to a precipitation-driven “drizzle-depletion” transition to open cells, however. The thermodynamic and dynamic changes caused by the WRF-simulated large-scale adjustments to smoke diabatic heating strongly influence cloud evolution in terms of both the rate of deepening (especially for changes in the inversion temperature jump and in subsidence) and in cloud fraction on the final day of the simulation (especially for the moisture “banding” effect). Such large-scale semi-direct effects would not have been possible to simulate using a small-domain LES model alone.

Exploring Satellite-Derived Relationships between Cloud Droplet Number Concentration and Liquid Water Path Using a Large-Domain Large-Eddy Simulation

Important aspects of the adjustments to aerosol-cloud interactions can be examined using the relationship between cloud droplet number concentration (N_d) and liquid water path (LWP). Specifically, this relation can constrain the role of aerosols in leading to thicker or thinner clouds in response to adjustment mechanisms. This study investigates the satellite retrieved relationship between N_d and LWP for a selected case of mid-latitude continental clouds using high-resolution Large-eddy simulations (LES) over a large domain in weather prediction mode. Since the satellite retrieval uses the adiabatic assumption to derive the N_d, we have also considered adiabatic N_d (N_Ad) from the LES model for comparison. The joint histogram analysis shows that the N_Ad-LWP relationship in the LES model and the satellite is in approximate agreement. In both cases, the peak conditional probability (CP) is confined to lower N_Ad and LWP; the corresponding mean LWP (LWP) shows a weak relation with N_Ad. The CP shows a larger spread at higher N_Ad (>50 cm^–3), and the LWP increases non-monotonically with increasing N_Ad in both cases. Nevertheless, both lack the negative N_Ad-LWP relationship at higher N_Ad, the entrainment effect on cloud droplets. In contrast, the model simulated N_d-LWP clearly illustrates a much more nonlinear (an increase in LWP with increasing N_d and a decrease in LWP at higher N_d) relationship, which clearly depicts the cloud lifetime and the entrainment effect. Additionally, our analysis demonstrates a regime dependency (marine and continental) in the N_Ad-LWP relation from the satellite retrievals. Comparing local vs large-scale statistics from satellite data shows that continental clouds exhibit only a weak nonlinear N_Ad-LWP relationship. Hence a regime-based N_d-LWP analysis is even more relevant when it comes to warm continental clouds and their comparison to satellite retrievals.

The influence of multiple groups of biological ice nucleating particles on microphysical properties of mixed-phase clouds observed during MC3E

Abstract. A new empirical parameterization (EP) for multiple groups of primary biological aerosol particles (PBAPs) is implemented in the aerosol–cloud model (AC) to investigate their roles as ice nucleating particles (INPs). The EP describes the heterogeneous ice nucleation by (1) fungal spores, (2) bacteria, (3) pollen, (4) detritus of plants, animals, and viruses, and (5) algae. Each group includes fragments from the originally emitted particles. A high-resolution simulation of a midlatitude mesoscale squall line by AC is validated against airborne and ground observations. Sensitivity tests are carried out by varying the initial vertical profiles of the loadings of individual PBAP groups. The resulting changes in warm and ice cloud microphysical parameters are investigated. The changes in warm microphysical parameters, including liquid water content and cloud droplet number concentration, are minimal (<10 %). Overall, PBAPs have little effect on the ice number concentration (<6 %) in the convective region. In the stratiform region, increasing the initial PBAP loadings by a factor of 1000 resulted in less than 40 % change in ice number concentrations. The total ice concentration is mostly controlled by various mechanisms of secondary ice production (SIP). However, when SIP is intentionally shut down in sensitivity tests, increasing the PBAP loading by a factor of 100 has an effect of less than 3 % on the ice phase. Further sensitivity tests revealed that PBAPs have little effect on surface precipitation and on the shortwave and longwave flux (<4 %) for a 100-fold perturbation in PBAPs.

Improving the Treatment of Subgrid Cloud Variability in Warm Rain Simulation in CESM2

Representing subgrid variability of cloud properties has always been a challenge in global climate models (GCMs). In many cloud microphysics schemes, the warm rain non-linear process rates calculated based on grid-mean cloud properties are usually scaled by an enhancement factor (EF) to account for the effects of subgrid cloud variability. In our study, we find that the EF derived from Cloud Layers Unified by Binormals in Community Atmosphere Model version 6 (CAM6) is severely overestimated in most of the cloudy oceanic areas, which leads to strong overestimation of the autoconversion rate. We improve the EF in warm rain simulation by developing a new formula for in-cloud subgrid cloud water variance. With the updated subgrid cloud water variance and EF treatment, the liquid cloud fraction (LCF) and cloud optical thickness (COT) increases noticeably for marine stratocumulus, and the shortwave cloud forcing (SWCF) matches better with observations. The updated formula improves the relationship between autoconversion rate and cloud droplet number concentration, and it decreases the sensitivity of autoconversion rate to aerosols. The sensitivity of liquid water path to aerosols decreases noticeably and is in better agreement with that in MODIS. Although the sensitivity of COT is similar to that in MODIS, CAM6 underestimates the sensitivity of grid-mean SWCF to aerosols because of the underestimation in the sensitivities of LCF and in-cloud SWCF. Our results indicate the importance of representing reasonable subgrid cloud variability in the simulation of cloud properties and aerosol-cloud interaction in GCMs.

Numerical simulation of the impact of COVID-19 lockdown on tropospheric composition and aerosol radiative forcing in Europe

Abstract. Aerosols influence the Earth's energy balance directly by modifying the radiation transfer and indirectly by altering the cloud microphysics. Anthropogenic aerosol emissions dropped considerably when the global COVID-19 pandemic resulted in severe restraints on mobility, production, and public life in spring 2020. We assess the effects of these reduced emissions on direct and indirect aerosol radiative forcing over Europe, excluding contributions from contrails. We simulate the atmospheric composition with the ECHAM5/MESSy Atmospheric Chemistry (EMAC) model in a baseline (business-as-usual) and a reduced emission scenario. The model results are compared to aircraft observations from the BLUESKY aircraft campaign performed in May–June 2020 over Europe. The model agrees well with most of the observations, except for sulfur dioxide, particulate sulfate, and nitrate in the upper troposphere, likely due to a biased representation of stratospheric aerosol chemistry and missing information about volcanic eruptions. The comparison with a baseline scenario shows that the largest relative differences for tracers and aerosols are found in the upper troposphere, around the aircraft cruise altitude, due to the reduced aircraft emissions, while the largest absolute changes are present at the surface. We also find an increase in all-sky shortwave radiation of 0.21 ± 0.05 W m−2 at the surface in Europe for May 2020, solely attributable to the direct aerosol effect, which is dominated by decreased aerosol scattering of sunlight, followed by reduced aerosol absorption caused by lower concentrations of inorganic and black carbon aerosols in the troposphere. A further increase in shortwave radiation from aerosol indirect effects was found to be much smaller than its variability. Impacts on ice crystal concentrations, cloud droplet number concentrations, and effective crystal radii are found to be negligible.

Source attribution of cloud condensation nuclei and their impact on stratocumulus clouds and radiation in the south-eastern Atlantic

Abstract. The semi-permanent stratocumulus clouds over the south-eastern Atlantic Ocean (SEA) can act as an “air conditioner” to the regional and global climate system. The interaction of aerosols and clouds becomes important in this region and can lead to negative radiative effects, partially offsetting the positive radiative forcing of greenhouse gases. A key pathway by which aerosols affect cloud properties is by acting as cloud condensation nuclei (CCN). In this paper, we use the United Kingdom Earth System Model (UKESM1) to investigate the sources of CCN (from emissions and atmospheric processes) in the SEA as well as the response of the cloud droplet number concentration (CDNC), the cloud liquid water path (LWP), and radiative forcing to these sources during 2016 and 2017. Overall, free and upper troposphere nucleated aerosols are the dominant source of the boundary layer CCN concentration at 0.2 % supersaturation (CCN0.2 %), contributing an annual average of ∼ 41 % as they subside and entrain into the marine boundary layer, which is consistent with observations highlighting the important role of nucleation in the boundary layer CCN concentration. In terms of emission sources, anthropogenic emissions (from energy, industry, agriculture, etc.) contribute the most to the annual average CCN0.2 % in the marine boundary layer (∼ 26 %), followed by biomass burning (BB, ∼ 17 %). In the cloud layer, BB contributes about 34 % of the annual CCN0.2 %, midway between the contributions from aerosol nucleation (36 %) and anthropogenic sources (31 %). The contribution of aerosols from different sources to the CDNC is consistent with their contribution to CCN0.2 % within the marine boundary layer, with free and upper troposphere aerosol nucleation being the most important source of the CDNC overall. In terms of emission sources, anthropogenic sources are also the largest contributors to the annual average CDNC, closely followed by BB. However, during the BB season, BB and free and upper troposphere aerosol nucleation are equally the most important sources of the CDNC. The contribution of BB to the CDNC is more significant than its increase to CCN0.2 %, mainly because BB aerosols are mostly located directly above the inversion layer in the model; thus, they can increase the in-cloud CDNC by enhancing the supersaturation through the dynamical feedback due to short-wave absorption. An aerosol source that shows an increase in the CDNC also shows an increase in the LWP resulting from a reduction in autoconversion. Due to the absorption effect, BB aerosol can enhance existing temperature inversions and reduce the entrainment of sub-saturated air, leading to a further increase in the LWP. As a result, the contribution of BB to the LWP is second only to aerosol nucleation on annual averages. These findings demonstrate that BB is not the dominant source of CCN within the marine boundary layer from an emission source perspective. However, as most BB aerosols are located directly above the inversion layer, their effect on clouds increases due to their absorption effect (about the same as anthropogenic sources for the CDNC and more than anthropogenic sources for the LWP), highlighting the crucial role of their radiative effect on clouds. The results on the radiative effects of aerosols show that BB aerosol exhibits an overall positive RFari (radiative forcing associated with aerosol–radiation interactions), but its net effective radiative forcing remains negative due to its effect on clouds (mainly due to its absorbing effect). By quantifying aerosol and cloud properties affected by different sources, this paper provides a framework for understanding the effects of aerosol sources on marine stratocumulus clouds and radiation in the SEA.

Use of Machine Learning to Reduce Uncertainties in Particle Number Concentration and Aerosol Indirect Radiative Forcing Predicted by Climate Models

AbstractThe radiative forcing of anthropogenic aerosols associated with aerosol‐cloud interactions (RFaci) remains the largest source of uncertainty in climate prediction. The calculation of particle number concentration (PNC), one of the critical parameters affecting RFaci, is generally simplified in climate models. Here we employ outputs from long‐term (30‐year) simulations of a global size‐resolved (sectional) aerosol microphysics model and a machine‐learning tool to develop a Random Forest Regression Model (RFRM) for PNC. We have implemented the PNC RFRM in GISS‐ModelE2.1 with a mass‐based One‐Moment Aerosol module, which is one of CMIP6 models. Compared to the default setting, the GISS‐ModelE2.1 simulation based on RFRM reduces the changes of cloud droplet number concentration associated with anthropogenic emissions, and decreases the RFaci from −1.46 to −1.11 W·m−2. This work highlights a promising approach based on machine learning to reduce uncertainties of climate models in predicting PNC and RFaci without compromising their computing efficiency.

Cloudwater Deposition Process of Radionuclides Based on Water Droplets Retrieved from Pollen Sensor Data.

Radionuclides released during the Fukushima Daiichi nuclear accident caused altitude-dependent surface contamination in the mountainous areas of Japan. To explore the possible cloudwater deposition that formed a distinctive contamination profile, data from pollen sensors deployed nationwide were analyzed. Utilizing the polarization of scattered light, Cedar pollen and water droplets were distinguished. On March 15, when surface contamination was simulated in previous studies, dense clouds with high droplet number concentrations were observed outside the 137Cs surface deposition areas, indicating that the sensor sites were immersed amid cloud layers. In contrast, cloud droplets with moderate number concentrations were measured at altitudes of approximately 570-840 m, which overlapped with the surface contamination areas. Considering the existing knowledge on vertical gradients of cloudwater composition, these suggest that contaminated cloud droplets were localized near the cloud base where a moderate number concentration of cloud droplets was measured. A formation process was proposed for the observed vertical distribution, that is, surface contamination occurred intensively at the contact line between the cloud base and mountain slopes via cloudwater deposition, and the descending cloud base formed the contamination zone. This study sheds light on the deposition processes of radionuclides, which have not previously been clarified.

Sensitivity analysis of an aerosol-aware microphysics scheme in Weather Research and Forecasting (WRF) during case studies of fog in Namibia

Abstract. Aerosol-aware microphysics parameterisation schemes are increasingly being introduced into numerical weather prediction models, allowing for regional and case-specific parameterisation of cloud condensation nuclei (CCN) and cloud droplet interactions. In this paper, the Thompson aerosol-aware microphysics scheme, within the Weather Research and Forecasting (WRF) model, is used for two fog cases during September 2017 over Namibia. Measurements of CCN and fog microphysics were undertaken during the AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA) field campaign at Henties Bay on the coast of Namibia during September 2017. A key concept of the microphysics scheme is the conversion of water-friendly aerosols to cloud droplets (hereafter referred to as CCN activation), which could be estimated from the observations. A fog monitor 100 (FM-100) provided cloud droplet size distribution, number concentration (Nt), liquid water content (LWC), and mean volumetric diameter (MVD). These measurements are used to evaluate and parameterise WRF model simulations of Nt, LWC, and MVD. A sensitivity analysis was conducted through variations to the initial CCN concentration, CCN radius, and the minimum updraft speed, which are important factors that influence droplet activation in the microphysics scheme of the model. The first model scenario made use of the default settings with a constant initial CCN number concentration of 300 cm−3 and underestimated the cloud droplet number concentration, while the LWC was in good agreement with the observations. This resulted in droplet size being larger than the observations. Another scenario used modelled data as CCN initial conditions, which were an order of magnitude higher than other scenarios. However, these provided the most realistic values of Nt, LWC, MVD, and droplet size distribution. From this, it was concluded that CCN activation of around 10 % in the simulations is too low, while the observed appears to be higher reaching between 20 % and 80 %, with a mean (median) of 0.55 (0.56) during fog events. To achieve this level of activation in the model, the minimum updraft speed for CCN activation was increased from 0.01 to 0.1 m s−1. This scenario provided Nt, LWC, MVD, and droplet size distribution in the range of the observations, with the added benefit of a realistic initial CCN concentration. These results demonstrate the benefits of a dynamic aerosol-aware scheme when parameterised with observations.

Sensitivity study of the impact of CCN size on simulated ground precipitation for deep convection case

A sensitivity study investigated the impact of the mean radius of the aerosol spectrum on surface precipitation using the two-moment bulk microphysical scheme. The numerical model solved the mixing ratios and corresponding number concentrations of the following seven hydrometeors: cloud droplets, raindrops, ice crystals, snow, graupel, frozen raindrops, and hail. Aerosol particles act as cloud condensation nuclei (CCN). The values of the mean radius of CCN varied from 0.01 μm to 1 μm, forming six sensitivity experiments. The mass production of various hydrometeors in the model domain and the amounts of surface rain and hail precipitation were analysed. Increasing the mean radius of the aerosol spectrum leads to an ever-widening of the precipitation area and a consequent increase in the amounts of rain and hail on the ground. The clouds that form in an environment characterized by a larger mean radius move faster and consist of multiple convective cells. In an environment with the smallest mean radius, there is a higher cloud droplet number concentration, which slows the transfer to rain via the autoconversion process. The higher amounts of frozen raindrops in the environment with larger aerosol particles lead to a pronounced source for hailstones, which further grows by accretion of cloud droplets. In such an atmosphere, a stronger melting of frozen raindrops and hail leads to faster conversion to the rain category compared to the cases with smaller aerosol particles.

Insect Herbivory Caused Plant Stress Emissions Increases the Negative Radiative Forcing of Aerosols.

Plant stress in a changing climate is predicted to increase plant volatile organic compound (VOC) emissions and thus can affect the formed secondary organic aerosol (SOA) concentrations, which in turn affect the radiative properties of clouds and aerosol. However, global aerosol‐climate models do not usually consider plant stress induced VOCs in their emission schemes. In this study, we modified the monoterpene emission factors in biogenic emission model to simulate biotic stress caused by insect herbivory on needleleaf evergreen boreal and broadleaf deciduous boreal trees and studied the consequent effects on SOA formation, aerosol‐cloud interactions as well as direct radiative effects of formed SOA. Simulations were done altering the fraction of stressed and healthy trees in the latest version of ECHAM‐HAMMOZ (ECHAM6.3‐HAM2.3‐MOZ1.0) global aerosol‐climate model. Our simulations showed that increasing the extent of stress to the aforementioned tree types, substantially increased the SOA burden especially over the areas where these trees are located. This indicates that increased VOC emissions due to increasing stress enhance the SOA formation via oxidation of VOCs to low VOCs. In addition, cloud droplet number concentration at the cloud top increased with increasing extent of biotic stress. This indicates that as SOA formation increases, it further enhances the number of particles acting as cloud condensation nuclei. The increase in SOA formation also decreased both all‐sky and clear‐sky radiative forcing. This was due to a shift in particle size distributions that enhanced aerosol reflecting and scattering of incoming solar radiation.

Impact of Holuhraun volcano aerosols on clouds in cloud-system-resolving simulations

Abstract. Increased anthropogenic aerosols result in an enhancement in cloud droplet number concentration (Nd), which consequently modifies the cloud and precipitation process. It is unclear how exactly the cloud liquid water path (LWP) and cloud fraction respond to aerosol perturbations. A volcanic eruption may help to better understand and quantify the cloud response to external perturbations, with a focus on the short-term cloud adjustments. The goal of the present study is to understand and quantify the response of clouds to a selected volcanic eruption and to thereby advance the fundamental understanding of the cloud response to external forcing. In this study we used the ICON (ICOsahedral Non-hydrostatic) model in its numerical weather prediction setup at a cloud-system-resolving resolution of 2.5 km horizontally, to simulate the region around the Holuhraun volcano for 1 week (1–7 September 2014). A pair of simulations, with and without the volcanic aerosol plume, allowed us to assess the simulated effective radiative forcing and its mechanisms, as well as its impact on adjustments of LWP and cloud fraction to the perturbations of Nd. In comparison to MODIS (Moderate Resolution Imaging Spectroradiometer) satellite retrievals, a clear enhancement of Nd due to the volcanic aerosol is detected and attributed. In contrast, no changes in either LWP or cloud fraction could be attributed. The on average almost unchanged LWP is a result of some LWP enhancement for thick clouds and a decrease for thin clouds.

Improving Global Weather Prediction in GFDL SHiELD Through an Upgraded GFDL Cloud Microphysics Scheme

AbstractWe describe the third version of the Geophysical Fluid Dynamics Laboratory cloud microphysics scheme (GFDL MP v3) implemented in the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD). Compared to the GFDL MP v2, the GFDL MP v3 is entirely reorganized, optimized, and modularized into functions. The particle size distribution (PSD) of all hydrometeor categories is redefined to better mimic observations, and the cloud droplet number concentration (CDNC) is calculated from the Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA2) aerosol data. In addition, the GFDL MP has been redesigned so all processes use the redefined PSD to ensure overall consistency and easily permit the introduction of new PSDs and microphysical processes. A year's worth of global 13‐km, 10‐day weather forecasts were performed with the new GFDL MP. Compared to the GFDL MP v2, the GFDL MP v3 significantly improves SHiELD's predictions of geopotential height, air temperature, and specific humidity in the Troposphere, as well as the high, middle and total cloud fractions and the liquid water path. The predictions are improved even further by the use of reanalysis aerosol data to calculate CDNC, and also by using the more realistic PSD available in GFDL MP v3. However, the upgrade of the GFDL MP shows little impact on the precipitation prediction. Degradations caused by the new scheme are discussed and provide a guide for future GFDL MP development.

The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data

Abstract. Cloud droplet number concentration (Nd) is of central importance to observation-based estimates of aerosol indirect effects, being used to quantify both the cloud sensitivity to aerosol and the base state of the cloud. However, the derivation of Nd from satellite data depends on a number of assumptions about the cloud and the accuracy of the retrievals of the cloud properties from which it is derived, making it prone to systematic biases. A number of sampling strategies have been proposed to address these biases by selecting the most accurate Nd retrievals in the satellite data. This work compares the impact of these strategies on the accuracy of the satellite retrieved Nd, using a selection of in situ measurements. In stratocumulus regions, the MODIS Nd retrieval is able to achieve a high precision (r2 of 0.5–0.8). This is lower in other cloud regimes but can be increased by appropriate sampling choices. Although the Nd sampling can have significant effects on the Nd climatology, it produces only a 20 % variation in the implied radiative forcing from aerosol–cloud interactions, with the choice of aerosol proxy driving the overall uncertainty. The results are summarised into recommendations for using MODIS Nd products and appropriate sampling.