scholarly journals On the relationship between cloud water composition and cloud droplet number concentration

2020 ◽  
Vol 20 (13) ◽  
pp. 7645-7665 ◽  
Author(s):  
Alexander B. MacDonald ◽  
Ali Hossein Mardi ◽  
Hossein Dadashazar ◽  
Mojtaba Azadi Aghdam ◽  
Ewan Crosbie ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Environmental Factors ◽  
Cloud Droplet ◽  
Cloud Water ◽  
Number Concentration ◽  
Sea Salt ◽  
Cloud Base ◽  
Cloud Height ◽  
Cloud Droplet Number Concentration ◽  
Total Sulfate

Abstract. Aerosol–cloud interactions are the largest source of uncertainty in quantifying anthropogenic radiative forcing. The large uncertainty is, in part, due to the difficulty of predicting cloud microphysical parameters, such as the cloud droplet number concentration (Nd). Even though rigorous first-principle approaches exist to calculate Nd, the cloud and aerosol research community also relies on empirical approaches such as relating Nd to aerosol mass concentration. Here we analyze relationships between Nd and cloud water chemical composition, in addition to the effect of environmental factors on the degree of the relationships. Warm, marine, stratocumulus clouds off the California coast were sampled throughout four summer campaigns between 2011 and 2016. A total of 385 cloud water samples were collected and analyzed for 80 chemical species. Single- and multispecies log–log linear regressions were performed to predict Nd using chemical composition. Single-species regressions reveal that the species that best predicts Nd is total sulfate (Radj2=0.40). Multispecies regressions reveal that adding more species does not necessarily produce a better model, as six or more species yield regressions that are statistically insignificant. A commonality among the multispecies regressions that produce the highest correlation with Nd was that most included sulfate (either total or non-sea-salt), an ocean emissions tracer (such as sodium), and an organic tracer (such as oxalate). Binning the data according to turbulence, smoke influence, and in-cloud height allowed for examination of the effect of these environmental factors on the composition–Nd correlation. Accounting for turbulence, quantified as the standard deviation of vertical wind speed, showed that the correlation between Nd with both total sulfate and sodium increased at higher turbulence conditions, consistent with turbulence promoting the mixing between ocean surface and cloud base. Considering the influence of smoke significantly improved the correlation with Nd for two biomass burning tracer species in the study region, specifically oxalate and iron. When binning by in-cloud height, non-sea-salt sulfate and sodium correlated best with Nd at cloud top, whereas iron and oxalate correlated best with Nd at cloud base.

scholarly journals On the Relationship Between Cloud Water Composition and Cloud Droplet Number Concentration

2020 ◽  
Author(s):  
Alexander B. MacDonald ◽  
Ali Hossein Mardi ◽  
Hossein Dadashazar ◽  
Mojtaba Azadi Aghdam ◽  
Ewan Crosbie ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Environmental Factors ◽  
Cloud Droplet ◽  
Cloud Water ◽  
Number Concentration ◽  
Sea Salt ◽  
Cloud Base ◽  
Cloud Height ◽  
Cloud Droplet Number Concentration ◽  
Total Sulfate

Abstract. Aerosol-cloud interactions are the largest source of uncertainty in quantifying anthropogenic radiative forcing. The large uncertainty is, in part, due to the difficulty of predicting cloud microphysical parameters, such as the cloud droplet number concentration (Nd). Even though rigorous first-principle approaches exist to calculate Nd, the cloud and aerosol research community also relies on empirical approaches such as relating Nd to aerosol mass concentration. Here we analyze relationships between Nd and cloud water chemical composition, in addition to the effect of environmental factors on the degree of the relationships. Warm, marine, stratocumulus clouds off the California coast were sampled throughout four summer campaigns between 2011 and 2016. A total of 385 cloud water samples were collected and analyzed for 79 chemical species. Single- and multi-species log-log linear regressions were performed to predict Nd using chemical composition. Single-species regressions reveal that the species that best predicts Nd is total sulfate (R2adj = 0.40). Multi-species regressions reveal that adding more species does not necessarily produce a better model, as six or more species yield regressions that are statistically insignificant. A commonality among the multi-species regressions that produce the highest correlation with Nd was that most included sulfate (either total or non-sea salt), an ocean emissions tracer (such as sodium), and an organic tracer (such as oxalate). Binning the data according to turbulence, smoke influence, and in-cloud height allowed examination of the effect of these environmental factors on the composition-Nd correlation. Accounting for turbulence, quantified as the standard deviation of vertical wind speed, showed that the correlation between Nd with both total sulfate and sodium increased at higher turbulence conditions, consistent with turbulence promoting the mixing between ocean surface and cloud base. Considering the influence of smoke significantly improved the correlation with Nd for two biomass burning tracer species in the study region, specifically oxalate and iron. When binning by in-cloud height, non-sea salt sulfate and sodium correlated best with Nd at cloud top, whereas iron and oxalate correlate best with Nd at cloud base.

scholarly journals Improvement of airborne retrievals of cloud droplet number concentration of trade wind cumulus using a synergetic approach

2019 ◽  
Vol 12 (3) ◽  
pp. 1635-1658 ◽  
Author(s):  
Kevin Wolf ◽  
André Ehrlich ◽  
Marek Jacob ◽  
Susanne Crewell ◽  
Martin Wirth ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Cloud Base ◽  
Trade Wind ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Research Aircraft ◽  
Cloud Droplet Number Concentration

Abstract. In situ measurements of cloud droplet number concentration N are limited by the sampled cloud volume. Satellite retrievals of N suffer from inherent uncertainties, spatial averaging, and retrieval problems arising from the commonly assumed strictly adiabatic vertical profiles of cloud properties. To improve retrievals of N it is suggested in this paper to use a synergetic combination of passive and active airborne remote sensing measurement, to reduce the uncertainty of N retrievals, and to bridge the gap between in situ cloud sampling and global averaging. For this purpose, spectral solar radiation measurements above shallow trade wind cumulus were combined with passive microwave and active radar and lidar observations carried out during the second Next Generation Remote Sensing for Validation Studies (NARVAL-II) campaign with the High Altitude and Long Range Research Aircraft (HALO) in August 2016. The common technique to retrieve N is refined by including combined measurements and retrievals of cloud optical thickness τ, liquid water path (LWP), cloud droplet effective radius reff, and cloud base and top altitude. Three approaches are tested and applied to synthetic measurements and two cloud scenarios observed during NARVAL-II. Using the new combined retrieval technique, errors in N due to the adiabatic assumption have been reduced significantly.

Quantification of aerosol effects on marine boundary layer clouds with machine learning

2021 ◽  
Author(s):  
Lukas Zipfel ◽  
Hendrik Andersen ◽  
Jan Cermak
Keyword(s):  
Machine Learning ◽  
Boundary Layer ◽  
Marine Boundary Layer ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Satellite Observations ◽  
Radiative Properties ◽  
Cloud Base ◽  
Boundary Layer Clouds ◽  
Cloud Droplet Number Concentration

<p>Satellite observations are used in regional machine learning models to quantify sensitivities of marine boundary-layer clouds (MBLC) to aerosol changes.</p><p>MBLCs make up a large part of the global cloud coverage as they are persistently present over more than 20% of the Earth’s oceans in the annual mean.They play an important role in Earth’s energy budget by reflecting solar radiation and interacting with thermal radiation from the surface, leading to a net cooling effect. Cloud properties and their radiative characteristics such as cloud albedo, horizontal and vertical extent, lifetime and precipitation susceptibility are dependent on environmental conditions. Aerosols in their role as condensation nuclei affect these cloud radiative properties through changes in the cloud droplet number concentration and subsequent cloud adjustments to this perturbation. However, the magnitude and sign of these effects remain among the largest uncertainties in future climate predictions.</p><p>In an effort to help improve these predictions a machine learning approach in combination with observational data is pursued:</p><p>Satellite observations from the collocated A-Train dataset (C3M) for 2006-2011 are used in combination with ECMWF atmospheric reanalysis data (ERA5) to train regional Gradient Boosting Regression Tree (GBRT) models to predict changes in key physical and radiative properties of MBLCs. The cloud droplet number concentration (N<sub>d</sub>) and the liquid water path (LWP) are simulated for the eastern subtropical oceans, which are characterised by a high annual coverage of MBLC due to the occurrence of semi-permanent stratocumulus sheets. Relative humidity above cloud, cloud top height and temperature below the cloud base and at the surface are identified as important predictors for both N<sub>d</sub> and LWP.  The impact of each predictor variable on the GBRT model's output is analysed using Shapley values as a method of explainable machine learning, providing novel sensitivity estimates that will improve process understanding and help constrain the parameterization of MBLC processes in Global Climate Models.</p>

On the Relation among Satellite Observed Liquid Water Path, Cloud Droplet Number Concentration and Cloud Base Rain Rate and Its Implication to the Auto-Conversion Parameterization in Stratocumulus Clouds

2021 ◽  
pp. 1
Author(s):  
Yasutaka Murakami ◽  
Christian D. Kummerow ◽  
Susan C. van den Heever
Keyword(s):  
Rain Rate ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Satellite Observations ◽  
Cloud Base ◽  
Liquid Water Path ◽  
Warm Rain ◽  
Accretion Process ◽  
Stratocumulus Clouds ◽  
Cloud Droplet Number Concentration

AbstractPrecipitation processes play a critical role in the longevity and spatial distribution of stratocumulus clouds through their interaction with the vertical profiles of humidity and temperature within the atmospheric boundary layer. One of the difficulties in understanding these processes is the limited amount of observational data. In this study, robust relations among liquid water path (LWP), cloud droplet number concentration (Nd) and cloud base rain rate (Rcb) from three subtropical stratocumulus decks are obtained from A-Train satellite observations in order to obtain a broad perspective on warm rain processes. Rcb has a positive correlation with LWP/Nd and the increase of Rcb becomes larger as LWP/Nd increases. However, the increase of Rcb with respect to LWP/Nd becomes more gradual in regions with larger Nd, which indicates the relation is moderated by Nd. These results are consistent with our theoretical understanding of warm rain processes and suggest that satellite observations are capable of elucidating the average manner of how precipitation processes are modulated by LWP and Nd. The sensitivity of the auto-conversion rate to Nd is investigated by examining pixels with small LWP in which the accretion process is assumed to have little influence on Rcb. The upper limit of the dependency of auto-conversion rate on Nd is assessed from the relation between Rcb and Nd, since the sensitivity is exaggerated by the accretion process, and was found to be a cloud droplet number concentration to the power of −1.44 ± 0.12.

scholarly journals Global impact of mineral dust on cloud droplet number concentration

2017 ◽  
Vol 17 (9) ◽  
pp. 5601-5621 ◽  
Author(s):  
Vlassis A. Karydis ◽  
Alexandra P. Tsimpidi ◽  
Sara Bacer ◽  
Andrea Pozzer ◽  
Athanasios Nenes ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Mineral Dust ◽  
Cloud Droplet ◽  
Droplet Formation ◽  
Number Concentration ◽  
Dust Particles ◽  
Southern Europe ◽  
Northeastern Asia ◽  
Adsorption Theory ◽  
Cloud Droplet Number Concentration

Abstract. The importance of wind-blown mineral dust for cloud droplet formation is studied by considering (i) the adsorption of water on the surface of insoluble particles, (ii) particle coating by soluble material (atmospheric aging) which augments cloud condensation nuclei (CCN) activity, and (iii) the effect of dust on inorganic aerosol concentrations through thermodynamic interactions with mineral cations. The ECHAM5/MESSy Atmospheric Chemistry (EMAC) model is used to simulate the composition of global atmospheric aerosol, while the ISORROPIA-II thermodynamic equilibrium model treats the interactions of K+-Ca2+-Mg2+-NH4+-Na+-SO42−-NO3−-Cl−-H2O aerosol with gas-phase inorganic constituents. Dust is considered a mixture of inert material with reactive minerals and its emissions are calculated online by taking into account the soil particle size distribution and chemical composition of different deserts worldwide. The impact of dust on droplet formation is treated through the unified dust activation parameterization that considers the inherent hydrophilicity from adsorption and acquired hygroscopicity from soluble salts during aging. Our simulations suggest that the presence of dust increases cloud droplet number concentration (CDNC) over major deserts (e.g., up to 20 % over the Sahara and the Taklimakan desert) and decreases CDNC over polluted areas (e.g., up to 10 % over southern Europe and 20 % over northeastern Asia). This leads to a global net decrease in CDNC by 11 %. The adsorption activation of insoluble aerosols and the mineral dust chemistry are shown to be equally important for the cloud droplet formation over the main deserts; for example, these effects increase CDNC by 20 % over the Sahara. Remote from deserts the application of adsorption theory is critically important since the increased water uptake by the large aged dust particles (i.e., due to the added hydrophilicity by the soluble coating) reduce the maximum supersaturation and thus cloud droplet formation from the relatively smaller anthropogenic particles (e.g., CDNC decreases by 10 % over southern Europe and 20 % over northeastern Asia by applying adsorption theory). The global average CDNC decreases by 10 % by considering adsorption activation, while changes are negligible when accounting for the mineral dust chemistry. Sensitivity simulations indicate that CDNC is also sensitive to the mineral dust mass and inherent hydrophilicity, and not to the chemical composition of the emitted dust.

scholarly journals Supplementary material to "On the Relationship Between Cloud Water Composition and Cloud Droplet Number Concentration"

2020 ◽  
Author(s):  
Alexander B. MacDonald ◽  
Ali Hossein Mardi ◽  
Hossein Dadashazar ◽  
Mojtaba Azadi Aghdam ◽  
Ewan Crosbie ◽  
...  
Keyword(s):  
Cloud Droplet ◽  
Cloud Water ◽  
Number Concentration ◽  
Water Composition ◽  
Cloud Droplet Number Concentration ◽  
Supplementary Material ◽  
The Relationship

scholarly journals Strong aerosol–cloud interaction in altocumulus during updraft periods: lidar observations over central Europe

2015 ◽  
Vol 15 (18) ◽  
pp. 10687-10700 ◽  
Author(s):  
J. Schmidt ◽  
A. Ansmann ◽  
J. Bühl ◽  
U. Wandinger
Keyword(s):  
Liquid Water ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Cloud Base ◽  
Pure Liquid ◽  
Raman Lidar ◽  
Aerosol Cloud ◽  
Cloud Properties ◽  
Cloud Dynamics ◽  
Cloud Droplet Number Concentration

Abstract. For the first time, a liquid-water cloud study of the aerosol–cloud-dynamics relationship, solely based on lidar, was conducted. Twenty-nine cases of pure liquid-water altocumulus layers were observed with a novel dual-field-of-view Raman lidar over the polluted central European site of Leipzig, Germany, between September 2010 and September 2012. By means of the novel Raman lidar technique, cloud properties such as the droplet effective radius and cloud droplet number concentration (CDNC) in the lower part of altocumulus layers are obtained. The conventional aerosol Raman lidar technique provides the aerosol extinction coefficient (used as aerosol proxy) below cloud base. A collocated Doppler lidar measures the vertical velocity at cloud base and thus updraft and downdraft occurrence. Here, we present the key results of our statistical analysis of the 2010–2012 observations. Besides a clear aerosol effect on cloud droplet number concentration in the lower part of the altocumulus layers during updraft periods, turbulent mixing and entrainment of dry air is assumed to be the main reason for the found weak correlation between aerosol proxy and CDNC higher up in the cloud. The corresponding aerosol–cloud interaction parameter based on changes in cloud droplet number concentration with aerosol loading was found to be close to 0.8 at 30–70 m above cloud base during updraft periods and below 0.4 when ignoring vertical-wind information in the analysis. Our findings are extensively compared with literature values and agree well with airborne observations.

scholarly journals Improvement of Airborne Retrievals of Cloud Droplet Number Concentration of Trade Wind Cumulus Using a Synergetic Approach

2018 ◽  
Author(s):  
Kevin Wolf ◽  
André Ehrlich ◽  
Marek Jacob ◽  
Susanne Crewell ◽  
Martin Wirth ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Liquid Water ◽  
Weather Prediction ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Global Climate Models ◽  
Cloud Base ◽  
Trade Wind ◽  
Cloud Droplet Number Concentration

Abstract. The cloud droplet number concentration N is a major variable in numerical weather prediction (NWP) and global climate models (GCMs). In combination with the liquid water content LWC it determines the cloud droplet effective radius reff, which is used to calculate the radiative effects of clouds. Especially, the solar cloud top reflectivity R of shallow trade wind cumulus is highly sensitive with respect to N and LWC. In-situ measurements of N are limited by the sampled cloud volume, not covering the significant natural variability. Satellite retrievals of N suffer from large uncertainties and strictly assume adiabatic vertical profiles of cloud properties. Therefore, it is suggested to use the synergy of passive and active airborne remote sensing to reduce the uncertainty of N retrievals and to bridge the gap between in-situ cloud sampling and global averaging. For this purpose, spectral solar radiation measurements above shallow trade-wind cumulus were combined with passive microwave and active radar and lidar observations carried out during the second Next Generation Remote Sensing for Validation Studies (NARVAL-II) campaign with the High Altitude and Long Range Research Aircraft (HALO) in August 2016. The common approach to retrieve N is further developed by including combined measurements and retrievals of cloud optical thickness τ, liquid water path LWP, reff, as well as cloud base and top altitude. Three approaches are tested and applied to synthetic measurements and two cloud cases of NARVAL-II. Using the new techniques, errors in N due to the adiabatic assumption have been reduced significantly.

scholarly journals Role of updrafts in aerosol-cloud interactions: lidar observations of layered warm clouds over central Europe

2014 ◽  
Vol 14 (22) ◽  
pp. 31409-31440 ◽  
Author(s):  
J. Schmidt ◽  
A. Ansmann ◽  
J. Bühl ◽  
U. Wandinger
Keyword(s):  
Cloud Droplet ◽  
Number Concentration ◽  
Cloud Base ◽  
Aerosol Cloud ◽  
Cloud Properties ◽  
Aerosol Effect ◽  
Cloud Dynamics ◽  
Cloud Droplet Number Concentration ◽  
Water Cloud ◽  
Aerosol Cloud Interactions

Abstract. Twenty nine cases of layered liquid-water cloud systems were observed with dual-field-of-view (dual-FOV) Raman lidar over the polluted central European site of Leipzig, Germany, between September 2010 and September 2012. For the first time, a detailed lidar-based study of aerosol-cloud-dynamics relationship was conducted. A collocated Doppler lidar provided information on vertical velocity and thus on updraft and downdraft occurrence. The novel dual-FOV lidar permits the retrieval of the particle extinction coefficient (used as aerosol proxy just below cloud base) and cloud properties such as droplet effective radius and cloud droplet number concentration in the lower part of optically thin cloud layers. Here, we present the key results of our statistical analysis of the 2010–2012 observations. Besides a clear aerosol effect on cloud droplet number concentration in the lower part of the convectively weak cloud layers during updraft periods, meteorological effects (turbulent mixing, entrainment of dry air) were found to diminish the observable aerosol effect higher up in the clouds. The corresponding aerosol-cloud interaction (ACI) parameter based on changes in cloud droplet number concentration with aerosol loading was found to be close to 0.8 at 30–70 m above cloud base during updraft periods which points to values around 1 at cloud base (0–30 m above cloud base). Our findings are extensively compared with literature values and agree well with airborne observations. As a conclusion, ACI studies over continental sites should include vertical wind observations to avoid a~bias (too low values) in the obtained ACI results.

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