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Athanasios Nenes - One of the best experts on this subject based on the ideXlab platform.
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global impact of mineral dust on Cloud Droplet number concentration
Atmospheric Chemistry and Physics, 2016Co-Authors: V A Karydis, Athanasios Nenes, Alexandra P Tsimpidi, Sara Bacer, Andrea Pozzer, Jos LelieveldAbstract: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.
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1 Parameterization of Cloud Droplet formation in large scale models: 1 including effects of entrainment 2
2015Co-Authors: Donifan Barahona, Athanasios NenesAbstract:Abstract 12 This work offers for the first time a comprehensive parameterization suitable for large 13 scale models which is robust, computationally efficient, and from first principles links 14 chemical effects, aerosol heterogeneity and entrainment with Cloud Droplet formation. 15 The parameterization is based on the entraining ascending parcel model framework; 16 mixing of outside air is parameterized in terms of a per-length entrainment rate. The 17 integration of the Droplet growth is done using the “population splitting ” concept of 18 Nenes and Seinfeld. Formulations for lognormal and sectional aerosol representations are 19 given, as well as simplifications that allow the treatment of entrainment with high 20 computational efficiency without loss of accuracy. The concept of “critical entrainment”, 21 a value beyond which Droplet activation is not favored, is introduced and shown that it is 22 important for defining i) whether or not entrainment effects have an impact on Droplet 23 formation, and, ii) the characteristic temperature and pressure for Cloud Droplet 24 formation. The performance of the parameterization was evaluated against a detailed 25 numerical parcel model over a comprehensive range of Droplet formation conditions. The 26 agreement is always very good (mean relative error 2.3 % ± 21%); errors tend to increase 27 as entrainment approaches the critical value, but are never above 40%. 28
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surfactants from the gas phase may promote Cloud Droplet formation
Proceedings of the National Academy of Sciences of the United States of America, 2013Co-Authors: N Sareen, Athanasios Nenes, Allison N Schwier, T L Lathem, Faye V McneillAbstract:Clouds, a key component of the climate system, form when water vapor condenses upon atmospheric particulates termed Cloud condensation nuclei (CCN). Variations in CCN concentrations can profoundly impact Cloud properties, with important effects on local and global climate. Organic matter constitutes a significant fraction of tropospheric aerosol mass, and can influence CCN activity by depressing surface tension, contributing solute, and influencing Droplet activation kinetics by forming a barrier to water uptake. We present direct evidence that two ubiquitous atmospheric trace gases, methylglyoxal (MG) and acetaldehyde, known to be surface-active, can enhance aerosol CCN activity upon uptake. This effect is demonstrated by exposing acidified ammonium sulfate particles to 250 parts per billion (ppb) or 8 ppb gas-phase MG and/or acetaldehyde in an aerosol reaction chamber for up to 5 h. For the more atmospherically relevant experiments, i.e., the 8-ppb organic precursor concentrations, significant enhancements in CCN activity, up to 7.5% reduction in critical dry diameter for activation, are observed over a timescale of hours, without any detectable limitation in activation kinetics. This reduction in critical diameter enhances the apparent particle hygroscopicity up to 26%, which for ambient aerosol would lead to Cloud Droplet number concentration increases of 8–10% on average. The observed enhancements exceed what would be expected based on Kohler theory and bulk properties. Therefore, the effect may be attributed to the adsorption of MG and acetaldehyde to the gas–aerosol interface, leading to surface tension depression of the aerosol. We conclude that gas-phase surfactants may enhance CCN activity in the atmosphere.
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on the effect of dust particles on global Cloud condensation nuclei and Cloud Droplet number
Journal of Geophysical Research, 2011Co-Authors: V A Karydis, Prashant Kumar, Donifan Barahona, Irina N Sokolik, Athanasios NenesAbstract:[1] Aerosol-Cloud interaction studies to date consider aerosol with a substantial fraction of soluble material as the sole source of Cloud condensation nuclei (CCN). Emerging evidence suggests that mineral dust can act as good CCN through water adsorption onto the surface of particles. This study provides a first assessment of the contribution of insoluble dust to global CCN and Cloud Droplet number concentration (CDNC). Simulations are carried out with the NASA Global Modeling Initiative chemical transport model with an online aerosol simulation, considering emissions from fossil fuel, biomass burning, marine, and dust sources. CDNC is calculated online and explicitly considers the competition of soluble and insoluble CCN for water vapor. The predicted annual average contribution of insoluble mineral dust to CCN and CDNC in Cloud-forming areas is up to 40 and 23.8%, respectively. Sensitivity tests suggest that uncertainties in dust size distribution and water adsorption parameters modulate the contribution of mineral dust to CDNC by 23 and 56%, respectively. Coating of dust by hygroscopic salts during the atmospheric aging causes a twofold enhancement of the dust contribution to CCN; the aged dust, however, can substantially deplete in-Cloud supersaturation during the initial stages of Cloud formation and can eventually reduce CDNC. Considering the hydrophilicity from adsorption and hygroscopicity from solute is required to comprehensively capture the dust-warm Cloud interactions. The framework presented here addresses this need and can be easily integrated in atmospheric models.
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characteristic updrafts for computing distribution averaged Cloud Droplet number and stratocumulus Cloud properties
Journal of Geophysical Research, 2010Co-Authors: Ricardo Morales, Athanasios NenesAbstract:[1] A computationally effective framework is presented that addresses the contribution of subgrid-scale vertical velocity variations in predictions of Cloud Droplet number concentration (CDNC) in large-scale models. Central to the framework is the concept of a “characteristic updraft velocity” , which yields CDNC value representative of integration over a probability density function (PDF) of updraft (i.e., positive vertical) velocity. Analytical formulations for are developed for computation of average CDNC over a Gaussian PDF using the Twomey Droplet parameterization. The analytical relationship also agrees with numerical integrations using a state-of-the-art Droplet activation parameterization. For situations where the variabilities of vertical velocity and liquid water content can be decoupled, the concept of is extended to the calculation of Cloud properties and process rates that complements existing treatments for subgrid variability of liquid water content. It is shown that using the average updraft velocity (instead of ) for calculations of Nd, re, and A (a common practice in atmospheric models) can overestimate PDF-averaged Nd by 10%, underestimate re by 10%–15%, and significantly underpredict autoconversion rate between a factor of 2–10. The simple expressions of presented here can account for an important source of parameterization “tuning” in a physically based manner.
Yangang Liu - One of the best experts on this subject based on the ideXlab platform.
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comment on Cloud Droplet spectral width relationship to ccn spectra and vertical velocity by hudson et al
Journal of Geophysical Research, 2014Co-Authors: Yangang Liu, Peter H DaumAbstract:Hudson et al. [2012, hereafter H12] have lately shown that the standard deviation (σ) of Droplet size distributions was inversely related to Cloud condensation nuclei (CCN) concentration at 1% supersaturation (NCCN) for the in situ aircraft measurements collected during the Rain in Cumulus over the Ocean (RICO) project. Using adiabatic parcel model simulations for various values of updraft velocity (w), they further reported a tendency for the σ -N CCN correlation to change signs from positive to negative as w increases beyond a certain value. The analysis of σ -N CCN correlation and its dependence on w certainly add new understanding of dispersion effect (e.g., Liu and Daum, 2002, LD02 hereafter; Liu et al., 2006b, LDY06 hereafter) and imply that like the more widely known aerosol effect on Cloud Droplet number concentration (N), dispersion effect may exhibit aerosol-limited and w-limited regimes such that dispersion effect can either diminish or enhance the cooling of number effect, depending on the regimes. However, there appears to be some misunderstanding/misinterpretation of LD02 and LDY06 concerning the use of relative dispersion as a measure of spectral width. Furthermore, our examination of the data reported in Table 1 of H12 shows that there is a positive correlation between NCCN and w, and thus it cannot be ruled out that the observed negative σ -N CCN correlation is a manifestation of the covariation in w, or arises from the indirect effect of aerosol on Cloud dynamics, or a combination of both. Below these points are detailed.
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analytical expression for the relative dispersion of the Cloud Droplet size distribution
Geophysical Research Letters, 2006Co-Authors: Yangang Liu, Peter H Daum, Seong Soo YumAbstract:[1] An analytical expression that relates the relative dispersion (ratio of standard deviation to mean radius) of the Cloud Droplet size distribution to CCN spectra and updraft velocity is derived from adiabatic growth theory of Cloud Droplets. Coupled with the Twomey expression for Droplet concentration, the analytical expression is used to examine the relationship of relative dispersion to Droplet concentration under different combinations of CCN spectra and updraft velocities. These analytical results compare favorably with the corresponding simulations of an adiabatic parcel model. The analytical expression theoretically demonstrates that an increase in aerosol loading (CCN concentration) leads to concurrent increases in the Droplet concentration and relative dispersion whereas a larger updraft velocity leads to a higher Droplet concentration but a smaller relative dispersion.
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sensitivity of the first indirect aerosol effect to an increase of Cloud Droplet spectral dispersion with Droplet number concentration
Journal of Climate, 2003Co-Authors: Leon Rotstayn, Yangang LiuAbstract:Observations show that an increase in anthropogenic aerosols leads to concurrent increases in the Cloud Droplet concentration and the relative dispersion of the Cloud Droplet spectrum, other factors being equal. It has been suggested that the increase in effective radius resulting from increased relative dispersion may substantially negate the indirect aerosol effect, but this is usually not parameterized in global climate models (GCMs). Empirical parameterizations, designed to represent the average of this effect, as well as its lower and upper bounds, are tested in the CSIRO GCM. Compared to a control simulation, in which the relative dispersion of the Cloud Droplet spectrum is prescribed separately over land and ocean, inclusion of this effect reduces the magnitude of the first indirect aerosol effect by between 12% and 35%.
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spectral dispersion of Cloud Droplet size distributions and the parameterization of Cloud Droplet effective radius
Geophysical Research Letters, 2000Co-Authors: Yangang Liu, Peter H DaumAbstract:Parameterization of effective radius (re) as proportional (Pontikis and Hicks, 1992; Bower and Choularton, 1992; Bower et to the cube root of the ratio of Cloud liquid water content (L) to al., 1994; Martin et al., 1994; Liu and Hallett, 1997; Reid et al., Hicks (1992) and by Liu and Hallet (1997) that account for the dependence of ot on the spectral dispersion are compared to each where re is the effective radius intm, L the liquid water content in other and to Cloud microphysical data collected during two recent gm -3, N the total Droplet concentration in cm -3, and ot the prefactor. field studies. The expression of Liu and Hallet describes the A key issue in use of this parameterization is the specification spectral dependence of ot (or re) more accurately than the Pontikis of or. Here we explore the dependence of ot on the spectral disper- and Hicks relation over the observed range of spectral dispersions. sion of Cloud Droplet size distributions. Values of ot derived from The comparison shows that the different treatments of ot as a func- other studies are compared to those derived from the data collected tion of spectral dispersion alone can result in substantial differ- during two Intensive Observation Periods (IOP) conducted at the ences in re estimated from different parameterization schemes, Atmospheric Radiation Measurements (ARM) program Southern suggesting that accurately representing re in climate models Great Plain (SGP) site in Oklahoma, in the spring and fall of 1997. requires predicting ot in addition to L and N. This analysis suggests the necessity and possibility of improving
Peter H Daum - One of the best experts on this subject based on the ideXlab platform.
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comment on Cloud Droplet spectral width relationship to ccn spectra and vertical velocity by hudson et al
Journal of Geophysical Research, 2014Co-Authors: Yangang Liu, Peter H DaumAbstract:Hudson et al. [2012, hereafter H12] have lately shown that the standard deviation (σ) of Droplet size distributions was inversely related to Cloud condensation nuclei (CCN) concentration at 1% supersaturation (NCCN) for the in situ aircraft measurements collected during the Rain in Cumulus over the Ocean (RICO) project. Using adiabatic parcel model simulations for various values of updraft velocity (w), they further reported a tendency for the σ -N CCN correlation to change signs from positive to negative as w increases beyond a certain value. The analysis of σ -N CCN correlation and its dependence on w certainly add new understanding of dispersion effect (e.g., Liu and Daum, 2002, LD02 hereafter; Liu et al., 2006b, LDY06 hereafter) and imply that like the more widely known aerosol effect on Cloud Droplet number concentration (N), dispersion effect may exhibit aerosol-limited and w-limited regimes such that dispersion effect can either diminish or enhance the cooling of number effect, depending on the regimes. However, there appears to be some misunderstanding/misinterpretation of LD02 and LDY06 concerning the use of relative dispersion as a measure of spectral width. Furthermore, our examination of the data reported in Table 1 of H12 shows that there is a positive correlation between NCCN and w, and thus it cannot be ruled out that the observed negative σ -N CCN correlation is a manifestation of the covariation in w, or arises from the indirect effect of aerosol on Cloud dynamics, or a combination of both. Below these points are detailed.
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analytical expression for the relative dispersion of the Cloud Droplet size distribution
Geophysical Research Letters, 2006Co-Authors: Yangang Liu, Peter H Daum, Seong Soo YumAbstract:[1] An analytical expression that relates the relative dispersion (ratio of standard deviation to mean radius) of the Cloud Droplet size distribution to CCN spectra and updraft velocity is derived from adiabatic growth theory of Cloud Droplets. Coupled with the Twomey expression for Droplet concentration, the analytical expression is used to examine the relationship of relative dispersion to Droplet concentration under different combinations of CCN spectra and updraft velocities. These analytical results compare favorably with the corresponding simulations of an adiabatic parcel model. The analytical expression theoretically demonstrates that an increase in aerosol loading (CCN concentration) leads to concurrent increases in the Droplet concentration and relative dispersion whereas a larger updraft velocity leads to a higher Droplet concentration but a smaller relative dispersion.
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spectral dispersion of Cloud Droplet size distributions and the parameterization of Cloud Droplet effective radius
Geophysical Research Letters, 2000Co-Authors: Yangang Liu, Peter H DaumAbstract:Parameterization of effective radius (re) as proportional (Pontikis and Hicks, 1992; Bower and Choularton, 1992; Bower et to the cube root of the ratio of Cloud liquid water content (L) to al., 1994; Martin et al., 1994; Liu and Hallett, 1997; Reid et al., Hicks (1992) and by Liu and Hallet (1997) that account for the dependence of ot on the spectral dispersion are compared to each where re is the effective radius intm, L the liquid water content in other and to Cloud microphysical data collected during two recent gm -3, N the total Droplet concentration in cm -3, and ot the prefactor. field studies. The expression of Liu and Hallet describes the A key issue in use of this parameterization is the specification spectral dependence of ot (or re) more accurately than the Pontikis of or. Here we explore the dependence of ot on the spectral disper- and Hicks relation over the observed range of spectral dispersions. sion of Cloud Droplet size distributions. Values of ot derived from The comparison shows that the different treatments of ot as a func- other studies are compared to those derived from the data collected tion of spectral dispersion alone can result in substantial differ- during two Intensive Observation Periods (IOP) conducted at the ences in re estimated from different parameterization schemes, Atmospheric Radiation Measurements (ARM) program Southern suggesting that accurately representing re in climate models Great Plain (SGP) site in Oklahoma, in the spring and fall of 1997. requires predicting ot in addition to L and N. This analysis suggests the necessity and possibility of improving
Florian Ewald - One of the best experts on this subject based on the ideXlab platform.
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remote sensing of Cloud Droplet radius profiles using solar reflectance from Cloud sides part 1 retrieval development and characterization
Atmospheric Measurement Techniques, 2019Co-Authors: Florian Ewald, Tobias Zinner, Tobias Kolling, Bernhard MayerAbstract:Abstract. Convective Clouds play an essential role for Earth's climate as well as for regional weather events since they have a large influence on the radiation budget and the water cycle. In particular, Cloud albedo and the formation of precipitation are influenced by aerosol particles within Clouds. In order to improve the understanding of processes from aerosol activation, from Cloud Droplet growth to changes in Cloud radiative properties, remote sensing techniques become more and more important. While passive retrievals for spaceborne observations have become sophisticated and commonplace for inferring Cloud optical thickness and Droplet size from Cloud tops, profiles of Droplet size have remained largely uncharted territory for passive remote sensing. In principle they could be derived from observations of Cloud sides, but faced with the small-scale heterogeneity of Cloud sides, “classical” passive remote sensing techniques are rendered inappropriate. In this work the feasibility is demonstrated to gain new insights into the vertical evolution of Cloud Droplet effective radius by using reflected solar radiation from Cloud sides. Central aspect of this work on its path to a working Cloud side retrieval is the analysis of the impact unknown Cloud surface geometry has on effective radius retrievals. This study examines the sensitivity of reflected solar radiation to Cloud Droplet size, using extensive 3-D radiative transfer calculations on the basis of realistic Droplet size resolving Cloud simulations. Furthermore, it explores a further technique to resolve ambiguities caused by illumination and Cloud geometry by considering the surroundings of each pixel. Based on these findings, a statistical approach is used to provide an effective radius retrieval. This statistical effective radius retrieval is focused on the liquid part of convective water Clouds, e.g., cumulus mediocris, cumulus congestus, and trade-wind cumulus, which exhibit well-developed Cloud sides. Finally, the developed retrieval is tested using known and unknown Cloud side scenes to analyze its performance.
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Remote sensing of Cloud Droplet radius profiles using solar reflectance from Cloud sides – Part 1: Retrieval development and characterization
Copernicus Publications, 2019Co-Authors: Florian Ewald, Tobias Kolling, T. Zinne, . MayeAbstract:Convective Clouds play an essential role for Earth's climate as well as for regional weather events since they have a large influence on the radiation budget and the water cycle. In particular, Cloud albedo and the formation of precipitation are influenced by aerosol particles within Clouds. In order to improve the understanding of processes from aerosol activation, from Cloud Droplet growth to changes in Cloud radiative properties, remote sensing techniques become more and more important. While passive retrievals for spaceborne observations have become sophisticated and commonplace for inferring Cloud optical thickness and Droplet size from Cloud tops, profiles of Droplet size have remained largely uncharted territory for passive remote sensing. In principle they could be derived from observations of Cloud sides, but faced with the small-scale heterogeneity of Cloud sides, “classical” passive remote sensing techniques are rendered inappropriate. In this work the feasibility is demonstrated to gain new insights into the vertical evolution of Cloud Droplet effective radius by using reflected solar radiation from Cloud sides. Central aspect of this work on its path to a working Cloud side retrieval is the analysis of the impact unknown Cloud surface geometry has on effective radius retrievals. This study examines the sensitivity of reflected solar radiation to Cloud Droplet size, using extensive 3-D radiative transfer calculations on the basis of realistic Droplet size resolving Cloud simulations. Furthermore, it explores a further technique to resolve ambiguities caused by illumination and Cloud geometry by considering the surroundings of each pixel. Based on these findings, a statistical approach is used to provide an effective radius retrieval. This statistical effective radius retrieval is focused on the liquid part of convective water Clouds, e.g., cumulus mediocris, cumulus congestus, and trade-wind cumulus, which exhibit well-developed Cloud sides. Finally, the developed retrieval is tested using known and unknown Cloud side scenes to analyze its performance.
Ralf Bennartz - One of the best experts on this subject based on the ideXlab platform.
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global assessment of marine boundary layer Cloud Droplet number concentration from satellite
Journal of Geophysical Research, 2007Co-Authors: Ralf BennartzAbstract:[1] Global satellite data are used to infer the Droplet number concentration of marine boundary layer Clouds. In a first step, two and a half years (July 2002 to December 2004) of Moderate Resolution Imaging Spectroradiometer (MODIS, on board NASA's Aqua satellite) level 3 data estimates of Cloud effective radius and optical thickness are used to derive Cloud Droplet number concentration N and Cloud geometrical thickness H under the assumption of adiabatically stratified Clouds. Theoretical error estimates show that for a liquid water path higher than 30 g/m2 and a Cloud fraction higher than 0.8, H (N) can be derived with a relative uncertainty of better than 20% (80%). To further validate the estimates of N and H, Cloud liquid water path is calculated and compared to independent observations of Cloud liquid water path from the passive microwave Advanced Microwave Scanning Radiometer (AMSR-E), also on board Aqua. Good agreement between the two different data sets is found. In a second step, the global distribution of Cloud Droplet number concentration in stratiform boundary layer Clouds is evaluated. The data are separated into observations that are likely to be drizzling and drizzle-free using published relations between drizzle rate and N and H. It is found that the mean Droplet number concentration over remote Northern Hemisphere oceans is higher than over the southern oceans (64–89 cm−3 in the Northern Hemisphere and 40–67 cm−3 in the Southern Hemisphere). Leeward of the major continents Cloud Droplet number concentration is generally high with maximum values close to the coasts. On the Southern Hemisphere, especially over the east Pacific Ocean, microphysical conditions were almost constant through the entire observation period. Over the southeast Atlantic Ocean, the Cloud microphysical variability appears to be strongly influenced by the dry biomass burning season in Africa.