scholarly journals Quantifying Albedo Susceptibility Biases in Shallow Clouds

2021 ◽  
Author(s):  
Graham Feingold ◽  
Tom Goren ◽  
Takanobu Yamaguchi
Keyword(s):  
Radiative Forcing ◽  
Radiation Effects ◽  
Spatial Aggregation ◽  
Climate Projections ◽  
Marine Stratocumulus ◽  
Aerosol Cloud ◽  
Large Eddy ◽  
Shallow Clouds ◽  
Highly Correlated ◽  
Aerosol Cloud Interactions

Abstract. The evaluation of radiative forcing associated with aerosol-cloud interactions remains a significant source of uncertainty in future climate projections. The problem is confounded by the fact that aerosol particles influence clouds locally, and that averaging to larger spatial and/or temporal scales carries biases that depend on the heterogeneity and spatial correlation of the interacting fields and the non-linearity of the responses. Mimicking commonly applied satellite data analyses for calculation of albedo susceptibility So, we quantify So aggregation biases using an ensemble of 127 large eddy simulations of marine stratocumulus. We explore the cloud field properties that control this spatial aggregation bias, and quantify the bias for a large range of shallow stratocumulus cloud conditions manifesting a variety of morphologies and range of cloud fractions. We show that So spatial aggregation biases can be on the order of 100s of percent, depending on methodology. Key uncertainties emanate from the typically applied adiabatic drop concentration Nd retrieval, the correlation between aerosol and cloud fields, and the extent to which averaging reduces the variance in cloud albedo Ac and Nd. Biases are more often positive than negative. So biases are highly correlated to biases in the adjustment. Temporal aggregation biases are shown to offset spatial averaging biases. Both spatial and temporal biases have significant implications for observationally based assessments of aerosol indirect effects and our inferences of underlying aerosol-cloud-radiation effects.

scholarly journals 100 Years of Progress in Cloud Physics, Aerosols, and Aerosol Chemistry Research

2019 ◽  
Vol 59 ◽  
pp. 11.1-11.72 ◽  
Author(s):  
Sonia M. Kreidenweis ◽  
Markus Petters ◽  
Ulrike Lohmann
Keyword(s):  
Radiative Forcing ◽  
Cloud Microphysics ◽  
Climate System ◽  
Mitigation Strategies ◽  
Climate Projections ◽  
Weather Modification ◽  
Theoretical Understanding ◽  
Aerosol Cloud ◽  
Chemistry Research ◽  
Aerosol Cloud Interactions

Abstract This chapter reviews the history of the discovery of cloud nuclei and their impacts on cloud microphysics and the climate system. Pioneers including John Aitken, Sir John Mason, Hilding Köhler, Christian Junge, Sean Twomey, and Kenneth Whitby laid the foundations of the field. Through their contributions and those of many others, rapid progress has been made in the last 100 years in understanding the sources, evolution, and composition of the atmospheric aerosol, the interactions of particles with atmospheric water vapor, and cloud microphysical processes. Major breakthroughs in measurement capabilities and in theoretical understanding have elucidated the characteristics of cloud condensation nuclei and ice nucleating particles and the role these play in shaping cloud microphysical properties and the formation of precipitation. Despite these advances, not all their impacts on cloud formation and evolution have been resolved. The resulting radiative forcing on the climate system due to aerosol–cloud interactions remains an unacceptably large uncertainty in future climate projections. Process-level understanding of aerosol–cloud interactions remains insufficient to support technological mitigation strategies such as intentional weather modification or geoengineering to accelerating Earth-system-wide changes in temperature and weather patterns.

Is there hope for reducing the uncertainty associated with aerosols in climate projections? 

2021 ◽  
Author(s):  
Ilona Riipinen ◽  
Annica Ekman ◽  
Matthew Salter ◽  
Karoliina Pulkkinen ◽  
Keyword(s):  
Knowledge Transfer ◽  
Radiative Forcing ◽  
Aerosol Particles ◽  
Scientific Understanding ◽  
Climate Projections ◽  
Earth System ◽  
Aerosol Cloud ◽  
The Earth ◽  
Aerosol Cloud Interactions ◽  
Aerosol Emissions

<p>Together with imminent climate action, building a sustainable future for the humanity requires striving for healthier environments. Atmospheric aerosol particles (also referred to as particulate matter, PM) play a key role in defining the air that the future generations will breathe but also the climates they live in, PM being an important short-lived climate forcer but also a key component of air quality and global environmental health hazard. The contribution of aerosol particles has been a key uncertainty in estimates of the Earth’s radiative forcing since the establishment of the Intergovernmental Panel for Climate Change (IPCC) and still remains as the single largest quoted source of uncertainty in the anthropogenic climate forcing during the industrial period. In the latest assessment by the IPCC, the radiative forcing by aerosol particles has been estimated to be -0.45 W m-2 (between -0.95 and 0.05 W m-2) for aerosol-radiation interactions (RFari) and -0.45 W m-2 (between -1.25 and 0 W m-2) for aerosol-cloud interactions (RFaci). Recent reviews indicate no significant reduction in the uncertainty. The large range of possible aerosol forcing values has serious consequences for climate projections and therefore developing strategies for reaching Paris agreement targets. It is currently not possible to say if a reduction in aerosol emissions due to air pollution mitigation and a phase-out of aerosol emissions will result in a noticeable increase in global mean temperature  or in a negligible climate effect. We will discuss the components and reasons of this uncertainty, focusing on those that are important for aerosol-cloud interactions. We will identify critical bottlenecks in 1) scientific understanding of fundamental aerosol and cloud microphysical processes; 2) method development for improving the understanding of aerosol, cloud, and aerosol-cloud processes as well as their representation in Earth System Models (ESMs); and 3) knowledge transfer within and between the relevant research communities. We will argue for key actions to overcome these bottlenecks, giving examples of good practices for breaking new ground in this long-standing problem that continues to intrigue the atmospheric and climate science communities. Besides enhancing the scientific understanding of the Earth system within the realm of natural science based on multiple lines of evidence, and developing novel (e.g. machine learning –based) methodologies for analyzing existing observational data and model output, we call for additional perspectives from social science and humanities on the communication and knowledge transfer practices within atmospheric and climate research. Making the relevant knowledge transfer pathways and processes transparent is urgently needed to enable systematic determination of the actions required to maximally utilize the existing knowledge, and to ensure effective implementation of new results that may help to narrow down the uncertainty associated with aerosols in climate projections. Improved understanding of the role of aerosols in the climate system will result in enhanced credibility of ESMs and hence also tighter constraints for policies aiming for simultaneous climate neutrality and zero pollution targets.</p>

scholarly journals Deconvolution of boundary layer depth and aerosol constraints on cloud water path in subtropical stratocumulus decks

2020 ◽  
Vol 20 (6) ◽  
pp. 3609-3621
Author(s):  
Anna Possner ◽  
Ryan Eastman ◽  
Frida Bender ◽  
Franziska Glassmeier
Keyword(s):  
Boundary Layer ◽  
Radiative Forcing ◽  
Cloud Droplet ◽  
Process Modelling ◽  
Liquid Water Path ◽  
Marine Stratocumulus ◽  
Water Path ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions ◽  
Relative Change

Abstract. The liquid water path (LWP) adjustment due to aerosol–cloud interactions in marine stratocumulus remains a considerable source of uncertainty for climate sensitivity estimates. An unequivocal attribution of LWP adjustments to changes in aerosol concentration from climatology remains difficult due to the considerable covariance between meteorological conditions alongside changes in aerosol concentrations. We utilise the susceptibility framework to quantify the potential change in LWP adjustment with boundary layer (BL) depth in subtropical marine stratocumulus. We show that the LWP susceptibility, i.e. the relative change in LWP scaled by the relative change in cloud droplet number concentration, in marine BLs triples in magnitude from −0.1 to −0.31 as the BL deepens from 300 to 1200 m and deeper. We further find deep BLs to be underrepresented in pollution tracks, process modelling, and in situ studies of aerosol–cloud interactions in marine stratocumulus. Susceptibility estimates based on these approaches are skewed towards shallow BLs of moderate LWP susceptibility. Therefore, extrapolating LWP susceptibility estimates from shallow BLs to the entire cloud climatology may underestimate the true LWP adjustment within subtropical stratocumulus and thus overestimate the effective aerosol radiative forcing in this region. Meanwhile, LWP susceptibility estimates in deep BLs remain poorly constrained. While susceptibility estimates in shallow BLs are found to be consistent with process modelling studies, they overestimate pollution track estimates.

scholarly journals Detection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model

2020 ◽  
Vol 20 (9) ◽  
pp. 5657-5678 ◽  
Author(s):  
Montserrat Costa-Surós ◽  
Odran Sourdeval ◽  
Claudia Acquistapace ◽  
Holger Baars ◽  
Cintia Carbajal Henken ◽  
...  
Keyword(s):  
Liquid Water ◽  
Radiative Forcing ◽  
Reference Conditions ◽  
Liquid Water Path ◽  
Large Domain ◽  
Aerosol Cloud ◽  
Detection And Attribution ◽  
Large Eddy ◽  
Ground Based Lidar ◽  
Aerosol Cloud Interactions

Abstract. Clouds and aerosols contribute the largest uncertainty to current estimates and interpretations of the Earth’s changing energy budget. Here we use a new-generation large-domain large-eddy model, ICON-LEM (ICOsahedral Non-hydrostatic Large Eddy Model), to simulate the response of clouds to realistic anthropogenic perturbations in aerosols serving as cloud condensation nuclei (CCN). The novelty compared to previous studies is that (i) the LEM is run in weather prediction mode and with fully interactive land surface over a large domain and (ii) a large range of data from various sources are used for the detection and attribution. The aerosol perturbation was chosen as peak-aerosol conditions over Europe in 1985, with more than fivefold more sulfate than in 2013. Observational data from various satellite and ground-based remote sensing instruments are used, aiming at the detection and attribution of this response. The simulation was run for a selected day (2 May 2013) in which a large variety of cloud regimes was present over the selected domain of central Europe. It is first demonstrated that the aerosol fields used in the model are consistent with corresponding satellite aerosol optical depth retrievals for both 1985 (perturbed) and 2013 (reference) conditions. In comparison to retrievals from ground-based lidar for 2013, CCN profiles for the reference conditions were consistent with the observations, while the ones for the 1985 conditions were not. Similarly, the detection and attribution process was successful for droplet number concentrations: the ones simulated for the 2013 conditions were consistent with satellite as well as new ground-based lidar retrievals, while the ones for the 1985 conditions were outside the observational range. For other cloud quantities, including cloud fraction, liquid water path, cloud base altitude and cloud lifetime, the aerosol response was small compared to their natural variability. Also, large uncertainties in satellite and ground-based observations make the detection and attribution difficult for these quantities. An exception to this is the fact that at a large liquid water path value (LWP > 200 g m−2), the control simulation matches the observations, while the perturbed one shows an LWP which is too large. The model simulations allowed for quantifying the radiative forcing due to aerosol–cloud interactions, as well as the adjustments to this forcing. The latter were small compared to the variability and showed overall a small positive radiative effect. The overall effective radiative forcing (ERF) due to aerosol–cloud interactions (ERFaci) in the simulation was dominated thus by the Twomey effect and yielded for this day, region and aerosol perturbation −2.6 W m−2. Using general circulation models to scale this to a global-mean present-day vs. pre-industrial ERFaci yields a global ERFaci of −0.8 W m−2.

scholarly journals Detection and attribution of aerosol-cloud interactions in large-domain large-eddy simulations with ICON

2019 ◽  
Author(s):  
Montserrat Costa-Surós ◽  
Odran Sourdeval ◽  
Claudia Acquistapace ◽  
Holger Baars ◽  
Cintia Carbajal Henken ◽  
...  
Keyword(s):  
Liquid Water ◽  
Radiative Forcing ◽  
Reference Conditions ◽  
Liquid Water Path ◽  
Large Domain ◽  
Aerosol Cloud ◽  
Detection And Attribution ◽  
Large Eddy ◽  
Ground Based Lidar ◽  
Aerosol Cloud Interactions

Abstract. Clouds and aerosols contribute the largest uncertainty to current estimates and interpretations of the Earth’s changing energy budget. Here we use a new-generation large-domain large-eddy model, ICON-LEM, to simulate the response of clouds to realistic anthropogenic perturbations in aerosols serving as cloud condensation nuclei (CCN). The novelty compared to previous studies is that (i) the LEM is run in weather prediction mode and with fully interactive land surface over a large domain, and (ii) a large range of data from various sources are used for the detection and attribution. The aerosol perturbation was chosen as peak-aerosol conditions over Europe in 1985, with more than five-fold more sulfate than in 2013. Observational data from various satellite and ground-based remote sensing instruments are used aiming at a detection and attribution of this response. The simulation was run for a selected day (2 May 2013) in which over the selected domain of central Europe a large variety of cloud regimes was present. It first is demonstrated, using satellite aerosol optical depth retrievals available for both 1985 and 2013, that the aerosol fields for the reference conditions and also for the perturbed ones, as well as the difference between the two, were consistent in the model and the satellite retrievals. In comparison to retrievals from ground-based lidar for 2013, CCN profiles for the reference conditions were consistent with the observations, while the ones for the 1985 conditions were not. Similarly, detection-and-attribution was successful for droplet number concentrations: the ones simulated for the 2013 conditions were consistent with satellite as well as new ground-based lidar retrievals, while the ones for the 1985 conditions were outside the observational range. For other cloud quantities, including cloud fraction, liquid water path, cloud-base altitude, and cloud lifetime, the aerosol response was small compared to their natural variability. Also, large uncertainties in satellite and ground-based observations make the detection-attribution difficult for these quantities. An exception to this is the fact that at large liquid water path, the control simulation matches the observations, while the perturbed one shows too large LWP. The model simulations allowed to quantify the radiative forcing due to aerosol-cloud interactions, as well as the adjustments to this forcing. The latter were small compared to the variability and showed overall a small positive radiative effect. The overall effective radiative forcing (ERF) due to aerosol-cloud interactions (ERFaci) in the simulation was dominated thus by the Twomey effect and yielded for this day, region, and aerosol perturbation −2.6 W m-2. Using general circulation models to scale this to a global-mean present-day vs. pre-industrial ERFaci yields a global ERFaci of −0.8 W m-2.

scholarly journals Significant underestimation of radiative forcing by aerosol–cloud interactions derived from satellite-based methods

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hailing Jia ◽  
Xiaoyan Ma ◽  
Fangqun Yu ◽  
Johannes Quaas
Keyword(s):  
Radiative Forcing ◽  
Cloud Fraction ◽  
Future Climate Change ◽  
Satellite Measurements ◽  
Aerosol Cloud ◽  
Fine Mode ◽  
Sampling Biases ◽  
Aerosol Cloud Interactions ◽  
Significant Underestimation ◽  
Weak Satellite

AbstractSatellite-based estimates of radiative forcing by aerosol–cloud interactions (RFaci) are consistently smaller than those from global models, hampering accurate projections of future climate change. Here we show that the discrepancy can be substantially reduced by correcting sampling biases induced by inherent limitations of satellite measurements, which tend to artificially discard the clouds with high cloud fraction. Those missed clouds exert a stronger cooling effect, and are more sensitive to aerosol perturbations. By accounting for the sampling biases, the magnitude of RFaci (from −0.38 to −0.59 W m−2) increases by 55 % globally (133 % over land and 33 % over ocean). Notably, the RFaci further increases to −1.09 W m−2 when switching total aerosol optical depth (AOD) to fine-mode AOD that is a better proxy for CCN than AOD. In contrast to previous weak satellite-based RFaci, the improved one substantially increases (especially over land), resolving a major difference with models.

Evaluation of Scalar Advection Schemes in the Advanced Research WRF Model Using Large-Eddy Simulations of Aerosol–Cloud Interactions

2009 ◽  
Vol 137 (8) ◽  
pp. 2547-2558 ◽  
Author(s):  
Hailong Wang ◽  
William C. Skamarock ◽  
Graham Feingold
Keyword(s):  
Large Eddy Simulations ◽  
Numerical Dispersion ◽  
Integration Scheme ◽  
Cloud Albedo ◽  
Flux Limiter ◽  
Aerosol Cloud ◽  
Advection Schemes ◽  
Large Eddy ◽  
Runge Kutta ◽  
Aerosol Cloud Interactions

Abstract In the Advanced Research Weather Research and Forecasting Model (ARW), versions 3.0 and earlier, advection of scalars was performed using the Runge–Kutta time-integration scheme with an option of using a positive-definite (PD) flux limiter. Large-eddy simulations of aerosol–cloud interactions using the ARW model are performed to evaluate the advection schemes. The basic Runge–Kutta scheme alone produces spurious oscillations and negative values in scalar mixing ratios because of numerical dispersion errors. The PD flux limiter assures positive definiteness but retains the oscillations with an amplification of local maxima by up to 20% in the tests. These numerical dispersion errors contaminate active scalars directly through the advection process and indirectly through physical and dynamical feedbacks, leading to a misrepresentation of cloud physical and dynamical processes. A monotonic flux limiter is introduced to correct the generally accurate but dispersive solutions given by high-order Runge–Kutta scheme. The monotonic limiter effectively minimizes the dispersion errors with little significant enhancement of numerical diffusion errors. The improvement in scalar advection using the monotonic limiter is discussed in the context of how the different advection schemes impact the quantification of aerosol–cloud interactions. The PD limiter results in 20% (10%) fewer cloud droplets and 22% (5%) smaller cloud albedo than the monotonic limiter under clean (polluted) conditions. Underprediction of cloud droplet number concentration by the PD limiter tends to trigger the early formation of precipitation in the clean case, leading to a potentially large impact on cloud albedo change.

scholarly journals Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles

2021 ◽  
Author(s):  
Arshad Nair ◽  
Fangqun Yu ◽  
Pedro Campuzano Jost ◽  
Paul DeMott ◽  
Ezra Levin ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Radiative Forcing ◽  
Climate Models ◽  
Cloud Condensation Nuclei ◽  
Global Climate Models ◽  
Aerosol Composition ◽  
Airborne Measurements ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Abstract Cloud condensation nuclei (CCN) are mediators of aerosol–cloud interactions, which contribute to the largest uncertainty in climate change prediction. Here, we present a machine learning/artificial intelligence model that quantifies CCN from variables of aerosol composition, atmospheric trace gases, and meteorology. Comprehensive multi-campaign airborne measurements, covering varied physicochemical regimes in the troposphere, confirm the validity of and help probe the inner workings of this machine learning model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. Machine learning extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust machine learning pathway in global climate models that only resolve aerosol composition; potentially mitigating the uncertainty of effective radiative forcing due to aerosol–cloud interactions (ERFaci) and improving confidence in assessment of anthropogenic contributions and climate change projections.

Quantifying how model assumptions affect aerosol-cloud interactions in large-eddy simulations of warm stratocumulus clouds

2021 ◽  
Author(s):  
Matthias Schwarz ◽  
Julien Savre ◽  
Annica Ekman
Keyword(s):  
Number Concentration ◽  
Large Eddy Simulations ◽  
Radiation Balance ◽  
Marine Stratocumulus ◽  
Aerosol Cloud ◽  
Reference Simulation ◽  
Model Aerosol ◽  
Large Eddy ◽  
Stratocumulus Clouds ◽  
Climate Interactions

<p>Subtropical low-level marine stratocumulus clouds effectively reflect downwelling shortwave radiation while having a small effect on outgoing longwave radiation. Hence, they impose a strong negative net radiative effect on the Earth’s radiation balance. The optical and microphysical properties of these clouds are susceptible to anthropogenic changes in aerosol abundance. Although these aerosol-cloud-climate interactions (ACI) are generally explicitly treated in state-of-the-art Earth System Models (ESMs), they are accountable for large uncertainties in current climate projections.</p><p>Here, we present preliminary work where we exploit Large-Eddy-Simulations (LES) of warm stratocumulus clouds to identify and constrain processes and model assumptions that affect the response of cloud droplet number concentration (N<sub>d</sub>) to changes in aerosol number concentration (N<sub>a</sub>). Our results are based on simulations with the MISU-MIT Cloud-Aerosol (MIMICA, Savre et al., 2014) LES, which has two-moment bulk microphysics (Seifert and Beheng, 2001) and a two-moment aerosol scheme (Ekman et al., 2006). The reference simulation is based on observations made during the Dynamics and Chemistry of Marine Stratocumulus Field Study (DYCOMS-II, Stevens et al., 2003) which were used extensively during previous LES studies (e.g., Ackerman et al., 2009).</p><p>Starting from the reference simulation, we conduct sensitivity experiments to examine how the susceptibility (β=dln(N<sub>d</sub>)/dln(N<sub>a</sub>)) changes depending on different model setups. We run the model with fixed and interactive aerosol concentrations, with and without saturation adjustment, with different aerosol populations, and with different model parameter choices. Our early results suggest that β is sensitive to these choices and can vary roughly between 0.6 to 0.9 depending on the setup. The overall purpose of our study is to guide future model developments and evaluations concerning aerosol-cloud-climate interactions.  </p><p> </p><p><strong>References</strong></p><p>Ackerman, A. S., vanZanten, M. C., Stevens, B., Savic-Jovcic, V., Bretherton, C. S., Chlond, A., et al. (2009). Large-Eddy Simulations of a Drizzling, Stratocumulus-Topped Marine Boundary Layer. Monthly Weather Review, 137(3), 1083–1110. https://doi.org/10.1175/2008MWR2582.1</p><p>Ekman, A. M. L., Wang, C., Ström, J., & Krejci, R. (2006). Explicit Simulation of Aerosol Physics in a Cloud-Resolving Model: Aerosol Transport and Processing in the Free Troposphere. Journal of the Atmospheric Sciences, 63(2), 682–696. https://doi.org/10.1175/JAS3645.1</p><p>Savre, J., Ekman, A. M. L., & Svensson, G. (2014). Technical note: Introduction to MIMICA, a large-eddy simulation solver for cloudy planetary boundary layers. Journal of Advances in Modeling Earth Systems, 6(3), 630–649. https://doi.org/10.1002/2013MS000292</p><p>Stevens, B., Lenschow, D. H., Vali, G., Gerber, H., Bandy, A., Blomquist, B., et al. (2003). Dynamics and Chemistry of Marine Stratocumulus—DYCOMS-II. Bulletin of the American Meteorological Society, 84(5), 579–594. https://doi.org/10.1175/BAMS-84-5-579</p>

scholarly journals Quantifying cloud adjustments and the radiative forcing due to aerosol–cloud interactions in satellite observations of warm marine clouds

2020 ◽  
Vol 20 (10) ◽  
pp. 6225-6241 ◽  
Author(s):  
Alyson Douglas ◽  
Tristan L'Ecuyer
Keyword(s):  
Radiative Forcing ◽  
Local Environment ◽  
Satellite Observations ◽  
Cloud Environment ◽  
Climate Scenarios ◽  
Liquid Water Path ◽  
Sources Of Uncertainty ◽  
Aerosol Cloud ◽  
Effective Radiative Forcing ◽  
Aerosol Cloud Interactions

Abstract. Aerosol–cloud interactions and their resultant forcing remains one of the largest sources of uncertainty in future climate scenarios. The effective radiative forcing due to aerosol–cloud interactions (ERFaci) is a combination of two different effects, namely how aerosols modify cloud brightness (RFaci, intrinsic) and how cloud extent reacts to aerosol (cloud adjustments CA; extrinsic). Using satellite observations of warm clouds from the NASA A-Train constellation from 2007 to 2010 along with MERRA-2 Reanalysis and aerosol from the SPRINTARS model, we evaluate the ERFaci in warm, marine clouds and its components, the RFaciwarm and CAwarm, while accounting for the liquid water path and local environment. We estimate the ERFaciwarm to be -0.32±0.16 Wm−2. The RFaciwarm dominates the ERFaciwarm contributing 80 % (-0.21±0.15 Wm−2), while the CAwarm enhances this cooling by 20 % (-0.05±0.03 Wm−2). Both the RFaciwarm and CAwarm vary in magnitude and sign regionally and can lead to opposite, negating effects under certain environmental conditions. Without considering the two terms separately and without constraining cloud–environment interactions, weak regional ERFaciwarm signals may be erroneously attributed to a damped susceptibility to aerosol.

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