scholarly journals Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements

2020 ◽  
Vol 20 (21) ◽  
pp. 12853-12869
Author(s):  
Arshad Arjunan Nair ◽  
Fangqun Yu
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Cloud Condensation Nuclei ◽  
Condensation Nuclei ◽  
Gaseous Species ◽  
Near Surface ◽  
Random Decision Forests ◽  
Kendall’S Τ ◽  
Good Agreement

Abstract. Cloud condensation nuclei (CCN) number concentrations are an important aspect of aerosol–cloud interactions and the subsequent climate effects; however, their measurements are very limited. We use a machine learning tool, random decision forests, to develop a random forest regression model (RFRM) to derive CCN at 0.4 % supersaturation ([CCN0.4]) from commonly available measurements. The RFRM is trained on the long-term simulations in a global size-resolved particle microphysics model. Using atmospheric state and composition variables as predictors, through associations of their variabilities, the RFRM is able to learn the underlying dependence of [CCN0.4] on these predictors, which are as follows: eight fractions of PM2.5 (NH4, SO4, NO3, secondary organic aerosol (SOA), black carbon (BC), primary organic carbon (POC), dust, and salt), seven gaseous species (NOx, NH3, O3, SO2, OH, isoprene, and monoterpene), and four meteorological variables (temperature (T), relative humidity (RH), precipitation, and solar radiation). The RFRM is highly robust: it has a median mean fractional bias (MFB) of 4.4 % with ≈96.33 % of the derived [CCN0.4] within a good agreement range of -60%<MFB<+60% and strong correlation of Kendall's τ coefficient ≈0.88. The RFRM demonstrates its robustness over 4 orders of magnitude of [CCN0.4] over varying spatial (such as continental to oceanic, clean to polluted, and near-surface to upper troposphere) and temporal (from the hourly to the decadal) scales. At the Atmospheric Radiation Measurement Southern Great Plains observatory (ARM SGP) in Lamont, Oklahoma, United States, long-term measurements for PM2.5 speciation (NH4, SO4, NO3, and organic carbon (OC)), NOx, O3, SO2, T, and RH, as well as [CCN0.4] are available. We modify, optimize, and retrain the developed RFRM to make predictions from 19 to 9 of these available predictors. This retrained RFRM (RFRM-ShortVars) shows a reduction in performance due to the unavailability and sparsity of measurements (predictors); it captures the [CCN0.4] variability and magnitude at SGP with ≈67.02 % of the derived values in the good agreement range. This work shows the potential of using the more commonly available measurements of PM2.5 speciation to alleviate the sparsity of CCN number concentrations' measurements.

scholarly journals Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements

2020 ◽  
Author(s):  
Arshad Arjunan Nair ◽  
Fangqun Yu
Keyword(s):  
Machine Learning ◽  
Cloud Condensation Nuclei ◽  
Condensation Nuclei ◽  
Gaseous Species ◽  
Random Forest Regression ◽  
Random Decision Forests ◽  
Atmospheric State ◽  
Aerosol Cloud Interactions ◽  
Good Agreement

Abstract. Cloud condensation nuclei (CCN) number concentrations are an important aspect of aerosol–cloud interactions and the subsequent climate effects; however, their measurements are very limited. We use a machine learning tool, random decision forests, to develop a Random Forest Regression Model (RFRM) to derive CCN at 0.4 % supersaturation ([CCN0.4]) from commonly available measurements. The RFRM is trained on the long-term simulations in a global size-resolved particle microphysics model. Using atmospheric state and composition variables as predictors, through associations of their variabilities, the RFRM is able to learn the underlying dependence of [CCN0.4] on these predictors, which are: 8 fractions of PM2.5 (NH4, SO4, NO3, secondary organic aerosol (SOA), black carbon (BC), primary organic carbon (POC), dust, and salt), 7 gaseous species (NOx, NH3, O3, SO2, OH, isoprene, and monoterpene), and 4 meteorological variables (temperature (T), relative humidity (RH), precipitation, and solar radiation). The RFRM is highly robust: median mean fractional bias (MFB) of 4.4 % with ~ 96.33 % of the derived [CCN0.4] within a good agreement range of −60 % 

scholarly journals Organic acids as cloud condensation nuclei: Laboratory studies of highly soluble and insoluble species

2003 ◽  
Vol 3 (1) ◽  
pp. 949-982 ◽  
Author(s):  
P. Pradeep Kumar ◽  
K. Broekhuizen ◽  
J. P. D. Abbatt
Keyword(s):  
Organic Acids ◽  
Room Temperature ◽  
Cloud Condensation Nuclei ◽  
Particle Diameter ◽  
Diffusion Chamber ◽  
Laboratory Studies ◽  
Condensation Nuclei ◽  
Gradient Diffusion ◽  
Kohler Theory ◽  
Good Agreement

Abstract. The ability of sub-micron-sized organic acid particles to act as cloud condensation nuclei (CCN) has been examined at room temperature using a newly constructed continuous-flow, thermal-gradient diffusion chamber (TGDC). The organic acids studied were: oxalic, malonic, glutaric, oleic and stearic. The CCN properties of the highly soluble acids – oxalic, malonic and glutaric – match very closely Kohler theory predictions which assume full dissolution of the dry particle and a surface tension of the growing droplet equal to that of water. In particular, for supersaturations between 0.3 and 0.6, agreement between the dry particle diameter which gives 50% activation and that calculated from Kohler theory is to within 3 nm on average. In the course of the experiments, considerable instability of glutaric acid particles was observed as a function of time and there is evidence that they fragment to some degree to smaller particles. Stearic acid and oleic acid, which are both highly insoluble in water, did not activate at supersaturations of 0.6% with dry diameters up to 140 nm. Finally, to validate the performance of the TGDC, we present results for the activation of ammonium sulfate particles that demonstrate good agreement with Kohler theory if solution non-ideality is considered. Our findings support earlier studies in the literature that showed highly soluble organics to be CCN active but insoluble species to be largely inactive.

scholarly journals Effect of aerosol sub-grid variability on aerosol optical depth and cloud condensation nuclei: Implications for global aerosol modelling

2016 ◽  
Author(s):  
N. Weigum ◽  
N. Schutgens ◽  
P. Stier
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Scales ◽  
Cloud Condensation Nuclei ◽  
Convective Transport ◽  
Condensation Nuclei ◽  
Gaseous Species ◽  
Climate Effects ◽  
Aerosol Model ◽  
The Impact

Abstract. A fundamental limitation of grid-based models is their inability to resolve variability on scales smaller than a grid box. Past research has shown that significant aerosol variability exists on scales smaller than these grid-boxes, which can lead to discrepancies in simulated aerosol climate effects between high and low resolution models. This study investigates the impact of neglecting sub-grid variability in present-day global microphysical aerosol models on aerosol optical depth (AOD) and cloud condensation nuclei (CCN). We introduce a novel technique to isolate the effect of aerosol variability from other sources of model variability by varying the resolution of aerosol and trace gas fields while maintaining a constant resolution in the rest of the model. We compare WRF-Chem runs in which aerosol and gases are simulated at 80 km and again at 10 km resolutions; in both simulations the other model components, such as meteorology and dynamics, are kept at the 10 km baseline resolution. We find that AOD is underestimated by 13 % and CCN is overestimated by 27 % when aerosol and gases are simulated at 80 km resolution compared to 10 km. Processes most affected by neglecting aerosol sub-grid variability are gas-phase chemistry and aerosol uptake of water through aerosol/gas equilibrium reactions. The inherent non-linearities in these processes result in large changes in aerosol parameters when aerosol and gaseous species are artificially mixed over large spatial scales. These changes in aerosol and gas concentrations are exaggerated by convective transport, which transports these altered concentrations to altitudes where their effect is more pronounced. These results demonstrate that aerosol variability can have a large impact on simulating aerosol climate effects, even when meteorology and dynamics are held constant. Future aerosol model development should focus on accounting for the effect of sub-grid variability on these processes at global scales in order to improve model predictions of the aerosol effect on climate.

scholarly journals Aerosol hygroscopicity and cloud condensation nuclei activity during the AC<sup>3</sup>Exp campaign: implications for cloud condensation nuclei parameterization

2014 ◽  
Vol 14 (24) ◽  
pp. 13423-13437 ◽  
Author(s):  
F. Zhang ◽  
Y. Li ◽  
Z. Li ◽  
L. Sun ◽  
R. Li ◽  
...  
Keyword(s):  
Cloud Condensation Nuclei ◽  
Particle Diameter ◽  
Condensation Nuclei ◽  
Particle Composition ◽  
Particle Activation ◽  
Heavy Pollution ◽  
Pollution Events ◽  
Aerosol Hygroscopicity

Abstract. Aerosol hygroscopicity and cloud condensation nuclei (CCN) activity under background conditions and during pollution events are investigated during the Aerosol-CCN-Cloud Closure Experiment (AC3Exp) campaign conducted at Xianghe, China in summer 2013. A gradual increase in size-resolved activation ratio (AR) with particle diameter (Dp) suggests that aerosol particles have different hygroscopicities. During pollution events, the activation diameter (Da) measured at low supersaturation (SS) was significantly increased compared to background conditions. An increase was not observed when SS was > 0.4%. The hygroscopicity parameter (κ) was ~ 0.31–0.38 for particles in accumulation mode under background conditions. This range in magnitude of κ was ~ 20%, higher than κ derived under polluted conditions. For particles in nucleation or Aitken mode, κ ranged from 0.20–0.34 for background and polluted cases. Larger particles were on average more hygroscopic than smaller particles. The situation was more complex for heavy pollution particles because of the diversity in particle composition and mixing state. A non-parallel observation CCN closure test showed that uncertainties in CCN number concentration estimates ranged from 30–40%, which are associated with changes in particle composition as well as measurement uncertainties associated with bulk and size-resolved CCN methods. A case study showed that bulk CCN activation ratios increased as total condensation nuclei (CN) number concentrations (NCN) increased on background days. The background case also showed that bulk AR correlated well with the hygroscopicity parameter calculated from chemical volume fractions. On the contrary, bulk AR decreased with increasing total NCN during pollution events, but was closely related to the fraction of the total organic mass signal at m/z 44 (f44), which is usually associated with the particle's organic oxidation level. Our study highlights the importance of chemical composition in determining particle activation properties and underlines the significance of long-term observations of CCN under different atmospheric environments, especially regions with heavy pollution.

scholarly journals Contribution of particulate sulfate and organic carbon to cloud condensation nuclei in the marine atmosphere

1997 ◽  
Vol 24 (6) ◽  
pp. 655-658 ◽  
Author(s):  
Kiyoshi Matsumoto ◽  
Hiroshi Tanaka ◽  
Ippei Nagao ◽  
Yutaka Ishizaka
Keyword(s):  
Organic Carbon ◽  
Cloud Condensation Nuclei ◽  
Marine Atmosphere ◽  
Condensation Nuclei

scholarly journals Long-term observations of cloud condensation nuclei in the Amazon rain forest – Part 1: Aerosol size distribution, hygroscopicity, and new model parametrizations for CCN prediction

2016 ◽  
Vol 16 (24) ◽  
pp. 15709-15740 ◽  
Author(s):  
Mira L. Pöhlker ◽  
Christopher Pöhlker ◽  
Florian Ditas ◽  
Thomas Klimach ◽  
Isabella Hrabe de Angelis ◽  
...  
Keyword(s):  
Size Distribution ◽  
Aerosol Particle ◽  
Rain Forest ◽  
Seasonal Cycle ◽  
Cloud Condensation Nuclei ◽  
Number Concentration ◽  
Aerosol Size Distribution ◽  
Condensation Nuclei ◽  
Central Amazon

Abstract. Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a 1-year period and full seasonal cycle (March 2014–February 2015). The measurements provide a climatology of CCN properties characteristic of a remote central Amazonian rain forest site.The CCN measurements were continuously cycled through 10 levels of supersaturation (S  =  0.11 to 1.10 %) and span the aerosol particle size range from 20 to 245 nm. The mean critical diameters of CCN activation range from 43 nm at S  =  1.10 % to 172 nm at S  =  0.11 %. The particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode (κAit  =  0.14 ± 0.03), higher values for the accumulation mode (κAcc  =  0.22 ± 0.05), and an overall mean value of κmean  =  0.17 ± 0.06, consistent with high fractions of organic aerosol.The hygroscopicity parameter, κ, exhibits remarkably little temporal variability: no pronounced diurnal cycles, only weak seasonal trends, and few short-term variations during long-range transport events. In contrast, the CCN number concentrations exhibit a pronounced seasonal cycle, tracking the pollution-related seasonality in total aerosol concentration. We find that the variability in the CCN concentrations in the central Amazon is mostly driven by aerosol particle number concentration and size distribution, while variations in aerosol hygroscopicity and chemical composition matter only during a few episodes.For modeling purposes, we compare different approaches of predicting CCN number concentration and present a novel parametrization, which allows accurate CCN predictions based on a small set of input data.

Rapid cloud removal of dimethyl sulfide oxidation products limits SO2 and cloud condensation nuclei production in the marine atmosphere

2021 ◽  
Vol 118 (42) ◽  
pp. e2110472118
Author(s):  
Gordon A. Novak ◽  
Charles H. Fite ◽  
Christopher D. Holmes ◽  
Patrick R. Veres ◽  
J. Andrew Neuman ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Dimethyl Sulfide ◽  
Cloud Condensation Nuclei ◽  
Sulfide Oxidation ◽  
Radiation Balance ◽  
Marine Atmosphere ◽  
Condensation Nuclei ◽  
Near Surface ◽  
Loss Process ◽  
The Impact

Oceans emit large quantities of dimethyl sulfide (DMS) to the marine atmosphere. The oxidation of DMS leads to the formation and growth of cloud condensation nuclei (CCN) with consequent effects on Earth’s radiation balance and climate. The quantitative assessment of the impact of DMS emissions on CCN concentrations necessitates a detailed description of the oxidation of DMS in the presence of existing aerosol particles and clouds. In the unpolluted marine atmosphere, DMS is efficiently oxidized to hydroperoxymethyl thioformate (HPMTF), a stable intermediate in the chemical trajectory toward sulfur dioxide (SO2) and ultimately sulfate aerosol. Using direct airborne flux measurements, we demonstrate that the irreversible loss of HPMTF to clouds in the marine boundary layer determines the HPMTF lifetime (τHPMTF < 2 h) and terminates DMS oxidation to SO2. When accounting for HPMTF cloud loss in a global chemical transport model, we show that SO2 production from DMS is reduced by 35% globally and near-surface (0 to 3 km) SO2 concentrations over the ocean are lowered by 24%. This large, previously unconsidered loss process for volatile sulfur accelerates the timescale for the conversion of DMS to sulfate while limiting new particle formation in the marine atmosphere and changing the dynamics of aerosol growth. This loss process potentially reduces the spatial scale over which DMS emissions contribute to aerosol production and growth and weakens the link between DMS emission and marine CCN production with subsequent implications for cloud formation, radiative forcing, and climate.

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