Long-term size-segregated cloud condensation nuclei counter (CCNc) measurements in a boreal environment and the implications for aerosol-cloud interactions

2013 ◽  
Author(s):  
Mikhail Paramonov ◽  
Mikko Äijälä ◽  
Pasi P. Aalto ◽  
Ari Asmi ◽  
No̸nne Prisle ◽  
...  
Keyword(s):  
Cloud Condensation Nuclei ◽  
Condensation Nuclei ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions ◽  
Boreal Environment

scholarly journals Incorporation of advanced aerosol activation treatments into CESM/CAM5: model evaluation and impacts on aerosol indirect effects

2014 ◽  
Vol 14 (14) ◽  
pp. 7485-7497 ◽  
Author(s):  
B. Gantt ◽  
J. He ◽  
X. Zhang ◽  
Y. Zhang ◽  
A. Nenes
Keyword(s):  
Indirect Effects ◽  
Radiative Forcing ◽  
Cloud Condensation Nuclei ◽  
Condensation Nuclei ◽  
Activation Kinetics ◽  
Aerosol Cloud ◽  
Model Version ◽  
Cloud Properties ◽  
Aerosol Cloud Interactions ◽  
Aerosol Activation

Abstract. One of the greatest sources of uncertainty in the science of anthropogenic climate change is from aerosol–cloud interactions. The activation of aerosols into cloud droplets is a direct microphysical linkage between aerosols and clouds; parameterizations of this process link aerosol with cloud condensation nuclei (CCN) and the resulting indirect effects. Small differences between parameterizations can have a large impact on the spatiotemporal distributions of activated aerosols and the resulting cloud properties. In this work, we incorporate a series of aerosol activation schemes into the Community Atmosphere Model version 5.1.1 within the Community Earth System Model version 1.0.5 (CESM/CAM5) which include factors such as insoluble aerosol adsorption and giant cloud condensation nuclei (CCN) activation kinetics to understand their individual impacts on global-scale cloud droplet number concentration (CDNC). Compared to the existing activation scheme in CESM/CAM5, this series of activation schemes increase the computation time by ~10% but leads to predicted CDNC in better agreement with satellite-derived/in situ values in many regions with high CDNC but in worse agreement for some regions with low CDNC. Large percentage changes in predicted CDNC occur over desert and oceanic regions, owing to the enhanced activation of dust from insoluble aerosol adsorption and reduced activation of sea spray aerosol after accounting for giant CCN activation kinetics. Comparison of CESM/CAM5 predictions against satellite-derived cloud optical thickness and liquid water path shows that the updated activation schemes generally improve the low biases. Globally, the incorporation of all updated schemes leads to an average increase in column CDNC of 150% and an increase (more negative) in shortwave cloud forcing of 12%. With the improvement of model-predicted CDNCs and better agreement with most satellite-derived cloud properties in many regions, the inclusion of these aerosol activation processes should result in better predictions of radiative forcing from aerosol–cloud interactions.

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 Method to retrieve cloud condensation nuclei number concentrations using lidar measurements

2019 ◽  
Vol 12 (7) ◽  
pp. 3825-3839 ◽  
Author(s):  
Wangshu Tan ◽  
Gang Zhao ◽  
Yingli Yu ◽  
Chengcai Li ◽  
Jian Li ◽  
...  
Keyword(s):  
Cloud Condensation Nuclei ◽  
Number Concentration ◽  
New Method ◽  
Cloud Base ◽  
Chemical Compositions ◽  
Condensation Nuclei ◽  
Aerosol Cloud ◽  
Lidar Measurements ◽  
Kohler Theory ◽  
Aerosol Cloud Interactions

Abstract. Determination of cloud condensation nuclei (CCN) number concentrations at cloud base is important to constrain aerosol–cloud interactions. A new method to retrieve CCN number concentrations using backscatter and extinction profiles from multiwavelength Raman lidars is proposed. The method implements hygroscopic enhancements of backscatter and extinction with relative humidity to derive dry backscatter and extinction and humidogram parameters. Humidogram parameters, Ångström exponents, and lidar extinction-to-backscatter ratios are then linked to the ratio of CCN number concentration to dry backscatter and extinction coefficient (ARξ). This linkage is established based on the datasets simulated by Mie theory and κ-Köhler theory with in-situ-measured particle size distributions and chemical compositions. CCN number concentration can thus be calculated with ARξ and dry backscatter and extinction. An independent theoretical simulated dataset is used to validate this new method and results show that the retrieved CCN number concentrations at supersaturations of 0.07 %, 0.10 %, and 0.20 % are in good agreement with theoretical calculated values. Sensitivity tests indicate that retrieval error in CCN arises mostly from uncertainties in extinction coefficients and RH profiles. The proposed method improves CCN retrieval from lidar measurements and has great potential in deriving scarce long-term CCN data at cloud base, which benefits aerosol–cloud interaction studies.

scholarly journals Method to retrieve cloud condensation nuclei number concentrations using lidar measurements

2019 ◽  
Author(s):  
Wangshu Tan ◽  
Gang Zhao ◽  
Yingli Yu ◽  
Chengcai Li ◽  
Jian Li ◽  
...  
Keyword(s):  
Cloud Condensation Nuclei ◽  
Number Concentration ◽  
New Method ◽  
Cloud Base ◽  
Chemical Compositions ◽  
Condensation Nuclei ◽  
Aerosol Cloud ◽  
Lidar Measurements ◽  
Kohler Theory ◽  
Aerosol Cloud Interactions

Abstract. Determination of cloud condensation nuclei (CCN) number concentrations at cloud base is important to constrain aerosol-cloud interactions. A new method to retrieve CCN number concentrations using backscatter and extinction profiles from multiwavelength Raman lidars is proposed. The method implements hygroscopic enhancements of backscatter/extinction with relative humidity to derive dry backscatter/extinction and humidogram parameters. Humidogram parameters, Ångström exponents, and lidar extinction-to-backscatter ratios are then linked to the ratio of CCN number concentration to dry backscatter/extinction coefficient (ARξ). This linkage is established based on the datasets simulated by Mie theory and κ-Köhler theory with in situ measured particle size distributions and chemical compositions. CCN number concentration can thus be calculated with ARξ and dry backscatter/extinction. An independent theoretical simulated datasets is used to validate this new method and results show that the retrieved CCN number concentrations at supersaturations of 0.07 %, 0.10 %, and 0.20 % are in good agreement with theoretical calculated values. Sensitivity tests indicate that retrieval error in CCN arise mostly from uncertainties in extinction coefficients and RH profiles. The proposed method improves CCN retrieval from lidar measurements and has great potential in deriving scarce long-term CCN data at cloud base which benefits aerosol-cloud interaction studies.

scholarly journals Limitations of passive satellite remote sensing to constrain global cloud condensation nuclei

2015 ◽  
Vol 15 (22) ◽  
pp. 32607-32637 ◽  
Author(s):  
P. Stier
Keyword(s):  
Remote Sensing ◽  
Cloud Condensation Nuclei ◽  
Radiative Properties ◽  
High Spectral Resolution ◽  
Cloud Base ◽  
Condensation Nuclei ◽  
Global Estimate ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions ◽  
Aerosol Radiative

Abstract. Aerosol–cloud interactions are considered a key uncertainty in our understanding of climate change (Boucher et al., 2013). Knowledge of the global abundance of aerosols suitable to act as cloud condensation nuclei (CCN) is fundamental to determine the strength of the anthropogenic climate perturbation. Direct measurements are limited and sample only a very small fraction of the globe so that remote sensing from satellites and ground based instruments is widely used as a proxy for cloud condensation nuclei (Nakajima et al., 2001; Andreae, 2009; Clarke and Kapustin, 2010; Boucher et al., 2013). However, the underlying assumptions cannot be robustly tested with the small number of measurements available so that no reliable global estimate of cloud condensation nuclei exists. This study overcomes this limitation using a fully self-consistent global model (ECHAM-HAM) of aerosol radiative properties and cloud condensation nuclei. An analysis of the correlation of simulated aerosol radiative properties and cloud condensation nuclei reveals that common assumptions about their relationships are violated for a significant fraction of the globe: 71 % of the area of the globe shows correlation coefficients between CCN0.2% at cloud base and aerosol optical depth (AOD) below 0.5, i.e. AOD variability explains only 25 % of the CCN variance. This has significant implications for satellite based studies of aerosol–cloud interactions. The findings also suggest that vertically resolved remote sensing techniques, such as satellite-based high spectral resolution lidars, have a large potential for global monitoring of cloud condensation nuclei.

scholarly journals Limitations of passive remote sensing to constrain global cloud condensation nuclei

2016 ◽  
Vol 16 (10) ◽  
pp. 6595-6607 ◽  
Author(s):  
Philip Stier
Keyword(s):  
Remote Sensing ◽  
Cloud Condensation Nuclei ◽  
Radiative Properties ◽  
High Spectral Resolution ◽  
Cloud Base ◽  
Condensation Nuclei ◽  
Global Estimate ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions ◽  
Aerosol Radiative

Abstract. Aerosol–cloud interactions are considered a key uncertainty in our understanding of climate change (Boucher et al., 2013). Knowledge of the global abundance of cloud condensation nuclei (CCN) is fundamental to determine the strength of the anthropogenic climate perturbation. Direct measurements are limited and sample only a very small fraction of the globe so that remote sensing from satellites and ground-based instruments is widely used as a proxy for cloud condensation nuclei (Nakajima et al., 2001; Andreae, 2009; Clarke and Kapustin, 2010; Boucher et al., 2013). However, the underlying assumptions cannot be robustly tested with the small number of measurements available so that no reliable global estimate of cloud condensation nuclei exists. This study overcomes this limitation using a self-consistent global model (ECHAM-HAM) of aerosol radiative properties and cloud condensation nuclei. An analysis of the correlation of simulated aerosol radiative properties and cloud condensation nuclei reveals that common assumptions about their relationships are violated for a significant fraction of the globe: 71 % of the area of the globe shows correlation coefficients between CCN0.2 % at cloud base and aerosol optical depth (AOD) below 0.5, i.e. AOD variability explains only 25 % of the CCN variance. This has significant implications for satellite based studies of aerosol–cloud interactions. The findings also suggest that vertically resolved remote-sensing techniques, such as satellite-based high spectral resolution lidars, have a large potential for global monitoring of cloud condensation nuclei.

scholarly journals Implementation of aerosol–cloud interactions in the regional atmosphere–aerosol model COSMO-MUSCAT(5.0) and evaluation using satellite data

2017 ◽  
Vol 10 (6) ◽  
pp. 2231-2246 ◽  
Author(s):  
Sudhakar Dipu ◽  
Johannes Quaas ◽  
Ralf Wolke ◽  
Jens Stoll ◽  
Andreas Mühlbauer ◽  
...  
Keyword(s):  
Satellite Data ◽  
Transport Model ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Aerosol Model ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Abstract. The regional atmospheric model Consortium for Small-scale Modeling (COSMO) coupled to the Multi-Scale Chemistry Aerosol Transport model (MUSCAT) is extended in this work to represent aerosol–cloud interactions. Previously, only one-way interactions (scavenging of aerosol and in-cloud chemistry) and aerosol–radiation interactions were included in this model. The new version allows for a microphysical aerosol effect on clouds. For this, we use the optional two-moment cloud microphysical scheme in COSMO and the online-computed aerosol information for cloud condensation nuclei concentrations (Cccn), replacing the constant Cccn profile. In the radiation scheme, we have implemented a droplet-size-dependent cloud optical depth, allowing now for aerosol–cloud–radiation interactions. To evaluate the models with satellite data, the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) has been implemented. A case study has been carried out to understand the effects of the modifications, where the modified modeling system is applied over the European domain with a horizontal resolution of 0.25°  ×  0.25°. To reduce the complexity in aerosol–cloud interactions, only warm-phase clouds are considered. We found that the online-coupled aerosol introduces significant changes for some cloud microphysical properties. The cloud effective radius shows an increase of 9.5 %, and the cloud droplet number concentration is reduced by 21.5 %.

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.

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