scholarly journals The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei

2013 ◽  
Vol 13 (17) ◽  
pp. 8879-8914 ◽  
Author(s):  
L. A. Lee ◽  
K. J. Pringle ◽  
C. L. Reddington ◽  
G. W. Mann ◽  
P. Stier ◽  
...  
Keyword(s):  
Climate Models ◽  
Cloud Condensation Nuclei ◽  
Grid Cell ◽  
Expert Elicitation ◽  
Model Parameters ◽  
Condensation Nuclei ◽  
Model Simulations ◽  
Aerosol Cloud ◽  
Aerosol Emission ◽  
Combustion Particles

Abstract. Aerosol–cloud interaction effects are a major source of uncertainty in climate models so it is important to quantify the sources of uncertainty and thereby direct research efforts. However, the computational expense of global aerosol models has prevented a full statistical analysis of their outputs. Here we perform a variance-based analysis of a global 3-D aerosol microphysics model to quantify the magnitude and leading causes of parametric uncertainty in model-estimated present-day concentrations of cloud condensation nuclei (CCN). Twenty-eight model parameters covering essentially all important aerosol processes, emissions and representation of aerosol size distributions were defined based on expert elicitation. An uncertainty analysis was then performed based on a Monte Carlo-type sampling of an emulator built for each model grid cell. The standard deviation around the mean CCN varies globally between about ±30% over some marine regions to ±40–100% over most land areas and high latitudes, implying that aerosol processes and emissions are likely to be a significant source of uncertainty in model simulations of aerosol–cloud effects on climate. Among the most important contributors to CCN uncertainty are the sizes of emitted primary particles, including carbonaceous combustion particles from wildfires, biomass burning and fossil fuel use, as well as sulfate particles formed on sub-grid scales. Emissions of carbonaceous combustion particles affect CCN uncertainty more than sulfur emissions. Aerosol emission-related parameters dominate the uncertainty close to sources, while uncertainty in aerosol microphysical processes becomes increasingly important in remote regions, being dominated by deposition and aerosol sulfate formation during cloud-processing. The results lead to several recommendations for research that would result in improved modelling of cloud–active aerosol on a global scale.

scholarly journals The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei

2013 ◽  
Vol 13 (3) ◽  
pp. 6295-6378 ◽  
Author(s):  
L. A. Lee ◽  
K. J. Pringle ◽  
C. L. Reddington ◽  
G. W. Mann ◽  
P. Stier ◽  
...  
Keyword(s):  
Cloud Condensation Nuclei ◽  
Expert Elicitation ◽  
Model Parameters ◽  
Condensation Nuclei ◽  
Model Simulations ◽  
Aerosol Cloud ◽  
Aerosol Emission ◽  
Combustion Particles ◽  
Uncertainty Space ◽  
The Mean

Abstract. The global distribution of cloud condensation nuclei (CCN) is the fundamental quantity that determines how changes in aerosols affect climate through changes in cloud drop concentrations, cloud albedo and precipitation. Aerosol-cloud interaction effects are a major source of uncertainty in climate models so it is important to quantify the sources of uncertainty and thereby direct research efforts. However, the computational expense of global aerosol models has prevented a full statistical analysis of their outputs. Here we perform a variance-based analysis of a global 3-D aerosol microphysics model to quantify the magnitude and leading causes of parametric uncertainty in model-estimated present-day CCN concentrations. Twenty-eight model parameters covering essentially all important aerosol processes, emissions and representation of aerosol size distributions were defined based on expert elicitation. An uncertainty analysis was then performed based on a Monte Carlo-type sampling of an emulator built for each monthly-mean model grid cell from an ensemble of 168 one-year model simulations covering the uncertainty space of the 28 parameters. The standard deviation around the mean CCN varies globally between about ±30% of the mean over some marine regions to ±40–100% over most land areas and high latitudes. The results imply that aerosol processes and emissions are likely to be a significant source of uncertainty in model simulations of aerosol-cloud effects on climate. Variance decomposition enables the importance of the parameters for CCN uncertainty to be quantified and ranked from local to global scales. Among the most important contributors to CCN uncertainty are the sizes of emitted primary particles, including carbonaceous combustion particles from wildfires, biomass burning and fossil fuel use, as well as sulphate particles formed on sub-grid scales. Emissions of carbonaceous combustion particles affect CCN uncertainty more than sulphur emissions. Aerosol emission-related parameters dominate the uncertainty close to sources, while uncertainty in aerosol microphysical processes becomes increasingly important in remote regions, being dominated by deposition and aerosol sulphate formation during cloud-processing. Most of the 28 parameters are important for CCN uncertainty somewhere on the globe. The results lead to several recommendations for research that would result in improved modelling of cloud-active aerosol on a global scale.

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.

A study on aerosol-cloud condensation nuclei (CCN) activation over eastern Himalaya in India

2017 ◽  
Vol 189 ◽  
pp. 69-81 ◽  
Author(s):  
Arindam Roy ◽  
Abhijit Chatterjee ◽  
Chirantan Sarkar ◽  
Sanat Kumar Das ◽  
Sanjay Kumar Ghosh ◽  
...  
Keyword(s):  
Cloud Condensation Nuclei ◽  
Eastern Himalaya ◽  
Condensation Nuclei ◽  
Aerosol Cloud

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 Reducing uncertainties in satellite estimates of aerosol-cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations

2019 ◽  
Author(s):  
David Painemal ◽  
Fu-Lung Chang ◽  
Richard Ferrare ◽  
Sharon Burton ◽  
Zhujun Li ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Global Climate Models ◽  
Orthogonal Polarization ◽  
Near Surface ◽  
Aerosol Cloud ◽  
Error Characterization ◽  
Aerosol Cloud Interactions

Abstract. Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of global climate models. We show that the use of AOD for assessing aerosol-cloud interactions (ACI) is inadequate over vast oceanic areas in the subtropics. Instead, we postulate that a more physical approach that consists of matching vertically resolved aerosol data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at the cloud-layer height with Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) cloud retrievals reduces uncertainties in satellite-based ACI estimates. Combined aerosol extinction coefficients (σ) below cloud-top (σBC) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud droplet number concentrations (Nd) from Aqua-MODIS yield high correlations across a broad range of σBC values, with σBC quartile correlations > 0.78. In contrast, CALIOP-based AOD yields correlations with MODIS Nd of less than 0.62 for the two lower AOD quartiles. Moreover, σBC explains 41 % of the spatial variance in MODIS Nd, whereas AOD only explains 17 %, primarily caused by the lack of spatial covariability in the eastern Pacific. Compared with σBC, near-surface σ weakly correlates in space with MODIS Nd, accounting for a 16 % variance. It is concluded that the linear regression calculated from ln(Nd)−ln(σBC) (the standard method for quantifying ACI) is more physically meaningful than that derived from the Nd−AOD pair.

scholarly journals Bacteria as Cloud Condensation Nuclei (CCN) in the Atmosphere

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 786
Author(s):  
Mihalis Lazaridis
Keyword(s):  
Contact Angle ◽  
Sulfuric Acid ◽  
Cloud Condensation Nuclei ◽  
Sulfuric Acid Concentration ◽  
Contact Angles ◽  
Nucleation Theory ◽  
Classical Nucleation Theory ◽  
Condensation Nuclei ◽  
Critical Cluster ◽  
Model Simulations

Bacteria activation and cloud condensation nuclei (CCN) formation have been studied in the atmosphere using the classical theory of heterogeneous nucleation. Simulations were performed for the binary system of sulfuric acid/water using laboratory-determined contact angles. Realistic model simulations were performed at different atmospheric heights for a set of 140 different bacteria. Model simulations showed that bacteria activation is a potentially favorable process in the atmosphere which may be enhanced at lower temperatures. CCN formation from bacteria nuclei is dependent on ambient atmospheric conditions (temperature, relative humidity), bacteria size, and sulfuric acid concentration. Furthermore, a critical parameter for the determination of bacteria activation is the value of the intermolecular potential between the bacteria’s surface and the critical cluster formed at their surface. In the classical nucleation theory, this is parameterized with the contact angle between substrate and critical cluster. Therefore, the dataset of laboratory values for the contact angle of water on different bacteria substrates needs to be enriched for realistic simulations of bacteria activation in the atmosphere.

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