scholarly journals Analysis of aerosol–cloud interactions and their implications for precipitation formation using aircraft observations over the United Arab Emirates

2021 ◽  
Vol 21 (16) ◽  
pp. 12543-12560
Author(s):  
Youssef Wehbe ◽  
Sarah A. Tessendorf ◽  
Courtney Weeks ◽  
Roelof Bruintjes ◽  
Lulin Xue ◽  
...  
Keyword(s):  
United Arab Emirates ◽  
Cloud Condensation Nuclei ◽  
Active Role ◽  
Cloud Base ◽  
Cloud Particle ◽  
Research Aircraft ◽  
Orographic Clouds ◽  
Cloud Cores ◽  
Aerosol Cloud Interactions ◽  
Capping Inversion

Abstract. Aerosol and cloud microphysical measurements were collected by a research aircraft during August 2019 over the United Arab Emirates (UAE). The majority of scientific flights targeted summertime convection along the eastern Al Hajar Mountains bordering Oman, while one flight sampled non-orographic clouds over the western UAE near the Saudi Arabian border. In this work, we study the evolution of growing cloud turrets from cloud base (9 ∘C) up to the capping inversion level (−12 ∘C) using coincident cloud particle imagery and particle size distributions from cloud cores under different forcing. Results demonstrate the active role of background dust and pollution as cloud condensation nuclei (CCN) with the onset of their deliquescence in the subcloud region. Subcloud aerosol sizes are shown to extend from submicron to 100 µm sizes, with higher concentrations of ultra-giant CCN (d>10 µm) from local sources closer to the Saudi border, compared with the eastern orographic region where smaller CCN are observed. Despite the presence of ultra-giant CCN from dust and pollution in both regions, an active collision–coalescence (C–C) process is not observed within the limited depths of warm cloud (<1000 m). The state-of-the-art observations presented in this paper can be used to initialize modeling case studies to examine the influence of aerosols on cloud and precipitation processes in the region and to better understand the impacts of hygroscopic cloud seeding on these clouds.

scholarly journals Analysis of aerosol-cloud interactions and their implications for precipitation formation using aircraft observations over the United Arab Emirates

2021 ◽  
Author(s):  
Youssef Wehbe ◽  
Sarah A. Tessendorf ◽  
Courtney Weeks ◽  
Roelof Bruintjes ◽  
Lulin Xue ◽  
...  
Keyword(s):  
United Arab Emirates ◽  
Cloud Condensation Nuclei ◽  
Active Role ◽  
Cloud Base ◽  
Cloud Particle ◽  
Research Aircraft ◽  
Orographic Clouds ◽  
Cloud Cores ◽  
Aerosol Cloud Interactions ◽  
Capping Inversion

Abstract. Aerosol and cloud microphysical measurements were collected by a research aircraft during August 2019 over the United Arab Emirates (UAE). The majority of science flights targeted summertime convection along the eastern Hajar mountains bordering Oman, while one flight sampled non-orographic clouds over the western UAE near the Saudi Arabian border. In this work, we study the evolution of growing cloud turrets from cloud base (9 °C) up to the capping inversion level (−12 °C) using coincident cloud particle imagery and particle size distributions from cloud cores under different forcing. Results demonstrate the active role of background dust and pollution as cloud condensation nuclei (CCN) with the onset of their deliquescence in the sub-cloud region. Sub-cloud aerosol sizes are shown to extend from submicron to 100 µm sizes, with higher concentrations of ultra-giant CCN (d >10 µm) from local sources closer to the Saudi border, compared to the eastern orographic region where smaller size CCN are observed. Despite the presence of ultra-giant CCN from dust and pollution in both regions, an active collision-coalescence (C-C) process is not observed within the limited depths of warm cloud (< 1000 m). The state-of-the-art observations presented in this paper can be used to initialize modelling case studies to study the influence of aerosols on cloud and precipitation processes in the region and to better understand the impacts of hygroscopic cloud-seeding on these clouds.

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 Nonlinear Advection Algorithms Applied to Interrelated Tracers: Errors and Implications for Modeling Aerosol–Cloud Interactions

2009 ◽  
Vol 137 (2) ◽  
pp. 632-644 ◽  
Author(s):  
Mikhail Ovtchinnikov ◽  
Richard C. Easter
Keyword(s):  
Positive Definiteness ◽  
Cloud Base ◽  
Atmospheric Models ◽  
Cloud Particle ◽  
Mixing Ratios ◽  
Monotonicity Constraints ◽  
Velocity Flow ◽  
Flux Corrections ◽  
Aerosol Cloud Interactions ◽  
The Individual

Abstract Monotonicity constraints and gradient-preserving flux corrections employed by many advection algorithms used in atmospheric models make these algorithms nonlinear. Consequently, any relations among model variables transported separately are not necessarily preserved in such models. These errors cannot be revealed by traditional algorithm testing based on advection of a single tracer. New types of tests are developed and conducted to evaluate the monotonicity of a sum of several number mixing ratios advected independently of each other—as is the case, for example, in models using bin or sectional representations of aerosol or cloud particle size distributions. The tests show that when three tracers with an initially constant sum are advected separately in one-dimensional constant velocity flow, local errors in their sum can be on the order of 10%. When cloudlike interactions are allowed among the tracers in the idealized “cloud base” test, errors in the sum of three mixing ratios can reach 30%. Several approaches to eliminate the error are suggested, all based on advecting the sum as a separate variable and then using it to normalize the sum of the individual tracers’ mixing ratios or fluxes. A simple scalar normalization ensures the monotonicity of the total number mixing ratio and positive definiteness of the variables, but the monotonicity of individual tracers is no longer maintained. More involved flux normalization procedures are developed for the flux-based advection algorithms to maintain the monotonicity for individual scalars and their sum.

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 The aerosol-cyclone indirect effect in observations and high-resolution simulations

2017 ◽  
Author(s):  
Daniel T. McCoy ◽  
Paul R. Field ◽  
Anja Schmidt ◽  
Daniel P. Grosvenor ◽  
Frida A.-M. Bender ◽  
...  
Keyword(s):  
High Resolution ◽  
Indirect Effect ◽  
Climate Models ◽  
Climate Model ◽  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
2014 Eruption ◽  
Cloud Droplet Number Concentration ◽  
Aerosol Cloud Interactions ◽  
First Time

Abstract. Aerosol-cloud interactions are a major source of uncertainty in predicting 21st century climate change. Using high-resolution, convection-permitting global simulations we predict that increased cloud condensation nuclei (CCN) interacting with midlatitude cyclones will increase their cloud droplet number concentration (CDNC), liquid water (CLWP), and albedo. For the first time this effect is shown with 13 years of satellite observations. Causality between enhanced CCN and enhanced cyclone liquid content is supported by the 2014 eruption of Holuhraun. The change in midlatitude cyclone albedo due to enhanced CCN in a surrogate climate model is around 70 % of the change in a high-resolution convection-permitting model, indicating that climate models may underestimate this indirect effect.

scholarly journals Droplet Clustering in Shallow Cumuli: The Effects of In–Cloud Location and Aerosol Number Concentration

2018 ◽  
Author(s):  
Dillon S. Dodson ◽  
Jennifer D. Small Griswold
Keyword(s):  
Pair Correlation ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Atmospheric Composition ◽  
Cloud Base ◽  
Arrival Times ◽  
Remotely Piloted Aircraft ◽  
Aerosol Number Concentration ◽  
Aerosol Cloud Interactions ◽  
Lifetime Effects

Abstract. Aerosol–cloud interactions are complex, including albedo and lifetime effects that cause modifications to cloud characteristics. With most cloud–aerosol interactions focused on the previously stated phenomena, there has been no in–situ studies that focus explicitly on how aerosols can affect droplet clustering within clouds. This research therefore aims to gain a better understanding of how droplet clustering within cumulus clouds can be influenced by in–cloud droplet location (cloud edge vs. center) and aerosol number concentration. The pair–correlation function (PCF) is used to identify the magnitude of droplet clustering from data collected onboard the Center for interdisciplinary Remotely–Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft, flown during the 2006 Gulf of Mexico Atmospheric Composition and Climate Study (GoMACCS). Time stamps (at 10−4 m spatial resolution) of cloud droplet arrival times were measured by the Artium Flight Phase–Doppler Interferometer (PDI). Using four complete days of data with 81 non–precipitating cloud penetrations organized into two flights of low (L1, L2) and high (H1, H2) pollution data shows more clustering near cloud edge as compared to cloud center for all four cases. Low pollution clouds are shown to have enhanced overall clustering, with flight L2 being solely responsible for this enhanced clustering. Analysis suggests cloud age plays a larger role in the clustering amount experienced than the aerosol number concentration, with dissipating clouds showing increased clustering as compared to growing or mature clouds. Results using a single, vertically developed cumulus cloud demonstrate more clustering near cloud top as compared to cloud base.

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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