Articles | Volume 23, issue 9
https://doi.org/10.5194/acp-23-5373-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-5373-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 May 2023
Research article |
| 12 May 2023
| 12 May 2023
Aerosol first indirect effect of African smoke at the cloud base of marine cumulus clouds over Ascension Island, southern Atlantic Ocean
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Karolina Sarna
Geosciences & Remote Sensing, Delft University of Technology (TUD), Delft, the Netherlands
Jessica Brown
Meteorology and Air Quality Group, Wageningen University & Research, Wageningen, the Netherlands
Elma V. Tenner
Geosciences & Remote Sensing, Delft University of Technology (TUD), Delft, the Netherlands
Manon Schenkels
Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
David P. Donovan
R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Related authors
Martin de Graaf, Maarten Sneep, Mark ter Linden, L. Gijsbert Tilstra, David P. Donovan, Gerd-Jan van Zadelhoff, and J. Pepijn Veefkind
Atmos. Meas. Tech., 18, 2553–2571, https://doi.org/10.5194/amt-18-2553-2025, https://doi.org/10.5194/amt-18-2553-2025, 2025
Short summary
Short summary
The aerosol layer height (ALH) from the TROPOspheric Monitoring Instrument (TROPOMI) has been constantly improved since its release in 2019. Over bright surfaces, fitting the albedo improved the retrieval, as shown for a set of situations, ranging from multiple layers of smoke to thick desert dust plumes and low-altitude industrial pollution. The latest results of the operational ALH are compared to profiles from the ATmospheric LIDar (ATLID) on board the recently launched EarthCARE mission.
Lieuwe G. Tilstra, Martin de Graaf, Victor J. H. Trees, Pavel Litvinov, Oleg Dubovik, and Piet Stammes
Atmos. Meas. Tech., 17, 2235–2256, https://doi.org/10.5194/amt-17-2235-2024, https://doi.org/10.5194/amt-17-2235-2024, 2024
Short summary
Short summary
This paper introduces a new surface albedo climatology of directionally dependent Lambertian-equivalent reflectivity (DLER) observed by TROPOMI on the Sentinel-5 Precursor satellite. The database contains monthly fields of DLER for 21 wavelength bands at a relatively high spatial resolution of 0.125 by 0.125 degrees. The anisotropy of the surface reflection is handled by parameterisation of the viewing angle dependence.
Konstantinos Michailidis, Maria-Elissavet Koukouli, Dimitris Balis, J. Pepijn Veefkind, Martin de Graaf, Lucia Mona, Nikolaos Papagianopoulos, Gesolmina Pappalardo, Ioanna Tsikoudi, Vassilis Amiridis, Eleni Marinou, Anna Gialitaki, Rodanthi-Elisavet Mamouri, Argyro Nisantzi, Daniele Bortoli, Maria João Costa, Vanda Salgueiro, Alexandros Papayannis, Maria Mylonaki, Lucas Alados-Arboledas, Salvatore Romano, Maria Rita Perrone, and Holger Baars
Atmos. Chem. Phys., 23, 1919–1940, https://doi.org/10.5194/acp-23-1919-2023, https://doi.org/10.5194/acp-23-1919-2023, 2023
Short summary
Short summary
Comparisons with ground-based correlative lidar measurements constitute a key component in the validation of satellite aerosol products. This paper presents the validation of the TROPOMI aerosol layer height (ALH) product, using archived quality assured ground-based data from lidar stations that belong to the EARLINET network. Comparisons between the TROPOMI ALH and co-located EARLINET measurements show good agreement over the ocean.
Jim M. Haywood, Steven J. Abel, Paul A. Barrett, Nicolas Bellouin, Alan Blyth, Keith N. Bower, Melissa Brooks, Ken Carslaw, Haochi Che, Hugh Coe, Michael I. Cotterell, Ian Crawford, Zhiqiang Cui, Nicholas Davies, Beth Dingley, Paul Field, Paola Formenti, Hamish Gordon, Martin de Graaf, Ross Herbert, Ben Johnson, Anthony C. Jones, Justin M. Langridge, Florent Malavelle, Daniel G. Partridge, Fanny Peers, Jens Redemann, Philip Stier, Kate Szpek, Jonathan W. Taylor, Duncan Watson-Parris, Robert Wood, Huihui Wu, and Paquita Zuidema
Atmos. Chem. Phys., 21, 1049–1084, https://doi.org/10.5194/acp-21-1049-2021, https://doi.org/10.5194/acp-21-1049-2021, 2021
Short summary
Short summary
Every year, the seasonal cycle of biomass burning from agricultural practices in Africa creates a huge plume of smoke that travels many thousands of kilometres over the Atlantic Ocean. This study provides an overview of a measurement campaign called the cloud–aerosol–radiation interaction and forcing for year 2017 (CLARIFY-2017) and documents the rationale, deployment strategy, observations, and key results from the campaign which utilized the heavily equipped FAAM atmospheric research aircraft.
Maurits L. Kooreman, Piet Stammes, Victor Trees, Maarten Sneep, L. Gijsbert Tilstra, Martin de Graaf, Deborah C. Stein Zweers, Ping Wang, Olaf N. E. Tuinder, and J. Pepijn Veefkind
Atmos. Meas. Tech., 13, 6407–6426, https://doi.org/10.5194/amt-13-6407-2020, https://doi.org/10.5194/amt-13-6407-2020, 2020
Short summary
Short summary
We investigated the influence of clouds on the Absorbing Aerosol Index (AAI), an indicator of the presence of small particles in the atmosphere. Clouds produce artifacts in AAI calculations on the individual measurement (7 km) scale, which was not seen with previous instruments, as well as on large (1000+ km) scales. To reduce these artefacts, we used three different AAI calculation techniques of varying complexity. We find that the AAI artifacts are reduced when using more complex techniques.
Victor J. H. Trees, Ping Wang, Job I. Wiltink, Piet Stammes, Daphne M. Stam, David P. Donovan, and A. Pier Siebesma
EGUsphere, https://doi.org/10.5194/egusphere-2025-2197, https://doi.org/10.5194/egusphere-2025-2197, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We present MONKI (Monte Carlo KNMI), an efficient and accurate radiative transfer code written in Fortran. MONKI computes total and polarised radiances reflected and transmitted by planetary atmospheres, accounting for polarisation in all scattering orders. MONKI handles both homogeneous atmospheres and 3D cloud structures. MONKI has been validated, and produces reliable results even for planets with optically thick, strongly polarising atmospheres.
Martin de Graaf, Maarten Sneep, Mark ter Linden, L. Gijsbert Tilstra, David P. Donovan, Gerd-Jan van Zadelhoff, and J. Pepijn Veefkind
Atmos. Meas. Tech., 18, 2553–2571, https://doi.org/10.5194/amt-18-2553-2025, https://doi.org/10.5194/amt-18-2553-2025, 2025
Short summary
Short summary
The aerosol layer height (ALH) from the TROPOspheric Monitoring Instrument (TROPOMI) has been constantly improved since its release in 2019. Over bright surfaces, fitting the albedo improved the retrieval, as shown for a set of situations, ranging from multiple layers of smoke to thick desert dust plumes and low-altitude industrial pollution. The latest results of the operational ALH are compared to profiles from the ATmospheric LIDar (ATLID) on board the recently launched EarthCARE mission.
Peristera Paschou, Nikolaos Siomos, Eleni Marinou, Antonis Gkikas, Samira Moussa Idrissa, Daniel Tetteh Quaye, Désire Degbe Fiogbe Attannon, Kalliopi Artemis Voudouri, Charikleia Meleti, David Patric Donovan, George Georgoussis, Tommaso Parrinello, Thorsten Fehr, Jonas von Bismarck, and Vassilis Amiridis
EGUsphere, https://doi.org/10.5194/egusphere-2025-1152, https://doi.org/10.5194/egusphere-2025-1152, 2025
Short summary
Short summary
This study presents the results from a validation study on the Level 2A products (aerosol optical properties) of the European Space Agency’s Aeolus mission. Measurements from the eVe lidar, a combined linear/circular polarization and Raman lidar and ESA’s ground reference system, that have been collected during the ASKOS/JATAC campaign are compared with collocated Aeolus Level 2A profiles obtained from the latest version (Baseline 16) of the available SCA, MLE, and AEL-PRO Aeolus algorithms.
Konstantinos Rizos, Emmanouil Proestakis, Thanasis Georgiou, Antonis Gkikas, Eleni Marinou, Peristera Paschou, Kalliopi Artemis Voudouri, Athanasios Tsikerdekis, David Donovan, Gerd-Jan van Zadelhoff, Angela Benedetti, Holger Baars, Athena Augusta Floutsi, Nikos Benas, Martin Stengel, Christian Retscher, Edward Malina, and Vassilis Amiridis
EGUsphere, https://doi.org/10.5194/egusphere-2025-1175, https://doi.org/10.5194/egusphere-2025-1175, 2025
Short summary
Short summary
The Aeolus satellite's lidar system had limitations in detecting certain atmospheric layers and distinguishing between aerosol and cloud types. To improve accuracy, a new dust detection product was developed. By combining data from various sources and validating it with ground-based measurements, this enhanced product performs better than the original. It helps improve dust transport models and weather predictions, making it a valuable tool for atmospheric monitoring and forecasting.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
Short summary
Short summary
Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Lev D. Labzovskii, Gerd-Jan van Zadelhoff, David P. Donovan, Jos de Kloe, L. Gijsbert Tilstra, Ad Stoffelen, Damien Josset, and Piet Stammes
Atmos. Meas. Tech., 17, 7183–7208, https://doi.org/10.5194/amt-17-7183-2024, https://doi.org/10.5194/amt-17-7183-2024, 2024
Short summary
Short summary
The Atmospheric Laser Doppler Instrument (ALADIN) on the Aeolus satellite was the first of its kind to measure high-resolution vertical profiles of aerosols and cloud properties from space. We present an algorithm that produces Aeolus lidar surface returns (LSRs), containing useful information for measuring UV reflectivity. Aeolus LSRs matched well with existing UV reflectivity data from other satellites, like GOME-2 and TROPOMI, and demonstrated excellent sensitivity to modeled snow cover.
Ping Wang, David Patrick Donovan, Gerd-Jan van Zadelhoff, Jos de Kloe, Dorit Huber, and Katja Reissig
Atmos. Meas. Tech., 17, 5935–5955, https://doi.org/10.5194/amt-17-5935-2024, https://doi.org/10.5194/amt-17-5935-2024, 2024
Short summary
Short summary
We describe the new feature mask (AEL-FM) and aerosol profile retrieval (AEL-PRO) algorithms developed for Aeolus lidar and present the evaluation of the Aeolus products using CALIPSO data for dust aerosols over Africa. We have found that Aeolus and CALIPSO show similar aerosol patterns in the collocated orbits and have good agreement for the extinction coefficients for the dust aerosols, especially for the cloud-free scenes. The finding is applicable to Aeolus L2A product Baseline 17.
David Patrick Donovan, Gerd-Jan van Zadelhoff, and Ping Wang
Atmos. Meas. Tech., 17, 5301–5340, https://doi.org/10.5194/amt-17-5301-2024, https://doi.org/10.5194/amt-17-5301-2024, 2024
Short summary
Short summary
ATLID (atmospheric lidar) is the lidar to be flown on the Earth Clouds and Radiation Explorer satellite (EarthCARE). EarthCARE is a joint European–Japanese satellite mission that was launched in May 2024. ATLID is an advanced lidar optimized for cloud and aerosol property profile measurements. This paper describes some of the key novel algorithms being applied to this lidar to retrieve cloud and aerosol properties. Example results based on simulated data are presented and discussed.
Nicole Docter, Anja Hünerbein, David P. Donovan, Rene Preusker, Jürgen Fischer, Jan Fokke Meirink, Piet Stammes, and Michael Eisinger
Atmos. Meas. Tech., 17, 2507–2519, https://doi.org/10.5194/amt-17-2507-2024, https://doi.org/10.5194/amt-17-2507-2024, 2024
Short summary
Short summary
MSI is the imaging spectrometer on board EarthCARE and will provide across-track information on clouds and aerosol properties. The MSI solar channels exhibit a spectral misalignment effect (SMILE) in the measurements. This paper describes and evaluates how the SMILE will affect the cloud and aerosol retrievals that do not account for it.
Lieuwe G. Tilstra, Martin de Graaf, Victor J. H. Trees, Pavel Litvinov, Oleg Dubovik, and Piet Stammes
Atmos. Meas. Tech., 17, 2235–2256, https://doi.org/10.5194/amt-17-2235-2024, https://doi.org/10.5194/amt-17-2235-2024, 2024
Short summary
Short summary
This paper introduces a new surface albedo climatology of directionally dependent Lambertian-equivalent reflectivity (DLER) observed by TROPOMI on the Sentinel-5 Precursor satellite. The database contains monthly fields of DLER for 21 wavelength bands at a relatively high spatial resolution of 0.125 by 0.125 degrees. The anisotropy of the surface reflection is handled by parameterisation of the viewing angle dependence.
Shannon L. Mason, Howard W. Barker, Jason N. S. Cole, Nicole Docter, David P. Donovan, Robin J. Hogan, Anja Hünerbein, Pavlos Kollias, Bernat Puigdomènech Treserras, Zhipeng Qu, Ulla Wandinger, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, https://doi.org/10.5194/amt-17-875-2024, 2024
Short summary
Short summary
When the EarthCARE mission enters its operational phase, many retrieval data products will be available, which will overlap both in terms of the measurements they use and the geophysical quantities they report. In this pre-launch study, we use simulated EarthCARE scenes to compare the coverage and performance of many data products from the European Space Agency production model, with the intention of better understanding the relation between products and providing a compact guide to users.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
Short summary
Short summary
The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
David P. Donovan, Pavlos Kollias, Almudena Velázquez Blázquez, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5327–5356, https://doi.org/10.5194/amt-16-5327-2023, https://doi.org/10.5194/amt-16-5327-2023, 2023
Short summary
Short summary
The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) is a multi-instrument cloud–aerosol–radiation-oriented satellite for climate and weather applications. For this satellite mission to be successful, the development and implementation of new techniques for turning the measured raw signals into useful data is required. This paper describes how atmospheric model data were used as the basis for creating realistic high-resolution simulated data sets to facilitate this process.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
Short summary
Short summary
The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Ulla Wandinger, Moritz Haarig, Holger Baars, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 4031–4052, https://doi.org/10.5194/amt-16-4031-2023, https://doi.org/10.5194/amt-16-4031-2023, 2023
Short summary
Short summary
We introduce the algorithms that have been developed to derive cloud top height and aerosol layer products from observations with the Atmospheric Lidar (ATLID) onboard the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE). The products provide information on the uppermost cloud and geometrical and optical properties of aerosol layers in an atmospheric column. They can be used individually but also serve as input for algorithms that combine observations with EarthCARE’s lidar and imager.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
Short summary
Short summary
The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Abdanour Irbah, Julien Delanoë, Gerd-Jan van Zadelhoff, David P. Donovan, Pavlos Kollias, Bernat Puigdomènech Treserras, Shannon Mason, Robin J. Hogan, and Aleksandra Tatarevic
Atmos. Meas. Tech., 16, 2795–2820, https://doi.org/10.5194/amt-16-2795-2023, https://doi.org/10.5194/amt-16-2795-2023, 2023
Short summary
Short summary
The Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID) aboard the EarthCARE satellite are used to probe the Earth's atmosphere by measuring cloud and aerosol profiles. ATLID is sensitive to aerosols and small cloud particles and CPR to large ice particles, snowflakes and raindrops. It is the synergy of the measurements of these two instruments that allows a better classification of the atmospheric targets and the description of the associated products, which are the subject of this paper.
Ulla Wandinger, Athena Augusta Floutsi, Holger Baars, Moritz Haarig, Albert Ansmann, Anja Hünerbein, Nicole Docter, David Donovan, Gerd-Jan van Zadelhoff, Shannon Mason, and Jason Cole
Atmos. Meas. Tech., 16, 2485–2510, https://doi.org/10.5194/amt-16-2485-2023, https://doi.org/10.5194/amt-16-2485-2023, 2023
Short summary
Short summary
We introduce an aerosol classification model that has been developed for the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE). The model provides a consistent description of microphysical, optical, and radiative properties of common aerosol types such as dust, sea salt, pollution, and smoke. It is used for aerosol classification and assessment of radiation effects based on the synergy of active and passive observations with lidar, imager, and radiometer of the multi-instrument platform.
Konstantinos Michailidis, Maria-Elissavet Koukouli, Dimitris Balis, J. Pepijn Veefkind, Martin de Graaf, Lucia Mona, Nikolaos Papagianopoulos, Gesolmina Pappalardo, Ioanna Tsikoudi, Vassilis Amiridis, Eleni Marinou, Anna Gialitaki, Rodanthi-Elisavet Mamouri, Argyro Nisantzi, Daniele Bortoli, Maria João Costa, Vanda Salgueiro, Alexandros Papayannis, Maria Mylonaki, Lucas Alados-Arboledas, Salvatore Romano, Maria Rita Perrone, and Holger Baars
Atmos. Chem. Phys., 23, 1919–1940, https://doi.org/10.5194/acp-23-1919-2023, https://doi.org/10.5194/acp-23-1919-2023, 2023
Short summary
Short summary
Comparisons with ground-based correlative lidar measurements constitute a key component in the validation of satellite aerosol products. This paper presents the validation of the TROPOMI aerosol layer height (ALH) product, using archived quality assured ground-based data from lidar stations that belong to the EARLINET network. Comparisons between the TROPOMI ALH and co-located EARLINET measurements show good agreement over the ocean.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 15, 3121–3140, https://doi.org/10.5194/amt-15-3121-2022, https://doi.org/10.5194/amt-15-3121-2022, 2022
Short summary
Short summary
Cloud shadows are observed by the TROPOMI satellite instrument as a result of its high spatial resolution. These shadows contaminate TROPOMI's air quality measurements, because shadows are generally not taken into account in the models that are used for aerosol and trace gas retrievals. We present the Detection AlgoRithm for CLOud Shadows (DARCLOS) for TROPOMI, which is the first cloud shadow detection algorithm for a satellite spectrometer.
Karolina Sarna, David P. Donovan, and Herman W. J. Russchenberg
Atmos. Meas. Tech., 14, 4959–4970, https://doi.org/10.5194/amt-14-4959-2021, https://doi.org/10.5194/amt-14-4959-2021, 2021
Short summary
Short summary
We show a method for obtaining cloud optical extinction with a lidar system. We use a scheme in which a lidar signal is inverted based on the estimated value of cloud extinction at the far end of the cloud and apply a correction for multiple scattering within the cloud and a range resolution correction. By applying our technique, we show that it is possible to obtain the cloud optical extinction with an error better than 5 % up to 90 m within the cloud.
Jim M. Haywood, Steven J. Abel, Paul A. Barrett, Nicolas Bellouin, Alan Blyth, Keith N. Bower, Melissa Brooks, Ken Carslaw, Haochi Che, Hugh Coe, Michael I. Cotterell, Ian Crawford, Zhiqiang Cui, Nicholas Davies, Beth Dingley, Paul Field, Paola Formenti, Hamish Gordon, Martin de Graaf, Ross Herbert, Ben Johnson, Anthony C. Jones, Justin M. Langridge, Florent Malavelle, Daniel G. Partridge, Fanny Peers, Jens Redemann, Philip Stier, Kate Szpek, Jonathan W. Taylor, Duncan Watson-Parris, Robert Wood, Huihui Wu, and Paquita Zuidema
Atmos. Chem. Phys., 21, 1049–1084, https://doi.org/10.5194/acp-21-1049-2021, https://doi.org/10.5194/acp-21-1049-2021, 2021
Short summary
Short summary
Every year, the seasonal cycle of biomass burning from agricultural practices in Africa creates a huge plume of smoke that travels many thousands of kilometres over the Atlantic Ocean. This study provides an overview of a measurement campaign called the cloud–aerosol–radiation interaction and forcing for year 2017 (CLARIFY-2017) and documents the rationale, deployment strategy, observations, and key results from the campaign which utilized the heavily equipped FAAM atmospheric research aircraft.
Cristofer Jimenez, Albert Ansmann, Ronny Engelmann, David Donovan, Aleksey Malinka, Jörg Schmidt, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 20, 15247–15263, https://doi.org/10.5194/acp-20-15247-2020, https://doi.org/10.5194/acp-20-15247-2020, 2020
Short summary
Short summary
A novel lidar method to study cloud microphysical properties (of liquid water clouds) and to study aerosol–cloud interaction (ACI) is developed and presented in this paper. In Part 1, the theoretical framework including an error analysis is given together with an overview of the aerosol information that the same lidar system can obtain. The ACI concept based on aerosol and cloud information is also explained. Applications of the proposed approach to lidar measurements are presented in Part 2.
Cristofer Jimenez, Albert Ansmann, Ronny Engelmann, David Donovan, Aleksey Malinka, Patric Seifert, Robert Wiesen, Martin Radenz, Zhenping Yin, Johannes Bühl, Jörg Schmidt, Boris Barja, and Ulla Wandinger
Atmos. Chem. Phys., 20, 15265–15284, https://doi.org/10.5194/acp-20-15265-2020, https://doi.org/10.5194/acp-20-15265-2020, 2020
Short summary
Short summary
Part 2 presents the application of the dual-FOV polarization lidar technique introduced in Part 1. A lidar system was upgraded with a second polarization telescope, and it was deployed at the southernmost tip of South America. A comparison with alternative remote sensing techniques and the evaluation of the aerosol–cloud–wind relation in a convective boundary layer in pristine marine conditions are presented in two case studies, demonstrating the potential of the approach for ACI studies.
Maurits L. Kooreman, Piet Stammes, Victor Trees, Maarten Sneep, L. Gijsbert Tilstra, Martin de Graaf, Deborah C. Stein Zweers, Ping Wang, Olaf N. E. Tuinder, and J. Pepijn Veefkind
Atmos. Meas. Tech., 13, 6407–6426, https://doi.org/10.5194/amt-13-6407-2020, https://doi.org/10.5194/amt-13-6407-2020, 2020
Short summary
Short summary
We investigated the influence of clouds on the Absorbing Aerosol Index (AAI), an indicator of the presence of small particles in the atmosphere. Clouds produce artifacts in AAI calculations on the individual measurement (7 km) scale, which was not seen with previous instruments, as well as on large (1000+ km) scales. To reduce these artefacts, we used three different AAI calculation techniques of varying complexity. We find that the AAI artifacts are reduced when using more complex techniques.
Cited articles
Ackerman, A. S., Toon, O. B., Stevens, D. E., Heymsfield, A. J., Ramanathan,
V., and Welton, E. J.: Reduction of Tropical Cloudiness by Soot, Science,
288, 1042–1047, https://doi.org/10.1126/science.288.5468.1042, 2000. a
Adebiyi, A. A. and Zuidema, P.: The role of the southern African easterly jet
in modifying the southeast Atlantic aerosol and cloud environments,
Q. J. Roy. Meteor. Soc., 142, 1574–1589,
https://doi.org/10.1002/qj.2765, 2016. a
Ajoku, O. F., Miller, A. J., and Norris, J. R.: Impacts of aerosols produced by
biomass burning on the stratocumulus-to-cumulus transition in the equatorial
Atlantic, Atmos. Sci. Lett., 22, e1025,
https://doi.org/10.1002/asl.1025, 2021. a
Albrecht, B.: Aerosols, Cloud Microphysics, and Fractional Cloudiness,
Science, 245, 1227–1230, 1989. a
Albrecht, B. A., Randall, D. A., and Nicholls, S.: Observations of Marine
Stratocumulus Clouds During FIRE, B. Am. Meteorol. Soc., 69, 618–626,
1988. a
Bretherton, C. S. and Wyant, M. C.: Moisture Transport, Lower-Tropospheric
Stability, and Decoupling of Cloud-Topped Boundary Layers, J. Atmos. Sci.,
54, 148–167, https://doi.org/10.1175/1520-0469(1997)054<0148:MTLTSA>2.0.CO;2, 1997. a
Brown, J.: Inverting ground-based polarisation lidar measurements to retrieve
cloud microphysical properties during the Ascension Island Initiative,
Internship report KNMI IR-2016-10, Royal Netherlands Meteorological Institute
(KNMI) and WUR,
https://cdn.knmi.nl/knmi/pdf/bibliotheek/knmipubIR/IR2016-10.pdf (last access: 2 May 2023),
2016. a, b
Cattrall, C., Reagan, J., Thome, K., and Dubovik, O.: Variability of aerosol
and spectral lidar and backscatter and extinction ratios of key aerosol types
derived from selected Aerosol Robotic Network locations, J.
Geophys. Res.-Atmos., 110, D10S11, https://doi.org/10.1029/2004JD005124,
2005. a
Collis, R. T. H. and Russel, P. B.: Lidar measurement of particles and gases by
elastic backscattering and differential absorption, in: Laser monitoring of
the atmosphere. Topics in Applied Physics, vol. 14., edited by: Hinkley, E. D.,
chap. 4, Springer, Berlin Heidelberg, 71–151,
https://doi.org/10.1007/3-540-07743-X_18, 1976. a
Conant, W. C., VanReken, T. M., Rissman, T. A., Varutbangkul, V., Jonsson,
H. H., Nenes, A., Jimenez, J. L., Delia, A. E., Bahreini, R., Roberts, G. C.,
Flagan, R. C., and Seinfeld, J. H.: Aerosol–cloud drop concentration closure
in warm cumulus, J. Geophys. Res., 109, D13204, https://doi.org/10.1029/2003JD004324, 2004. a
Dadashazar, H., Painemal, D., Alipanah, M., Brunke, M., Chellappan, S., Corral, A. F., Crosbie, E., Kirschler, S., Liu, H., Moore, R. H., Robinson, C., Scarino, A. J., Shook, M., Sinclair, K., Thornhill, K. L., Voigt, C., Wang, H., Winstead, E., Zeng, X., Ziemba, L., Zuidema, P., and Sorooshian, A.: Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors, Atmos. Chem. Phys., 21, 10499–10526, https://doi.org/10.5194/acp-21-10499-2021, 2021. a
Dang, C., Segal-Rozenhaimer, M., Che, H., Zhang, L., Formenti, P., Taylor, J., Dobracki, A., Purdue, S., Wong, P.-S., Nenes, A., Sedlacek III, A., Coe, H., Redemann, J., Zuidema, P., Howell, S., and Haywood, J.: Biomass burning and marine aerosol processing over the southeast Atlantic Ocean: a TEM single-particle analysis, Atmos. Chem. Phys., 22, 9389–9412, https://doi.org/10.5194/acp-22-9389-2022, 2022. a
Davidson, K. L., Fairall, C. W., Boyle, P. J., and Schacher, G. E.:
Verification of an Atmospheric Mixed-Layer Model for a Coastal Region,
J. Clim. Appl. Meteorol., 23, 617–636,
https://doi.org/10.1175/1520-0450(1984)023<0617:VOAAML>2.0.CO;2, 1984. a
de Graaf, M. and Donovan, D. P.: KNMI UV-polarisation lidar Ascension Island initiative (ASCII) campaign data, KNMI [data set], https://doi.org/10.21944/5qqy-0c37, 2023. a
de Graaf, M., Tilstra, L. G., Aben, I., and Stammes, P.: Satellite
observations of the seasonal cycles of absorbing aerosols in Africa related
to the monsoon rainfall, 1995–2008, Atmos. Environ., 44, 1274–1283,
https://doi.org/10.1016/j.atmosenv.2009.12.038, 2010. a
de Roode, S. R. and Los, A.: The effect of temperature and humidity
fluctuations on the liquid water path of non-precipitating closed-cell
stratocumulus clouds, Q. J. Roy. Meteor. Soc.,
134, 403–416, https://doi.org/10.1002/qj.222, 2008. a
Diamond, M. S., Dobracki, A., Freitag, S., Small Griswold, J. D., Heikkila, A., Howell, S. G., Kacarab, M. E., Podolske, J. R., Saide, P. E., and Wood, R.: Time-dependent entrainment of smoke presents an observational challenge for assessing aerosol–cloud interactions over the southeast Atlantic Ocean, Atmos. Chem. Phys., 18, 14623–14636, https://doi.org/10.5194/acp-18-14623-2018, 2018. a
Diamond, M. S., Saide, P. E., Zuidema, P., Ackerman, A. S., Doherty, S. J., Fridlind, A. M., Gordon, H., Howes, C., Kazil, J., Yamaguchi, T., Zhang, J., Feingold, G., and Wood, R.: Cloud adjustments from large-scale smoke–circulation interactions strongly modulate the southeastern Atlantic stratocumulus-to-cumulus transition, Atmos. Chem. Phys., 22, 12113–12151, https://doi.org/10.5194/acp-22-12113-2022, 2022. a
Dorman, C. E. and Bourke, R. H.: Precipitation over the Atlantic Ocean,
30∘ S to 70∘ N, Mon. Weather Rev., 109, 554–563,
https://doi.org/10.1175/1520-0493(1981)109<0554:POTAOT>2.0.CO;2, 1981. a
Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov, A., O'Neill,
N. T., Slutsker, I., and Kinne, S.: Wavelength dependence of the optical
depth of biomass burning, urban, and desert dust aerosols,
J. Geophys. Res., 104, 31333–31349, https://doi.org/10.1029/1999JD900923, 1999. a
Fairall, C. W., Hare, J. E., and Snider, J. B.: An Eight-Month Sample of Marine
Stratocumulus Cloud Fraction, Albedo, and Integrated Liquid Water, J.
Climate, 3, 847–864, https://doi.org/10.1175/1520-0442(1990)003<0847:AEMSOM>2.0.CO;2,
1990. a
Feingold, G., Remer, L. A., Ramaprasad, J., and Kaufman, Y. J.: Analysis of
smoke impact on clouds in Brazilian biomass burning regions: An extension of
Twomey's approach, J. Geophys. Res., 106, 22907–22922,
https://doi.org/10.1029/2001JD000732, 2001. a
Fernald, F. G.: Analysis of atmospheric lidar observations: some comments,
Appl. Opt., 23, 652–653, https://doi.org/10.1364/AO.23.000652, 1984. a
Formenti, P., D'Anna, B., Flamant, C., Mallet, M., Piketh, S. J., Schepanski,
K., Waquet, F., Auriol, F., Brogniez, G., Burnet, F., Chaboureau, J.-P.,
Chauvigné, A., Chazette, P., Denjean, C., Desboeufs, K., Doussin, J.-F.,
Elguindi, N., Feuerstein, S., Gaetani, M., Giorio, C., Klopper, D., Mallet,
M. D., Nabat, P., Monod, A., Solmon, F., Namwoonde, A., Chikwililwa, C.,
Mushi, R., Welton, E. J., and Holben, B.: The Aerosols, Radiation and Clouds
in Southern Africa Field Campaign in Namibia: Overview, Illustrative
Observations, and Way Forward, B. Am. Meteorol. Soc., 100, 1277–1298,
https://doi.org/10.1175/BAMS-D-17-0278.1, 2019. a
Fox, N. I. and Illingworth, A. J.: The Retrieval of Stratocumulus Cloud
Properties by Ground-Based Cloud Radar, J. Appl. Meteorol., 36, 485–492,
https://doi.org/10.1175/1520-0450(1997)036<0485:TROSCP>2.0.CO;2, 1997. a
Frisch, A. S., Fairall, C. W., and Snider, J. B.: Measurement of Stratus Cloud
and Drizzle Parameters in ASTEX with a Ka-Band Doppler Radar and a Microwave
Radiometer, J. Atmos. Sci., 52, 2788–2799,
https://doi.org/10.1175/1520-0469(1995)052<2788:MOSCAD>2.0.CO;2, 1995. a, b, c
Frisch, A. S., Shupe, M., Djalalova, I., Feingold, G., and Poellot, M.: The
Retrieval of Stratus Cloud Droplet Effective Radius with Cloud Radars,
J. Atmos. Ocean. Tech., 19, 835–842,
https://doi.org/10.1175/1520-0426(2002)019<0835:TROSCD>2.0.CO;2, 2002. a, b, c, d
Garrett, T. J., Zhao, C., Dong, X., Mace, G. G., and Hobbs, P. V.: Effects of
varying aerosol regimes on low-level Arctic stratus, Geophys. Res. Lett.,
31, L17105, https://doi.org/10.1029/2004GL019928, 2004. a
Garstang, M., Tyson, P. D., Swap, R., Edwards, M., Kållberg, P., and
Lindesay, J. A.: Horizontal and vertical transport of air over southern
Africa, J. Geophys. Res., 101, 23721–23736, https://doi.org/10.1029/95JD00844, 1996. a
Greatwood, C., Richardson, T., Freer, J., Thomas, R., Rob Mackenzie, A.,
Brownlow, R., Lowry, D., Fisher, R., and Nisbet, E.: Atmospheric sampling on
ascension island using multirotor UAVs, Sensors, 17, 1189,
https://doi.org/10.3390/s17061189, 2017. a
Gupta, S., McFarquhar, G. M., O'Brien, J. R., Poellot, M. R., Delene, D. J., Chang, I., Gao, L., Xu, F., and Redemann, J.: In situ and satellite-based estimates of cloud properties and aerosol–cloud interactions over the southeast Atlantic Ocean, Atmos. Chem. Phys., 22, 12923–12943, https://doi.org/10.5194/acp-22-12923-2022, 2022. a
Guzzi, R.: Exploring the Atmosphere by Remote Sensing Techniques, Lecture Notes
in Physics, Springer Berlin Heidelberg,
https://books.google.nl/books?id=RPRpCQAAQBAJ (last access: 2 May 2023), 2008. a
Haywood, J. M., Abel, S. J., Barrett, P. A., Bellouin, N., Blyth, A., Bower, K. N., Brooks, M., Carslaw, K., Che, H., Coe, H., Cotterell, M. I., Crawford, I., Cui, Z., Davies, N., Dingley, B., Field, P., Formenti, P., Gordon, H., de Graaf, M., Herbert, R., Johnson, B., Jones, A. C., Langridge, J. M., Malavelle, F., Partridge, D. G., Peers, F., Redemann, J., Stier, P., Szpek, K., Taylor, J. W., Watson-Parris, D., Wood, R., Wu, H., and Zuidema, P.: The CLoud–Aerosol–Radiation Interaction and Forcing: Year 2017 (CLARIFY-2017) measurement campaign, Atmos. Chem. Phys., 21, 1049–1084, https://doi.org/10.5194/acp-21-1049-2021, 2021. a, b
Holben, B., Eck, T., Slutsker, I., Tanré, D., Buis, J., Setzer, A., Vermote,
E., Reagan, J., Kaufman, Y., Nakajima, T., Lavenu, F., Jankowiak, I., and
Smirnov, A.: AERONET – A Federated Instrument Network and Data Archive for
Aerosol Characterization, Remote Sens. Environ., 66, 1–16,
https://doi.org/10.1016/S0034-4257(98)00031-5, 1998. a
Jimenez, C., Ansmann, A., Engelmann, R., Donovan, D., Malinka, A., Seifert, P., Wiesen, R., Radenz, M., Yin, Z., Bühl, J., Schmidt, J., Barja, B., and Wandinger, U.: The dual-field-of-view polarization lidar technique: a new concept in monitoring aerosol effects in liquid-water clouds – case studies, Atmos. Chem. Phys., 20, 15265–15284, https://doi.org/10.5194/acp-20-15265-2020, 2020. a, b
Johnson, B. T., Shine, K. P., and Forster, P. M.: The semi–direct aerosol
effect: Impact of absorbing aerosols on marine stratocumulus,
Q. J. Roy. Meteor. Soc., 130, 1407–1422, https://doi.org/10.1256/qj.03.61, 2004. a, b
Kim, B.-G., Miller, M. A., Schwartz, S. E., Liu, Y., and Min, Q.: The role of
adiabaticity in the aerosol first indirect effect, J. Geophys. Res., 113, D05210,
https://doi.org/10.1029/2007JD008961, 2008. a
Kim, J.-H., Schneider, R. R., Mulitza, S., and Müller, P. J.: Reconstruction
of SE trade-wind intensity based on sea-surface temperature gradients in the
Southeast Atlantic over the last 25 kyr, Geophys. Res. Lett., 30, 2144,
https://doi.org/10.1029/2003GL017557, 2003. a
Klett, J. D.: Stable analytical inversion solution for processing lidar
returns, Appl. Opt., 20, 211–220, https://doi.org/10.1364/AO.20.000211, 1981. a
Küchler, N., Kneifel, S., Kollias, P., and Löhnert, U.: Revisiting Liquid
Water Content Retrievals in Warm Stratified Clouds: The Modified Frisch,
Geophys. Res. Lett., 45, 9323–9330, https://doi.org/10.1029/2018GL079845, 2018. a
Li, J., Jian, B., Huang, J., Hu, Y., Zhao, C., Kawamoto, K., Liao, S., and Wu,
M.: Long-term variation of cloud droplet number concentrations from
space-based Lidar, Remote Sens. Environ., 213, 144–161,
https://doi.org/10.1016/j.rse.2018.05.011, 2018. a
Liou, K.-N. and Schotland, R. M.: Multiple Backscattering and Depolarization
from Water Clouds for a Pulsed Lidar System, J. Atmos.
Sci., 28, 772–784,
https://doi.org/10.1175/1520-0469(1971)028<0772:MBADFW>2.0.CO;2, 1971. a
Martin, G. M., Johnson, D. W., and Spice, A.: The Measurement and
Parameterization of Effective Radius of Droplets in Warm Stratocumulus
Clouds, J. Atmos. Sci., 51, 1823–1842,
https://doi.org/10.1175/1520-0469(1994)051<1823:TMAPOE>2.0.CO;2, 1994. a
McComiskey, A., Feingold, G., Frisch, A. S., Turner, D. D., Miller, M. A.,
Chiu, J. C., Min, Q., and Ogren, J. A.: An assessment of aerosol-cloud
interactions in marine stratus clouds based on surface remote sensing,
J. Geophys. Res., 114, D09203, https://doi.org/10.1029/2008JD011006, d09203, 2009. a, b, c, d
Miles, N. L., Verlinde, J., and Clothiaux, E. E.: Cloud Droplet Size
Distributions in Low-Level Stratiform Clouds, J. Atmos.
Sci., 57, 295–311,
https://doi.org/10.1175/1520-0469(2000)057<0295:CDSDIL>2.0.CO;2, 2000. a, b
Miller, M. A., Mages, Z., Zheng, Q., Trabachino, L., Russell, L. M., Shilling,
J. E., and Zawadowicz, M. A.: Observed Relationships Between Cloud Droplet
Effective Radius and Biogenic Gas Concentrations in Summertime Marine
Stratocumulus Over the Eastern North Atlantic, Earth and Space Science, 9,
e2021EA001929, https://doi.org/10.1029/2021EA001929, 2021. a
Müller, D., Ansmann, A., Mattis, I., Tesche, M., Wandinger, U., Althausen, D.,
and Pisani, G.: Aerosol-type-dependent lidar ratios observed with Raman
lidar, J. Geophys. Res.-Atmos., 112, D16202,
https://doi.org/10.1029/2006JD008292, d16202, 2007. a
Painemal, D., Kato, S., and Minnis, P.: Boundary layer regulation in the
southeast Atlantic cloud microphysics during the biomass burning season as
seen by the A-train satellite constellation, J. Geophys. Res.-Atmos., 119, 11288–11302, https://doi.org/10.1002/2014JD022182, 2014. a
Paluch, I. R. and Lenschow, D. H.: Stratiform Cloud Formation in the Marine
Boundary Layer, J. Atmos. Sci., 48, 2141–2158,
https://doi.org/10.1175/1520-0469(1991)048<2141:SCFITM>2.0.CO;2, 1991. a
Pappalardo, G., Amodeo, A., Apituley, A., Comeron, A., Freudenthaler, V., Linné, H., Ansmann, A., Bösenberg, J., D'Amico, G., Mattis, I., Mona, L., Wandinger, U., Amiridis, V., Alados-Arboledas, L., Nicolae, D., and Wiegner, M.: EARLINET: towards an advanced sustainable European aerosol lidar network, Atmos. Meas. Tech., 7, 2389–2409, https://doi.org/10.5194/amt-7-2389-2014, 2014. a
Rajapakshe, C., Zhang, Z., Yorks, J. E., Yu, H., Tan, Q., Meyer, K., Platnick,
S., and Winker, D. M.: Seasonally transported aerosol layers over southeast
Atlantic are closer to underlying clouds than previously reported,
Geophys. Res. Lett., 44, 5818–5825,
https://doi.org/10.1002/2017GL073559, 2017. a
Ramanathan, V., Crutzen, P. J., Lelieveld, J., Mitra, A. P., Althausen, D.,
Anderson, J., Andreae, M. O., Cantrell, W., Cass, G. R., Chung, C. E.,
Clarke, A. D., Coakley, J. A., Collins, W. D., Conant, W. C., Dulac, F.,
Heintzenberg, J., Heymsfield, A. J., Holben, B., Howell, S., Hudson, J.,
Jayaraman, A., Kiehl, J. T., Krishnamurti, T. N., Lubin, D., McFarquhar, G.,
Novakov, T., Ogren, J. A., Podgorny, I. A., Prather, K., Priestley, K.,
Prospero, J. M., Quinn, P. K., Rajeev, K., Rasch, P., Rupert, S., Sadourny,
R., Satheesh, S. K., Shaw, G. E., Sheridan, P., and Valero, F. P. J.: Indian
Ocean Experiment: An integrated analysis of the climate forcing and effects
of the great Indo-Asian haze, J. Geophys. Res., 106, 28371–28398, https://doi.org/10.1029/2001JD900133, 2001. a
Randall, D. A., Coakley, J. A., Lenschow, D. H., Fairall, C. W., and Kropfli,
R. A.: Outlook for research on subtropical marine stratiform clouds,
B. Am. Meteorol. Soc., 65, 1290–1301,
https://doi.org/10.1175/1520-0477(1984)065<1290:Ofrosm>2.0.Co;2, 1984. a
Redemann, J., Wood, R., Zuidema, P., Doherty, S. J., Luna, B., LeBlanc, S. E., Diamond, M. S., Shinozuka, Y., Chang, I. Y., Ueyama, R., Pfister, L., Ryoo, J.-M., Dobracki, A. N., da Silva, A. M., Longo, K. M., Kacenelenbogen, M. S., Flynn, C. J., Pistone, K., Knox, N. M., Piketh, S. J., Haywood, J. M., Formenti, P., Mallet, M., Stier, P., Ackerman, A. S., Bauer, S. E., Fridlind, A. M., Carmichael, G. R., Saide, P. E., Ferrada, G. A., Howell, S. G., Freitag, S., Cairns, B., Holben, B. N., Knobelspiesse, K. D., Tanelli, S., L'Ecuyer, T. S., Dzambo, A. M., Sy, O. O., McFarquhar, G. M., Poellot, M. R., Gupta, S., O'Brien, J. R., Nenes, A., Kacarab, M., Wong, J. P. S., Small-Griswold, J. D., Thornhill, K. L., Noone, D., Podolske, J. R., Schmidt, K. S., Pilewskie, P., Chen, H., Cochrane, S. P., Sedlacek, A. J., Lang, T. J., Stith, E., Segal-Rozenhaimer, M., Ferrare, R. A., Burton, S. P., Hostetler, C. A., Diner, D. J., Seidel, F. C., Platnick, S. E., Myers, J. S., Meyer, K. G., Spangenberg, D. A., Maring, H., and Gao, L.: An overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project: aerosol–cloud–radiation interactions in the southeast Atlantic basin, Atmos. Chem. Phys., 21, 1507–1563, https://doi.org/10.5194/acp-21-1507-2021, 2021. a
Rodgers, C.: Inverse Methods for Atmospheric Sounding: Theory and Practice,
Series on atmospheric, oceanic and planetary physics, World Scientific,
https://books.google.nl/books?id=FjxqDQAAQBAJ (last access: 2 May 2023), 2000. a
Ryoo, J.-M., Pfister, L., Ueyama, R., Zuidema, P., Wood, R., Chang, I., and Redemann, J.: A meteorological overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign over the southeastern Atlantic during 2016–2018: Part 2 – Daily and synoptic characteristics, Atmos. Chem. Phys., 22, 14209–14241, https://doi.org/10.5194/acp-22-14209-2022, 2022. a, b
Sarna, K. and Russchenberg, H. W. J.: Ground-based remote sensing scheme for monitoring aerosol–cloud interactions, Atmos. Meas. Tech., 9, 1039–1050, https://doi.org/10.5194/amt-9-1039-2016, 2016. a, b
Sarna, K., Donovan, D. P., and Russchenberg, H. W. J.: Estimating the optical extinction of liquid water clouds in the cloud base region, Atmos. Meas. Tech., 14, 4959–4970, https://doi.org/10.5194/amt-14-4959-2021, 2021. a
Sassen, K. and Petrilla, R. L.: Lidar depolarization from multiple scattering
in marine stratus clouds, Appl. Opt., 25, 1450–1459,
https://doi.org/10.1364/AO.25.001450, 1986. a
Schenkels, M.: Aerosol Optical Depth and Cloud Parameters from Ascension Island
retrieved with a UV-depolarisation Lidar: An outlook on the validation,
Master thesis KNMI TR-366, Royal Netherlands Meteorological Institute (KNMI)
and UU,
https://studenttheses.uu.nl/handle/20.500.12932/30472 (last access: 2 May 2023), 2018. a, b, c, d
Swap, R., Garstang, M., Macko, S. A., Tyson, P. D., Maenhaut, W., Artaxo, P.,
Kållberg, P., and Talbot, R.: The long-range transport of
southern African aerosols to the tropical South Atlantic,
J. Geophys. Res., 101, 23777–23791, https://doi.org/10.1029/95JD01049, 1996. a, b
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8,
1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974. a
Twomey, S. A.: The Influence of Pollution on the Shortwave Albedo of Clouds,
J. Atmos. Sci., 34, 1149–1152, 1977. a
Wandinger, U., Baars, H., Engelmann, R., Hünerbein, A., Horn, S., Kanitz, T.,
Donovan, D., van Zadelhoff, G.-J., Daou, D., Fischer, J., von Bismarck, J.,
Filipitsch, F., Docter, N., Eisinger, M., Lajas, D., and Wehr, T.: HETEAC:
The Aerosol Classification Model for EarthCARE, EPJ Web Conf., 119,
01004, https://doi.org/10.1051/epjconf/201611901004, 2016. a, b, c, d
Wang, J. and Geerts, B.: Identifying drizzle within marine stratus with W-band
radar reflectivity, Atmos. Res., 69, 1–27,
https://doi.org/10.1016/j.atmosres.2003.08.001, 2003. a
Wang, S., Wang, Q., and Feingold, G.: Turbulence, Condensation, and Liquid
Water Transport in Numerically Simulated Nonprecipitating Stratocumulus
Clouds, J. Atmos. Sci., 60, 262–278,
https://doi.org/10.1175/1520-0469(2003)060<0262:TCALWT>2.0.CO;2, 2003. a, b
Wyant, M. C., Bretherton, C. S., Rand, H. A., and Stevens, D. E.: Numerical
Simulations and a Conceptual Model of the Stratocumulus to Trade Cumulus
Transition, J. Atmos. Sci., 54, 168–192,
https://doi.org/10.1175/1520-0469(1997)054<0168:NSAACM>2.0.CO;2, 1997. a
Xue, H. and Feingold, G.: Large-Eddy Simulations of Trade Wind Cumuli:
Investigation of Aerosol Indirect Effects, J. Atmos. Sci., 63, 1605–1622,
https://doi.org/10.1175/JAS3706.1, 2006. a, b
Yamaguchi, T., Feingold, G., Kazil, J., and McComiskey, A.: Stratocumulus to
cumulus transition in the presence of elevated smoke layers,
Geophys. Res. Lett., 42, 10,478–10,485,
https://doi.org/10.1002/2015GL066544, 2015. a
Yamaguchi, T., Feingold, G., and Kazil, J.: Stratocumulus to Cumulus Transition
by Drizzle, J. Adv. Model Earth Sy., 9, 2333–2349,
https://doi.org/10.1002/2017MS001104, 2017. a
Zhang, J. and Zuidema, P.: The diurnal cycle of the smoky marine boundary layer observed during August in the remote southeast Atlantic, Atmos. Chem. Phys., 19, 14493–14516, https://doi.org/10.5194/acp-19-14493-2019, 2019. a
Zhang, J. and Zuidema, P.: Sunlight-absorbing aerosol amplifies the seasonal cycle in low-cloud fraction over the southeast Atlantic, Atmos. Chem. Phys., 21, 11179–11199, https://doi.org/10.5194/acp-21-11179-2021, 2021. a
Zhang, S., Xue, H., and Feingold, G.: Vertical profiles of droplet effective radius in shallow convective clouds, Atmos. Chem. Phys., 11, 4633–4644, https://doi.org/10.5194/acp-11-4633-2011, 2011. a
Zhou, X., Ackerman, A. S., Fridlind, A. M., Wood, R., and Kollias, P.: Impacts of solar-absorbing aerosol layers on the transition of stratocumulus to trade cumulus clouds, Atmos. Chem. Phys., 17, 12725–12742, https://doi.org/10.5194/acp-17-12725-2017, 2017.
a, b
Zuidema, P., Redemann, J., Haywood, J., Wood, R., Piketh, S., Hipondoka, M.,
and Formenti, P.: Smoke and Clouds above the Southeast Atlantic: Upcoming
Field Campaigns Probe Absorbing Aerosol's Impact on Climate,
B. Am. Meteorol. Soc., 97, 1131–1135, https://doi.org/10.1175/BAMS-D-15-00082.1,
2016. a
Zuidema, P., Sedlacek III, A. J., Flynn, C., Springston, S., Delgadillo, R.,
Zhang, J., Aiken, A. C., Koontz, A., and Muradyan, P.: The Ascension Island
Boundary Layer in the Remote Southeast Atlantic is Often Smoky,
Geophys. Res. Lett., 45, 4456–4465, https://doi.org/10.1002/2017GL076926, 2018. a, b
Short summary
Clouds over the oceans reflect sunlight and cool the earth. Simultaneous measurements were performed of cloud droplet sizes and smoke particles in and near the cloud base over Ascension Island, a remote island in the Atlantic Ocean, to determine the sensitivity of cloud droplets to smoke from the African continent. The smoke was found to reduce cloud droplet sizes, which makes the cloud droplets more susceptible to evaporation, reducing cloud lifetime.
Clouds over the oceans reflect sunlight and cool the earth. Simultaneous measurements were...
Altmetrics
Final-revised paper
Preprint