Articles | Volume 25, issue 6
https://doi.org/10.5194/acp-25-3841-2025
© Author(s) 2025. 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-25-3841-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
02 Apr 2025
Research article | Highlight paper |
| 02 Apr 2025
| 02 Apr 2025
Pristine oceans are a significant source of uncertainty in quantifying global cloud condensation nuclei
Goutam Choudhury
CORRESPONDING AUTHOR
Department of Environment, Planning and Sustainability, Bar-Ilan University, Ramat Gan, 5290002, Israel
Karoline Block
Leipzig Institute for Meteorology, Leipzig University, 04103 Leipzig, Germany
Mahnoosh Haghighatnasab
Leipzig Institute for Meteorology, Leipzig University, 04103 Leipzig, Germany
Research and Development, German Weather Service, 63067 Offenbach am Main, Germany
Johannes Quaas
Leipzig Institute for Meteorology, Leipzig University, 04103 Leipzig, Germany
Tom Goren
Department of Environment, Planning and Sustainability, Bar-Ilan University, Ramat Gan, 5290002, Israel
Matthias Tesche
Leipzig Institute for Meteorology, Leipzig University, 04103 Leipzig, Germany
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Johannes Mülmenstädt, Edward Gryspeerdt, Sudhakar Dipu, Johannes Quaas, Andrew S. Ackerman, Ann M. Fridlind, Florian Tornow, Susanne E. Bauer, Andrew Gettelman, Yi Ming, Youtong Zheng, Po-Lun Ma, Hailong Wang, Kai Zhang, Matthew W. Christensen, Adam C. Varble, L. Ruby Leung, Xiaohong Liu, David Neubauer, Daniel G. Partridge, Philip Stier, and Toshihiko Takemura
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Atmos. Meas. Tech., 17, 397–406, https://doi.org/10.5194/amt-17-397-2024, https://doi.org/10.5194/amt-17-397-2024, 2024
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Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Rebecca J. Murray-Watson, Edward Gryspeerdt, and Tom Goren
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Clouds formed in Arctic marine cold air outbreaks undergo a distinct evolution, but the factors controlling their transition from high-coverage to broken cloud fields are poorly understood. We use satellite and reanalysis data to study how these clouds develop in time and the different influences on their evolution. The aerosol concentration is correlated with cloud break-up; more aerosol is linked to prolonged coverage and a stronger cooling effect, with implications for a more polluted Arctic.
Goutam Choudhury and Matthias Tesche
Earth Syst. Sci. Data, 15, 3747–3760, https://doi.org/10.5194/essd-15-3747-2023, https://doi.org/10.5194/essd-15-3747-2023, 2023
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Aerosols in the atmosphere that can form liquid cloud droplets are called cloud condensation nuclei (CCN). Accurate measurements of CCN, especially CCN of anthropogenic origin, are necessary to quantify the effect of anthropogenic aerosols on the present-day as well as future climate. In this paper, we describe a novel global 3D CCN data set calculated from satellite measurements. We also discuss the potential applications of the data in the context of aerosol–cloud interactions.
Hao Luo, Johannes Quaas, and Yong Han
Atmos. Chem. Phys., 23, 8169–8186, https://doi.org/10.5194/acp-23-8169-2023, https://doi.org/10.5194/acp-23-8169-2023, 2023
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Preprint archived
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We improve the ability of WRF-Chem model to simulate aerosol-cloud physical and chemical processes by coupling a spectral-bin cloud microphysics scheme and online aerosol module, and consequently explore the aerosol-cloud interactions over eastern China and its adjacent ocean in boreal winter. Our study highlights the differences in aerosol-cloud interactions between land and ocean, precipitation clouds and non-precipitation clouds, and differentiates and quantifies their underlying mechanisms.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
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Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
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In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Peter Bräuer and Matthias Tesche
Geosci. Model Dev., 15, 7557–7572, https://doi.org/10.5194/gmd-15-7557-2022, https://doi.org/10.5194/gmd-15-7557-2022, 2022
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This paper presents a tool for (i) finding temporally and spatially resolved intersections between two- or three-dimensional geographical tracks (trajectories) and (ii) extracting of data in the vicinity of intersections to achieve the optimal combination of various data sets.
Johannes Quaas, Hailing Jia, Chris Smith, Anna Lea Albright, Wenche Aas, Nicolas Bellouin, Olivier Boucher, Marie Doutriaux-Boucher, Piers M. Forster, Daniel Grosvenor, Stuart Jenkins, Zbigniew Klimont, Norman G. Loeb, Xiaoyan Ma, Vaishali Naik, Fabien Paulot, Philip Stier, Martin Wild, Gunnar Myhre, and Michael Schulz
Atmos. Chem. Phys., 22, 12221–12239, https://doi.org/10.5194/acp-22-12221-2022, https://doi.org/10.5194/acp-22-12221-2022, 2022
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Pollution particles cool climate and offset part of the global warming. However, they are washed out by rain and thus their effect responds quickly to changes in emissions. We show multiple datasets to demonstrate that aerosol emissions and their concentrations declined in many regions influenced by human emissions, as did the effects on clouds. Consequently, the cooling impact on the Earth energy budget became smaller. This change in trend implies a relative warming.
Matthias Tesche and Vincent Noel
Atmos. Meas. Tech., 15, 4225–4240, https://doi.org/10.5194/amt-15-4225-2022, https://doi.org/10.5194/amt-15-4225-2022, 2022
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Mid-level and high clouds can be considered natural laboratories for studying cloud glaciation in the atmosphere. While they can be conveniently observed from ground with lidar, such measurements require a clear line of sight between the instrument and the target cloud. Here, observations of clouds with two spaceborne lidars are used to assess where ground-based lidar measurements of mid- and upper-level clouds are least affected by the light-attenuating effect of low-level clouds.
Ovid O. Krüger, Bruna A. Holanda, Sourangsu Chowdhury, Andrea Pozzer, David Walter, Christopher Pöhlker, Maria Dolores Andrés Hernández, John P. Burrows, Christiane Voigt, Jos Lelieveld, Johannes Quaas, Ulrich Pöschl, and Mira L. Pöhlker
Atmos. Chem. Phys., 22, 8683–8699, https://doi.org/10.5194/acp-22-8683-2022, https://doi.org/10.5194/acp-22-8683-2022, 2022
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The abrupt reduction in human activities during the first COVID-19 lockdown created unprecedented atmospheric conditions. We took the opportunity to quantify changes in black carbon (BC) as a major anthropogenic air pollutant. Therefore, we measured BC on board a research aircraft over Europe during the lockdown and compared the results to measurements from 2017. With model simulations we account for different weather conditions and find a lockdown-related decrease in BC of 41 %.
Mahnoosh Haghighatnasab, Jan Kretzschmar, Karoline Block, and Johannes Quaas
Atmos. Chem. Phys., 22, 8457–8472, https://doi.org/10.5194/acp-22-8457-2022, https://doi.org/10.5194/acp-22-8457-2022, 2022
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The impact of aerosols emitted by the Holuhraun volcanic eruption on liquid clouds was assessed from a pair of cloud-system-resolving simulations along with satellite retrievals. Inside and outside the plume were compared in terms of their statistical distributions. Analyses indicated enhancement for cloud droplet number concentration inside the volcano plume in model simulations and satellite retrievals, while there was on average a small effect on both liquid water path and cloud fraction.
Hailing Jia, Johannes Quaas, Edward Gryspeerdt, Christoph Böhm, and Odran Sourdeval
Atmos. Chem. Phys., 22, 7353–7372, https://doi.org/10.5194/acp-22-7353-2022, https://doi.org/10.5194/acp-22-7353-2022, 2022
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Aerosol–cloud interaction is the most uncertain component of the anthropogenic forcing of the climate. By combining satellite and reanalysis data, we show that the strength of the Twomey effect (S) increases remarkably with vertical velocity. Both the confounding effect of aerosol–precipitation interaction and the lack of vertical co-location between aerosol and cloud are found to overestimate S, whereas the retrieval biases in aerosol and cloud appear to underestimate S.
Goutam Choudhury, Albert Ansmann, and Matthias Tesche
Atmos. Chem. Phys., 22, 7143–7161, https://doi.org/10.5194/acp-22-7143-2022, https://doi.org/10.5194/acp-22-7143-2022, 2022
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Lidars provide height-resolved type-specific aerosol properties and are key in studying vertically collocated aerosols and clouds. In this study, we compare the aerosol number concentrations derived from spaceborne lidar with the in situ flight measurements. Our results show a reasonable agreement between both datasets. Such an agreement has not been achieved yet. It shows the potential of spaceborne lidar in studying aerosol–cloud interactions, which is needed to improve our climate forecasts.
Juseon Shin, Juhyeon Sim, Naghmeh Dehkhoda, Sohee Joo, Taekyung Kim, Gahyung Kim, Detlef Müller, Matthias Tesche, Sungkyun Shin, Dongho Shin, and Youngmin Noh
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-219, https://doi.org/10.5194/acp-2022-219, 2022
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We analyzed long-term AERONET sun/sky radiometer for 6 continentals to verify the trend of aerosol physical properties depending on sources (dust or pollution) and size (fine or coarse mode). We identified the trend of classified aerosol optical depth (AOD) and size change over 9 years. Especially, we find out aerosol properties causing AOD variations are different from regions and fine aerosol particle in most regions has become smaller using MK-test for trend analysis.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Graham Feingold, Tom Goren, and Takanobu Yamaguchi
Atmos. Chem. Phys., 22, 3303–3319, https://doi.org/10.5194/acp-22-3303-2022, https://doi.org/10.5194/acp-22-3303-2022, 2022
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The evaluation of radiative forcing associated with aerosol–cloud interactions remains a significant source of uncertainty in future climate projections. Using high-resolution numerical model output, we mimic typical satellite retrieval methodologies to show that data aggregation can introduce significant error (hundreds of percent) in the cloud albedo susceptibility metric. Spatial aggregation errors tend to be countered by temporal aggregation errors.
Goutam Choudhury and Matthias Tesche
Atmos. Meas. Tech., 15, 639–654, https://doi.org/10.5194/amt-15-639-2022, https://doi.org/10.5194/amt-15-639-2022, 2022
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Aerosols are tiny particles suspended in the atmosphere. A fraction of these particles can form clouds and are called cloud condensation nuclei (CCN). Measurements of such aerosol particles are necessary to study the aerosol–cloud interactions and reduce the uncertainty in our future climate predictions. We present a novel methodology to estimate global 3D CCN concentrations from the CALIPSO satellite measurements. The final data set will be used to study the aerosol–cloud interactions.
Jianhao Zhang, Xiaoli Zhou, Tom Goren, and Graham Feingold
Atmos. Chem. Phys., 22, 861–880, https://doi.org/10.5194/acp-22-861-2022, https://doi.org/10.5194/acp-22-861-2022, 2022
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Oceanic liquid-form clouds are effective sunlight reflectors. Their brightness is highly sensitive to changes in the amount of aerosol particles in the atmosphere and the state of the atmosphere they reside in. This study quantifies this sensitivity using long-term satellite observations and finds an overall cloud brightening (a cooling effect) potential and an essential role of the covarying meteorological conditions in governing this sensitivity for northeastern Pacific stratocumulus.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
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Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, https://doi.org/10.5194/acp-21-17291-2021, 2021
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The article introduces the ACP readership to ongoing research in Germany on cloud- and precipitation-related process information inherent in polarimetric radar measurements, outlines pathways to inform atmospheric models with radar-based information, and points to remaining challenges towards an improved fusion of radar polarimetry and atmospheric modelling.
Maria Kezoudi, Matthias Tesche, Helen Smith, Alexandra Tsekeri, Holger Baars, Maximilian Dollner, Víctor Estellés, Johannes Bühl, Bernadett Weinzierl, Zbigniew Ulanowski, Detlef Müller, and Vassilis Amiridis
Atmos. Chem. Phys., 21, 6781–6797, https://doi.org/10.5194/acp-21-6781-2021, https://doi.org/10.5194/acp-21-6781-2021, 2021
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Mineral dust concentrations in the diameter range from 0.4 to 14.0 μm were measured with the balloon-borne UCASS optical particle counter. Launches were coordinated with ground-based remote-sensing and airborne in situ measurements during a Saharan dust outbreak over Cyprus. Particle number concentrations reached 50 cm−3 for the diameter range 0.8–13.9 μm. Comparisons with aircraft data show reasonable agreement in magnitude and shape of the particle size distribution.
Edward Gryspeerdt, Tom Goren, and Tristan W. P. Smith
Atmos. Chem. Phys., 21, 6093–6109, https://doi.org/10.5194/acp-21-6093-2021, https://doi.org/10.5194/acp-21-6093-2021, 2021
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Cloud responses to aerosol are time-sensitive, but this development is rarely observed. This study uses isolated aerosol perturbations from ships to measure this development and shows that macrophysical (width, cloud fraction, detectability) and microphysical (droplet number) properties of ship tracks vary strongly with time since emission, background cloud and meteorological state. This temporal development should be considered when constraining aerosol–cloud interactions with observations.
Matthias Tesche, Peggy Achtert, and Michael C. Pitts
Atmos. Chem. Phys., 21, 505–516, https://doi.org/10.5194/acp-21-505-2021, https://doi.org/10.5194/acp-21-505-2021, 2021
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We combine spaceborne lidar observations of clouds in the troposphere and stratosphere to assess the outcome of ground-based polar stratospheric cloud (PSC) observations that are often performed at the mercy of tropospheric clouds. We find that the outcome of ground-based lidar measurements of PSCs depends on the location of the measurement. We also provide recommendations regarding the most suitable sites in the Arctic and Antarctic.
Goutam Choudhury, Bhishma Tyagi, Naresh Krishna Vissa, Jyotsna Singh, Chandan Sarangi, Sachchida Nand Tripathi, and Matthias Tesche
Atmos. Chem. Phys., 20, 15389–15399, https://doi.org/10.5194/acp-20-15389-2020, https://doi.org/10.5194/acp-20-15389-2020, 2020
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This study uses 17 years (2001–2017) of observed rain rate, aerosol optical depth (AOD), meteorological reanalysis fields and outgoing long-wave radiation to investigate high precipitation events at the foothills of the Himalayas. Composite analysis of all data sets for high precipitation events (daily rainfall > 95th percentile) indicates clear and robust associations between high precipitation events, high aerosol loading and high moist static energy values.
Johannes Quaas, Antti Arola, Brian Cairns, Matthew Christensen, Hartwig Deneke, Annica M. L. Ekman, Graham Feingold, Ann Fridlind, Edward Gryspeerdt, Otto Hasekamp, Zhanqing Li, Antti Lipponen, Po-Lun Ma, Johannes Mülmenstädt, Athanasios Nenes, Joyce E. Penner, Daniel Rosenfeld, Roland Schrödner, Kenneth Sinclair, Odran Sourdeval, Philip Stier, Matthias Tesche, Bastiaan van Diedenhoven, and Manfred Wendisch
Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, https://doi.org/10.5194/acp-20-15079-2020, 2020
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Anthropogenic pollution particles – aerosols – serve as cloud condensation nuclei and thus increase cloud droplet concentration and the clouds' reflection of sunlight (a cooling effect on climate). This Twomey effect is poorly constrained by models and requires satellite data for better quantification. The review summarizes the challenges in properly doing so and outlines avenues for progress towards a better use of aerosol retrievals and better retrievals of droplet concentrations.
Jan Kretzschmar, Johannes Stapf, Daniel Klocke, Manfred Wendisch, and Johannes Quaas
Atmos. Chem. Phys., 20, 13145–13165, https://doi.org/10.5194/acp-20-13145-2020, https://doi.org/10.5194/acp-20-13145-2020, 2020
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This study compares simulations with the ICON model at the kilometer scale to airborne radiation and cloud microphysics observations that have been derived during the ACLOUD aircraft campaign around Svalbard, Norway, in May/June 2017. We find an overestimated surface warming effect of clouds compared to the observations in our setup. This bias was reduced by considering subgrid-scale vertical motion in the activation of cloud condensation nuclei in the two-moment microphysical scheme used.
Martina Krämer, Christian Rolf, Nicole Spelten, Armin Afchine, David Fahey, Eric Jensen, Sergey Khaykin, Thomas Kuhn, Paul Lawson, Alexey Lykov, Laura L. Pan, Martin Riese, Andrew Rollins, Fred Stroh, Troy Thornberry, Veronika Wolf, Sarah Woods, Peter Spichtinger, Johannes Quaas, and Odran Sourdeval
Atmos. Chem. Phys., 20, 12569–12608, https://doi.org/10.5194/acp-20-12569-2020, https://doi.org/10.5194/acp-20-12569-2020, 2020
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To improve the representations of cirrus clouds in climate predictions, extended knowledge of their properties and geographical distribution is required. This study presents extensive airborne in situ and satellite remote sensing climatologies of cirrus and humidity, which serve as a guide to cirrus clouds. Further, exemplary radiative characteristics of cirrus types and also in situ observations of tropical tropopause layer cirrus and humidity in the Asian monsoon anticyclone are shown.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003. a
Alexandri, F., Müller, F., Choudhury, G., Achtert, P., Seelig, T., and Tesche, M.: A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations, Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, 2024. a
Allen, G., Coe, H., Clarke, A., Bretherton, C., Wood, R., Abel, S. J., Barrett, P., Brown, P., George, R., Freitag, S., McNaughton, C., Howell, S., Shank, L., Kapustin, V., Brekhovskikh, V., Kleinman, L., Lee, Y.-N., Springston, S., Toniazzo, T., Krejci, R., Fochesatto, J., Shaw, G., Krecl, P., Brooks, B., McMeeking, G., Bower, K. N., Williams, P. I., Crosier, J., Crawford, I., Connolly, P., Allan, J. D., Covert, D., Bandy, A. R., Russell, L. M., Trembath, J., Bart, M., McQuaid, J. B., Wang, J., and Chand, D.: South East Pacific atmospheric composition and variability sampled along 20° S during VOCALS-REx, Atmos. Chem. Phys., 11, 5237–5262, https://doi.org/10.5194/acp-11-5237-2011, 2011. a
Aravindhavel, A., Choudhury, G., Prabhakaran, T., Murugavel, P., and Tesche, M.: Retrieval and validation of cloud condensation nuclei from satellite and airborne measurements over the Indian Monsoon region, Atmos. Res., 290, 106802, https://doi.org/10.1016/j.atmosres.2023.106802, 2023. a, b
Asmi, E., Kondratyev, V., Brus, D., Laurila, T., Lihavainen, H., Backman, J., Vakkari, V., Aurela, M., Hatakka, J., Viisanen, Y., Uttal, T., Ivakhov, V., and Makshtas, A.: Aerosol size distribution seasonal characteristics measured in Tiksi, Russian Arctic, Atmos. Chem. Phys., 16, 1271–1287, https://doi.org/10.5194/acp-16-1271-2016, 2016. a
Ayers, G. P. and Gras, J. L.: Seasonal relationship between cloud condensation nuclei and aerosol methanesulphonate in marine air, Nature, 353, 834–835, https://doi.org/10.1038/353834a0, 1991. a, b, c, d
Bègue, N., Tulet, P., Pelon, J., Aouizerats, B., Berger, A., and Schwarzenboeck, A.: Aerosol processing and CCN formation of an intense Saharan dust plume during the EUCAARI 2008 campaign, Atmos. Chem. Phys., 15, 3497–3516, https://doi.org/10.5194/acp-15-3497-2015, 2015. a
Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R. J., Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W., Kinne, S., Mangold, A., Razinger, M., Simmons, A. J., and Suttie, M.: Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation, J. Geophys. Res.-Atmos., 114, D13205, https://doi.org/10.1029/2008JD011115, 2009. a
Block, K.: Cloud condensation nuclei (CCN) numbers derived from CAMS reanalysis EAC4 (Version 1), World Data Center for Climate [data set], https://doi.org/10.26050/WDCC/QUAERERE_CCNCAMS_v1, 2023. a
Brendecke, J., Dong, X., Xi, B., and Zheng, X.: Maritime Aerosol and CCN Profiles Derived From Ship-Based Measurements Over Eastern North Pacific During MAGIC, Earth Space Sci., 9, e2022EA002319, https://doi.org/10.1029/2022EA002319, 2022. a
Choudhury, G. and Tesche, M.: Assessment of CALIOP-Derived CCN Concentrations by In Situ Surface Measurements, Remote Sens., 14, 3342, https://doi.org/10.3390/rs14143342, 2022b. a, b, c, d
Choudhury, G. and Tesche, M.: A first global height-resolved cloud condensation nuclei data set derived from spaceborne lidar measurements, Earth Syst. Sci. Data, 15, 3747–3760, https://doi.org/10.5194/essd-15-3747-2023, 2023a. a, b, c, d
Choudhury, G. and Tesche, M.: Global multiyear 3D dataset of cloud condensation nuclei derived from spaceborne lidar measurements, Pangaea [data set], https://doi.org/10.1594/PANGAEA.956215, 2023b. a
Choudhury, G., Ansmann, A., and Tesche, M.: Evaluation of aerosol number concentrations from CALIPSO with ATom airborne in situ measurements, Atmos. Chem. Phys., 22, 7143–7161, https://doi.org/10.5194/acp-22-7143-2022, 2022. a, b
Collaud Coen, M., Andrews, E., Alastuey, A., Arsov, T. P., Backman, J., Brem, B. T., Bukowiecki, N., Couret, C., Eleftheriadis, K., Flentje, H., Fiebig, M., Gysel-Beer, M., Hand, J. L., Hoffer, A., Hooda, R., Hueglin, C., Joubert, W., Keywood, M., Kim, J. E., Kim, S.-W., Labuschagne, C., Lin, N.-H., Lin, Y., Lund Myhre, C., Luoma, K., Lyamani, H., Marinoni, A., Mayol-Bracero, O. L., Mihalopoulos, N., Pandolfi, M., Prats, N., Prenni, A. J., Putaud, J.-P., Ries, L., Reisen, F., Sellegri, K., Sharma, S., Sheridan, P., Sherman, J. P., Sun, J., Titos, G., Torres, E., Tuch, T., Weller, R., Wiedensohler, A., Zieger, P., and Laj, P.: Multidecadal trend analysis of in situ aerosol radiative properties around the world, Atmos. Chem. Phys., 20, 8867–8908, https://doi.org/10.5194/acp-20-8867-2020, 2020. a
Copernicus Climate Change Service: Precipitation monthly and daily gridded data from 1979 to present derived from satellite measurement, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.c14d9324, 2021. a
Doelling, D. R., Loeb, N. G., Keyes, D. F., Nordeen, M. L., Morstad, D., Nguyen, C., Wielicki, B. A., Young, D. F., and Sun, M.: Geostationary Enhanced Temporal Interpolation for CERES Flux Products, J. Atmos. Ocean. Technol., 30, 1072–1090, https://doi.org/10.1175/JTECH-D-12-00136.1, 2013. a
Désalmand, F.: Observations of CCN concentrations south of the Sahara during a Dust Haze, Atmos. Res., 21, 13–28, https://doi.org/10.1016/0169-8095(87)90014-7, 1987. a
Errera, Q., Bennouna, Y., Schulz, M., Eskes, H. J., Basart, S., Benedictow, A., Blechschmidt, A.-M., Chabrillat, S., Clark, H., Cuevas, E., Flentje, H., Hansen, K. M., Im, U., Kapsomenakis, J., Langerock, B., Petersen, K., Richter, A., Sudarchikova, N., Thouret, V., Wagner, A., Wang, Y., Warneke, T., and Zerefos, C.: Validation report of the CAMS global Reanalysis of aerosols and reactive gases, years 2003–2020, Copernicus Atmosphere Monitoring Service (CAMS) report, https://doi.org/10.24380/8gf9-k005, 2021. a
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., 923–1054 pp., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157896.009, 2021. a, b, c
Fossum, K. N., Ovadnevaite, J., Ceburnis, D., Dall’Osto, M., Marullo, S., Bellacicco, M., Simó, R., Liu, D., Flynn, M., Zuend, A., and O’Dowd, C.: Summertime Primary and Secondary Contributions to Southern Ocean Cloud Condensation Nuclei, Sci. Rep., 8, 13844, https://doi.org/10.1038/s41598-018-32047-4, 2018. a
Garimella, S., Huang, Y.-W., Seewald, J. S., and Cziczo, D. J.: Cloud condensation nucleus activity comparison of dry- and wet-generated mineral dust aerosol: the significance of soluble material, Atmos. Chem. Phys., 14, 6003–6019, https://doi.org/10.5194/acp-14-6003-2014, 2014. a
Gasteiger, J. and Wiegner, M.: MOPSMAP v1.0: a versatile tool for the modeling of aerosol optical properties, Geosci. Model Dev., 11, 2739–2762, https://doi.org/10.5194/gmd-11-2739-2018, 2018. a
Gras, J. L.: Cloud condensation nuclei over the Southern Ocean, Geophys. Res. Lett., 17, 1565–1567, https://doi.org/10.1029/GL017i010p01565, 1990. a, b
Gras, J. L. and Keywood, M.: Cloud condensation nuclei over the Southern Ocean: wind dependence and seasonal cycles, Atmos. Chem. Phys., 17, 4419–4432, https://doi.org/10.5194/acp-17-4419-2017, 2017. a, b, c, d
Grosvenor, D. P. and Wood, R.: The effect of solar zenith angle on MODIS cloud optical and microphysical retrievals within marine liquid water clouds, Atmos. Chem. Phys., 14, 7291–7321, https://doi.org/10.5194/acp-14-7291-2014, 2014. a
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M. D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M., Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A., Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D., Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K., Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang, Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives, Rev. Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018. a
Gryspeerdt, E., Quaas, J., Ferrachat, S., Gettelman, A., Ghan, S., Lohmann, U., Morrison, H., Neubauer, D., Partridge, D. G., Stier, P., Takemura, T., Wang, H., Wang, M., and Zhang, K.: Constraining the instantaneous aerosol influence on cloud albedo, P. Natl. Acad. Sci. USA, 114, 4899–4904, https://doi.org/10.1073/pnas.1617765114, 2017. a, b
Gryspeerdt, E., Smith, T. W. P., O'Keeffe, E., Christensen, M. W., and Goldsworth, F. W.: The Impact of Ship Emission Controls Recorded by Cloud Properties, Geophys. Res. Lett., 46, 12547–12555, https://doi.org/10.1029/2019GL084700, 2019. a, b
Gryspeerdt, E., McCoy, D., Crosbie, E., Moore, R. H., Nott, G. J., Painemal, D., Small-Griswold, J. D., Sorooshian, A., and Ziemba, L.: Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/864a46cc65054008857ee5bb772a2a2b, 2022. a, b
Gryspeerdt, E., Povey, A. C., Grainger, R. G., Hasekamp, O., Hsu, N. C., Mulcahy, J. P., Sayer, A. M., and Sorooshian, A.: Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions, Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, 2023. a
Hasekamp, O. P., Gryspeerdt, E., and Quaas, J.: Analysis of polarimetric satellite measurements suggests stronger cooling due to aerosol-cloud interactions, Nat. Commun., 10, 5405, https://doi.org/10.1038/s41467-019-13372-2, 2019. a
Humphries, R. S., Keywood, M. D., Ward, J. P., Harnwell, J., Alexander, S. P., Klekociuk, A. R., Hara, K., McRobert, I. M., Protat, A., Alroe, J., Cravigan, L. T., Miljevic, B., Ristovski, Z. D., Schofield, R., Wilson, S. R., Flynn, C. J., Kulkarni, G. R., Mace, G. G., McFarquhar, G. M., Chambers, S. D., Williams, A. G., and Griffiths, A. D.: Measurement report: Understanding the seasonal cycle of Southern Ocean aerosols, Atmos. Chem. Phys., 23, 3749–3777, https://doi.org/10.5194/acp-23-3749-2023, 2023. a, b, c, d, e, f, g, h, i
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019a. a, b, c, d
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A., Dominguez, J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: CAMS global reanalysis (EAC4) monthly averaged fields, copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS) [data set], https://ads.atmosphere.copernicus.eu/datasets/cams-global-reanalysis-eac4-monthly?tab=overview (last access: 20 October 2024), 2019b. a, b
Jayachandran, V. N., Varghese, M., Murugavel, P., Todekar, K. S., Bankar, S. P., Malap, N., Dinesh, G., Safai, P. D., Rao, J., Konwar, M., Dixit, S., and Prabha, T. V.: Cloud condensation nuclei characteristics during the Indian summer monsoon over a rain-shadow region, Atmos. Chem. Phys., 20, 7307–7334, https://doi.org/10.5194/acp-20-7307-2020, 2020. a
Jin, S., Ma, Y., Huang, Z., Huang, J., Gong, W., Liu, B., Wang, W., Fan, R., and Li, H.: A comprehensive reappraisal of long-term aerosol characteristics, trends, and variability in Asia, Atmos. Chem. Phys., 23, 8187–8210, https://doi.org/10.5194/acp-23-8187-2023, 2023. a
Kendall, M. G.: Rank Correlation Methods, Charles Griffin, London, 1975. a
Kumar, P., Nenes, A., and Sokolik, I. N.: Importance of adsorption for CCN activity and hygroscopic properties of mineral dust aerosol, Geophys. Res. Lett., 36, L24804, https://doi.org/10.1029/2009GL040827, 2009. a
Lana, A., Bell, T. G., Simó, R., Vallina, S. M., Ballabrera-Poy, J., Kettle, A. J., Dachs, J., Bopp, L., Saltzman, E. S., Stefels, J., Johnson, J. E., and Liss, P. S.: An updated climatology of surface dimethlysulfide concentrations and emission fluxes in the global ocean, Global Biogeochem. Cy., 25, GB1004, https://doi.org/10.1029/2010GB003850, 2011. a
Mace, G. G. and Avey, S.: Seasonal variability of warm boundary layer cloud and precipitation properties in the Southern Ocean as diagnosed from A-Train data, J. Geophys. Res.-Atmos., 122, 1015–1032, https://doi.org/10.1002/2016JD025348, 2017. a
Mace, G. G., Benson, S., Humphries, R., Gombert, P. M., and Sterner, E.: Natural marine cloud brightening in the Southern Ocean, Atmos. Chem. Phys., 23, 1677–1685, https://doi.org/10.5194/acp-23-1677-2023, 2023. a
Mace, G. G., Benson, S., Sterner, E., Protat, A., Humphries, R., and Hallar, A. G.: The Association Between Cloud Droplet Number over the Summer Southern Ocean and Air Mass History, J. Geophys. Res.-Atmos., 129, e2023JD040673, https://doi.org/10.1029/2023JD040673, 2024. a
Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945. a
McCoy, D. T., Burrows, S. M., Wood, R., Grosvenor, D. P., Elliott, S. M., Ma, P.-L., Rasch, P. J., and Hartmann, D. L.: Natural aerosols explain seasonal and spatial patterns of Southern Ocean cloud albedo, Sci. Adv., 1, e1500157, https://doi.org/10.1126/sciadv.1500157, 2015. a
McCoy, D. T., Bender, F. A.-M., Grosvenor, D. P., Mohrmann, J. K., Hartmann, D. L., Wood, R., and Field, P. R.: Predicting decadal trends in cloud droplet number concentration using reanalysis and satellite data, Atmos. Chem. Phys., 18, 2035–2047, https://doi.org/10.5194/acp-18-2035-2018, 2018. a
McCoy, I. L., Bretherton, C. S., Wood, R., Twohy, C. H., Gettelman, A., Bardeen, C. G., and Toohey, D. W.: Influences of Recent Particle Formation on Southern Ocean Aerosol Variability and Low Cloud Properties, J. Geophys. Res.-Atmos., 126, e2020JD033529, https://doi.org/10.1029/2020JD033529, 2021. a
Moore, R. H., Karydis, V. A., Capps, S. L., Lathem, T. L., and Nenes, A.: Droplet number uncertainties associated with CCN: an assessment using observations and a global model adjoint, Atmos. Chem. Phys., 13, 4235–4251, https://doi.org/10.5194/acp-13-4235-2013, 2013. a, b, c, d
NASA Langley Research Center: CERES SYN1deg Level 3 Data Product, Version 4.1., NASA Atmospheric Science Data Center (ASDC) [data set], https://doi.org/10.5067/Terra+Aqua/CERES/SYN1deg_L3.004A, 2021. a
NASA/LARC/SD/ASDC: CALIPSO Lidar Level 2 Aerosol Profile, V4-20, EarthData [data set], https://doi.org/10.5067/CALIOP/CALIPSO/LID_L2_05, 2018. a, b
O'Connor, F. M., Johnson, C. E., Morgenstern, O., Abraham, N. L., Braesicke, P., Dalvi, M., Folberth, G. A., Sanderson, M. G., Telford, P. J., Voulgarakis, A., Young, P. J., Zeng, G., Collins, W. J., and Pyle, J. A.: Evaluation of the new UKCA climate-composition model – Part 2: The Troposphere, Geosci. Model Dev., 7, 41–91, https://doi.org/10.5194/gmd-7-41-2014, 2014. a
Paramonov, M., Kerminen, V.-M., Gysel, M., Aalto, P. P., Andreae, M. O., Asmi, E., Baltensperger, U., Bougiatioti, A., Brus, D., Frank, G. P., Good, N., Gunthe, S. S., Hao, L., Irwin, M., Jaatinen, A., Jurányi, Z., King, S. M., Kortelainen, A., Kristensson, A., Lihavainen, H., Kulmala, M., Lohmann, U., Martin, S. T., McFiggans, G., Mihalopoulos, N., Nenes, A., O'Dowd, C. D., Ovadnevaite, J., Petäjä, T., Pöschl, U., Roberts, G. C., Rose, D., Svenningsson, B., Swietlicki, E., Weingartner, E., Whitehead, J., Wiedensohler, A., Wittbom, C., and Sierau, B.: A synthesis of cloud condensation nuclei counter (CCNC) measurements within the EUCAARI network, Atmos. Chem. Phys., 15, 12211–12229, https://doi.org/10.5194/acp-15-12211-2015, 2015. a
Platnick, S., King, M., and Hubanks, P.: MODIS Atmosphere L3 Monthly Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], https://doi.org/10.5067/MODIS/MYD08_M3.061, 2017a. a
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., and Riedi, J.: The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua, IEEE T. Geosci. Remote Sens., 55, 502–525, https://doi.org/10.1109/TGRS.2016.2610522, 2017b. a
Pöhlker, M. L., Pöhlker, C., Quaas, J., Mülmenstädt, J., Pozzer, A., Andreae, M. O., Artaxo, P., Block, K., Coe, H., Ervens, B., Gallimore, P., Gaston, C. J., Gunthe, S. S., Henning, S., Herrmann, H., Krüger, O. O., McFiggans, G., Poulain, L., Raj, S. S., Reyes-Villegas, E., Royer, H. M., Walter, D., Wang, Y., and Pöschl, U.: Global organic and inorganic aerosol hygroscopicity and its effect on radiative forcing, Nat. Commun., 14, 6139, https://doi.org/10.1038/s41467-023-41695-8, 2023. a
Popp, T., De Leeuw, G., Bingen, C., Brühl, C., Capelle, V., Chedin, A., Clarisse, L., Dubovik, O., Grainger, R., Griesfeller, J., Heckel, A., Kinne, S., Klüser, L., Kosmale, M., Kolmonen, P., Lelli, L., Litvinov, P., Mei, L., North, P., Pinnock, S., Povey, A., Robert, C., Schulz, M., Sogacheva, L., Stebel, K., Stein Zweers, D., Thomas, G., Tilstra, L. G., Vandenbussche, S., Veefkind, P., Vountas, M., and Xue, Y.: Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci), Remote Sens., 8, 421, https://doi.org/10.3390/rs8050421, 2016. a
Quaas, J., Arola, A., Cairns, B., Christensen, M., Deneke, H., Ekman, A. M. L., Feingold, G., Fridlind, A., Gryspeerdt, E., Hasekamp, O., Li, Z., Lipponen, A., Ma, P.-L., Mülmenstädt, J., Nenes, A., Penner, J. E., Rosenfeld, D., Schrödner, R., Sinclair, K., Sourdeval, O., Stier, P., Tesche, M., van Diedenhoven, B., and Wendisch, M.: Constraining the Twomey effect from satellite observations: issues and perspectives, Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, 2020. a, b
Quaas, J., Jia, H., Smith, C., Albright, A. L., Aas, W., Bellouin, N., Boucher, O., Doutriaux-Boucher, M., Forster, P. M., Grosvenor, D., Jenkins, S., Klimont, Z., Loeb, N. G., Ma, X., Naik, V., Paulot, F., Stier, P., Wild, M., Myhre, G., and Schulz, M.: Robust evidence for reversal of the trend in aerosol effective climate forcing, Atmos. Chem. Phys., 22, 12221–12239, https://doi.org/10.5194/acp-22-12221-2022, 2022. a, b, c, d, e
Redemann, J. and Gao, L.: A machine learning paradigm for necessary observations to reduce uncertainties in aerosol climate forcing, Nat. Commun., 15, 8343, https://doi.org/10.1038/s41467-024-52747-y, 2024. 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
Revell, L. E., Kremser, S., Hartery, S., Harvey, M., Mulcahy, J. P., Williams, J., Morgenstern, O., McDonald, A. J., Varma, V., Bird, L., and Schuddeboom, A.: The sensitivity of Southern Ocean aerosols and cloud microphysics to sea spray and sulfate aerosol production in the HadGEM3-GA7.1 chemistry–climate model, Atmos. Chem. Phys., 19, 15447–15466, https://doi.org/10.5194/acp-19-15447-2019, 2019. a, b
Rosenfeld, D., Kokhanovsky, A., Goren, T., Gryspeerdt, E., Hasekamp, O., Jia, H., Lopatin, A., Quaas, J., Pan, Z., and Sourdeval, O.: Frontiers in Satellite-Based Estimates of Cloud-Mediated Aerosol Forcing, Rev. Geophys., 61, e2022RG000799, https://doi.org/10.1029/2022RG000799, 2023. a, b
Ross, K. E., Piketh, S. J., Bruintjes, R. T., Burger, R. P., Swap, R. J., and Annegarn, H. J.: Spatial and seasonal variations in CCN distribution and the aerosol-CCN relationship over southern Africa, J. Geophys. Res.-Atmos., 108, 8481, https://doi.org/10.1029/2002JD002384, 2003. a
Shen, Y., Virkkula, A., Ding, A., Luoma, K., Keskinen, H., Aalto, P. P., Chi, X., Qi, X., Nie, W., Huang, X., Petäjä, T., Kulmala, M., and Kerminen, V.-M.: Estimating cloud condensation nuclei number concentrations using aerosol optical properties: role of particle number size distribution and parameterization, Atmos. Chem. Phys., 19, 15483–15502, https://doi.org/10.5194/acp-19-15483-2019, 2019. a, b, c
Shinozuka, Y., Clarke, A. D., Nenes, A., Jefferson, A., Wood, R., McNaughton, C. S., Ström, J., Tunved, P., Redemann, J., Thornhill, K. L., Moore, R. H., Lathem, T. L., Lin, J. J., and Yoon, Y. J.: The relationship between cloud condensation nuclei (CCN) concentration and light extinction of dried particles: indications of underlying aerosol processes and implications for satellite-based CCN estimates, Atmos. Chem. Phys., 15, 7585–7604, https://doi.org/10.5194/acp-15-7585-2015, 2015. a
Tesche, M., Ansmann, A., Müller, D., Althausen, D., Engelmann, R., Freudenthaler, V., and Groß, S.: Vertically resolved separation of dust and smoke over Cape Verde using multiwavelength Raman and polarization lidars during Saharan Mineral Dust Experiment 2008, J. Geophys. Res.-Atmos., 114, D13202, https://doi.org/10.1029/2009JD011862, 2009. a
West, R. E. L., Stier, P., Jones, A., Johnson, C. E., Mann, G. W., Bellouin, N., Partridge, D. G., and Kipling, Z.: The importance of vertical velocity variability for estimates of the indirect aerosol effects, Atmos. Chem. Phys., 14, 6369–6393, https://doi.org/10.5194/acp-14-6369-2014, 2014. a
Wood, R., Stemmler, J. D., Rémillard, J., and Jefferson, A.: Low-CCN concentration air masses over the eastern North Atlantic: Seasonality, meteorology, and drivers, J. Geophys. Res.-Atmos., 122, 1203–1223, https://doi.org/10.1002/2016JD025557, 2017. a
Yu, L., Zhong, S., and Sun, B.: The Climatology and Trend of Surface Wind Speed over Antarctica and the Southern Ocean and the Implication to Wind Energy Application, Atmosphere, 11, 108, https://doi.org/10.3390/atmos11010108, 2020. a
Executive editor
Measurement datasets of cloud condensation nuclei (CCN) are vital for our understanding of aerosol-cloud interaction and reliable climate modelling. This study analyses and compares the only two global CCN datasets derived from satellite and reanalysis data. These key datasets are found to disagree over pristine oceans in terms of their climatology as well as seasonal and annual variations. Given the importance of CCN as a fundamental property in climate model simulations, further research is needed to reconcile these differences and to produce an observation-based dataset that can be confidently used to evaluate our understanding.
Measurement datasets of cloud condensation nuclei (CCN) are vital for our understanding of...
Short summary
Aerosol particles in the atmosphere increase cloud reflectivity, thereby cooling the Earth. Accurate global measurements of these particles are crucial for estimating this cooling effect. This study compares and harmonizes two newly developed global aerosol datasets, offering insights for their future use and refinement. We identify pristine oceans as a significant source of uncertainty in the datasets and, therefore, in quantifying the role of aerosols in Earth's climate.
Aerosol particles in the atmosphere increase cloud reflectivity, thereby cooling the Earth....
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