Articles | Volume 22, issue 13
https://doi.org/10.5194/acp-22-8457-2022
© Author(s) 2022. 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-22-8457-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of Holuhraun volcano aerosols on clouds in cloud-system-resolving simulations
Mahnoosh Haghighatnasab
CORRESPONDING AUTHOR
Institute for Meteorology, Universität Leipzig, Leipzig, Germany
Jan Kretzschmar
Institute for Meteorology, Universität Leipzig, Leipzig, Germany
Karoline Block
Institute for Meteorology, Universität Leipzig, Leipzig, Germany
Johannes Quaas
Institute for Meteorology, Universität Leipzig, Leipzig, Germany
Related authors
Goutam Choudhury, Karoline Block, Mahnoosh Haghighatnasab, Johannes Quaas, Tom Goren, and Matthias Tesche
Atmos. Chem. Phys., 25, 3841–3856, https://doi.org/10.5194/acp-25-3841-2025, https://doi.org/10.5194/acp-25-3841-2025, 2025
Short summary
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.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Karoline Block, Mahnoosh Haghighatnasab, Daniel G. Partridge, Philip Stier, and Johannes Quaas
Earth Syst. Sci. Data, 16, 443–470, https://doi.org/10.5194/essd-16-443-2024, https://doi.org/10.5194/essd-16-443-2024, 2024
Short summary
Short summary
Aerosols being able to act as condensation nuclei for cloud droplets (CCNs) are a key element in cloud formation but very difficult to determine. In this study we present a new global vertically resolved CCN dataset for various humidity conditions and aerosols. It is obtained using an atmospheric model (CAMS reanalysis) that is fed by satellite observations of light extinction (AOD). We investigate and evaluate the abundance of CCNs in the atmosphere and their temporal and spatial occurrence.
Charlotte Lange and Johannes Quaas
Atmos. Chem. Phys., 25, 10337–10359, https://doi.org/10.5194/acp-25-10337-2025, https://doi.org/10.5194/acp-25-10337-2025, 2025
Short summary
Short summary
We studied how the Earth’s climate system adjusts to sudden changes in the energy budget by analysing data of four climate models, which simulated a 4 % reduction in incoming solar energy. We found rapid cooling of the atmosphere and shifts in cloud cover and atmospheric circulation patterns like land–sea circulation. Our research helps to better understand cloud adjustments, which are a major source of uncertainty in climate models. This can improve future climate predictions.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Tong Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2555, https://doi.org/10.5194/egusphere-2025-2555, 2025
Short summary
Short summary
We use the WRF-Chem-SBM model to investigate how the meteorological conditions impact aerosol-cloud interactions (ACI) under different pollution regimes for marine liquid-phase clouds. Our findings highlight the changes in aerosol effects on clouds and precipitation with thermodynamic conditions, as well as the sensitivity of ACI to meteorological conditions under different pollution regimes, which help to advance the understanding of ACI.
Sajedeh Marjani, Sina Mehrdad, and Johannes Quaas
EGUsphere, https://doi.org/10.5194/egusphere-2025-1847, https://doi.org/10.5194/egusphere-2025-1847, 2025
Short summary
Short summary
A large part of aviation's climate impact comes from persistent contrails, not just carbon dioxide. This study examined how they evolve under different ice-supersaturated conditions, usually with background clouds. Using high-resolution simulations, we found that contrail growth depends on both the thickness of the supersaturated layer and available water vapor. Contrails can alter surrounding clouds and contribute to warming. Simple thresholds are not enough to predict their impact.
Goutam Choudhury, Karoline Block, Mahnoosh Haghighatnasab, Johannes Quaas, Tom Goren, and Matthias Tesche
Atmos. Chem. Phys., 25, 3841–3856, https://doi.org/10.5194/acp-25-3841-2025, https://doi.org/10.5194/acp-25-3841-2025, 2025
Short summary
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.
Tom Goren, Goutam Choudhury, Jan Kretzschmar, and Isabel McCoy
Atmos. Chem. Phys., 25, 3413–3423, https://doi.org/10.5194/acp-25-3413-2025, https://doi.org/10.5194/acp-25-3413-2025, 2025
Short summary
Short summary
Many studies have identified an inverted-V relationship between the liquid water path (LWP) and droplet concentration (Nd), where LWP increases and then decreases with Nd. Using satellite observations and meteorological data, we demonstrate that the inverted V primarily reflects co-variability between LWP and Nd. We suggest taking a holistic approach that considers this co-variability when assessing the climatological sensitivity of LWP to anthropogenic aerosols.
Donifan Barahona, Katherine Breen, Karoline Block, and Anton Darmenov
EGUsphere, https://doi.org/10.5194/egusphere-2025-482, https://doi.org/10.5194/egusphere-2025-482, 2025
Short summary
Short summary
Particulate matter impacts Earth's radiation, clouds, and human health, but modeling their size is challenging due to computational and observational limits. We developed a machine learning model to predict aerosol size distributions, which accurately replicates advanced models and field measurements.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Johannes Mülmenstädt, Andrew S. Ackerman, Ann M. Fridlind, Meng Huang, Po-Lun Ma, Naser Mahfouz, Susanne E. Bauer, Susannah M. Burrows, Matthew W. Christensen, Sudhakar Dipu, Andrew Gettelman, L. Ruby Leung, Florian Tornow, Johannes Quaas, Adam C. Varble, Hailong Wang, Kai Zhang, and Youtong Zheng
Atmos. Chem. Phys., 24, 13633–13652, https://doi.org/10.5194/acp-24-13633-2024, https://doi.org/10.5194/acp-24-13633-2024, 2024
Short summary
Short summary
Stratocumulus clouds play a large role in Earth's climate by reflecting incoming solar energy back to space. Turbulence at stratocumulus cloud top mixes in dry, warm air, which can lead to cloud dissipation. This process is challenging for coarse-resolution global models to represent. We show that global models nevertheless agree well with our process understanding. Global models also think the process is less important for the climate than other lines of evidence have led us to conclude.
Jianqi Zhao, Xiaoyan Ma, and Johannes Quaas
EGUsphere, https://doi.org/10.5194/egusphere-2024-3662, https://doi.org/10.5194/egusphere-2024-3662, 2024
Preprint archived
Short summary
Short summary
We conduct a comparative analysis of aerosol-cloud responses in liquid-phase clouds under different aerosol and meteorological conditions based on simulations using the WRF-Chem-SBM model. Our findings highlight the different effects of aerosols on clouds and precipitation, as well as variations in the roles of aerosol and meteorological factors influencing aerosol-cloud interactions, in different environment.
Iris Papakonstantinou-Presvelou and Johannes Quaas
EGUsphere, https://doi.org/10.5194/egusphere-2024-3293, https://doi.org/10.5194/egusphere-2024-3293, 2024
Preprint archived
Short summary
Short summary
As the Arctic warms and the sea ice retreats, more open ocean is exposed, changing how aerosols impact clouds. Our previous 10-year satellite analysis found higher ice crystal numbers over sea ice than over ocean. Using model simulations and aircraft observations we identify here two factors as potential causes at colder temperatures; ice nucleating particles over sea ice and blowing snow. With further sea ice loss, these processes may weaken, leading to fewer ice particles in the future.
Sina Mehrdad, Dörthe Handorf, Ines Höschel, Khalil Karami, Johannes Quaas, Sudhakar Dipu, and Christoph Jacobi
Weather Clim. Dynam., 5, 1223–1268, https://doi.org/10.5194/wcd-5-1223-2024, https://doi.org/10.5194/wcd-5-1223-2024, 2024
Short summary
Short summary
This study introduces a novel deep learning (DL) approach to analyze how regional radiative forcing in Europe impacts the Arctic climate. By integrating atmospheric poleward energy transport with DL-based clustering of atmospheric patterns and attributing anomalies to specific clusters, our method reveals crucial, nuanced interactions within the climate system, enhancing our understanding of intricate climate dynamics.
Julien Lenhardt, Johannes Quaas, Dino Sejdinovic, and Daniel Klocke
EGUsphere, https://doi.org/10.5194/egusphere-2024-2724, https://doi.org/10.5194/egusphere-2024-2724, 2024
Short summary
Short summary
Clouds come in various shapes and sizes and constitute a fundamental element of the Earth’s climate system. Different cloud types show variable impacts on climate change. We present a new cloud type classification method called CloudViT relying on spatial patterns of cloud properties obtained from satellite data using machine learning. We can thus help understanding the effects of different cloud types on climate change.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
Short summary
Short summary
Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia
Atmos. Chem. Phys., 24, 9101–9118, https://doi.org/10.5194/acp-24-9101-2024, https://doi.org/10.5194/acp-24-9101-2024, 2024
Short summary
Short summary
We explore aerosol–cloud interactions in liquid-phase clouds over eastern China and its adjacent ocean in winter based on the WRF-Chem–SBM model, which couples a spectral-bin microphysics scheme and an online aerosol module. Our study highlights the differences in aerosol–cloud interactions between land and ocean and between precipitation clouds and non-precipitation clouds, and it differentiates and quantifies their underlying mechanisms.
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
Atmos. Chem. Phys., 24, 7331–7345, https://doi.org/10.5194/acp-24-7331-2024, https://doi.org/10.5194/acp-24-7331-2024, 2024
Short summary
Short summary
Human activities release copious amounts of small particles called aerosols into the atmosphere. These particles change how much sunlight clouds reflect to space, an important human perturbation of the climate, whose magnitude is highly uncertain. We found that the latest climate models show a negative correlation but a positive causal relationship between aerosols and cloud water. This means we need to be very careful when we interpret observational studies that can only see correlation.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
Short summary
Short summary
Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Karoline Block, Mahnoosh Haghighatnasab, Daniel G. Partridge, Philip Stier, and Johannes Quaas
Earth Syst. Sci. Data, 16, 443–470, https://doi.org/10.5194/essd-16-443-2024, https://doi.org/10.5194/essd-16-443-2024, 2024
Short summary
Short summary
Aerosols being able to act as condensation nuclei for cloud droplets (CCNs) are a key element in cloud formation but very difficult to determine. In this study we present a new global vertically resolved CCN dataset for various humidity conditions and aerosols. It is obtained using an atmospheric model (CAMS reanalysis) that is fed by satellite observations of light extinction (AOD). We investigate and evaluate the abundance of CCNs in the atmosphere and their temporal and spatial occurrence.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
Short summary
Short summary
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.
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
Short summary
Short summary
Clouds exhibit a wide range of vertical structures with varying microphysical and radiative properties. We show a global survey of spatial distribution, vertical extent and radiative effect of various classified cloud vertical structures using joint satellite observations from the new CCCM datasets during 2007–2010. Moreover, the long-term trends in CVSs are investigated based on different CMIP6 future scenarios to capture the cloud variations with different, increasing anthropogenic forcings.
Samuel Kwakye, Heike Kalesse-Los, Maximilian Maahn, Patric Seifert, Roel van Klink, Christian Wirth, and Johannes Quaas
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-69, https://doi.org/10.5194/amt-2023-69, 2023
Publication in AMT not foreseen
Short summary
Short summary
Insect numbers in the atmosphere can be calculated using polarimetric weather radar but they have to be identified and separated from other echoes, especially weather phenomena. Here, the separation is demonstrated using three machine-learning algorithms and insect count data from suction traps and the nature of radar measurements of different radar echoes is revealed. Random forest is the best separating algorithm and insect echoes radar measurements are distinct.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia
EGUsphere, https://doi.org/10.5194/egusphere-2023-331, https://doi.org/10.5194/egusphere-2023-331, 2023
Preprint archived
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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 %.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Bjorn Stevens, Sandrine Bony, David Farrell, Felix Ament, Alan Blyth, Christopher Fairall, Johannes Karstensen, Patricia K. Quinn, Sabrina Speich, Claudia Acquistapace, Franziska Aemisegger, Anna Lea Albright, Hugo Bellenger, Eberhard Bodenschatz, Kathy-Ann Caesar, Rebecca Chewitt-Lucas, Gijs de Boer, Julien Delanoë, Leif Denby, Florian Ewald, Benjamin Fildier, Marvin Forde, Geet George, Silke Gross, Martin Hagen, Andrea Hausold, Karen J. Heywood, Lutz Hirsch, Marek Jacob, Friedhelm Jansen, Stefan Kinne, Daniel Klocke, Tobias Kölling, Heike Konow, Marie Lothon, Wiebke Mohr, Ann Kristin Naumann, Louise Nuijens, Léa Olivier, Robert Pincus, Mira Pöhlker, Gilles Reverdin, Gregory Roberts, Sabrina Schnitt, Hauke Schulz, A. Pier Siebesma, Claudia Christine Stephan, Peter Sullivan, Ludovic Touzé-Peiffer, Jessica Vial, Raphaela Vogel, Paquita Zuidema, Nicola Alexander, Lyndon Alves, Sophian Arixi, Hamish Asmath, Gholamhossein Bagheri, Katharina Baier, Adriana Bailey, Dariusz Baranowski, Alexandre Baron, Sébastien Barrau, Paul A. Barrett, Frédéric Batier, Andreas Behrendt, Arne Bendinger, Florent Beucher, Sebastien Bigorre, Edmund Blades, Peter Blossey, Olivier Bock, Steven Böing, Pierre Bosser, Denis Bourras, Pascale Bouruet-Aubertot, Keith Bower, Pierre Branellec, Hubert Branger, Michal Brennek, Alan Brewer, Pierre-Etienne Brilouet, Björn Brügmann, Stefan A. Buehler, Elmo Burke, Ralph Burton, Radiance Calmer, Jean-Christophe Canonici, Xavier Carton, Gregory Cato Jr., Jude Andre Charles, Patrick Chazette, Yanxu Chen, Michal T. Chilinski, Thomas Choularton, Patrick Chuang, Shamal Clarke, Hugh Coe, Céline Cornet, Pierre Coutris, Fleur Couvreux, Susanne Crewell, Timothy Cronin, Zhiqiang Cui, Yannis Cuypers, Alton Daley, Gillian M. Damerell, Thibaut Dauhut, Hartwig Deneke, Jean-Philippe Desbios, Steffen Dörner, Sebastian Donner, Vincent Douet, Kyla Drushka, Marina Dütsch, André Ehrlich, Kerry Emanuel, Alexandros Emmanouilidis, Jean-Claude Etienne, Sheryl Etienne-Leblanc, Ghislain Faure, Graham Feingold, Luca Ferrero, Andreas Fix, Cyrille Flamant, Piotr Jacek Flatau, Gregory R. Foltz, Linda Forster, Iulian Furtuna, Alan Gadian, Joseph Galewsky, Martin Gallagher, Peter Gallimore, Cassandra Gaston, Chelle Gentemann, Nicolas Geyskens, Andreas Giez, John Gollop, Isabelle Gouirand, Christophe Gourbeyre, Dörte de Graaf, Geiske E. de Groot, Robert Grosz, Johannes Güttler, Manuel Gutleben, Kashawn Hall, George Harris, Kevin C. Helfer, Dean Henze, Calvert Herbert, Bruna Holanda, Antonio Ibanez-Landeta, Janet Intrieri, Suneil Iyer, Fabrice Julien, Heike Kalesse, Jan Kazil, Alexander Kellman, Abiel T. Kidane, Ulrike Kirchner, Marcus Klingebiel, Mareike Körner, Leslie Ann Kremper, Jan Kretzschmar, Ovid Krüger, Wojciech Kumala, Armin Kurz, Pierre L'Hégaret, Matthieu Labaste, Tom Lachlan-Cope, Arlene Laing, Peter Landschützer, Theresa Lang, Diego Lange, Ingo Lange, Clément Laplace, Gauke Lavik, Rémi Laxenaire, Caroline Le Bihan, Mason Leandro, Nathalie Lefevre, Marius Lena, Donald Lenschow, Qiang Li, Gary Lloyd, Sebastian Los, Niccolò Losi, Oscar Lovell, Christopher Luneau, Przemyslaw Makuch, Szymon Malinowski, Gaston Manta, Eleni Marinou, Nicholas Marsden, Sebastien Masson, Nicolas Maury, Bernhard Mayer, Margarette Mayers-Als, Christophe Mazel, Wayne McGeary, James C. McWilliams, Mario Mech, Melina Mehlmann, Agostino Niyonkuru Meroni, Theresa Mieslinger, Andreas Minikin, Peter Minnett, Gregor Möller, Yanmichel Morfa Avalos, Caroline Muller, Ionela Musat, Anna Napoli, Almuth Neuberger, Christophe Noisel, David Noone, Freja Nordsiek, Jakub L. Nowak, Lothar Oswald, Douglas J. Parker, Carolyn Peck, Renaud Person, Miriam Philippi, Albert Plueddemann, Christopher Pöhlker, Veronika Pörtge, Ulrich Pöschl, Lawrence Pologne, Michał Posyniak, Marc Prange, Estefanía Quiñones Meléndez, Jule Radtke, Karim Ramage, Jens Reimann, Lionel Renault, Klaus Reus, Ashford Reyes, Joachim Ribbe, Maximilian Ringel, Markus Ritschel, Cesar B. Rocha, Nicolas Rochetin, Johannes Röttenbacher, Callum Rollo, Haley Royer, Pauline Sadoulet, Leo Saffin, Sanola Sandiford, Irina Sandu, Michael Schäfer, Vera Schemann, Imke Schirmacher, Oliver Schlenczek, Jerome Schmidt, Marcel Schröder, Alfons Schwarzenboeck, Andrea Sealy, Christoph J. Senff, Ilya Serikov, Samkeyat Shohan, Elizabeth Siddle, Alexander Smirnov, Florian Späth, Branden Spooner, M. Katharina Stolla, Wojciech Szkółka, Simon P. de Szoeke, Stéphane Tarot, Eleni Tetoni, Elizabeth Thompson, Jim Thomson, Lorenzo Tomassini, Julien Totems, Alma Anna Ubele, Leonie Villiger, Jan von Arx, Thomas Wagner, Andi Walther, Ben Webber, Manfred Wendisch, Shanice Whitehall, Anton Wiltshire, Allison A. Wing, Martin Wirth, Jonathan Wiskandt, Kevin Wolf, Ludwig Worbes, Ethan Wright, Volker Wulfmeyer, Shanea Young, Chidong Zhang, Dongxiao Zhang, Florian Ziemen, Tobias Zinner, and Martin Zöger
Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
Short summary
Short summary
The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Abdul-Razzak, H. and Ghan, J.: Dri Dw, J. Geophys. Res., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000. a
Ackerman, A. S., Kirkpatrick, M. P., Stevens, D. E., and Toon, O. B.: The
impact of humidity above stratiform clouds on indirect aerosol climate
forcing, Nature, 432, 1014–1017, https://doi.org/10.1038/nature03174, 2004. a, b
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness,
Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M., Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales, Q. J. Roy. Meteor. Soc., 134, 1337–1351, https://doi.org/10.1002/qj.289, 2008. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A. L., Dufresne, J. L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate Change, Rev. Geophys., 58, 1–45, https://doi.org/10.1029/2019RG000660, 2020. a
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011. a
Brenguier, J.-L., Burnet, F., and Geoffroy, O.: Cloud optical thickness and liquid water path – does the k coefficient vary with droplet concentration?, Atmos. Chem. Phys., 11, 9771–9786, https://doi.org/10.5194/acp-11-9771-2011, 2011. a
Bretherton, C. S., Blossey, P. N., and Uchida, J.: Cloud droplet sedimentation, entrainment efficiency, and subtropical stratocumulus albedo,
Geophys. Res. Lett., 34, L03813, https://doi.org/10.1029/2006GL027648, 2007. a
Christensen, M. W., Gettelman, A., Cermak, J., Dagan, G., Diamond, M., Douglas, A., Feingold, G., Glassmeier, F., Goren, T., Grosvenor, D. P., Gryspeerdt, E., Kahn, R., Li, Z., Ma, P.-L., Malavelle, F., McCoy, I. L., McCoy, D. T., McFarquhar, G., Mülmenstädt, J., Pal, S., Possner, A., Povey, A., Quaas, J., Rosenfeld, D., Schmidt, A., Schrödner, R., Sorooshian, A., Stier, P., Toll, V., Watson-Parris, D., Wood, R., Yang, M., and Yuan, T.: Opportunistic experiments to constrain aerosol effective radiative forcing, Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, 2022. a
Cole-Dai, J.: Volcanoes and climate, WIRES Climate Change, 1, 824–839, https://doi.org/10.1002/wcc.76, 2010. a
Copernicus Atmosphere Data Storage: CAMS global reanalysis (EAC4), Copernicus [data set], https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=form, last access: 27 June 2022. a
Costa-Surós, M., Sourdeval, O., Acquistapace, C., Baars, H., Carbajal Henken, C., Genz, C., Hesemann, J., Jimenez, C., König, M., Kretzschmar, J., Madenach, N., Meyer, C. I., Schrödner, R., Seifert, P., Senf, F., Brueck, M., Cioni, G., Engels, J. F., Fieg, K., Gorges, K., Heinze, R., Siligam, P. K., Burkhardt, U., Crewell, S., Hoose, C., Seifert, A., Tegen, I., and Quaas, J.: Detection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model, Atmos. Chem. Phys., 20, 5657–5678, https://doi.org/10.5194/acp-20-5657-2020, 2020. a, b, c, d
Fioletov, V., McLinden, C. A., Griffin, D., Theys, N., Loyola, D. G., Hedelt, P., Krotkov, N. A., and Li, C.: Anthropogenic and volcanic point source SO2 emissions derived from TROPOMI on board Sentinel-5 Precursor: first results, Atmos. Chem. Phys., 20, 5591–5607, https://doi.org/10.5194/acp-20-5591-2020, 2020. a
Ghan, S. J., Abdul-Razzak, H., Nenes, A., Ming, Y., Liu, X., Ovchinnikov, M.,
Shipway, B., Meskhidze, N., Xu, J., and Shi, X.: Droplet nucleation:
Physically-based parameterizations and comparative evaluation, J.
Adv. Model. Earth Syst., 3, M10001, https://doi.org/10.1029/2011ms000074, 2011. a
Giorgetta, M. A., Brokopf, R., Crueger, T., Esch, M., Fiedler, S., Helmert, J., Hohenegger, C., Kornblueh, L., Köhler, M., Manzini, E., Mauritsen, T., Nam, C., Raddatz, T., Rast, S., Reinert, D., Sakradzija, M., Schmidt, H., Schneck, R., Schnur, R., Silvers, L., Wan, H., Zängl, G., and Stevens, B.: ICON-A, the Atmosphere Component of the ICON Earth System Model: I. Model Description, J. Adv. Model. Earth Syst., 10, 1613–1637, https://doi.org/10.1029/2017MS001242, 2018. a
Goto, D., Sato, Y., Yashiro, H., Suzuki, K., Oikawa, E., Kudo, R., Nagao, T. M., and Nakajima, T.: Global aerosol simulations using NICAM.16 on a 14 km grid spacing for a climate study: improved and remaining issues relative to a lower-resolution model, Geosci. Model Dev., 13, 3731–3768, https://doi.org/10.5194/gmd-13-3731-2020, 2020. 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, b
Gryspeerdt, E., Goren, T., Sourdeval, O., Quaas, J., Mülmenstädt, J., Dipu, S., Unglaub, C., Gettelman, A., and Christensen, M.: Constraining the aerosol influence on cloud liquid water path, Atmos. Chem. Phys., 19, 5331–5347, https://doi.org/10.5194/acp-19-5331-2019, 2019. a, b
Hande, L. B., Engler, C., Hoose, C., and Tegen, I.: Parameterizing cloud condensation nuclei concentrations during HOPE, Atmos. Chem. Phys., 16, 12059–12079, https://doi.org/10.5194/acp-16-12059-2016, 2016. a, b
Heinze, R., Dipankar, A., Henken, C. C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C., Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B., Meyer, C. I., Muppa, S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F., Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G., Zhang, D., and Quaas, J.: Large‐eddy simulations over Germany using ICON: a comprehensive evaluation, Q. J. Roy. Meteor. Soc., 143, 69–100, https://doi.org/10.1002/qj.2947, 2017. a
Ialongo, I., Hakkarainen, J., Kivi, R., Anttila, P., Krotkov, N. A., Yang, K., Li, C., Tukiainen, S., Hassinen, S., and Tamminen, J.: Comparison of operational satellite SO2 products with ground-based observations in northern Finland during the Icelandic Holuhraun fissure eruption, Atmos. Meas. Tech., 8, 2279–2289, https://doi.org/10.5194/amt-8-2279-2015, 2015. a
Ilyinskaya, E., Schmidt, A., Mather, T. A., Pope, F. D., Witham, C., Baxter, P., Jóhannsson, T., Pfeffer, M., Barsotti, S., Singh, A., Sanderson, P., Bergsson, B., McCormick Kilbride, B., Donovan, A., Peters, N., Oppenheimer, C., and Edmonds, M.: Understanding the environmental impacts of large fissure eruptions: Aerosol and gas emissions from the 2014–2015 Holuhraun eruption (Iceland), Earth Planet. Sc. Lett., 472, 309–322, https://doi.org/10.1016/j.epsl.2017.05.025, 2017. a
Inguaggiato, S., Diliberto, I. S., Federico, C., Paonita, A., and Vita, F.: Review of the evolution of geochemical monitoring, networks and methodologies applied to the volcanoes of the Aeolian Arc (Italy), Earth-Sci. Rev., 176, 241–276, https://doi.org/10.1016/j.earscirev.2017.09.006, 2018. a
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, 2019. a
Khain, A. P., BenMoshe, N., and Pokrovsky, A.: Factors determining the impact of aerosols on surface precipitation from clouds: An attempt at classification, J. Atmos. Sci., 65, 1721–1748, https://doi.org/10.1175/2007JAS2515.1, 2008. a
Klocke, D., Brueck, M., Hohenegger, C., and Stevens, B.: Rediscovery of the doldrums in storm-resolving simulations over the tropical Atlantic /704/106/ 704/106/35/ 704/106/35/823 perspective, Nat. Geosci., 10, 891–896,
https://doi.org/10.1038/s41561-017-0005-4, 2017. a
Köhler, H.: The nucleus in and the growth of hygroscopic droplets,
T. Faraday Soc., 32, 1152–1161, https://doi.org/10.1039/TF9363201152, 1936. a
Kolzenburg, S., Giordano, D., Thordarson, T., Höskuldsson, A., and
Dingwell, D. B.: The rheological evolution of the 2014/2015 eruption at
Holuhraun, central Iceland, B. Volcanol., 79, 45,
https://doi.org/10.1007/s00445-017-1128-6, 2017. a
Kretzschmar, J., Salzmann, M., Mülmenstädt, J., and Quaas, J.: Arctic clouds in ECHAM6 and their sensitivity to cloud microphysics and surface fluxes, Atmos. Chem. Phys., 19, 10571–10589, https://doi.org/10.5194/acp-19-10571-2019, 2019. a
Kretzschmar, J., Stapf, J., Klocke, D., Wendisch, M., and Quaas, J.: Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic, Atmos. Chem. Phys., 20, 13145–13165, https://doi.org/10.5194/acp-20-13145-2020, 2020. a, b
Lai, R., Teng, S., Yi, B., Letu, H., Min, M., Tang, S., and Liu, C.: Comparison of cloud properties from Himawari-8 and FengYun-4A geostationary
satellite radiometers with MODIS cloud retrievals, Remote Sens., 11, 1703,
https://doi.org/10.3390/rs11141703, 2019. a
Lebo, Z. J. and Feingold, G.: On the relationship between responses in cloud water and precipitation to changes in aerosol, Atmos. Chem. Phys., 14, 11817–11831, https://doi.org/10.5194/acp-14-11817-2014, 2014. a
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013.
Liu, Y. and Daum, P. H.: Indirect warming effect from dispersion forcing,
Nature, 419, 580–581, https://doi.org/10.1038/419580a, 2002. a
Loeb, N. G., Manalo-Smith, N., Su, W., Shankar, M., and Thomas, S.: CERES top-of-atmosphere earth radiation budget climate data record: Accounting for in-orbit changes in instrument calibration, Remote Sens., 8, 182,
https://doi.org/10.3390/rs8030182, 2016. a
Luo, Y., Xu, K. M., Morrison, H., and McFarquhar, G.: Arctic mixed-phase clouds simulated by a cloud-resolving model: Comparison with ARM observations
and sensitivity to microphysics parameterizations, J. Atmos. Sci., 65, 1285–1303, https://doi.org/10.1175/2007JAS2467.1, 2008. a
Malavelle, F. F., Haywood, J. M., Jones, A., Gettelman, A., Clarisse, L., Bauduin, S., Allan, R. P., Karset, I. H. H., Kristjánsson, J. E., Oreopoulos, L., Cho, N., Lee, D., Bellouin, N., Boucher, O., Grosvenor, D. P., Carslaw, K. S., Dhomse, S., Mann, G. W., Schmidt, A., Coe, H., Hartley, M. E., Dalvi, M., Hill, A. A., Johnson, B. T., Johnson, C. E., Knight, J. R., O'Connor, F. M., Stier, P., Myhre, G., Platnick, S., Stephens,
G. L., Takahashi, H., and Thordarson, T.: Strong constraints on
aerosol-cloud interactions from volcanic eruptions, Nature, 546, 485–491,
https://doi.org/10.1038/nature22974, 2017. a, b, c
Mather, T. A., Pyle, D. M., and Allen, A. G.: Volcanic source for fixed
nitrogen in the early Earth's atmosphere, Geology, 32, 905–908,
https://doi.org/10.1130/G20679.1, 2004. 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
Nakajima, T. and King, M. D.: Determination of the Optical Thickness and
Effective Particle Radius of Clouds from Reflected Solar Radiation
Measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990. a, b
NASA Worldview: https://worldview.earthdata.nasa.gov/, last access: 2 March 2022. a
Pincus, R. and Baker, M. B.: Effect of precipitation on the albedo
susceptibility of clouds in the marine boundary layer, Nature, 372,
250–252, https://doi.org/10.1038/372250a0, 1994. a
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Patrick Hofmann, R. J.: Reconciling simulated and observed views of clouds: MODIS,
ISCCP, and the limits of instrument simulators, J. Climate, 25,
4699–4720, https://doi.org/10.1175/JCLI-D-11-00267.1, 2012. 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, 55, 502–525, https://doi.org/10.1109/TGRS.2016.2610522, 2017a. a
Platnick, S., Ackerman, S., King, M. D., Wind, G., Meyer, K., Menzel, P., Frey, R., Holz, R. E., Baum, B., and Yang, P.: MODIS atmosphere L2 cloud product (06_L2), NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA [data set], https://doi.org/10.5067/MODIS/MYD06_L2.061, 2017b. a
Quaas, J., Boucher, O., and Lohmann, U.: Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data, Atmos. Chem. Phys., 6, 947–955, https://doi.org/10.5194/acp-6-947-2006, 2006. a
Robock, A.: A latitudinally dependent volcanic dust veil index, and its effect on climate simulations, J. Volcanol. Geoth. Res., 11,
67–80, https://doi.org/10.1016/0377-0273(81)90076-7, 1981. a
Roh, W., Satoh, M., Hashino, T., Okamoto, H., and Seiki, T.: Evaluations of
the thermodynamic phases of clouds in a cloud-system-resolving model using
calipso and a satellite simulator over the southern ocean, J. Atmos. Sci., 77, 3781–3801, https://doi.org/10.1175/JAS-D-19-0273.1, 2020. a
Rose, W. I., Bluth, G. J. S., Schneider, D. J., Ernst, G. G. J., Riley, C. M., Henderson, L. J., and McGimsey, R. G.: Observations of Volcanic Clouds in Their First Few Days of Atmospheric Residence: The 1992 Eruptions of Crater Peak, Mount Spurr Volcano, Alaska, J. Geol., 109, 677–694,
https://doi.org/10.1086/323189, 2001. a
Sahyoun, M., Freney, E., Brito, J., Duplissy, J., Gouhier, M., Colomb, A., Dupuy, R., Bourianne, T., Nowak, J. B., Yan, C., Petäjä, T., Kulmala, M., Schwarzenboeck, A., Planche, C., and Sellegri, K.: Evidence of New Particle Formation Within Etna and Stromboli Volcanic Plumes and Its Parameterization From Airborne In Situ Measurements, J. Geophys. Res.-Atmos., 124, 5650–5668, https://doi.org/10.1029/2018JD028882, 2019. a
Saponaro, G., Sporre, M. K., Neubauer, D., Kokkola, H., Kolmonen, P., Sogacheva, L., Arola, A., de Leeuw, G., Karset, I. H. H., Laaksonen, A., and Lohmann, U.: Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 1607–1626, https://doi.org/10.5194/acp-20-1607-2020, 2020. a
Sato, Y., Goto, D., Michibata, T., Suzuki, K., Takemura, T., Tomita, H., and
Nakajima, T.: Aerosol effects on cloud water amounts were successfully
simulated by a global cloud-system resolving model, Nat. Commun.,
9, 985, https://doi.org/10.1038/s41467-018-03379-6, 2018. a
Seifert, A. and Beheng, K. D.: A two-moment cloud microphysics
parameterization for mixed-phase clouds. Part 1: Model description,
Meteorol. Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2006. a
Seifert, A., Köhler, C., and Beheng, K. D.: Aerosol-cloud-precipitation effects over Germany as simulated by a convective-scale numerical weather prediction model, Atmos. Chem. Phys., 12, 709–725, https://doi.org/10.5194/acp-12-709-2012, 2012. a
Seifert, A., Heus, T., Pincus, R., and Stevens, B.: Large-eddy simulation of
the transient and near-equilibrium behavior of precipitating shallow
convection, J. Adv. Model. Earth Syst., 7, 1918–1937,
https://doi.org/10.1002/2015MS000489, 2015. a
Small, J. D., Chuang, P. Y., Feingold, G., and Jiang, H.: Can aerosol decrease cloud lifetime?, Geophys. Res. Lett., 36, 1–5,
https://doi.org/10.1029/2009GL038888, 2009. a
Stevens, B. and Feingold, G.: Untangling aerosol effects on clouds and
precipitation in a buffered system, Nature, 461, 607–613,
https://doi.org/10.1038/nature08281, 2009. a
Stevens, B., Acquistapace, C., Hansen, A., Heinze, R., Klinger, C., Klocke, D., Rybka, H., Schubotz, W., Windmiller, J., Adamidis, P., Arka, I., Barlakas, V., Biercamp, J., Brueck, M., Brune, S., Buehler, S. A., Burkhardt, U., Cioni, G., Costa-Surós, M., Crewell, S., Crüger, T., Deneke, H., Friederichs, P., Henken, C. C., Hohenegger, C., Jacob, M., Jakub, F., Kalthoff, N., Köhler, M., van LAAR, T. W., Li, P., Löhnert, U., Macke, A., Madenach, N., Mayer, B., Nam, C., Naumann, A. K., Peters, K., Poll, S., Quaas, J., Röber, N., Rochetin, N., Scheck, L., Schemann, V., Schnitt, S., Seifert, A., Senf, F., Shapkalijevski, M., Simmer, C., Singh, S., Sourdeval, O., Spickermann, D., Strandgren, J., Tessiot, O., Vercauteren, N., Vial, J., Voigt, A., and Zängl, G.: The added value of large-eddy and storm-resolving models for simulating clouds and precipitation, J. Meteorol. Soc. Jpn., 98, 395–435, https://doi.org/10.2151/jmsj.2020-021, 2020. a
Su, W., Corbett, J., Eitzen, Z., and Liang, L.: Next-generation angular distribution models for top-of-atmosphere radiative flux calculation from CERES instruments: methodology, Atmos. Meas. Tech., 8, 611–632, https://doi.org/10.5194/amt-8-611-2015, 2015. a, b
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization in Large-Scale Models, Mon. Weather Rev., 117, 1779–1800,
https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989. a, b
Toll, V., Christensen, M., Gassó, S., and Bellouin, N.: Volcano and Ship Tracks Indicate Excessive Aerosol-Induced Cloud Water Increases in a Climate Model, Geophys. Res. Lett., 44, 12492–12500,
https://doi.org/10.1002/2017GL075280, 2017. a
Twomey, S.: The nuclei of natural cloud formation part II: The supersaturation in natural clouds and the variation of cloud droplet concentration, Geofisica Pura e Applicata, 43, 243–249, https://doi.org/10.1007/BF01993560, 1959. a
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974. a, b
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, 2017. 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
Xue, H., Feingold, G., and Stevens, B.: Aerosol effects on clouds, precipitation, and the organization of shallow cumulus convection, J. Atmos. Sci., 65, 392–406, https://doi.org/10.1175/2007JAS2428.1, 2008. a
Yang, K.: OMPS-NPP L2 NM Sulfur Dioxide (SO2) Total and Tropospheric Column swath orbital V2, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/A9O02ZH0J94R, 2017. a, b, c
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core, Q. J. Roy. Meteor. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a
Short summary
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.
The impact of aerosols emitted by the Holuhraun volcanic eruption on liquid clouds was assessed...
Altmetrics
Final-revised paper
Preprint