Articles | Volume 24, issue 9
https://doi.org/10.5194/acp-24-5165-2024
© Author(s) 2024. 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-24-5165-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Lagrangian perspective on the lifecycle and cloud radiative effect of deep convective clouds over Africa
William K. Jones
CORRESPONDING AUTHOR
Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK
Invited contribution by William K. Jones, recipient of the EGU Atmospheric Sciences (AS) Outstanding Student Poster and PICO Award 2023.
Martin Stengel
Climate Monitoring Satellite Application Facility (CM SAF), Deutscher Wetterdienst (DWD), Offenbach, Germany
Philip Stier
Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK
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Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
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Geostationary weather satellites have been used to detect storm clouds since their earliest applications. However, this task remains difficult as imaging satellites cannot observe the strong vertical winds that are characteristic of storm clouds. Here we introduce a new method that allows us to detect the early development of storms and continue to track them throughout their lifetime, allowing us to study how their early behaviour affects subsequent weather.
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Atmos. Chem. Phys., 25, 7789–7814, https://doi.org/10.5194/acp-25-7789-2025, https://doi.org/10.5194/acp-25-7789-2025, 2025
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Clouds exist at scales that climate models struggle to represent, limiting our knowledge of how climate change may impact clouds. Here we use a new kilometer-scale global model representing an important step towards the necessary scale. We focus on how aerosol particles modify clouds, radiation, and precipitation. We find the magnitude and manner of responses tend to vary from region to region, highlighting the potential of global kilometer-scale simulations and a need to represent aerosols in climate models.
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Atmos. Chem. Phys., 25, 6957–6973, https://doi.org/10.5194/acp-25-6957-2025, https://doi.org/10.5194/acp-25-6957-2025, 2025
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This study examines how ship emissions affect clouds over a shipping corridor in the southeastern Atlantic. Using satellite data from 2004 to 2023, we find that ship emissions increase the number of cloud droplets while reducing their size and slightly decrease cloud water content. Effects on seasonal and daily patterns vary based on regional factors. The impact of emissions weakened after stricter regulations were implemented in 2020.
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Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, https://doi.org/10.5194/gmd-18-3877-2025, 2025
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Konstantinos Rizos, Emmanouil Proestakis, Thanasis Georgiou, Antonis Gkikas, Eleni Marinou, Peristera Paschou, Kalliopi Artemis Voudouri, Athanasios Tsikerdekis, David Donovan, Gerd-Jan van Zadelhoff, Angela Benedetti, Holger Baars, Athena Augusta Floutsi, Nikos Benas, Martin Stengel, Christian Retscher, Edward Malina, and Vassilis Amiridis
EGUsphere, https://doi.org/10.5194/egusphere-2025-1175, https://doi.org/10.5194/egusphere-2025-1175, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
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Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes W. Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang
Atmos. Chem. Phys., 25, 1545–1567, https://doi.org/10.5194/acp-25-1545-2025, https://doi.org/10.5194/acp-25-1545-2025, 2025
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We compared smoke plume simulations from 11 global models to each other and to satellite smoke amount observations aimed at constraining smoke source strength. In regions where plumes are thick and background aerosol is low, models and satellites compare well. However, the input emission inventory tends to underestimate in many places, and particle property and loss rate assumptions vary enormously among models, causing uncertainties that require systematic in situ measurements to resolve.
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G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
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Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, and Veronika Eyring
Earth Syst. Sci. Data, 16, 3001–3016, https://doi.org/10.5194/essd-16-3001-2024, https://doi.org/10.5194/essd-16-3001-2024, 2024
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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
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
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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
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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
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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.
Nikos Benas, Irina Solodovnik, Martin Stengel, Imke Hüser, Karl-Göran Karlsson, Nina Håkansson, Erik Johansson, Salomon Eliasson, Marc Schröder, Rainer Hollmann, and Jan Fokke Meirink
Earth Syst. Sci. Data, 15, 5153–5170, https://doi.org/10.5194/essd-15-5153-2023, https://doi.org/10.5194/essd-15-5153-2023, 2023
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This paper describes CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI, which was created based on observations from geostationary Meteosat satellites. CLAAS-3 cloud properties are evaluated using a variety of reference datasets, with very good overall results. The demonstrated quality of CLAAS-3 ensures its usefulness in a wide range of applications, including studies of local- to continental-scale cloud processes and evaluation of climate models.
Cunbo Han, Corinna Hoose, Martin Stengel, Quentin Coopman, and Andrew Barrett
Atmos. Chem. Phys., 23, 14077–14095, https://doi.org/10.5194/acp-23-14077-2023, https://doi.org/10.5194/acp-23-14077-2023, 2023
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Cloud phase has been found to significantly impact cloud thermodynamics and Earth’s radiation budget, and various factors influence it. This study investigates the sensitivity of the cloud-phase distribution to the ice-nucleating particle concentration and thermodynamics. Multiple simulation experiments were performed using the ICON model at the convection-permitting resolution of 1.2 km. Simulation results were compared to two different retrieval products based on SEVIRI measurements.
Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
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This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
Peter Manshausen, Duncan Watson-Parris, Matthew W. Christensen, Jukka-Pekka Jalkanen, and Philip Stier
Atmos. Chem. Phys., 23, 12545–12555, https://doi.org/10.5194/acp-23-12545-2023, https://doi.org/10.5194/acp-23-12545-2023, 2023
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Aerosol from burning fuel changes cloud properties, e.g., the number of droplets and the content of water. Here, we study how clouds respond to different amounts of shipping aerosol. Droplet numbers increase linearly with increasing aerosol over a broad range until they stop increasing, while the amount of liquid water always increases, independently of emission amount. These changes in cloud properties can make them reflect more or less sunlight, which is important for the earth's climate.
Hendrik Andersen, Jan Cermak, Alyson Douglas, Timothy A. Myers, Peer Nowack, Philip Stier, Casey J. Wall, and Sarah Wilson Kemsley
Atmos. Chem. Phys., 23, 10775–10794, https://doi.org/10.5194/acp-23-10775-2023, https://doi.org/10.5194/acp-23-10775-2023, 2023
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This study uses an observation-based cloud-controlling factor framework to study near-global sensitivities of cloud radiative effects to a large number of meteorological and aerosol controls. We present near-global sensitivity patterns to selected thermodynamic, dynamic, and aerosol factors and discuss the physical mechanisms underlying the derived sensitivities. Our study hopes to guide future analyses aimed at constraining cloud feedbacks and aerosol–cloud interactions.
Leighton A. Regayre, Lucia Deaconu, Daniel P. Grosvenor, David M. H. Sexton, Christopher Symonds, Tom Langton, Duncan Watson-Paris, Jane P. Mulcahy, Kirsty J. Pringle, Mark Richardson, Jill S. Johnson, John W. Rostron, Hamish Gordon, Grenville Lister, Philip Stier, and Ken S. Carslaw
Atmos. Chem. Phys., 23, 8749–8768, https://doi.org/10.5194/acp-23-8749-2023, https://doi.org/10.5194/acp-23-8749-2023, 2023
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Aerosol forcing of Earth’s energy balance has persisted as a major cause of uncertainty in climate simulations over generations of climate model development. We show that structural deficiencies in a climate model are exposed by comprehensively exploring parametric uncertainty and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. This provides a future pathway towards building models with greater physical realism and lower uncertainty.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
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Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
Ross Herbert and Philip Stier
Atmos. Chem. Phys., 23, 4595–4616, https://doi.org/10.5194/acp-23-4595-2023, https://doi.org/10.5194/acp-23-4595-2023, 2023
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We provide robust evidence from multiple sources showing that smoke from fires in the Amazon rainforest significantly modifies the diurnal cycle of convection and cools the climate. Low to moderate amounts of smoke increase deep convective clouds and rain, whilst beyond a threshold amount, the smoke starts to suppress the convection and rain. We are currently at this threshold, suggesting increases in fires from agricultural practices or droughts will reduce cloudiness and rain over the region.
William K. Jones, Matthew W. Christensen, and Philip Stier
Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, https://doi.org/10.5194/amt-16-1043-2023, 2023
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Geostationary weather satellites have been used to detect storm clouds since their earliest applications. However, this task remains difficult as imaging satellites cannot observe the strong vertical winds that are characteristic of storm clouds. Here we introduce a new method that allows us to detect the early development of storms and continue to track them throughout their lifetime, allowing us to study how their early behaviour affects subsequent weather.
Leighton A. Regayre, Lucia Deaconu, Daniel P. Grosvenor, David Sexton, Christopher C. Symonds, Tom Langton, Duncan Watson-Paris, Jane P. Mulcahy, Kirsty J. Pringle, Mark Richardson, Jill S. Johnson, John Rostron, Hamish Gordon, Grenville Lister, Philip Stier, and Ken S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2022-1330, https://doi.org/10.5194/egusphere-2022-1330, 2022
Preprint archived
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We show that potential structural deficiencies in a climate model can be exposed by comprehensively exploring its parametric uncertainty, and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. Combined consideration of parametric and structural uncertainties provides a future pathway towards building models that have greater physical realism and lower uncertainty.
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.
Haochi Che, Philip Stier, Duncan Watson-Parris, Hamish Gordon, and Lucia Deaconu
Atmos. Chem. Phys., 22, 10789–10807, https://doi.org/10.5194/acp-22-10789-2022, https://doi.org/10.5194/acp-22-10789-2022, 2022
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Extensive stratocumulus clouds over the south-eastern Atlantic (SEA) can lead to a cooling effect on the climate. A key pathway by which aerosols affect cloud properties is by acting as cloud condensation nuclei (CCN). Here, we investigated the source attribution of CCN in the SEA as well as the cloud responses. Our results show that aerosol nucleation contributes most to CCN in the marine boundary layer. In terms of emissions, anthropogenic sources contribute most to the CCN and cloud droplets.
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.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
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The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Maria Sand, Bjørn H. Samset, Gunnar Myhre, Jonas Gliß, Susanne E. Bauer, Huisheng Bian, Mian Chin, Ramiro Checa-Garcia, Paul Ginoux, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Philippe Le Sager, Marianne T. Lund, Hitoshi Matsui, Twan van Noije, Dirk J. L. Olivié, Samuel Remy, Michael Schulz, Philip Stier, Camilla W. Stjern, Toshihiko Takemura, Kostas Tsigaridis, Svetlana G. Tsyro, and Duncan Watson-Parris
Atmos. Chem. Phys., 21, 15929–15947, https://doi.org/10.5194/acp-21-15929-2021, https://doi.org/10.5194/acp-21-15929-2021, 2021
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Absorption of shortwave radiation by aerosols can modify precipitation and clouds but is poorly constrained in models. A total of 15 different aerosol models from AeroCom phase III have reported total aerosol absorption, and for the first time, 11 of these models have reported in a consistent experiment the contributions to absorption from black carbon, dust, and organic aerosol. Here, we document the model diversity in aerosol absorption.
Shipeng Zhang, Philip Stier, and Duncan Watson-Parris
Atmos. Chem. Phys., 21, 10179–10197, https://doi.org/10.5194/acp-21-10179-2021, https://doi.org/10.5194/acp-21-10179-2021, 2021
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The relationship between aerosol-induced changes in atmospheric energetics and precipitation responses across different scales is studied in terms of fast (radiatively or microphysically mediated) and slow (temperature-mediated) responses. We introduced a method to decompose rainfall changes into contributions from clouds, aerosols, and clear–clean sky from an energetic perspective. It provides a way to better interpret and quantify the precipitation changes caused by aerosol perturbations.
Nick Schutgens, Oleg Dubovik, Otto Hasekamp, Omar Torres, Hiren Jethva, Peter J. T. Leonard, Pavel Litvinov, Jens Redemann, Yohei Shinozuka, Gerrit de Leeuw, Stefan Kinne, Thomas Popp, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 21, 6895–6917, https://doi.org/10.5194/acp-21-6895-2021, https://doi.org/10.5194/acp-21-6895-2021, 2021
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Absorptive aerosol has a potentially large impact on climate change. We evaluate and intercompare four global satellite datasets of absorptive aerosol optical depth (AAOD) and single-scattering albedo (SSA). We show that these datasets show reasonable correlations with the AErosol RObotic NETwork (AERONET) reference, although significant biases remain. In a follow-up paper we show that these observations nevertheless can be used for model evaluation.
Jens Redemann, Robert Wood, Paquita Zuidema, Sarah J. Doherty, Bernadette Luna, Samuel E. LeBlanc, Michael S. Diamond, Yohei Shinozuka, Ian Y. Chang, Rei Ueyama, Leonhard Pfister, Ju-Mee Ryoo, Amie N. Dobracki, Arlindo M. da Silva, Karla M. Longo, Meloë S. Kacenelenbogen, Connor J. Flynn, Kristina Pistone, Nichola M. Knox, Stuart J. Piketh, James M. Haywood, Paola Formenti, Marc Mallet, Philip Stier, Andrew S. Ackerman, Susanne E. Bauer, Ann M. Fridlind, Gregory R. Carmichael, Pablo E. Saide, Gonzalo A. Ferrada, Steven G. Howell, Steffen Freitag, Brian Cairns, Brent N. Holben, Kirk D. Knobelspiesse, Simone Tanelli, Tristan S. L'Ecuyer, Andrew M. Dzambo, Ousmane O. Sy, Greg M. McFarquhar, Michael R. Poellot, Siddhant Gupta, Joseph R. O'Brien, Athanasios Nenes, Mary Kacarab, Jenny P. S. Wong, Jennifer D. Small-Griswold, Kenneth L. Thornhill, David Noone, James R. Podolske, K. Sebastian Schmidt, Peter Pilewskie, Hong Chen, Sabrina P. Cochrane, Arthur J. Sedlacek, Timothy J. Lang, Eric Stith, Michal Segal-Rozenhaimer, Richard A. Ferrare, Sharon P. Burton, Chris A. Hostetler, David J. Diner, Felix C. Seidel, Steven E. Platnick, Jeffrey S. Myers, Kerry G. Meyer, Douglas A. Spangenberg, Hal Maring, and Lan Gao
Atmos. Chem. Phys., 21, 1507–1563, https://doi.org/10.5194/acp-21-1507-2021, https://doi.org/10.5194/acp-21-1507-2021, 2021
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Southern Africa produces significant biomass burning emissions whose impacts on regional and global climate are poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA investigation designed to study the key processes that determine these climate impacts. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project, the dataset it produced, and the most important initial findings.
Jim M. Haywood, Steven J. Abel, Paul A. Barrett, Nicolas Bellouin, Alan Blyth, Keith N. Bower, Melissa Brooks, Ken Carslaw, Haochi Che, Hugh Coe, Michael I. Cotterell, Ian Crawford, Zhiqiang Cui, Nicholas Davies, Beth Dingley, Paul Field, Paola Formenti, Hamish Gordon, Martin de Graaf, Ross Herbert, Ben Johnson, Anthony C. Jones, Justin M. Langridge, Florent Malavelle, Daniel G. Partridge, Fanny Peers, Jens Redemann, Philip Stier, Kate Szpek, Jonathan W. Taylor, Duncan Watson-Parris, Robert Wood, Huihui Wu, and Paquita Zuidema
Atmos. Chem. Phys., 21, 1049–1084, https://doi.org/10.5194/acp-21-1049-2021, https://doi.org/10.5194/acp-21-1049-2021, 2021
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Every year, the seasonal cycle of biomass burning from agricultural practices in Africa creates a huge plume of smoke that travels many thousands of kilometres over the Atlantic Ocean. This study provides an overview of a measurement campaign called the cloud–aerosol–radiation interaction and forcing for year 2017 (CLARIFY-2017) and documents the rationale, deployment strategy, observations, and key results from the campaign which utilized the heavily equipped FAAM atmospheric research aircraft.
Haochi Che, Philip Stier, Hamish Gordon, Duncan Watson-Parris, and Lucia Deaconu
Atmos. Chem. Phys., 21, 17–33, https://doi.org/10.5194/acp-21-17-2021, https://doi.org/10.5194/acp-21-17-2021, 2021
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The south-eastern Atlantic is semi-permanently covered by some of the largest stratocumulus clouds and is influenced by one-third of the biomass burning emissions from African fires. A UKEMS1 model simulation shows that the absorption effect of biomass burning aerosols is the most significant on clouds and radiation. The dominate cooling and rapid adjustments induced by the radiative effects of biomass burning aerosols result in an overall cooling in the south-eastern Atlantic.
Jane P. Mulcahy, Colin Johnson, Colin G. Jones, Adam C. Povey, Catherine E. Scott, Alistair Sellar, Steven T. Turnock, Matthew T. Woodhouse, Nathan Luke Abraham, Martin B. Andrews, Nicolas Bellouin, Jo Browse, Ken S. Carslaw, Mohit Dalvi, Gerd A. Folberth, Matthew Glover, Daniel P. Grosvenor, Catherine Hardacre, Richard Hill, Ben Johnson, Andy Jones, Zak Kipling, Graham Mann, James Mollard, Fiona M. O'Connor, Julien Palmiéri, Carly Reddington, Steven T. Rumbold, Mark Richardson, Nick A. J. Schutgens, Philip Stier, Marc Stringer, Yongming Tang, Jeremy Walton, Stephanie Woodward, and Andrew Yool
Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, https://doi.org/10.5194/gmd-13-6383-2020, 2020
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Aerosols are an important component of the Earth system. Here, we comprehensively document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 and UKESM1. This study provides a useful characterisation of the aerosol climatology in both models, facilitating the understanding of the numerous aerosol–climate interaction studies that will be conducted for CMIP6 and beyond.
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.
Nick Schutgens, Andrew M. Sayer, Andreas Heckel, Christina Hsu, Hiren Jethva, Gerrit de Leeuw, Peter J. T. Leonard, Robert C. Levy, Antti Lipponen, Alexei Lyapustin, Peter North, Thomas Popp, Caroline Poulsen, Virginia Sawyer, Larisa Sogacheva, Gareth Thomas, Omar Torres, Yujie Wang, Stefan Kinne, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 20, 12431–12457, https://doi.org/10.5194/acp-20-12431-2020, https://doi.org/10.5194/acp-20-12431-2020, 2020
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We intercompare 14 different datasets of satellite observations of aerosol. Such measurements are challenging but also provide the best opportunity to globally observe an atmospheric component strongly related to air pollution and climate change. Our study shows that most datasets perform similarly well on a global scale but that locally errors can be quite different. We develop a technique to estimate satellite errors everywhere, even in the absence of surface reference data.
Caroline A. Poulsen, Gregory R. McGarragh, Gareth E. Thomas, Martin Stengel, Matthew W. Christensen, Adam C. Povey, Simon R. Proud, Elisa Carboni, Rainer Hollmann, and Roy G. Grainger
Earth Syst. Sci. Data, 12, 2121–2135, https://doi.org/10.5194/essd-12-2121-2020, https://doi.org/10.5194/essd-12-2121-2020, 2020
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We have created a satellite cloud and radiation climatology from the ATSR-2 and AATSR on board ERS-2 and Envisat, respectively, which spans the period 1995–2012. The data set was created using a combination of optimal estimation and neural net techniques. The data set was created as part of the ESA Climate Change Initiative program. The data set has been compared with active CALIOP lidar measurements and compared with MAC-LWP AND CERES-EBAF measurements and is shown to have good performance.
Cited articles
Agard, V. and Emanuel, K.: Clausius–Clapeyron Scaling of Peak CAPE in Continental Convective Storm Environments, J. Atmos. Sci., 74, 3043–3054, https://doi.org/10.1175/JAS-D-16-0352.1, 2017. a
Aminou, D. M. A.: MSG's SEVIRI Instrument, ESA Bulletin, 111, 15–17, https://www.esa.int/esapub/bulletin/bullet111/chapter4_bul111.pdf (last access: 1 May 2024), 2002. a
Berry, E. and Mace, G. G.: Cloud Properties and Radiative Effects of the Asian Summer Monsoon Derived from A-Train Data, J. Geophys. Res.-Atmos., 119, 9492–9508, https://doi.org/10.1002/2014JD021458, 2014. a
Beydoun, H., Caldwell, P. M., Hannah, W. M., and Donahue, A. S.: Dissecting Anvil Cloud Response to Sea Surface Warming, Geophys. Res. Lett., 48, e2021GL094049, https://doi.org/10.1029/2021GL094049, 2021. a
Bony, S., Stevens, B., Coppin, D., Becker, T., Reed, K. A., Voigt, A., and Medeiros, B.: Thermodynamic Control of Anvil Cloud Amount, P. Natl. Acad. Sci. USA, 113, 8927–8932, https://doi.org/10.1073/pnas.1601472113, 2016. a
Bouniol, D., Roca, R., Fiolleau, T., and Poan, D. E.: Macrophysical, Microphysical, and Radiative Properties of Tropical Mesoscale Convective Systems over Their Life Cycle, J. Climate, 29, 3353–3371, https://doi.org/10.1175/JCLI-D-15-0551.1, 2016. a
Bouniol, D., Roca, R., Fiolleau, T., and Raberanto, P.: Life Cycle–Resolved Observation of Radiative Properties of Mesoscale Convective Systems, J. Appl. Meteorol. Clim., 60, 1091–1104, https://doi.org/10.1175/JAMC-D-20-0244.1, 2021. a
Chen, S. S. and Houze Jr., R. A.: Diurnal Variation and Life-Cycle of Deep Convective Systems over the Tropical Pacific Warm Pool, Q. J. Roy. Meteor. Soc., 123, 357–388, https://doi.org/10.1002/qj.49712353806, 1997. a
Del Genio, A. D. and Kovari, W.: Climatic Properties of Tropical Precipitating Convection under Varying Environmental Conditions, J. Climate, 15, 2597–2615, https://doi.org/10.1175/1520-0442(2002)015<2597:CPOTPC>2.0.CO;2, 2002. a
Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T., and Bugliaro, L.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, 2016. a
Feng, Z., Leung, L. R., Liu, N., Wang, J., Houze Jr., R. A., Li, J., Hardin, J. C., Chen, D., and Guo, J.: A Global High-Resolution Mesoscale Convective System Database Using Satellite-Derived Cloud Tops, Surface Precipitation, and Tracking, J. Geophys. Res.-Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202, 2021. a
Feng, Z., Hardin, J., Barnes, H. C., Li, J., Leung, L. R., Varble, A., and Zhang, Z.: PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis, Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, 2023. a
Feng, Z., Leung, L. R., Hardin, J., Terai, C. R., Song, F., and Caldwell, P.: Mesoscale Convective Systems in DYAMOND Global Convection-Permitting Simulations, Geophys. Res. Lett., 50, e2022GL102603, https://doi.org/10.1029/2022GL102603, 2023. a
Fiolleau, T. and Roca, R.: An Algorithm for the Detection and Tracking of Tropical Mesoscale Convective Systems Using Infrared Images From Geostationary Satellite, IEEE T. Geosci. Remote, 51, 4302–4315, https://doi.org/10.1109/TGRS.2012.2227762, 2013. a
Futyan, J. M. and Del Genio, A. D.: Deep Convective System Evolution over Africa and the Tropical Atlantic, J. Climate, 20, 5041–5060, https://doi.org/10.1175/JCLI4297.1, 2007. a, b, c
Gasparini, B., Sokol, A. B., Wall, C. J., Hartmann, D. L., and Blossey, P. N.: Diurnal Differences in Tropical Maritime Anvil Cloud Evolution, J. Climate, 35, 1655–1677, https://doi.org/10.1175/JCLI-D-21-0211.1, 2022. a
Gasparini, B., Sullivan, S. C., Sokol, A. B., Kärcher, B., Jensen, E., and Hartmann, D. L.: Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts, Atmos. Chem. Phys., 23, 15413–15444, https://doi.org/10.5194/acp-23-15413-2023, 2023. a, b
Grainger, R. G., Carboni, E., Cox, C., Hollman, R., Poulsen, C., Povey, A. C., Prata, A. T., Proud, S. R., Siddans, R., and Thomas, G. E.: ORAC, GitHub [code], https://github.com/ORAC-CC/orac, 2024, last access: 1 May 2024.
Harrop, B. E. and Hartmann, D. L.: The Role of Cloud Radiative Heating within the Atmosphere on the High Cloud Amount and Top-of-Atmosphere Cloud Radiative Effect, J. Adv. Model. Earth Sy., 8, 1391–1410, https://doi.org/10.1002/2016MS000670, 2016. a
Hartmann, D. L.: Tropical Anvil Clouds and Climate Sensitivity, P. Natl. Acad. Sci. USA, 113, 8897–8899, https://doi.org/10.1073/pnas.1610455113, 2016. a
Hartmann, D. L. and Larson, K.: An Important Constraint on Tropical Cloud – Climate Feedback, Geophys. Res. Lett., 29, 12-1–12-4, https://doi.org/10.1029/2002GL015835, 2002. a
Hartmann, D. L., Ockert-Bell, M. E., and Michelsen, M. L.: The Effect of Cloud Type on Earth's Energy Balance: Global Analysis, J. Climate, 5, 1281–1304, https://doi.org/10.1175/1520-0442(1992)005<1281:TEOCTO>2.0.CO;2, 1992. a
Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019. a
Held, I. M. and Soden, B. J.: Robust Responses of the Hydrological Cycle to Global Warming, J. Climate, 19, 5686–5699, https://doi.org/10.1175/JCLI3990.1, 2006. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hill, P. G., Holloway, C. E., Byrne, M. P., Lambert, F. H., and Webb, M. J.: Climate Models Underestimate Dynamic Cloud Feedbacks in the Tropics, Geophys. Res. Lett., 50, e2023GL104573, https://doi.org/10.1029/2023GL104573, 2023. a
Horner, G. and Gryspeerdt, E.: The evolution of deep convective systems and their associated cirrus outflows, Atmos. Chem. Phys., 23, 14239–14253, https://doi.org/10.5194/acp-23-14239-2023, 2023. a, b
Houze, R. A.: Mesoscale Convective Systems, Rev. Geophys., 42, RG4003, https://doi.org/10.1029/2004RG000150, 2004. a
Igel, M. R., Drager, A. J., and van den Heever, S. C.: A CloudSat Cloud Object Partitioning Technique and Assessment and Integration of Deep Convective Anvil Sensitivities to Sea Surface Temperature, J. Geophys. Res.-Atmos., 119, 10515–10535, https://doi.org/10.1002/2014JD021717, 2014. a
Jeevanjee, N. and Fueglistaler, S.: Simple Spectral Models for Atmospheric Radiative Cooling, J. Atmos. Sci., 77, 479–497, https://doi.org/10.1175/JAS-D-18-0347.1, 2020. a
Jones, W. K.: Cloud-CCI+ SEVIRI CRE Case Study Dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.8317025, 2023a. a
Jones, W. K.: Tobac-Flow v1.7.6, Zenodo [code], https://doi.org/10.5281/zenodo.8317062, 2023b. a
Jones, W. K.: Material for Preparation of “A Lagrangian Perspective on the Lifecycle and Cloud Radiative Effect of Deep Convective Clouds Over Africa”, Zenodo [code], https://doi.org/10.5281/zenodo.10834939, 2024. a
Jones, W. K., Christensen, M. W., and Stier, P.: A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations, Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, 2023. a
Lakshmanan, V. and Smith, T.: An Objective Method of Evaluating and Devising Storm-Tracking Algorithms, Weather Forecast., 25, 701–709, https://doi.org/10.1175/2009WAF2222330.1, 2010. a
Lin, B., Wong, T., Wielicki, B. A., and Hu, Y.: Examination of the Decadal Tropical Mean ERBS Nonscanner Radiation Data for the Iris Hypothesis, J. Climate, 17, 1239–1246, https://doi.org/10.1175/1520-0442(2004)017<1239:EOTDTM>2.0.CO;2, 2004. a
Lindzen, R. S., Chou, M.-D., and Hou, A. Y.: Does the Earth Have an Adaptive Infrared Iris?, B. Am. Meteorol. Soc., 82, 417–432, https://doi.org/10.1175/1520-0477(2001)082<0417:DTEHAA>2.3.CO;2, 2001. a
Liu, H., Koren, I., and Altaratz, O.: Observed Decreasing Trend in the Upper-Tropospheric Cloud Top Temperature, npj Climate and Atmospheric Science, 6, 1–8, https://doi.org/10.1038/s41612-023-00465-5, 2023. a
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., Liang, L., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product, J. Climate, 31, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1, 2018. a
Martin, P. P., Durand, Y., Aminou, D., Gaudin-Delrieu, C., and Lamard, J.-L.: FCI Instrument On-Board MeteoSat Third Generation Satellite: Design and Development Status, in: International Conference on Space Optics – ICSO 2020, online, 30 March–2 April 2021, vol. 11852, SPIE, 125–140, https://doi.org/10.1117/12.2599152, 2021. a
McGarragh, G. R., Poulsen, C. A., Thomas, G. E., Povey, A. C., Sus, O., Stapelberg, S., Schlundt, C., Proud, S., Christensen, M. W., Stengel, M., Hollmann, R., and Grainger, R. G.: The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach, Atmos. Meas. Tech., 11, 3397–3431, https://doi.org/10.5194/amt-11-3397-2018, 2018. a
Müller, R., Haussler, S., and Jerg, M.: The Role of NWP Filter for the Satellite Based Detection of Cumulonimbus Clouds, Remote Sens.-Basel, 10, 386, https://doi.org/10.3390/rs10030386, 2018. a
Müller, R., Haussler, S., Jerg, M., and Heizenreder, D.: A Novel Approach for the Detection of Developing Thunderstorm Cells, Remote Sens.-Basel, 11, 443, https://doi.org/10.3390/rs11040443, 2019. a
Nicholson, S. E.: A Revised Picture of the Structure of the “Monsoon” and Land ITCZ over West Africa, Clim. Dynam., 32, 1155–1171, https://doi.org/10.1007/s00382-008-0514-3, 2009. a
Nicholson, S. E.: The ITCZ and the Seasonal Cycle over Equatorial Africa, B. Am. Meteorol. Soc., 99, 337–348, https://doi.org/10.1175/BAMS-D-16-0287.1, 2018. a
Norris, J. R., Allen, R. J., Evan, A. T., Zelinka, M. D., O'Dell, C. W., and Klein, S. A.: Evidence for Climate Change in the Satellite Cloud Record, Nature, 536, 72–75, https://doi.org/10.1038/nature18273, 2016. a
Nowicki, S. M. J. and Merchant, C. J.: Observations of Diurnal and Spatial Variability of Radiative Forcing by Equatorial Deep Convective Clouds, J. Geophys. Res.-Atmos., 109, D11602, https://doi.org/10.1029/2003JD004176, 2004. a
Núñez Ocasio, K. M., Evans, J. L., and Young, G. S.: Tracking Mesoscale Convective Systems That Are Potential Candidates for Tropical Cyclogenesis, Mon. Weather Rev., 148, 655–669, https://doi.org/10.1175/MWR-D-19-0070.1, 2020. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A Review on Regional Convection-Permitting Climate Modeling: Demonstrations, Prospects, and Challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015. a
Protopapadaki, S. E., Stubenrauch, C. J., and Feofilov, A. G.: Upper tropospheric cloud systems derived from IR sounders: properties of cirrus anvils in the tropics, Atmos. Chem. Phys., 17, 3845–3859, https://doi.org/10.5194/acp-17-3845-2017, 2017. a
Ramanathan, V., Cess, R. D., Harrison, E. F., Minnis, P., Barkstrom, B. R., Ahmad, E., and Hartmann, D.: Cloud-Radiative Forcing and Climate: Results from the Earth Radiation Budget Experiment, Science, 243, 57–63, https://doi.org/10.1126/science.243.4887.57, 1989. a, b
Riehl, H. and Malkus, J. S.: On the Heat Balance in the Equatorial Trough Zone, Geophysica, 6, 503–538, 1958. a
Roberts, R. D. and Rutledge, S.: Nowcasting Storm Initiation and Growth Using GOES-8 and WSR-88D Data, Weather Forecast., 18, 562–584, https://doi.org/10.1175/1520-0434(2003)018<0562:NSIAGU>2.0.CO;2, 2003. a
Roca, R., Fiolleau, T., and Bouniol, D.: A Simple Model of the Life Cycle of Mesoscale Convective Systems Cloud Shield in the Tropics, J. Climate, 30, 4283–4298, https://doi.org/10.1175/JCLI-D-16-0556.1, 2017. a
Seeley, J. T. and Romps, D. M.: Why Does Tropical Convective Available Potential Energy (CAPE) Increase with Warming?, Geophys. Res. Lett., 42, 10429–10437, https://doi.org/10.1002/2015GL066199, 2015. a
Seeley, J. T., Jeevanjee, N., and Romps, D. M.: FAT or FiTT: Are Anvil Clouds or the Tropopause Temperature Invariant?, Geophys. Res. Lett., 46, 1842–1850, https://doi.org/10.1029/2018GL080096, 2019. a
Seidel, S. D. and Yang, D.: Temperatures of Anvil Clouds and Radiative Tropopause in a Wide Array of Cloud-Resolving Simulations, J. Climate, 35, 8065–8078, https://doi.org/10.1175/JCLI-D-21-0962.1, 2022. a
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a
Sokol, A. B. and Hartmann, D. L.: Tropical Anvil Clouds: Radiative Driving Toward a Preferred State, J. Geophys. Res.-Atmos., 125, e2020JD033107, https://doi.org/10.1029/2020JD033107, 2020. a, b
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C., L'Ecuyer, T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten Years of Actively Observing the Earth System, B. Am. Meteorol. Soc., 99, 569–581, https://doi.org/10.1175/BAMS-D-16-0324.1, 2018. a
Stephens, G. L., Gabriel, P. M., and Partain, P. T.: Parameterization of Atmospheric Radiative Transfer. Part I: Validity of Simple Models, J. Atmos. Sci., 58, 3391–3409, https://doi.org/10.1175/1520-0469(2001)058<3391:POARTP>2.0.CO;2, 2001. a
Sus, O., Stengel, M., Stapelberg, S., McGarragh, G., Poulsen, C., Povey, A. C., Schlundt, C., Thomas, G., Christensen, M., Proud, S., Jerg, M., Grainger, R., and Hollmann, R.: The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors, Atmos. Meas. Tech., 11, 3373–3396, https://doi.org/10.5194/amt-11-3373-2018, 2018. a
Takahashi, H., Luo, Z. J., and Stephens, G. L.: Level of Neutral Buoyancy, Deep Convective Outflow, and Convective Core: New Perspectives Based on 5 Years of CloudSat Data, J. Geophys. Res.-Atmos., 122, 2958–2969, https://doi.org/10.1002/2016JD025969, 2017. a
Takahashi, H., Lebsock, M. D., Richardson, M., Marchand, R., and Kay, J. E.: When Will Spaceborne Cloud Radar Detect Upward Shifts in Cloud Heights?, J. Geophys. Res.-Atmos., 124, 7270–7285, https://doi.org/10.1029/2018JD030242, 2019. a
Takahashi, H., Luo, Z. J., Stephens, G., and Mulholland, J. P.: Revisiting the Land-Ocean Contrasts in Deep Convective Cloud Intensity Using Global Satellite Observations, Geophys. Res. Lett., 50, e2022GL102089, https://doi.org/10.1029/2022GL102089, 2023. a
Taylor, S., Stier, P., White, B., Finkensieper, S., and Stengel, M.: Evaluating the diurnal cycle in cloud top temperature from SEVIRI, Atmos. Chem. Phys., 17, 7035–7053, https://doi.org/10.5194/acp-17-7035-2017, 2017. a
Vecchi, G. A. and Soden, B. J.: Global Warming and the Weakening of the Tropical Circulation, J. Climate, 20, 4316–4340, https://doi.org/10.1175/JCLI4258.1, 2007. a
Vizy, E. K. and Cook, K. H.: Understanding the Summertime Diurnal Cycle of Precipitation over Sub-Saharan West Africa: Regions with Daytime Rainfall Peaks in the Absence of Significant Topographic Features, Clim. Dynam., 52, 2903–2922, https://doi.org/10.1007/s00382-018-4315-z, 2019. a
Vondou, D. A., Nzeukou, A., Lenouo, A., and Mkankam Kamga, F.: Seasonal Variations in the Diurnal Patterns of Convection in Cameroon–Nigeria and Their Neighboring Areas, Atmos. Sci. Lett., 11, 290–300, https://doi.org/10.1002/asl.297, 2010. a
Wall, C. J. and Hartmann, D. L.: Balanced Cloud Radiative Effects Across a Range of Dynamical Conditions Over the Tropical West Pacific, Geophys. Res. Lett., 45, 11490–11498, https://doi.org/10.1029/2018GL080046, 2018. a
Wall, C. J., Norris, J. R., Gasparini, B., Smith, W. L., Thieman, M. M., and Sourdeval, O.: Observational Evidence That Radiative Heating Modifies the Life Cycle of Tropical Anvil Clouds, J. Climate, 33, 8621–8640, https://doi.org/10.1175/JCLI-D-20-0204.1, 2020. a
Wang, D., Jensen, M. P., D'Iorio, J. A., Jozef, G., Giangrande, S. E., Johnson, K. L., Luo, Z. J., Starzec, M., and Mullendore, G. L.: An Observational Comparison of Level of Neutral Buoyancy and Level of Maximum Detrainment in Tropical Deep Convective Clouds, J. Geophys. Res.-Atmos., 125, e2020JD032637, https://doi.org/10.1029/2020JD032637, 2020. a
Weisman, M. L.: MESOSCALE METEOROLOGY | Convective Storms: Overview, in: Encyclopedia of Atmospheric Sciences, 2nd edn., edited by: North, G. R., Pyle, J., and Zhang, F., Academic Press, Oxford, 401–410, https://doi.org/10.1016/B978-0-12-382225-3.00490-4, 2015. a
Westra, S., Fowler, H. J., Evans, J. P., Alexander, L. V., Berg, P., Johnson, F., Kendon, E. J., Lenderink, G., and Roberts, N. M.: Future Changes to the Intensity and Frequency of Short-Duration Extreme Rainfall, Rev. Geophys., 52, 522–555, https://doi.org/10.1002/2014RG000464, 2014. a
Zelinka, M. D. and Hartmann, D. L.: Why Is Longwave Cloud Feedback Positive?, J. Geophys. Res.-Atmos., 115, D16117, https://doi.org/10.1029/2010JD013817, 2010. a
Zinner, T., Mannstein, H., and Tafferner, A.: Cb-TRAM: Tracking and Monitoring Severe Convection from Onset over Rapid Development to Mature Phase Using Multi-Channel Meteosat-8 SEVIRI Data, Meteorol. Atmos. Phys., 101, 191–210, https://doi.org/10.1007/s00703-008-0290-y, 2008. a
Zinner, T., Forster, C., de Coning, E., and Betz, H.-D.: Validation of the Meteosat storm detection and nowcasting system Cb-TRAM with lightning network data – Europe and South Africa, Atmos. Meas. Tech., 6, 1567–1583, https://doi.org/10.5194/amt-6-1567-2013, 2013. a
Short summary
Storm clouds cover large areas of the tropics. These clouds both reflect incoming sunlight and trap heat from the atmosphere below, regulating the temperature of the tropics. Over land, storm clouds occur in the late afternoon and evening and so exist both during the daytime and at night. Changes in this timing could upset the balance of the respective cooling and heating effects of these clouds. We find that isolated storms have a larger effect on this balance than their small size suggests.
Storm clouds cover large areas of the tropics. These clouds both reflect incoming sunlight and...
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