Articles | Volume 25, issue 23
https://doi.org/10.5194/acp-25-17473-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-17473-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Dec 2025
Research article |
| 03 Dec 2025
| 03 Dec 2025
Regime-based aerosol–cloud interactions from CALIPSO-MODIS and the Energy Exascale Earth System Model version 2 (E3SMv2) over the Eastern North Atlantic
Xiaojian Zheng
CORRESPONDING AUTHOR
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
David Painemal
Science Directorate, NASA Langley Research Center, Hampton, VA, USA
Meng Zhang
Lawrence Livermore National Laboratory, Livermore, CA, USA
now at: Department of Earth and Atmospheric Science, University of Houston, Houston, TX, USA
Shaocheng Xie
Lawrence Livermore National Laboratory, Livermore, CA, USA
Zhujun Li
Science Directorate, NASA Langley Research Center, Hampton, VA, USA
Analytical Mechanics Associates, Hampton, VA, USA
Robert Jacob
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Bethany Lusch
Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, IL, USA
Related authors
Zhujun Li, David Painemal, Yan Feng, and Xiaojian Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-4769, https://doi.org/10.5194/egusphere-2025-4769, 2025
Short summary
Short summary
This study is the first global assessment of aerosol-cloud interactions (ACI) and cloud adjustments that relies on vertically resolved aerosol retrievals that are vertically matched with the location of the cloud layer. We computed ACI metrics and cloud adjustments over the global ocean by combining retrievals from active and passive satellite sensors and found high sensitivity of clouds to changes in their cloud droplet number concentration due to aerosols.
Anik Das, Baike Xi, Xiaojian Zheng, and Xiquan Dong
EGUsphere, https://doi.org/10.5194/egusphere-2025-874, https://doi.org/10.5194/egusphere-2025-874, 2025
Preprint withdrawn
Short summary
Short summary
This study presents a method to identify cloud boundaries and phases for marine boundary layer clouds over the Southern Ocean using airborne cloud radar and in situ probe data collected during the SOCRATES campaign. Single-layer low-level clouds (<3 km) were found to be dominating (85 %) across all observed samples. Phase classification showed 48.8 % liquid, 23.3 % mixed, and 6.9 % ice. Results support cloud process understanding and improve satellite and climate model studies.
Travis D. Toth, Marian B. Clayton, Zhujun Li, David Painemal, Sharon D. Rodier, Jayanta Kar, Tyler J. Thorsen, Richard A. Ferrare, Mark A. Vaughan, Jason L. Tackett, Huisheng Bian, Mian Chin, Anne E. Garnier, Ellsworth J. Welton, Robert A. Ryan, Charles R. Trepte, and David M. Winker
Atmos. Meas. Tech., 18, 6765–6793, https://doi.org/10.5194/amt-18-6765-2025, https://doi.org/10.5194/amt-18-6765-2025, 2025
Short summary
Short summary
NASA's CALIPSO satellite mission observed aerosols (airborne particles) globally from 2006 to 2023. Its final data products update improves its aerosol optical parameters over oceans by adjusting for regional and seasonal differences in a new measurement-model synergistic approach. This results in a more realistic aerosol characterization, specifically near coastlines (where sea salt mixes with pollution), with potential impacts to future studies of science applications (e.g., climate effects).
Naser Mahfouz, Hassan Beydoun, Johannes Mülmenstädt, Noel Keen, Adam C. Varble, Luca Bertagna, Peter Bogenschutz, Andrew Bradley, Matthew W. Christensen, T. Conrad Clevenger, Aaron Donahue, Jerome Fast, James Foucar, Jean-Christophe Golaz, Oksana Guba, Walter Hannah, Benjamin Hillman, Robert Jacob, Wuyin Lin, Po-Lun Ma, Yun Qian, Balwinder Singh, Christopher Terai, Hailong Wang, Mingxuan Wu, Kai Zhang, Andrew Gettelman, Mark Taylor, L. Ruby Leung, Peter Caldwell, and Susannah Burrows
Atmos. Chem. Phys., 25, 15105–15120, https://doi.org/10.5194/acp-25-15105-2025, https://doi.org/10.5194/acp-25-15105-2025, 2025
Short summary
Short summary
Our study assesses the aerosol effective radiative forcing in a global cloud-resolving atmosphere model at ultra-high resolution. We demonstrate that global aerosol forcing signal can be robustly reproduced across resolutions when aerosol activation processes are carefully parameterized. Further, we argue that simplified prescribed aerosol schemes will open the door for further process/mechanism studies under controlled conditions.
Jianhao Zhang, David Painemal, Tom Dror, Jung-Sub Lim, Armin Sorooshian, and Graham Feingold
EGUsphere, https://doi.org/10.5194/egusphere-2025-5119, https://doi.org/10.5194/egusphere-2025-5119, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Clouds over the ocean can change quickly from overcast to broken fields, affecting how much sunlight reaches the Earth's surface. We studied these transitions during cold-air outbreaks using satellite images and a new method that tracks cloud evolution through time and space. We found that ice-related processes play a key role in shaping these transitions, highlighting the power of space-borne observations in revealing physical insights.
Allen Hu, Ziming Ke, Xiaohong Liu, Benjamin Wagman, Hunter Brown, Zheng Lu, Mingxuan Wu, Hailong Wang, Qi Tang, Diana Bull, Kara Peterson, and Shaocheng Xie
Atmos. Chem. Phys., 25, 12137–12157, https://doi.org/10.5194/acp-25-12137-2025, https://doi.org/10.5194/acp-25-12137-2025, 2025
Short summary
Short summary
Volcanic eruptions have major effects on atmospheric temperature and can be studied as a proxy for geo-engineering. The original aerosol module in the Energy Exascale Earth System Model v2 (E3SMv2) has problems simulating volcanic aerosols. We alter the aerosol module to simulate the 1991 Pinatubo eruption and implement a more complex chemistry scheme, producing results that better agree with observations. Process analyses of the volcanic aerosols help explain how they grow in the stratosphere.
Zhujun Li, David Painemal, Yan Feng, and Xiaojian Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-4769, https://doi.org/10.5194/egusphere-2025-4769, 2025
Short summary
Short summary
This study is the first global assessment of aerosol-cloud interactions (ACI) and cloud adjustments that relies on vertically resolved aerosol retrievals that are vertically matched with the location of the cloud layer. We computed ACI metrics and cloud adjustments over the global ocean by combining retrievals from active and passive satellite sensors and found high sensitivity of clouds to changes in their cloud droplet number concentration due to aerosols.
Mingxuan Wu, Hailong Wang, Zheng Lu, Xiaohong Liu, Huisheng Bian, David D. Cohen, Yan Feng, Mian Chin, Didier A. Hauglustaine, Vlassis A. Karydis, Marianne T. Lund, Gunnar Myhre, Andrea Pozzer, Michael Schulz, Ragnhild B. Skeie, Alexandra P. Tsimpidi, Svetlana G. Tsyro, and Shaocheng Xie
Atmos. Chem. Phys., 25, 10049–10074, https://doi.org/10.5194/acp-25-10049-2025, https://doi.org/10.5194/acp-25-10049-2025, 2025
Short summary
Short summary
A key challenge in simulating the life cycle of nitrate aerosol in global models is accurately representing the mass size distribution of nitrate aerosol, which lacks sufficient observational constraints. We found that most global models underestimate the mass fraction of fine-mode nitrate at the surface in all regions. Our study highlights the importance of gas–aerosol partitioning parameterization and the simulation of dust and sea salt in correctly simulating the mass size distribution of nitrate.
Jingyi Chen, Hailong Wang, Bo Zhang, Hongyu Liu, David Painemal, Armin Sorooshian, Sheng-Lun Tai, and Christiane Voigt
EGUsphere, https://doi.org/10.22541/essoar.175376670.02806644/v1, https://doi.org/10.22541/essoar.175376670.02806644/v1, 2025
Short summary
Short summary
NASA-validated modeling shows +4K SST & +25 % gradients distinctly alter boundary layer dynamics, cloud physics in cold-air outbreaks. Warmer SST reduces cloud cover; increases size, elongation; hydrometeors shift to ice. Sharper Gradients boost liquid water (cold upwind); reduces ice; disrupts organization. Also, SST changes alter cloud-top properties via entrained airmass origin. Resolving ocean-atmosphere coupling in global models is essential for accurate cloud feedback projections.
Xinzhu Li, Longlei Li, Yan Feng, and Xin Xi
EGUsphere, https://doi.org/10.5194/egusphere-2025-3013, https://doi.org/10.5194/egusphere-2025-3013, 2025
Short summary
Short summary
This study evaluates the dust emission variability and relationship with its physical drivers within 21 ESMs, and uncovers new insights about model discrepancies in dust variability and the relative importance of wind and hydroclimate drivers. The study underscores the importance of improving hydroclimate and land surface representations for reducing uncertainties in dust emission responses to climate variability and change.
Anik Das, Baike Xi, Xiaojian Zheng, and Xiquan Dong
EGUsphere, https://doi.org/10.5194/egusphere-2025-874, https://doi.org/10.5194/egusphere-2025-874, 2025
Preprint withdrawn
Short summary
Short summary
This study presents a method to identify cloud boundaries and phases for marine boundary layer clouds over the Southern Ocean using airborne cloud radar and in situ probe data collected during the SOCRATES campaign. Single-layer low-level clouds (<3 km) were found to be dominating (85 %) across all observed samples. Phase classification showed 48.8 % liquid, 23.3 % mixed, and 6.9 % ice. Results support cloud process understanding and improve satellite and climate model studies.
Vincent Larson, Zhun Guo, Benjamin Stephens, Colin Zarzycki, Gerhard Dikta, Yun Qian, and Shaocheng Xie
EGUsphere, https://doi.org/10.5194/egusphere-2025-1593, https://doi.org/10.5194/egusphere-2025-1593, 2025
Short summary
Short summary
Global models of the atmosphere contain errors that lead to inaccurate simulations. A software tool ("QuadTune") is presented that attempts to mitigate some of the inaccuracies. It also displays diagnostic plots that provide hints about where the errors might lie in the model.
Florian Tornow, Ann Fridlind, George Tselioudis, Brian Cairns, Andrew Ackerman, Seethala Chellappan, David Painemal, Paquita Zuidema, Christiane Voigt, Simon Kirschler, and Armin Sorooshian
Atmos. Chem. Phys., 25, 5053–5074, https://doi.org/10.5194/acp-25-5053-2025, https://doi.org/10.5194/acp-25-5053-2025, 2025
Short summary
Short summary
The recent NASA campaign ACTIVATE (Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment) performed 71 tandem flights in mid-latitude marine cold-air outbreaks off the US eastern seaboard. We provide meteorological and cloud transition stage context, allowing us to identify days that are most suitable for Lagrangian modeling and analysis. Surveyed cloud properties show signatures of cloud microphysical processes, such as cloud-top entrainment and secondary ice formation.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Hongyu Liu, Bo Zhang, Richard H. Moore, Luke D. Ziemba, Richard A. Ferrare, Hyundeok Choi, Armin Sorooshian, David Painemal, Hailong Wang, Michael A. Shook, Amy Jo Scarino, Johnathan W. Hair, Ewan C. Crosbie, Marta A. Fenn, Taylor J. Shingler, Chris A. Hostetler, Gao Chen, Mary M. Kleb, Gan Luo, Fangqun Yu, Mark A. Vaughan, Yongxiang Hu, Glenn S. Diskin, John B. Nowak, Joshua P. DiGangi, Yonghoon Choi, Christoph A. Keller, and Matthew S. Johnson
Atmos. Chem. Phys., 25, 2087–2121, https://doi.org/10.5194/acp-25-2087-2025, https://doi.org/10.5194/acp-25-2087-2025, 2025
Short summary
Short summary
We use the GEOS-Chem model to simulate aerosol distributions and properties over the western North Atlantic Ocean (WNAO) during the winter and summer deployments in 2020 of the NASA ACTIVATE mission. Model results are evaluated against aircraft, ground-based, and satellite observations. The improved understanding of life cycle, composition, transport pathways, and distribution of aerosols has important implications for characterizing aerosol–cloud–meteorology interactions over WNAO.
Ewan Crosbie, Luke D. Ziemba, Michael A. Shook, Taylor Shingler, Johnathan W. Hair, Armin Sorooshian, Richard A. Ferrare, Brian Cairns, Yonghoon Choi, Joshua DiGangi, Glenn S. Diskin, Chris Hostetler, Simon Kirschler, Richard H. Moore, David Painemal, Claire Robinson, Shane T. Seaman, K. Lee Thornhill, Christiane Voigt, and Edward Winstead
Atmos. Chem. Phys., 24, 6123–6152, https://doi.org/10.5194/acp-24-6123-2024, https://doi.org/10.5194/acp-24-6123-2024, 2024
Short summary
Short summary
Marine clouds are found to clump together in regions or lines, readily discernible from satellite images of the ocean. While clustering is also a feature of deep storm clouds, we focus on smaller cloud systems associated with fair weather and brief localized showers. Two aircraft sampled the region around these shallow systems: one incorporated measurements taken within, adjacent to, and below the clouds, while the other provided a survey from above using remote sensing techniques.
Leong Wai Siu, Joseph S. Schlosser, David Painemal, Brian Cairns, Marta A. Fenn, Richard A. Ferrare, Johnathan W. Hair, Chris A. Hostetler, Longlei Li, Mary M. Kleb, Amy Jo Scarino, Taylor J. Shingler, Armin Sorooshian, Snorre A. Stamnes, and Xubin Zeng
Atmos. Meas. Tech., 17, 2739–2759, https://doi.org/10.5194/amt-17-2739-2024, https://doi.org/10.5194/amt-17-2739-2024, 2024
Short summary
Short summary
An unprecedented 3-year aerosol dataset was collected from a recent NASA field campaign over the western North Atlantic Ocean, which offers a special opportunity to evaluate two state-of-the-art remote sensing instruments, one lidar and the other polarimeter, on the same aircraft. Special attention has been paid to validate aerosol optical depth data and their uncertainties when no reference dataset is available. Physical reasons for the disagreement between two instruments are discussed.
Alejandro Baró Pérez, Michael S. Diamond, Frida A.-M. Bender, Abhay Devasthale, Matthias Schwarz, Julien Savre, Juha Tonttila, Harri Kokkola, Hyunho Lee, David Painemal, and Annica M. L. Ekman
Atmos. Chem. Phys., 24, 4591–4610, https://doi.org/10.5194/acp-24-4591-2024, https://doi.org/10.5194/acp-24-4591-2024, 2024
Short summary
Short summary
We use a numerical model to study interactions between humid light-absorbing aerosol plumes, clouds, and radiation over the southeast Atlantic. We find that the warming produced by the aerosols reduces cloud cover, especially in highly polluted situations. Aerosol impacts on drizzle play a minor role. However, aerosol effects on cloud reflectivity and moisture-induced changes in cloud cover dominate the climatic response and lead to an overall cooling by the biomass burning plumes.
Shaoyue Qiu, Xue Zheng, David Painemal, Christopher R. Terai, and Xiaoli Zhou
Atmos. Chem. Phys., 24, 2913–2935, https://doi.org/10.5194/acp-24-2913-2024, https://doi.org/10.5194/acp-24-2913-2024, 2024
Short summary
Short summary
The aerosol indirect effect (AIE) depends on cloud states, which exhibit significant diurnal variations in the northeastern Atlantic. Yet the AIE diurnal cycle remains poorly understood. Using satellite retrievals, we find a pronounced “U-shaped” diurnal variation in the AIE, which is contributed to by the transition of cloud states combined with the lagged cloud responses. This suggests that polar-orbiting satellites with overpass times at noon underestimate daytime mean values of the AIE.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
Short summary
Short summary
Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Simon Kirschler, Christiane Voigt, Bruce E. Anderson, Gao Chen, Ewan C. Crosbie, Richard A. Ferrare, Valerian Hahn, Johnathan W. Hair, Stefan Kaufmann, Richard H. Moore, David Painemal, Claire E. Robinson, Kevin J. Sanchez, Amy J. Scarino, Taylor J. Shingler, Michael A. Shook, Kenneth L. Thornhill, Edward L. Winstead, Luke D. Ziemba, and Armin Sorooshian
Atmos. Chem. Phys., 23, 10731–10750, https://doi.org/10.5194/acp-23-10731-2023, https://doi.org/10.5194/acp-23-10731-2023, 2023
Short summary
Short summary
In this study we present an overview of liquid and mixed-phase clouds and precipitation in the marine boundary layer over the western North Atlantic Ocean. We compare microphysical properties of pure liquid clouds to mixed-phase clouds and show that the initiation of the ice phase in mixed-phase clouds promotes precipitation. The observational data presented in this study are well suited for investigating the processes that give rise to liquid and mixed-phase clouds, ice, and precipitation.
Armin Sorooshian, Mikhail D. Alexandrov, Adam D. Bell, Ryan Bennett, Grace Betito, Sharon P. Burton, Megan E. Buzanowicz, Brian Cairns, Eduard V. Chemyakin, Gao Chen, Yonghoon Choi, Brian L. Collister, Anthony L. Cook, Andrea F. Corral, Ewan C. Crosbie, Bastiaan van Diedenhoven, Joshua P. DiGangi, Glenn S. Diskin, Sanja Dmitrovic, Eva-Lou Edwards, Marta A. Fenn, Richard A. Ferrare, David van Gilst, Johnathan W. Hair, David B. Harper, Miguel Ricardo A. Hilario, Chris A. Hostetler, Nathan Jester, Michael Jones, Simon Kirschler, Mary M. Kleb, John M. Kusterer, Sean Leavor, Joseph W. Lee, Hongyu Liu, Kayla McCauley, Richard H. Moore, Joseph Nied, Anthony Notari, John B. Nowak, David Painemal, Kasey E. Phillips, Claire E. Robinson, Amy Jo Scarino, Joseph S. Schlosser, Shane T. Seaman, Chellappan Seethala, Taylor J. Shingler, Michael A. Shook, Kenneth A. Sinclair, William L. Smith Jr., Douglas A. Spangenberg, Snorre A. Stamnes, Kenneth L. Thornhill, Christiane Voigt, Holger Vömel, Andrzej P. Wasilewski, Hailong Wang, Edward L. Winstead, Kira Zeider, Xubin Zeng, Bo Zhang, Luke D. Ziemba, and Paquita Zuidema
Earth Syst. Sci. Data, 15, 3419–3472, https://doi.org/10.5194/essd-15-3419-2023, https://doi.org/10.5194/essd-15-3419-2023, 2023
Short summary
Short summary
The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol–cloud–meteorology interactions. HU-25 Falcon and King Air aircraft conducted systematic and spatially coordinated flights over the northwest Atlantic Ocean. This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Ian Chang, Lan Gao, Connor J. Flynn, Yohei Shinozuka, Sarah J. Doherty, Michael S. Diamond, Karla M. Longo, Gonzalo A. Ferrada, Gregory R. Carmichael, Patricia Castellanos, Arlindo M. da Silva, Pablo E. Saide, Calvin Howes, Zhixin Xue, Marc Mallet, Ravi Govindaraju, Qiaoqiao Wang, Yafang Cheng, Yan Feng, Sharon P. Burton, Richard A. Ferrare, Samuel E. LeBlanc, Meloë S. Kacenelenbogen, Kristina Pistone, Michal Segal-Rozenhaimer, Kerry G. Meyer, Ju-Mee Ryoo, Leonhard Pfister, Adeyemi A. Adebiyi, Robert Wood, Paquita Zuidema, Sundar A. Christopher, and Jens Redemann
Atmos. Chem. Phys., 23, 4283–4309, https://doi.org/10.5194/acp-23-4283-2023, https://doi.org/10.5194/acp-23-4283-2023, 2023
Short summary
Short summary
Abundant aerosols are present above low-level liquid clouds over the southeastern Atlantic during late austral spring. The model simulation differences in the proportion of aerosol residing in the planetary boundary layer and in the free troposphere can greatly affect the regional aerosol radiative effects. This study examines the aerosol loading and fractional aerosol loading in the free troposphere among various models and evaluates them against measurements from the NASA ORACLES campaign.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
Short summary
Short summary
Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Vijay S. Mahadevan, Jorge E. Guerra, Xiangmin Jiao, Paul Kuberry, Yipeng Li, Paul Ullrich, David Marsico, Robert Jacob, Pavel Bochev, and Philip Jones
Geosci. Model Dev., 15, 6601–6635, https://doi.org/10.5194/gmd-15-6601-2022, https://doi.org/10.5194/gmd-15-6601-2022, 2022
Short summary
Short summary
Coupled Earth system models require transfer of field data between multiple components with varying spatial resolutions to determine the correct climate behavior. We present the Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol to evaluate the accuracy, conservation properties, monotonicity, and local feature preservation of four different remapper algorithms for various unstructured mesh problems of interest. Future extensions to more practical use cases are also discussed.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
Short summary
Short summary
Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Edward Gryspeerdt, Daniel T. McCoy, Ewan Crosbie, Richard H. Moore, Graeme J. Nott, David Painemal, Jennifer Small-Griswold, Armin Sorooshian, and Luke Ziemba
Atmos. Meas. Tech., 15, 3875–3892, https://doi.org/10.5194/amt-15-3875-2022, https://doi.org/10.5194/amt-15-3875-2022, 2022
Short summary
Short summary
Droplet number concentration is a key property of clouds, influencing a variety of cloud processes. It is also used for estimating the cloud response to aerosols. The satellite retrieval depends on a number of assumptions – different sampling strategies are used to select cases where these assumptions are most likely to hold. Here we investigate the impact of these strategies on the agreement with in situ data, the droplet number climatology and estimates of the indirect radiative forcing.
Zhujun Li, David Painemal, Gregory Schuster, Marian Clayton, Richard Ferrare, Mark Vaughan, Damien Josset, Jayanta Kar, and Charles Trepte
Atmos. Meas. Tech., 15, 2745–2766, https://doi.org/10.5194/amt-15-2745-2022, https://doi.org/10.5194/amt-15-2745-2022, 2022
Short summary
Short summary
For more than 15 years, CALIPSO has revolutionized our understanding of the role of aerosols in climate. Here we evaluate CALIPSO aerosol typing over the ocean using an independent CALIPSO–CloudSat product. The analysis suggests that CALIPSO correctly categorizes clean marine aerosol over the open ocean, elevated smoke over the SE Atlantic, and dust over the tropical Atlantic. Similarities between clean and dusty marine over the open ocean implies that algorithm modifications are warranted.
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
Geosci. Model Dev., 15, 3433–3445, https://doi.org/10.5194/gmd-15-3433-2022, https://doi.org/10.5194/gmd-15-3433-2022, 2022
Short summary
Short summary
In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
Aurore Voldoire, Romain Roehrig, Hervé Giordani, Robin Waldman, Yunyan Zhang, Shaocheng Xie, and Marie-Nöelle Bouin
Geosci. Model Dev., 15, 3347–3370, https://doi.org/10.5194/gmd-15-3347-2022, https://doi.org/10.5194/gmd-15-3347-2022, 2022
Short summary
Short summary
A single-column version of the global climate model CNRM-CM6-1 has been designed to ease development and validation of the model physics at the air–sea interface in a simplified environment. This model is then used to assess the ability to represent the sea surface temperature diurnal cycle. We conclude that the sea surface temperature diurnal variability is reasonably well represented in CNRM-CM6-1 with a 1 h coupling time step and the upper-ocean model resolution of 1 m.
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.
Sarah J. Doherty, Pablo E. Saide, Paquita Zuidema, Yohei Shinozuka, Gonzalo A. Ferrada, Hamish Gordon, Marc Mallet, Kerry Meyer, David Painemal, Steven G. Howell, Steffen Freitag, Amie Dobracki, James R. Podolske, Sharon P. Burton, Richard A. Ferrare, Calvin Howes, Pierre Nabat, Gregory R. Carmichael, Arlindo da Silva, Kristina Pistone, Ian Chang, Lan Gao, Robert Wood, and Jens Redemann
Atmos. Chem. Phys., 22, 1–46, https://doi.org/10.5194/acp-22-1-2022, https://doi.org/10.5194/acp-22-1-2022, 2022
Short summary
Short summary
Between July and October, biomass burning smoke is advected over the southeastern Atlantic Ocean, leading to climate forcing. Model calculations of forcing by this plume vary significantly in both magnitude and sign. This paper compares aerosol and cloud properties observed during three NASA ORACLES field campaigns to the same in four models. It quantifies modeled biases in properties key to aerosol direct radiative forcing and evaluates how these biases propagate to biases in forcing.
David Painemal, Douglas Spangenberg, William L. Smith Jr., Patrick Minnis, Brian Cairns, Richard H. Moore, Ewan Crosbie, Claire Robinson, Kenneth L. Thornhill, Edward L. Winstead, and Luke Ziemba
Atmos. Meas. Tech., 14, 6633–6646, https://doi.org/10.5194/amt-14-6633-2021, https://doi.org/10.5194/amt-14-6633-2021, 2021
Short summary
Short summary
Cloud properties derived from satellite sensors are critical for the global monitoring of climate. This study evaluates satellite-based cloud properties over the North Atlantic using airborne data collected during NAAMES. Satellite observations of droplet size and cloud optical depth tend to compare well with NAAMES data. The analysis indicates that the satellite pixel resolution and the specific viewing geometry need to be taken into account in research applications.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Hossein Dadashazar, David Painemal, Majid Alipanah, Michael Brunke, Seethala Chellappan, Andrea F. Corral, Ewan Crosbie, Simon Kirschler, Hongyu Liu, Richard H. Moore, Claire Robinson, Amy Jo Scarino, Michael Shook, Kenneth Sinclair, K. Lee Thornhill, Christiane Voigt, Hailong Wang, Edward Winstead, Xubin Zeng, Luke Ziemba, Paquita Zuidema, and Armin Sorooshian
Atmos. Chem. Phys., 21, 10499–10526, https://doi.org/10.5194/acp-21-10499-2021, https://doi.org/10.5194/acp-21-10499-2021, 2021
Short summary
Short summary
This study investigates the seasonal cycle of cloud drop number concentration (Nd) over the western North Atlantic Ocean (WNAO) using multiple datasets. Reasons for the puzzling discrepancy between the seasonal cycles of Nd and aerosol concentration were identified. Results indicate that Nd is highest in winter (when aerosol proxy values are often lowest) due to conditions both linked to cold-air outbreaks and that promote greater droplet activation.
Yong Wang, Guang J. Zhang, Shaocheng Xie, Wuyin Lin, George C. Craig, Qi Tang, and Hsi-Yen Ma
Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021, https://doi.org/10.5194/gmd-14-1575-2021, 2021
Short summary
Short summary
A stochastic deep convection parameterization is implemented into the US Department of Energy Energy Exascale Earth System Model Atmosphere Model version 1 (EAMv1). Compared to the default model, the well-known problem of
too much light rain and too little heavy rainis largely alleviated over the tropics with the stochastic scheme. Results from this study provide important insights into the model performance of EAMv1 when stochasticity is included in the deep convective parameterization.
Qi Tang, Michael J. Prather, Juno Hsu, Daniel J. Ruiz, Philip J. Cameron-Smith, Shaocheng Xie, and Jean-Christophe Golaz
Geosci. Model Dev., 14, 1219–1236, https://doi.org/10.5194/gmd-14-1219-2021, https://doi.org/10.5194/gmd-14-1219-2021, 2021
Hsi-Yen Ma, Chen Zhou, Yunyan Zhang, Stephen A. Klein, Mark D. Zelinka, Xue Zheng, Shaocheng Xie, Wei-Ting Chen, and Chien-Ming Wu
Geosci. Model Dev., 14, 73–90, https://doi.org/10.5194/gmd-14-73-2021, https://doi.org/10.5194/gmd-14-73-2021, 2021
Short summary
Short summary
We propose an experimental design of a suite of multi-year, short-term hindcasts and compare them with corresponding observations or measurements for periods based on different weather and climate phenomena. This atypical way of evaluating model performance is particularly useful and beneficial, as these hindcasts can give scientists a robust picture of modeled precipitation, and cloud and radiation processes from their diurnal variation to year-to-year variability.
Cited articles
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989.
Albrecht, B. A., Fairall, C. W., Thomson, D. W., White, A. B., Snider, J. B., and Schubert, W. H.: Surface-based remote sensing of the observed and the Adiabatic liquid water content of stratocumulus clouds, Geophys. Res. Lett., 17, 89–92, https://doi.org/10.1029/GL017i001p00089, 1990.
Albrecht, B. A., Bretherton, C. S., Johnson, D., Scubert, W. H., and Frisch, A. S.: The Atlantic Stratocumulus Transition Experiment – ASTEX, B. Am. Meteorol. Soc., 76, 889–904, https://doi.org/10.1175/1520-0477(1995)076<0889:Taste>2.0.Co;2, 1995.
Alexandri, F., Müller, F., Choudhury, G., Achtert, P., Seelig, T., and Tesche, M.: A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations, Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, 2024.
Altaratz, O., Koren, I., Remer, L. A., and Hirsch, E.: Review: Cloud invigoration by aerosols – Coupling between microphysics and dynamics, Atmos. Res., 140–141, 38–60, https://doi.org/10.1016/j.atmosres.2014.01.009, 2014.
Arola, A., Lipponen, A., Kolmonen, P., Virtanen, T. H., Bellouin, N., Grosvenor, D. P., Gryspeerdt, E., Quaas, J., and Kokkola, H.: Aerosol effects on clouds are concealed by natural cloud heterogeneity and satellite retrieval errors, Nature Communications, 13, 7357, https://doi.org/10.1038/s41467-022-34948-5, 2022.
Barnes, W. L., Pagano, T. S., and Salomonson, V. V.: Prelaunch characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-AM1, IEEE T. Geosci. Remote, 36, 1088–1100, https://doi.org/10.1109/36.700993, 1998.
Baynard, T., Garland, R. M., Ravishankara, A. R., Tolbert, M. A., and Lovejoy, E. R.: Key factors influencing the relative humidity dependence of aerosol light scattering, Geophys. Res. Lett., 33, L06813, https://doi.org/10.1029/2005GL024898, 2006.
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, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020.
Bogenschutz, P. A., Eldred, C., and Caldwell, P. M.: Horizontal resolution sensitivity of the Simple Convection-Permitting E3SM Atmosphere Model in a doubly-periodic configuration, J. Adv. Model. Earth Syst., 15, e2022MS003466, https://doi.org/10.1029/2022MS003466, 2023.
Bretherton, C. S., Blossey, P. N., and Uchida, J.: Cloud droplet sedimentation, entrainment efficiency, and subtropical stratocumulus albedo, Geophys. Res. Lett., 34, https://doi.org/10.1029/2006GL027648, 2007.
Burrows, S. M., Easter, R. C., Liu, X., Ma, P. L., Wang, H., Elliott, S. M., Singh, B., Zhang, K., and Rasch, P. J.: OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) sea spray organic aerosol emissions – implementation in a global climate model and impacts on clouds, Atmos. Chem. Phys., 22, 5223-5251, https://doi.org/10.5194/acp-22-5223-2022, 2022.
Carrillo, J., Guerra, J. C., Cuevas, E., and Barrancos, J.: Characterization of the Marine Boundary Layer and the Trade-Wind Inversion over the Sub-tropical North Atlantic, Bound.-Lay. Meteorol., 158, 311–330, https://doi.org/10.1007/s10546-015-0081-1, 2016.
Chen, Y. C., Xue, L., Lebo, Z. J., Wang, H., Rasmussen, R. M., and Seinfeld, J. H.: A comprehensive numerical study of aerosol-cloud-precipitation interactions in marine stratocumulus, Atmos. Chem. Phys., 11, 9749-9769, https://doi.org/10.5194/acp-11-9749-2011, 2011.
Christensen, M. W., Ma, P.-L., Wu, P., Varble, A. C., Mülmenstädt, J., and Fast, J. D.: Evaluation of aerosol–cloud interactions in E3SM using a Lagrangian framework, Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023, 2023.
Christensen, M. W., Wu, P., Varble, A. C., Xiao, H., and Fast, J. D.: Aerosol-induced closure of marine cloud cells: enhanced effects in the presence of precipitation, Atmos. Chem. Phys., 24, 6455–6476, https://doi.org/10.5194/acp-24-6455-2024, 2024.
Chun, J.-Y., Wood, R., Blossey, P., and Doherty, S. J.: Microphysical, macrophysical, and radiative responses of subtropical marine clouds to aerosol injections, Atmos. Chem. Phys., 23, 1345–1368, https://doi.org/10.5194/acp-23-1345-2023, 2023.
Dedrick, J. L., Pelayo, C. N., Russell, L. M., Lubin, D., Mülmenstädt, J., and Miller, M.: Competition response of cloud supersaturation explains diminished Twomey effect for smoky aerosol in the tropical Atlantic, P. Natl. Acad. Sci. USA, 122, e2412247122, https://doi.org/10.1073/pnas.2412247122, 2025.
Diamond, M. S., Director, H. M., Eastman, R., Possner, A., and Wood, R.: Substantial cloud brightening from shipping in subtropical low clouds, AGU Adv., 1, e2019AV000111, https://doi.org/10.1029/2019AV000111, 2020.
Dong, X., Zheng, X., Xi, B., and Xie, S.: A Climatology of Midlatitude Maritime Cloud Fraction and Radiative Effect Derived from the ARM ENA Ground-Based Observations, J. Climate, 36, 531–546, https://doi.org/10.1175/JCLI-D-22-0290.1, 2023.
Erfani, E., Blossey, P., Wood, R., Mohrmann, J., Doherty, S. J., Wyant, M., and O, K.-T.: Simulating Aerosol Lifecycle Impacts on the Subtropical Stratocumulus-to-Cumulus Transition Using Large-Eddy Simulations, J. Geophys. Res.-Atmos., 127, e2022JD037258, https://doi.org/10.1029/2022JD037258, 2022.
Faruque, O., Nji, F. N., Cham, M., Salvi, R. M., Zheng, X., and Wang, J.: Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data, ArXiv, https://arxiv.org/abs/2304.14541, 2023.
Feingold, G., Ghate, V. P., Russell, L. M., Blossey, P., Cantrell, W., Christensen, M. W., Diamond, M. S., Gettelman, A., Glassmeier, F., Gryspeerdt, E., Haywood, J., Hoffmann, F., Kaul, C. M., Lebsock, M., McComiskey, A. C., McCoy, D. T., Ming, Y., Mülmenstädt, J., Possner, A., Prabhakaran, P., Quinn, P. K., Schmidt, K. S., Shaw, R. A., Singer, C. E., Sorooshian, A., Toll, V., Wan, J. S., Wood, R., Yang, F., Zhang, J., and Zheng, X.: Physical science research needed to evaluate the viability and risks of marine cloud brightening, Sci. Adv., 10, eadi8594, https://doi.org/10.1126/sciadv.adi8594, 2024.
Feingold, G., Glassmeier, F., Zhang, J., and Hoffmann, F.: Opinion: Inferring process from snapshots of cloud systems, Atmos. Chem. Phys., 25, 10869–10885, https://doi.org/10.5194/acp-25-10869-2025, 2025.
Feng, Y., Cadeddu, M., Kotamarthi, V. R., Renju, R., and Raju, C. S.: Humidity bias and effect on simulated aerosol optical properties during the Ganges Valley Experiment, Curr. Sci., 111, 93–100, https://doi.org/10.18520/cs/v111/i1/93-100, 2016.
Feng, Y., Wang, H., Rasch, P. J., Zhang, K., Lin, W., Tang, Q., Xie, S., Hamilton, D. S., Mahowald, N., and Yu, H.: Global Dust Cycle and Direct Radiative Effect in E3SM Version 1: Impact of Increasing Model Resolution, Journal of Advances in Modeling Earth Systems, 14, e2021MS002909, https://doi.org/10.1029/2021MS002909, 2022.
Gettelman, A. and Morrison, H.: Advanced Two-Moment Bulk Microphysics for Global Models. Part I: Off-Line Tests and Comparison with Other Schemes, J. Climate, 28, 1268–1287, https://doi.org/10.1175/JCLI-D-14-00102.1, 2015.
Gettelman, A., Gagne, D. J., Chen, C.-C., Christensen, M. W., Lebo, Z. J., Morrison, H., and Gantos, G.: Machine learning the warm rain process, J. Adv. Model. Earth Syst., 13, e2020MS002268, https://doi.org/10.1029/2020MS002268, 2021.
Ghate, V. P., Cadeddu, M. P., Zheng, X., and O'Connor, E.: Turbulence in the Marine Boundary Layer and Air Motions below Stratocumulus Clouds at the ARM Eastern North Atlantic Site, J. Appl. Meteorol. Clim., 60, 1495–1510, https://doi.org/10.1175/JAMC-D-21-0087.1, 2021.
Ghate, V. P., Surleta, T., Magaritz-Ronen, L., Raveh-Rubin, S., Gallo, F., Carlton, A. G., and Azevedo, E. B.: Drivers of Cloud Condensation Nuclei in the Eastern North Atlantic as Observed at the ARM Site, J. Geophys. Res.-Atmos., 128, e2023JD038636, https://doi.org/10.1029/2023JD038636, 2023.
Gläser, G., Wernli, H., Kerkweg, A., and Teubler, F.: The transatlantic dust transport from North Africa to the Americas – Its characteristics and source regions, J. Geophys. Res.-Atmos., 120, 11231–211252, https://doi.org/10.1002/2015JD023792, 2015.
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W., Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution, Journal of Advances in Modeling Earth Systems, 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019.
Golaz, J.-C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., Bradley, A. M., Tang, Q., Maltrud, M. E., Forsyth, R. M., Zhang, C., Zhou, T., Zhang, K., Zender, C. S., Wu, M., Wang, H., Turner, A. K., Singh, B., Richter, J. H., Qin, Y., Petersen, M. R., Mametjanov, A., Ma, P.-L., Larson, V. E., Krishna, J., Keen, N. D., Jeffery, N., Hunke, E. C., Hannah, W. M., Guba, O., Griffin, B. M., Feng, Y., Engwirda, D., Di Vittorio, A. V., Dang, C., Conlon, L. M., Chen, C.-C.-J., Brunke, M. A., Bisht, G., Benedict, J. J., Asay-Davis, X. S., Zhang, Y., Zhang, M., Zeng, X., Xie, S., Wolfram, P. J., Vo, T., Veneziani, M., Tesfa, T. K., Sreepathi, S., Salinger, A. G., Reeves Eyre, J. E. J., Prather, M. J., Mahajan, S., Li, Q., Jones, P. W., Jacob, R. L., Huebler, G. W., Huang, X., Hillman, B. R., Harrop, B. E., Foucar, J. G., Fang, Y., Comeau, D. S., Caldwell, P. M., Bartoletti, T., Balaguru, K., Taylor, M. A., McCoy, R. B., Leung, L. R., and Bader, D. C.: The DOE E3SM Model Version 2: Overview of the Physical Model and Initial Model Evaluation, Journal of Advances in Modeling Earth Systems, 14, e2022MS003156, https://doi.org/10.1029/2022MS003156, 2022.
Gong, X., Wang, Y., Xie, H., Zhang, J., Lu, Z., Wood, R., Stratmann, F., Wex, H., Liu, X., and Wang, J.: Maximum Supersaturation in the Marine Boundary Layer Clouds Over the North Atlantic, AGU Advances, 4, e2022AV000855, https://doi.org/10.1029/2022AV000855, 2023.
Goren, T., Rosenfeld, D., Sourdeval, O., and Quaas, J.: Satellite observations of precipitating marine stratocumulus show greater cloud fraction for decoupled clouds in comparison to coupled clouds, Geophysical Research Letters, 45, 5126–5134, https://doi.org/10.1029/2018GL078122, 2018.
Goren, T., Sourdeval, O., Kretzschmar, J., and Quaas, J.: Spatial Aggregation of Satellite Observations Leads to an Overestimation of the Radiative Forcing due to Aerosol-Cloud Interactions, Geophys. Res. Lett., 50, e2023GL105282, https://doi.org/10.1029/2023GL105282, 2023.
Goren, T., Choudhury, G., Kretzschmar, J., and McCoy, I.: Co-variability drives the inverted-V sensitivity between liquid water path and droplet concentrations, Atmos. Chem. Phys., 25, 3413–3423, https://doi.org/10.5194/acp-25-3413-2025, 2025.
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.
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.
Gryspeerdt, E., McCoy, D. T., Crosbie, E., Moore, R. H., Nott, G. J., Painemal, D., Small-Griswold, J., Sorooshian, A., and Ziemba, L.: The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data, Atmos. Meas. Tech., 15, 3875–3892, https://doi.org/10.5194/amt-15-3875-2022, 2022.
Gryspeerdt, E., Povey, A. C., Grainger, R. G., Hasekamp, O., Hsu, N. C., Mulcahy, J. P., Sayer, A. M., and Sorooshian, A.: Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions, Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, 2023.
Gupta, S., McFarquhar, G. M., O'Brien, J. R., Delene, D. J., Poellot, M. R., Dobracki, A., Podolske, J. R., Redemann, J., LeBlanc, S. E., Segal-Rozenhaimer, M., and Pistone, K.: Impact of the variability in vertical separation between biomass burning aerosols and marine stratocumulus on cloud microphysical properties over the Southeast Atlantic, Atmos. Chem. Phys., 21, 4615–4635, https://doi.org/10.5194/acp-21-4615-2021, 2021.
Hassan, T., Zhang, K., Li, J., Singh, B., Zhang, S., Wang, H., and Ma, P.-L.: Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model, Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, 2024.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023.
Huang, M., Ma, P.-L., Fast, J., Hassan, T., Li, J., Qin, Y., Tang, S., Ullrich, P., Varble, A., and Yao, Yu: Evaluation of E3SM simulated aerosols and aerosol-cloud interactions across GCM and convection-permitting scales, ESS Open Archive, https://doi.org/10.22541/essoar.173179990.05795821/v1, 2024.
Hudson, J. G. and Noble, S.: CCN and Vertical Velocity Influences on Droplet Concentrations and Supersaturations in Clean and Polluted Stratus Clouds, J. Atmos. Sci., 71, 312–331, https://doi.org/10.1175/JAS-D-13-086.1, 2014.
IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to The Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Pe'an, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157896, 2021.
Jensen, M. P., Ghate, V. P., Wang, D., Apoznanski, D. K., Bartholomew, M. J., Giangrande, S. E., Johnson, K. L., and Thieman, M. M.: Contrasting characteristics of open- and closed-cellular stratocumulus cloud in the eastern North Atlantic, Atmos. Chem. Phys., 21, 14557-14571, https://doi.org/10.5194/acp-21-14557-2021, 2021.
Jeong, J.-H., Witte, M. K., Glenn, I. B., Smalley, M., Lebsock, M. D., Lamer, K., and Zhu, Z.: Distinct Dynamical and Structural Properties of Marine Stratocumulus and Shallow Cumulus Clouds in the Eastern North Atlantic, J. Geophys. Res.-Atmos., 127, e2022JD037021, https://doi.org/10.1029/2022JD037021, 2022.
Jia, H., Ma, X., Yu, F., Quaas, J., and Rosenfeld, D.: Significant underestimation of radiative forcing by aerosol–cloud interactions derived from satellite-based methods, Nat. Commun., 12, 3649, https://doi.org/10.1038/s41467-021-23888-1, 2021.
Jia, H., Quaas, J., Gryspeerdt, E., Böhm, C., and Sourdeval, O.: Addressing the difficulties in quantifying droplet number response to aerosol from satellite observations, Atmos. Chem. Phys., 22, 7353–7372, https://doi.org/10.5194/acp-22-7353-2022, 2022.
Kang, L., Marchand, R. T., Ma, P.-L., Huang, M., Wood, R., and Jongebloed, U., and Becky, A.: Understanding Biases in E3SMv2 Simulated Cloud Droplet Number and Aerosol Concentrations over the Southern Ocean, ESS Open Archive, https://doi.org/10.22541/essoar.172574475.50852357/v1, 2024.
King, M. D., Platnick, S., Menzel, W. P., Ackerman, S. A., and Hubanks, P. A.: Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites, IEEE T. Geosci. Remote, 51, 3826–3852, https://doi.org/10.1109/TGRS.2012.2227333, 2013.
Kirschler, S., Voigt, C., Anderson, B., Campos Braga, R., Chen, G., Corral, A. F., Crosbie, E., Dadashazar, H., Ferrare, R. A., Hahn, V., Hendricks, J., Kaufmann, S., Moore, R., Pöhlker, M. L., Robinson, C., Scarino, A. J., Schollmayer, D., Shook, M. A., Thornhill, K. L., Winstead, E., Ziemba, L. D., and Sorooshian, A.: Seasonal updraft speeds change cloud droplet number concentrations in low-level clouds over the western North Atlantic, Atmos. Chem. Phys., 22, 8299–8319, https://doi.org/10.5194/acp-22-8299-2022, 2022.
Larson, V.: CLUBB-SILHS: A parameterization of subgrid variability in the atmosphere, arXiv, https://doi.org/10.48550/arXiv.1711.03675, 2017.
Lee, H.-H., Bogenschutz, P., and Yamaguchi, T.: Resolving away stratocumulus biases in modern global climate models, Geophys. Res. Lett., 49, e2022GL099422, https://doi.org/10.1029/2022GL099422, 2022.
Lee, H.-H., Zheng, X., Qiu, S., and Wang, Y.: Numerical case study of the aerosol–cloud interactions in warm boundary layer clouds over the eastern North Atlantic with an interactive chemistry module, Atmos. Chem. Phys., 25, 6069–6091, https://doi.org/10.5194/acp-25-6069-2025, 2025.
Li, Z., Painemal, D., Schuster, G., Clayton, M., Ferrare, R., Vaughan, M., Josset, D., Kar, J., and Trepte, C.: Assessment of tropospheric CALIPSO Version 4.2 aerosol types over the ocean using independent CALIPSO–SODA lidar ratios, Atmos. Meas. Tech., 15, 2745–2766, https://doi.org/10.5194/amt-15-2745-2022, 2022.
Li, Z., Painemal, D., Feng, Y., and Zheng, X.: Progress in the quantification of aerosol-cloud interactions estimated from the CALIPSO-CloudSat-Aqua/MODIS record, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-4769, 2025.
Liu, J., Zhu, Y., Wang, M., Rosenfeld, D., Cao, Y., and Yuan, T.: Cloud Susceptibility to Aerosols: Comparing Cloud-Appearance Versus Cloud-Controlling Factors Regimes, J. Geophys. Res.-Atmos., 129, e2024JD041216, https://doi.org/10.1029/2024JD041216, 2024.
Liu, X., Ma, P.-L., Wang, H., Tilmes, S., Singh, B., Easter, R. C., Ghan, S. J., and Rasch, P. J.: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model, Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, 2016.
Logan, T., Xi, B., and Dong, X.: Aerosol properties and their influences on marine boundary layer cloud condensation nuclei at the ARM mobile facility over the Azores, J. Geophys. Res.-Atmos., 119, 4859–4872, https://doi.org/10.1002/2013JD021288, 2014.
Ma, P.-L., Harrop, B. E., Larson, V. E., Neale, R. B., Gettelman, A., Morrison, H., Wang, H., Zhang, K., Klein, S. A., Zelinka, M. D., Zhang, Y., Qian, Y., Yoon, J.-H., Jones, C. R., Huang, M., Tai, S.-L., Singh, B., Bogenschutz, P. A., Zheng, X., Lin, W., Quaas, J., Chepfer, H., Brunke, M. A., Zeng, X., Mülmenstädt, J., Hagos, S., Zhang, Z., Song, H., Liu, X., Pritchard, M. S., Wan, H., Wang, J., Tang, Q., Caldwell, P. M., Fan, J., Berg, L. K., Fast, J. D., Taylor, M. A., Golaz, J.-C., Xie, S., Rasch, P. J., and Leung, L. R.: Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1, Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, 2022.
Martin, G. M., Johnson, D. W., and Spice, A.: The Measurement and Parameterization of Effective Radius of Droplets in Warm Stratocumulus Clouds, J. Atmos. Sci., 51, 1823–1842, https://doi.org/10.1175/1520-0469(1994)051<1823:TMAPOE>2.0.CO;2, 1994.
McCoy, D. T., Bender, F. A.-M., Mohrmann, J. K. C., Hartmann, D. L., Wood, R., and Grosvenor, D. P.: The global aerosol-cloud first indirect effect estimated using MODIS, MERRA, and AeroCom, J. Geophys. Res. Atmos., 122, 1779–1796, https://doi.org/10.1002/2016JD026141, 2017.
McCoy, D. T., Field, P., Gordon, H., Elsaesser, G. S., and Grosvenor, D. P.: Untangling causality in midlatitude aerosol–cloud adjustments, Atmos. Chem. Phys., 20, 4085–4103, https://doi.org/10.5194/acp-20-4085-2020, 2020.
Mechem, D. B., Wittman, C. S., Miller, M. A., Yuter, S. E., and de Szoeke, S. P.: Joint synoptic and cloud variability over the Northeast Atlantic near the Azores, J. Appl. Meteorol. Climatol., 57, 1273–1290, https://doi.org/10.1175/JAMC-D-17-0211.1, 2018.
Minnis, P., Sun-Mack, S., Chen, Y., Chang, F. L., Yost, C. R., Smith, W. L., Heck, P. W., Arduini, R. F., Bedka, S. T., Yi, Y., Hong, G., Jin, Z., Painemal, D., Palikonda, R., Scarino, B. R., Spangenberg, D. A., Smith, R. A., Trepte, Q. Z., Yang, P., and Xie, Y.: CERES MODIS Cloud Product Retrievals for Edition 4 – Part I: Algorithm Changes, IEEE T. Geosci. Remote, 59, 2744–2780, https://doi.org/10.1109/TGRS.2020.3008866, 2021.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
Mülmenstädt, J., and Feingold, G.: The Radiative Forcing of Aerosol–Cloud Interactions in Liquid Clouds: Wrestling and Embracing Uncertainty, Curr Clim Change Rep 4, 23–40, https://doi.org/10.1007/s40641-018-0089-y, 2018.
Mülmenstädt, J., Lubin, D., Russell, L. M., and Vogelmann, A. M.: Cloud Properties over the North Slope of Alaska: Identifying the Prevailing Meteorological Regimes, J. Climate, 25, 8238–8258, https://doi.org/10.1175/JCLI-D-11-00636.1, 2012.
Mülmenstädt, J., Ackerman, A. S., Fridlind, A. M., Huang, M., Ma, P.-L., Mahfouz, N., Bauer, S. E., Burrows, S. M., Christensen, M. W., Dipu, S., Gettelman, A., Leung, L. R., Tornow, F., Quaas, J., Varble, A. C., Wang, H., Zhang, K., and Zheng, Y.: Can general circulation models (GCMs) represent cloud liquid water path adjustments to aerosol–cloud interactions?, Atmos. Chem. Phys., 24, 13633–13652, https://doi.org/10.5194/acp-24-13633-2024, 2024a.
Mülmenstädt, J., Gryspeerdt, E., Dipu, S., Quaas, J., Ackerman, A. S., Fridlind, A. M., Tornow, F., Bauer, S. E., Gettelman, A., Ming, Y., Zheng, Y., Ma, P.-L., Wang, H., Zhang, K., Christensen, M. W., Varble, A. C., Leung, L. R., Liu, X., Neubauer, D., Partridge, D. G., Stier, P., and Takemura, T.: General circulation models simulate negative liquid water path–droplet number correlations, but anthropogenic aerosols still increase simulated liquid water path, Atmos. Chem. Phys., 24, 7331–7345, https://doi.org/10.5194/acp-24-7331-2024, 2024b.
O, K.-T., Wood, R., and Bretherton, C. S.: Ultraclean Layers and Optically Thin Clouds in the Stratocumulus-to-Cumulus Transition. Part II: Depletion of Cloud Droplets and Cloud Condensation Nuclei through Collision–Coalescence, J. Atmos. Sci., 75, 1653–1673, https://doi.org/10.1175/JAS-D-17-0218.1, 2018.
Ovchinnikov, M., Ma, P.-L., Kaul, C. M., Pressel, K. G., Huang, M., Shpund, J., and Tang, S.: Evaluation of Autoconversion Representation in E3SMv2 Using an Ensemble of Large-Eddy Simulations of Low-Level Warm Clouds, Journal of Advances in Modeling Earth Systems, 16, e2024MS004280, https://doi.org/10.1029/2024MS004280, 2024.
Painemal, D. and Zuidema, P.: Assessment of MODIS cloud effective radius and optical thickness retrievals over the Southeast Pacific with VOCALS-REx in situ measurements, J. Geophys. Res.-Atmos., 116, https://doi.org/10.1029/2011JD016155, 2011.
Painemal, D., Greenwald, T., Cadeddu, M., and Minnis, P.: First extended validation of satellite microwave liquid water path with ship-based observations of marine low clouds, Geophys. Res. Lett., 43, 6563–6570, https://doi.org/10.1002/2016GL069061, 2016.
Painemal, D., Clayton, M., Ferrare, R., Burton, S., Josset, D., and Vaughan, M.: Novel aerosol extinction coefficients and lidar ratios over the ocean from CALIPSO–CloudSat: evaluation and global statistics, Atmos. Meas. Tech., 12, 2201–2217, https://doi.org/10.5194/amt-12-2201-2019, 2019.
Painemal, D., Chang, F.-L., Ferrare, R., Burton, S., Li, Z., Smith Jr., W. L., Minnis, P., Feng, Y., and Clayton, M.: Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations, Atmos. Chem. Phys., 20, 7167–7177, https://doi.org/10.5194/acp-20-7167-2020, 2020.
Painemal, D., Chellappan, S., Smith Jr., W. L., Spangenberg, D., Park, J. M., Ackerman, A., Chen, J., Crosbie, E., Ferrare, R., Hair, J., Kirschler, S., Li, X.-Y., McComiskey, A., Moore, R. H., Sanchez, K., Sorooshian, A., Tornow, F., Voigt, C., Wang, H., Winstead, E., Zeng, X., Ziemba, L., and Zuidema, P.: Wintertime Synoptic Patterns of Midlatitude Boundary Layer Clouds Over the Western North Atlantic: Climatology and Insights From In Situ ACTIVATE Observations, J. Geophys. Res.-Atmos., 128, e2022JD037725, https://doi.org/10.1029/2022JD037725, 2023.
Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum, B. A., Riedi, J. C., and Frey, R. A.: The MODIS cloud products: algorithms and examples from Terra, IEEE T. Geosci. Remote, 41, 459–473, https://doi.org/10.1109/TGRS.2002.808301, 2003.
Possner, A., Eastman, R., Bender, F., and Glassmeier, F.: Deconvolution of boundary layer depth and aerosol constraints on cloud water path in subtropical stratocumulus decks, Atmos. Chem. Phys., 20, 3609–3621, https://doi.org/10.5194/acp-20-3609-2020, 2020.
Qin, Y., Zheng, X., Klein, S. A., Zelinka, M. D., Ma, P.-L., Golaz, J.-C., and Xie, S.: Causes of Reduced Climate Sensitivity in E3SM From Version 1 to Version 2, Journal of Advances in Modeling Earth Systems, 16, e2023MS003875, https://doi.org/10.1029/2023MS003875, 2024.
Qiu, S., Zheng, X., Painemal, D., Terai, C. R., and Zhou, X.: Daytime variation in the aerosol indirect effect for warm marine boundary layer clouds in the eastern North Atlantic, Atmos. Chem. Phys., 24, 2913–2935, https://doi.org/10.5194/acp-24-2913-2024, 2024.
Quaas, J., Arola, A., Cairns, B., Christensen, M., Deneke, H., Ekman, A. M. L., Feingold, G., Fridlind, A., Gryspeerdt, E., Hasekamp, O., Li, Z., Lipponen, A., Ma, P.-L., Mülmenstädt, J., Nenes, A., Penner, J. E., Rosenfeld, D., Schrödner, R., Sinclair, K., Sourdeval, O., Stier, P., Tesche, M., van Diedenhoven, B., and Wendisch, M.: Constraining the Twomey effect from satellite observations: issues and perspectives, Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, 2020.
Rasch, P. J., Xie, S., Ma, P. L., Lin, W., Wang, H., Tang, Q., Burrows, S. M., Caldwell, P., Zhang, K., Easter, R. C., Cameron-Smith, P., Singh, B., Wan, H., Golaz, J. C., Harrop, B. E., Roesler, E., Bacmeister, J., Larson, V. E., Evans, K. J., Qian, Y., Taylor, M., Leung, L. R., Zhang, Y., Brent, L., Branstetter, M., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J. H., Yoon, J. H., Zender, C. S., Bader, D., Flanner, M., Foucar, J. G., Jacob, R., Keen, N., Klein, S. A., Liu, X., Salinger, A. G., Shrivastava, M., and Yang, Y.: An Overview of the Atmospheric Component of the Energy Exascale Earth System Model, Journal of Advances in Modeling Earth Systems, 11, 2377–2411, https://doi.org/10.1029/2019MS001629, 2019.
Rémillard, J. and Tselioudis, G.: Cloud Regime Variability over the Azores and Its Application to Climate Model Evaluation, J. Climate, 28, 9707–9720, https://doi.org/10.1175/JCLI-D-15-0066.1, 2015.
Rodríguez, S. and López-Darias, J.: Extreme Saharan dust events expand northward over the Atlantic and Europe, prompting record-breaking PM10 and PM2.5 episodes, Atmos. Chem. Phys., 24, 12031–12053, https://doi.org/10.5194/acp-24-12031-2024, 2024.
Sanchez, K. J., Chen, C.-L., Russell, L. M., Betha, R., Liu, J., Price, D. J., Massoli, P., Ziemba, L. D., Crosbie, E. C., Moore, R. H., Müller, M., Schiller, S. A., Wisthaler, A., Lee, A. K. Y., Quinn, P. K., Bates, T. S., Porter, J., Bell, T. G., Saltzman, E. S., Vaillancourt, R. D., and Behrenfeld, M. J.: Substantial Seasonal Contribution of Observed Biogenic Sulfate Particles to Cloud Condensation Nuclei, Sci. Rep., 8, 3235, https://doi.org/10.1038/s41598-018-21590-9, 2018.
Sanchez, K. J., Roberts, G. C., Diao, M., and Russell, L. M.: Measured Constraints on Cloud Top Entrainment to Reduce Uncertainty of Nonprecipitating Stratocumulus Shortwave Radiative Forcing in the Southern Ocean, Geophys. Res. Lett., 47, e2020GL090513, https://doi.org/10.1029/2020GL090513, 2020.
Shan, Y., Fan, J., Zhang, K., Shpund, J., Terai, C., Zhang, G. J., Song, X., Chen, C.-C.-J., Lin, W., Liu, X., Shrivastava, M., Wang, H., and Xie, S.: Improving Aerosol Radiative Forcing and Climate in E3SM: Impacts of New Cloud Microphysics and Improved Wet Removal Treatments, Journal of Advances in Modeling Earth Systems, 16, e2023MS004059, https://doi.org/10.1029/2023MS004059, 2024.
Tang, S., Varble, A. C., Fast, J. D., Zhang, K., Wu, P., Dong, X., Mei, F., Pekour, M., Hardin, J. C., and Ma, P.-L.: Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations, Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, 2023.
Tang, S., Wang, H., Li, X.-Y., Chen, J., Sorooshian, A., Zeng, X., Crosbie, E., Thornhill, K. L., Ziemba, L. D., and Voigt, C.: Understanding aerosol–cloud interactions using a single-column model for a cold-air outbreak case during the ACTIVATE campaign, Atmos. Chem. Phys., 24, 10073–10092, https://doi.org/10.5194/acp-24-10073-2024, 2024.
Tian, J., Comstock, J., Geiss, A., Wu, P., Silber, I., Zhang, D., Kooloth, P., and Feng, Y.-C.: Mesoscale Cellular Convection Detection and Classification Using Convolutional Neural Networks: Insights From Long-Term Observations at ARM Eastern North Atlantic Site, Journal of Geophysical Research: Machine Learning and Computation, 2, e2024JH000486, https://doi.org/10.1029/2024JH000486, 2025.
Twomey, S.: The Influence of Pollution on the Shortwave Albedo of Clouds, J. Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2, 1977.
Varble, A. C., Ma, P.-L., Christensen, M. W., Mülmenstädt, J., Tang, S., and Fast, J.: Evaluation of liquid cloud albedo susceptibility in E3SM using coupled eastern North Atlantic surface and satellite retrievals, Atmos. Chem. Phys., 23, 13523–13553, https://doi.org/10.5194/acp-23-13523-2023, 2023.
Wall, C. J., Norris, J. R., Possner, A., McCoy, D. T., McCoy, I. L., and Lutsko, N. J.: Assessing effective radiative forcing from aerosol–cloud interactions over the global ocean, P. Natl. Acad. Sci. USA, 119, e2210481119, https://doi.org/10.1073/pnas.2210481119, 2022.
Wall, C. J., Storelvmo, T., and Possner, A.: Global observations of aerosol indirect effects from marine liquid clouds, Atmos. Chem. Phys., 23, 13125–13141, https://doi.org/10.5194/acp-23-13125-2023, 2023.
Wan, H., Yenpure, A., Geveci, B., Easter, R. C., Rasch, P. J., Zhang, K., and Zeng, X.: Features of mid- and high-latitude low-level clouds and their relation to strong aerosol effects in the Energy Exascale Earth System Model version 2 (E3SMv2), Geosci. Model Dev., 18, 5655–5680, https://doi.org/10.5194/gmd-18-5655-2025, 2025.
Wang, H., Easter, R. C., Zhang, R., Ma, P.-L., Singh, B., Zhang, K., Ganguly, D., Rasch, P. J., Burrows, S. M., Ghan, S. J., Lou, S., Qian, Y., Yang, Y., Feng, Y., Flanner, M., Leung, L. R., Liu, X., Shrivastava, M., Sun, J., Tang, Q., Xie, S., and Yoon, J.-H.: Aerosols in the E3SM Version 1: New Developments and Their Impacts on Radiative Forcing, Journal of Advances in Modeling Earth Systems, 12, e2019MS001851, https://doi.org/10.1029/2019MS001851, 2020a.
Wang, J., Wood, R., Jensen, M. P., Chiu, J. C., Liu, Y., Lamer, K., Desai, N., Giangrande, S. E., Knopf, D. A., Kollias, P., Laskin, A., Liu, X., Lu, C., Mechem, D., Mei, F., Starzec, M., Tomlinson, J., Wang, Y., Yum, S. S., Zheng, G., Aiken, A. C., Azevedo, E. B., Blanchard, Y., China, S., Dong, X., Gallo, F., Gao, S., Ghate, V. P., Glienke, S., Goldberger, L., Hardin, J. C., Kuang, C., Luke, E. P., Matthews, A. A., Miller, M. A., Moffet, R., Pekour, M., Schmid, B., Sedlacek, A. J., Shaw, R. A., Shilling, J. E., Sullivan, A., Suski, K., Veghte, D. P., Weber, R., Wyant, M., Yeom, J., Zawadowicz, M., and Zhang, Z.: Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA), B. Am. Meteorol. Soc., 103, E619-E641, https://doi.org/10.1175/BAMS-D-19-0220.1, 2022.
Wang, Y., Zheng, X., Dong, X., Xi, B., Wu, P., Logan, T., and Yung, Y. L.: Impacts of long-range transport of aerosols on marine-boundary-layer clouds in the eastern North Atlantic, Atmos. Chem. Phys., 20, 14741–14755, https://doi.org/10.5194/acp-20-14741-2020, 2020b.9
Wang, Y., Zheng, G., Jensen, M. P., Knopf, D. A., Laskin, A., Matthews, A. A., Mechem, D., Mei, F., Moffet, R., Sedlacek, A. J., Shilling, J. E., Springston, S., Sullivan, A., Tomlinson, J., Veghte, D., Weber, R., Wood, R., Zawadowicz, M. A., and Wang, J.: Vertical profiles of trace gas and aerosol properties over the eastern North Atlantic: variations with season and synoptic condition, Atmos. Chem. Phys., 21, 11079–11098, https://doi.org/10.5194/acp-21-11079-2021, 2021.
Wang, Y., Zheng, X., Dong, X., Xi, B., and Yung, Y. L.: Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations, Atmos. Chem. Phys., 23, 8591–8605, https://doi.org/10.5194/acp-23-8591-2023, 2023.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms, J. Atmos. Ocean. Tech., 26, 2310-2323, https://doi.org/10.1175/2009JTECHA1281.1, 2009.
Wood, R.: Drizzle in Stratiform Boundary Layer Clouds. Part I: Vertical and Horizontal Structure, J. Atmos. Sci., 62, 3011-3033, https://doi.org/10.1175/JAS3529.1, 2005.
Wood, R.: Stratocumulus Clouds, Mon. Weather Rev., 140, 2373–2423, https://doi.org/10.1175/MWR-D-11-00121.1, 2012.
Wood, R., Leon, D., Lebsock, M., Snider, J., and Clarke, A. D.: Precipitation driving of droplet concentration variability in marine low clouds, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2012JD018305, 2012.
Wood, R., Wyant, M., Bretherton, C. S., Rémillard, J., Kollias, P., Fletcher, J., Stemmler, J., de Szoeke, S., Yuter, S., Miller, M., Mechem, D., Tselioudis, G., Chiu, J. C., Mann, J. A. L., O'Connor, E. J., Hogan, R. J., Dong, X., Miller, M., Ghate, V., Jefferson, A., Min, Q., Minnis, P., Palikonda, R., Albrecht, B., Luke, E., Hannay, C., and Lin, Y.: Clouds, Aerosols, and Precipitation in the Marine Boundary Layer: An Arm Mobile Facility Deployment, B. Am. Meteorol. Soc., 96, 419–440, https://doi.org/10.1175/BAMS-D-13-00180.1, 2015.
Wu, P., Dong, X., Xi, B., Liu, Y., Thieman, M., and Minnis, P.: Effects of environment forcing on marine boundary layer cloud-drizzle processes, J. Geophys. Res.-Atmos., 122, 4463–4478, https://doi.org/10.1002/2016JD026326, 2017.
Wu, P., Dong, X., and Xi, B.: A Climatology of Marine Boundary Layer Cloud and Drizzle Properties Derived from Ground-Based Observations over the Azores, J. Climate, 33, 10133–10148, https://doi.org/10.1175/JCLI-D-20-0272.1, 2020.
Xi, B., Dong, X., Minnis, P., and Sun-Mack, S.: Comparison of marine boundary layer cloud properties from CERES-MODIS Edition 4 and DOE ARM AMF measurements at the Azores, J. Geophys. Res.-Atmos., 119, 9509–9529, https://doi.org/10.1002/2014JD021813, 2014.
Xie, S., Lin, W., Rasch, P. J., Ma, P.-L., Neale, R., Larson, V. E., Qian, Y., Bogenschutz, P. A., Caldwell, P., Cameron-Smith, P., Golaz, J.-C., Mahajan, S., Singh, B., Tang, Q., Wang, H., Yoon, J.-H., Zhang, K., and Zhang, Y.: Understanding Cloud and Convective Characteristics in Version 1 of the E3SM Atmosphere Model, Journal of Advances in Modeling Earth Systems, 10, 2618–2644, https://doi.org/10.1029/2018MS001350, 2018.
Xie, S., Wang, Y.-C., Lin, W., Ma, H.-Y., Tang, Q., Tang, S., Zheng, X., Golaz, J.-C., Zhang, G. J., and Zhang, M.: Improved Diurnal Cycle of Precipitation in E3SM With a Revised Convective Triggering Function, Journal of Advances in Modeling Earth Systems, 11, 2290–2310, https://doi.org/10.1029/2019MS001702, 2019.
Yuan, T., Song, H., Painemal, D., and Platnick, S.: Observational evidence of strong forcing from aerosol effect on low cloud coverage, Sci. Adv., 9, eadh7716, https://doi.org/10.1126/sciadv.adh7716, 2023.
Zhang, J. and Feingold, G.: Distinct regional meteorological influences on low-cloud albedo susceptibility over global marine stratocumulus regions, Atmos. Chem. Phys., 23, 1073–1090, https://doi.org/10.5194/acp-23-1073-2023, 2023.
Zhang, G. J., and McFarlane, N. A.: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian climate centre general circulation model, Atmosphere-Ocean, 33(3), 407–446, https://doi.org/10.1080/07055900.1995.9649539, 1995.
Zhang, J., Zhou, X., Goren, T., and Feingold, G.: Albedo susceptibility of northeastern Pacific stratocumulus: the role of covarying meteorological conditions, Atmos. Chem. Phys., 22, 861–880, https://doi.org/10.5194/acp-22-861-2022, 2022.
Zhang, J., Chen, Y.-S., Gryspeerdt, E., Yamaguchi, T., and Feingold, G.: Radiative forcing from the 2020 shipping fuel regulation is large but hard to detect, Communications Earth and Environment, 6, 18, https://doi.org/10.1038/s43247-024-01911-9, 2025.
Zhang, Y., Xie, S., Qin, Y., Lin, W., Golaz, J.-C., Zheng, X., Ma, P.-L., Qian, Y., Tang, Q., Terai, C. R., and Zhang, M.: Understanding changes in cloud simulations from E3SM version 1 to version 2, Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, 2024.
Zheng, G., Wang, Y., Aiken, A. C., Gallo, F., Jensen, M. P., Kollias, P., Kuang, C., Luke, E., Springston, S., Uin, J., Wood, R., and Wang, J.: Marine boundary layer aerosol in the eastern North Atlantic: seasonal variations and key controlling processes, Atmos. Chem. Phys., 18, 17615–17635, https://doi.org/10.5194/acp-18-17615-2018, 2018.
Zheng, X., Xi, B., Dong, X., Logan, T., Wang, Y., and Wu, P.: Investigation of aerosol–cloud interactions under different absorptive aerosol regimes using Atmospheric Radiation Measurement (ARM) southern Great Plains (SGP) ground-based measurements, Atmos. Chem. Phys., 20, 3483–3501, https://doi.org/10.5194/acp-20-3483-2020, 2020.
Zheng, X., Dong, X., Ward, D. M., Xi, B., Wu, P., and Wang, Y.: Aerosol-Cloud-Precipitation Interactions in a Closed-cell and Non-homogenous MBL Stratocumulus Cloud, Adv. Atmos. Sci., 39, 2107–2123, https://doi.org/10.1007/s00376-022-2013-6, 2022a.
Zheng, X., Xi, B., Dong, X., Wu, P., Logan, T., and Wang, Y.: Environmental effects on aerosol–cloud interaction in non-precipitating marine boundary layer (MBL) clouds over the eastern North Atlantic, Atmos. Chem. Phys., 22, 335–354, https://doi.org/10.5194/acp-22-335-2022, 2022b.
Zheng, X., Dong, X., Xi, B., Logan, T., and Wang, Y.: Distinctive aerosol–cloud–precipitation interactions in marine boundary layer clouds from the ACE-ENA and SOCRATES aircraft field campaigns, Atmos. Chem. Phys., 24, 10323–10347, https://doi.org/10.5194/acp-24-10323-2024, 2024.
Zheng, X., Faruque, O., Wang, J., and Nji, F.: Deep Spatiotemporal Clustering Model: A Temporal Clustering Approach for Multi-dimensional Climate Data, Zenodo [code], https://doi.org/10.5281/zenodo.14720991, 2025a.
Zheng, X., Feng, Y., Zhang, M., and Xie, S.: E3SMv2 Nudged Model Simulation over ENA region for Zheng et al., 2025, Zenodo [data set], https://doi.org/10.5281/zenodo.15670340, 2025b.
Zheng, X., Qiu, S., Zhang, D., Adebiyi, A., Zheng, X., Faruque, O., Wu, P., and Wang, J.: Variability of Eastern North Atlantic Summertime Marine Boundary Layer Clouds and Aerosols Across Different Synoptic Regimes Identified With Multiple Conditions, J. Geophys. Res.-Atmos., 130, e2024JD042240, https://doi.org/10.1029/2024JD042240, 2025c.
Zhou, X., Painemal, D., Gettleman, A., and Feingold, G.: Exploring causal relationships and adjustment timescales of aerosol–cloud interactions in geostationary satellite observations and CAM6 using wavelet phase coherence analysis, Geophys. Res. Lett., 52, e2024GL111961, https://doi.org/10.1029/2024GL111961, 2025.
Short summary
This study used satellite observations and climate model simulations to examine how aerosols affect marine clouds over the Eastern North Atlantic. Cloud responses to aerosols differed significantly across meteorological regimes. The model captured overall trends but exaggerated cloud responses, performing better for shallower than deeper clouds. The results highlight the need to untangle aerosol effects from meteorology and improve cloud process representation in climate models.
This study used satellite observations and climate model simulations to examine how aerosols...
Altmetrics
Final-revised paper
Preprint