Articles | Volume 25, issue 4
https://doi.org/10.5194/acp-25-2123-2025
© 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-25-2123-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Feb 2025
Research article |
| 19 Feb 2025
| 19 Feb 2025
A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate models
Scripps Institution of Oceanography, University of California at San Diego, La Jolla, CA, USA
Casey J. Wall
Department of Geosciences, University of Oslo, Oslo, Norway
now at: Department of Meteorology, Stockholm University, 106 91 Stockholm, Sweden
Nicholas J. Lutsko
Scripps Institution of Oceanography, University of California at San Diego, La Jolla, CA, USA
Takuro Michibata
Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan
Po-Lun Ma
Pacific Northwest National Laboratory, Richland, WA, USA
Pacific Northwest National Laboratory, Richland, WA, USA
Margaret L. Duffy
NSF National Center for Atmospheric Research, Boulder, CO, USA
now at: Department of Earth and Planetary Sciences, University of California, Berkeley, CA, USA
Brian Medeiros
NSF National Center for Atmospheric Research, Boulder, CO, USA
Matvey Debolskiy
Department of Geosciences, University of Oslo, Oslo, Norway
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Matthew W. Christensen, Andrew Geiss, Adam C. Varble, and Po-Lun Ma
EGUsphere, https://doi.org/10.5194/egusphere-2025-3850, https://doi.org/10.5194/egusphere-2025-3850, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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We used satellite data and machine learning to better understand how tiny particles in the atmosphere affect clouds and their brightness. At higher spatial resolution, we discovered a new “M”-shaped pattern in the relationship between cloud water and droplet concentration unlike the inverted-V shape observed at coarsely gridded scales. Cloud water increases more with droplet concentration when rain is present. These findings support the development of next-generation atmospheric models.
Forrest M. Hoffman, Birgit Hassler, Ranjini Swaminathan, Jared Lewis, Bouwe Andela, Nathaniel Collier, Dóra Hegedűs, Jiwoo Lee, Charlotte Pascoe, Mika Pflüger, Martina Stockhause, Paul Ullrich, Min Xu, Lisa Bock, Felicity Chun, Bettina K. Gier, Douglas I. Kelley, Axel Lauer, Julien Lenhardt, Manuel Schlund, Mohanan G. Sreeush, Katja Weigel, Ed Blockley, Rebecca Beadling, Romain Beucher, Demiso D. Dugassa, Valerio Lembo, Jianhua Lu, Swen Brands, Jerry Tjiputra, Elizaveta Malinina, Brian Mederios, Enrico Scoccimarro, Jeremy Walton, Philip Kershaw, André L. Marquez, Malcolm J. Roberts, Eleanor O’Rourke, Elisabeth Dingley, Briony Turner, Helene Hewitt, and John P. Dunne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2685, https://doi.org/10.5194/egusphere-2025-2685, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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As Earth system models become more complex, rapid and comprehensive evaluation through comparison with observational data is necessary. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require fast analysis. This paper describes a new Rapid Evaluation Framework (REF) that was developed for the Assessment Fast Track that will be run at the Earth System Grid Federation (ESGF) to inform the community about the performance of models.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-1868, https://doi.org/10.5194/egusphere-2025-1868, 2025
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Our study assesses the aerosol effective radiative forcing in a global cloud-resolving atmosphere model at ultra-high resolution. We demonstrate that global ERFaer 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.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
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
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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.
Mark D. Zelinka, Li-Wei Chao, Timothy A. Myers, Yi Qin, and Stephen A. Klein
Atmos. Chem. Phys., 25, 1477–1495, https://doi.org/10.5194/acp-25-1477-2025, https://doi.org/10.5194/acp-25-1477-2025, 2025
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Clouds lie at the heart of uncertainty in both climate sensitivity and radiative forcing, making it imperative to properly diagnose their radiative effects. Here we provide a recommended methodology and code base for the community to use in performing such diagnoses using cloud radiative kernels. We show that properly accounting for changes in obscuration of lower-level clouds by upper-level clouds is important for accurate diagnosis and attribution of cloud feedbacks and adjustments.
Johannes Mülmenstädt, Andrew S. Ackerman, Ann M. Fridlind, Meng Huang, Po-Lun Ma, Naser Mahfouz, Susanne E. Bauer, Susannah M. Burrows, Matthew W. Christensen, Sudhakar Dipu, Andrew Gettelman, L. Ruby Leung, Florian Tornow, Johannes Quaas, Adam C. Varble, Hailong Wang, Kai Zhang, and Youtong Zheng
Atmos. Chem. Phys., 24, 13633–13652, https://doi.org/10.5194/acp-24-13633-2024, https://doi.org/10.5194/acp-24-13633-2024, 2024
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Stratocumulus clouds play a large role in Earth's climate by reflecting incoming solar energy back to space. Turbulence at stratocumulus cloud top mixes in dry, warm air, which can lead to cloud dissipation. This process is challenging for coarse-resolution global models to represent. We show that global models nevertheless agree well with our process understanding. Global models also think the process is less important for the climate than other lines of evidence have led us to conclude.
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.
Charlotte M. Beall, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Adam Varble, Kentaroh Suzuki, and Takuro Michibata
Atmos. Chem. Phys., 24, 5287–5302, https://doi.org/10.5194/acp-24-5287-2024, https://doi.org/10.5194/acp-24-5287-2024, 2024
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Single-layer warm liquid clouds cover nearly one-third of the Earth's surface, and uncertainties regarding the impact of aerosols on their radiative properties pose a significant challenge to climate prediction. Here, we demonstrate how satellite observations can be used to constrain Earth system model estimates of the radiative forcing from the interactions of aerosols with clouds due to warm rain processes.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
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Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Yu Yao, Po-Lun Ma, Yi Qin, Matthew W. Christensen, Hui Wan, Kai Zhang, Balwinder Singh, Meng Huang, and Mikhail Ovchinnikov
EGUsphere, https://doi.org/10.5194/egusphere-2024-523, https://doi.org/10.5194/egusphere-2024-523, 2024
Preprint withdrawn
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Giant aerosols have substantial effects on warm rain formation. However, it remains challenging to quantify the impact of giant particles at global scale. In this work, we applied earth system model to investigate its impacts by implementing new giant aerosol treatments to consider its physical process. We found this approach substantially affect liquid cloud and improved model's precipitation response to aerosols. Our findings demonstrate the significant impact of giant aerosols on climate.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
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
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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.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
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To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Adam C. Varble, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Shuaiqi Tang, and Jerome Fast
Atmos. Chem. Phys., 23, 13523–13553, https://doi.org/10.5194/acp-23-13523-2023, https://doi.org/10.5194/acp-23-13523-2023, 2023
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We evaluate how clouds change in response to changing atmospheric particle (aerosol) concentrations in a climate model and find that the model-predicted cloud brightness increases too much as aerosols increase because the cloud drop number increases too much. Excessive drizzle in the model mutes this difference. Many differences between observational and model estimates are explained by varying assumptions of how much liquid has been lost in clouds, which impacts the estimated cloud drop number.
Casey J. Wall, Trude Storelvmo, and Anna Possner
Atmos. Chem. Phys., 23, 13125–13141, https://doi.org/10.5194/acp-23-13125-2023, https://doi.org/10.5194/acp-23-13125-2023, 2023
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Interactions between aerosol pollution and liquid clouds are one of the largest sources of uncertainty in the effective radiative forcing of climate over the industrial era. We use global satellite observations to decompose the forcing into components from changes in cloud-droplet number concentration, cloud water content, and cloud amount. Our results reduce uncertainty in these forcing components and clarify their relative importance.
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.
Mark D. Zelinka, Christopher J. Smith, Yi Qin, and Karl E. Taylor
Atmos. Chem. Phys., 23, 8879–8898, https://doi.org/10.5194/acp-23-8879-2023, https://doi.org/10.5194/acp-23-8879-2023, 2023
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The primary uncertainty in how strongly Earth's climate has been perturbed by human activities comes from the unknown radiative impact of aerosol changes. Accurately quantifying these forcings – and their sub-components – in climate models is crucial for understanding the past and future simulated climate. In this study we describe biases in previously published estimates of aerosol radiative forcing in climate models and provide corrected estimates along with code for users to compute them.
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
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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.
Robert Pincus, Paul A. Hubanks, Steven Platnick, Kerry Meyer, Robert E. Holz, Denis Botambekov, and Casey J. Wall
Earth Syst. Sci. Data, 15, 2483–2497, https://doi.org/10.5194/essd-15-2483-2023, https://doi.org/10.5194/essd-15-2483-2023, 2023
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This paper describes a new global dataset of cloud properties observed by a specific satellite program created to facilitate comparison with a matching observational proxy used in climate models. Statistics are accumulated over daily and monthly timescales on an equal-angle grid. Statistics include cloud detection, cloud-top pressure, and cloud optical properties. Joint histograms of several variable pairs are also available.
Aishwarya Raman, Thomas Hill, Paul J. DeMott, Balwinder Singh, Kai Zhang, Po-Lun Ma, Mingxuan Wu, Hailong Wang, Simon P. Alexander, and Susannah M. Burrows
Atmos. Chem. Phys., 23, 5735–5762, https://doi.org/10.5194/acp-23-5735-2023, https://doi.org/10.5194/acp-23-5735-2023, 2023
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Ice-nucleating particles (INPs) play an important role in cloud processes and associated precipitation. Yet, INPs are not accurately represented in climate models. This study attempts to uncover these gaps by comparing model-simulated INP concentrations against field campaign measurements in the SO for an entire year, 2017–2018. Differences in INP concentrations and variability between the model and observations have major implications for modeling cloud properties in high latitudes.
Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin
Geosci. Model Dev., 16, 2355–2370, https://doi.org/10.5194/gmd-16-2355-2023, https://doi.org/10.5194/gmd-16-2355-2023, 2023
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Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Matthew W. Christensen, Po-Lun Ma, Peng Wu, Adam C. Varble, Johannes Mülmenstädt, and Jerome D. Fast
Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023, https://doi.org/10.5194/acp-23-2789-2023, 2023
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An increase in aerosol concentration (tiny airborne particles) is shown to suppress rainfall and increase the abundance of droplets in clouds passing over Graciosa Island in the Azores. Cloud drops remain affected by aerosol for several days across thousands of kilometers in satellite data. Simulations from an Earth system model show good agreement, but differences in the amount of cloud water and its extent remain despite modifications to model parameters that control the warm-rain process.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
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Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023, https://doi.org/10.5194/gmd-16-315-2023, 2023
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool was designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows for reading and processing native climate model output, i.e., data that have not been reformatted before.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
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The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
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
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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.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
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We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Susannah M. Burrows, Richard C. Easter, Xiaohong Liu, Po-Lun Ma, Hailong Wang, Scott M. Elliott, Balwinder Singh, Kai Zhang, and Philip J. Rasch
Atmos. Chem. Phys., 22, 5223–5251, https://doi.org/10.5194/acp-22-5223-2022, https://doi.org/10.5194/acp-22-5223-2022, 2022
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Sea spray particles are composed of a mixture of salts and organic substances from oceanic microorganisms. In prior work, our team developed an approach connecting sea spray chemistry to ocean biology, called OCEANFILMS. Here we describe its implementation within an Earth system model, E3SM. We show that simulated sea spray chemistry is consistent with observed seasonal cycles and that sunlight reflected by simulated Southern Ocean clouds increases, consistent with analysis of satellite data.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, Rodrigo Guzman, Cyprien Gindre, Po-Lun Ma, and Marjolaine Chiriaco
Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, https://doi.org/10.5194/amt-15-1055-2022, 2022
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Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).
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.
Zhonghua Zheng, Matthew West, Lei Zhao, Po-Lun Ma, Xiaohong Liu, and Nicole Riemer
Atmos. Chem. Phys., 21, 17727–17741, https://doi.org/10.5194/acp-21-17727-2021, https://doi.org/10.5194/acp-21-17727-2021, 2021
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Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. We present a framework for evaluating the error in aerosol mixing state induced by aerosol representation assumptions, which is one of the important contributors to structural uncertainty in aerosol models. Our study provides insights into potential improvements to model process representation for aerosols.
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
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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.
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
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Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, and Daniel Rothenberg
Geosci. Model Dev., 14, 3067–3077, https://doi.org/10.5194/gmd-14-3067-2021, https://doi.org/10.5194/gmd-14-3067-2021, 2021
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The activation of aerosol into cloud droplets is an important but uncertain process in the Earth system. The physical and chemical interactions that govern this process are too computationally expensive to explicitly resolve in modern Earth system models. Here, we demonstrate how hybrid machine learning approaches can provide a potential path forward, enabling the representation of the more detailed physics and chemistry at a reduced computational cost while still retaining physical information.
Jingyu Wang, Jiwen Fan, Robert A. Houze Jr., Stella R. Brodzik, Kai Zhang, Guang J. Zhang, and Po-Lun Ma
Geosci. Model Dev., 14, 719–734, https://doi.org/10.5194/gmd-14-719-2021, https://doi.org/10.5194/gmd-14-719-2021, 2021
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This paper presents an evaluation of the E3SM model against NEXRAD radar observations for the warm seasons during 2014–2016. The COSP forward simulator package is implemented in the model to generate radar reflectivity, and the NEXRAD observations are coarsened to the model resolution for comparison. The model severely underestimates the reflectivity above 4 km. Sensitivity tests on the parameters from cumulus parameterization and cloud microphysics do not improve this model bias.
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.
Takuro Michibata, Kentaroh Suzuki, and Toshihiko Takemura
Atmos. Chem. Phys., 20, 13771–13780, https://doi.org/10.5194/acp-20-13771-2020, https://doi.org/10.5194/acp-20-13771-2020, 2020
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This work reveals that prognostic precipitation significantly reduces the magnitude of aerosol–cloud interactions (ERFaci), mainly due to the collection process associated with snowflakes and underlying cloud droplets. This precipitation-driven buffering effect, which is missing in traditional GCMs, can explain the model–observation discrepancy in ERFaci. These results underscore the necessity for a prognostic precipitation framework in GCMs for more reliable climate simulations.
Cited articles
Ackerman, A. S., Kirkpatrick, M. P., Stevens, D. E., and Toon, O. B.: The impact of humidity above stratiform clouds on indirect aerosol climate forcing, Nature, 432, 1014–1017, https://doi.org/10.1038/nature03174, 2004. a, b, c
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Andreae, M. O., Jones, C. D., and Cox, P. M.: Strong present-day aerosol cooling implies a hot future, Nature, 435, 1187–1190, https://doi.org/10.1038/nature03671, 2005. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate Change, Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020. a, b, c, d
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011. a
Bretherton, C. S., Blossey, P. N., and Uchida, J.: Cloud droplet sedimentation, entrainment efficiency, and subtropical stratocumulus albedo, Geophys. Res. Lett., 34, L03813, https://doi.org/10.1029/2006GL027648, 2007. a
CFMIP: COSPv2.0, GitHub [code], https://github.com/CFMIP/COSPv2.0, last access: 12 September 2024a. a
Chen, Y.-C., Christensen, M. W., Stephens, G. L., and Seinfeld, J. H.: Satellite-based estimate of global aerosol–cloud radiative forcing by marine warm clouds, Nat. Geosci., 7, 643–646, https://doi.org/10.1038/ngeo2214, 2014. a
Clough, S., Shephard, M., Mlawer, E., Delamere, J., Iacono, M., Cady-Pereira, K., Boukabara, S., and Brown, P.: Atmospheric radiative transfer modeling: a summary of the AER codes, J. Quant. Spectrosc. Ra., 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058, 2005. a
Colman, R. A. and McAvaney, B. J.: A study of general circulation model climate feedbacks determined from perturbed sea surface temperature experiments, J. Geophys. Res.-Atmos., 102, 19383–19402, https://doi.org/10.1029/97JD00206, 1997. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
Debolskiy, M. V.: Release release-noresm2.1.1-cosp2-update mvdebolskiy/NorESM, Github [code], https://github.com/mvdebolskiy/NorESM/releases/tag/release-noresm2.1.1-cosp2-update (last access: 8 September 2024), 2024. a
Diamond, M. S., Director, H. M., Eastman, R., Possner, A., and Wood, R.: Substantial Cloud Brightening From Shipping in Subtropical Low Clouds, AGU Advances, 1, e2019AV000111, https://doi.org/10.1029/2019AV000111, 2020. a
Donner, L. J., Wyman, B. L., Hemler, R. S., Horowitz, L. W., Ming, Y., Zhao, M., Golaz, J.-C., Ginoux, P., Lin, S.-J., Schwarzkopf, M. D., Austin, J., Alaka, G., Cooke, W. F., Delworth, T. L., Freidenreich, S. M., Gordon, C. T., Griffies, S. M., Held, I. M., Hurlin, W. J., Klein, S. A., Knutson, T. R., Langenhorst, A. R., Lee, H.-C., Lin, Y., Magi, B. I., Malyshev, S. L., Milly, P. C. D., Naik, V., Nath, M. J., Pincus, R., Ploshay, J. J., Ramaswamy, V., Seman, C. J., Shevliakova, E., Sirutis, J. J., Stern, W. F., Stouffer, R. J., Wilson, R. J., Winton, M., Wittenberg, A. T., and Zeng, F.: The Dynamical Core, Physical Parameterizations, and Basic Simulation Characteristics of the Atmospheric Component AM3 of the GFDL Global Coupled Model CM3, J. Climate, 24, 3484–3519, https://doi.org/10.1175/2011JCLI3955.1, 2011. a
Duffy, M. L., Medeiros, B., Gettelman, A., and Eidhammer, T.: Perturbing Parameters to Understand Cloud Contributions to Climate Change, J. Climate, 37, 213–227, https://doi.org/10.1175/JCLI-D-23-0250.1, 2024. a
Duran, B. M.: modis_cloud_radiative_kernels, Zenodo [code], https://doi.org/10.5281/zenodo.13839356, 2024. a, b, c
Duran, B. M., Wall, C. J., Lutsko, N. J., Michibata, T., Ma, P.-L., Qin, Y., Duffy, M. L., Debolskiy, M., and Medeiros, B.: Data from: A new method for diagnosing effective radiative forcing from aerosol-cloud interactions in climate models, UC San Diego Library Digital Collections [data set], https://doi.org/10.6075/J0P26ZF1, 2024. a
Dvorak, M. T., Armour, K. C., Frierson, D. M. W., Proistosescu, C., Baker, M. B., and Smith, C. J.: Estimating the timing of geophysical commitment to 1.5 and 2.0 °C of global warming, Nat. Clim. Change, 12, 547–552, https://doi.org/10.1038/s41558-022-01372-y, 2022. a
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth's Energy Budget, Climate Feedbacks and Climate Sensitivity, in: Climate Change 2021 – The Physical Science Basis Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, 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, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021. a, b, c
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Gettelman, A., Morrison, H., Santos, S., Bogenschutz, P., and Caldwell, P. M.: Advanced Two-Moment Bulk Microphysics for Global Models. Part II: Global Model Solutions and Aerosol–Cloud Interactions, J. Climate, 28, 1288–1307, https://doi.org/10.1175/JCLI-D-14-00103.1, 2015. a, b
Ghan, S. J.: Technical Note: Estimating aerosol effects on cloud radiative forcing, Atmos. Chem. Phys., 13, 9971–9974, https://doi.org/10.5194/acp-13-9971-2013, 2013. a, b, c
Glassmeier, F., Hoffmann, F., Johnson, J. S., Yamaguchi, T., Carslaw, K. S., and Feingold, G.: Aerosol-cloud-climate cooling overestimated by ship-track data, Science, 371, 485–489, https://doi.org/10.1126/science.abd3980, 2021. a
Golaz, J.-C., Larson, V. E., and Cotton, W. R.: A PDF-based model for boundary layer clouds. Part I: Method and model description, J. Atmos. Sci., 59, 3540–3551, 2002. a
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, J. Adv. Model. Earth Sy., 14, e2022MS003156, https://doi.org/10.1029/2022MS003156, 2022. a, b
Griffies, S. M., Winton, M., Donner, L. J., Horowitz, L. W., Downes, S. M., Farneti, R., Gnanadesikan, A., Hurlin, W. J., Lee, H.-C., Liang, Z., Palter, J. B., Samuels, B. L., Wittenberg, A. T., Wyman, B. L., Yin, J., and Zadeh, N.: The GFDL CM3 Coupled Climate Model: Characteristics of the Ocean and Sea Ice Simulations, J. Climate, 24, 3520–3544, https://doi.org/10.1175/2011JCLI3964.1, 2011. a
Grosvenor, D. P. and Carslaw, K. S.: The decomposition of cloud–aerosol forcing in the UK Earth System Model (UKESM1), Atmos. Chem. Phys., 20, 15681–15724, https://doi.org/10.5194/acp-20-15681-2020, 2020. a
Gryspeerdt, E., Quaas, J., and Bellouin, N.: Constraining the aerosol influence on cloud fraction, J. Geophys.l Res.-Atmos., 121, 3566–3583, https://doi.org/10.1002/2015JD023744, 2016. a
Gryspeerdt, E., Mülmenstädt, J., Gettelman, A., Malavelle, F. F., Morrison, H., Neubauer, D., Partridge, D. G., Stier, P., Takemura, T., Wang, H., Wang, M., and Zhang, K.: Surprising similarities in model and observational aerosol radiative forcing estimates, Atmos. Chem. Phys., 20, 613–623, https://doi.org/10.5194/acp-20-613-2020, 2020. a, b, c, d, e
Gryspeerdt, E., Goren, T., and Smith, T. W. P.: Observing the timescales of aerosol–cloud interactions in snapshot satellite images, Atmos. Chem. Phys., 21, 6093–6109, https://doi.org/10.5194/acp-21-6093-2021, 2021. a, b
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
Jia, H., Ma, X., Yu, F., and Quaas, J.: Significant underestimation of radiative forcing by aerosol–cloud interactions derived from satellite-based methods, Na. Commun., 12, 3649, https://doi.org/10.1038/s41467-021-23888-1, 2021. a
Jiang, H., Xue, H., Teller, A., Feingold, G., and Levin, Z.: Aerosol effects on the lifetime of shallow cumulus, Geophys. Res. Lett., 33, L14806, https://doi.org/10.1029/2006GL026024, 2006. a
King, M., Menzel, W., Kaufman, Y., Tanre, D., Bo-Cai Gao, Platnick, S., Ackerman, S., Remer, L., Pincus, R., and Hubanks, P.: Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS, IEEE T. Geosci. Remote, 41, 442–458, https://doi.org/10.1109/TGRS.2002.808226, 2003. a
King, M. D., Platnick, S., Wind, G., Arnold, G. T., and Dominguez, R. T.: Remote sensing of radiative and microphysical properties of clouds during TC4: Results from MAS, MASTER, MODIS, and MISR, J. Geophys. Res.-Atmos., 115, 2009JD013277, https://doi.org/10.1029/2009JD013277, 2010. a
Klein, S. A. and Jakob, C.: Validation and Sensitivities of Frontal Clouds Simulated by the ECMWF Model, Mon. Weather Rev., 127, 2514–2531, https://doi.org/10.1175/1520-0493(1999)127<2514:VASOFC>2.0.CO;2, 1999. a
Larson, V. E.: CLUBB-SILHS: A parameterization of subgrid variability in the atmosphere, arXiv [preprint], https://doi.org/10.48550/arXiv.1711.03675, 26 March 2022. a
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. a
Malavelle, F. F., Haywood, J. M., Jones, A., Gettelman, A., Clarisse, L., Bauduin, S., Allan, R. P., Karset, I. H. H., Kristjánsson, J. E., Oreopoulos, L., Cho, N., Lee, D., Bellouin, N., Boucher, O., Grosvenor, D. P., Carslaw, K. S., Dhomse, S., Mann, G. W., Schmidt, A., Coe, H., Hartley, M. E., Dalvi, M., Hill, A. A., Johnson, B. T., Johnson, C. E., Knight, J. R., O’Connor, F. M., Partridge, D. G., Stier, P., Myhre, G., Platnick, S., Stephens, G. L., Takahashi, H., and Thordarson, T.: Strong constraints on aerosol–cloud interactions from volcanic eruptions, Nature, 546, 485–491, https://doi.org/10.1038/nature22974, 2017. a
Mauritsen, T. and Pincus, R.: Committed warming inferred from observations, Nat. Clim. Change, 7, 652–655, https://doi.org/10.1038/nclimate3357, 2017. a
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. a, b, c
Michibata, T. and Suzuki, K.: Reconciling Compensating Errors Between Precipitation Constraints and the Energy Budget in a Climate Model, Geophys. Res. Lett., 47, e2020GL088340, https://doi.org/10.1029/2020GL088340, 2020. a
Michibata, T., Suzuki, K., Sato, Y., and Takemura, T.: The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals, Atmos. Chem. Phys., 16, 15413–15424, https://doi.org/10.5194/acp-16-15413-2016, 2016. a
Michibata, T., Suzuki, K., and Takemura, T.: Snow-induced buffering in aerosol–cloud interactions, Atmos. Chem. Phys., 20, 13771–13780, https://doi.org/10.5194/acp-20-13771-2020, 2020. a, b, c
Mülmenstädt, J., Gryspeerdt, E., Salzmann, M., Ma, P.-L., Dipu, S., and Quaas, J.: Separating radiative forcing by aerosol–cloud interactions and rapid cloud adjustments in the ECHAM–HAMMOZ aerosol–climate model using the method of partial radiative perturbations, Atmos. Chem. Phys., 19, 15415–15429, https://doi.org/10.5194/acp-19-15415-2019, 2019. a, b, c
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, 2024. a
Nam, C., Bony, S., Dufresne, J.-L., and Chepfer, H.: The 'too few, too bright' tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39, L21801, https://doi.org/10.1029/2012GL053421, 2012. a
NASA: MCD06COSP_M3_MODIS – MODIS (Aqua/Terra) Cloud Properties Level 3 monthly, 1x1 degree grid, Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD06COSP_M3_MODIS.062, 2023. a
NCAR Computational and Information Systems Laboratory: Derecho: HPE Cray EX System (NCAR Community Computing), NCAR, Boulder, CO, https://doi.org/10.5065/qx9a-pg09, 2023. a
Olivié, D.: Release of NorESM2.1 – results from longer simulations, NorESM Documentation Pages, https://noresm-docs.readthedocs.io/en/noresm2.1/noresm2.1/noresm2.1_test_runs.html (last access: 9 September 2024), 2024. a
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Patrick Hofmann, R. J.: Reconciling Simulated and Observed Views of Clouds: MODIS, ISCCP, and the Limits of Instrument Simulators, J. Climate, 25, 4699–4720, https://doi.org/10.1175/JCLI-D-11-00267.1, 2012. a, b
Pincus, R., Forster, P. M., and Stevens, B.: The Radiative Forcing Model Intercomparison Project (RFMIP): experimental protocol for CMIP6, Geosci. Model Dev., 9, 3447–3460, https://doi.org/10.5194/gmd-9-3447-2016, 2016. a
Pincus, R., Hubanks, P. A., Platnick, S., Meyer, K., Holz, R. E., Botambekov, D., and Wall, C. J.: Updated observations of clouds by MODIS for global model assessment, Earth Syst. Sci. Data, 15, 2483–2497, https://doi.org/10.5194/essd-15-2483-2023, 2023. a, b
Posselt, R. and Lohmann, U.: Introduction of prognostic rain in ECHAM5: design and single column model simulations, Atmos. Chem. Phys., 8, 2949–2963, https://doi.org/10.5194/acp-8-2949-2008, 2008. a
Posselt, R. and Lohmann, U.: Sensitivity of the total anthropogenic aerosol effect to the treatment of rain in a global climate model, Geophys. Res. Lett., 36, L02805, https://doi.org/10.1029/2008GL035796, 2009. a
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. a
Sato, Y., Goto, D., Michibata, T., Suzuki, K., Takemura, T., Tomita, H., and Nakajima, T.: Aerosol effects on cloud water amounts were successfully simulated by a global cloud-system resolving model, Nat. Commun., 9, 985, https://doi.org/10.1038/s41467-018-03379-6, 2018. a
Scott, R. C., Myers, T. A., Norris, J. R., Zelinka, M. D., Klein, S. A., Sun, M., and Doelling, D. R.: Observed Sensitivity of Low-Cloud Radiative Effects to Meteorological Perturbations over the Global Oceans, J. Climate, 33, 7717–7734, https://doi.org/10.1175/JCLI-D-19-1028.1, 2020. a
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020. a
Smith, C. J., Kramer, R. J., Myhre, G., Alterskjær, K., Collins, W., Sima, A., Boucher, O., Dufresne, J.-L., Nabat, P., Michou, M., Yukimoto, S., Cole, J., Paynter, D., Shiogama, H., O'Connor, F. M., Robertson, E., Wiltshire, A., Andrews, T., Hannay, C., Miller, R., Nazarenko, L., Kirkevåg, A., Olivié, D., Fiedler, S., Lewinschal, A., Mackallah, C., Dix, M., Pincus, R., and Forster, P. M.: Effective radiative forcing and adjustments in CMIP6 models, Atmos. Chem. Phys., 20, 9591–9618, https://doi.org/10.5194/acp-20-9591-2020, 2020. a, b, c, d, e, f
Song, C., McCoy, D. T., Eidhammer, T., Gettelman, A., McCoy, I. L., Watson-Parris, D., Wall, C. J., Elsaesser, G., and Wood, R.: Buffering of Aerosol-Cloud Adjustments by Coupling Between Radiative Susceptibility and Precipitation Efficiency, Geophys. Res. Lett., 51, e2024GL108663, https://doi.org/10.1029/2024GL108663, 2024. a
Swales, D. J., Pincus, R., and Bodas-Salcedo, A.: The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2, Geosci. Model Dev., 11, 77–81, https://doi.org/10.5194/gmd-11-77-2018, 2018. a
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019. a
Taylor, K. E., Crucifix, M., Braconnot, P., Hewitt, C. D., Doutriaux, C., Broccoli, A. J., Mitchell, J. F. B., and Webb, M. J.: Estimating Shortwave Radiative Forcing and Response in Climate Models, J. Climate, 20, 2530–2543, https://doi.org/10.1175/JCLI4143.1, 2007. a, b
Toll, V., Christensen, M., Gassó, S., and Bellouin, N.: Volcano and Ship Tracks Indicate Excessive Aerosol-Induced Cloud Water Increases in a Climate Model, Geophys. Res. Lett., 44, 12492–12500, https://doi.org/10.1002/2017GL075280, 2017. a
Toll, V., Christensen, M., Quaas, J., and Bellouin, N.: Weak average liquid-cloud-water response to anthropogenic aerosols, Nature, 572, 51–55, https://doi.org/10.1038/s41586-019-1423-9, 2019. a
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. a
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. a
Wall, C. J., Debolskiy, M., and Bodas-Salcedo, A.: New MODIS Joint Histograms, GitHub [code], https://github.com/CFMIP/COSPv2.0/commit/d252f193137b54
adff4cc5b8f40604f1832472fa, last access: 12 September 2024. a
Wang, C., Soden, B. J., Yang, W., and Vecchi, G. A.: Compensation Between Cloud Feedback and Aerosol-Cloud Interaction in CMIP6 Models, Geophys. Res. Lett., 48, e2020GL091024, https://doi.org/10.1029/2020GL091024, 2021. a
Wang, S., Wang, Q., and Feingold, G.: Turbulence, Condensation, and Liquid Water Transport in Numerically Simulated Nonprecipitating Stratocumulus Clouds, J. Atmos. Sci., 60, 262–278, https://doi.org/10.1175/1520-0469(2003)060<0262:TCALWT>2.0.CO;2, 2003. a
Watson-Parris, D. and Smith, C. J.: Large uncertainty in future warming due to aerosol forcing, Nat. Clim. Change, 12, 1111–1113, https://doi.org/10.1038/s41558-022-01516-0, 2022. a
Webb, M., Senior, C., Bony, S., and Morcrette, J.-J.: Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models, Clim. Dynam., 17, 905–922, https://doi.org/10.1007/s003820100157, 2001. a, b
Wetherald, R. T. and Manabe, S.: Cloud Feedback Processes in a General Circulation Model, J. Atmos. Sci., 45, 1397–1416, https://doi.org/10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2, 1988. a
Williamson, M. S., Thackeray, C. W., Cox, P. M., Hall, A., Huntingford, C., and Nijsse, F. J. M. M.: Emergent constraints on climate sensitivities, Rev. Mod. Phys., 93, 025004, https://doi.org/10.1103/RevModPhys.93.025004, 2021. a
Xue, H. and Feingold, G.: Large-Eddy Simulations of Trade Wind Cumuli: Investigation of Aerosol Indirect Effects, J. Atmos. Sci., 63, 1605–1622, https://doi.org/10.1175/JAS3706.1, 2006. a
Zelinka, M.: aprp, Zenodo [code], https://doi.org/10.5281/zenodo.5514141, 2023. a
Zelinka, M.: cloud-radiative-kernels, Zenodo [code], https://doi.org/10.5281/zenodo.5514136, 2024. a
Zelinka, M. D., Klein, S. A., and Hartmann, D. L.: Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth, J. Climate, 25, 3736–3754, https://doi.org/10.1175/JCLI-D-11-00249.1, 2012b. a
Zelinka, M. D., Klein, S. A., Taylor, K. E., Andrews, T., Webb, M. J., Gregory, J. M., and Forster, P. M.: Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5, J. Climate, 26, 5007–5027, https://doi.org/10.1175/JCLI-D-12-00555.1, 2013. a, b, c
Zelinka, M. D., Andrews, T., Forster, P. M., and Taylor, K. E.: Quantifying components of aerosol-cloud-radiation interactions in climate models, J. Geophys. Res.-Atmos., 119, 7599–7615, https://doi.org/10.1002/2014JD021710, 2014. a
Zelinka, M. D., Chao, L.-W., Myers, T. A., Qin, Y., and Klein, S. A.: Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels, Atmos. Chem. Phys., 25, 1477–1495, https://doi.org/10.5194/acp-25-1477-2025, 2025. a, b
Zhang, D., Wang, Z., and Liu, D.: A global view of midlevel liquid-layer topped stratiform cloud distribution and phase partition from CALIPSO and CloudSat measurements, J. Geophys. Res.-Atmos., 115, D00H13, https://doi.org/10.1029/2009JD012143, 2010. a
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. a, b, c
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. a
Zhao, X., Liu, X., Lin, L., Qin, Y., Zelinka, M. D., Klein, S. A., Zhang, M., Zhang, K., Ma, P.-L., Zhu, J., Lu, Z., and Saravanan, R.: Larger Cloud Liquid Water Enhances Both Aerosol Indirect Forcing and Cloud Radiative Feedback in Two Earth System Models, Geophys. Res. Lett., 51, e2023GL105529, https://doi.org/10.1029/2023GL105529, 2024. a
Zhou, C. and Penner, J. E.: Why do general circulation models overestimate the aerosol cloud lifetime effect? A case study comparing CAM5 and a CRM, Atmos. Chem. Phys., 17, 21–29, https://doi.org/10.5194/acp-17-21-2017, 2017. a
Short summary
We use satellite simulator data generated by global climate models to investigate how aerosol particles impact the radiative properties of liquid clouds. Specifically, we quantify the radiative perturbations arising from aerosol-driven changes in the number density of cloud droplets, the vertically integrated cloud water mass, and the cloud amount. Our results show that, in models, aerosol effects on the number density of cloud droplets contribute the most to anthropogenic climate forcing.
We use satellite simulator data generated by global climate models to investigate how aerosol...
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