Articles | Volume 26, issue 6
https://doi.org/10.5194/acp-26-4289-2026
© Author(s) 2026. 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-26-4289-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Mar 2026
Research article |
| 27 Mar 2026
| 27 Mar 2026
Meteorological drivers of the low-cloud radiative feedback pattern effect and its uncertainty
Department of Atmospheric Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Timothy A. Myers
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
Physical Sciences Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA
Mark D. Zelinka
Lawrence Livermore National Laboratory, Livermore, CA, USA
Cristian Proistosescu
Department of Atmospheric Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Department of Earth Sciences and Environmental Change, University of Illinois at Urbana-Champaign, Urbana, IL, USA
Yuan-Jen Lin
Center for Climate Systems Research, Columbia University, New York, NY, USA
NASA Goddard Institute for Space Studies, New York, NY, USA
Kate Marvel
NASA Goddard Institute for Space Studies, New York, NY, USA
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Paulo Ceppi, Sarah Wilson Kemsley, Hendrik Andersen, Timothy Andrews, Ryan J. Kramer, Peer Nowack, Casey J. Wall, and Mark D. Zelinka
Atmos. Chem. Phys., 26, 4153–4171, https://doi.org/10.5194/acp-26-4153-2026, https://doi.org/10.5194/acp-26-4153-2026, 2026
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Recent decades have seen a marked decrease in global low-level cloud cover, leading to more sunlight heating the Earth. This trend is poorly understood, raising the concern that clouds may amplify global warming more than previously thought. We show that the cloud decrease is mostly caused by human forcing on climate, and that it agrees with previous estimates of how clouds respond to decreasing aerosol pollution, increasing greenhouse gas concentration, and their effects on global temperature.
Nicola Bodini, Joseph Olson, Brian Gaudet, Giacomo Valerio Iungo, Mojtaba Shams Solari, Sayahnya Roy, Julie K. Lundquist, Nathan Agarwal, Timothy A. Myers, Bianca Adler, Jeffrey D. Mirocha, Eric James, Laura Bianco, James M. Wilczak, and David D. Turner
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-17, https://doi.org/10.5194/wes-2026-17, 2026
Preprint under review for WES
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To improve offshore wind forecasts, the Third Wind Forecast Improvement Project monitored the United States East Coast for eighteen months. We compiled a daily log of weather events using advanced scanners and expert notes. This public dataset identifies important wind patterns, helping scientists test computer models and choose specific cases to study.
Paulo Ceppi, Alejandro Bodas-Salcedo, Mark D. Zelinka, Timothy Andrews, Florent Brient, Robin Chadwick, Jonathan M. Gregory, Yen-Ting Hwang, Sarah M. Kang, Jennifer E. Kay, Thorsten Mauritsen, Tomoo Ogura, George Tselioudis, Masahiro Watanabe, Mark J. Webb, and Allison A. Wing
EGUsphere, https://doi.org/10.5194/egusphere-2026-398, https://doi.org/10.5194/egusphere-2026-398, 2026
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Clouds constitute a key uncertainty for climate change projections. The Cloud Feedback Model Intercomparison Project (CFMIP) aims to address this challenge by evaluating and understanding clouds and their impacts on atmospheric circulation, precipitation, and climate sensitivity. The present paper describes the CFMIP experiment protocol for the Coupled Model Intercomparison Project phase 7 (CMIP7), and discusses the accompanying science questions and opportunities for progress.
Anna Zehrung, Andrew D. King, Zebedee Nicholls, Mark D. Zelinka, and Malte Meinshausen
Geosci. Model Dev., 18, 9433–9450, https://doi.org/10.5194/gmd-18-9433-2025, https://doi.org/10.5194/gmd-18-9433-2025, 2025
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The Gregory method is a common approach for calculating the equilibrium climate sensitivity (ECS). However, studies which apply this method lack transparency in how model data is processed prior to calculating the ECS, inhibiting replicability. Different choices of global weighting, net radiative flux variable, anomaly calculation, and linear regression fit can affect the ECS estimates. We investigate the impact of these choices and propose a standardised method for future ECS calculations.
Ram Singh, Kostas Tsigaridis, Diana Bull, Laura P. Swiler, Benjamin M. Wagman, and Kate Marvel
Atmos. Chem. Phys., 25, 16511–16532, https://doi.org/10.5194/acp-25-16511-2025, https://doi.org/10.5194/acp-25-16511-2025, 2025
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Analysis of post-eruption climate conditions using the impact metrics is crucial for understanding the hydroclimatic responses. We used NASA’s Earth system model to perform the experiments and utilized the moisture-based impact metrics and hydrological variables to investigate the effect of volcanically induced conditions that govern plant productivity. This study highlights Mt. Pinatubo's impact on the drivers of plant productivity and regional and seasonal dependence of these drivers.
Beth Dingley, James A. Anstey, Marta Abalos, Carsten Abraham, Tommi Bergman, Lisa Bock, Sonya Fiddes, Birgit Hassler, Ryan J. Kramer, Fei Luo, Fiona M. O'Connor, Petr Šácha, Isla R. Simpson, Laura J. Wilcox, and Mark D. Zelinka
EGUsphere, https://doi.org/10.5194/egusphere-2025-3189, https://doi.org/10.5194/egusphere-2025-3189, 2025
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This manuscript defines as a list of variables and scientific opportunities which are requested from the CMIP7 Assessment Fast Track to address open atmospheric science questions. The list reflects the output of a large public community engagement effort, coordinated across autumn 2025 through to summer 2025.
Prasanth Prabhakaran, Timothy A. Myers, Fabian Hoffmann, and Graham Feingold
EGUsphere, https://doi.org/10.5194/egusphere-2025-2935, https://doi.org/10.5194/egusphere-2025-2935, 2025
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We explore how climate change and aerosol affect the evolution of marine low-clouds. Using high-resolution simulations, we find that warming has a stronger impact on these clouds, but aerosol becomes more important after the clouds form precipitation. Our results suggest that attempts to brighten these clouds using aerosol may become less effective in a warmer future due to the decrease in cloud cover.
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.
Bianca Adler, David D. Turner, Laura Bianco, Irina V. Djalalova, Timothy Myers, and James M. Wilczak
Atmos. Meas. Tech., 17, 6603–6624, https://doi.org/10.5194/amt-17-6603-2024, https://doi.org/10.5194/amt-17-6603-2024, 2024
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Continuous profile observations of temperature and humidity in the lowest part of the atmosphere are essential for the evaluation of numerical weather prediction models and data assimilation for better weather forecasts. Such profiles can be retrieved from passive ground-based remote sensing instruments like infrared spectrometers and microwave radiometers. In this study, we describe three recent modifications to the retrieval framework TROPoe for improved temperature and humidity profiles.
Laura Bianco, Bianca Adler, Ludovic Bariteau, Irina V. Djalalova, Timothy Myers, Sergio Pezoa, David D. Turner, and James M. Wilczak
Atmos. Meas. Tech., 17, 3933–3948, https://doi.org/10.5194/amt-17-3933-2024, https://doi.org/10.5194/amt-17-3933-2024, 2024
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The Tropospheric Remotely Observed Profiling via Optimal Estimation physical retrieval is used to retrieve temperature and humidity profiles from various combinations of passive and active remote sensing instruments, surface platforms, and numerical weather prediction models. The retrieved profiles are assessed against collocated radiosonde in non-cloudy conditions to assess the sensitivity of the retrievals to different input combinations. Case studies with cloudy conditions are also inspected.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
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.
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.
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Zhou, C., Zelinka, M. D., Dessler, A. E., and Yang, P.: An Analysis of the Short-Term Cloud Feedback Using MODIS Data, J. Clim., 26, 4803–4815, https://doi.org/10.1175/JCLI-D-12-00547.1, 2013. a
Zhou, C., Zelinka, M. D., and Klein, S. A.: Impact of decadal cloud variations on the Earth's energy budget, Nat. Geosci., 9, 871–874, https://doi.org/10.1038/ngeo2828, 2016. a, b, c
Zhou, C., Zelinka, M. D., and Klein, S. A.: Analyzing the dependence of global cloud feedback on the spatial pattern of sea surface temperature change with a Green's function approach, J. Adv. Model. Earth Sy., 9, 2174–2189, https://doi.org/10.1002/2017MS001096, 2017. a, b
Short summary
This work identifies the key driver to the change of present and future climate response, known as the pattern effect, by breaking down low-cloud feedback as the radiative changes to meteorology and the meteorology changes to warming using a cloud controlling factor framework. We identify inversion strength in the Southern Ocean and the South East Pacific as the main driver to the pattern effect, and larger uncertainty remains in the sensitivities of radiative flux to meteorology.
This work identifies the key driver to the change of present and future climate response, known...
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