Articles | Volume 25, issue 3
https://doi.org/10.5194/acp-25-1477-2025
© 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-25-1477-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
03 Feb 2025
Technical note |
| 03 Feb 2025
| 03 Feb 2025
Technical note: Recommendations for diagnosing cloud feedbacks and rapid cloud adjustments using cloud radiative kernels
Lawrence Livermore National Laboratory, 7000 East Avenue, L-103, Livermore, CA, USA
Li-Wei Chao
Lawrence Livermore National Laboratory, 7000 East Avenue, L-103, Livermore, CA, 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
Pacific Northwest National Laboratory, Richland, WA, USA
Stephen A. Klein
Lawrence Livermore National Laboratory, 7000 East Avenue, L-103, Livermore, CA, USA
Related authors
Rachel Yuen Sum Tam, Timothy Myers, Mark Zelinka, Cristian Proistosescu, Yuan-Jen Lin, and Kate Marvel
EGUsphere, https://doi.org/10.5194/egusphere-2025-3177, https://doi.org/10.5194/egusphere-2025-3177, 2025
Short summary
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.
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
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
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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.
Anna Zehrung, Andrew D. King, Zebedee Nicholls, Mark D. Zelinka, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2252, https://doi.org/10.5194/egusphere-2025-2252, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
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 and annual mean weighting, 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.
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
Short summary
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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.
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
Short summary
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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
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.
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.
Rachel Yuen Sum Tam, Timothy Myers, Mark Zelinka, Cristian Proistosescu, Yuan-Jen Lin, and Kate Marvel
EGUsphere, https://doi.org/10.5194/egusphere-2025-3177, https://doi.org/10.5194/egusphere-2025-3177, 2025
Short summary
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.
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
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
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
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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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.
Anna Zehrung, Andrew D. King, Zebedee Nicholls, Mark D. Zelinka, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2252, https://doi.org/10.5194/egusphere-2025-2252, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
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 and annual mean weighting, 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.
Brandon M. Duran, Casey J. Wall, Nicholas J. Lutsko, Takuro Michibata, Po-Lun Ma, Yi Qin, Margaret L. Duffy, Brian Medeiros, and Matvey Debolskiy
Atmos. Chem. Phys., 25, 2123–2146, https://doi.org/10.5194/acp-25-2123-2025, https://doi.org/10.5194/acp-25-2123-2025, 2025
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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.
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
Short summary
Short summary
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.
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.
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.
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
Short summary
Short summary
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.
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.
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.
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
Bony, S., Colman, R., Kattsov, V. M., Allan, R. P., Bretherton, C. S., Dufresne, J. L., Hall, A., Hallegatte, S., Holland, M. M., Ingram, W., Randall, D. A., Soden, B. J., Tselioudis, G., and Webb, M. J.: How well do we understand and evaluate climate change feedback processes?, J. Climate, 19, 3445–3482, https://doi.org/10.1175/JCLI3819.1, 2006. a
Ceppi, P., Myers, T. A., Nowack, P., Wall, C. J., and Zelinka, M. D.: Implications of a pervasive climate model bias for low-cloud feedback, Geophys. Res. Lett., 51, e2024GL110525, https://doi.org/10.1029/2024GL110525, 2024. a
Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Ghan, S. J., Kiehl, J. T., Treut, H. L., Li, Z. X., Liang, X. Z., Mitchell, J. F. B., Morcrette, J. J., Randall, D. A., Riches, M. R., Roeckner, E., Schlese, U., Slingo, A., Taylor, K. E., Washington, W. M., Wetherald, R. T., and Yagai, I.: Interpretation of cloud-climate feedback as produced by 14 atmospheric general circulation models, Science, 245, 513–516, https://doi.org/10.1126/science.245.4917.513, 1989. a
Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Genio, A. D. D., Déqué, M., Dymnikov, V., Galin, V., Gates, W. L., Ghan, S. J., Kiehl, J. T., Lacis, A. A., Treut, H. L., Li, Z.-X., Liang, X.-Z., McAvaney, B. J., Meleshko, V. P., Mitchell, J. F. B., Morcrette, J.-J., Randall, D. A., Rikus, L., Roeckner, E., Royer, J. F., Schlese, U., Sheinin, D. A., Slingo, A., Sokolov, A. P., Taylor, K. E., Washington, W. M., Wetherald, R. T., Yagai, I., and Zhang, M.-H.: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models, J. Geophys. Res.-Atmos., 95, 16601–16615, https://doi.org/10.1029/JD095iD10p16601, 1990. a
Chao, L.-W., Zelinka, M. D., and Dessler, A. E.: Evaluating cloud feedback components in observations and their representation in climate models, J. Geophys. Res.-Atmos., 129, e2023JD039427, https://doi.org/10.1029/2023JD039427, 2024. a, b, c, d
Colman, R. and McAvaney, B.: On tropospheric adjustment to forcing and climate feedbacks, Clim. Dynam., 36, 1649–1658, https://doi.org/10.1007/s00382-011-1067-4, 2011. a
Earth System Grid Federation: https://esgf.github.io/, last access: 19 November 2024. a
Garay, M. J., de Szoeke, S. P., and Moroney, C. M.: Comparison of marine stratocumulus cloud top heights in the southeastern Pacific retrieved from satellites with coincident ship-based observations, J. Geophys. Res., 113, D18204, https://doi.org/10.1029/2008JD009975, 2008. a
Held, I. M. and Shell, K. M.: Using relative humidity as a state variable in climate feedback analysis, J. Climate, 25, 2578–2582, https://doi.org/10.1175/JCLI-D-11-00721.1, 2012. a, b
Kamae, Y. and Watanabe, M.: Tropospheric adjustment to increasing CO2: Its timescale and the role of land-sea contrast, Clim. Dynam., 41, 3007–3024, https://doi.org/10.1007/s00382-012-1555-1, 2012. a
Kamae, Y., Watanabe, M., Ogura, T., Yoshimori, M., and Shiogama, H.: Rapid adjustments of cloud and hydrological cycle to increasing CO2: a review, Current Climate Change Reports, 1, 103–113, https://doi.org/10.1007/s40641-015-0007-5, 2015. a
Lutsko, N. J., Luongo, M. T., Wall, C. J., and Myers, T. A.: Correlation between cloud adjustments and cloud feedbacks responsible for larger range of climate sensitivities in CMIP6, J. Geophys. Res.-Atmos., 127, e2022JD037486, https://doi.org/10.1029/2022JD037486, 2022. a
Marchand, R. and Ackerman, T.: An analysis of cloud cover in multiscale modeling framework global climate model simulations using 4 and 1 km horizontal grids, J. Geophys. Res.-Atmos., 115, D16207, https://doi.org/10.1029/2009JD013423, 2010. a
Myers, T. A., Scott, R. C., Zelinka, M. D., Klein, S. A., Norris, J. R., and Caldwell, P. M.: Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity, Nat. Clim. Change, 11, 501–507, https://doi.org/10.1038/s41558-021-01039-0, 2021. a, b, c, d
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Hofmann, R. J. P.: 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
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
Qin, Y., Zelinka, M. D., and Klein, S. A.: On the correspondence between atmosphere-only and coupled simulations for radiative feedbacks and forcing from CO2, J. Geophys. Res.-Atmos., 127, e2021JD035460, https://doi.org/10.1029/2021JD035460, 2022. a
Raghuraman, S. P., Medeiros, B., and Gettelman, A.: Observational quantification of tropical high cloud changes and feedbacks, J. Geophys. Res.-Atmos., 129, e2023JD039364, https://doi.org/10.1029/2023JD039364, 2024. a, b
Ringer, M. A., Andrews, T., and Webb, M. J.: Global-mean radiative feedbacks and forcing in atmosphere-only and coupled atmosphere-ocean climate change experiments, Geophys. Res. Lett., 41, 4035–4042, https://doi.org/10.1002/2014gl060347, 2014. a
Rossow, W. B. and Schiffer, R. A.: Advances in understanding clouds from ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2 1999. 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, b, c
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., Heydt, A. S. v. d., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An assessment of earth's climate sensitivity using multiple lines of evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a
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
Soden, B. J. and Vecchi, G. A.: The vertical distribution of cloud feedback in coupled ocean-atmosphere models, Geophys. Res. Lett., 38, L12704, https://doi.org/10.1029/2011GL047632, 2011. 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
Tselioudis, G., Lipat, B. R., Konsta, D., Grise, K. M., and Polvani, L. M.: Midlatitude cloud shifts, their primary link to the Hadley cell, and their diverse radiative effects, Geophys. Res. Lett., 43, 4594–4601, https://doi.org/10.1002/2016gl068242, 2016. a
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, 2017. a
Wyant, M. C., Bretherton, C. S., Blossey, P. N., and Khairoutdinov, M.: Fast cloud adjustment to increasing CO2 in a superparameterized climate model, J. Adv. Model. Earth Sy., 4, M05001, https://doi.org/10.1029/2011MS000092, 2012. 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, b
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, d
Zelinka, M. D., Zhou, C., and Klein, S. A.: Insights from a refined decomposition of cloud feedbacks, Geophys. Res. Lett., 43, 9259–9269, https://doi.org/10.1002/2016gl069917, 2016. a
Zelinka, M. D., Grise, K. M., Klein, S. A., Zhou, C., DeAngelis, A. M., and Christensen, M. W.: Drivers of the low-cloud response to poleward jet shifts in the North Pacific in observations and models, J. Climate, 31, 7925–7947, https://doi.org/10.1175/jcli-d-18-0114.1, 2018. a, b
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of higher climate sensitivity in CMIP6 models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a
Zhou, C., Zelinka, M. D., Dessler, A. E., and Yang, P.: An analysis of the short-term cloud feedback using MODIS data, J. Climate, 26, 4803–4815, https://doi.org/10.1175/JCLI-D-12-00547.1, 2013. a, b
Short summary
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.
Clouds lie at the heart of uncertainty in both climate sensitivity and radiative forcing, making...
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