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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Prasanth Prabhakaran, Timothy A. Myers, Fabian Hoffmann, and Graham Feingold
Atmos. Chem. Phys., 26, 5151–5167, https://doi.org/10.5194/acp-26-5151-2026, https://doi.org/10.5194/acp-26-5151-2026, 2026
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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.
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
Geosci. Model Dev., 19, 2945–2984, https://doi.org/10.5194/gmd-19-2945-2026, https://doi.org/10.5194/gmd-19-2945-2026, 2026
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This manuscript defines as a list of variables and scientific opportunities which are requested from the Coupled Model Intercomparison Project Phase 7 (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.
Abigail L. S. Swann, Charles D. Koven, Cristian Proistosecu, Rosie A. Fisher, Benjamin M. Sanderson, Victor Brovkin, Tomohiro Hajima, Chris D. Jones, Nancy Y. Kiang, David M. Lawrence, Spencer Liddicoat, Hannah Liddy, Anastasia Romanou, Roland Séférian, Lori T. Sentman, Norman J. Steinert, Jerry Tjiputra, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2026-1673, https://doi.org/10.5194/egusphere-2026-1673, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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We analyzed simulations from Earth system models (ESM) with a constant rate of emissions, zero emissions, and negative emissions of CO2 to quantify the response of land carbon sinks. We found that under positive emissions vegetation in the tropics gained carbon. Under zero emissions and negative emissions most ESMs lost carbon from vegetation in the tropics but gained carbon in mid- and high-latitudes, mostly in soils. Our findings imply that tropical carbon is vulnerable under zero emissions.
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
Revised manuscript 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.
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.
Cited articles
Andrews, T. and Ringer, M. A.: Cloud Feedbacks, Rapid Adjustments, and the Forcing–Response Relationship in a Transient CO2 Reversibility Scenario, J. Clim., 27, 1799–1818, https://doi.org/10.1175/JCLI-D-13-00421.1, 2014. a
Andrews, T., Gregory, J. M., Webb, M. J., and Taylor, K. E.: Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models: Climate Sensitivity in CMIP5 Models, Geophys. Res. Lett., 39, https://doi.org/10.1029/2012GL051607, 2012. a
Andrews, T., Gregory, J. M., Paynter, D., Silvers, L. G., Zhou, C., Mauritsen, T., Webb, M. J., Armour, K. C., Forster, P. M., and Titchner, H.: Accounting for Changing Temperature Patterns Increases Historical Estimates of Climate Sensitivity, Geophys. Res. Lett., 45, 8490–8499, https://doi.org/10.1029/2018GL078887, 2018. a
Andrews, T., BodasSalcedo, A., Gregory, J. M., Dong, Y., Armour, K. C., Paynter, D., Lin, P., Modak, A., Mauritsen, T., Cole, J. N. S., Medeiros, B., Benedict, J. J., Douville, H. e., Roehrig, R., Koshiro, T., Kawai, H., Ogura, T., Dufresne, J., Allan, R. P., and Liu, C.: On the Effect of Historical SST Patterns on Radiative Feedback, J. Geophys. Res.-Atmos., 127, https://doi.org/10.1029/2022JD036675, 2022. a, b, c, d
Armour, K. C.: Energy budget constraints on climate sensitivity in light of inconstant climate feedbacks, Nat. Clim. Change, 7, 331–335, https://doi.org/10.1038/nclimate3278, 2017. a
Armour, K. C., Bitz, C. M., and Roe, G. H.: Time-Varying Climate Sensitivity from Regional Feedbacks, J. Clim., 26, 4518–4534, https://doi.org/10.1175/JCLI-D-12-00544.1, 2013. a
Armour, K. C., Proistosescu, C., Dong, Y., Hahn, L. C., Blanchard-Wrigglesworth, E., Pauling, A. G., Jnglin Wills, R. C., Andrews, T., Stuecker, M. F., Po-Chedley, S., Mitevski, I., Forster, P. M., and Gregory, J. M.: Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity, P. Natl. Acad. Sci. USA, 121, e2312093121, https://doi.org/10.1073/pnas.2312093121, 2024. a
Bader, D. C., Leung, R., Taylor, M., and McCoy, R. B.: E3SM-Project E3SM1.0 model output prepared for CMIP6 CMIP amip, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.4492, 2019a. a
Bader, D. C., Leung, R., Taylor, M., and McCoy, R. B.: E3SM-Project E3SM1.0 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.4497, 2019b. a
Bader, D. C., Leung, R., Taylor, M., and McCoy, R. B.: E3SM-Project E3SM1.0 model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.4491, 2019c. a
Bellouin, N., Boucher, O., Haywood, J., Johnson, C., Jones, A., Rae, J., and Woodward, S.: Improved representation of aerosols for HadGEM2, Met Office Hadley Centre, Technical Note No. HCTN 73, Met Office Hadley Centre, Exeter, UK, 43 pp., 2007. a
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, Bull. Am. Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011. a
Bony, S. and Dufresne, J.-L.: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models, Geophys. Res. Lett., 32, L20806, https://doi.org/10.1029/2005GL023851, 2005. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., Cheruy, F., Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP amip, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5113, 2018a. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., Cheruy, F., Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5195, 2018b. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., Cheruy, F., Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5109, 2018c. a
Bretherton, C. S.: Insights into low-latitude cloud feedbacks from high-resolution models, Philos. T. R. Soc. A, 373, 20140415, https://doi.org/10.1098/rsta.2014.0415, 2015. a
Brient, F. and Schneider, T.: Constraints on climate sensitivity from space-based measurements of low-cloud reflection, J. Clim., 29, 5821–5835, 2016. a
Ceppi, P. and Gregory, J. M.: Relationship of tropospheric stability to climate sensitivity and Earth’s observed radiation budget, P. Natl. Acad. Sci. USA, 114, 13126–13131, https://doi.org/10.1073/pnas.1714308114, 2017. a
Ceppi, P. and Gregory, J. M.: A refined model for the Earth’s global energy balance, Clim. Dynam., 53, 4781–4797, https://doi.org/10.1007/s00382-019-04825-x, 2019. a, b
Cesana, G. V. and Del Genio, A. D.: Observational constraint on cloud feedbacks suggests moderate climate sensitivity, Nat. Clim. Change, 11, 213–218, https://doi.org/10.1038/s41558-020-00970-y, 2021. a, b
Clement, A. C., Seager, R., Cane, M. A., and Zebiak, S. E.: An Ocean Dynamical Thermostat, J. Clim., 9, 2190–2196, https://doi.org/10.1175/1520-0442(1996)009<2190:AODT>2.0.CO;2, 1996. a
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Hinton, T., Jones, C. D., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Totterdell, I., Woodward, S., Reichler, T., and Kim, J.: Evaluation of the HadGEM2 model, Met Office Hadley Centre, Technical Note No. HCTN 74, Met Office Hadley Centre, Exeter, UK, 2008. a
Cooper, V. T., Armour, K. C., Hakim, G. J., Tierney, J. E., Osman, M. B., Proistosescu, C., Dong, Y., Burls, N. J., Andrews, T., Amrhein, D. E., Zhu, J., Dong, W., Ming, Y., and Chmielowiec, P.: Last Glacial Maximum pattern effects reduce climate sensitivity estimates, Sci. Adv., 10, eadk9461, https://doi.org/10.1126/sciadv.adk9461, 2024. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 Monthly Averaged Data on Single Levels from 1979 to Present, Copernicus Climate Change Service, https://doi.org/10.24381/cds.f17050d764, 2019a. a
Hersbach, H. et al.: ERA5 Monthly Averaged Data on Pressure Levels from 1979 to Present, Copernicus Climate Change Service, https://doi.org/10.24381/cds.6860a573, 2019b. a
Doelling, D.: CERES Monthly Daytime Mean Regionally Averaged Terra and Aqua TOA Fluxes and Associated Cloud Properties Stratified by Optical Depth and Effective Pressure Edition4A, NASA Atmospheric Science Data Center (ASDC), https://doi.org/10.5067/TERRA-AQUA/CERES/FLUXBYCLDTYP-MONTH_L3.004A, 2020. a
Dong, Y., Proistosescu, C., Armour, K. C., and Battisti, D. S.: Attributing Historical and Future Evolution of Radiative Feedbacks to Regional Warming Patterns using a Green’s Function Approach: The Preeminence of the Western Pacific, J. Clim., 32, 5471–5491, https://doi.org/10.1175/JCLI-D-18-0843.1, 2019. a, b, c
Dong, Y., Armour, K. C., Zelinka, M. D., Proistosescu, C., Battisti, D. S., Zhou, C., and Andrews, T.: Intermodel Spread in the Pattern Effect and Its Contribution to Climate Sensitivity in CMIP5 and CMIP6 Models, J. Clim., 33, 7755–7775, https://doi.org/10.1175/JCLI-D-19-1011.1, 2020. a
Dong, Y., Armour, K. C., Proistosescu, C., Andrews, T., Battisti, D. S., Forster, P. M., Paynter, D., Smith, C. J., and Shiogama, H.: Biased Estimates of Equilibrium Climate Sensitivity and Transient Climate Response Derived From Historical CMIP6 Simulations, Geophys. Res. Lett., 48, https://doi.org/10.1029/2021GL095778, 2021. a
Dong, Y., Armour, K. C., Battisti, D. S., and Blanchard-Wrigglesworth, E.: Two-way teleconnections between the Southern Ocean and the tropical Pacific via a dynamic feedback, J. Clim., 35, 6267–6282, 2022. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C., Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M., Worley, P. H., Yang, Z.-L., and Zhang, M.: The Community Climate System Model Version 4, J. Clim., 24, 4973–4991, https://doi.org/10.1175/2011JCLI4083.1, 2011. a
Guo, H., John, J. G., Blanton, C., McHugh, C., Nikonov, S., Radhakrishnan, A., Rand, K., Zadeh, N. T., Balaji, V., Durachta, J., Dupuis, C., Menzel, R., Robinson, T., Underwood, S., Vahlenkamp, H., Bushuk, M., Dunne, K. A., Dussin, R., Gauthier, P. P., Ginoux, P., Griffies, S. M., Hallberg, R., Harrison, M., Hurlin, W., Lin, P., Malyshev, S., Naik, V., Paulot, F., Paynter, D. J., Ploshay, J., Reichl, B. G., Schwarzkopf, D. M., Seman, C. J., Shao, A., Silvers, L., Wyman, B., Yan, X., Zeng, Y., Adcroft, A., Dunne, J. P., Held, I. M., Krasting, J. P., Horowitz, L. W., Milly, P., Shevliakova, E., Winton, M., Zhao, M., and Zhang, R.: NOAA-GFDL GFDL-CM4 model output amip, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.8494, 2018a. a
Guo, H., John, J. G., Blanton, C., McHugh, C., Nikonov, S., Radhakrishnan, A., Rand, K., Zadeh, N. T., Balaji, V., Durachta, J., Dupuis, C., Menzel, R., Robinson, T., Underwood, S., Vahlenkamp, H., Bushuk, M., Dunne, K. A., Dussin, R., Gauthier, P. P., Ginoux, P., Griffies, S. M., Hallberg, R., Harrison, M., Hurlin, W., Lin, P., Malyshev, S., Naik, V., Paulot, F., Paynter, D. J., Ploshay, J., Reichl, B. G., Schwarzkopf, D. M., Seman, C. J., Shao, A., Silvers, L., Wyman, B., Yan, X., Zeng, Y., Adcroft, A., Dunne, J. P., Held, I. M., Krasting, J. P., Horowitz, L. W., Milly, P., Shevliakova, E., Winton, M., Zhao, M., and Zhang, R.: NOAA-GFDL GFDL-CM4 model output historical, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.8594, 2018b. a
Guo, H., John, J. G., Blanton, C., McHugh, C., Nikonov, S., Radhakrishnan, A., Rand, K., Zadeh, N. T., Balaji, V., Durachta, J., Dupuis, C., Menzel, R., Robinson, T., Underwood, S., Vahlenkamp, H., Bushuk, M., Dunne, K. A., Dussin, R., Gauthier, P. P., Ginoux, P., Griffies, S. M., Hallberg, R., Harrison, M., Hurlin, W., Lin, P., Malyshev, S., Naik, V., Paulot, F., Paynter, D. J., Ploshay, J., Reichl, B. G., Schwarzkopf, D. M., Seman, C. J., Shao, A., Silvers, L., Wyman, B., Yan, X., Zeng, Y., Adcroft, A., Dunne, J. P., Held, I. M., Krasting, J. P., Horowitz, L. W., Milly, P., Shevliakova, E., Winton, M., Zhao, M., and Zhang, R.: NOAA-GFDL GFDL-CM4 model output abrupt-4xCO2, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.8486, 2018c. a
Hajima, T., Abe, M., Arakawa, O., Suzuki, T., Komuro, Y., Ogura, T., Ogochi, K., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Ohgaito, R., Ito, A., Yamazaki, D., Ito, A., Takata, K., Watanabe, S., Kawamiya, M., and Tachiiri, K.: MIROC MIROC-ES2L model output prepared for CMIP6 CMIP historical, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5602, 2019a. a
Hajima, T., Abe, M., Arakawa, O., Suzuki, T., Komuro, Y., Ogura, T., Ogochi, K., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Ohgaito, R., Ito, A., Yamazaki, D., Ito, A., Takata, K., Watanabe, S., Kawamiya, M., and Tachiiri, K.: MIROC MIROC-ES2L model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5410, 2019b. a
Hajima, T., Abe, M., Arakawa, O., Suzuki, T., Komuro, Y., Ogura, T., Ogochi, K., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Ohgaito, R., Ito, A., Yamazaki, D., Ito, A., Takata, K., Watanabe, S., Kawamiya, M., and Tachiiri, K.: MIROC MIROC-ES2L model output prepared for CMIP6 CMIP amip, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5421, 2020. a
Hedemann, C., Mauritsen, T., Jungclaus, J., and Marotzke, J.: Reconciling Conflicting Accounts of Local Radiative Feedbacks in Climate Models, J. Clim., 35, 3131–3146, https://doi.org/10.1175/JCLI-D-21-0513.1, 2022. a
Heede, U. K. and Fedorov, A. V.: Eastern equatorial Pacific warming delayed by aerosols and thermostat response to CO2 increase, Nat. Clim. Change, 11, 696–703, https://doi.org/10.1038/s41558-021-01101-x, 2021. a, b
Heidinger, A. K., Foster, M. J., Walther, A., and Zhao, X. T.: The Pathfinder Atmospheres–Extended AVHRR Climate Dataset, Bull. Am. Meteorol. Soc., 95, 909–922, https://doi.org/10.1175/BAMS-D-12-00246.1, 2014. a
Held, I. M., Winton, M., Takahashi, K., Delworth, T., Zeng, F., and Vallis, G. K.: Probing the Fast and Slow Components of Global Warming by Returning Abruptly to Preindustrial Forcing, J. Clim., 23, 2418–2427, https://doi.org/10.1175/2009JCLI3466.1, 2010. a
Huang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., Smith, T., and Zhang, H.-M.: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1, J. Clim., 34, 2923–2939, https://doi.org/10.1175/JCLI-D-20-0166.1, 2021. a
Johns, T. C., Durman, C. F., Banks, H. T., Roberts, M. J., McLaren, A. J., Ridley, J. K., Senior, C. A., Williams, K. D., Jones, A., Rickard, G. J., Cusack, S., Ingram, W. J., Crucifix, M., Sexton, D. M. H., Joshi, M. M., Dong, B.-W., Spencer, H., Hill, R. S. R., Gregory, J. M., Keen, A. B., Pardaens, A. K., Lowe, J. A., Bodas-Salcedo, A., Stark, S., and Searl, Y.: The New Hadley Centre Climate Model (HadGEM1): Evaluation of Coupled Simulations, J. Clim., 19, 1327–1353, https://doi.org/10.1175/JCLI3712.1, 2006. a
Kang, S. M., Yu, Y., Deser, C., Zhang, X., Kang, I.-S., Lee, S.-S., Rodgers, K. B., and Ceppi, P.: Global impacts of recent Southern Ocean cooling, P. Natl. Acad. Sci. USA, 120, e2300881120, https://doi.org/10.1073/pnas.2300881120, 2023. a
Klein, S. A., Hall, A., Norris, J. R., and Pincus, R.: Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review, Surv. Geophys., 38, 1307–1329, https://doi.org/10.1007/s10712-017-9433-3, 2017. a, b
Knutti, R. and Rugenstein, M. A. A.: Feedbacks, climate sensitivity and the limits of linear models, Philos. T. R. Soc. A, 373, 20150146, https://doi.org/10.1098/rsta.2015.0146, 2015. a
Lewis, H., Bellon, G., and Dinh, T.: Upstream Large-Scale Control of Subtropical Low-Cloud Climatology, J. Clim., 36, 3289–3303, https://doi.org/10.1175/JCLI-D-22-0676.1, 2023. a
Li, C., Von Storch, J.-S., and Marotzke, J.: Deep-ocean heat uptake and equilibrium climate response, Clim. Dynam., 40, 1071–1086, https://doi.org/10.1007/s00382-012-1350-z, 2013. a
Lilly, D. K.: Models of cloud‐topped mixed layers under a strong inversion, Q. J. Roy. Meteorol. Soc.y, 94, 292–309, https://doi.org/10.1002/qj.49709440106, 1968. a
Lin, Y.-J., Cesana, G. V., Proistosescu, C., Zelinka, M. D., and Armour, K. C.: The Relative Importance of Forced and Unforced Temperature Patterns in Driving the Time Variation of Low-Cloud Feedback, J. Clim., 38, 513–529, https://doi.org/10.1175/JCLI-D-24-0014.1, 2025. a
Martin, G. M., Ringer, M. A., Pope, V. D., Jones, A., Dearden, C., and Hinton, T. J.: The Physical Properties of the Atmosphere in the New Hadley Centre Global Environmental Model (HadGEM1). Part I: Model Description and Global Climatology, J. Clim., 19, 1274–1301, https://doi.org/10.1175/JCLI3636.1, 2006. a
Mauger, G. S. and Norris, J. R.: Assessing the Impact of Meteorological History on Subtropical Cloud Fraction, J. Clim., 23, 2926–2940, https://doi.org/10.1175/2010JCLI3272.1, 2010. a
Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen, Y., Spangenberg, D. A., Arduini, R. F., Trepte, Q. Z., Smith, W. L., Ayers, J. K., Gibson, S. C., Miller, W. F., Hong, G., Chakrapani, V., Takano, Y., Liou, K.-N., Xie, Y., and Yang, P.: CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part I: Algorithms, IEEE T. Geosci. Remote Sens., 49, 4374–4400, https://doi.org/10.1109/TGRS.2011.2144601, 2011. a
Modak, A. and Mauritsen, T.: Better-constrained climate sensitivity when accounting for dataset dependency on pattern effect estimates, Atmos. Chem. Phys., 23, 7535–7549, https://doi.org/10.5194/acp-23-7535-2023, 2023. a
Myers, T. A. and Norris, J. R.: On the Relationships between Subtropical Clouds and Meteorology in Observations and CMIP3 and CMIP5 Models, J. Clim., 28, 2945–2967, https://doi.org/10.1175/JCLI-D-14-00475.1, 2015. a
Myers, T. A. and Norris, J. R.: Reducing the uncertainty in subtropical cloud feedback, Geophys. Res. Lett., 43, 2144–2148, https://doi.org/10.1002/2015GL067416, 2016. a
Myers, T. A. and Zelinka, M.: Meteorological Cloud Radiative Kernel code, GitHub [code], https://github.com/tamyers87/meteorological_cloud_radiative_kernels (last access: 4 April 2022), 2022. 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
Myers, T. A., Zelinka, M. D., and Klein, S. A.: Observational Constraints on the Cloud Feedback Pattern Effect, J. Clim., pp. 1–31, https://doi.org/10.1175/JCLI-D-22-0862.1, 2023. a, b, c, d
Olonscheck, D., Rugenstein, M., and Marotzke, J.: Broad Consistency Between Observed and Simulated Trends in Sea Surface Temperature Patterns, Geophys. Res. Lett., 47, e2019GL086773, https://doi.org/10.1029/2019GL086773, 2020. a
Pincus, R., Baker, M. B., and Bretherton, C. S.: What Controls Stratocumulus Radiative Properties? Lagrangian Observations of Cloud Evolution, J. Atmos. Sci., 54, 2215–2236, https://doi.org/10.1175/1520-0469(1997)054<2215:WCSRPL>2.0.CO;2, 1997. a
Platnick, S., Ackerman, S. A., King, M. D., Meyer, K., Menzel, W. P., Holz, R. E., Baum, B. A., and Yang, P.: MODIS atmosphere L2 cloud product (06 L2), NASA MODIS Adaptive Processing System, Goddard Space Flight Center, https://doi.org/10.5067/MODIS/MOD06_L2.006, 2015. a
Proistosescu, C. and Huybers, P. J.: Slow climate mode reconciles historical and model-based estimates of climate sensitivity, Sci. Adv., 3, e1602821, https://doi.org/10.1126/sciadv.1602821, 2017. a
Proistosescu, C., Donohoe, A., Armour, K. C., Roe, G. H., Stuecker, M. F., and Bitz, C. M.: Radiative feedbacks from stochastic variability in surface temperature and radiative imbalance, Geophys. Res. Lett., 45, 5082–5094, 2018. a
Qu, X., Hall, A., Klein, S. A., and DeAngelis, A. M.: Positive tropical marine lowcloud cover feedback inferred from cloudcontrolling factors, Geophys. Res. Lett., 42, 7767–7775, https://doi.org/10.1002/2015GL065627, 2015. a
Raddatz, T. J., Reick, C. H., Knorr, W., Kattge, J., Roeckner, E., Schnur, R., Schnitzler, K.-G., Wetzel, P., and Jungclaus, J.: Will the tropical land biosphere dominate the climate–carbon cycle feedback during the twenty-first century?, Clim. Dynam., 29, 565–574, https://doi.org/10.1007/s00382-007-0247-8, 2007. a
Ridley, J., Menary, M., Kuhlbrodt, T., Andrews, M., and Andrews, T.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP amip, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5853, 2019a. a
Ridley, J., Menary, M., Kuhlbrodt, T., Andrews, M., and Andrews, T.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP historical, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.6109, 2019b. a
Ridley, J., Menary, M., Kuhlbrodt, T., Andrews, M., and Andrews, T.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5839, 2019c. a
Ringer, M. A., Martin, G. M., Greeves, C. Z., Hinton, T. J., James, P. M., Pope, V. D., Scaife, A. A., Stratton, R. A., Inness, P. M., Slingo, J. M., and Yang, G.-Y.: The Physical Properties of the Atmosphere in the New Hadley Centre Global Environmental Model (HadGEM1). Part II: Aspects of Variability and Regional Climate, J. Clim., 19, 1302–1326, https://doi.org/10.1175/JCLI3713.1, 2006. a
Rossow, W., Golea, V., Walker, A., Knapp, K., Young, A., Hankins, B., and Inamdar, A.: International Satellite Cloud Climatology Project (ISCCP) Climate Data Record, H-Series, NOAA National Centers for Environmental Information, https://doi.org/10.7289/V5QZ281S, 2017. a
Rugenstein, M., Bloch-Johnson, J., Abe-Ouchi, A., Andrews, T., Beyerle, U., Cao, L., Chadha, T., Danabasoglu, G., Dufresne, J.-L., Duan, L., Foujols, M.-A., Frölicher, T., Geoffroy, O., Gregory, J., Knutti, R., Li, C., Marzocchi, A., Mauritsen, T., Menary, M., Moyer, E., Nazarenko, L., Paynter, D., Saint-Martin, D., Schmidt, G. A., Yamamoto, A., and Yang, S.: LongRunMIP: Motivation and Design for a Large Collection of Millennial-Length AOGCM Simulations, Bull. Am. Meteorol. Soc., 100, 2551–2570, https://doi.org/10.1175/BAMS-D-19-0068.1, 2019. a
Rugenstein, M., Bloch‐Johnson, J., Gregory, J., Andrews, T., Mauritsen, T., Li, C., Frölicher, T. L., Paynter, D., Danabasoglu, G., Yang, S., Dufresne, J., Cao, L., Schmidt, G. A., Abe‐Ouchi, A., Geoffroy, O., and Knutti, R.: Equilibrium Climate Sensitivity Estimated by Equilibrating Climate Models, Geophys. Res. Lett., 47, e2019GL083898, https://doi.org/10.1029/2019GL083898, 2020. a
Rugenstein, M., Dhame, S., Olonscheck, D., Wills, R. J., Watanabe, M., and Seager, R.: Connecting the SST pattern problem and the hot model problem, Geophys. Res. Lett., 50, e2023GL105488, https://doi.org/10.1029/2023GL105488, 2023a. a
Rugenstein, M., Zelinka, M., Karnauskas, K., Ceppi, P., and Andrews, T.: Patterns of surface warming matter for climate sensitivity, Eos, 104, https://doi.org/10.1029/2023EO230411, 2023b. a
Rugenstein, M. A. A., Gregory, J. M., Schaller, N., Sedláček, J., and Knutti, R.: Multiannual Ocean–Atmosphere Adjustments to Radiative Forcing, J. Clim., 29, 5643–5659, https://doi.org/10.1175/JCLI-D-16-0312.1, 2016. 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. Clim., 33, 7717–7734, https://doi.org/10.1175/JCLI-D-19-1028.1, 2020. a, b, c, d, e, f
Seager, R., Cane, M., Henderson, N., Lee, D.-E., Abernathey, R., and Zhang, H.: Strengthening tropical Pacific zonal sea surface temperature gradient consistent with rising greenhouse gases, Nat. Clim. Change, 9, 517–522, https://doi.org/10.1038/s41558-019-0505-x, 2019. a
Senior, C. A. and Mitchell, J. F. B.: The time-dependence of climate sensitivity, Geophys. Res. Lett., 27, 2685–2688, https://doi.org/10.1029/2000GL011373, 2000. a
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence, Rev. Geophys., 58, https://doi.org/10.1029/2019RG000678, 2020. a, b, c, d
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G., Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A., et al.: Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX radiation panel, Bull. Am. Meteorol. Soc., 94, 1031–1049, 2013. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 CMIP amip, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.3535, 2019a. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.3610, 2019b. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.3532, 2019c. a
Tam, R. Y. S., Myers, T. A., Zelinka, M., Proistosescu, C., Lin, Y. J., and Marvel, K.: ccf_project: Analysis code for Meteorological Drivers of the Low-Cloud Radiative Feedback Pattern Effect and its Uncertainty, Zenodo [code], https://doi.org/10.5281/zenodo.4417809, 2026. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP amip, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5857, 2019a. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.6113, 2019b. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5843, 2019c. a
Tatebe, H. and Watanabe, M.: MIROC MIROC6 model output prepared for CMIP6 CMIP amip, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5422, 2018a. a
Tatebe, H. and Watanabe, M.: MIROC MIROC6 model output prepared for CMIP6 CMIP historical, https://doi.org/10.22033/ESGF/CMIP6.5603, 2018b. a
Tatebe, H. and Watanabe, M.: MIROC MIROC6 model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.5411, 2018c. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, Bull. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
Von Salzen, K., Scinocca, J. F., McFarlane, N. A., Li, J., Cole, J. N. S., Plummer, D., Verseghy, D., Reader, M. C., Ma, X., Lazare, M., and Solheim, L.: The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4), Part I: Representation of Physical Processes, Atmos.-Ocean, 51, 104–125, https://doi.org/10.1080/07055900.2012.755610, 2013. a
Watanabe, M., Suzuki, T., O’ishi, R., Komuro, Y., Watanabe, S., Emori, S., Takemura, T., Chikira, M., Ogura, T., Sekiguchi, M., Takata, K., Yamazaki, D., Yokohata, T., Nozawa, T., Hasumi, H., Tatebe, H., and Kimoto, M.: Improved Climate Simulation by MIROC5: Mean States, Variability, and Climate Sensitivity, J. Clim., 23, 6312–6335, https://doi.org/10.1175/2010JCLI3679.1, 2010. a
Watanabe, M., Dufresne, J.-L., Kosaka, Y., Mauritsen, T., and Tatebe, H.: Enhanced warming constrained by past trends in equatorial Pacific sea surface temperature gradient, Nat. Clim. Change, 11, 33–37, https://doi.org/10.1038/s41558-020-00933-3, 2021. a
Watanabe, M., Kang, S. M., Collins, M., Hwang, Y.-T., McGregor, S., and Stuecker, M. F.: Possible shift in controls of the tropical Pacific surface warming pattern, Nature, 630, 315–324, 2024. a
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872, https://doi.org/10.5194/gmd-4-845-2011, 2011. a
Wills, R. C. J., Dong, Y., Proistosecu, C., Armour, K. C., and Battisti, D. S.: Systematic Climate Model Biases in the LargeScale Patterns of Recent SeaSurface Temperature and SeaLevel Pressure Change, Geophys. Res. Lett., 49, https://doi.org/10.1029/2022GL100011, 2022. a, b, c, d
Yukimoto, S., Yoshimura, H., Hosaka, M., Sakami, T., Tsujino, H., Hirabara, M., Tanaka, T. Y., Deushi, M., Obata, A., Nakano, H., Adachi, Y., Shindo, E., Yabu, S., Ose, T., and Kitoh, A.: Meteorological Research Institute-Earth System Model Version 1 (MRI-ESM1) -Model Description-, Meteorological Research Institute, https://doi.org/10.11483/mritechrepo.64, 2011. a
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP amip, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.6758, 2019a. a
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.6842, 2019b. a
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation, https://doi.org/10.22033/ESGF/CMIP6.6755, 2019c. 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. Clim., 25, 3736–3754, https://doi.org/10.1175/JCLI-D-11-00249.1, 2012. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., PoChedley, 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, 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. 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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