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Journal articles on the topic 'Cloud feedback'

Author: Grafiati

Published: 22 February 2025

Last updated: 25 July 2025

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1

Zhou, Chen, Mark D. Zelinka, Andrew E. Dessler, and Ping Yang. "An Analysis of the Short-Term Cloud Feedback Using MODIS Data." Journal of Climate 26, no. 13 (2013): 4803–15. http://dx.doi.org/10.1175/jcli-d-12-00547.1.

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Abstract The cloud feedback in response to short-term climate variations is estimated from cloud measurements combined with offline radiative transfer calculations. The cloud measurements are made by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite and cover the period 2000–10. Low clouds provide a strong negative cloud feedback, mainly because of their impact in the shortwave (SW) portion of the spectrum. Midlevel clouds provide a positive net cloud feedback that is a combination of a positive SW feedback partially canceled by a negative feedback in the long

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2

Zelinka, Mark D., Stephen A. Klein, Karl E. Taylor, et al. "Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5*." Journal of Climate 26, no. 14 (2013): 5007–27. http://dx.doi.org/10.1175/jcli-d-12-00555.1.

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Abstract Using five climate model simulations of the response to an abrupt quadrupling of CO2, the authors perform the first simultaneous model intercomparison of cloud feedbacks and rapid radiative adjustments with cloud masking effects removed, partitioned among changes in cloud types and gross cloud properties. Upon CO2 quadrupling, clouds exhibit a rapid reduction in fractional coverage, cloud-top pressure, and optical depth, with each contributing equally to a 1.1 W m−2 net cloud radiative adjustment, primarily from shortwave radiation. Rapid reductions in midlevel clouds and optically th

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3

Zelinka, Mark D., Stephen A. Klein, and Dennis L. Hartmann. "Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels." Journal of Climate 25, no. 11 (2012): 3715–35. http://dx.doi.org/10.1175/jcli-d-11-00248.1.

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This study proposes a novel technique for computing cloud feedbacks using histograms of cloud fraction as a joint function of cloud-top pressure (CTP) and optical depth (τ). These histograms were generated by the International Satellite Cloud Climatology Project (ISCCP) simulator that was incorporated into doubled-CO2 simulations from 11 global climate models in the Cloud Feedback Model Intercomparison Project. The authors use a radiative transfer model to compute top of atmosphere flux sensitivities to cloud fraction perturbations in each bin of the histogram for each month and latitude. Mult

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Yoshimori, Masakazu, F. Hugo Lambert, Mark J. Webb, and Timothy Andrews. "Fixed Anvil Temperature Feedback: Positive, Zero, or Negative?" Journal of Climate 33, no. 7 (2020): 2719–39. http://dx.doi.org/10.1175/jcli-d-19-0108.1.

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AbstractThe fixed anvil temperature (FAT) theory describes a mechanism for how tropical anvil clouds respond to global warming and has been used to argue for a robust positive longwave cloud feedback. A constant cloud anvil temperature, due to increased anvil altitude, has been argued to lead to a “zero cloud emission change” feedback, which can be considered positive relative to the negative feedback associated with cloud anvil warming when cloud altitude is unchanged. Here, partial radiative perturbation (PRP) analysis is used to quantify the radiative feedback caused by clouds that follow t

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Dawson, Emma, and Kathleen A. Schiro. "Tropical High Cloud Feedback Relationships to Climate Sensitivity." Journal of Climate 38, no. 2 (2025): 583–96. https://doi.org/10.1175/jcli-d-24-0218.1.

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Abstract Clouds constitute a large portion of uncertainty in predictions of equilibrium climate sensitivity (ECS). While low cloud feedbacks have been the focus of intermodel studies due to their high variability among global climate models, tropical high cloud feedbacks also exhibit considerable uncertainty. Here, we apply the cloud radiative kernel technique of Zelinka et al. to 22 models across the CMIP5 and CMIP6 ensembles to survey tropical high cloud feedbacks and analyze their relationship to ECS. We find that the net high cloud feedback and its altitude and optical depth feedback compo

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6

Zhu, Ping, James J. Hack, and Jeffrey T. Kiehl. "Diagnosing Cloud Feedbacks in General Circulation Models." Journal of Climate 20, no. 11 (2007): 2602–22. http://dx.doi.org/10.1175/jcli4140.1.

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Abstract In this study, it is shown that the NCAR and GFDL GCMs exhibit a marked difference in climate sensitivity of clouds and radiative fluxes in response to doubled CO2 and ±2-K SST perturbations. The GFDL model predicted a substantial decrease in cloud amount and an increase in cloud condensate in the warmer climate, but produced a much weaker change in net cloud radiative forcing (CRF) than the NCAR model. Using a multiple linear regression (MLR) method, the full-sky radiative flux change at the top of the atmosphere was successfully decomposed into individual components associated with

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Zelinka, Mark D., Stephen A. Klein, and Dennis L. Hartmann. "Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth." Journal of Climate 25, no. 11 (2012): 3736–54. http://dx.doi.org/10.1175/jcli-d-11-00249.1.

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Cloud radiative kernels and histograms of cloud fraction, both as functions of cloud-top pressure and optical depth, are used to quantify cloud amount, altitude, and optical depth feedbacks. The analysis is applied to doubled-CO2 simulations from 11 global climate models in the Cloud Feedback Model Intercomparison Project. Global, annual, and ensemble mean longwave (LW) and shortwave (SW) cloud feedbacks are positive, with the latter nearly twice as large as the former. The robust increase in cloud-top altitude in both the tropics and extratropics is the dominant contributor to the positive LW

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Sun, De-Zheng, Yongqiang Yu, and Tao Zhang. "Tropical Water Vapor and Cloud Feedbacks in Climate Models: A Further Assessment Using Coupled Simulations." Journal of Climate 22, no. 5 (2009): 1287–304. http://dx.doi.org/10.1175/2008jcli2267.1.

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Abstract By comparing the response of clouds and water vapor to ENSO forcing in nature with that in Atmospheric Model Intercomparison Project (AMIP) simulations by some leading climate models, an earlier evaluation of tropical cloud and water vapor feedbacks has revealed the following two common biases in the models: 1) an underestimate of the strength of the negative cloud albedo feedback and 2) an overestimate of the positive feedback from the greenhouse effect of water vapor. Extending the same analysis to the fully coupled simulations of these models as well as other Intergovernmental Pane

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9

Zhang, Minghua, and Christopher Bretherton. "Mechanisms of Low Cloud–Climate Feedback in Idealized Single-Column Simulations with the Community Atmospheric Model, Version 3 (CAM3)." Journal of Climate 21, no. 18 (2008): 4859–78. http://dx.doi.org/10.1175/2008jcli2237.1.

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Abstract This study investigates the physical mechanism of low cloud feedback in the Community Atmospheric Model, version 3 (CAM3) through idealized single-column model (SCM) experiments over the subtropical eastern oceans. Negative cloud feedback is simulated from stratus and stratocumulus that is consistent with previous diagnostics of cloud feedbacks in CAM3 and its predecessor versions. The feedback occurs through the interaction of a suite of parameterized processes rather than from any single process. It is caused by the larger amount of in-cloud liquid water in stratus clouds from conve

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Lohmann, Ulrike, and David Neubauer. "The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2." Atmospheric Chemistry and Physics 18, no. 12 (2018): 8807–28. http://dx.doi.org/10.5194/acp-18-8807-2018.

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Abstract. How clouds change in a warmer climate remains one of the largest uncertainties for the equilibrium climate sensitivity (ECS). While a large spread in the cloud feedback arises from low-level clouds, it was recently shown that mixed-phase clouds are also important for ECS. If mixed-phase clouds in the current climate contain too few supercooled cloud droplets, too much ice will change to liquid water in a warmer climate. As shown by Tan et al. (2016), this overestimates the negative cloud-phase feedback and underestimates ECS in the CAM global climate model (GCM). Here we use the newe

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Dagan, Guy. "Equilibrium climate sensitivity increases with aerosol concentration due to changes in precipitation efficiency." Atmospheric Chemistry and Physics 22, no. 24 (2022): 15767–75. http://dx.doi.org/10.5194/acp-22-15767-2022.

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Abstract. How Earth's climate reacts to anthropogenic forcing is one of the most burning questions faced by today's scientific community. A leading source of uncertainty in estimating this sensitivity is related to the response of clouds. Under the canonical climate-change perspective of forcings and feedbacks, the effect of anthropogenic aerosols on clouds is categorized under the forcing component, while the modifications of the radiative properties of clouds due to climate change are considered in the feedback component. Each of these components contributes the largest portion of uncertaint

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12

Lee, Wan-Ho, and Richard C. J. Somerville. "Effects of alternative cloud radiation parameterizations in a general circulation model." Annales Geophysicae 14, no. 1 (1996): 107–14. http://dx.doi.org/10.1007/s00585-996-0107-6.

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Abstract. Using the National Center for Atmospheric Research (NCAR) general circulation model (CCM2), a suite of alternative cloud radiation parameterizations has been tested. Our methodology relies on perpetual July integrations driven by ±2 K sea surface temperature forcing. The tested parameterizations include relative humidity based clouds and versions of schemes involving a prognostic cloud water budget. We are especially interested in testing the effect of cloud optical thickness feedbacks on global climate sensitivity. All schemes exhibit negative cloud radiation feedbacks, i.e., cloud

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13

Webb, Mark J., Adrian P. Lock, Christopher S. Bretherton, et al. "The impact of parametrized convection on cloud feedback." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2054 (2015): 20140414. http://dx.doi.org/10.1098/rsta.2014.0414.

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We investigate the sensitivity of cloud feedbacks to the use of convective parametrizations by repeating the CMIP5/CFMIP-2 AMIP/AMIP + 4K uniform sea surface temperature perturbation experiments with 10 climate models which have had their convective parametrizations turned off. Previous studies have suggested that differences between parametrized convection schemes are a leading source of inter-model spread in cloud feedbacks. We find however that ‘ConvOff’ models with convection switched off have a similar overall range of cloud feedbacks compared with the standard configurations. Furthermore

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14

Fu, Q., M. Baker, and D. L. Hartmann. "Tropical cirrus and water vapor: an effective Earth infrared iris feedback?" Atmospheric Chemistry and Physics 2, no. 1 (2002): 31–37. http://dx.doi.org/10.5194/acp-2-31-2002.

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Abstract. We revisit a model of feedback processes proposed by Lindzen et al. (2001), in which an assumed 22% reduction in the area of tropical high clouds per degree increase in sea surface temperature produces negative feedbacks associated with upper tropospheric water vapor and cloud radiative effects. We argue that the water vapor feedback is overestimated in Lindzen et al. (2001) by at least 60%, and that the high cloud feedback is small. Although not mentioned by Lindzen et al. (2001), tropical low clouds make a significant contribution to their negative feedback, which is also overestim

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15

Gettelman, A., J. E. Kay, and J. T. Fasullo. "Spatial Decomposition of Climate Feedbacks in the Community Earth System Model." Journal of Climate 26, no. 11 (2013): 3544–61. http://dx.doi.org/10.1175/jcli-d-12-00497.1.

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Abstract An ensemble of simulations from different versions of the Community Atmosphere Model in the Community Earth System Model (CESM) is used to investigate the processes responsible for the intermodel spread in climate sensitivity. In the CESM simulations, the climate sensitivity spread is primarily explained by shortwave cloud feedbacks on the equatorward flank of the midlatitude storm tracks. Shortwave cloud feedbacks have been found to explain climate sensitivity spread in previous studies, but the location of feedback differences was in the subtropics rather than in the storm tracks as

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16

Lauer, Axel, Kevin Hamilton, Yuqing Wang, Vaughan T. J. Phillips, and Ralf Bennartz. "The Impact of Global Warming on Marine Boundary Layer Clouds over the Eastern Pacific—A Regional Model Study." Journal of Climate 23, no. 21 (2010): 5844–63. http://dx.doi.org/10.1175/2010jcli3666.1.

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Abstract Cloud simulations and cloud–climate feedbacks in the tropical and subtropical eastern Pacific region in 16 state-of-the-art coupled global climate models (GCMs) and in the International Pacific Research Center (IPRC) Regional Atmospheric Model (iRAM) are examined. The authors find that the simulation of the present-day mean cloud climatology for this region in the GCMs is very poor and that the cloud–climate feedbacks vary widely among the GCMs. By contrast, iRAM simulates mean clouds and interannual cloud variations that are quite similar to those observed in this region. The model a

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17

Yue, Qing, Brian H. Kahn, Eric J. Fetzer, Sun Wong, Xianglei Huang, and Mathias Schreier. "Temporal and Spatial Characteristics of Short-Term Cloud Feedback on Global and Local Interannual Climate Fluctuations from A-Train Observations." Journal of Climate 32, no. 6 (2019): 1875–93. http://dx.doi.org/10.1175/jcli-d-18-0335.1.

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AbstractObservations from multiple sensors on the NASA Aqua satellite are used to estimate the temporal and spatial variability of short-term cloud responses (CR) and cloud feedbacks λ for different cloud types, with respect to the interannual variability within the A-Train era (July 2002–June 2017). Short-term cloud feedbacks by cloud type are investigated both globally and locally by three different definitions in the literature: 1) the global-mean cloud feedback parameter λGG from regressing the global-mean cloud-induced TOA radiation anomaly ΔRG with the global-mean surface temperature cha

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18

Gettelman, A., L. Lin, B. Medeiros, and J. Olson. "Climate Feedback Variance and the Interaction of Aerosol Forcing and Feedbacks." Journal of Climate 29, no. 18 (2016): 6659–75. http://dx.doi.org/10.1175/jcli-d-16-0151.1.

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Abstract Aerosols can influence cloud radiative effects and, thus, may alter interpretation of how Earth’s radiative budget responds to climate forcing. Three different ensemble experiments from the same climate model with different greenhouse gas and aerosol scenarios are used to analyze the role of aerosols in climate feedbacks and their spread across initial condition ensembles of transient climate simulations. The standard deviation of global feedback parameters across ensemble members is low, typically 0.02 W m−2 K−1. Feedbacks from high (8.5 W m−2) and moderate (4.5 W m−2) year 2100 forc

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19

Fu, Q., M. Baker, and D. L. Hartmann. "Tropical cirrus and water vapor: an effective Earth infrared iris feedback?" Atmospheric Chemistry and Physics Discussions 1, no. 1 (2001): 221–38. http://dx.doi.org/10.5194/acpd-1-221-2001.

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Abstract. We revisit a model of feedback processes proposed by Lindzen et al. (2001), in which an assumed 22% reduction in the area of tropical high clouds per degree of sea surface temperature increase produces negative feedbacks associated with upper tropospheric water vapor and cloud radiative effects. We argue that the water vapor feedback is overestimated in Lindzen et al. (2001) by at least 60%, and that the high cloud feedback should be small. Although not mentioned by Lindzen et al, tropical low clouds make a significant contribution to their negative feedback, which is also overestima

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20

Bretherton, Christopher S. "Insights into low-latitude cloud feedbacks from high-resolution models." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2054 (2015): 20140415. http://dx.doi.org/10.1098/rsta.2014.0415.

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Cloud feedbacks are a leading source of uncertainty in the climate sensitivity simulated by global climate models (GCMs). Low-latitude boundary-layer and cumulus cloud regimes are particularly problematic, because they are sustained by tight interactions between clouds and unresolved turbulent circulations. Turbulence-resolving models better simulate such cloud regimes and support the GCM consensus that they contribute to positive global cloud feedbacks. Large-eddy simulations using sub-100 m grid spacings over small computational domains elucidate marine boundary-layer cloud response to green

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21

Dessler, A. E. "A Determination of the Cloud Feedback from Climate Variations over the Past Decade." Science 330, no. 6010 (2010): 1523–27. http://dx.doi.org/10.1126/science.1192546.

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Estimates of Earth's climate sensitivity are uncertain, largely because of uncertainty in the long-term cloud feedback. I estimated the magnitude of the cloud feedback in response to short-term climate variations by analyzing the top-of-atmosphere radiation budget from March 2000 to February 2010. Over this period, the short-term cloud feedback had a magnitude of 0.54 ± 0.74 (2σ) watts per square meter per kelvin, meaning that it is likely positive. A small negative feedback is possible, but one large enough to cancel the climate’s positive feedbacks is not supported by these observations. Bot

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22

Zhang, Yuanchong, Zhonghai Jin, and Matteo Ottaviani. "Comparison of Clouds and Cloud Feedback between AMIP5 and AMIP6." Atmosphere 14, no. 6 (2023): 978. http://dx.doi.org/10.3390/atmos14060978.

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We examine the changes in clouds and cloud feedback between Phase 5 (AMIP5) and Phase 6 (AMIP6) of the Atmospheric Model Intercomparison Project. Each model is perturbed by uniformly increasing the sea surface temperature by 4 K. The simulated cloud fraction, the perturbed states and cloud radiative kernels are used to derive cloud feedback in the shortwave (SW), longwave (LW) and their sum (Net). Compared to AMIP5, the cloud fraction in AMIP6 increases by 9.1%, while the perturbation leads to a 0.25% decrease. The Net cloud feedback at the top of the atmosphere (TOA) is almost double (174%).

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23

Lin, Jia-Lin, Taotao Qian, and Toshiaki Shinoda. "Stratocumulus Clouds in Southeastern Pacific Simulated by Eight CMIP5–CFMIP Global Climate Models." Journal of Climate 27, no. 8 (2014): 3000–3022. http://dx.doi.org/10.1175/jcli-d-13-00376.1.

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Abstract This study examines the stratocumulus clouds and associated cloud feedback in the southeast Pacific (SEP) simulated by eight global climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and Cloud Feedback Model Intercomparison Project (CFMIP) using long-term observations of clouds, radiative fluxes, cloud radiative forcing (CRF), sea surface temperature (SST), and large-scale atmosphere environment. The results show that the state-of-the-art global climate models still have significant difficulty in simulating the SEP stratocumulus clouds and ass

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Sun, De-Zheng, John Fasullo, Tao Zhang, and Andres Roubicek. "On the Radiative and Dynamical Feedbacks over the Equatorial Pacific Cold Tongue." Journal of Climate 16, no. 14 (2003): 2425–32. http://dx.doi.org/10.1175/2786.1.

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Abstract An analysis of the climatic feedbacks in the NCAR Community Climate Model, version 3 (CCM3) over the equatorial Pacific cold tongue is presented. Using interannual signals in the underlying SST, the radiative and dynamical feedbacks have been calculated using both observations and outputs from the NCAR CCM3. The results show that the positive feedback from the greenhouse effect of water vapor in the model largely agrees with that from observations. The dynamical feedback from the atmospheric transport in the model is also comparable to that from observations. However, the negative fee

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25

Masters, T. "On the determination of the global cloud feedback from satellite measurements." Earth System Dynamics Discussions 3, no. 1 (2012): 73–90. http://dx.doi.org/10.5194/esdd-3-73-2012.

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Abstract. A detailed analysis is presented in order to determine the sensitivity of the estimated short-term cloud feedback to choices of temperature datasets, sources of top-of-atmosphere (TOA) radiative flux data, and temporal averaging. It is shown that the results of a previous analysis, which suggested a likely positive value for the short-term cloud feedback, depended upon combining radiative fluxes from satellite and reanalysis data when determining the cloud radiative forcing (CRF). These results are contradicted when ΔCRF is derived from NASA's Clouds and Earth's Radiant Energy System

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26

Stephens, Graeme L. "Cloud Feedbacks in the Climate System: A Critical Review." Journal of Climate 18, no. 2 (2005): 237–73. http://dx.doi.org/10.1175/jcli-3243.1.

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Abstract This paper offers a critical review of the topic of cloud–climate feedbacks and exposes some of the underlying reasons for the inherent lack of understanding of these feedbacks and why progress might be expected on this important climate problem in the coming decade. Although many processes and related parameters come under the influence of clouds, it is argued that atmospheric processes fundamentally govern the cloud feedbacks via the relationship between the atmospheric circulations, cloudiness, and the radiative and latent heating of the atmosphere. It is also shown how perturbatio

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27

Webb, Mark J., Timothy Andrews, Alejandro Bodas-Salcedo, et al. "The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6." Geoscientific Model Development 10, no. 1 (2017): 359–84. http://dx.doi.org/10.5194/gmd-10-359-2017.

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Abstract. The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud–climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted exper

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Chen, Lin, Yongqiang Yu, and De-Zheng Sun. "Cloud and Water Vapor Feedbacks to the El Niño Warming: Are They Still Biased in CMIP5 Models?" Journal of Climate 26, no. 14 (2013): 4947–61. http://dx.doi.org/10.1175/jcli-d-12-00575.1.

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Abstract Previous evaluations of model simulations of the cloud and water vapor feedbacks in response to El Niño warming have singled out two common biases in models from phase 3 of the Coupled Model Intercomparison Project (CMIP3): an underestimate of the negative feedback from the shortwave cloud radiative forcing (SWCRF) and an overestimate of the positive feedback from the greenhouse effect of water vapor. Here, the authors check whether these two biases are alleviated in the CMIP5 models. While encouraging improvements are found, particularly in the simulation of the negative SWCRF feedba

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Chen, Ying-Wen, Tatsuya Seiki, Chihiro Kodama, Masaki Satoh, Akira T. Noda, and Yohei Yamada. "High Cloud Responses to Global Warming Simulated by Two Different Cloud Microphysics Schemes Implemented in the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)." Journal of Climate 29, no. 16 (2016): 5949–64. http://dx.doi.org/10.1175/jcli-d-15-0668.1.

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Abstract This study examines cloud responses to global warming using a global nonhydrostatic model with two different cloud microphysics schemes. The cloud microphysics schemes tested here are the single- and double-moment schemes with six water categories: these schemes are referred to as NSW6 and NDW6, respectively. Simulations of one year for NSW6 and one boreal summer for NDW6 are performed using the nonhydrostatic icosahedral atmospheric model with a mesh size of approximately 14 km. NSW6 and NDW6 exhibit similar changes in the visible cloud fraction under conditions of global warming. Th

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Zhang, Bosong, Ryan J. Kramer, and Brian J. Soden. "Radiative Feedbacks Associated with the Madden–Julian Oscillation." Journal of Climate 32, no. 20 (2019): 7055–65. http://dx.doi.org/10.1175/jcli-d-19-0144.1.

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Abstract Radiative kernels derived from CloudSat/CALIPSO measurements are used to diagnose radiative feedbacks induced by the Madden–Julian oscillation (MJO). Over the Indo-Pacific warm pool, positive cloud and water vapor feedbacks are coincident with the convective envelope of the MJO during its active phases, whereas the lapse rate feedback shows faster eastward propagation than the convective envelope. During phase 2/3, when the convective envelope is over the Indian Ocean, water vapor exhibits a vertically coherent response, with the largest anomalies and strongest feedback in the midtrop

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Crook, Julia A., Piers M. Forster, and Nicola Stuber. "Spatial Patterns of Modeled Climate Feedback and Contributions to Temperature Response and Polar Amplification." Journal of Climate 24, no. 14 (2011): 3575–92. http://dx.doi.org/10.1175/2011jcli3863.1.

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Abstract Spatial patterns of local climate feedback and equilibrium partial temperature responses are produced from eight general circulation models with slab oceans forced by doubling carbon dioxide (CO2). The analysis is extended to other forcing mechanisms with the Met Office Hadley Centre slab ocean climate model version 3 (HadSM3). In agreement with previous studies, the greatest intermodel differences are in the tropical cloud feedbacks. However, the greatest intermodel spread in the equilibrium temperature response comes from the water vapor plus lapse rate feedback, not clouds, disagre

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Stephens, Graeme L., Susan van den Heever, and Lyle Pakula. "Radiative–Convective Feedbacks in Idealized States of Radiative–Convective Equilibrium." Journal of the Atmospheric Sciences 65, no. 12 (2008): 3899–916. http://dx.doi.org/10.1175/2008jas2524.1.

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Abstract This paper examines feedbacks between the radiative heating of clouds and convection in the context of numerical radiative–convective equilibrium experiments conducted using both 2D and 3D versions of a cloud-resolving model. The experiments are conducted on a large domain, and equilibria develop as juxtaposed regions of dry and moist air that are connected and sustained by circulations between them. The scales of such variability are large and differ significantly between the 2D and 3D versions of the experiments. Three sensitivity experiments were conducted which, when compared to t

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Kuma, Peter, Frida A. M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland. "Machine learning of cloud types in satellite observations and climate models." Atmospheric Chemistry and Physics 23, no. 1 (2023): 523–49. http://dx.doi.org/10.5194/acp-23-523-2023.

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Abstract. Uncertainty in cloud feedbacks in climate models is a major limitation in projections of future climate. Therefore, evaluation and improvement of cloud simulation are essential to ensure the accuracy of climate models. We analyse cloud biases and cloud change with respect to global mean near-surface temperature (GMST) in climate models relative to satellite observations and relate them to equilibrium climate sensitivity, transient climate response and cloud feedback. For this purpose, we develop a supervised deep convolutional artificial neural network for determination of cloud type

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Kuma, Peter, Frida A.-M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland. "Machine learning of cloud types in satellite observations and climate models." Atmospheric Chemistry and Physics 23, no. 1 (2023): 523–49. https://doi.org/10.5281/zenodo.7533870.

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Uncertainty in cloud feedbacks in climate models is a major limitation in projections of future climate. Therefore, evaluation and improvement of cloud simulation are essential to ensure the accuracy of climate models. We analyse cloud biases and cloud change with respect to global mean near-surface temperature (GMST) in climate models relative to satellite observations and relate them to equilibrium climate sensitivity, transient climate response and cloud feedback. For this purpose, we develop a supervised deep convolutional artificial neural network for determination of cloud types from low

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Larson, Kristin, and Dennis L. Hartmann. "Interactions among Cloud, Water Vapor, Radiation, and Large-Scale Circulation in the Tropical Climate. Part I: Sensitivity to Uniform Sea Surface Temperature Changes." Journal of Climate 16, no. 10 (2003): 1425–40. http://dx.doi.org/10.1175/1520-0442-16.10.1425.

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Abstract The responses of tropical clouds and water vapor to SST variations are investigated with simple numerical experiments. The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model is used with doubly periodic boundary conditions and a uniform constant sea surface temperature (SST). The SST is varied and the equilibrium statistics of cloud properties, water vapor, and circulation at different temperatures are compared. The top of the atmosphere (TOA) radiative fluxes have the same sensitivities to SST as in observations averaged

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Wilson Kemsley, Sarah, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack. "A systematic evaluation of high-cloud controlling factors." Atmospheric Chemistry and Physics 24, no. 14 (2024): 8295–316. http://dx.doi.org/10.5194/acp-24-8295-2024.

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Abstract. Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. “Cloud controlling factor” (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to system

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Sejas, Sergio A., Ming Cai, Aixue Hu, Gerald A. Meehl, Warren Washington, and Patrick C. Taylor. "Individual Feedback Contributions to the Seasonality of Surface Warming." Journal of Climate 27, no. 14 (2014): 5653–69. http://dx.doi.org/10.1175/jcli-d-13-00658.1.

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Abstract Using the climate feedback response analysis method, the authors examine the individual contributions of the CO2 radiative forcing and climate feedbacks to the magnitude, spatial pattern, and seasonality of the transient surface warming response in a 1% yr−1 CO2 increase simulation of the NCAR Community Climate System Model, version 4 (CCSM4). The CO2 forcing and water vapor feedback warm the surface everywhere throughout the year. The tropical warming is predominantly caused by the CO2 forcing and water vapor feedback, while the evaporation feedback reduces the warming. Most feedback

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Ceppi, Paulo, and Dennis L. Hartmann. "Clouds and the Atmospheric Circulation Response to Warming." Journal of Climate 29, no. 2 (2016): 783–99. http://dx.doi.org/10.1175/jcli-d-15-0394.1.

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Abstract The authors study the effect of clouds on the atmospheric circulation response to CO2 quadrupling in an aquaplanet model with a slab ocean lower boundary. The cloud effect is isolated by locking the clouds to either the control or 4xCO2 state in the shortwave (SW) or longwave (LW) radiation schemes. In the model, cloud radiative changes explain more than half of the total poleward expansion of the Hadley cells, midlatitude jets, and storm tracks under CO2 quadrupling, even though they cause only one-fourth of the total global-mean surface warming. The effect of clouds on circulation r

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Eitzen, Zachary A., Kuan-Man Xu, and Takmeng Wong. "An Estimate of Low-Cloud Feedbacks from Variations of Cloud Radiative and Physical Properties with Sea Surface Temperature on Interannual Time Scales." Journal of Climate 24, no. 4 (2011): 1106–21. http://dx.doi.org/10.1175/2010jcli3670.1.

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Abstract Simulations of climate change have yet to reach a consensus on the sign and magnitude of the changes in physical properties of marine boundary layer clouds. In this study, the authors analyze how cloud and radiative properties vary with SST anomaly in low-cloud regions, based on five years (March 2000–February 2005) of Clouds and the Earth’s Radiant Energy System (CERES)–Terra monthly gridded data and matched European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological reanalaysis data. In particular, this study focuses on the changes in cloud radiative effect, cloud frac

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Lefèvre, Maxence, Xianyu Tan, Elspeth K. H. Lee, and R. T. Pierrehumbert. "Cloud-convection Feedback in Brown Dwarf Atmospheres." Astrophysical Journal 929, no. 2 (2022): 153. http://dx.doi.org/10.3847/1538-4357/ac5e2d.

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Abstract Numerous observational evidence has suggested the presence of active meteorology in the atmospheres of brown dwarfs. A near-infrared brightness variability has been observed. Clouds have a major role in shaping the thermal structure and spectral properties of these atmospheres. The mechanism of such variability is still unclear, and neither 1D nor global circulation models can fully study this topic due to resolution. In this study, a convective-resolving model is coupled to gray-band radiative transfer in order to study the coupling between the convective atmosphere and the variabili

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Lefèvre, Maxence, Xianyu Tan, Elspeth K. H. Lee, and R. T. Pierrehumbert. "Cloud-convection Feedback in Brown Dwarf Atmospheres." Astrophysical Journal 929, no. 2 (2022): 153. http://dx.doi.org/10.3847/1538-4357/ac5e2d.

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Abstract Numerous observational evidence has suggested the presence of active meteorology in the atmospheres of brown dwarfs. A near-infrared brightness variability has been observed. Clouds have a major role in shaping the thermal structure and spectral properties of these atmospheres. The mechanism of such variability is still unclear, and neither 1D nor global circulation models can fully study this topic due to resolution. In this study, a convective-resolving model is coupled to gray-band radiative transfer in order to study the coupling between the convective atmosphere and the variabili

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Vaillant de Guélis, Thibault, Hélène Chepfer, Vincent Noel, et al. "The link between outgoing longwave radiation and the altitude at which a spaceborne lidar beam is fully attenuated." Atmospheric Measurement Techniques 10, no. 12 (2017): 4659–85. http://dx.doi.org/10.5194/amt-10-4659-2017.

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Abstract. According to climate model simulations, the changing altitude of middle and high clouds is the dominant contributor to the positive global mean longwave cloud feedback. Nevertheless, the mechanisms of this longwave cloud altitude feedback and its magnitude have not yet been verified by observations. Accurate, stable, and long-term observations of a metric-characterizing cloud vertical distribution that are related to the longwave cloud radiative effect are needed to achieve a better understanding of the mechanism of longwave cloud altitude feedback. This study shows that the direct m

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Virgin, John G., Christopher G. Fletcher, Jason N. S. Cole, Knut von Salzen, and Toni Mitovski. "Cloud Feedbacks from CanESM2 to CanESM5.0 and their influence on climate sensitivity." Geoscientific Model Development 14, no. 9 (2021): 5355–72. http://dx.doi.org/10.5194/gmd-14-5355-2021.

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Abstract. The newest iteration of the Canadian Earth System Model (CanESM5.0.3) has an effective climate sensitivity (EffCS) of 5.65 K, which is a 54 % increase relative to the model's previous version (CanESM2 – 3.67 K), and the highest sensitivity of all current models participating in the sixth phase of the coupled model inter-comparison project (CMIP6). Here, we explore the underlying causes behind CanESM5's increased EffCS via comparison of forcing and feedbacks between CanESM2 and CanESM5. We find only modest differences in radiative forcing as a response to CO2 between model versions. W

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Li, R. L., T. Storelvmo, A. V. Fedorov, and Y. S. Choi. "A Positive Iris Feedback: Insights from Climate Simulations with Temperature-Sensitive Cloud–Rain Conversion." Journal of Climate 32, no. 16 (2019): 5305–24. http://dx.doi.org/10.1175/jcli-d-18-0845.1.

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AbstractEstimates for equilibrium climate sensitivity from current climate models continue to exhibit a large spread, from 2.1 to 4.7 K per carbon dioxide doubling. Recent studies have found that the treatment of precipitation efficiency in deep convective clouds—specifically the conversion rate from cloud condensate to rain Cp—may contribute to the large intermodel spread. It is common for convective parameterization in climate models to carry a constant Cp, although its values are model and resolution dependent. In this study, we investigate how introducing a potential iris feedback, the clo

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Ceppi, Paulo, and Peer Nowack. "Observational evidence that cloud feedback amplifies global warming." Proceedings of the National Academy of Sciences 118, no. 30 (2021): e2026290118. http://dx.doi.org/10.1073/pnas.2026290118.

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Global warming drives changes in Earth’s cloud cover, which, in turn, may amplify or dampen climate change. This “cloud feedback” is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)—the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. C

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Middlemas, Eleanor A., Amy C. Clement, Brian Medeiros, and Ben Kirtman. "Cloud Radiative Feedbacks and El Niño–Southern Oscillation." Journal of Climate 32, no. 15 (2019): 4661–80. http://dx.doi.org/10.1175/jcli-d-18-0842.1.

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Abstract Cloud radiative feedbacks are disabled via “cloud-locking” in the Community Earth System Model, version 1.2 (CESM1.2), to result in a shift in El Niño–Southern Oscillation (ENSO) periodicity from 2–7 years to decadal time scales. We hypothesize that cloud radiative feedbacks may impact the periodicity in three ways: by 1) modulating heat flux locally into the equatorial Pacific subsurface through negative shortwave cloud feedback on sea surface temperature anomalies (SSTA), 2) damping the persistence of subtropical southeast Pacific SSTA such that the South Pacific meridional mode imp

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Caldwell, Peter M., Mark D. Zelinka, Karl E. Taylor, and Kate Marvel. "Quantifying the Sources of Intermodel Spread in Equilibrium Climate Sensitivity." Journal of Climate 29, no. 2 (2016): 513–24. http://dx.doi.org/10.1175/jcli-d-15-0352.1.

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Abstract This study clarifies the causes of intermodel differences in the global-average temperature response to doubled CO2, commonly known as equilibrium climate sensitivity (ECS). The authors begin by noting several issues with the standard approach for decomposing ECS into a sum of forcing and feedback terms. This leads to a derivation of an alternative method based on linearizing the effect of the net feedback. Consistent with previous studies, the new method identifies shortwave cloud feedback as the dominant source of intermodel spread in ECS. This new approach also reveals that covaria

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Yoshimori, Masakazu, Tokuta Yokohata, and Ayako Abe-Ouchi. "A Comparison of Climate Feedback Strength between CO2 Doubling and LGM Experiments." Journal of Climate 22, no. 12 (2009): 3374–95. http://dx.doi.org/10.1175/2009jcli2801.1.

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Abstract Studies of the climate in the past potentially provide a constraint on the uncertainty of climate sensitivity, but previous studies warn against a simple scaling to the future. Climate sensitivity is determined by a number of feedback processes, and they may vary according to climate states and forcings. In this study, the similarities and differences in feedbacks for CO2 doubling, a Last Glacial Maximum (LGM), and LGM greenhouse gas (GHG) forcing experiments are investigated using an atmospheric general circulation model coupled to a slab ocean model. After computing the radiative fo

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Abbot, Dorian S., Chris C. Walker, and Eli Tziperman. "Can a Convective Cloud Feedback Help to Eliminate Winter Sea Ice at High CO2 Concentrations?" Journal of Climate 22, no. 21 (2009): 5719–31. http://dx.doi.org/10.1175/2009jcli2854.1.

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Abstract Winter sea ice dramatically cools the Arctic climate during the coldest months of the year and may have remote effects on global climate as well. Accurate forecasting of winter sea ice has significant social and economic benefits. Such forecasting requires the identification and understanding of all of the feedbacks that can affect sea ice. A convective cloud feedback has recently been proposed in the context of explaining equable climates, for example, the climate of the Eocene, which might be important for determining future winter sea ice. In this feedback, CO2-initiated warming le

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Yokohata, Tokuta, Mark J. Webb, Matthew Collins, et al. "Structural Similarities and Differences in Climate Responses to CO2 Increase between Two Perturbed Physics Ensembles." Journal of Climate 23, no. 6 (2010): 1392–410. http://dx.doi.org/10.1175/2009jcli2917.1.

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Abstract The equilibrium climate sensitivity (ECS) of the two perturbed physics ensembles (PPE) generated using structurally different GCMs, Model for Interdisciplinary Research on Climate (MIROC3.2) and the Third Hadley Centre Atmospheric Model with slab ocean (HadSM3), is investigated. A method to quantify the shortwave (SW) cloud feedback by clouds with different cloud-top pressure is developed. It is found that the difference in the ensemble means of the ECS between the two ensembles is mainly caused by differences in the SW low-level cloud feedback. The ensemble mean SW cloud feedback and

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