Identifying Robust Cloud Feedbacks in Observations and Models

Average longwave (LW), shortwave (SW), and net cloud feedbacks as simulated by global climate models


For more than 30 years, scientists have known that the inability to predict how clouds will respond to a climate change hinders a confident prediction of the magnitude of global warming resulting from a given increase in greenhouse gases. As a result, we are not able to confidently identify the magnitude of carbon emission reductions necessary to avoid dangerous anthropogenic interference in the climate system. Thus it is imperative to perform research aimed at reducing the uncertainty range associated with the response of clouds to a warming of the planet, also known as the “cloud feedback”.

To reduce the substantial uncertainties associated with long-term climate-change, a team of researchers at Lawrence Livermore National Laboratory and the University of California at Los Angeles are working to reduce these uncertainties by identifying robust cloud feedbacks in today’s climate models and constraining them with available observations. The team scrutinizes the results from simulations of future climate made by the most recent climate models assessed by the Intergovernmental Panel on Climate Change to answer a variety of questions:


Cloud feedbacks are extremely variable between different climate models. However, it is not always clear what is the relative contribution of cloud types from various regions to the global mean cloud feedback and its inter-model spread. Researchers in this project have developed novel techniques to separate the contribution of different cloud types and have found that cloud feedbacks are not the result of a single cloud type but that we must consider the feedbacks from many cloud types including low clouds, high clouds, mid-latitude clouds and polar clouds.


A key aspect of the project is the identification of cloud feedbacks where similarities are found in simulations of both current-climate variability and of projected climate change (so called “timescale invariant feedbacks”). For example, if fluctuations of clouds with day-to-day or season-to-season variations of temperature are similar to those shown over climate change time-scales, we could use observations from the current climate to constrain cloud feedbacks. Researchers are working to identify which cloud types exhibit time-scale invariance as well as the observations that can quantitatively constrain these feedbacks.


Our confidence in any given cloud feedback is dependent on our ability to understand the physical processes from which the feedbacks result and our confidence in those processes. Researchers are working towards identifying the physical mechanisms of various feedbacks simulated by complex climate models and critiquing their realism. One technique to accomplish this is the application of more realistic models of cloud processes to the changes in the large-scale environment predicted by global climate models.

This Cloud Feedback project is supported by the Regional and Global Climate Modeling (RGCM) Programs of the U.S. Department of Energy’s Office of Science/Biological and Environmental Research (BER).

Project Participants

Former Participants:

Research Highlights




    • Ceppi, P., F. Brient, M. D. Zelinka, and D. L. Hartmann, 2017: Cloud feedback mechanisms and their representation in global climate models. WIREs Climate Change, e465, doi:10.1002/wcc.465.
    • Klein, S. A., A. Hall, J. R. Norris, and R. Pincus, 2017: Low-Cloud Feedbacks from Cloud Controlling Factors: A Review. Surv. Geophys., in press.
    • Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M., 2017: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6. Geo. Mod. Dev., 10, 359-384,
    • Zhou, C., M. D. Zelinka, and S. A. Klein, 2017: 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 Syst., 9, doi:10.1002/2017MS001096.


    • Caldwell, P. M., M. D. Zelinka, K. E. Taylor, and K. Marvel, 2016: Quantifying the sources of inter-model spread in equilibrium climate sensitivity. J. Clim., 29, 513-524, doi:10.1175/JCLI-D-15-0352.1.
    • Danco, J. F., A. M. DeAngelis, B. K. Raney, and A. J. Broccoli, 2016: Effects of a Warming Climate on Daily Snowfall Events in the Northern Hemisphere. J. Climate, 29, 6295-6318, doi:10.1175/JCLI-D-15-0687.1.
    • DeAngelis, A., X. Qu, M. D. Zelinka, and A. Hall, 2016: Corrigendum: An observational radiative constraint on hydrologic cycle intensification. Nature, doi:10.1038/nature17621.
    • Norris, J. R., R. J. Allen, A. T. Evan, M. D. Zelinka, C. W. O’Dell, and S. A. Klein, 2016: Evidence for climate change in the satellite cloud record. Nature, 536, 72-75, doi:10.1038/nature18273.
    • Terai, C. R., S. A. Klein, and M. D. Zelinka, 2016; Constraining the low-cloud optical depth feedback at middle and high latitudes using satellite observations. Journal of Geophysical Research-Atmospheres, 121, 9696-9716, doi:10.1002/2016JD025233.
    • Yuan, T., L. Oreopoulos, M. Zelinka, H. Yu, J. R. Norris, M. Chin, S. Platnick, and K. Meyer, 2016: Positive low cloud and dust feedbacks amplify tropical North Atlantic Multidecadal Oscillation, Geophys. Res. Lett., 43, 1349-1356, doi:10.1002/2016GL067679.
    • Zelinka, M. D., C. Zhou, and S. A. Klein, 2016: Insights from a Refined Decomposition of Cloud Feedbacks, Geophys. Res. Lett., 43, 9259-9269, doi:10.1002/2016GL069917.
    • Zhou, C., M. D. Zelinka, and S. A. Klein, 2016: Impact of decadal cloud variations on the Earth's energy budget. Nature Geoscience, 9, 871-874, doi:10.1038/ngeo2828.


    • Brient, F., T. Schneider, Z. Tan, S. Bony, X. Qu, and A. Hall, 2015: Shallowness of tropical low clouds as a predictor of climate models' response to warming. Clim. Dyn., doi: 10.1007/s00382-015-2846-0.
    • DeAngelis, A. M., X. Qu, M. D. Zelinka, and A. Hall, 2015: An observational radiative constraint on hydrologic cycle intensification. Nature, doi: 10.1038/nature15770.
    • Dessler, A.E. and M. D. Zelinka, 2015. Climate Feedbacks. In: Gerald R. North (editor-in-chief), John Pyle and Fuqing Zhang (editors). Encyclopedia of Atmospheric Sciences, 2nd edition, Vol 2, pp. 18-25.
    • Klein, S. A., and A. Hall, 2015: Emergent constraints for cloud feedbacks. Current Climate Change Reports, 1, 276-287, doi: 10.1007/s40641-015-0027-1.
    • Marvel, K., M. D. Zelinka, S. A. Klein, C. Bonfils, P. M. Caldwell, C. Doutriaux, B. D. Santer, and K. E. Taylor, 2015: External influences on modeled and observed cloud trends, J. Climate, 28, 4820-4840, doi:10.1175/JCLI-D-14-00734.1.
    • Qu, X., A. Hall, S. A. Klein and P. M. Caldwell, 2015: The strength of the tropical inversion and its response to climate change in 18 CMIP5 models. Clim. Dyn., 45, 375-396, doi: 10.1007/s00382-014-2441-9.
    • Qu, X., A. Hall, S. A. Klein, and A. M. DeAngelis, 2015: Positive tropical marine low-cloud cover feedback inferred from cloud-controlling factors. Geophys. Res. Lett., 42, 7767-7775, doi:10.1002/2015GL065627.
    • Sanderson, B. M., R. Knutti, and P. M. Caldwell, 2015: Addressing interdepedency in a mutli-model ensemble by interpolation of climate models. J. Clim., 28, 5150-5170, doi:
    • Sanderson, B. M., R. Knutti, and P. M. Caldwell, 2015: A representative democracy to reduce interdependency in a multi-model ensemble. J. Clim., 28, 5171-5194, doi:
    • Zhou, C., A. E. Dessler, M. D. Zelinka, P. Yang, and T. Wang, 2015: Cirrus feedback on interannual climate fluctuations, Geophys. Res. Lett., 41, 9166-9173, doi:10.1002/2014GL062095.
    • Zhou, C., M. D. Zelinka, A. Dessler, and S. A. Klein, 2015: Relationship between inter-annual and long-term cloud feedbacks. Geophys. Res. Lett., 42, 10,463-10,469, doi:10.1002/2015GL066698.


    • Caldwell, P. M., C. S. Bretherton, M. D. Zelinka, S. A. Klein, B. D. Santer, and B. M. Sanderson, 2014: Statistical significance of climate sensitivity predictors obtained by data mining. Geophys. Res. Lett., 41, 1803-1808, doi:10.1002/2014GL059205.
    • Ceppi, P., M. D. Zelinka, and D. L. Hartmann, 2014: The Response of the Southern Hemispheric Eddy-Driven Jet to Future Changes in Shortwave Radiation in CMIP5, Geophys. Res. Lett., 41, 3244-3250, doi:10.1002/2014GL060043.
    • Gordon, N. D. and S. A. Klein, 2014: Low-cloud optical depth feedback in climate models. J. Geophys. Res., 119, 6052-6065, doi:10.1002/2013JD021052.
    • Johnston, M. S., Eliasson, S., Eriksson, P., Forbes, R. M., Gettelman, A., Raisanen, P., and Zelinka, M. D., 2014: Diagnosing the average spatio-temporal impact of convective systems - Part 2: A model intercomparison using satellite data, Atmos. Chem. Phys., 14, 8701-8721, doi:10.5194/acp-14-8701-2014.
    • Qu, X. and A. Hall, 2014: On the persistent spread in snow-albedo feedback. Clim. Dyn., 42, 69-81, doi: 10.1007/s00382-013-1774-0.
    • Qu, X., A. Hall, S. A. Klein, and P. M. Caldwell, 2014: On the spread of changes in marine low cloud cover in climate model simulations of the 21st century. Clim. Dyn., 42, 2603-2626, 10.1007/s00382-013-1945-z
    • Zelinka, M. D., T. Andrews, P. M. Forster, and K. E. Taylor, 2014: Quantifying Components of Aerosol-Cloud-Radiation Interactions in Climate Models. Journal of Geophysical Research - Atmospheres 119:7599-7615. doi:10.1002/2014JD021710.


    • Caldwell, P. M., Y. Zhang and S. A. Klein, 2013: CMIP3 subtropical stratocumulus feedback interpreted through a mixed-layer model. J. Clim., 26, 1607-1625, doi: 10.1175/JCLI-D-12-00188.1.
    • Grise, K. M., L. M. Polvani, G. Tselioudis, Y. Wu, and M. D. Zelinka, 2013: The ozone hole indirect effect: Cloud-radiative anomalies accompanying the poleward shift of the eddy-driven jet in the Southern Hemisphere. Geophys. Res. Lett., 40, doi:10.1002/grl.50675.
    • Johnston, M. S., S. Eliasson, P. Eriksson, R. M. Forbes, K. Wyser, and M. D. Zelinka, 2013: Diagnosing the average spatio-temporal impact of convective systems - Part 1: A methodology for evaluating climate models, Atmos. Chem. Phys., 13, 12043 - 12058, doi:10.5194/acp-13-12043-2013.
    • Klein, S. A., Y. Zhang, M. D. Zelinka, R. N. Pincus, J. Boyle, and P. J. Gleckler, 2013: Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator. J. Geophys. Res., 118, 1 - 14, doi:10.1002/jgrd.50141 .
    • Zelinka, M. D., S. A. Klein, K. E. Taylor, T. Andrews, M. J. Webb, J. M. Gregory, and P. M. Forster, 2013: Contributions of different cloud types to feedbacks and rapid adjustments in CMIP5. J. Clim., 26, 5007 - 5027, 10.1175/JCLI-D-12-00555.1.
    • Zhou, C., M. D. Zelinka, A. E. Dessler, P. Yang, 2013: An analysis of the short-term cloud feedback using MODIS data. J. Clim., 26, 4803 - 4815, 10.1175/JCLI-D-12-00547.1.


    • Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012a: Computing and partitioning cloud feedbacks using cloud property histograms. Part I: Cloud radiative kernels. J. Clim., 25, 3715 - 3735, doi: 10.1175/JCLI-D-11-00248.1.
    • Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012b: 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, doi: 10.1175/JCLI-D-11-00249.1.