Quantifying the Cloud Particle‐Size Feedback in an Earth System Model
dc.contributor.author | Zhu, Jiang | |
dc.contributor.author | Poulsen, Christopher J. | |
dc.date.accessioned | 2019-11-12T16:21:26Z | |
dc.date.available | WITHHELD_12_MONTHS | |
dc.date.available | 2019-11-12T16:21:26Z | |
dc.date.issued | 2019-10-16 | |
dc.identifier.citation | Zhu, Jiang; Poulsen, Christopher J. (2019). "Quantifying the Cloud Particle‐Size Feedback in an Earth System Model." Geophysical Research Letters 46(19): 10910-10917. | |
dc.identifier.issn | 0094-8276 | |
dc.identifier.issn | 1944-8007 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/151978 | |
dc.description.abstract | Physical process‐based two‐moment cloud microphysical parameterizations, in which effective cloud particle size evolves prognostically with climate change, have recently been incorporated into global climate models. The impacts of cloud particle‐size change on the cloud feedback, however, have never been explicitly quantified. Here we develop a partial radiative perturbation‐based method to estimate the cloud feedback associated with particle‐size changes in the Community Earth System Model. We find an increase of cloud particle size in the upper troposphere in response to an instantaneous doubling of atmospheric CO2. The associated net, shortwave, and longwave cloud feedbacks are estimated to be 0.18, 0.33, and −0.15 Wm−2 K−1, respectively. The cloud particle‐size feedback is dominated by its shortwave component with a maximum greater than 1.0 Wm−2 K−1 in the tropics and the Southern Ocean. We suggest that the cloud particle‐size feedback is an underappreciated contributor to the spread of cloud feedback and climate sensitivity among current models.Plain Language SummaryEffects of clouds on Earth’s radiation budget vary with their spatial and temporal distribution and their physical properties, including water content and its partitioning between liquid and ice, and cloud particle size. Changes in cloud distribution and physical properties can amplify or damp anthropogenic global warming and is the largest source of uncertainty in predictions of future climate. The simulation of cloud physical properties in climate models is limited due to a lack of understanding from theory and observations about what controls these properties. Recent progress has been made in some models to predict cloud particle sizes based on physical processes. In this study, we find an increase of cloud particle size in response to anthropogenic warming and estimate the resulting cloud radiative effects. The larger particles increase scattering of solar radiation in the downward direction leading to an amplification of surface warming. We suggest cloud particle‐size changes play a role in the large spread of warming in model predictions of future climate.Key PointsCloud particle size increases with warming in an Earth system modelThe associated cloud particle‐size feedback is estimated to be 0.18, 0.33, and −0.15 Wm−2 K−1 for net, shortwave, and longwave componentsCloud particle‐size feedback is an underappreciated contributor to the spread of climate sensitivity in current models | |
dc.publisher | Academic Press | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | cloud radiative effects | |
dc.subject.other | cloud droplet size | |
dc.subject.other | cloud feedback | |
dc.title | Quantifying the Cloud Particle‐Size Feedback in an Earth System Model | |
dc.type | Article | |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Geological Sciences | |
dc.subject.hlbtoplevel | Science | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/151978/1/grl59600.pdf | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/151978/2/grl59600_am.pdf | |
dc.identifier.doi | 10.1029/2019GL083829 | |
dc.identifier.source | Geophysical Research Letters | |
dc.identifier.citedreference | Slingo, A. ( 1988 ). A GCM parameterization for the shortwave radiative properties of water clouds. Journal of the Atmospheric Sciences, 46 ( 10 ), 1419 – 1427. https://doi.org/10.1175/1520‐0469(1989)046<1419:AGPFTS>2.0.CO;2 | |
dc.identifier.citedreference | Gettelman, A., Hannay, C., Bacmeister, J. T., Neale, R. B., Pendergrass, A. G., Danabasoglu, G., Lamarque, J. F., Fasullo, J. T., Bailey, D. A., Lawrence, D. M., & Mills, M. J. ( 2019 ). High climate sensitivity in the Community Earth System Model Version 2 (CESM2). Geophysical Research Letters, 46, 8329 – 8337. https://doi.org/10.1029/2019GL083978 | |
dc.identifier.citedreference | Gettelman, A., Kay, J. E., & Shell, K. M. ( 2012 ). The evolution of climate sensitivity and climate feedbacks in the Community Atmosphere Model. Journal of Climate, 25 ( 5 ), 1453 – 1469. https://doi.org/10.1175/JCLI‐D‐11‐00197.1 | |
dc.identifier.citedreference | Gettelman, A., Morrison, H., & Ghan, S. J. ( 2008 ). A new two‐moment bulk stratiform cloud microphysics scheme in the Community Atmosphere Model, Version 3 (CAM3). Part II: Single‐column and global results. Journal of Climate, 21 ( 15 ), 3660 – 3679. https://doi.org/10.1175/2008JCLI2116.1 | |
dc.identifier.citedreference | Gettelman, A., & Sherwood, S. C. ( 2016 ). Processes responsible for cloud feedback. Current Climate Change Reports, 2 ( 4 ), 179 – 189. https://doi.org/10.1007/s40641‐016‐0052‐8 | |
dc.identifier.citedreference | Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J. F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., & Marshall, S. ( 2013 ). The community earth system model: A framework for collaborative research. Bulletin of the American Meteorological Society, 94 ( 9 ), 1339 – 1360. https://doi.org/10.1175/BAMS‐D‐12‐00121.1 | |
dc.identifier.citedreference | Kay, J. E., Hillman, B. R., Klein, S. A., Zhang, Y., Medeiros, B., Pincus, R., Gettelman, A., Eaton, B., Boyle, J., Marchand, R., & Ackerman, T. P. ( 2012 ). Exposing global cloud biases in the Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators. Journal of Climate, 25 ( 15 ), 5190 – 5207. https://doi.org/10.1175/JCLI‐D‐11‐00469.1 | |
dc.identifier.citedreference | Kiehl, J. T. ( 1994 ). Sensitivity of a GCM climate simulation to differences in continental versus maritime cloud drop size. Journal of Geophysical Research, 99 ( D11 ), 23,107 – 23,115. https://doi.org/10.1029/94JD01117 | |
dc.identifier.citedreference | Kiehl, J. T., & Shields, C. A. ( 2013 ). Sensitivity of the Palaeocene–Eocene Thermal Maximum climate to cloud properties. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371 ( 2001 ). https://doi.org/10.1098/rsta.2013.0093 | |
dc.identifier.citedreference | Liou, K. N. ( 2002 ). An introduction to atmospheric radiation, Int. Geophys. Ser. (Vol. 84 ). San Diego, CA: Academic Press. https://doi.org/10.1016/S0074‐6142(02)80015‐8 | |
dc.identifier.citedreference | Lohmann, U., Stier, P., Hoose, C., Ferrachat, S., Kloster, S., Roeckner, E., & Zhang, J. ( 2007 ). Cloud microphysics and aerosol indirect effects in the global climate model ECHAM5‐HAM. Atmospheric Chemistry and Physics, 7 ( 13 ), 3425 – 3446. https://doi.org/10.5194/acp‐7‐3425‐2007 | |
dc.identifier.citedreference | Martin, G. M., Johnson, D. W., & Spice, A. ( 1994 ). The measurement and parameterization of effective radius of droplets in warm stratocumulus clouds. Journal of the Atmospheric Sciences, 51 ( 13 ), 1823 – 1842. https://doi.org/10.1175/1520‐0469(1994)051<1823:Tmapoe>2.0.Co;2 | |
dc.identifier.citedreference | Park, S., Bretherton, C. S., & Rasch, P. J. ( 2014 ). Integrating cloud processes in the Community Atmosphere Model, Version 5. Journal of Climate, 27 ( 18 ), 6821 – 6856. https://doi.org/10.1175/JCLI‐D‐14‐00087.1 | |
dc.identifier.citedreference | Pendergrass, A. G., Conley, A., & Vitt, F. M. ( 2018 ). Surface and top‐of‐atmosphere radiative feedback kernels for CESM‐CAM5. Earth System Science Data, 10 ( 1 ), 317 – 324. https://doi.org/10.5194/essd‐10‐317‐2018 | |
dc.identifier.citedreference | Salzmann, M., Ming, Y., Golaz, J. C., Ginoux, P. A., Morrison, H., Gettelman, A., Krämer, M., & Donner, L. J. ( 2010 ). Two‐moment bulk stratiform cloud microphysics in the GFDL AM3 GCM: Description, evaluation, and sensitivity tests. Atmospheric Chemistry and Physics, 10 ( 16 ), 8037 – 8064. https://doi.org/10.5194/acp‐10‐8037‐2010 | |
dc.identifier.citedreference | Slingo, A. ( 1990 ). Sensitivity of the Earth’s radiation budget to changes in low clouds. Nature, 343 ( 6253 ), 49 – 51. https://doi.org/10.1038/343049a0 | |
dc.identifier.citedreference | Stephens, G. L. ( 1978 ). Radiation profiles in extended water clouds. II: Parameterization schemes. Journal of the Atmospheric Sciences, 35 ( 11 ), 2123 – 2132. https://doi.org/10.1175/1520‐0469(1978)035<2123:RPIEWC>2.0.CO;2 | |
dc.identifier.citedreference | Wetherald, R. T., & Manabe, S. ( 1988 ). Cloud feedback processes in a general circulation model. Journal of the Atmospheric Sciences, 45 ( 8 ), 1397 – 1416. https://doi.org/10.1175/1520‐0469(1988)045<1397:CFPIAG>2.0.CO;2 | |
dc.identifier.citedreference | Zelinka, M. D., Klein, S. A., & Hartmann, D. L. ( 2012 ). 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 ( 11 ), 3736 – 3754. https://doi.org/10.1175/jcli‐d‐11‐00249.1 | |
dc.identifier.citedreference | Zhu, J., Poulsen, C. J., & Tierney, J. E. ( 2019 ). Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks. Science Advances, 5 ( 9 ), eaax1874. https://doi.org/10.1126/sciadv.aax1874 | |
dc.identifier.citedreference | Bitz, C. M., Shell, K. M., Gent, P. R., Bailey, D. A., Danabasoglu, G., Armour, K. C., Holland, M. M., & Kiehl, J. T. ( 2011 ). Climate sensitivity of the Community Climate System Model, Version 4. Journal of Climate, 25 ( 9 ), 3053 – 3070. https://doi.org/10.1175/JCLI‐D‐11‐00290.1 | |
dc.identifier.citedreference | Ceppi, P., Brient, F., Zelinka, M. D., & Hartmann, D. L. ( 2017 ). Cloud feedback mechanisms and their representation in global climate models. Wiley Interdisciplinary Reviews: Climate Change, 8 ( 4 ), e465. https://doi.org/10.1002/wcc.465 | |
dc.identifier.citedreference | Colman, R., Fraser, J., & Rotstayn, L. ( 2001 ). Climate feedbacks in a general circulation model incorporating prognostic clouds. Climate Dynamics, 18 ( 1‐2 ), 103 – 122. https://doi.org/10.1007/s003820100162 | |
dc.identifier.citedreference | Conley, A. J., Lamarque, J. F., Vitt, F., Collins, W. D., & Kiehl, J. ( 2013 ). PORT, a CESM tool for the diagnosis of radiative forcing. Geoscientific Model Development, 6 ( 2 ), 469 – 476. https://doi.org/10.5194/gmd‐6‐469‐2013 | |
dc.identifier.citedreference | Ebert, E. E., & Curry, J. A. ( 1992 ). A parameterization of ice cloud optical properties for climate models. Journal of Geophysical Research, 97 ( D4 ), 3831 – 3836. https://doi.org/10.1029/91JD02472 | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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