Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks

Cloud radiative feedback is central to our projection of future climate change. It can be estimated using the cloud radiative kernel (CRK) method or adjustment method. This study, for the first time, examines the contributions of each spectral band to the longwave (LW) cloud radiative feedbacks (CRF...

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Published in:Geophysical Research Letters
Main Authors: Huang, Xianglei, Chen, Xiuhong, Yue, Qing
Format: Article in Journal/Newspaper
Language:unknown
Published: Cambridge University Press 2019
Subjects:
radiative kernel
climate model
spectral longwave radiation
cloud radiative feedback
Geological Sciences
Science
Arctic
Online Access:https://hdl.handle.net/2027.42/150592
https://doi.org/10.1029/2019GL083466
id ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/150592
record_format openpolar
institution Open Polar
collection University of Michigan: Deep Blue
op_collection_id ftumdeepblue
language unknown
topic radiative kernel
climate model
spectral longwave radiation
cloud radiative feedback
Geological Sciences
Science
spellingShingle radiative kernel
climate model
spectral longwave radiation
cloud radiative feedback
Geological Sciences
Science
Huang, Xianglei
Chen, Xiuhong
Yue, Qing
Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks
topic_facet radiative kernel
climate model
spectral longwave radiation
cloud radiative feedback
Geological Sciences
Science
description Cloud radiative feedback is central to our projection of future climate change. It can be estimated using the cloud radiative kernel (CRK) method or adjustment method. This study, for the first time, examines the contributions of each spectral band to the longwave (LW) cloud radiative feedbacks (CRFs). Simulations of three warming scenarios are analyzed, including +2 K sea surface temperature, 2 × CO2, and 4 × CO2 experiments. While the LW broadband CRFs derived from the CRK and adjustment methods agree with each other, they disagree on the relative contributions from the far‐infrared and window bands. The CRK method provides a consistent band‐by‐band decomposition of LW CRF for different warming scenarios. The simulated and observed short‐term broadband CRFs for the 2003–2013 period are similar to the long‐term counterparts, but their band‐by‐band decompositions are different, which can be further related to the cloud fraction changes in respective simulations and observation.Plain Language SummaryWe studied how the cloud change in response to surface temperature change leads to the changes of radiation at the top of the atmosphere (referred to as cloud radiative feedback) over different frequency ranges in the longwave (referred to as spectral bands). While different methods can provide a similar estimate of broadband cloud radiative feedbacks, the decomposition to different longwave spectral bands can be different from one method to another. The cloud radiative kernel method can provide a more consistent band‐by‐band decomposition of the longwave cloud radiative feedback for different warming scenarios. The decomposition for cloud radiative feedback derived from the warming experiments is considerably different from that derived from decadal‐scale observations and simulations. Such differences in spectral band decomposition can be related to the specific cloud fraction changes for different types of clouds defined with respect to cloud top pressure and cloud opacity.Key PointsThe band‐by‐band decomposition of ...
format Article in Journal/Newspaper
author Huang, Xianglei
Chen, Xiuhong
Yue, Qing
author_facet Huang, Xianglei
Chen, Xiuhong
Yue, Qing
author_sort Huang, Xianglei
title Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks
title_short Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks
title_full Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks
title_fullStr Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks
title_full_unstemmed Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks
title_sort band‐by‐band contributions to the longwave cloud radiative feedbacks
publisher Cambridge University Press
publishDate 2019
url https://hdl.handle.net/2027.42/150592
https://doi.org/10.1029/2019GL083466
genre Arctic
genre_facet Arctic
op_relation Huang, Xianglei; Chen, Xiuhong; Yue, Qing (2019). "Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks." Geophysical Research Letters 46(12): 6998-7006.
0094-8276
1944-8007
https://hdl.handle.net/2027.42/150592
doi:10.1029/2019GL083466
Geophysical Research Letters
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Huang, X. L., Yang, W. Z., Loeb, N. G., & Ramaswamy, V. ( 2008 ). Spectrally resolved fluxes derived from collocated AIRS and CERES measurements and their application in model evaluation: 1. Clear sky over the tropical oceans. Journal of Geophysical Research, 113, D09110. https://doi.org/10.1029/2007JD009219
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Zhou, C., Zelinka, M. D., Dessler, A. E., & Klein, S. A. ( 2015 ). The relationship between interannual and long‐term cloud feedbacks. Geophysical Research Letters, 42, 10,463 – 10,469. https://doi.org/10.1002/2015GL066698
Zhou, C., Zelinka, M. D., Dessler, A. E., & Yang, P. ( 2013 ). An analysis of the short‐term cloud feedback using MODIS data. Journal of Climate, 26 ( 13 ), 4803 – 4815. https://doi.org/10.1175/JCLI‐D‐12‐00547.1
Chen, X. H., Huang, X. L., & Liu, X. ( 2013 ). Non‐negligible effects of cloud vertical overlapping assumptions on longwave spectral fingerprinting studies. Journal of Geophysical Research: Atmospheres, 118, 7309 – 7320. https://doi.org/10.1002/jgrd.50562
Ebert, E. E., & Curry, J. A. ( 1992 ). A parametrization of ice cloud optical properties for climate models. Journal of Geophysical Research, 97 ( D4 ), 3831 – 3836. https://doi.org/10.1029/91JD02472
Fouquart, Y. ( 1987 ). Radiative transfer in climate models. In M. E. Schlesinger (Ed.), Physically based modelling and simulation of climate and climate changes (pp. 223 – 284 ). Mass: Kluwer Acad., Norwell.
Schwarkzopf, M. D., & Ramasamy, V. ( 1999 ). Radiative effects of CH4, N2O, halocarbons and the foreign‐broadened H2O continuum: A GCM experiment. Journal of Geophysical Research, 104 ( D8 ), 9467 – 9488. https://doi.org/10.1029/1999JD900003
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., & John, V. O. ( 2011 ). COSP: Satellite simulation software for model assessment. Bulletin of the American Meteorological Society, 92 ( 8 ), 1023 – 1043. https://doi.org/10.1175/2011BAMS2856.1
Schwarkzopf, M. D., & Ramasamy, V. ( 1999 ). Radiative effects of CH 4, N 2 O, halocarbons and the foreign‐broadened H 2 O continuum: A GCM experiment. Journal of Geophysical Research, 104 ( D8 ), 9467 – 9488. https://doi.org/10.1029/1999JD900003
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Chen, X. H., Huang, X. L., Loeb, N. G., & Wei, H. L. ( 2013 ). Comparisons of clear‐sky outgoing far‐IR flux inferred from satellite observations and computed from three most recent reanalysis products. Journal of Climate, 26 ( 2 ), 478 – 494. https://doi.org/10.1175/JCLI‐D‐12‐00212.1
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spelling ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/150592 2023-08-20T04:03:12+02:00 Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks Huang, Xianglei Chen, Xiuhong Yue, Qing 2019-06-28 application/pdf https://hdl.handle.net/2027.42/150592 https://doi.org/10.1029/2019GL083466 unknown Cambridge University Press Wiley Periodicals, Inc. Huang, Xianglei; Chen, Xiuhong; Yue, Qing (2019). "Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks." Geophysical Research Letters 46(12): 6998-7006. 0094-8276 1944-8007 https://hdl.handle.net/2027.42/150592 doi:10.1029/2019GL083466 Geophysical Research Letters Yue, Q., Kahn, B. H., Fetzer, E. J., Wong, S., Frey, R., & Meyer, K. G. ( 2017 ). On the response of MODIS cloud coverage to global mean surface air temperature. Journal of Geophysical Research: Atmospheres, 122, 966 – 979. https://doi.org/10.1002/2016JD025174 Huang, X. L., Yang, W. Z., Loeb, N. G., & Ramaswamy, V. ( 2008 ). Spectrally resolved fluxes derived from collocated AIRS and CERES measurements and their application in model evaluation: 1. Clear sky over the tropical oceans. Journal of Geophysical Research, 113, D09110. https://doi.org/10.1029/2007JD009219 Klein, S., & Hall, A. ( 2015 ). Emergent constraints for cloud feedbacks. Current Climate Change Reports, 1, 1 – 12. Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. ( 1997 ). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated‐k model for the longwave. Journal of Geophysical Research, 102 ( D14 ), 16663 – 16682. https://doi.org/10.1029/97JD00237 Rossow, W. B., & Schiffer, R. A. ( 1999 ). Advances in understanding clouds from ISCCP. Bulletin of the American Meteorological Society, 80 ( 11 ), 2261 – 2287. https://doi.org/10.1175/1520‐0477 Sedlar, J., & Tjernström, M. ( 2017 ). Clouds, warm air, and a climate cooling signal over the summer Arctic. Geophysical Research Letters, 44, 1095 – 1103. https://doi.org/10.1002/2016GL071959 Shell, K. M., Kiehl, J. T., & Shields, C. A. ( 2008 ). Using the radiative kernel technique to calculate climate feedbacks in NCAR’s community atmospheric model. Journal of Climate, 21 ( 10 ), 2269 – 2282. https://doi.org/10.1175/2007JCLI2044.1 Soden, B. J., Broccoli, A. J., & Hemler, R. S. ( 2004 ). On the use of cloud forcing to estimate cloud feedback. Journal of Climate, 17 ( 19 ), 3661 – 3665. https://doi.org/10.1175/1520‐0442(2004)017<3661:OTUOCF>2.0.CO;2 Soden, B. J., Held, I. M., Colman, R., Shell, K. M., Kiehl, J. T., & Shields, C. ( 2008 ). Quantifying climate feedbacks using radiative kernels. Journal of Climate, 21 ( 14 ), 3504 – 3520. https://doi.org/10.1175/2007JCLI2110.1 Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C., L’Ecuyer, T., & Lebsock, M. ( 2018 ). CloudSat and CALIPSO within the A‐Train: Ten years of actively observing the Earth system. Bulletin of the American Meteorological Society, 99 ( 3 ), 569 – 581. https://doi.org/10.1175/BAMS‐D‐16‐0324.1. Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O’connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., & the CloudSat Science Team ( 2002 ). The CloudSat mission and the A‐Train: A new dimension of space‐based observations of clouds and precipitation. Bulletin of the American Meteorological Society, 83 ( 12 ), 1771 – 1790. https://doi.org/10.1175/BAMS‐83‐12‐1771 Vial, J., Dufresne, J.‐L., & Bony, S. ( 2013 ). On the interpretation of inter‐model spread in CMIP5 climate sensitivity estimates. Climate Dynamics, 41 ( 11‐12 ), 3339 – 3362. https://doi.org/10.1007/s00382‐013‐1725‐9 Yue, Q., Kahn, B. H., Fetzer, E. J., Schreier, M., Wong, S., Chen, X. H., & Huang, X. L. ( 2016 ). Observation‐based longwave cloud radiative kernels derived from the A‐Train. Journal of Climate, 29 ( 6 ), 2023 – 2040. https://doi.org/10.1175/JCLI‐D‐15‐0257.1 Zelinka, M., Klein, S., Taylor, K., Andrews, T., Webb, M., Gregory, J., & Forster, P. ( 2013 ). Contributions of different cloud types to feedbacks and rapid adjustments in CMIP5. Journal of Climate, 26 ( 14 ), 5007 – 5027. https://doi.org/10.1175/JCLI‐D‐12‐00555.1 Zelinka, M. D., & Hartmann, D. L. ( 2010 ). Why is longwave cloud feedback positive? Journal of Geophysical Research, 115, D16117. https://doi.org/10.1029/2010JD013817 Zelinka, M. D., Klein, S. A., & Hartmann, D. L. ( 2012 ). Computing and partitioning cloud feedbacks using cloud property histograms. Part I: Cloud radiative kernels. Journal of Climate, 25 ( 11 ), 3715 – 3735. https://doi.org/10.1175/JCLI‐D‐11‐00248.1 Zhou, C., Zelinka, M. D., Dessler, A. E., & Klein, S. A. ( 2015 ). The relationship between interannual and long‐term cloud feedbacks. Geophysical Research Letters, 42, 10,463 – 10,469. https://doi.org/10.1002/2015GL066698 Zhou, C., Zelinka, M. D., Dessler, A. E., & Yang, P. ( 2013 ). An analysis of the short‐term cloud feedback using MODIS data. Journal of Climate, 26 ( 13 ), 4803 – 4815. https://doi.org/10.1175/JCLI‐D‐12‐00547.1 Chen, X. H., Huang, X. L., & Liu, X. ( 2013 ). Non‐negligible effects of cloud vertical overlapping assumptions on longwave spectral fingerprinting studies. Journal of Geophysical Research: Atmospheres, 118, 7309 – 7320. https://doi.org/10.1002/jgrd.50562 Ebert, E. E., & Curry, J. A. ( 1992 ). A parametrization of ice cloud optical properties for climate models. Journal of Geophysical Research, 97 ( D4 ), 3831 – 3836. https://doi.org/10.1029/91JD02472 Fouquart, Y. ( 1987 ). Radiative transfer in climate models. In M. E. Schlesinger (Ed.), Physically based modelling and simulation of climate and climate changes (pp. 223 – 284 ). Mass: Kluwer Acad., Norwell. Schwarkzopf, M. D., & Ramasamy, V. ( 1999 ). Radiative effects of CH4, N2O, halocarbons and the foreign‐broadened H2O continuum: A GCM experiment. Journal of Geophysical Research, 104 ( D8 ), 9467 – 9488. https://doi.org/10.1029/1999JD900003 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., & John, V. O. ( 2011 ). COSP: Satellite simulation software for model assessment. Bulletin of the American Meteorological Society, 92 ( 8 ), 1023 – 1043. https://doi.org/10.1175/2011BAMS2856.1 Schwarkzopf, M. D., & Ramasamy, V. ( 1999 ). Radiative effects of CH 4, N 2 O, halocarbons and the foreign‐broadened H 2 O continuum: A GCM experiment. Journal of Geophysical Research, 104 ( D8 ), 9467 – 9488. https://doi.org/10.1029/1999JD900003 Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.‐M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., Stevens, B., & Zhang, X. Y. ( 2013 ). Clouds and aerosols. In T. F. Stocker, D. Qin, G.‐K. Plattner, M. Tignor, S. K. Allen, J. Boschung, et al. (Eds.), Climate change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Chap. 7, pp. 571 – 657 ). Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. Caldwell, P. M., Zelinka, M. D., Taylor, K. E., & Marvel, K. ( 2016 ). Quantifying the sources of intermodel spread in equilibrium climate sensitivity. Journal of Climate, 29 ( 2 ), 513 – 524. https://doi.org/10.1175/JCLI‐D‐15‐0352.1 Ceppi, P., McCoy, T, D., & Hartmann, D. L. ( 2016 ). Observational evidence for a negative shortwave cloud feedback in middle to high latitudes. Geophysical Research Letters, 43, 1331 – 1339. https://doi.org/10.1002/2015GL06749 Chen, X. H., Huang, X. L., Loeb, N. G., & Wei, H. L. ( 2013 ). Comparisons of clear‐sky outgoing far‐IR flux inferred from satellite observations and computed from three most recent reanalysis products. Journal of Climate, 26 ( 2 ), 478 – 494. https://doi.org/10.1175/JCLI‐D‐12‐00212.1 Clough, S. A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M. J., Cady‐Pereira, K., Boukabara, S., & Brown, P. D. ( 2005 ). Atmospheric radiative transfer modeling: A summary of the AER codes. Journal of Quantitative Spectroscopy and Radiative Transfer, 91 ( 2 ), 233 – 244. https://doi.org/10.1016/j.jqsrt.2004.05.058 Dessler, A. E. ( 2010 ). A determination of the cloud feedback from climate variations over the past decade. Science, 330 ( 6010 ), 1523 – 1527. https://doi.org/10.1126/science.1192546 Dessler, A. E., & Loeb, N. G. ( 2013 ). Impact of dataset choice on calculations of the short‐term cloud feedback. Journal of Geophysical Research: Atmospheres, 118, 2821 – 2826. https://doi.org/10.1002/jgrd.50199 Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., & Rummukainen, M. ( 2013 ). Evaluation of climate models. In T. F. Stocker, D. Qin, G.‐K. Plattner, M. Tignor, S. K. Allen, J. Boschung, et al. (Eds.), Climate change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Chap. 9, pp. 741 – 866 ). Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. Fouquart, Y. ( 1987 ). Radiative transfer in climate models. In M. E. Schlesinger (Ed.), Physically based modelling and simulation of climate and climate changes, (pp. 223 – 284 ). Mass: Kluwer Acad., Norwell. Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. 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L., Loeb, N. G., & Yang, W. Z. ( 2010 ). Spectrally resolved fluxes derived from collocated AIRS and CERES measurements and their application in model evaluation: 2. Cloudy sky and band‐by‐band cloud radiative forcing over the tropical oceans. Journal of Geophysical Research, 115, D21101. https://doi.org/10.1029/2010JD013932 IndexNoFollow radiative kernel climate model spectral longwave radiation cloud radiative feedback Geological Sciences Science Article 2019 ftumdeepblue https://doi.org/10.1029/2019GL08346610.1126/science.1192546 2023-07-31T20:40:02Z Cloud radiative feedback is central to our projection of future climate change. It can be estimated using the cloud radiative kernel (CRK) method or adjustment method. This study, for the first time, examines the contributions of each spectral band to the longwave (LW) cloud radiative feedbacks (CRFs). Simulations of three warming scenarios are analyzed, including +2 K sea surface temperature, 2 × CO2, and 4 × CO2 experiments. While the LW broadband CRFs derived from the CRK and adjustment methods agree with each other, they disagree on the relative contributions from the far‐infrared and window bands. The CRK method provides a consistent band‐by‐band decomposition of LW CRF for different warming scenarios. The simulated and observed short‐term broadband CRFs for the 2003–2013 period are similar to the long‐term counterparts, but their band‐by‐band decompositions are different, which can be further related to the cloud fraction changes in respective simulations and observation.Plain Language SummaryWe studied how the cloud change in response to surface temperature change leads to the changes of radiation at the top of the atmosphere (referred to as cloud radiative feedback) over different frequency ranges in the longwave (referred to as spectral bands). While different methods can provide a similar estimate of broadband cloud radiative feedbacks, the decomposition to different longwave spectral bands can be different from one method to another. The cloud radiative kernel method can provide a more consistent band‐by‐band decomposition of the longwave cloud radiative feedback for different warming scenarios. The decomposition for cloud radiative feedback derived from the warming experiments is considerably different from that derived from decadal‐scale observations and simulations. Such differences in spectral band decomposition can be related to the specific cloud fraction changes for different types of clouds defined with respect to cloud top pressure and cloud opacity.Key PointsThe band‐by‐band decomposition of ... Article in Journal/Newspaper Arctic University of Michigan: Deep Blue Geophysical Research Letters 46 12 6998 7006

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