Assessing the Coupled Influences of Clouds on the Atmospheric Energy and Water Cycles in Reanalyses with A-Train Observations

A. S. Daloz Space and Science Engineering Center, and Center for Climatic Research, University of Wisconsin–Madison, Madison, Wisconsin

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E. Nelson Atmospheric and Oceanic Sciences Department, University of Wisconsin–Madison, Madison, Wisconsin

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T. L’Ecuyer Center for Climatic Research, and Atmospheric and Oceanic Sciences Department, University of Wisconsin–Madison, Madison, Wisconsin

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A. D. Rapp Department of Atmospheric Sciences, Texas A&M University, College Station, Texas

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L. Sun Department of Atmospheric Sciences, Texas A&M University, College Station, Texas

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Abstract

The lack of complete knowledge concerning the complex interactions among clouds, circulation, and climate hinders our ability to simulate the Earth’s climate correctly. This study contributes to a broader understanding of the implications of cloud and precipitation biases on the representation of coupled energy and water exchanges by bringing together a suite of cloud impact parameters (CIPs). These parameters measure the coupled impact of cloud systems on regional energy balance and hydrology by simultaneously capturing the absolute strength of the cloud albedo and greenhouse effects, the relative importance of these two radiative effects, and the efficiency of precipitating clouds to radiatively heat the atmosphere and cool the surface per unit of heating through rain production. Global distribution of these CIPs is derived using satellite observations from CloudSat and used to evaluate energy and water cycle coupling in four reanalysis datasets [both versions of the Modern-Era Retrospective Analysis for Research and Applications (MERRA and MERRA-2); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Japanese 55-year Reanalysis (JRA-55)]. The results show that the reanalyses provide a more accurate representation of the three radiation-centric parameters than the radiative efficiencies. Of the four reanalyses, MERRA and ERA-Interim provide the best overall representation of the different cloud processes but can still show significant biases. JRA-55 exhibits some clear deficiencies in many parameters, while MERRA-2 seems to introduce biases that were not evident in MERRA.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Anne Sophie Daloz, adaloz@wisc.edu

Abstract

The lack of complete knowledge concerning the complex interactions among clouds, circulation, and climate hinders our ability to simulate the Earth’s climate correctly. This study contributes to a broader understanding of the implications of cloud and precipitation biases on the representation of coupled energy and water exchanges by bringing together a suite of cloud impact parameters (CIPs). These parameters measure the coupled impact of cloud systems on regional energy balance and hydrology by simultaneously capturing the absolute strength of the cloud albedo and greenhouse effects, the relative importance of these two radiative effects, and the efficiency of precipitating clouds to radiatively heat the atmosphere and cool the surface per unit of heating through rain production. Global distribution of these CIPs is derived using satellite observations from CloudSat and used to evaluate energy and water cycle coupling in four reanalysis datasets [both versions of the Modern-Era Retrospective Analysis for Research and Applications (MERRA and MERRA-2); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Japanese 55-year Reanalysis (JRA-55)]. The results show that the reanalyses provide a more accurate representation of the three radiation-centric parameters than the radiative efficiencies. Of the four reanalyses, MERRA and ERA-Interim provide the best overall representation of the different cloud processes but can still show significant biases. JRA-55 exhibits some clear deficiencies in many parameters, while MERRA-2 seems to introduce biases that were not evident in MERRA.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Anne Sophie Daloz, adaloz@wisc.edu
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  • Adler, R. F., and Coauthors, 2003: The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–present). J. Hydrometeor., 4, 11471167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adler, R. F., G. Gu, and G. J. Huffman, 2012: Estimating climatological bias errors for the Global Precipitation Climatology Project (GPCP). J. Appl. Meteor. Climatol., 51, 8499, https://doi.org/10.1175/JAMC-D-11-052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ahlgrimm, M., and R. Forbes, 2014: Improving the representation of low clouds and drizzle in the ECMWF model based on ARM observations from the Azores. Mon. Wea. Rev., 142, 668685, https://doi.org/10.1175/MWR-D-13-00153.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bacmeister, J. T., M. J. Suarez, and F. R. Robertson, 2006: Rain reevaporation, boundary layer–convection interactions, and Pacific rainfall patterns in an AGCM. J. Atmos. Sci., 63, 33833403, https://doi.org/10.1175/JAS3791.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bloom, S., L. Takacs, A. da Silva, and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 12561271, https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bony, S., and J.-L. Dufresne, 2005: 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bony, S., and Coauthors, 2015: Clouds, circulation and climate sensitivity. Nat. Geosci., 8, 261268, https://doi.org/10.1038/ngeo2398.

  • Bosilovich, M. G., and Coauthors, 2015: MERRA-2: Initial evaluation of the climate. NASA Tech. Rep. NASA/TM-2015-104606, 139 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich803.pdf.

  • Bretherton, C. S., and A. H. Sobel, 2002: A simple model of a convectively coupled Walker circulation using the weak temperature gradient approximation. J. Climate, 15, 29072920, https://doi.org/10.1175/1520-0442(2002)015<2907:ASMOAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., P. N. Blossey, and M. Khairoutdinov, 2005: An energy-balance analysis of deep convective self-aggregation above uniform SST. J. Atmos. Sci., 62, 42734292, https://doi.org/10.1175/JAS3614.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choi, Y.-S., W. Kim, S.-W. Yeh, H. Masunaga, M.-J. Kwon, H.-S. Jo, and L. Huang, 2017: Revisiting the iris effect of tropical cirrus clouds with TRMM and A‐Train satellite data. J. Geophys. Res. Atmos., 122, 59175931, https://doi.org/10.1002/2016JD025827.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chou, M. D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. NASA Tech. Rep. NASA/TM-1999-104606, 40 pp., https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19990060930.pdf.

  • Chou, M. D., M. J. Suarez, X. Z. Liang, and M. M. H. Yan, 2001: A thermal infrared radiation parameterization for atmospheric studies. NASA Tech. Rep. NASA/TM-2001-104606, 56 pp., https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20010072848.pdf.

  • Clark, J. V., and J. E. Walsh, 2010: Observed and reanalysis cloud fraction. J. Geophys. Res., 115, D23121, https://doi.org/10.1029/2009JD013235.

  • Coakley, J. A., R. D. Cess, and F. B. Yurevich, 1983: The effect of tropospheric aerosols on the Earth’s radiation budget: A parameterization for climate models. J. Atmos. Sci., 40, 116138, https://doi.org/10.1175/1520-0469(1983)040<0116:TEOTAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cullather, R. I., S. M. Nowicki, B. Zhao, and M. J. Suarez, 2014: Evaluation of the surface representation of the Greenland Ice Sheet in a general circulation model. J. Climate, 27, 48354856, https://doi.org/10.1175/JCLI-D-13-00635.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daloz, A. S., F. Chauvin, K. Walsh, S. Lavender, D. Abbs, and F. Roux, 2012: The ability of general circulation models to simulate tropical cyclones and their precursors over the North Atlantic main development region. Climate Dyn., 39, 15591576, https://doi.org/10.1007/s00382-012-1290-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Deckker, P., 2016: The Indo-Pacific warm pool: Critical to world oceanography and world climate. Geosci. Lett., 3, 20, https://doi.org/10.1186/s40562-016-0054-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., Y. Chen, D. Kim, and M. Yao, 2012: The MJO transition from shallow to deep convection in CloudSat/CALIPSO data and GISS GCM simulations. J. Climate, 25, 37553770, https://doi.org/10.1175/JCLI-D-11-00384.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dixon, R. D., A. S. Daloz, D. J. Vimont, and M. Biasutti, 2017: Saharan heat low biases in CMIP5 models. J. Climate, 30, 28672884, https://doi.org/10.1175/JCLI-D-16-0134.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dolinar, E. K., X. Dong, and B. Xi, 2016: Evaluation and intercomparison of clouds, precipitation, and radiation budgets in recent reanalyses using satellite-surface observations. Climate Dyn., 46, 21232144, https://doi.org/10.1007/s00382-015-2693-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duynkerke, P. G., and J. Teixeira, 2001: Comparison of the ECMWF reanalysis with FIRE I observations: Diurnal variation of marine stratocumulus. J. Climate, 14, 14661478, https://doi.org/10.1175/1520-0442(2001)014<1466:COTERW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gibson, J. K., and Coauthors, 1997: ERA description. ECMWF Re-Analysis Project Report Series, 72 pp.

  • Hartmann, D. L., H. H. Hendon, and R. A. Houze, 1984: Some implications of the mesoscale circulations in tropical cloud clusters for large-scale dynamics and climate. J. Atmos. Sci., 41, 113121, https://doi.org/10.1175/1520-0469(1984)041<0113:SIOTMC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haynes, J. M., T. S. L’Ecuyer, G. L. Stephens, S. D. Miller, C. Mitrescu, N. B. Wood, and S. Tanelli, 2009: Rainfall retrieval over the ocean with spaceborne W-band radar. J. Geophys. Res., 114, D00A22, https://doi.org/10.1029/2008JD009973.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699, https://doi.org/10.1175/JCLI3990.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henderson, D. S., T. L’Ecuyer, G. Stephens, P. Partain, and M. Sekiguchi, 2013: A multisensor perspective on the radiative impacts of clouds and aerosols. J. Appl. Meteor. Climatol., 52, 853871, https://doi.org/10.1175/JAMC-D-12-025.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heng, Z., Y. Fu, G. Liu, R. Zhou, Y. Wang, R. Yuan, J. Guo, and X. Dong, 2014: A study of the distribution and variability of cloud water using ISCCP, SSM/I cloud product, and reanalysis datasets. J. Climate, 27, 31143128, https://doi.org/10.1175/JCLI-D-13-00031.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holloway, C. E., A. A. Wing, S. Bony, C. Muller, H. Masunaga, T. L’Ecuyer, D. Turner, and P. Zuidema, 2017: Observing convective aggregation. Surv. Geophys., 38, 11991236, https://doi.org/10.1007/s10712-017-9419-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., 1982: Cloud clusters and large-scale vertical motions in the tropics. J. Meteor. Soc. Japan, 60, 396410, https://doi.org/10.2151/jmsj1965.60.1_396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., 1989: Observed structure of mesoscale convective systems and implications for large-scale heating. Quart. J. Roy. Meteor. Soc., 115, 425461, https://doi.org/10.1002/qj.49711548702.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, P., and T. Li, 2012: Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden–Julian oscillation. J. Climate, 25, 49144931, https://doi.org/10.1175/JCLI-D-11-00310.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the global precipitation record: GPCP Version 2.1. Geophys. Res. Lett., 36, L17808, https://doi.org/10.1029/2009GL040000.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jakob, C., 1999: Cloud cover in ECMWF reanalysis. J. Climate, 12, 947959, https://doi.org/10.1175/1520-0442(1999)012<0947:CCITER>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jakob, C., and S. A. Klein, 1999: The role of vertically varying cloud fraction in the parametrization of microphysical processes in the ECMWF model. Quart. J. Roy. Meteor. Soc., 125, 941965, https://doi.org/10.1002/qj.49712555510.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jakob, C., and S. A. Klein, 2000: A parametrization of the effects of cloud and precipitation overlap for use in general-circulation models. Quart. J. Roy. Meteor. Soc., 126, 25252544, https://doi.org/10.1002/qj.49712656809.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joseph, J. H., W. J. Wiscombe, and J. A. Weinman, 1976: The delta-Eddington approximation for radiative flux transfer. J. Atmos. Sci., 33, 24522459, https://doi.org/10.1175/1520-0469(1976)033<2452:TDEAFR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and Coauthors, 2012: Exposing global cloud biases in the Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators. J. Climate, 25, 51905207, https://doi.org/10.1175/JCLI-D-11-00469.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M. F., and K. A. Emanuel, 2013: Rotating radiative-convective equilibrium simulated by a cloud-resolving model. J. Adv. Model. Earth Syst., 5, 816825, https://doi.org/10.1002/2013MS000253.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiehl, J. T., 1994: On the observed near cancellation between longwave and shortwave cloud forcing in tropical regions. J. Climate, 7, 559565, https://doi.org/10.1175/1520-0442(1994)007<0559:OTONCB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, J., and M. J. Alexander, 2013: Tropical precipitation variability and convectively coupled equatorial waves on submonthly time scales in reanalyses and TRMM. J. Climate, 26, 30133030, https://doi.org/10.1175/JCLI-D-12-00353.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6, 15871606, https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, C., H. Endo, T. Ota, S. Kobayashi, H. Onoda, Y. Harada, K. Onogi, and H. Kamahori, 2014: Preliminary results of the JRA-55C, an atmospheric reanalysis assimilating conventional observations only. SOLA, 10, 7882, https://doi.org/10.2151/sola.2014-016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, S., and Coauthors, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 548, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Köhler, M., M. Ahlgrimm, and A. C. M. Beljaars, 2011: Unified treatment of dry convective and stratocumulus-topped boundary layers in the ECMWF model. Quart. J. Roy. Meteor. Soc., 137, 4357, https://doi.org/10.1002/qj.713.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., S. Ringerud, J. Crook, D. Randel, and W. Berg, 2011: An observationally generated a priori database for microwave rainfall retrievals. J. Atmos. Oceanic Technol., 28, 113130, https://doi.org/10.1175/2010JTECHA1468.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K., and L. Peng, 1987: Origin of low-frequency (intraseasonal) oscillations in the tropical atmosphere. Part I: Basic theory. J. Atmos. Sci., 44, 950972, https://doi.org/10.1175/1520-0469(1987)044<0950:OOLFOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • L’Ecuyer, T. S., and J. H. Jiang, 2010: Touring the atmosphere aboard the A-Train. Phys. Today, 63, 3641, https://doi.org/10.1063/1.3463626.

  • L’Ecuyer, T. S., H. Masunaga, and C. Kummerow, 2006: Variability in the characteristics of precipitation systems in the tropical Pacific. Part II: Implications for atmospheric heating. J. Climate, 19, 13881406, https://doi.org/10.1175/JCLI3698.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • L’Ecuyer, T. S., N. B. Wood, T. Haladay, G. L. Stephens, and P. W. Stackhouse Jr., 2008: Impact of clouds on atmospheric heating based on the R04 CloudSat fluxes and heating rates data set. J. Geophys. Res., 113, D00A15, https://doi.org/10.1029/2008JD009951.

    • Search Google Scholar
    • Export Citation
  • Lee, M.-I., I.-S. Kang, J.-K. Kim, and B. E. Mapes, 2001: Influence of cloud-radiation interaction on simulating tropical intraseasonal oscillation with an atmospheric general circulation model. J. Geophys. Res., 106, 14 21914 233, https://doi.org/10.1029/2001JD900143.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lenaerts, J. T. M., K. Van Tricht, S. Lhermitte, and T. S. L’Ecuyer, 2017: Polar clouds and radiation in satellite observations, reanalyses, and climate models. Geophys. Res. Lett., 44, 33553364, https://doi.org/10.1002/2016GL072242.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J., W. C. Wang, X. Dong, and J. Mao, 2017: Cloud-radiation-precipitation associations over the Asian monsoon region: An observational analysis. Climate Dyn., 49, 32373255, https://doi.org/10.1007/s00382-016-3509-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., and J. R. Key, 2016: Assessment of Arctic cloud cover anomalies in atmospheric reanalysis products using satellite data. J. Climate, 29, 60656083, https://doi.org/10.1175/JCLI-D-15-0861.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lock, A. P., A. R. Brown, M. R. Bush, G. M. Martin, and R. N. B. Smith, 2000: A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests. Mon. Wea. Rev., 128, 31873199, https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Louis, J., M. Tiedtke, and J. Geleyn, 1982: A short history of the PBL parameterization at ECMWF. Proc. ECMWF Workshop on Planetary Boundary Layer Parameterization, Reading, United Kingdom, ECMWF, 59–80, https://www.ecmwf.int/en/elibrary/10845-short-history-pbl-parameterization-ecmwf.

  • Mapes, B. E., and R. A. Houze Jr., 1995: Diabatic divergence profiles in western Pacific mesoscale convective systems. J. Atmos. Sci., 52, 18071828, https://doi.org/10.1175/1520-0469(1995)052<1807:DDPIWP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matus, A. V., and T. S. L’Ecuyer, 2017: The role of cloud phase in Earth’s radiation budget. J. Geophys. Res. Atmos., 122, 25592578, https://doi.org/10.1002/2016JD025951.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitchell, J. F., C. A. Wilson, and W. M. Cunnington, 1987: On Co2 climate sensitivity and model dependence of results. Quart. J. Roy. Meteor. Soc., 113, 293322, https://doi.org/10.1256/smsqj.47516.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molod, A., L. Takacs, M. Suarez, and J. Bacmeister, 2015: Development of the GEOS-5 atmospheric general circulation model: Evolution from MERRA to MERRA2. Geosci. Model Dev., 8, 13391356, https://doi.org/10.5194/gmd-8-1339-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moorthi, S., and M. J. Suarez, 1992: Relaxed Arakawa-Schubert. A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 9781002, https://doi.org/10.1175/1520-0493(1992)120<0978:RASAPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nam, C., and J. Quaas, 2012: Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data. J. Climate, 25, 49754992, https://doi.org/10.1175/JCLI-D-11-00347.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nelson, E. L., and T. S. L’Ecuyer, 2018: Global character of latent heat release in oceanic warm rain systems. J. Geophys. Res. Atmos., 123, 47974817, https://doi.org/10.1002/2017JD027844.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nelson, E. L., T. S. L’Ecuyer, S. M. Saleeby, W. Berg, S. R. Herbener, and S. C. van den Heever, 2016: Toward an algorithm for estimating latent heat release in warm rain systems. J. Atmos. Oceanic Technol., 33, 13091329, https://doi.org/10.1175/JTECH-D-15-0205.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nesbitt, S. W., and E. J. Zipser, 2003: The diurnal cycle of rainfall and convective intensity according to three years of TRMM measurements. J. Climate, 16, 14561475, https://doi.org/10.1175/1520-0442-16.10.1456.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nolan, D. S., Y. Moon, and D. P. Stern, 2007: Tropical cyclone intensification from asymmetric convection: Energetics and efficiency. J. Atmos. Sci., 64, 33773405, https://doi.org/10.1175/JAS3988.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., and D. L. Hartmann, 2014: The atmospheric energy constraint on global-mean precipitation change. J. Climate, 27, 757768, https://doi.org/10.1175/JCLI-D-13-00163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pincus, R., C. P. Batstone, R. J. P. Hofmann, K. E. Taylor, and P. J. Glecker, 2008: Evaluating the present-day simulation of clouds, precipitation, and radiation in climate models. J. Geophys. Res., 113, D14209, https://doi.org/10.1029/2007JD009334.

    • Search Google Scholar
    • Export Citation
  • Räisänen, P., 1998: Effective longwave cloud fraction and maximum-random overlap of clouds: A problem and a solution. Mon. Wea. Rev., 126, 33363340, https://doi.org/10.1175/1520-0493(1998)126<3336:ELCFAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichler, T., and J. Kim, 2008: Uncertainties in the climate mean state of global observations, reanalyses, and the GFDL climate model. J. Geophys. Res., 113, D05106, https://doi.org/10.1029/2007JD009278.

    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, C., R. A. Houze Jr., and I. Kraucunas, 2004: The tropical dynamical response to latent heating estimates derived from the TRMM precipitation radar. J. Atmos. Sci., 61, 13411358, https://doi.org/10.1175/1520-0469(2004)061<1341:TTDRTL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sherwood, S. C., S. Bony, and J.-L. Dufresne, 2014: Spread in model climate sensitivity traced to atmospheric convective mixing. Nature, 505, 3742, https://doi.org/10.1038/nature12829.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slingo, A., and J. M. Slingo, 1988: The response of a general circulation model to cloud longwave radiative forcing. I: Introduction and initial experiments. Quart. J. Roy. Meteor. Soc., 114, 10271062, https://doi.org/10.1002/qj.49711448209.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slingo, A., and J. M. Slingo, 1991: Response of the National Center for Atmospheric Research community climate model to improvements in the representation of clouds. J. Geophys. Res., 96, 15 34115 357, https://doi.org/10.1029/91JD00930.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobel, A. H., and C. S. Bretherton, 2000: Modeling tropical precipitation in a single column. J. Climate, 13, 43784392, https://doi.org/10.1175/1520-0442(2000)013<4378:MTPIAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobel, A. H., and H. Gildor, 2003: A simple time-dependent model of SST hot spots. J. Climate, 16, 39783992, https://doi.org/10.1175/1520-0442(2003)016<3978:ASTMOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soden, B. J., 2000: The sensitivity of the tropical hydrologic cycle to ENSO. J. Climate, 13, 538549, https://doi.org/10.1175/1520-0442(2000)013<0538:TSOTTH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soden, B. J., and I. M. Held, 2006: An assessment of climate feedbacks in coupled ocean–atmosphere models. J. Climate, 19, 33543360, https://doi.org/10.1175/JCLI3799.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stein, T. H., C. E. Holloway, I. Tobin, and S. Bony, 2017: Observed relationships between cloud vertical structure and convective aggregation over tropical ocean. J. Climate, 30, 21872207, https://doi.org/10.1175/JCLI-D-16-0125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18, 237273, https://doi.org/10.1175/JCLI-3243.1.

  • Stephens, G. L., and T. J. Greenwald, 1991: The Earth’s radiation budget and its relation to atmospheric hydrology: 2. Observations of cloud effects. J. Geophys. Res., 96, 15 32515 340, https://doi.org/10.1029/91JD00972.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and T. D. Ellis, 2008: Controls of global-mean precipitation increases in global warming GCM experiments. J. Climate, 21, 61416155, https://doi.org/10.1175/2008JCLI2144.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2008: CloudSat mission: Performance and early science after the first year of operation. J. Geophys. Res., 113, D00A18, https://doi.org/10.1029/2008JD009982.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., D. Winker, J. Pelon, C. Trepte, D. Vane, C. Yuhas, T. L’Ecuyer, and M. Lebsock, 2017: CloudSat and CALIPSO within the A-Train: Ten years of actively observing the Earth system. Bull. Amer. Meteor. Soc., 99, 569581, https://doi.org/10.1175/BAMS-D-16-0324.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, B., and S. Bony, 2013: What are climate models missing? Science, 340, 10531054, https://doi.org/10.1126/science.1237554.

  • Stevens, B., A. Beljaars, S. Bordoni, C. Holloway, M. Köhler, S. Krueger, V. Savic-Jovcic, and Y. Y. Zhang, 2007: On the structure of the lower troposphere in the summertime stratocumulus regime of the northeast Pacific. Mon. Wea. Rev., 135, 9851005, https://doi.org/10.1175/MWR3427.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanelli, S., S. L. Durden, E. Im, K. S. Pak, D. G. Reinke, P. Partain, J. M. Haynes, and R. T. Marchand, 2008: CloudSat’s Cloud Profiling Radar after two years in orbit: Performance, calibration, and processing. IEEE Trans. Geosci. Remote Sens., 46, 35603573, https://doi.org/10.1109/TGRS.2008.2002030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teixeira, J., and Coauthors, 2011: Tropical and subtropical cloud transitions in weather and climate prediction models: The GCSS/WGNE Pacific Cross-Section Intercomparison (GPCI). J. Climate, 24, 52235256, https://doi.org/10.1175/2011JCLI3672.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tobin, I., S. Bony, and R. Roca, 2012: Observational evidence for relationships between the degree of aggregation of deep convection, water vapor, surface fluxes, and radiation. J. Climate, 25, 68856904, https://doi.org/10.1175/JCLI-D-11-00258.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uppala, S. M., and Coauthors, 2005: The ERA‐40 re‐analysis. Quart. J. Roy. Meteor. Soc., 131, 29613012, https://doi.org/10.1256/qj.04.176.

  • Waliser, D. E., and N. E. Graham, 1993: Convective cloud systems and warm‐pool sea surface temperatures: Coupled interactions and self‐regulation. J. Geophys. Res., 98, 12 88112 893, https://doi.org/10.1029/93JD00872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., W. L. Chapman, and D. H. Portis, 2009: Arctic cloud fraction and radiative fluxes in atmospheric reanalyses. J. Climate, 22, 23162334, https://doi.org/10.1175/2008JCLI2213.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, M., and Coauthors, 2012: Constraining cloud lifetime effects of aerosols using A-Train satellite observations. Geophys. Res. Lett., 39, L15709, https://doi.org/10.1029/2012GL052204.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weaver, A. J., 1999: Extratropical subduction and decadal modulation of El Niño. Geophys. Res. Lett., 26, 743746, https://doi.org/10.1029/1999GL900102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S., and M. Zhang, 2000: Impact of the convection triggering function on single-column model simulations. J. Geophys. Res., 105, 14 98314 996, https://doi.org/10.1029/2000JD900170.

    • Crossref
    • Search Google Scholar
    • Export Citation
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