• Abdul-Razzak, H., and S. J. Ghan, 2000: A parameterization of aerosol activation: 2. Multiple aerosol types. J. Geophys. Res., 105, 68376844, https://doi.org/10.1029/1999JD901161.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Albrecht, B. A., 1989: Aerosols, cloud microphysics, and fractional cloudiness. Science, 245, 12271230, https://doi.org/10.1126/science.245.4923.1227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashcroft, P., and F. Wentz, 2013: AMSR-E/Aqua L2A global swath spatially-resampled brightness temperatures, version 3. National Snow and Ice Data Center, accessed October 2017, https://doi.org/10.5067/AMSR-E/AE_L2A.003.

    • Crossref
    • Export Citation
  • Berry, E., G. G. Mace, and A. Gettleman, 2019: Using A-train observations to evaluate cloud occurrence and radiative effects in the Community Atmosphere Model during the Southeast Asia summer monsoon. J. Climate, 32, 41454165, https://doi.org/10.1175/JCLI-D-18-0693.1.

    • 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
  • Boutle, I. A., S. J. Abel, P. G. Hill, and C. J. Morcrette, 2014: Spatial variability of liquid cloud and rain: Observations and microphysical effects. Quart. J. Roy. Meteor. Soc., 140, 583594, https://doi.org/10.1002/qj.2140.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., and S. Park, 2009: A new moist turbulence parameterization in the Community Atmosphere Model. J. Climate, 22, 34223448, https://doi.org/10.1175/2008JCLI2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, A. R., R. J. Beare, J. M. Edwards, A. P. Lock, S. J. Keogh, S. F. Milton, and D. N. Walters, 2008: Upgrades to the boundary-layer scheme in the Met Office numerical weather prediction model. Bound.-Layer Meteor., 128, 117132, https://doi.org/10.1007/s10546-008-9275-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ceppi, P., F. Brient, M. D. Zelinka, and D. L. Hartmann, 2017: Cloud feedback mechanisms and their representation in global climate models. Wiley Interdiscip. Rev.: Climate Change, 8, e465, https://doi.org/10.1002/wcc.465.

    • Search Google Scholar
    • Export Citation
  • Chen, T., W. B. Rossow, and Y. C. Zhang, 2000: Radiative effects of cloud-type variations. J. Climate, 13, 264286, https://doi.org/10.1175/1520-0442(2000)013<0264:REOCTV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Christensen, M. W., W. K. Jones, and P. Stier, 2020: Aerosols enhance cloud lifetime and brightness along the stratus-to-cumulus transition. Proc. Natl. Acad. Sci. USA, 117, 17 59117 598, https://doi.org/10.1073/pnas.1921231117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Copernicus Climate Change Service, 2017: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store, accessed December 2019, https://cds.climate.copernicus.eu/cdsapp#!/home.

  • Cusack, S., A. Slingo, J. M. Edwards, and M. Wild, 1998: The radiative impact of a simple aerosol climatology on the Hadley Centre atmospheric GCM. Quart. J. Roy. Meteor. Soc., 124, 25172526, https://doi.org/10.1002/qj.49712455117.

    • 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
  • de Szoeke, S. P., and K. L. Verlinden, S. E. Yuter, and D. B. Mechem, 2016: The time scales of variability of marine low clouds. J. Climate, 29, 64636481, https://doi.org/10.1175/JCLI-D-15-0460.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eastman, R., and R. Wood, 2016: Factors controlling low-cloud evolution over the eastern subtropical oceans: A Lagrangian perspective using the A-train satellites. J. Atmos. Sci., 73, 331351, https://doi.org/10.1175/JAS-D-15-0193.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eastman, R., and R. Wood, 2018: The competing effects of stability and humidity on subtropical stratocumulus entrainment and cloud evolution from a Lagrangian perspective. J. Atmos. Sci., 75, 25632578, https://doi.org/10.1175/JAS-D-18-0030.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eastman, R., R. Wood, and C. S. Bretherton, 2016: Time scales of clouds and cloud-controlling variables in subtropical stratocumulus from a Lagrangian perspective. J. Atmos. Sci., 73, 30793091, https://doi.org/10.1175/JAS-D-16-0050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eastman, R., R. Wood, and K. Ting O, 2017: The subtropical stratocumulus-topped planetary boundary layer: A climatology and the Lagrangian evolution. J. Atmos. Sci., 74, 26332656, https://doi.org/10.1175/JAS-D-16-0336.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eastman, R., M. Lebsock, and R. Wood, 2019: Warm rain rates from AMSR-E 89-GHz brightness temperatures trained using CloudSat 380 rain-rate observations. J. Atmos. Oceanic Technol., 36, 10331051, https://doi.org/10.1175/JTECH-D-18-0185.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Edwards, J. M., and A. Slingo, 1996: Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model. Quart. J. Roy. Meteor. Soc., 122, 689719, https://doi.org/10.1002/qj.49712253107.

    • 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
  • Grosvenor, D. P., and R. Wood, 2018: Daily MODIS (Moderate Imaging Spectroradiometer) derived cloud droplet number concentration global dataset for 2003-2015. Centre for Environmental Data Analysis, accessed January 2020, https://catalogue.ceda.ac.uk/uuid/cf97ccc802d348ec8a3b6f2995dfbbff.

  • Grosvenor, D. P., and K. S. Carslaw, 2020: The decomposition of cloud–aerosol forcing in the UK Earth System Model (UKESM1). Atmos. Chem. Phys., 20, 15 68115 724, https://doi.org/10.5194/acp-20-15681-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grosvenor, D. P., and Coauthors, 2018: Remote sensing of cloud droplet number concentration in warm clouds: Review of current state of knowledge and perspectives. Rev. Geophys., 56, 409453, https://doi.org/10.1029/2017RG000593.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hahn, C. J., and S. G. Warren, 2007: A gridded climatology of clouds over land (1971-96) and ocean (1954-97) from surface observations worldwide. CDIAC Rep. ORNL/CDIAC-153, 71 pp., https://doi.org/10.3334/CDIAC/cli.ndp026e.

    • Crossref
    • Export Citation
  • Hubanks, P. A., M. D. King, S. Platnick, and R. Pincus, 2008: MODIS atmosphere L3 gridded product algorithm theoretical basis document. MODIS Tech. Doc. ATBD-MOD-30, 96 pp.

  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karlsson, J., G. Svensson, S. Cardoso, J. Teixeira, and S. Paradise, 2010: Subtropical cloud-regime transitions: Boundary layer depth and cloud-top height evolution in models and observations. J. Appl. Meteor. Climatol., 49, 18451858, https://doi.org/10.1175/2010JAMC2338.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J., and Coauthors, 2019: The Climate Data Guide: COSP: Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. UCAR, https://climatedataguide.ucar.edu/climate-data/cosp-cloud-feedback-model-intercomparison-project-cfmip-observation-simulator-package.

  • King, M. D., and Coauthors, 2003: Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. IEEE Trans. Geosci. Remote Sens., 41, 442458, https://doi.org/10.1109/TGRS.2002.808226.

    • 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
  • Lebsock, M. D., and T. S. L’Ecuyer, 2011: The retrieval of warm rain from CloudSat. J. Geophys. Res., 116, D20209, https://doi.org/10.1029/2011JD016076.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lebsock, M. D., and H. Su, 2014: Application of active spaceborne remote sensing for understanding biases between passive cloud water path retrievals. J. Geophys. Res. Atmos., 119, 89628979, https://doi.org/10.1002/2014JD021568.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • L’Ecuyer, T. S., and G. L. Stephens, 2002: An estimation-based precipitation retrieval algorithm for attenuating radars. J. Appl. Meteor., 41, 272285, https://doi.org/10.1175/1520-0450(2002)041<0272:AEBPRA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lock, A. P., 2001: The numerical representation of entrainment in parametrizations of boundary layer turbulent mixing. Mon. Wea. Rev., 129, 11481163, https://doi.org/10.1175/1520-0493(2001)129<1148:TNROEI>2.0.CO;2.

    • 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
  • Maddux, B. C., S. A. Ackerman, and S. Platnick, 2010: Viewing geometry dependencies in MODIS cloud products. J. Atmos. Oceanic Technol., 27, 15191528, https://doi.org/10.1175/2010JTECHA1432.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martin, G. M., D. W. Johnson, and A. Spice, 1994: The measurement and parameterization of effective radius of droplets in warm stratocumulus clouds. J. Atmos. Sci., 51, 18231842, https://doi.org/10.1175/1520-0469(1994)051<1823:TMAPOE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCoy, D. T., F. A. M. Bender, J. K. C. Mohrmann, D. L. Hartmann, R. Wood, and D. P. Grosvenor, 2017: The global aerosol-cloud first indirect effect estimated using MODIS, MERRA and AeroCom. J. Geophys. Res. Atmos., 122, 17791796, https://doi.org/10.1002/2016JD026141.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mellado, J. P., 2017: Cloud-top entrainment in stratocumulus clouds. Annu. Rev. Fluid Mech., 49, 145169, https://doi.org/10.1146/annurev-fluid-010816-060231.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, M. A., and S. E. Yuter, 2013: Detection and characterization of heavy drizzle cells within subtropical marine stratocumulus using AMSR-E 89-GHz passive microwave measurements. Atmos. Meas. Tech., 6, 113, https://doi.org/10.5194/amt-6-1-2013.

    • 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
  • Morcrette, C. J., 2012: Improvements to a prognostic cloud scheme through changes to its cloud erosion parametrization. Atmos. Sci. Lett., 13, 95102, https://doi.org/10.1002/asl.374.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morgenstern, O., P. Braesicke, F. M. O’Connor, A. C. Bushell, C. E. Johnson, S. M. Osprey, and J. A. Pyle, 2009: Evaluation of the new UKCA climate-composition model—Part I: The stratosphere. Geosci. Model Dev., 2, 4357, https://doi.org/10.5194/gmd-2-43-2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., and A. Gettelman, 2008: A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: Description and numerical tests. J. Climate, 21, 36423659, https://doi.org/10.1175/2008JCLI2105.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Myers, T. A., and J. R. Norris, 2013: Observational evidence that enhanced subsidence reduces subtropical marine boundary layer cloudiness. J. Climate, 26, 75077524, https://doi.org/10.1175/JCLI-D-12-00736.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nam, C., S. Bony, J.-L. Dufresne, and H. Chepfer, 2012: The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models. Geophys. Res. Lett., 39, L21801, https://doi.org/10.1029/2012GL053421.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Naud, C. M., J. F. Booth, J. Jeyaratnam, L. J. Donner, C. J. Seman, M. Zhao, H. Guo, and Y. Ming, 2019: Extratropical cyclone clouds in the GFDL climate model: Diagnosing biases and the associated causes. J. Climate, 32, 66856701, https://doi.org/10.1175/JCLI-D-19-0421.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neale, R. B., and Coauthors, 2010: Description of the NCAR Community Atmosphere Model (CAM5.0). NCAR Tech. Note NCAR/TN-486+STR, 268 pp., www.cesm.ucar.edu/models/cesm1.1/cam/docs/description/cam5_desc.pdf.

  • O’Connor, F. M., and Coauthors, 2014: Evaluation of the new UKCA climate-composition model—Part 2: The troposphere. Geosci. Model Dev., 7, 4191, https://doi.org/10.5194/gmd-7-41-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oreopoulos, L., 2005: The impact of subsampling on MODIS level-3 statistics of cloud optical thickness and effective radius. IEEE Trans. Geosci. Remote Sens., 43, 366373, https://doi.org/10.1109/TGRS.2004.841247.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parishani, H., M. S. Pritchard, C. S. Bretherton, M. C. Wyant, and M. Khairoutdinov, 2019: Toward low-cloud-permitting cloud superparameterization with explicit boundary layer turbulence. J. Adv. Model. Earth Syst., 9, 15421571, https://doi.org/10.1002/2017MS000968.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, S., and C. S. Bretherton, 2009: The University of Washington shallow convection scheme and moist turbulence schemes and their impact on climate simulations with the Community Atmosphere Model. J. Climate, 22, 34493469, https://doi.org/10.1175/2008JCLI2557.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Platnick, S., and Coauthors, 2017: The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua. IEEE Trans. Geosci. Remote Sens., 55, 502525, https://doi.org/10.1109/TGRS.2016.2610522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qu, X., A. Hall, S. Klein, and P. M. Caldwell, 2013: On the spread of changes in marine low cloud cover in climate model simulations of the 21st century. Climate Dyn., 42, 26032626, https://doi.org/10.1007/s00382-013-1945-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramanathan, V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom, E. Ahmed, and D. L. Hartmann, 1989: Cloud-radiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science, 243, 5763, https://doi.org/10.1126/science.243.4887.57.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seethala, C., and Á. Horváth, 2010: Global assessment of AMSR-E and MODIS cloud liquid water path retrievals in warm oceanic clouds. J. Geophys. Res., 115, D13202, https://doi.org/10.1029/2009JD012662.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seidel, D. J., C. O. Ao, and K. Li, 2010: Estimating climatological planetary boundary layer heights from radiosonde observations: Comparison of methods and uncertainty analysis. J. Geophys. Res., 115, D16113, https://doi.org/10.1029/2009JD013680.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shin, S.-H., O.-Y. Kim, D. Kim, and M.-I. Lee, 2017: Cloud radiative effects and changes simulated by the Coupled Model Intercomparison Project phase 5 models. Adv. Atmos. Sci., 34, 859876, https://doi.org/10.1007/s00376-017-6089-3.

    • 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 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Touma, J. S., 1977: Dependence of the wind profile power law on stability for various locations. J. Air Pollut. Control Assoc., 27, 863866, https://doi.org/10.1080/00022470.1977.10470503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vaughan, M., S. Young, D. Winker, K. Powell, A. Omar, Z. Liu, Y. Hu, and C. Hostetler, 2004: Fully automated analysis of space-based lidar data: An overview of the CALIPSO retrieval algorithms and data products. Proc. SPIE, 5575, 1630, https://doi.org/10.1117/12.572024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walters, D., and Coauthors, 2017: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations. Geosci. Model Dev., 10, 14871520, https://doi.org/10.5194/gmd-10-1487-2017.

    • 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
  • Wentz, F. J., and T. Meissner, 2004: AMSR-E/Aqua L2B global swath ocean products derived from Wentz algorithm, version 2. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 13 May 2016, https://doi.org/10.5067/AMSR-E/AE_OCEAN.002.

    • Crossref
    • Export Citation
  • Wentz, F. J., T. Meissner, C. Gentemann, and M. Brewer, 2014: Remote Sensing Systems Aqua AMSR-E [daily] environmental suite on 0.25 deg grid. Remote Sensing Systems, www.remss.com/missions/amsr.

  • West, R. E. L., P. Stier, A. Jones, C. E. Johnson, G. W. Mann, N. Bellouin, D. G. Partridge, and Z. Kipling, 2014: The importance of vertical velocity variability for estimates of the indirect aerosol effects. Atmos. Chem. Phys., 14, 63696393, https://doi.org/10.5194/acp-14-6369-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, D. R., and S. P. Ballard, 1999: A microphysically based precipitation scheme for the UK Meteorological Office Unified Model. Quart. J. Roy. Meteor. Soc., 125, 16071636, https://doi.org/10.1002/qj.49712555707.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, D. R., A. C. Bushell, A. M. Kerr-Munslow, J. D. Price, and C. J. Morcrette, 2008a: PC2: A prognostic cloud fraction and condensation scheme. I: Scheme description. Quart. J. Roy. Meteor. Soc., 134, 20932107, https://doi.org/10.1002/qj.333.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, D. R., A. C. Bushell, A. M. Kerr-Munslow, J. D. Price, C. J. Morcrette, and A. Bodas-Salcedo, 2008b: PC2: A prognostic cloud fraction and condensation scheme. II: Climate model simulations. Quart. J. Roy. Meteor. Soc., 134, 21092125, https://doi.org/10.1002/qj.332.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, R., 2000: Parametrization of the effect of drizzle upon the droplet effective radius in stratocumulus clouds. Quart. J. Roy. Meteor. Soc., 126, 33093324, https://doi.org/10.1002/qj.49712657015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, R., 2012: Stratocumulus clouds. Mon. Wea. Rev., 140, 23732423, https://doi.org/10.1175/MWR-D-11-00121.1.

  • Wood, R., and Coauthors, 2018: Ultraclean layers and optically thin clouds in the stratocumulus to cumulus transition. Part I: Observations. J. Atmos. Sci., 75, 16311652, https://doi.org/10.1175/JAS-D-17-0213.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyant, M. C., C. S. Bretherton, H. A. Rand, and D. E. Stevens, 1997: Numerical simulations and a conceptual model of the stratocumulus to trade cumulus transition. J. Atmos. Sci., 54, 168192, https://doi.org/10.1175/1520-0469(1997)054<0168:NSAACM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuter, S. E., J. D. Hader, M. A. Miller, and D. B. Mechem, 2018: Abrupt cloud clearing of marine stratocumulus in the subtropical southeast Atlantic. Science, 361, 697701, https://doi.org/10.1126/science.aar5836.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.–Ocean, 33, 407446, https://doi.org/10.1080/07055900.1995.9649539.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, K., and Coauthors, 2014: Technical note: On the use of nudging for aerosol–climate model intercomparison studies. Atmos. Chem. Phys., 14, 86318645, https://doi.org/10.5194/acp-14-8631-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evaluating the Lagrangian Evolution of Subtropical Low Clouds in GCMs Using Observations: Mean Evolution, Time Scales, and Responses to Predictors

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  • 1 Department of Atmospheric Sciences, University of Washington, Seattle, Washington
  • 2 Department of Earth System Science, University of California, Irvine, Irvine, California
  • 3 Lawrence Livermore National Laboratory, Livermore, California
  • 4 National Centre for Atmospheric Sciences, School of Earth and Environment, University of Leeds, Leeds, United Kingdom
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Abstract

A Lagrangian framework is developed to show the daily-scale time evolution of low clouds over the eastern subtropical oceans. An identical framework is applied to two general circulation models (GCMs): the CAM5 and UKMET and a set of satellite observations. This approach follows thousands of parcels as they advect downwind in the subtropical trade winds, comparing cloud evolution in time and space. This study tracks cloud cover, in-cloud liquid water path (CLWP), droplet concentration Nd, planetary boundary layer (PBL) depth, and rain rate as clouds transition from regions with predominately stratiform clouds to regions containing mostly trade cumulus. The two models generate fewer clouds with greater Nd relative to observations. Models show stronger Lagrangian cloud cover decline and greater PBL deepening when compared with observations. In comparing frequency distributions of cloud variables over time, it is seen that models generate increasing frequencies of nearly clear conditions at the expense of overcast conditions, whereas observations show transitions from overcast to cloud amounts between 50% and 90%. Lagrangian decorrelation time scales (e-folding time τ) of cloud cover and CLWP are between 11 and 19 h for models and observations, although they are a bit shorter for models. A Lagrangian framework applied here resolves and compares the time evolution of cloud systems as they adjust to environmental perturbations in models and observations. Increasing subsidence in the overlying troposphere leads to declining cloud cover, CLWP, PBL depth, and rain rates in models and observations. Modeled cloud responses to other meteorological variables are less consistent with observations, suggesting a need for continuing mechanical improvements in GCMs.

© 2021 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: Ryan Eastman, rmeast@atmos.washington.edu

Abstract

A Lagrangian framework is developed to show the daily-scale time evolution of low clouds over the eastern subtropical oceans. An identical framework is applied to two general circulation models (GCMs): the CAM5 and UKMET and a set of satellite observations. This approach follows thousands of parcels as they advect downwind in the subtropical trade winds, comparing cloud evolution in time and space. This study tracks cloud cover, in-cloud liquid water path (CLWP), droplet concentration Nd, planetary boundary layer (PBL) depth, and rain rate as clouds transition from regions with predominately stratiform clouds to regions containing mostly trade cumulus. The two models generate fewer clouds with greater Nd relative to observations. Models show stronger Lagrangian cloud cover decline and greater PBL deepening when compared with observations. In comparing frequency distributions of cloud variables over time, it is seen that models generate increasing frequencies of nearly clear conditions at the expense of overcast conditions, whereas observations show transitions from overcast to cloud amounts between 50% and 90%. Lagrangian decorrelation time scales (e-folding time τ) of cloud cover and CLWP are between 11 and 19 h for models and observations, although they are a bit shorter for models. A Lagrangian framework applied here resolves and compares the time evolution of cloud systems as they adjust to environmental perturbations in models and observations. Increasing subsidence in the overlying troposphere leads to declining cloud cover, CLWP, PBL depth, and rain rates in models and observations. Modeled cloud responses to other meteorological variables are less consistent with observations, suggesting a need for continuing mechanical improvements in GCMs.

© 2021 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: Ryan Eastman, rmeast@atmos.washington.edu
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