Abstract
Cloud microphysical properties are significantly affected by entrainment and mixing processes. However, it is unclear how the entrainment rate affects the relative dispersion of cloud droplet size distribution. Previously, the relationship between relative dispersion and entrainment rate was found to be positive or negative. To reconcile the contrasting relationships, the Explicit Mixing Parcel Model is used to determine the underlying mechanisms. When evaporation is dominated by small droplets, and the entrained environmental air is further saturated during mixing, the relationship is negative. However, when the evaporation of big droplets is dominant, the relationship is positive. Whether or not the cloud condensation nuclei are considered in the entrained environmental air is a key factor as condensation on the entrained condensation nuclei is the main source of small droplets. However, if cloud condensation nuclei are not entrained, the relationship is positive. If cloud condensation nuclei are entrained, the relationship is dependent on many other factors. High values of vertical velocity, relative humidity of environmental air, and liquid water content, and low values of droplet number concentration, are more likely to cause the negative relationship since new saturation is easier to achieve by evaporation of small droplets. Further, the signs of the relationship are not strongly affected by the turbulence dissipation rate, but the higher dissipation rate causes the positive relationship to be more significant for a larger entrainment rate. A conceptual model is proposed to reconcile the contrasting relationships. This work enhances the understanding of relative dispersion and lays a foundation for the quantification of entrainment-mixing mechanisms.
摘要
云与环境空气之间的夹卷混合过程能够显著影响云的微物理特性. 然而, 目前尚不清楚夹卷率如何影响云滴谱的离散度. 在以往的观测结果中发现, 离散度和夹卷率之间的关系可以为正相关或负相关. 为了调和两者关系的不一致性, 本文使用显式混合气泡模式来揭示决定两者关系的物理机制. 结果显示, 当蒸发以小云滴为主, 并使卷入云中的环境空气在混合过程中达到饱和时, 两者关系为负相关. 当大云滴的蒸发占主导地位时, 两者关系为正相关. 在卷入的环境空气中是否考虑云凝结核是决定两者关系的一个关键因素, 因为通过凝结核凝结是产生小云滴的主要途径. 如果未卷入凝结核, 两者关系为正相关. 如果卷入凝结核, 两者关系亦受到其他因素的影响. 垂直速度、 环境空气的相对湿度和含水量较大时以及云滴数浓度较小时更有利于两者形成负相关, 因为新的饱和更容易通过小云滴的蒸发来实现. 此外, 两者关系受湍流耗散率的影响较小, 但夹卷率较大时较强的耗散率导致正相关关系更为显著. 本文提出了一个概念模型来调和两者关系的不一致性, 该工作增强了对离散度的理解, 并为进一步定量化夹卷混合机制奠定了基础.
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References
Abbott, T. H., and T. W. Cronin, 2021: Aerosol invigoration of atmospheric convection through increases in humidity. Science, 371, 83–85, https://doi.org/10.1126/science.abc5181.
Ackerman, A. S., and Coauthors, 2009: Large-eddy simulations of a drizzling, stratocumulus-topped marine boundary layer. Mon. Wea. Rev., 137, 1083–1110, https://doi.org/10.1175/2008MWR2582.1.
Axelsen, S. L., 2005: The role of relative humidity on shallow cumulus dynamics; results from a large eddy simulation model. Utrecht, Netherlands: Utrecht University.
Baker, M. B., R. G. Corbin, and J. Latham, 1980: The influence of entrainment on the evolution of cloud droplet spectra: I. A model of inhomogeneous mixing. Quart. J. Roy. Meteor. Soc., 106, 581–598, https://doi.org/10.1002/qj.49710644914.
Bera, S., 2021: Droplet spectral dispersion by lateral mixing process in continental deep cumulus clouds. Journal of Atmospheric and Solar-Terrestrial Physics, 214, 105550, https://doi.org/10.1016/j.jastp.2021.105550.
Bera, S., T. V. Prabha, and W. W. Grabowski, 2016a: Observations of monsoon convective cloud microphysics over India and role of entrainment-mixing. J. Geophys. Res., 121, 9767–9788, https://doi.org/10.1002/2016JD025133.
Bera, S., G. Pandithurai, and T. V. Prabha, 2016b: Entrainment and droplet spectral characteristics in convective clouds during transition to monsoon. Atmospheric Science Letters, 17, 286–293, https://doi.org/10.1002/asl.657.
Burnet, F., and J.-L. Brenguier, 2007: Observational study of the entrainment-mixing process in warm convective clouds. J. Atmos. Sci., 64, 1995–2011, https://doi.org/10.1175/JAS3928.1.
Chandrakar, K. K., W. Cantrell, K. Chang, D. Ciochetto, D. Niedermeier, M. Ovchinnikov, R. A. Shaw, and F. Yang, 2016: Aerosol indirect effect from turbulence-induced broadening of cloud-droplet size distributions. Proceedings of the National Academy of Sciences of the United States of America, 113, 14 243–14 248, https://doi.org/10.1073/pnas.1612686113.
Chen, J. Y., Y. G. Liu, M. H. Zhang, and Y. R. Peng, 2016: New understanding and quantification of the regime dependence of aerosol-cloud interaction for studying aerosol indirect effects. Geophys. Res. Lett., 43, 1780–1787, https://doi.org/10.1002/2016GL067683.
Chen, J. Y., Y. G. Liu, and M. H. Zhang, 2020: Effects of lateral entrainment mixing with entrained aerosols on cloud microphysics. Geophys. Res. Lett., 47, e2020GL087667, https://doi.org/10.1029/2020GL087667.
Cooper, W. A., 1989: Effects of variable droplet growth histories on droplet size distributions. Part I: Theory. J. Atmos. Sci., 46, 1301–1311, https://doi.org/10.1175/1520-0469(1989)046<1301:EOVDGH>2.0.CO;2.
Cooper, W. A., S. G. Lasher-Trapp, and A. M. Blyth, 2013: The influence of entrainment and mixing on the initial formation of rain in a warm cumulus cloud. J. Atmos. Sci., 70, 1727–1743, https://doi.org/10.1175/JAS-D-12-0128.1.
de Rooy, W. C., and Coauthors, 2013: Entrainment and detrainment in cumulus convection: An overview. Quart. J. Roy. Meteor. Soc., 139, 1–19, https://doi.org/10.1002/qj.1959.
Devenish, B. J., and Coauthors, 2012: Droplet growth in warm turbulent clouds. Quart. J. Roy. Meteor. Soc., 138, 1401–1429, https://doi.org/10.1002/qj.1897.
Frisch, U., 1980: Fully developed turbulence and intermittency. Annals of the New York Academy of Sciences, 357, 359–367, https://doi.org/10.1111/j.1749-6632.1980.tb29703.x.
Fukuta, N., and L. A. Walter, 1970: Kinetics of hydrometeor growth from a vapor-spherical model. J. Atmos. Sci., 27, 1160–1172, https://doi.org/10.1175/1520-0469(1970)027<1160:KOHGFA>2.0.CO;2.
Gao, S. N., C. S. Lu, Y. G. Liu, F. Mei, J. Wang, L. Zhu, and S. Q. Yan, 2020: Contrasting scale dependence of entrainment-mixing mechanisms in stratocumulus clouds. Geophys. Res. Lett., 47, e2020GL086970, https://doi.org/10.1029/2020GL086970.
Gao, S., and Coauthors, 2021: Comprehensive quantification of height dependence of entrainment mixing between stratiform cloud top and environment. Atmospheric Chemistry and Physics, 21, 11 225–11 241, https://doi.org/10.5194/acp-21-11225-2021.
Gao, Z., Y. G. Liu, X. L. Li, and C. S. Lu, 2018: Investigation of turbulent entrainment-mixing processes with a new particle-resolved direct numerical simulation model. J. Geophys. Res., 123, 2194–2214, https://doi.org/10.1002/2017JD027507.
Gerber, H. E., G. M. Frick, J. B. Jensen, and J. G. Hudson, 2008: Entrainment, mixing, and microphysics in trade-wind cumulus. J. Meteor. Soc. Japan, 86A, 87–106, https://doi.org/10.2151/jmsj.86A.87.
Guo, X. H., C. S. Lu, T. L. Zhao, Y. G. Liu, G. J. Zhang, and S. Luo, 2018: Observational study of the relationship between entrainment rate and relative dispersion in deep convective clouds. Atmospheric Research, 199, 186–192, https://doi.org/10.1016/j.atmosres.2017.09.013.
Hoffmann, F., H. Siebert, J. Schumacher, T. Riechelmann, J. Katzwinkel, B. Kumar, P. Götzfried, and S. Raasch, 2014: Entrainment and mixing at the interface of shallow cumulus clouds: Results from a combination of observations and simulations. Meteor. Z., 23, 349–368, https://doi.org/10.1127/0941-2948/2014/0597.
Hoffmann, F., S. Raasch, and Y. Noh, 2015: Entrainment of aerosols and their activation in a shallow cumulus cloud studied with a coupled LCM-LES approach. Atmospheric Research, 156, 43–57, https://doi.org/10.1016/j.atmosres.2014.12.008.
Houze, R. A. Jr., 1993: Cloud Dynamics. Academic Press.
Hsieh, W. C., H. Jonsson, L.-P. Wang, G. Buzorius, R. C. Flagan, J. H. Seinfeld, and A. Nenes, 2009: On the representation of droplet coalescence and autoconversion: Evaluation using ambient cloud droplet size distributions. J. Geophys. Res., 114, D07201, https://doi.org/10.1029/2008JD010502.
Hudson, J. G., S. Noble, and V. Jha, 2012: Cloud droplet spectral width relationship to CCN spectra and vertical velocity. J. Geophys. Res., 117, D11211, https://doi.org/10.1029/2012JD017546.
Johnson, D. B., 1993: The onset of effective coalescence growth in convective clouds. Quart. J. Roy. Meteor. Soc., 119, 925–933, https://doi.org/10.1002/qj.49711951304.
Jonas, P. R., 1990: Observations of cumulus cloud entrainment. Atmospheric Research, 25, 105–127, https://doi.org/10.1016/0169-8095(90)90008-Z.
Kerstein, A. R., 1991: Linear-eddy modelling of turbulent transport. Part 6. Microstructure of diffusive scalar mixing fields. J. Fluid Mech., 231, 361–394, https://doi.org/10.1017/S0022112091003439.
Kerstein, A. R., 1992: Linear eddy modeling of turbulent transport. Part 7 Finite rate chemistry and multi-stream mixing. J. Fluid Mech., 240, 289–313, https://doi.org/10.1017/S0022112092000107.
Khain, A., M. Pinsky, and L. Magaritz-Ronen, 2018: Physical interpretation of mixing diagrams. J. Geophys. Res., 123, 529–542, https://doi.org/10.1002/2017JD027124.
Krueger, S. K., 1993: Linear eddy modeling of entrainment and mixing in stratus clouds. J. Atmos. Sci., 50, 3078–3090, https://doi.org/10.1175/1520-0469(1993)050<3078:LEMOEA>2.0.CO;2.
Krueger, S. K., P. Lehr, and C. Su, 2006: How entrainment and mixing scenarios affect droplet spectra in cumulus clouds. Preprints, 12th Conference on Cloud Physics.
Krueger, S. K., C.-W. Su, and P. A. McMurtry, 1997: Modeling entrainment and finescale mixing in cumulus clouds. J. Atmos. Sci., 54, 2697–2712, https://doi.org/10.1175/1520-0469(1997)054<2697:MEAFMI>2.0.CO;2.
Krueger, S. K., H. Schlueter, and P. Lehr, 2008: Fine-scale modeling of entrainment and mixing of cloudy and clear air. Preprints, 15th International Conference on Clouds and Precipitation, Cancun, Mexico.
Kumar, B., F. Janetzko, J. Schumacher, and R. A. Shaw, 2012: Extreme responses of a coupled scalar-particle system during turbulent mixing. New Journal of Physics, 14, 115020, https://doi.org/10.1088/1367-2630/14/11/115020.
Kumar, B., J. Schumacher, and R. A. Shaw, 2014: Lagrangian mixing dynamics at the cloudy-clear air interface. J. Atmos. Sci., 71, 2564–2580, https://doi.org/10.1175/JAS-D-13-0294.1.
Kumar, B., S. Bera, T. V. Prabha, and W. W. Grabowski, 2017: Cloud-edge mixing: Direct numerical simulation and observations in Indian Monsoon clouds. Journal of Advances in Modeling Earth Systems, 9, 332–353, https://doi.org/10.1002/2016MS000731.
Lasher-Trapp, S. G., W. A. Cooper, and A. M. Blyth, 2005: Broadening of droplet size distributions from entrainment and mixing in a cumulus cloud. Quart. J. Roy. Meteor. Soc., 131, 195–220, https://doi.org/10.1256/qj.03.199.
Lee, S. H., P. D. Williams, and T. H. A. Frame, 2019: Increased shear in the North Atlantic upper-level jet stream over the past four decades. Nature, 572, 639–642, https://doi.org/10.1038/s41586-019-1465-z.
Lehmann, K., H. Siebert, and R. A. Shaw, 2009: Homogeneous and inhomogeneous mixing in cumulus clouds: Dependence on local turbulence structure. J. Atmos. Sci., 66, 3641–3659, https://doi.org/10.1175/2009JAS3012.1.
Li, Y. Y., and M. H. Zhang, 2017: The role of shallow convection over the Tibetan Plateau. J. Climate, 30, 5791–5803, https://doi.org/10.1175/JCLI-D-16-0599.1.
Liu, Y., P. H. Daum, S. K. Chai, and F. Liu, 2002: Cloud parameterizations, cloud physics, and their connections: An overview. BNL-68995, 26 pp.
Liu, Y. G., 2019: Introduction to the special section on fast physics in climate models: Parameterization, evaluation, and observation. J. Geophys. Res., 124, 8631–8644, https://doi.org/10.1029/2019JD030422.
Liu, Y. G., and P. H. Daum, 2002: Indirect warming effect from dispersion forcing. Nature, 419, 580–581, https://doi.org/10.1038/419580a.
Liu, Y. G., P. H. Daum, and S. S. Yum, 2006: Analytical expression for the relative dispersion of the cloud droplet size distribution. Geophys. Res. Lett., 33, L02810, https://doi.org/10.1029/2005GL024052.
Lohse, D., and S. Grossmann, 1993: Intermittency in turbulence. Physica A: Statistical Mechanics and its Applications, 194, 519–531, https://doi.org/10.1016/0378-4371(93)90382-E.
Lu, C., and Coauthors, 2018c: Observational relationship between entrainment rate and environmental relative humidity and implications for convection parameterization. Geophys. Res. Lett., 45, 13 495–13 504, https://doi.org/10.1029/2018GL080264.
Lu, C. S., Y. G. Liu, S. S. Yum, S. J. Niu, and S. Endo, 2012: A new approach for estimating entrainment rate in cumulus clouds. Geophys. Res. Lett., 39, L04802, https://doi.org/10.1029/2011GL050546.
Lu, C. S., S. J. Niu, Y. G. Liu, and A. M. Vogelmann, 2013a: Empirical relationship between entrainment rate and microphysics in cumulus clouds. Geophys. Res. Lett., 40, 2333–2338, https://doi.org/10.1002/grl.50445.
Lu, C. S., Y. G. Liu, S. J. Niu, S. Krueger, and T. Wagner, 2013b: Exploring parameterization for turbulent entrainment-mixing processes in clouds. J. Geophys. Res., 118, 185–194, https://doi.org/10.1029/2012JD018464.
Lu, C.-S., Y.-G. Liu, S.-J. Niu, and Y.-Q. Xue, 2018a: Broadening of cloud droplet size distributions and warm rain initiation associated with turbulence: An overview. Atmospheric and Oceanic Science Letters, 11, 123–135, https://doi.org/10.1080/16742834.2018.1410057.
Lu, C. S., Y. G. Liu, B. Zhu, S. S. Yum, S. K. Krueger, Y. J. Qiu, S. J. Niu, and S. Luo, 2018b: On which microphysical time scales to use in studies of entrainment-mixing mechanisms in clouds. J. Geophys. Res., 123, 3740–3756, https://doi.org/10.1002/2017JD027985.
Lu, C. S., and Coauthors, 2020: Reconciling contrasting relationships between relative dispersion and volume-mean radius of cloud droplet size distributions. J. Geophys. Res., 125, e2019JD031868, https://doi.org/10.1029/2019JD031868.
Luo, S., and Coauthors, 2020: Parameterizations of entrainment-mixing mechanisms and their effects on cloud droplet spectral width based on numerical simulations. J. Geophys. Res., 125, e2020JD032972, https://doi.org/10.1029/2020JD032972.
Luo, S., C. S. Lu, Y. G. Liu, W. H. Gao, L. Zhu, X. Q. Xu, J. J. Li, and X. H. Guo, 2021: Consideration of initial cloud droplet size distribution shapes in quantifying different entrainment-mixing mechanisms. J. Geophys. Res., 126, e2020JD034455, https://doi.org/10.1029/2020JD034455.
Ma, J. Z., Y. Chen, W. Wang, P. Yang, H. J. Liu, S. Y. Yang, Z. J. Hu, and J. Lelieveld, 2010: Strong air pollution causes widespread haze-clouds over China. J. Geophys. Res., 115, D18204, https://doi.org/10.1029/2009JD013065.
Mahrt, L., 1989: Intermittency of atmospheric turbulence. J. Atmos. Sci., 46, 79–95, https://doi.org/10.1175/1520-0469(1989)046<0079:IOAT>2.0.CO;2.
McFarquhar, G. M., T.-L. Hsieh, M. Freer, J. Mascio, and B. F. Jewett, 2015: The characterization of ice hydrometeor gamma size distributions as volumes in N0−λ−μ phase space: Implications for microphysical process modeling. J. Atmos. Sci., 72, 892–909, https://doi.org/10.1175/JAS-D-14-0011.1.
Nolan, D. S., and E. D. Rappin, 2008: Increased sensitivity of tropical cyclogenesis to wind shear in higher SST environments. Geophys. Res. Lett., 35, L14805, https://doi.org/10.1029/2008GL034147.
Pandithurai, G., S. Dipu, T. V. Prabha, R. S. Maheskumar, J. R. Kulkarni, and B. N. Goswami, 2012: Aerosol effect on droplet spectral dispersion in warm continental cumuli. J. Geophys. Res., 117, D16202, https://doi.org/10.1029/2011JD016532.
Peng, Y. R., U. Lohmann, R. Leaitch, and M. Kulmala, 2007: An investigation into the aerosol dispersion effect through the activation process in marine stratus clouds. J. Geophys. Res., 112, D11117, https://doi.org/10.1029/2006JD007401.
Pinsky, M., and A. Khain, 2018: Theoretical analysis of mixing in liquid clouds-Part IV: DSD evolution and mixing diagrams. Atmospheric Chemistry and Physics, 18, 3659–3676, https://doi.org/10.5194/acp-18-3659-2018.
Pinsky, M., A. Khain, A. Korolev, and L. Magaritz-Ronen, 2016: Theoretical investigation of mixing in warm clouds-Part 2: Homogeneous mixing. Atmospheric Chemistry and Physics, 16, 9255–9272, https://doi.org/10.5194/acp-16-9255-2016.
Prabha, T. V., and Coauthors, 2012: Spectral width of premonsoon and monsoon clouds over Indo-Gangetic valley. J. Geophys. Res., 117, D20205, https://doi.org/10.1029/2011JD016837.
Prabhakaran, P., A. S. M. Shawon, G. Kinney, S. Thomas, W. Cantrell, and R. A. Shaw, 2020: The role of turbulent fluctuations in aerosol activation and cloud formation. Proceedings of the National Academy of Sciences of the United States of America, 117, 16 831–16 838, https://doi.org/10.1073/pnas.2006426117.
Raga, G. B., J. B. Jensen, and M. B. Baker, 1990: Characteristics of cumulus band clouds off the coast of hawaii. J. Atmos. Sci., 47, 338–356, https://doi.org/10.1175/1520-0469(1990)047<0338:COCBCO>2.0.CO;2.
Randall, D. A., and Coauthors, 2018: 100 years of earth system model development. Meteor. Monogr., 59, 12.1–12.66, https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0018.1.
Rotstayn, L. D., and Y. G. Liu, 2009: Cloud droplet spectral dispersion and the indirect aerosol effect: Comparison of two treatments in a GCM. Geophys. Res. Lett., 36, L10801, https://doi.org/10.1029/2009GL038216.
She, Z.-S., E. Jackson, and S. A. Orszag, 1990: Intermittent vortex structures in homogeneous isotropic turbulence. Nature, 344, 226–228, https://doi.org/10.1038/344226a0.
Siebert, H., K. Lehmann, and M. Wendisch, 2006a: Observations of small-scale turbulence and energy dissipation rates in the cloudy boundary layer. J. Atmos. Sci., 63, 1451–1466, https://doi.org/10.1175/JAS3687.1.
Siebert, H., H. Franke, K. Lehmann, R. Maser, E. W. Saw, D. Schell, R. A. Shaw, and M. Wendisch, 2006b: Probing finescale dynamics and microphysics of clouds with helicopter-borne measurements. Bull. Amer. Meteor. Soc., 87, 1727–1738, https://doi.org/10.1175/BAMS-87-12-1727.
Slawinska, J., W. W. Grabowski, H. Pawlowska, and H. Morrison, 2012: Droplet activation and mixing in large-eddy simulation of a shallow cumulus field. J. Atmos. Sci., 69, 444–462, https://doi.org/10.1175/JAS-D-11-054.1.
Small, J. D., and P. Y. Chuang, 2008: New observations of precipitation initiation in warm cumulus clouds. J. Atmos. Sci., 65, 2972–2982, https://doi.org/10.1175/2008JAS2600.1.
Small, J. D., P. Y. Chuang, and H. H. Jonsson, 2013: Microphysical imprint of entrainment in warm cumulus. Tellus B: Chemical and Physical Meteorology, 65, 19922, https://doi.org/10.3402/tellusb.v65i0.19922.
Sreenivasan, K., 1985: On the fine-scale intermittency of turbulence. J. Fluid Mech., 151, 81–103, https://doi.org/10.1017/S0022112085000878.
Stanfield, R. E., and Coauthors, 2019: Convective entrainment rates estimated from Aura CO and CloudSat/CALIPSO observations and comparison with GEOS — 5. J. Geophys. Res., 124, 9796–9807, https://doi.org/10.1029/2019JD030846.
Su, C.-W., S. K. Krueger, P. A. McMurtry, and P. H. Austin, 1998: Linear eddy modeling of droplet spectral evolution during entrainment and mixing in cumulus clouds. Atmospheric Research, 47–48, 41–58, https://doi.org/10.1016/S0169-8095(98)00039-8.
Tas, E., I. Koren, and O. Altaratz, 2012: On the sensitivity of droplet size relative dispersion to warm cumulus cloud evolution. Geophys. Res. Lett., 39, L13807, https://doi.org/10.1029/2012GL052157.
Tas, E., A. Teller, O. Altaratz, D. Axisa, R. Bruintjes, Z. Levin, and I. Koren, 2015: The relative dispersion of cloud droplets: Its robustness with respect to key cloud properties. Atmospheric Chemistry and Physics, 15, 2009–2017, https://doi.org/10.5194/acp-15-2009-2015.
Telford, J. W., and S. K. Chai, 1980: A new aspect of condensation theory. Pure and Applied Geophysics, 118, 720–742, https://doi.org/10.1007/BF01593025.
Tölle, M. H., and S. K. Krueger, 2014: Effects of entrainment and mixing on droplet size distributions in warm cumulus clouds. Journal of Advances in Modeling Earth Systems, 6, 281–299, https://doi.org/10.1002/2012MS000209.
Turner, J. S., 1962: The ‘starting plume’ in neutral surroundings. J. Fluid Mech., 13, 356–368, https://doi.org/10.1017/S0022112062000762.
Wallace, J. M., and P. V. Hobbs, 2006: Atmospheric Science: An Introductory Survey. 2nd ed. Elsevier.
Wang, M. Q., Y. R. Peng, Y. G. Liu, Y. Liu, X. N. Xie, and Z. Y. Guo, 2020a: Understanding cloud droplet spectral dispersion effect using empirical and semi-analytical parameterizations in NCAR CAM5.3. Earth and Space Science, 7, e2020EA001276, https://doi.org/10.1029/2020EA001276.
Wang, Y., and Coauthors, 2019: A new method for distinguishing unactivated particles in cloud condensation nuclei measurements: Implications for aerosol indirect effect evaluation. Geophys. Res. Lett., 46, 14 185–14 194, https://doi.org/10.1029/2019GL085379.
Wang, Y., and Coauthors, 2020b: Microphysical properties of generating cells over the Southern Ocean: Results from SOCRATES. J. Geophys. Res., 125, e2019JD032237, https://doi.org/10.1029/2019JD032237.
Wang, Y., H. Su, J. H. Jiang, F. Xu, and Y. L. Yung, 2020c: Impact of cloud ice particle size uncertainty in a climate model and implications for future satellite missions. J. Geophys. Res., 125, e2019JD032119, https://doi.org/10.1029/2019JD032119.
Wang, Y., C. F. Zhao, G. M. McFarquhar, W. Wu, M. Reeves, and J. M. Li, 2021a: Dispersion of droplet size distributions in supercooled non-precipitating stratocumulus from aircraft observations obtained during the Southern Ocean cloud radiation aerosol transport experimental study. J. Geophys. Res., 126, e2020JD033720, https://doi.org/10.1029/2020JD033720.
Wang, Y., S. J. Niu, C. S. Lu, S. X. Fan, J. J. Lv, X. Q. Xu, Y. C. Jin, and W. Sun, 2021b: A new CCN activation parameterization and its potential influences on aerosol indirect effects. Atmospheric Research, 253, 105491, https://doi.org/10.1016/j.atmosres.2021.105491.
Xie, X. N., X. D. Liu, Y. R. Peng, Y. Wang, Z. G. Yue, and X. Z. Li, 2013: Numerical simulation of clouds and precipitation depending on different relationships between aerosol and cloud droplet spectral dispersion. Tellus B: Chemical and Physical Meteorology, 65, 19054, https://doi.org/10.3402/tellusb.v65i0.19054.
Xu, X. Q., C. S. Lu, Y. G. Liu, W. H. Gao, Y. Wang, Y. M. Cheng, S. Luo, and K. Van Weverberg, 2020: Effects of cloud liquid-phase microphysical processes in mixed-phase cumuli over the Tibetan Plateau. J. Geophys. Res., 125, e2020JD033371, https://doi.org/10.1029/2020JD033371.
Xu, X. Q., C. Sun, C. S. Lu, Y. G. Liu, G. J. Zhang, and Q. Chen, 2021: Factors affecting entrainment rate in deep convective clouds and parameterizations. J. Geophys. Res., 126, e2021JD034881, https://doi.org/10.1029/2021JD034881.
Xu, X., and Coauthors, 2022: Influences of an entrainment-mixing parameterization on numerical simulations of cumulus and stratocumulus clouds. Atmos. Chem. Phys., 22, 5459–5475, https://doi.org/10.5194/acp-22-5459-2022.
Xue, H. W., and G. Feingold, 2006: Large-eddy simulations of trade wind cumuli: Investigation of aerosol indirect effects. J. Atmos. Sci., 63, 1605–1622, https://doi.org/10.1175/JAS3706.1.
Yang, F., P. Kollias, R. A. Shaw, and A. M. Vogelmann, 2018: Cloud droplet size distribution broadening during diffusional growth: Ripening amplified by deactivation and reactivation. Atmospheric Chemistry and Physics, 18, 7313–7328, https://doi.org/10.5194/acp-18-7313-2018.
Yeom, J. M., S. S. Yum, F. Mei, B. Schmid, J. Comstock, L. A. T. Machado, and M. A. Cecchini, 2019: Impact of secondary droplet activation on the contrasting cloud microphysical relationships during the wet and dry seasons in the Amazon. Atmospheric Research, 230, 104648, https://doi.org/10.1016/j.atmosres.2019.104648.
Yum, S. S., and J. G. Hudson, 2005: Adiabatic predictions and observations of cloud droplet spectral broadness. Atmospheric Research, 73, 203–223, https://doi.org/10.1016/j.atmosres.2004.10.006.
Yum, S. S., J. Wang, Y. G. Liu, G. Senum, S. Springston, R. McGraw, and J. M. Yeom, 2015: Cloud microphysical relationships and their implication on entrainment and mixing mechanism for the stratocumulus clouds measured during the VOCALS project. J. Geophys. Res., 120, 5047–5069, https://doi.org/10.1002/2014JD022802.
Zelinka, M. D., D. A. Randall, M. J. Webb, and S. A. Klein, 2017: Clearing clouds of uncertainty. Nature Climate Change, 7, 674–678, https://doi.org/10.1038/nclimate3402.
Zhang, G. J., X. Q. Wu, X. P. Zeng, and T. Mitovski, 2016: Estimation of convective entrainment properties from a cloud-resolving model simulation during TWP-ICE. Climate Dyn., 47, 2177–2192, https://doi.org/10.1007/s00382-015-2957-7.
Zhao, C. F., Y. M. Qiu, X. B. Dong, Z. E. Wang, Y. R. Peng, B. D. Li, Z. H. Wu, and Y. Wang, 2018: Negative aerosol-cloud re relationship from aircraft observations over Hebei, China. Earth and Space Science, 5, 19–29, https://doi.org/10.1002/2017EA000346.
Zhao, C. S., and Coauthors, 2006: Aircraft measurements of cloud droplet spectral dispersion and implications for indirect aerosol radiative forcing. Geophys. Res. Lett., 33, L16809, https://doi.org/10.1029/2006GL026653.
Zheng, Y. T., Y. N. Zhu, D. Rosenfeld, and Z. Q. Li, 2021: Climatology of cloud-top radiative cooling in marine shallow clouds. Geophys. Res. Lett., 48, e2021GL094676, https://doi.org/10.1029/2021GL094676.
Zhu, L., and Coauthors, 2021: A new approach for simultaneous estimation of entrainment and detrainment rates in non-precipitating shallow cumulus. Geophys. Res. Lett., 48, e2021GL093817, https://doi.org/10.1029/2021GL093817.
Acknowledgements
The authors thank Sinan GAO and Zhuangzhuang ZHOU in NUIST for helpful discussions. This research is supported by the National Natural Science Foundation of China (Grant Nos. 41822504, 42175099, 42027804, 42075073 and 42075077), and the National Center of Meteorology, Abu Dhabi, UAE under the UAE Research Program for Rain Enhancement Science. LIU is supported by the U.S. Department of Energy Atmospheric System Research (ASR) Program (DE-SC00112704) and Solar Energy Technologies Office (SETO) under Award 33504. LUO is supported by Research Fund of Civil Aviation Flight University of China (J2022-037), LI is supported by Research Fund of Civil Aviation Flight University of China (09005001), and WU is supported by Research on Key of Manmachine Ring in Plateau Flight (FZ2020ZZ03). The simulation data are stored in https://data.mendeley.com/datasets/v38k2hd95b/draft?a=ad380244-94c2-4762-9bc2-ec24a164f234. The numerical calculations in this paper have been performed at the Supercomputing Center of Nanjing University of Information Science and Technology.
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Article Highlights
• The observed contrasting relationships between the relative dispersion of cloud droplet size distribution and entrainment rate are reproduced, and their impacting factors are examined.
• The different relationships are mainly determined by the relative importance of evaporation of small and big droplets, and entrained cloud condensation nuclei.
• The negative relationship is more likely to occur for high values of vertical velocity, relative humidity of environmental air, and liquid water content, and low values of droplet number concentration.
This paper is a contribution to the special issue on Cloud-Aerosol-Radiation-Precipitation Interaction: Progress and Challenges
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Luo, S., Lu, C., Liu, Y. et al. Relationships between Cloud Droplet Spectral Relative Dispersion and Entrainment Rate and Their Impacting Factors. Adv. Atmos. Sci. 39, 2087–2106 (2022). https://doi.org/10.1007/s00376-022-1419-5
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DOI: https://doi.org/10.1007/s00376-022-1419-5