Resolving both entrainment-mixing and number of activated CCN in deep convective clouds

Abstract

The number concentration of activated CCN (<i>N</i><sub>a</sub>) is the most fundamental microphysical property of a convective cloud. It determines the rate of droplet growth with cloud depth and conversion into precipitation-sized particles and affects the radiative properties of the clouds. However, measuring <i>N</i><sub>a</sub> is not always possible, even in the cores of the convective clouds, because entrainment of sub-saturated ambient air deeper into the cloud lowers the concentrations by dilution and may cause partial or total droplet evaporation, depending on whether the mixing is homogeneous or extreme inhomogeneous, respectively. <br><br> Here we describe a methodology to derive <i>N</i><sub>a</sub> based on the rate of cloud droplet effective radius (<i>R</i><sub>e</sub>) growth with cloud depth and with respect to the cloud mixing with the entrained ambient air. We use the slope of the tight linear relationship between the adiabatic water and <i>R</i><sub>e</sub><sup>3</sup> to derive an upper limit for <i>N</i><sub>a</sub> assuming extreme inhomogeneous mixing. Then we tune <i>N</i><sub>a</sub> down to find the theoretical relative humidity that the entrained ambient air would have for each horizontal cloud penetration, in case of homogeneous mixing. This allows us to evaluate both the entrainment and mixing process in the vertical dimension in addition to getting a better estimation for <i>N</i><sub>a</sub>. <br><br> We found that the derived <i>N</i><sub>a</sub> from the entire profile data is highly correlated with the independent CCN measurements from below cloud base. Moreover, it was found that mixing of sub-saturated ambient air into the cloud is inclined towards the extreme inhomogeneous limit, i.e. that the time scale of droplet evaporation is significantly smaller than that for turbulent mixing. This means that ambient air that entrains the cloud is pre-moistened by total evaporation of cloud droplets before it mixes deeper into the clouds where it can hardly change the droplet size distribution, hence <i>R</i><sub>e</sub> remains close to its adiabatic value at any given cloud depth. However, the tendency towards the extreme inhomogeneous mixing appeared to slightly decrease with altitude, possibly due to enhanced turbulence and larger cloud drops aloft. <br><br> Quantifying these effects, based on more examples from other projects and high resolution cloud models is essential for improving our understanding of the interactions between the cloud and its environment. These interactions may play an important role in cloud dynamics and microphysics, by affecting cloud depth and droplet size spectra, for example, and may therefore influence the cloud precipitation formation processes

Combined satellite and radar retrievals of drop concentration and CCN at convective cloud base

“…Some other studies showed a smaller-drop growth rate with height that indicates a larger component of homogeneous mixing [e.g., Paluch and Baumgardner, 1989], although part of this component may be explained by instrumental artifacts [Burnet and Brenguier, 2007]. An extensive survey of aircraft measurements in the Amazon, California, India, and Israel showed the dominance of the near-extreme inhomogeneous mixing mode [Freud et al, 2011]. N a was calculated by Freud et al [2011] as follows:…”

“…An extensive survey of aircraft measurements in the Amazon, California, India, and Israel showed the dominance of the near-extreme inhomogeneous mixing mode [Freud et al, 2011]. N a was calculated by Freud et al [2011] as follows:…”

Exploring parameterization for turbulent entrainment‐mixing processes in clouds

“…The reason for the negative values is that n along each of these two legs is smaller than n i , which may be related to the uncertainties of the cloud properties determined from observations, such as n a . Freud et al [2011] pointed out that n a determined by observed maximum number concentration may be sensitive to small-scale processes (e.g., local strong updraft near cloud base, since n a is sensitive to updraft [e.g., Lu et al, 2012a]) and be affected by the extent of dilution that the measured cloud has experienced; they developed a new approach to estimate n a but this approach is not applicable to the stratocumulus clouds in this study, because applying this approach requires penetrations at different levels in convective clouds. Fortunately, these two legs with negative homogeneous mixing degrees have small N La and N L0 (<10) and the data points for these legs would be located in the bottom left corner if Figure 2 was plotted in the linear space, still supporting the positive relationships of ψ 1 , ψ 2 , ψ 3 to N La , N L0 .…”

“…Although the conceptual model is well established, our understanding is still far from complete. Some observations suggested that the entrainment-mixing process was close to homogeneous [e.g., Jensen et al, 1985;Burnet and Brenguier, 2007;Lehmann et al, 2009]; others pointed to an inhomogeneous scenario [Pawlowska et al, 2000;Burnet and Brenguier, 2007;Haman et al, 2007;Freud et al, 2008Freud et al, , 2011Gerber et al, 2008;Lehmann et al, 2009].…”

Satellite retrieval of convective cloud base temperature based on the NPP/VIIRS Imager

“…The calculation of N d depends on the knowledge of cloud base temperature, T b , because the methodology is based in part on the calculated adiabatic cloud liquid water content as a function of height above cloud base [Freud et al, 2011]. Here we develop the methodology and show the feasibility to measure cloud base temperature from an existing satellite with a standard retrieval error of only 1.1°C.…”