Aerosol indirect effect from turbulence-induced broadening of cloud-droplet size distributions

Abstract

The influence of aerosol concentration on the cloud-droplet size distribution is investigated in a laboratory chamber that enables turbulent cloud formation through moist convection. The experiments allow steady-state microphysics to be achieved, with aerosol input balanced by cloud-droplet growth and fallout. As aerosol concentration is increased, the cloud-droplet mean diameter decreases, as expected, but the width of the size distribution also decreases sharply. The aerosol input allows for cloud generation in the limiting regimes of fast microphysics (τc < τ t ) for high aerosol concentration, and slow microphysics (τc > τ t ) for low aerosol concentration; here, τc is the phase-relaxation time and τ t is the turbulence-correlation time. The increase in the width of the droplet size distribution for the low aerosol limit is consistent with larger variability of supersaturation due to the slow microphysical response. A stochastic differential equation for supersaturation predicts that the standard deviation of the squared droplet radius should increase linearly with a system time scale defined as τ, and the measurements are in excellent agreement with this finding. The result underscores the importance of droplet size dispersion for aerosol indirect effects: increasing aerosol concentration changes the albedo and suppresses precipitation formation not only through reduction of the mean droplet diameter but also by narrowing of the droplet size distribution due to reduced supersaturation fluctuations. Supersaturation fluctuations in the low aerosol/slow microphysics limit are likely of leading importance for precipitation formation.aerosol indirect effect | cloud-droplet size distribution | cloud-turbulence interactions T he optical properties of warm clouds depend on the droplet size distribution and its moments such as number density and effective radius, which, in turn, are influenced by the aerosol particles that act as nuclei for the formation of cloud droplets (1, 2). Thus, aerosol indirect effects are considered among the largest uncertainties in climate response to changes in radiative forcing (3). This work addresses how the aerosol number concentration affects the cloud-droplet size distribution in a turbulent environment, which is relevant to both the aerosol first and second indirect effects (albedo and lifetime effects). The lifetime effect links the development of precipitation, and thus cloud lifetime, to aerosol number concentration. The logic is that a higher aerosol concentration leads to smaller cloud droplets and narrower size distributions, and therefore suppression of the collision and coalescence of droplets, thereby increasing cloud lifetime and maintaining higher cloud liquid water content (4-7). The microphysical details of the transition from condensation growth to collision growth are not fully understood, however, and it is fair to say that the underlying mechanism of the second indirect effect is still a matter of active research (2, 8). Initiation of precipitation in warm clouds ...

Cloud droplet size distribution broadening during diffusional growth: ripening amplified by deactivation and reactivation

“…Results from the sensitivity studies show that the relative dispersion is larger than 1.5 for relatively polluted conditions when both deactivation and reactivation occur (see Table 2), which is consistent with the values from observations and simulations (e.g., Miles et al, 2000;Liu and Daum, 2002;Chandrakar et al, 2016). However the relative dispersion has also been found to be larger than 1.5 for relatively clean conditions (e.g., Miles et al, 2000;Lu and Seinfeld, 2006;Chandrakar et al, 2016).…”

“…However the relative dispersion has also been found to be larger than 1.5 for relatively clean conditions (e.g., Miles et al, 2000;Lu and Seinfeld, 2006;Chandrakar et al, 2016). This might be due to other mechanisms, such as supersaturation fluctuations in a turbulent environment or the collision coalescence process.…”

“…A qualitative description of this mechanism is that some "lucky" cloud droplets experience relatively larger supersaturation or stay a relatively longer time in the cloud compared with the other cloud droplets; therefore they can grow larger in size and broaden the CDSD. Recent theoretical and experimental studies support this mechanism and provide ways to quantify the resulting width of the droplet size distribution (e.g., McGraw and Liu, 2006;Sardina et al, 2015;Chandrakar et al, 2016;Grabowski and Abade, 2017;Siewert et al, 2017). Turbulence can also modulate the condensational growth of cloud droplets through mixing and entrainment (e.g., Lasher-Trapp et al, 2005;Cooper et al, 2013;Korolev et al, 2013;Yang et al, 2016).…”

Cloud droplet size distribution broadening during diffusional growth: ripening amplified by deactivation and reactivation

“…The original version of the model was designed to study the warm cloud processes by Feingold et al (1998) and since has been modified and applied to investigate various of microphysical problems (e.g., Feingold and Kreidenweis, 2000;Xue and Feingold, 2004;Ervens and Feingold, 2012;Yang et al, 2012;Li et al, 2013;Yang et al, 2016). In this study, the parcel starts rising at about 300 m below cloud base and starts 10 descending at about 300 m above cloud base, which is similar to Jensen and Nugent (2017), except that our cloud parcel then experiences upward and downward oscillations between 50 m above cloud base and 300 m above cloud base (see Figure 1a).…”

“…An interesting question is to explain why the CDSD is wider than predicted and the presence of the large droplet sizes in the tail of the distribution (e.g., Siebert and Shaw, 2017), which might be related to the fast-rain process in the atmosphere (e.g., Göke et al, 2007). Several pos- 15 sible mechanisms have been proposed, including the existence of giant cloud condensational nuclei (GCCN, usually defined as aerosols with dry diameter larger than few µm) (e.g., Feingold et al, 1999;Yin et al, 2000;Jensen and Lee, 2008;Cheng et al, 2009;Jensen and Nugent, 2017), lucky cloud droplets (e.g., Kostinski and Shaw, 2005;Naumann and Seifert, 2015;Lozar and Muessle, 2016), mixing with environmental air (e.g., Lasher-Trapp et al, 2005;Cooper et al, 2013;Korolev et al, 2013;Yang et al, 2016), supersaturation fluctuations (e.g., Chandrakar et al, 2016;Siebert and Shaw, 2017), and enhancement of collision 20 efficiency due to turbulence or charge (e.g., Paluch, 1970;Grabowski and Wang, 2013;Falkovich and Pumir, 2015;Lu and Shaw, 2015). Recently, Jensen and Nugent (2017) investigated the effect of GCCN on droplet growth and rain formation using a cloud parcel model.…”