Notes on the state-of-the-art numerical modeling of cloud microphysics

Хаин, А.; Ovtchinnikov, Mikhail; Pinsky, Mark; Pokrovsky, A.; Krugliak, H.

doi:10.1016/s0169-8095(00)00064-8

Cited by 332 publications

(282 citation statements)

References 149 publications

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“…Regeneration upon evaporation of cloud drops was shown to be a very important source of aerosols, especially in polluted conditions (Yin et al, 2005). The aerosol serves as potential cloud condensation nuclei (CCN), and it is activated based on the Köhler theory (the scheme is described in Khain et al, 2000). The aerosol (water drop) size distribution is calculated between 5 nm and 2 µm (2 µm-3.2 mm).…”

Section: Methodsologymentioning

confidence: 99%

Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading

et al. 2017

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Abstract. Large eddy simulations (LESs) with bin microphysics are used here to study cloud fields' sensitivity to changes in aerosol loading and the time evolution of this response. Similarly to the known response of a single cloud, we show that the mean field properties change in a nonmonotonic trend, with an optimum aerosol concentration for which the field reaches its maximal water mass or rain yield. This trend is a result of competition between processes that encourage cloud development versus those that suppress it. However, another layer of complexity is added when considering clouds' impact on the field's thermodynamic properties and how this is dependent on aerosol loading. Under polluted conditions, rain is suppressed and the non-precipitating clouds act to increase atmospheric instability. This results in warming of the lower part of the cloudy layer (in which there is net condensation) and cooling of the upper part (net evaporation). Evaporation at the upper part of the cloudy layer in the polluted simulations raises humidity at these levels and thus amplifies the development of the next generation of clouds (preconditioning effect). On the other hand, under clean conditions, the precipitating clouds drive net warming of the cloudy layer and net cooling of the sub-cloud layer due to rain evaporation. These two effects act to stabilize the atmospheric boundary layer with time (consumption of the instability). The evolution of the field's thermodynamic properties affects the cloud properties in return, as shown by the migration of the optimal aerosol concentration toward higher values.

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Section: Methodsologymentioning

confidence: 99%

Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading

et al. 2017

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“…Along with the mass concentration, the number concentration is often used as a prognostic variable in two-moment bulk microphysics schemes that predict the evolution of clouds in recent climate and 15 numerical weather prediction models (Khain et al, 2000;Seifert and Beheng, 2006). An absence of global observational constraints therefore limits the evaluation of model predictions to sparser in situ measurements (e.g.…”

Section: Introductionmentioning

confidence: 99%

Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

Sourdeval¹,

Gryspeerdt²,

Krämer³

et al. 2018

Preprint

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Abstract. The number concentration of cloud particles is a key quantity for understanding aerosol-cloud interactions and describing clouds in climate and numerical weather prediction models. In contrast with recent advances for liquid clouds, few observational constraints exist on the ice crystal number concentration (N i ). This study investigates how combined lidar-radar measurements can be used to provide satellite estimates of N i , using a methodology that constrains moments of a parameterized particle size distribution (PSD). The operational liDAR-raDAR (DARDAR) product serves as an existing base for this method, 5 which focuses on ice clouds with temperatures T c < -30°C.Theoretical considerations demonstrate the capability for accurate retrievals of N i , apart from a possible bias in the concentration in small crystals when T c -50°C, due to the assumption of a monomodal PSD shape in the current method. This is verified by comparing satellite estimates to co-incident in situ measurements, which additionally demonstrates the sufficient sensitivity of lidar-radar observations to N i . Following these results, satellite estimates of N i are evaluated in the context of a 10 case study and a preliminary climatological analysis based on 10 years of global data. Despite of a lack of other large-scale references, this evaluation shows a reasonable physical consistency in N i spatial distribution patterns. Notably, increases in N i are found towards cold temperatures and, more significantly, in the presence of strong updraughts, such as those related to convective or orographic uplifts. Further evaluation and improvements of this method are necessary but these results already constitute a first encouraging step towards large-scale observational constraints for N i . Part two of this series uses this new 15 dataset to examine the controls on N i .

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“…The first condition is that droplets form on polydisperse aerosol particles where larger cloud droplets contain more solute. This is a very general occurrence in the atmosphere due to the complexity of aerosol size and composition (Murphy et al, 1998;Khain et al, 2000). The second condition is that a cloud experiences upward and downward oscillations.…”

Section: Conclusion and Atmospheric Implicationsmentioning

confidence: 99%

“…First, turbulenceinduced mixing and entrainment can trigger in-cloud activation of haze particles, which can broaden the left branch of the size distribution (e.g., Khain et al, 2000;Devenish et al, 2012;Yang et al, 2016;Grabowski et al, 2018). Secondly, giant cloud condensational nuclei (GCCN, usually defined as aerosols with a dry diameter larger than a few µm) provides an embryo for large droplets, which can broaden the right branch of the size distribution and can be important for warm rain initiation (e.g., Johnson, 1982;Feingold et al, 1999;Yin et al, 2000;Jensen and Lee, 2008;Cheng et al, 2009).…”

mentioning

confidence: 99%

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

et al. 2018

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Abstract. Cloud droplet size distributions (CDSDs), which are related to cloud albedo and rain formation, are usually broader in warm clouds than predicted from adiabatic parcel calculations. We investigate a mechanism for the CDSD broadening using a moving-size-grid cloud parcel model that considers the condensational growth of cloud droplets formed on polydisperse, submicrometer aerosols in an adiabatic cloud parcel that undergoes vertical oscillations, such as those due to cloud circulations or turbulence. Results show that the CDSD can be broadened during condensational growth as a result of Ostwald ripening amplified by droplet deactivation and reactivation, which is consistent with early work. The relative roles of the solute effect, curvature effect, deactivation and reactivation on CDSD broadening are investigated. Deactivation of smaller cloud droplets, which is due to the combination of curvature and solute effects in the downdraft region, enhances the growth of larger cloud droplets and thus contributes particles to the larger size end of the CDSD. Droplet reactivation, which occurs in the updraft region, contributes particles to the smaller size end of the CDSD. In addition, we find that growth of the largest cloud droplets strongly depends on the residence time of cloud droplet in the cloud rather than the magnitude of local variability in the supersaturation fluctuation. This is because the environmental saturation ratio is strongly buffered by numerous smaller cloud droplets. Two necessary conditions for this CDSD broadening, which generally occur in the atmosphere, are as follows: (1) droplets form on aerosols of different sizes, and (2) the cloud parcel experiences upwards and downwards motions. Therefore we expect that this mechanism for CDSD broadening is possible in real clouds. Our results also suggest it is important to consider both curvature and solute effects before and after cloud droplet activation in a cloud model. The importance of this mechanism compared with other mechanisms on cloud properties should be investigated through in situ measurements and 3-D dynamic models.

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Notes on the state-of-the-art numerical modeling of cloud microphysics

Cited by 332 publications

References 149 publications

Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading

Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading

Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

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

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