Process‐Based Simulation of Aerosol‐Cloud Interactions in a One‐Dimensional Cirrus Model

Abstract

A new microphysical cirrus model to simulate ice crystal nucleation, depositional growth, and gravitational settling is described. The model tracks individual simulation ice particles in a vertical column of air and allows moisture and heat profiles to be affected by turbulent diffusion. Ice crystal size‐ and supersaturation‐dependent deposition coefficients are employed in a one‐dimensional model framework. This enables the detailed simulation of microphysical feedbacks influencing the outcome of ice nucleation processes in cirrus. The use of spheroidal water vapor fluxes enables the prediction of primary ice crystal shapes once microscopic models describing the vapor uptake on the surfaces of cirrus ice crystals are better constrained. Two applications addressing contrail evolution and cirrus formation demonstrate the potential of the model for advanced studies of aerosol‐cirrus interactions. It is shown that supersaturation in, and microphysical and optical properties of, cirrus are affected by variable deposition coefficients. Vertical variability in ice supersaturation, ice crystal sedimentation, and high turbulent diffusivity all tend to decrease homogeneously nucleated ice number mixing ratios over time, but low ice growth efficiencies counteract this tendency. Vertical mixing induces a tendency to delay the onset of homogeneous freezing. In situations of sustained large‐scale cooling, natural cirrus clouds may often form in air surrounding persistent contrails.

Soot PCF: pore condensation and freezing framework for soot aggregates

“…Only at temperatures of around 230 K, where the homogeneous ice nucleation rate may be critical, can the stochastic nature of ice nucleation be relevant. Therefore, vertical velocity, which is a determining factor for the number of homogeneously nucleated ice crystals (Hoyle et al, 2005;Kärcher and Lohmann, 2002;Sullivan et al, 2016), does not influence the number of ice crystals nucleated through soot PCF and can be neglected in the soot-PCF framework. It should further be noted that the function P N (RH) can either be used to bring AF(RH) into agreement with an experimental dataset or be derived from soot properties by inspecting the different onset RH required to nucleate and grow ice out of ring pores, as shown in Appendix D. Note that within aggregates, there may be structures that are not fully closed to form ring pores.…”

Response of cirrus clouds to idealised perturbations from aviation

“…Exact reproduction of the observed and modelled GW (Yang et al, 2012) and WCB outflow (Spichtinger et al, 2005) cases is not expected because of differences between our model configurations and initial conditions. However, Krämer et al (2016;2020) and Li et al (2023) offer an opportunity to compare our simulations to a representative sample of observed cirrus clouds and hence determine whether the cirrus we simulate can be considered realistic. Table 2 shows summary statistics averaged over the entire domain during the stable phase of all ICNC simulations for both cases, while Figure 3 shows the distribution of ice particle size and number for each control case, with an indication of the size and number ranges that occur most frequently within the cloud.…”

“…Recent studies have determined that both heterogeneous and homogeneous ice nucleation occur within cirrus clouds, although for some time there were contrasting ideas about the dominant mechanism (for example, compare Czizco et al, 2013and Kärcher & Lohmann et al, 2002or Sölch & Kärcher, 2011. It is now apparent that different ice nucleation mechanisms dominate in different cirrus temperature, aerosol and meteorological regimes (Fan et al, 2016;Krämer et al, 2016;2020;Froyd et al, 2022) and can inhibit one another by competing for available water vapour (Spichtinger & Gierens 2009a;2009b;Penner et al, 2018). In regions dominated by heterogeneous freezing and a low aerosol background, model configurations where aviation aerosols increase ice crystal number concentrations (ICNC) globally simulate a strongly positive RF associated with aerosol-cloud interactions.…”

“…Aerosol-cirrus interactions take place in a variety of meteorological conditions, so it is important to characterise these in different natural cirrus regimes. Krämer et al (2016;2020) produced a comprehensive climatology of cirrus clouds in various meteorological settings. They use the climatology to categorise different types of cirrus based on formation mechanism and dynamical situation.…”

Studies on the Competition Between Homogeneous and Heterogeneous Ice Nucleation in Cirrus Formation

“…Laboratory measurements have shown that deposition coefficients vary with ambient conditions (i.e., ice supersaturation and temperature), but discrepancies between laboratory data at cirrus temperatures exist and have not been reconciled (Asakawa et al., 2014; Harrington et al., 2019; Nelson, 2001). The development of ice crystal shapes (habits) are caused by variations in deposition coefficients across ice crystal surfaces (Chen & Lamb, 1994); variability in α may also cause microphysical‐dynamical feedbacks in simulations of mixed‐phase and cirrus clouds relevant for their evolution (Ervens et al., 2011; Kärcher, 2020). Nonetheless, most cloud models resort to constant α in the absence of a complete theory of crystal growth from the vapor encompassing the full cirrus regime, from temperatures, T , below about 233 K at the boundary to mixed‐phase clouds to values prevailing in the TTL (<200 K).…”