1D and super-particle aerosol-cloud modeling to assess the role of cloud conditions and immersion freezing parameterizations on the INP reservoir

Description

High-latitude mixed-phase clouds contribute significantly to the uncertainty in estimated equilibrium climate sensitivity. This uncertainty is primarily driven by deficient knowledge of cloud processes, which determine the supercooled liquid and ice fraction, and hence, the cloud’s reflectivity on mid-to-large scales. To advance our understanding of the underlying cloud microphysical processes we employ a minimalistic 1D aerosol-cloud model and a super-particle model to prognostically evaluate the evolution of the ice-nucleating particle (INP) reservoir. Both model environments apply time-independent (singular) and time-dependent (classical nucleation theory, CNT) parameterization schemes of immersion freezing. In the 1D model, we apply three different aerosol particle types including mineral dust, sea spray aerosol, and organic (humic-like substances). Furthermore, the effect of varying particle number concentration and cloud microphysical parameters, such as cloud top radiative cooling rate, cloud top entrainment rate, and ice crystal fall velocity on the INP reservoir and ice crystal number concentrations are assessed. Only CNT-based description can sustain INP and ice crystal number concentrations over 10 h cloud lifetime and the cloud top radiative cooling has the strongest impact on the INP budget.

The super-particle simulations are carried out with box-model and prescribed-flow setups, with comprehensive particle-resolved treatment of aerosol-cloud microphysics. For both immersion freezing models, we use a Monte-Carlo simulation approach in which the attribute space of aerosol properties is randomly sampled at initialization. In the case of the singular model, the attribute is the freezing temperature whereas for the CNT-model it is the immersed insoluble surface area. The singular- and CNT-based simulations match only for one specific cooling rate which is characteristic for the employed laboratory measurements. Despite the time-dependent approach being computationally costlier than the singular approach, it thus provides the added value of robustness to differing flow regimes or flow patterns precluding nucleation with the singular scheme (downdrafts or quiescent flow). The CNT approach therefore opens possibilities for online coupling of the immersion freezing scheme with aerosol physio-chemical dynamics that are represented with high fidelity in particle-based models and that accommodate the evolution of the immersed INP surface.