Aerosol-cloud interactions: The representation of heterogeneous ice activation in cloud models

Abstract

Abstract. The homogeneous nucleation of ice in supercooled liquid water clouds is characterized by time-dependent freezing rates. By contrast, water phase transitions induced heterogeneously by ice nucleating particles (INPs) are described by time-independent ice-active fractions depending on ice supersaturation (s). Laboratory studies report ice-active particle number fractions (AFs) that are cumulative in s. Cloud models budget INP and ice crystal numbers to conserve total particle number during water phase transitions. Here, we show that ice formation from INPs with time-independent nucleation behavior is overpredicted when models budget particle numbers and at the same time derive ice crystal numbers from s-cumulative AFs. This causes a bias towards heterogeneous ice formation in situations where INPs compete with homogeneous droplet freezing during cloud formation. We resolve this issue by introducing differential AFs, moving us one step closer to more robust simulations of aerosol-cloud interactions.

Technical Note: assessing predicted cirrus ice properties between two deterministic ice formation parameterizations

“…A common method to simulate the number of ice crystals that form from a heterogeneous nucleation event is based on the AF of available INPs. This quantity is derived from cloud or continuous flow chamber experiments of the number of frozen particles (Kärcher and Marcolli, 2021). Vali (1971) and Vali (2019) defined two approaches for determining the number of INPs that become ice active.…”

“…On the other hand, the latter approach can be used to determine the ice nucleation ability of a population of particles, which is relevant to modeling cirrus ice formation processes that can occur on numerous particles. However, as Kärcher and Marcolli (2021) highlight, care must be taken when determining which approach to use when calculating the number of ice crystals that can form on INPs. For example, if a model explicitly removes INPs from the total available population after each ice formation event and adds them to the newly nucleated ice number concentrations (i.e.…”

“…"INP-budgeting"), then using the cumulative AF approach, which is based on the total (integrated) number of active INPs, may overpredict the number of heterogeneously nucleated ice crystals. Kärcher and Marcolli (2021) introduced a new parameterization to simulate the number of ice particles resulting from a heterogeneous nucleation event that is based on the differential AF (Vali, 1971(Vali, , 2019 when employing an INP-budgeting approach (Section 2.1). This method demonstrated that it is able to counteract the potential over-prediction of heterogeneous nucleation in cirrus.…”

“…Meanwhile, Muench and Lohmann (2020) reformulated the ice nucleation mechanisms for cirrus in the ECHAM-HAM GCM (Stevens et al, 2013;Neubauer et al, 2019;Tegen et al, 2019) to also include an AF-based approach that is, instead, based on cumulative AF. Note, this new approach in ECHAM-HAM is not the same as the cumulative AF approach described by Kärcher and Marcolli (2021), as Muench and Lohmann (2020) introduced implicit INP-budgeting by using a differential ice crystal number concentration (ICNC) variable that accounts for the issue stated by Kärcher and Marcolli (2021).…”

“…In addition, determining whether a heterogeneous nucleation process follows a time-dependent (stochastic) or a time-independent (deterministic) approach increases the level of complexity in numerical modeling applications. Kärcher and Marcolli (2021) introduced a new deterministic ice formation parameterization based on the differential activated fraction (AF), arguing that with explicit INP-budgeting this approach could help correct a potential over-prediction of the importance of heterogeneous nucleation within cirrus. We formulated a general circulation model (GCM)-compatible version of the differential AF parameterization and compared it to the method currently employed in the ECHAM6.3-HAM2.3 GCM that is based on cumulative AF, with implicit INP-budgeting.…”