Unraveling the continuous ice formation in arctic mixed-phase clouds with a novel column model
Knopf, Daniel — Stony Brook University
Riemer, Nicole — University of Illinois Urbana-Champaign
Area of research
Arctic mixed-phase clouds, wherein supercooled droplets and ice crystals coexist, play a crucial role in the arctic climate system, which is experiencing accelerated warming that is poorly captured by today's climate models. Arctic mixed-phase clouds are widespread and long-lived, with sustained ice crystal formation that challenges current understanding. The longevity of such clouds is often poorly represented by global climate models. Development and application of a simplified column model allows us to evaluate the impact of different freezing parameterizations on predicting the number concentration of ice-nucleating particles (INPs) available for ice crystal formation.
The reason for the longevity of arctic clouds has been a longstanding question. The choice of freezing parameterization is essential in defining the number of ice-nucleating particles available for ice crystal formation. The two main categories of freezing parameterizations, time-independent and stochastic freezing models, can both result in continuous ice production, but for differing reasons: in time-independent schemes, ice-nucleating particles are quickly lost to ice crystal formation and render a system that is highly dependent on cloud entrainment and cooling, whereas a stochastic freezing model allows for a much larger reservoir of ice-nucleating particles that is negligibly sensitive to entrainment and weakly sensitive to cloud top temperature changes.
Mixed-phase clouds have been identified as significant contributors to uncertainties in climate projections, attributable to model representation of processes controlling the formation and loss of supercooled water droplets and ice particles from the atmosphere. Using a simplified 1D aerosol-cloud model, this study examines the budget of ice-nucleating particles available for ice formation within a well-mixed boundary-layer cloud system, termed INP reservoir. The model setup includes prescribed dynamical forcings and thermodynamic profiles. The INPs are treated as multicomponent and polydisperse particle size distributions. Different immersion freezing parameterization are compared, including time-independent (singular) number- and surface area-based descriptions and a time-dependent description following classical nucleation theory (CNT). The CNT-based description yields an orders-of-magnitude larger INP reservoir than the singular parameterizations, which is the dominant factor for the magnitude of sustained ice crystal formation rate. INP loss should be generally considered when simulating mixed-phase cloud evolution but could be neglected when the INP reservoir size is large and INP depletion weak. Resolving the source of differences in INP reservoir dynamics due to model implementation is a high priority for advancing climate model physics.