Abstract

Abstract. The time dependence of ice-nucleating particle (INP) activity is known to exist, yet for simplicity it is often omitted in atmospheric models as an approximation. Hitherto, only limited experimental work has been done to quantify this time dependency, for which published data are especially scarce regarding ambient aerosol samples and longer timescales. In this study, the time dependence of INP activity is quantified experimentally for six ambient environmental samples. The experimental approach includes a series of hybrid experiments with alternating constant cooling and isothermal experiments using a recently developed cold-stage setup called the Lund University Cold-Stage (LUCS). This approach of observing ambient aerosol samples provides the optimum realism for representing their time dependence in any model. Six ambient aerosol samples were collected at a station in rural Sweden representing aerosol conditions likely influenced by various types of INPs: marine, mineral dust, continental pristine, continental-polluted, combustion-related and rural continental aerosol. Active INP concentrations were seen to be augmented by about 40 % to 100 % (or 70 % to 200 %), depending on the sample, over 2 h (or 10 h). Mineral dust and rural continental samples displayed the most time dependence. This degree of time dependence observed was comparable to, but weaker than, that seen in previous published works. A general tendency was observed for the natural timescale of the freezing to dilate increasingly with time. The fractional freezing rate was observed to decline steadily with the time since the start of isothermal conditions following a power law. A representation of time dependence for incorporation into schemes of heterogeneous ice nucleation that currently omit it is proposed. Our measurements are inconsistent with the simplest purely stochastic model of INP activity, which assumes that the fractional freezing rate of all unfrozen drops is somehow constant and would eventually overpredict active INPs. In reality, the variability of efficiencies among INPs must be treated with any stochastic theory.

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