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.