Estimating the variability of contact parameter temperature dependence with the Monte Carlo Markov Chain method

Recent datasets on heterogeneous deposition mode ice nucleation have revealed a strong dependence of the contact parameter m on temperature, ranging from linear to exponential, depending on the experiments. We analyze recent datasets using a Monte Carlo Markov Chain method with the full classical nucleation theory including spherical and planar geometry. The method we use allows us to test models of the temperature dependence of the contact parameter and evaluate their performance. We estimate the applicability of different forms of contact parameter temperature dependence, including a new well-behaved suggestion. Such a function has a more physical behavior at high and low temperatures and might thus be more easily applicable in atmospheric modeling. However, because of their limited temperature range, the present datasets are unable to reveal the behavior of the contact parameter in low temperatures, and we are unable to fully validate the proposed function. We thus call for more heterogeneous nucleation experiments reaching low temperatures (<170 K). Such datasets may be significant for studies on, for example, polar mesospheric clouds, Mars ice clouds, and perhaps exoplanet clouds. This work provides a new framework, valid even for very small ice nucleus sizes, for analyzing heterogeneous nucleation datasets.