The phase of midlevel and lowlevel clouds over the Southern Ocean and the Arctic Ocean and their dependence on cloud top temperature, sea ice, and aerosol concentrations

Abstract

The accurate representation of clouds and their phase is crucial to enable a correct representation of the Earth’s radiation balance. This has been demonstrated by large radiative errors in global models over the Southern Ocean mainly caused by an incorrect representation of supercooled liquid lowlevel clouds. In contrast to lowlevel clouds, midlevel clouds are rarely investigated. To fill this gap, this study investigates active satellite observations of midlevel clouds over the Southern Ocean and the Arctic Ocean from 2007 and 2008 with a comprehensive comparison to observations of lowlevel clouds. Midlevel and lowlevel clouds are distinguished by cloud base height. The DARDAR dataset provides a detailed phase categorization based on CloudSat and CALIPSO measurements. We have analyzed the cloud phase partitioning as a function of cloud top temperature, vertical cloud thickness, and the horizontal cloud extent. A local minimum in the mean liquid fraction within a cloud column can be observed for a cloud top temperature of -15 °C. An exception to this observation occurs for lowlevel clouds over the Arctic Ocean, which feature a plateau instead of a minimum. This hints at processes producing ice at these temperatures, which could be habit dependent vapor growth, secondary ice production, or a combination of both processes, as already discussed in other studies. Furthermore, daily sea ice concentrations from a passive microwave instrument are collocated to investigate their correlation with the cloud phase. At equal cloud top temperature, lowlevel clouds over the Southern Ocean and the Arctic Ocean have a higher liquid fraction, if they occur over sea ice. Midlevel clouds over the Southern Ocean show the same behaviour, while midlevel clouds over the Arctic Ocean show no significant phase dependence on the sea ice concentration. In addition, collocated CAMS reanalysis data are used to investigate the influence of different concentrations of various aerosol types such as sea salt, dust, black carbon, or organic matter on the cloud phase. Preliminary results show a stronger influence of the mixing ratio of sea salt on the phase of lowlevel clouds, but also the phase of midlevel clouds over the Southern Ocean seems to be influenced. Future work will further investigate the influence of different parameters, such as sea ice concentration, aerosol concentration, and cloud top temperature on the cloud phase by applying a machine learning model and exploring the relative importance of these parameters for the cloud phase, as well as their interactions and synergies.