Insights on water-ice partition in stratiform mixed-phase clouds based on long-term ARM observations

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Description

Our poor understanding of ice generation in the atmosphere results in large uncertainties in simulating ice and mixed-phase clouds in weather and climate models. Recent analyses on global distribution of mixed-phase cloud distributions and IPCC inter-model differences in simulated cloud radiative forcing under doubling CO2 condition indicate that mixed-phase cloud representations in climate models contribute significantly to current climate predication uncertainties. A novel multi-sensor retrieval algorithm is applied to long-term ARM observations at the NSA site and provides an important data set to better understand stratiform mixed-phase clouds. Statistical results show that widely used temperature dependence of water-ice partition based on in situ observations from frontal clouds cannot represent the water-ice partition in this type of mixed-phase clouds. Significant difference in temperature dependence of water-ice partition is found between the spring season and the other seasons, which can be attributed to more effective ice generation and/or growth linked to high aerosol loading during the spring season over the Arctic region. Although temperature is an important controlling factor on water-ice partition in mixed-phase clouds, the water-ice partition in these stratiform mixed-phase clouds also depends on cloud microphysical properties. These new insights provide important guidance on developing new mixed-phase cloud parameterization for large-scale models.