There are significant gaps in both experimental and theoretical understanding of mixed-phase clouds, their impacts on the hydrological cycle as well as their effects on atmospheric radiation. Accurately identifying liquid water layers in mixed-phase clouds is crucial for estimating cloud radiative effects. A proof-of-concept study utilizing a machine-learning-based liquid-layer detection method called VOODOO is presented. This method was applied alongside a single-column radiative transfer model to compare downwelling shortwave fluxes of mixed-phase clouds detected by the standard Cloudnet processing chain and VOODOO to ground-based pyranometer observations. Our findings reveal that VOODOO creates more realistic liquid water content distributions and significantly influences profiles of heating rates. Moreover, our study demonstrates a substantial enhancement in the estimation of shortwave cloud radiative effects of VOODOO compared to conventional method Cloudnet. Specifically, we observe a remarkable reduction in the mean absolute error of simulated shortwave radiation at the surface of 70\%, particularly in homogeneous cloud conditions. The mean percentage error of SW cloud radiative effects between Cloudnet and pyranometer observations is 44\%, while VOODOO+Cloudnet reduces this error to 8\%. Overall, our results underscore the potential of VOODOO to provide new insights into deep mixed-phase clouds, which were previously inaccessible using traditional lidar-based remote sensing techniques.

Accurately identifying liquid water layers in mixed-phase clouds is crucial for estimating cloud radiative effects. Lidar-based retrievals are limited in optically thick or multilayer clouds, leading to positive biases in simulated shortwave radiative fluxes. At the same time, general circulation models also tend to overestimate the downwelling shortwave radiation at the surface especially in the Southern Ocean regions. To reduce this SW radiation bias in models, we first need better observational-based retrievals for liquid detection which can later be used for model validation. To address this, a machine-learning-based liquid-layer detection method called VOODOO was employed in a proof-of-concept study using a single column radiative transfer model to compare shortwave cloud radiative effects of liquid-containing clouds detected by Cloudnet and VOODOO to ground-based and satellite observations. Results showed a reduction in shortwave radiation bias, indicating that liquid-layer detection with machine-learning retrievals can improve radiative transfer simulations.

An improved understanding of the seasonality of the Arctic snowpack properties related to the timing and intensity of snowmelt processes is the key driver to better quantify atmosphere-ice-ocean interactions, and in particular the seasonal energy and mass budgets of the ice-covered polar oceans. Various satellite data products over the last decades have shown a trend towards an earlier snowmelt onset in the Arctic, thus contributing to Arctic amplification and sea-ice decline, underlining the need to better understand these processes. We present here the physical snow properties from spring 2020 examined during the “Multidisciplinary drifting Observatory for the Study of Arctic Climate” (MOSAiC). We focus on southerly air mass advection events in mid-April that were associated with near-surface air temperatures near freezing at the MOSAiC floe. In doing so, we emphasize a single sampling site that was revisited daily-to-weekly throughout the spring. At the sampling site, snow depth ranged from 10 to 14 cm with the bulk density varying between 200 to 350 kg m-3, mainly driven by freshly fallen snow. The vertical snow structure prior to the warm event was characterized by large pores with distinct snow crystal structures and widespread depth hoar crystals, both related to the strong temperature gradient in the snowpack. During the warm air intrusion, increasing temperatures temporarily reversed the thermal gradient in the snow. The warm snow surface, now above a relatively cold snow/ice interface, resulted temporary negative vertical heat flux values observed to be up to -12 Wm-2. Because the snow/ice interface is close to freezing, the negative flux is an indicator that melt may have occurred. Once temperatures dropped again, the vertical temperature and heat flux gradients returned back to the previous patterns. However, the decreased snow grain sizes throughout the snowpack due to the warming and the associated compacted lower layers now dominated the snowpack. Such a temporary warm spell event has decisive impacts on the sea-ice energy and mass budget of the MOSAiC floe. Understanding this effect on a local scale will help to transfer that knowledge to larger spatial scales, and thus to quantify the influence of warm air intrusions during winter and/or spring in the ice-covered Arctic basin.