Seasonality of spectral radiative fluxes and optical properties of Arctic sea ice during the spring-summer transition

The reflection, absorption, and transmittance of solar (shortwave) radiation by sea ice play a crucial role in physical and biological processes in the ice-covered Arctic Ocean and atmosphere. These sea ice optical properties are of great importance, in particular during the melt season, as they significantly impact energy fluxes within and the total energy budget of the coupled atmosphere-ice-ocean system. In this paper, we analyse data from autonomous drifting stations to investigate the seasonal evolution of the spectral albedo, transmittance and absorptivity for different sea ice, snow, and surface conditions as measured during the MOSAiC expedition in 2019-2020. We find that the spatial variability of these quantities was small during spring, and that it strongly increased after the melt onset on May 26, 2020, when the liquid water content on the surface increased. The enhanced variability was then mostly determined by the formation of melt ponds. The formation of a single melt pond can increase the energy absorption of the sea ice by 50% compared to adjacent bare ice sites. The temporal evolution of the surface albedo and the sea ice transmittance was mostly event-driven and, thus, neither continuous nor linear. Furthermore, absorptivity and transmittance showed strong temporal and spatial variabilities, which depended on internal sea ice properties and under-ice biological processes and not only surface conditions. The spatial and temporal heterogeneity of sea ice conditions strongly impacted the partitioning of the solar short-wave radiation. This study shows that the formation and development of melt ponds can reduce albedo to 1/3, enhancing the total (summer) heat deposition. Individual ponding events can lead to more energy deposition than an earlier melt onset. The small-scale heterogeneity and the timing and duration of ponding events have to be considered when comparing (local) in-situ observations with large-scale satellite remote sensing datasets, and can help to improve numerical models.