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

The reflection, absorption, and transmittance of shortwave solar radiation by sea ice play crucial roles in physical and biological processes in the ice-covered Arctic Ocean and atmosphere. These sea-ice optical properties, particularly during the melt season, significantly impact energy fluxes within and the total energy budget of the coupled atmosphere-ice-ocean system. We analyzed 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 measured during the MOSAiC expedition in 2019-2020. The spatial variability of these properties was small during spring and increased strongly after melt onset on May 26, 2020, when liquid water content on the surface increased, largely accounting for the enhanced variability. The temporal evolution of surface albedo and sea-ice transmittance was mostly event-driven, thus containing episodic elements. Melt ponds reduced the local surface albedo by 31%-45%. Over the melting season, single ponding events increased the energy deposition of the sea ice by 35% compared to adjacent bare ice. Thus, single melt ponds may impact the summer energy budget as much as seasonal evolution over 1 month. Absorptivity and transmittance showed strong temporal and spatial variabilities independently of surface conditions, possibly due to the different internal sea-ice properties and under-ice biological processes. The differences in seasonal evolution shown for different sea-ice conditions strongly impacted the partitioning of shortwave solar radiation. This study shows that the formation and development of melt ponds, in reducing albedo by a third of bare ice sites, can notably increase the total summer heat deposition. The vastly different seasonal evolutions, different sea-ice conditions, and timing and duration of ponding events need to be considered when comparing local in-situ observations with large-scale satellite remote sensing datasets, which we suggest can help to improve numerical models.