Melt ponds greatly increase the transmission of solar radiation through sea ice relative to snow covered or bare ice. This rise in transmittance has the potential to enhance water column heating and primary production. I examine how spatially variable sea ice surfaces control the under-ice salinity, temperature and photosynthetically active radiation (PAR) and provide estimates of solar heating and primary production during melt. Conductivity, temperature and PAR profiles were measured in the Canadian Arctic under snow covered ice, leads, bare ice and melt ponds. The under-ice light field to a depth of 10 to 13 m was highly variable, controlled by increased transmission under melt ponds and shading by bare ice. Below, the light field became relatively homogeneous showing the depth the surface heterogeneity had an effect on transmitted PAR. Furthermore, one water column profile is not representative of the PAR, salinity or temperature under a spatially heterogeneous surface.

Melt ponds greatly increase the transmission of solar radiation through sea ice relative to snow covered or bare ice. This rise in transmittance has the potential to enhance water column heating and primary production. I examine how spatially variable sea ice surfaces control the under-ice salinity, temperature and photosynthetically active radiation (PAR) and provide estimates of solar heating and primary production during melt. Conductivity, temperature and PAR profiles were measured in the Canadian Arctic under snow covered ice, leads, bare ice and melt ponds. The under-ice light field to a depth of 10 to 13 m was highly variable, controlled by increased transmission under melt ponds and shading by bare ice. Below, the light field became relatively homogeneous showing the depth the surface heterogeneity had an effect on transmitted PAR. Furthermore, one water column profile is not representative of the PAR, salinity or temperature under a spatially heterogeneous surface.

Arctic sea ice is rapidly declining in extent, thickness, volume and age, with the majority of the decline in extent observed at the end of the melt season. Advanced melt is a thermodynamic regime and is characterized by the formation of melt ponds on the sea ice surface, which have a lower surface albedo (0.2-0.4) than the surrounding ice (0.5-0.7) allowing more shortwave radiation to enter the system. The loss of multiyear ice (MYI) may have a profound impact on the energy balance of the system because melt ponds on first-year ice (FYI) comprise up to 70% of the ice surface during advanced melt, compared to 40% on MYI. Despite the importance of advanced melt to the ocean-sea ice-atmosphere system, advanced melt and the extent to which winter conditions influence it remain poorly understood due to the highly dynamic nature of melt pond formation and evolution, and a lack of reliable observations during this time. In order to establish quantitative links between winter and subsequent advanced melt conditions, and assess the effects of scale and choice of aggregation features on the relationships, three data aggregation approaches at varied spatial scales were used to compare high resolution satellite GeoEye-1 optical images of melt pond covered sea ice to winter airborne laser scanner surface roughness and electromagnetic induction sea ice thickness measurements. The findings indicate that winter sea ice thickness has a strong association with melt pond fraction (fp) for FYI and MYI. FYI winter surface roughness is correlated with fp, whereas for MYI no association with fp was found. Satellite-borne synthetic aperture radar (SAR) data are heavily relied upon for sea ice observation; however, during advanced melt the reliability of observations is reduced. In preparation for the upcoming launch of the RADARSAT Constellation Mission (RCM), the Kolmogorov-Smirnov (KS) statistical test was used to assess the ability of simulated RCM parameters and grey level co-occurrence matrix (GLCM) derived texture features to discriminate between major ice types during winter and advanced melt, with a focus on advanced melt. RCM parameters with highest discrimination ability in conjunction with optimal GLCM texture features were used as input parameters for Support Vector Machine (SVM) supervised classifications. The results indicate that steep incidence angle RCM parameters show promise for distinguishing between FYI and MYI during advanced melt with an overall classification accuracy of 77.06%. The addition of GLCM texture parameters improved accuracy to 85.91%. This thesis provides valuable contributions to the growing body of literature on fp parameterization and SAR ice type discrimination during advanced melt. / Graduate / 2019-03-21