Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

Abstract

Abstract:The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (f p ) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two~5.2 km long profiles over FYI-and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring f p using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring f p . A stronger negative correlation was observed between FYI thickness and f p (Spearman r s = −0.85) compared to FYI roughness and f p (r s = −0.52). The association between MYI thickness and f p was also negative (r s = −0.56), whereas there was no association between MYI roughness and f p . 47% of spring f p variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high f p ) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low f p ). This work provides an important contribution towards the parameterizations of f p in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring f p .

Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar

“…As a means to aggregate and compare aerial survey and SAR image data, image segments representing zones of homogeneous sea ice, called objects, were created along the survey track following Nasonova et al 2017. Specifically, two sequential Sentinel-1 C-band SAR scenes covering the entire study area were used to create objects.…”

“…Objects were created based on user defined criteria, including a scale criterion, which controls the object size, spatial heterogeneity, which is related to the shape of the object, and spectral heterogeneity, which describes the variance of data within the objects (Benz et al 2004). OBIA is advantageous for analysis of homogeneous portions of sea ice and integration of multisensor data for inter-comparisons when compared to gridded approaches commonly used in the analysis of remote sensing imagery (Nasonova et al 2017).…”

“…In April/May, the snow thickness in the CAA ranges between 0.2 m and 0.4 m (Brown and Cote 1992;Melling 2002;Haas and Howell 2015). The immobile nature of the landfast ice in this region is ideal for studying sea ice evolution because there is negligible ice drift between acquisition times of the SAR imagery in the winter and melting conditions (Barber et al, 1992;Nasonova et al 2017).…”

“…Late-winter surface roughness and thickness were observed with an airborne sensor package operated from a DC3/Basler BT67 aircraft flown on April 8 th , 2016. This survey captured late winter conditions at the time of maximum sea ice thickness when it is assumed that little subsequent surface variability will occur until melt onset (Nasonova et al 2017). (Beckers et al 2015).…”

Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice

“…The retrieval of parameters, such as the spectral albedo of sea ice or snow and properties of melt ponds and leads, relies on spectral information; the retrieval of optical properties of target surfaces and waterbodies therefore is necessary. Optical sensors such as the Advanced Visible High Resolution Radiometer (AVHRR; e.g., Huck et al (2007)) and the MODerate Resolution Imaging Spectroradiometer (MODIS; e.g., Rösel et al (2012), Tschudi et al (2008)) have long been used in Arctic research (Pope et al, 2014;Nasonova et al, 2017) providing observations of different parameters such as sea ice extent, sea ice thickness and albedo. The Sentinel-3 satellites (S-3) from the European Space Agency's (ESA) Copernicus program, carrying the Ocean and Land Color Instrument (OLCI), continue this tradition (Donlon et al, 2012;Malenovský et al, 2012).…”