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

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dc.contributor.author Nasonova, Sasha
dc.contributor.author Scharien, Randall K.
dc.contributor.author Haas, Christian
dc.contributor.author Howell, Stephen E. L.
dc.date.accessioned 2018-11-23T14:26:05Z
dc.date.available 2018-11-23T14:26:05Z
dc.date.copyright 2017 en_US
dc.date.issued 2017
dc.identifier.citation Nasonova, S., Scharien, R.K., Haas, C. & Howell, S.E.L. (2018). Linking regional winter sea ice thickness and surface roughness to spring melt pond fraction on landfast artic sea ice. Remote Sensing, 10(1), 37. https://doi.org/10.3390/rs10010037 en_US
dc.identifier.uri https://doi.org/10.3390/rs10010037
dc.identifier.uri http://hdl.handle.net/1828/10355
dc.description.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 (fp) 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 fp 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 fp. A stronger negative correlation was observed between FYI thickness and fp (Spearman rs = −0.85) compared to FYI roughness and fp (rs = −0.52). The association between MYI thickness and fp was also negative (rs = −0.56), whereas there was no association between MYI roughness and fp. 47% of spring fp 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 fp) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low fp). This work provides an important contribution towards the parameterizations of fp in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring fp. en_US
dc.description.sponsorship The research was conducted as part of the interdisciplinary Ice Covered Ecosystem- CAMbridge Bay Process Studies (ICE-CAMPS) project. Funding was provided by Marine Environmental Observation Prediction and Response Network (MEOPAR) and the National Sciences and Engineering Research Council of Canada (NSERC)—Discovery Grants Program. Additional support was provided by the Northern Scientific Training Program (NSTP). en_US
dc.language.iso en en_US
dc.publisher Remote Sensing en_US
dc.subject Arctic en_US
dc.subject sea ice thickness en_US
dc.subject roughness en_US
dc.subject melt pond fraction en_US
dc.subject object-based image analysis (OBIA) en_US
dc.title Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice en_US
dc.type Article en_US
dc.description.scholarlevel Faculty en_US
dc.description.reviewstatus Reviewed en_US


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