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.
Highlights
Due to its sensitivity to fluctuations in climate, Arctic sea ice is often pointed to as a clear indicator of climate change
The primary goal of this study was to quantitatively link winter sea ice thickness and surface roughness to spring fp in order to better understand how the ice evolves from winter to spring thermodynamic states and inform parameterizations of fp in climate models. This was achieved by evaluating the utility of object-based image analysis, hybrid-object analysis, which takes into account the footprints of the thickness and roughness sensors, as well as the traditional grid-cell data aggregation approaches
It was found that winter sea ice thickness has a strong association with fp for first-year ice (FYI) and multiyear ice (MYI) using hybrid aggregation approach, as well as the object approach for FYI and MYI
Summary
Due to its sensitivity to fluctuations in climate, Arctic sea ice is often pointed to as a clear indicator of climate change. It has been well established that in recent decades the Arctic sea ice cover has been decreasing in extent, thickness and volume [1,2]. These changes are accompanied by a longer melt season and a transition from a mainly multiyear ice (MYI) regime to a first-year ice (FYI)-dominated. Key aspects of sea ice decay that are poorly represented in climate models are melt pond formation and evolution. The current understanding of melt evolution is based primarily on detailed in situ observations, and macro-scale satellite-based studies necessary to initialize the models are hindered by pervasive cloud cover during the spring and summer months
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