Abstract
Monitoring the trend of sea ice breakup and formation in Hudson Bay is vital for maritime operations, such as local hunting or shipping, particularly in response to the lengthening of the ice-free period in the Bay driven by climate change. Satellite passive microwave sea ice concentration products are commonly used for large-scale sea ice monitoring and predictive modelling; however, these product algorithms are known to underperform during the summer melt period due to the changes in sea ice thermophysical properties. This study investigates the evolution of in situ and satellite-retrieved brightness temperature (TB) throughout the melt season using a combination of in situ passive microwave measurements, thermophysical sampling, unmanned aerial vehicle (UAV) surveys, and satellite-retrieved TB. In situ data revealed a strong positive correlation between the presence of liquid water in the snow matrix and in situ TB in the 37 and 89 GHz frequencies. When considering TB ratios utilized by popular sea ice concentration algorithms (e.g., NASA Team 2), liquid water presence in the snow matrix was shown to increase the in situ TB gradient ratio of 37/19V. In situ gradient ratios of 89/19V and 89/19H were shown to correlate positively with UAV-derived melt pond coverage across the ice surface. Multi-scale comparison between in situ TB measurements and satellite-retrieved TB (by Advanced Microwave Scanning Radiometer 2) showed a distinct pattern of passive microwave TB signature at different stages of melt, confirmed by data from in situ thermophysical measurements. This pattern allowed for both in situ and satellite-retrieved TB to be partitioned into three discrete stages of sea ice melt: late spring, early melt and advanced melt. The results of this study thus advance the goal of achieving more accurate modeled predictions of the sea ice cover during the critical navigation and breakup period in Hudson Bay.
Highlights
Decline in the extent and concentration of Arctic sea ice has become a commonly used indicator of our rapidly changing climate (Peng and Meier, 2018)
Stations progress from having a thick snow layer and no melt ponds to a thin snow layer with extensive melt ponding (Table 2, Figure 2)
This progression indicates that a representative set of ice conditions throughout the melt period were captured in this dataset
Summary
Decline in the extent and concentration of Arctic sea ice has become a commonly used indicator of our rapidly changing climate (Peng and Meier, 2018). SIC algorithms predict ice concentration from satellite retrievals of microwave emissions from the Earth’s surface. Using empirical knowledge of the microwave emissivity of various Earth surface types, algorithms have been generated to estimate the fractional coverage of surface types contributing to a single satellite measurement (Comiso et al, 1997; Hwang et al, 2007). This calculation is accurate during the winter, when emissions from the dry ice surface and ocean are distinct and well understood. The average accuracy of commonly used SIC algorithms in the winter is reported to be within ±5% in areas of high ice concentration (Ivanova et al, 2015)
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