A Multi-Sensor and Modeling Approach for Mapping Light Under Sea Ice During the Ice-Growth Season

Arctic sea ice is shifting from a year-round to a seasonal sea ice cover. This substantial transformation, via a reduction in Arctic sea ice extent and a thinning of its thickness, influences the amount of light entering the upper ocean. This in turn impacts under ice algal growth and associated ecosystem dynamics. Field campaigns have provided valuable insights as to how snow and ice properties impact light penetration at fixed locations in the Arctic, but to understand the spatial variability in the under-ice light field there is a need to scale up to the pan-Arctic level. Combining information from satellites with state-of-the-art parameterizations is one means to achieve this. This study combines satellite and modeled data products to map under-ice light on a monthly time-scale from 2011 through 2018. Key limitations pertain to the availability of satellite derived sea ice thickness, which for radar altimetry, is only available during the sea ice growth season. We clearly show that year-to-year variability in snow depth, along with the fraction of thin ice, plays a key role in how much light enters the Arctic Ocean. This is particularly significant in April, which in some regions, coincides with the beginning of the under-ice algal bloom, whereas we find that ice thickness is the main driver of under-ice light availability at the end of the melt season in October. The extension to the melt season due to a warmer Arctic means that snow accumulation has reduced, which is leading to positive trends in light transmission through snow. This, combined with a thinner ice cover, should lead to increased under-ice PAR also in the summer months.

Automated summary

This study examines the impact of Arctic sea ice changes on under-ice light and algae growth, highlighting the importance of snow depth and thin ice fraction in light penetration into the Arctic Ocean. It also suggests a method to map under-ice light using satellite and modeled data, while identifying changing factors in different seasons.