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

Abstract

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.

Under-Ice Light Field in the Western Arctic Ocean During Late Summer

“…Light transmission through Arctic sea-ice has been the subject of various studies through in situ observations, remote sensing, and modelling (e.g., Mundy et al, 2007;Light et al, 2008;Frey et al, 2011;Nicolaus et al, 2012;Katlein et al, 2019;Castellani et al, 2020;Stroeve et al, 2021;Pinkerton and Hayward 2021;Mundy et al, 2007;Light et al, 2008;Frey et al, 2011;Nicolaus et al, 2012;Katlein et al, 2019;Castellani et al, 2020;Stroeve et al, 2021;Pinkerton and Hayward, 2021). In situ optical measurements of under-ice light are performed using radiometers on static devices, as transect lines using remotely operated vehicles (ROV), or systems towed by ships (Lange et al, 2017;Massicotte et al, 2019;Castellani et al, 2020).…”

Sub-kilometre scale distribution of snow depth on Arctic sea ice from Soviet drifting stations

“…For instance, the amount of shortwave solar radiation incident on the ice surface in a multi-kilometre gridcell is sensitive to the fractional coverage of snow which is optically thin ( 15 cm for dry snow; Warren, 2019). This area cannot be straightforwardly gleaned from modelling or satellite observations of the mean snow depth in the gridcell (Stroeve and others, 2021).…”

Uncertainty quantification for ecological models with random parameters

“…Heterogeneity of the sea ice ecosystem over meter scales complicates both empirical and modelling studies of sea ice ecology (Cimoli et al, 2017; Mundy et al, 2005; Swadling et al, 1997). Important physical characteristics such as snow depth (Massom et al, 2001; Perovich et al, 1998), ice thickness (Stroeve et al, 2021; Thorndike et al, 1975), and salinity (Tucker III et al, 1984), vary over these small spatial scales, resulting in patchy ice algal distributions (Gosselin et al, 1986; Meiners et al, 2017; Rysgaard et al, 2001). Because of this heterogeneity and the nonlinearity of bloom dynamics, regional mean values of snow depth or ice thickness cannot be expected to accurately predict regional algal dynamics (Abraham et al, 2015; Stroeve et al, 2021).…”