Snow-related variability of spectral light transmittance of Arctic First-Year-Ice in the Lincoln Sea

• Main Authors: Anhaus, Philipp, Katlein, Christian, Nicolaus, Marcel, Matero, Ilkka, Arndt, Stefanie, Jutila, Arttu, Haas, Christian
• Format: Conference Object
• Language: unknown
• Published: 2019
• Subjects: Arctic; Lincoln Sea; Sea ice
• Online Access: https://epic.awi.de/id/eprint/50256/; https://epic.awi.de/id/eprint/50256/1/IGS2019_Winnipeg_20190821_ANHAUS_AWI_EPIC.pdf; https://hdl.handle.net/10013/epic.78920ae1-d069-4f23-8e01-5a0988212dcf

Light transmittance through Arctic sea ice and snow has an important impact on both the ocean heat content and the ice-associated ecosystem. The partitioning of the radiation is a key factor of the mass and energy balance of Arctic sea ice. It is therefore crucial to measure sea ice transmittance and understand which parameters determine its variation on temporal and spatial scales. Ice and snow imprint characteristic features in the spectral shape of transmitted light. Transmitted spectral irradiance was recorded at the underside of levelled landfast First-Year-Ice (FYI) in a refrozen lead using a hyper-spectral radiometer mounted on a remotely operated vehicle (ROV) during the Last Ice Area campaign off Alert in the Lincoln Sea in May 2018. The main benefits of using the ROV are large spatial coverage in comparably short survey times and non-destructive measurements under sea ice. Snow depth was obtained using a Magna Probe and a Terrestrial Laser Scanner measured the surface topography. The total ice thickness was recorded with a ground-based electromagnetic induction sounding device whereas an upward-looking single-beam sonar also mounted on the ROV recorded ice draft. This unique co-located data set enables to categorize groups of spectral transmittances. Due to the relatively constant FYI thickness it was possible to separate the spectral effect of snow depth on the light transmittance. Further we discuss how to retrieve snow depth and ice thickness based only on spectral transmittance data by developing a new observation-based inverse algorithm. Three methods are envisioned: First, to fit a multiplicative exponential function to the spectra which includes wavelength-dependent extinction coefficients of snow and sea ice. Second, to follow a statistical approach using normalized difference indices (NDIs) to construct spectral correlation coefficients between the NDIs with snow depth and ice thickness. Third, to generate synthetic spectra from snow depth and ice thickness using the radiative transfer model ...