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Influence of ice thickness and surface properties on light transmission through A rctic sea ice

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ABSTRACT

The observed changes in physical properties of sea ice such as decreased thickness and increased melt pond cover severely impact the energy budget of Arctic sea ice. Increased light transmission leads to increased deposition of solar energy in the upper ocean and thus plays a crucial role for amount and timing of sea‐ice‐melt and under‐ice primary production. Recent developments in underwater technology provide new opportunities to study light transmission below the largely inaccessible underside of sea ice. We measured spectral under‐ice radiance and irradiance using the new Nereid Under‐Ice (NUI) underwater robotic vehicle, during a cruise of the R/V Polarstern to 83°N 6°W in the Arctic Ocean in July 2014. NUI is a next generation hybrid remotely operated vehicle (H‐ROV) designed for both remotely piloted and autonomous surveys underneath land‐fast and moving sea ice. Here we present results from one of the first comprehensive scientific dives of NUI employing its interdisciplinary sensor suite. We combine under‐ice optical measurements with three dimensional under‐ice topography (multibeam sonar) and aerial images of the surface conditions. We investigate the influence of spatially varying ice‐thickness and surface properties on the spatial variability of light transmittance during summer. Our results show that surface properties such as melt ponds dominate the spatial distribution of the under‐ice light field on small scales (<1000 m2), while sea ice‐thickness is the most important predictor for light transmission on larger scales. In addition, we propose the use of an algorithm to obtain histograms of light transmission from distributions of sea ice thickness and surface albedo.

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Histograms of light transmission as obtained from the (a) irradiance and (b) radiance sensors. (c) Light transmission histograms generated with the presented algorithm from the distribution of surface albedo and ice thickness using a extinction coefficient of κ =1.5 m−1 for the case of independent source distribution functions. (d) Same histograms presented as cumulative probability functions.
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jgrc21358-fig-0006: Histograms of light transmission as obtained from the (a) irradiance and (b) radiance sensors. (c) Light transmission histograms generated with the presented algorithm from the distribution of surface albedo and ice thickness using a extinction coefficient of κ =1.5 m−1 for the case of independent source distribution functions. (d) Same histograms presented as cumulative probability functions.

Mentions: Histograms derived from our data set are shown in Figure 6. Using independent source distribution functions and an extinction coefficient of κ=1.5 m−1 as commonly used in the literature [Grenfell, 1977; Perovich, 1996], the algorithm correctly estimates the main mode at 0.035 but slightly underestimates the occurrence of small light transmittance values (Figure 6c). As explained above, the deviations between measured and derived histogram at both ends of the distribution are expected. Comparing the cumulative distribution functions (Figure 6d) confirms a good agreement between measured and derived light transmittance distributions. While a final conclusion as to the possibility of estimating transmission histograms from distributions of ice thickness and surface albedo is not possible using this limited data set, our results encourage future exploration now that extensive spatial data sets of sea ice optics are attainable.


Influence of ice thickness and surface properties on light transmission through A rctic sea ice
Histograms of light transmission as obtained from the (a) irradiance and (b) radiance sensors. (c) Light transmission histograms generated with the presented algorithm from the distribution of surface albedo and ice thickness using a extinction coefficient of κ =1.5 m−1 for the case of independent source distribution functions. (d) Same histograms presented as cumulative probability functions.
© Copyright Policy - creativeCommonsBy-nc-nd
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC5016760&req=5

jgrc21358-fig-0006: Histograms of light transmission as obtained from the (a) irradiance and (b) radiance sensors. (c) Light transmission histograms generated with the presented algorithm from the distribution of surface albedo and ice thickness using a extinction coefficient of κ =1.5 m−1 for the case of independent source distribution functions. (d) Same histograms presented as cumulative probability functions.
Mentions: Histograms derived from our data set are shown in Figure 6. Using independent source distribution functions and an extinction coefficient of κ=1.5 m−1 as commonly used in the literature [Grenfell, 1977; Perovich, 1996], the algorithm correctly estimates the main mode at 0.035 but slightly underestimates the occurrence of small light transmittance values (Figure 6c). As explained above, the deviations between measured and derived histogram at both ends of the distribution are expected. Comparing the cumulative distribution functions (Figure 6d) confirms a good agreement between measured and derived light transmittance distributions. While a final conclusion as to the possibility of estimating transmission histograms from distributions of ice thickness and surface albedo is not possible using this limited data set, our results encourage future exploration now that extensive spatial data sets of sea ice optics are attainable.

View Article: PubMed Central - PubMed

ABSTRACT

The observed changes in physical properties of sea ice such as decreased thickness and increased melt pond cover severely impact the energy budget of Arctic sea ice. Increased light transmission leads to increased deposition of solar energy in the upper ocean and thus plays a crucial role for amount and timing of sea‐ice‐melt and under‐ice primary production. Recent developments in underwater technology provide new opportunities to study light transmission below the largely inaccessible underside of sea ice. We measured spectral under‐ice radiance and irradiance using the new Nereid Under‐Ice (NUI) underwater robotic vehicle, during a cruise of the R/V Polarstern to 83°N 6°W in the Arctic Ocean in July 2014. NUI is a next generation hybrid remotely operated vehicle (H‐ROV) designed for both remotely piloted and autonomous surveys underneath land‐fast and moving sea ice. Here we present results from one of the first comprehensive scientific dives of NUI employing its interdisciplinary sensor suite. We combine under‐ice optical measurements with three dimensional under‐ice topography (multibeam sonar) and aerial images of the surface conditions. We investigate the influence of spatially varying ice‐thickness and surface properties on the spatial variability of light transmittance during summer. Our results show that surface properties such as melt ponds dominate the spatial distribution of the under‐ice light field on small scales (<1000 m2), while sea ice‐thickness is the most important predictor for light transmission on larger scales. In addition, we propose the use of an algorithm to obtain histograms of light transmission from distributions of sea ice thickness and surface albedo.

No MeSH data available.


Related in: MedlinePlus