Katlein Christian

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Katlein
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Christian
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  • Article
    Influence of ice thickness and surface properties on light transmission through Arctic sea ice
    (John Wiley & Sons, 2015-09-04) Katlein, Christian ; Arndt, Stefanie ; Nicolaus, Marcel ; Perovich, Donald K. ; Jakuba, Michael V. ; Suman, Stefano ; Elliott, Stephen M. ; Whitcomb, Louis L. ; McFarland, Christopher J. ; Gerdes, Rudiger ; Boetius, Antje ; German, Christopher R.
    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.
  • Article
    The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms
    (University of California Press, 2024-05-10) Rabe, Benjamin ; Cox, Christopher J. ; Fang, Ying-Chih ; Goessling, Helge ; Granskog, Mats A. ; Hoppmann, Mario ; Hutchings, Jennifer K. ; Krumpen, Thomas ; Kuznetsov, Ivan ; Lei, Ruibo ; Li, Tao ; Maslowski, Wieslaw ; Nicolaus, Marcel ; Perovich, Don ; Persson, Ola ; Regnery, Julia ; Rigor, Ignatius ; Shupe, Matthew D. ; Sokolov, Vladimir T. ; Spreen, Gunnar ; Stanton, Tim ; Watkins, Daniel M. ; Blockley, Ed ; Buenger, H. Jakob ; Cole, Sylvia T. ; Fong, Allison A. ; Haapala, Jari ; Heuze, Celine ; Hoppe, Clara J. M. ; Janout, Markus A. ; Jutila, Arttu ; Katlein, Christian ; Krishfield, Richard A. ; Lin, Long ; Ludwig, Valentin ; Morgenstern, Anne ; O’Brien, Jeff ; Zurita, Alejandra Quintanilla ; Rackow, Thomas ; Riemann-Campe, Kathrin ; Rohde, Jan ; Shaw, William J. ; Smolyanitsky, Vasily ; Solomon, Amy ; Sperling, Anneke ; Tao, Ran ; Toole, John M. ; Tsamados, Michel ; Zhu, Jialiang ; Zuo, Guangyu
    Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.