Smolyanitsky Vasily

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Smolyanitsky
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Vasily
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  • Article
    A new structure for the Sea Ice Essential Climate variables of the Global Climate Observing System
    (American Meteorological Society, 2022-06-01) Lavergne, Thomas ; Kern, Stefan ; Aaboe, Signe ; Derby, Lauren ; Dybkjaer, Gorm ; Garric, Gilles ; Heil, Petra ; Hendricks, Stefan ; Holfort, Jürgen ; Howell, Stephen ; Key, Jeffrey ; Lieser, Jan ; Maksym, Ted ; Maslowski, Wieslaw ; Meier, Walt ; Muñoz-Sabater, Joaquín ; Nicolas, Julien ; Ozsoy, Burcu ; Rabe, Benjamin ; Rack, Wolfgang ; Raphael, Marilyn ; de Rosnay, Patricia ; Smolyanitsky, Vasily ; Tietsche, Steffen ; Ukita, Jinro ; Vichi, Marcello ; Wagner, Penelope M. ; Willmes, Sascha ; Zhao, Xi
    Climate observations inform about the past and present state of the climate system. They underpin climate science, feed into policies for adaptation and mitigation, and increase awareness of the impacts of climate change. The Global Climate Observing System (GCOS), a body of the World Meteorological Organization (WMO), assesses the maturity of the required observing system and gives guidance for its development. The Essential Climate Variables (ECVs) are central to GCOS, and the global community must monitor them with the highest standards in the form of Climate Data Records (CDR). Today, a single ECV—the sea ice ECV—encapsulates all aspects of the sea ice environment. In the early 1990s it was a single variable (sea ice concentration) but is today an umbrella for four variables (adding thickness, edge/extent, and drift). In this contribution, we argue that GCOS should from now on consider a set of seven ECVs (sea ice concentration, thickness, snow depth, surface temperature, surface albedo, age, and drift). These seven ECVs are critical and cost effective to monitor with existing satellite Earth observation capability. We advise against placing these new variables under the umbrella of the single sea ice ECV. To start a set of distinct ECVs is indeed critical to avoid adding to the suboptimal situation we experience today and to reconcile the sea ice variables with the practice in other ECV domains.
  • 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.