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https://doi.org/10.5194/tc-2020-65
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-65
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 09 Mar 2020

Submitted as: research article | 09 Mar 2020

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This preprint is currently under review for the journal TC.

Inter-comparison of snow depth over sea ice from multiple methods

Lu Zhou1, Julienne Stroeve2,3,4, Shiming Xu1,5, Alek Petty6,7, Rachel Tilling6,7, Mai Winstrup8,9, Philip Rostosky10, Isobel R. Lawrence11, Glen E. Liston12, Andy Ridout2, Michel Tsamados2, and Vishnu Nandan3 Lu Zhou et al.
  • 1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
  • 2Centre for Polar Observation and Modelling, Earth Sciences, University College London, London, UK
  • 3Centre for Earth Observation Science, University of Manitoba, Winnipeg, Canada
  • 4National Snow and Ice Data Center, University of Colorado, Boulder, CO, USA
  • 5University Corporation for Polar Research, Beijing, China
  • 6NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 7Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
  • 8DTU Space, Technical University of Denmark, Lyngby, Denmark
  • 9Danish Meteorological Institute (DMI), Copenhagen, Denmark
  • 10University of Bremen, Institute of Environmental Physics, Bremen, Germany
  • 11Centre for Polar Observation and Modelling, University of Leeds, UK
  • 12Colorado State University, Cooperative Institute for Research in the Atmosphere (CIRA), Fort Collins, CO, USA

Abstract. In this study, we compare eight recently developed snow depth products that use satellite observations, modeling or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations.

Large snow depth discrepancies between the different snow depth data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest spring (March-April) snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest spring snow depths. There is no significant trend in the mean snow depth among all snow products since the 2000s, despite the great differences in regional snow depth. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model (NESOSIM), also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. The snow density in SnowModel-LG is generally higher than climatology, whereas NESOSIM density is generally lower. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology.

Inconsistencies in the reconstructed snow parameters among the products, as well as differences between in-situ and airborne observations can in part be attributed to differences in effective footprint and spatial/temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in-situ and remote sensing observations.

Lu Zhou et al.

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Latest update: 13 Jul 2020
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Short summary
Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal discrepancies among the eight snow depth products are analyzed together with their seasonal variability and long-term trends. These snow products are further compared against various ground-truth observations. More analysis on representation error of sea ice parameters are needed for systematic comparison and fusion of airborne, in-situ and remote sensing observations.
Snow on sea ice plays an important role in the Arctic climate system. Large spatial and temporal...
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