EGU2020-19051
https://doi.org/10.5194/egusphere-egu2020-19051
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Lu Zhou1, Julienne Stroeve2,3,4, and Shiming Xu1,5
Lu Zhou et al.
  • 1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China (zhou-l15@mails.tsinghua.edu.cn)
  • 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

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 differences between 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 snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest Spring snow depths. There is no significant trend for mean snow depth among all snow products since the 2000s, however, those in regional varies larhely. 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. Compared to climatology, snow density from SnowModel-LG is generally denser, whereas that from NESOSIM is less. 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 and with 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.

How to cite: Zhou, L., Stroeve, J., and Xu, S.: Inter-comparison of snow depth over sea ice from multiple methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19051, https://doi.org/10.5194/egusphere-egu2020-19051, 2020

Displays

Display file