SCIENCE CHINA Earth Sciences, Volume 60, Issue 5: 858-865(2017) https://doi.org/10.1007/s11430-017-9023-8

Spatial and temporal variability of sea ice deformation rates in the Arctic Ocean observed by RADARSAT-1

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  • ReceivedFeb 8, 2017
  • AcceptedFeb 23, 2017
  • PublishedMar 22, 2017


Sea ice deformation parameters are important for elucidation of the properties and characteristics of ice-ocean models. Observations of sea ice motion over 11.5 year period (November 1996–April 2008) are used to calculate ice motion divergence and shear rates, and thus, to construct total deformation rate (TDR) estimates with respect to spatial and temporal variability in the Arctic Ocean. Strong sea ice deformation signal (SDS) rates are identified when TDR>0.01 day‒1, and very strong SDS events, when TDR>0.05 day‒1. These calculations are based on measurements made by the RADARSAT-1 Geophysical Processer System (RGPS). Statistical analysis of the SDS data suggest the following features: (1) Mean SDS and the SDS probability distributions are larger in “low latitudes” of the Arctic Ocean (less than 80°N) than in “high latitudes” (above 80°N), in both summer and winter; (2) very high SDS probabilities distributions and mean SDS values occur in coastal areas, e.g. the East Siberian Sea, Chukchi Sea and Beaufort Sea; (3) areas with relatively low TDR values, in the range from 0.01 day‒1 to 0.05 day‒1, cover much of the Arctic Ocean, in summer and winter; (4) of the entire TDR dataset, 45.89% belong to SDS, with summer the SDS percentage, 59.06%, and the winter SDS percentage, 40.50%. Statistically, the summer mean SDS, SDS percentage and very strong SDS are larger than corresponding values in the winter for each year, and show slight increasing tendencies during the years from 1997 to 2007. These results suggest important constraints for accurate simulations of very strong SDS in ice-ocean models.

Funded by

Global Change Research Program of China(2015CB953901)

The National Key Research and Development Program of China(2016YFC1401007)

Canadian Program on Energy Research and Development(OERD)

Office of Naval Research(Code 322,“Arctic,Global Prediction”,Grant number,Principal Investigator: William Perrie,Grant No. N00014-15-1-2611)


This work was supported by the Global Change Research Program of China (Grant No. 2015CB953901), the National Key Research and Development Program of China (Grant No. 2016YFC1401007), the Canadian Program on Energy Research and Development (OERD), the Office of Naval Research (Code 322, “Arctic and Global Prediction”, Grant Number and Principal Investigator: William Perrie, Grant No. N00014-15-1-2611).


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  • Figure 1

    Ascending sequences of TDR for 15948148 effective samples derived from gridded RGPS data from 871 SAR images based on 3 day intervals.

  • Figure 2

    Gradients of TDR corresponding to Figure 1.

  • Figure 3

    Variation of probability density function distributions with TDR, for the whole dataset (black line), for summer (red line) and for winter (blue line).

  • Figure 4

    Spatial and probability distributions of SDS: (a) mean of SDS in summer; (b) probability distribution of SDS in summer; (c) mean of SDS in winter; (d) probability distribution of SDS in winter.

  • Figure 5

    Spatial distributions of the four interval ranges from TDR time averages at each grid in summer (a) and in winter (b); the pie chart of percentages of samples in the whole dataset in summer (c) and in winter (d). Colours Blue, Cyan, Yellow and Red represent TDR samples whose values are in interval 0–0.01, 0.01–0.05, 0.05–0.10 and >0.10 day−1, respectively.

  • Figure 6

    Temporal variability of yearly and seasonal (a) mean SDS; (b) percentage of SDS larger than 0.01 day−1; (c) percentage of SDS larger than 0.05 day−1, where data in 2000 were not used owing to very few samples in that year.

  • Table 1   Days of missing RGPS data, for the entire dataset (10 November, 1996–29 April, 2008)














    Data starts on November 10, 1996




    August 1 to November 4




    Jul 28 to November 2



    August 4 to November 3



    May 3 to November 9



    August 2 to November 10


    April 29 to May 21

    August 3 to December 31




    August 1 to December 9





    August 16 to November 18




    August 2 to December 4



    August 3 to December 8



    August 1 to December 6


    Data end on: April 29, 2008

    Underline the data means no data

  • Table 2   “Locations”, for TDR, the proportion of different area samples in all samples of the whole dataset

    Boundaries name

    First data





    Last data

    Sample “locations” in index number







    TDR (day−1)







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