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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

Abstract

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

Office of Naval Research(Code 322)

Canadian Program on Energy Research and Development(OERD)

Global Change Research Program of China(2015CB953901)

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


Acknowledgment

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)

    Year

    January

    February

    April

    May

    June

    July

    August

    September

    October

    November

    December

    1996

    Data starts on November 10, 1996

    1997

    423

    1519

    August 1 to November 4

    1998

    212

    215

    Jul 28 to November 2

    1999

    310

    August 4 to November 3

    5–6

    2000

    May 3 to November 9

    2001

    1017

    August 2 to November 10

    2002

    April 29 to May 21

    August 3 to December 31

    2003

    216

    1718

    August 1 to December 9

    2004

    610

    1228

    2729

    August 16 to November 18

    2005

    711

    420

    August 2 to December 4

    2006

    224

    August 3 to December 8

    2007

    319

    August 1 to December 6

    2008

    Data end on: April 29, 2008

    a)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

    A

    B

    C

    D

    Last data

    Sample “locations” in index number

    1

    1330648

    8629671

    14475592

    15910352

    15948148

    TDR (day−1)

    0

    0.001

    0.01

    0.05

    0.10

    58.10

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