Emodnet-Arctic

Ice cover (grid)

Sub-challenge: Change in average ice cover (kg m2/year) over past 10 years, past 50 years and past 100 years, shown on a grid.

Results
The task was to find data on changes in average ice cover over past 10 years, past 50 years and past 100 years, and present these on a grid. This means the sub-challenge can be divided into three different scenarios:

  • Change in average ice cover (kg m2/year) over past 10 years shown on a grid for the entire study area;
  • Change in average ice cover (kg m2/year) over past 50 years shown on a grid for the entire study area;
  • Change in average ice cover (kg m2/year) over past 100 years shown on a grid for the entire study area.

To calculate change in ice cover (kg m2/year) estimates of ice thickness and, ideally, ice density are necessary in addition to ice extent and concentration. There are different methods for measuring ice thickness, e.g. in situ from the surface, upward looking sonars (mounted on submarines or moorings), or remote sensing (satellite and airborne LIDAR observations (Light Detection and Ranging – a detection system similar to radar but uses laser generated light). An overview of available data and associated uncertainty are presented in a recent paper by Lindsay and Schweiger (2015). According to Lindsay and Schweiger; "Historically, a great number of ice thickness measurements have been made at specific locations using drill holes or ground based electromagnetic methods; however, these point measurements are difficult to translate into area-averaged mean ice thickness because of the highly heterogeneous nature of the ice pack." For this reason, no historical observational data could be used to map changes in ice thickness over the last 50 and 100 years.

There are several datasets available from U.S. National Snow and Ice Data Center (NSIDC) at the University of Colorado. The most useful in this context is the Unified Sea Ice Thickness Climate Data Record Collection Spanning 1947-2012 (Lindsay 2013). This is the dataset used and described in Lindsay and Schweiger (2015). Starting from 2010 estimates of ice thickness are available from the European Space Agency’s CryoSat-2 satellite. ESA Data are available from Centre for Polar Observation and Modelling at the University College London (cpom.ucl.ac.uk) (Laxon, Giles et al. 2013). However, there is no dataset available on change in average ice cover as requested, and estimates must therefore be made from available data on ice extent and ice thickness (assuming a constant density of sea ice). Obtaining good measurements of average ice thickness over the last decade is challenging because of the sparsity of data in space and time. For the past 50 and 100 years it was not possible, as discussed above. An alternative method is using model data. Data from the University of Washington’s Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) is available (Zhang et al. 2013). A description and evaluation of the performance of this system to simulate ice thickness is found in Schweiger, Lindsay et al. (2011). We downloaded PIOMAS area and ice thickness data. By assuming a constant ice density of 930 kg/m3 we have calculated the trend in average ice cover (kg m2/year) over the last decade (2006-2015) (Figure 1).Trend ice cover figure1

Figure 1: Trend in average ice cover (kg m-2 y-1) over the last decade (2006-2015).

Data use, availability and gaps
There was not enough data available to complete this sub-challenge . We were unable to locate reliable data specific to average ice cover in kg m2 /year for the past 50 or 100 years. A ‘grid’ was added to the maps for optimal viewing. Data are from U.S. NOAA and from European Space Agency and University of Washington, U.S.A.

Conclusion and lessons learned
In all the data sets, the decline in the extent of solid ice is notable, though more so in the Atlantic sector than in the Bering Sea sector. However, as sea ice thickness is different from extent, different research techniques are necessary and these produce different data. Lessons learned during this sub-challenge:

  • Data on sea-ice thickness (especially in kg m2 /year) is not readily available as it is a hard to measure.
  • More data on ice thickness is becoming available relatively recently with new measuring techniques and the use of models.

Recommendations
Sea-ice thickness is an important parameter for climate change research and we recommend a yearly monitoring programme.


References:

  • Laxon, S. W., K. A. Giles, A. L. Ridout, D. J. Wingham, R. Willatt, R. Cullen, R. Kwok, A. Schweiger, J. Zhang, C. Haas, S. Hendricks, R. Krishfield, N. Kurtz, S. Farrell and M. Davidson (2013). "CryoSat-2 estimates of Arctic sea ice thickness and volume." Geophysical Research Letters 40(4): 732-737.
  • Lindsay, R. (2013). Unified Sea Ice Thickness Climate Data Record Collection Spanning 1947-2012, version 1. Boulder, Colorado, USA, National Oceanic and Atmospheric Administration, National Snow and Ice Data Center.
  • Lindsay, R. and A. Schweiger (2015). "Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations." The Cryosphere 9(1): 269-283.
  • Zhang, Jinlun and D.A. Rothrock: Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates, Mon. Wea. Rev. 131(5), 681-697, 2003. http://psc.apl.washington.edu/zhang/IDAO/data_piomas.html
  • http://www.cpom.ucl.ac.uk/csopr/seaice.html