Sea Ice Thickness Data Sets: Overview & Comparison Table

While satellite observations of sea ice extent and concentration are available from 1979, long-term high quality (daily and high spatial resolution) observations of sea ice thickness remain limited as a result of few satellite and in situ observations. Reconstructions using numerous observational sources show a 65% decline in annual mean sea ice thickness in the central Arctic since the 1970s (Lindsay and Schweiger , 2015). Existing observations of sea ice thickness can differ through spatial and temporal coverage, measurement uncertainties, and methods of estimation.  (excerpted from Zachary Labe's expert-user guidance).

Key Strengths:

  • Sea ice thickness data is necessary for assessing sea ice mass balance, the surface energy budget, seasonal and annual sea ice prediction, and changes in the polar climate system
  • Total sea ice volume can be estimated through sea ice thickness and sea ice concentration

Key Limitations:

  • Numerous uncertainties as a result of different methods, assumptions, and data sources
  • Major limitations in the spatial and temporal coverage of sea ice thickness, which prevent a consistent record of long-term change and variability
  • Methods for assessing snow depth on top of the sea ice (i.e. use of climatology) may lead to biases in sea ice thickness estimates

Expert User Guidance

The following was contributed by Zachary Labe (University of California, Irvine), August, 2017:

While satellite observations of sea ice extent and concentration are available from 1979, long-term high quality (daily and high spatial resolution) observations of sea ice thickness remain limited as a result of few satellite and in situ observations. Reconstructions using numerous observational sources show a 65% decline in annual mean sea ice thickness in the central Arctic since the 1970s (Lindsay and Schweiger , 2015). Existing observations of sea ice thickness can differ through spatial and temporal coverage, measurement uncertainties, and methods of estimation.

In the last decade, satellite-based altimetry has improved our spatial and temporal coverage of sea ice thickness. Prior to these satellite observations, spare sea ice thickness data are available from submarine upward looking sonar and moorings. Limited data is even available from 1947 in the Canadian Arctic Archipelago. More recently, ERS-1/ERS-2 (1993-2001), ICESat (2003-2009) and now CryoSat-2 (2010-2017) provide nearly pan-Arctic observations of sea ice thickness. These altimetry-based estimates of sea ice thickness can be derived by measuring the height above the water level (freeboard), using snow and sea ice densities, estimating the snow depth on top of the ice, and assuming hydrostatic equilibrium. Satellites estimates of sea ice thickness can also be derived from brightness temperature, such as in the Soil Moisture and Ocean Salinity (SMOS) Mission. Meanwhile, estimates of sea ice thickness from upward-looking sonar and mooring data measure ice drift before converting to sea ice thickness. This can also induce uncertainties and errors through changing bottom roughness.

Through these various observational data sets and methodological assumptions, sea ice thickness remains one of the more poorly observed variables in the Arctic. Sources of error and uncertainty include: freeboard measurements, snow depth, and density estimates for sea ice and snow (Zygmuntowska et al., 2014). Most satellite sea ice thickness data is not available during the melt season due to the formation of melt ponds.

As a result of the limited temporal and spatial estimates of sea ice thickness, ice-ocean models with data assimilation are also a useful tool in providing sea ice thickness “reanalysis.” The Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) is a coupled ice and ocean model with sea ice thickness data available over the satellite era (from 1979) (Zhang and Rothrock , 2003). PIOMAS has been widely validated against sea ice thickness data sets (such as ICESat), and its uncertainties are addressed in Schweiger et al.(2011). PIOMAS has the capabilities of assimilating sea surface temperatures, sea ice concentration, and sea ice velocity data. The model is driven by atmospheric surface forcings from daily mean NCEP-NCAR (R1) reanalysis.

The following list of data sets and links provide a starting point for using sea ice thickness data. Particularly, the “Unified Sea Ice Thickness Climate Data Record” is a comprehensive archive of various sea ice thickness observational data sets, which are available at a 50 km resolution from 1947 to 2017 (Lindsay, 2010). The archive identifies each data set type along with any associated uncertainty statistics and additional concise documentation.

The use of these data sets requires an understanding of the documentation and potential sources of error. For example, estimates of snow depth on the top of sea ice often use a climatology from Warren et al. (1999), which may not reflect the actual snow depth at the time of sea ice thickness estimation. Comparisons and validations between many of these sea ice thickness products can be found in recent studies (e.g., Stroeve et al., 2014; Wanget al., 2016).##

temporal metadataID:

Formats

Timestep

Daily | Monthly | Weekly | Irregular

Domain

Spatial Resolution

varies

Ocean or Land

Ocean Only

Missing Data Flag

missing data present

Vertical Levels

Earth system components and main variables

Data Access: Please Cite data sources, following the data providers' instructions.

  1. Please see the table, below.

  1. Kwok, R., and G. F. Cunningham (2015), Variability of Arctic sea ice thickness and vol- ume from CryoSat-2., Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 373 (2045), 20140,157–, doi:10.1098/rsta.2014.0157
  2. Lindsay, R. (2010), New Unified Sea Ice Thickness Climate Data Record, Eos, Transactions American Geophysical Union, 91 (44), 405, doi:10.1029/2010EO440001
  3. Lindsay, R., and A. Schweiger (2015), Arctic sea ice thickness loss determined using subsur- face, aircraft, and satellite observations, The Cryosphere, 9 (1), 269–283, doi:10.5194/tc- 9-269-2015.
  4. Schweiger, A., R. Lindsay, J. Zhang, M. Steele, H. Stern, and R. Kwok (2011), Uncertainty in modeled Arctic sea ice volume, Journal of Geophysical Research, 116 (C8), C00D06, doi:10.1029/2011JC007084.
  5. Stroeve, J., A. Barrett, M. Serreze, and A. Schweiger (2014), Using records from submarine, aircraft and satellites to evaluate climate model simulations of Arctic sea ice thickness, The Cryosphere, 8 (5), 1839–1854, doi:10.5194/tc-8-1839-2014.
  6. Wang, X., J. Key, R. Kwok, and J. Zhang (2016), Comparison of Arctic Sea Ice Thickness from Satellites, Aircraft, and PIOMAS Data, Remote Sensing, 8 (9), 713, doi:10.3390/ rs8090713.
  7. Warren, S. G., I. G. Rigor, N. Untersteiner, V. F. Radionov, N. N. Bryazgin, Y. I. Aleksan- drov, R. Colony, S. G. Warren, I. G. Rigor, N. Untersteiner, V. F. Radionov, N. N. Bryazgin, Y. I. Aleksandrov, and R. Colony (1999), Snow Depth on Arctic Sea Ice,
  8. Zhang, J., and D. A. Rothrock (2003), Modeling Global Sea Ice with a Thickness and Enthalpy Distribution Model in Generalized Curvilinear Coordinates, Monthly Weather Review, 131 (5), 845–861, doi:10.1175/1520-0493(2003)131 0845:MGSIWA 2.0.CO;2.
  9. Zygmuntowska, M., P. Rampal, N. Ivanova, and L. H. Smedsrud (2014), Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends, The Cryosphere, 8 (2), 705–720, doi:10.5194/tc-8-705-2014.

Key Figures

Click the thumbnails to view larger sizes

Thumbnails

Captions

sea ice volume anomalies in PIOMASS (provided by Z Labe) Sea ice volume anomalies in PIOMAS (Zhang and Rothrock , 2003) calculated from a 1979 to 2016 baseline. The linear trend is shown in blue. The shaded areas are highlighted for one and two standard deviations from the trend. Figure by the Polar Science Center at the University of Washington. (provided by Z Labe)
Arctic sea ice thickness in ICESat and CryoSat2 (provided by Z Labe) Winter Arctic sea ice thickness in ICESat (2004-2008) and CryoSat-2 (2011-2014) [top]. Sea ice volume in winter and summer along with corresponding trends for ICESat and CryoSat-2 [bottom]. Figure by Ron Kwok (Jet Propulsion Laboratory) and adapted from Kwok and Cunningham (2015). (provided by Z Labe)

Cite this page

Labe, Zachary & National Center for Atmospheric Research Staff (Eds). Last modified 11 Sep 2017. "The Climate Data Guide: Sea Ice Thickness Data Sets: Overview & Comparison Table." Retrieved from https://climatedataguide.ucar.edu/climate-data/sea-ice-thickness-data-sets-overview-comparison-table.

Acknowledgement of any material taken from this page is appreciated. On behalf of experts who have contributed data, advice, and/or figures, please cite their work as well.