Sea Ice Thickness Data Sets: Overview & Comparison Table

Main content

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

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

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

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).##

Key Figures

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)

Other Information

Earth system components and main variables
Type of data product
None
Dataset collections

Years of record
None
Data time period extended
None
Timestep
Daily, Monthly, Weekly, Irregular
Domain
Formats:
Input Data
None
Vertical Levels:
Missing Data Flag
Missing data present
Ocean or Land
Ocean Only
Spatial Resolution

varies

Model Resolution (reanalysis)
None
Data Assimilation Method
None
Model Vintage (reanalysis)
None

Please cite data sources, following the data providers' instructions
Suggested Data Citation
Dataset DOIs
None
Hosted Climate Index Files
None
Data Access
None
Usage Restrictions
None

Key Publications
Not available...

Summary of sea ice thickness datasets

Name Source Domain Period of Record Timesteps Resolution Formats Formats
Unified Sea Ice Thickness Climate Data Record NSIDC/Axel Schweiger and others Antarctic, Arctic 1947/01 to present Daily, Weekly, Monthly 50 km ascii aircraft, satellites, moorings
Sea Ice Thickness Data from Gridded Products NSIDC Arctic 1993/01 to 2014/12 Monthly 100 km netCDF ULS, airborne laser, and radar altimetry
Sea Ice Freeboard and Thickness from ICESat (G) NSIDC/Donghui Yi, H. Zwally Arctic 2003/02/20 to 2008/10/19 Campaigns, Daily 70 m ascii GGLAS, SSM/I, snow climatology
Sea Ice Thickness from ICESat (J) JPL/Ron Kwok Antarctic, Arctic 2003/09/24 to 2008/03/21 Campaigns 25 km ascii ICESat
Sea Ice Freeboard, Thickness and Snow Depth from IceBridge
NSIDC/Nathan Kurtz and others Antarctic, Arctic 2009/03/19 to 2013/04/25 Varies varies ascii Airborne altimetry
Near Real-time (NRT) Sea Ice Thickness from CryoSat-2 ESA, processed at the NERC Center for Polar Observation and Modelling (CPOM), UCL Arctic 2010/10 to present 2 Days, 14 Days, 28 Days 5 km netCDF, ascii, GeoTIFF CryoSat2
Sea Ice Thickness from AWI-CryosSat-2 Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research Arctic 2010/11 to present Monthly 25 km netCDF CryoSat2
Daily Thickness of Thin Sea Ice from SMOS Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, University of Hamburg Antarctic, Arctic 2010/10 to present Daily 12.5 km netCDF SMOS
Weekly Sea Ice Thickness from CryoSat2/SMOS Data Fusion Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, University of Hamburg Antarctic, Arctic 2010/11/15 to present Weekly 25 km netCDF CS2-SMOS Data Merging
Simulated Sea Ice Thickness from the Pan-Arctic Ice Ocean Modeleing and Assimilation System (PIOMAS) Polar Science Center at the University of Washing- ton/Jinlun Zhang Arctic 1979/01 to present Daily, Monthly mean 22 km binary coupled Parallel Ocean and Sea Ice model and assimilation
Simulated Sea Ice Thickness from the Global Ice-Ocean Modeleing and Assimilation System (GIOMAS) Polar Science Center at the University of Washing- ton/Jinlun Zhang Global 1979/01 to present Monthly mean 22 km binary coupled Parallel Ocean and Sea Ice model and assimilation