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.

In the last two decades, satellite-based altimetry has improved our spatial and temporal coverage of sea ice thickness. Prior to these satellite observations, sparse sea ice thickness data are available from submarine upward looking sonar and moorings. These measure the depth of ice below the water level (draft), which can then be converted to ice thickness. Limited data is even available from 1947 in the Canadian Arctic Archipelago. Since the early 1990s satellite altimeters including ERS-1/ERS-2 (1993-2001), Envisat (2002-2012) and ICESat (2003-2009) have been used to estimate sea ice thickness, but their orbital coverage meant measurements were limited to the region of oldest, thickest ice in the Arctic Ocean. Currently, CryoSat-2 (2010-) and ICESat-2 (2018-) provide nearly pan-Arctic observations of sea ice thickness up to 88N. They also measure the sea ice in much finer detail than their predecessors. ICESat-2, for example, is able to detect individual photons form the sea ice surface, from an area as small as 11 m diameter. 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.

Sources of error and uncertainty in altimeter sea ice thickness include: freeboard, snow depth, and the densities of sea ice and snow (Zygmuntowska et al., 2014). Of these, snow depth is the main contributor of uncertainty. Most satellite sea ice thickness data is not available during the melt season due to the formation of melt ponds, which complicate the satellite return signal.

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 in altimeter sea ice thickness include: freeboard, freeboard measurements, snow depth, and the and densitiesy ofestimates for sea ice and snow (Zygmuntowska et al., 2014). Of these, snow depth is the main contributor of uncertainty. Most satellite sea ice thickness data is not available during the melt season due to the formation of melt ponds, which complicate the satellite return signal. 

New techniques are being developed to overcome uncertainties in sea ice thickness estimates from altimetry. A major advantage of having CryoSat-2 and ICESat-2 orbiting at the same time is the ability to directly estimate snow depth from their freeboard differences, rather than relying on outdated in situ measurements or model estimates (Kacimi and Kwok, 2022). This is possible because CryoSat-2 is a radar that reflects from the ice surface, and ICESat-2 is a lidar that reflects from the overlying snow surface. In 2020, the European Space Agency started their Cryo2Ice campaign, where the orbit of CryoSat-2 was shifted so it periodically aligns with ICESat-2, marking a huge international effort to further improve the accuracy of snow depth and sea ice thickness observations from space. Additionally, machine learning techniques have been applied to CryoSat-2 data to produce sea ice thickness estimates in summer (Landy et al., 2022). Satellites estimates of sea ice thickness can also be derived from brightness temperature, such as in the Soil Moisture and Ocean Salinity (SMOS) Mission. 

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).##