In this study, six Arctic sea ice thickness products are compared: the AVHRR Polar Pathfinder-extended (APP-x), ICESat, CryoSat-2, SMOS, NASA IceBridge aircraft flights, and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). The satellite products are based on three different retrieval methods: an energy budget approach, measurements of ice freeboard, and the relationship between passive microwave brightness temperatures and thin ice thickness. Inter-comparisons are done for the periods of overlap from 2003 to 2013. Results show that ICESat sea ice is thicker than APP-x and PIOMAS overall, particularly along the north coast of Greenland and Canadian Archipelago. The relative differences of APP-x and PIOMAS with ICESat are −0.48 m and −0.31 m, respectively. APP-x underestimates thickness relative to CryoSat-2, with a mean difference of −0.19 m. The biases for APP-x, PIOMAS, and CryoSat-2 relative to IceBridge thicknesses are 0.18 m, 0.18 m, and 0.29 m. The mean difference between SMOS and CryoSat-2 for 0~1 m thick ice is 0.13 m in March and −0.24 m in October. All satellite-retrieved ice thickness products and PIOMAS overestimate the thickness of thin ice (1 m or less) compared to IceBridge for which SMOS has the smallest bias (0.26 m). The spatial correlation between the datasets indicates that APP-x and PIOMAS are the most similar, followed by APP-x and CryoSat-2.

With recent, dramatic changes in Arctic sea ice and the Antarctic ice sheets, the importance of monitoring the climate of the polar regions has never been greater. While many individual global satellite products exist, the AVHRR Polar Pathfinder products provide a comprehensive set of variables that can be used to study trends and interactions within the Arctic and Antarctic climate systems. This paper describes the AVHRR Polar Pathfinder (APP), which is a fundamental climate data record that provides channel reflectances and brightness temperatures, and the AVHRR Polar Pathfinder—Extended (APP-x), which is a thematic climate data record that builds on APP to provide information on surface and cloud properties and radiative fluxes. Both datasets cover the period from 1982 through the present, twice daily, over both polar regions. APP-x has been used in the study of trends in surface properties, cloud cover, and radiative fluxes, interactions between clouds and sea ice, and the role of land surface changes in summer warming.

Measurements from the Russian “North Pole” series of drifting stations, the United States drifting stations“T-3” and “Arlis II,” land stations, and, where necessary, over the northern North Atlantic and coastal Greenland, empirically derived values from earlier Russian studies are used to compile a new gridded monthly climatology of global (downwelling shortwave) radiation for the region north of 65°N. Spatio-temporal patterns of fluxes and effective cloud transmittance are examined and comparisons are made with fields from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis and those derived from the International Satellite Cloud Climatology Project (ISCCP) C2 (monthly) cloud product. All months examined (March–October) show peak fluxes over the Greenland ice sheet. March, September, and October feature a strong zonal component. Other months exhibit an asymmetric pattern related to cloud fraction and optical depth, manifested by an Atlantic side flux minimum. For June, the month of maximum insolation, fluxes increase from less than 200 W m⁻² in the Norwegian and Barents seas to more than 300 W m⁻² over the Pacific side of central Arctic Ocean extending into the Beaufort Sea. June fluxes of more than 340 W m⁻² are found over the Greenland ice sheet. Effective cloud transmittance, taken as the ratio of the observed flux to the modeled clear sky flux, is examined for April–September. Values for the Atlantic sector range from 0.50–0.60, contrasting with the central Arctic Ocean where values peak in April at 0.75–0.80, falling to 0.60–0.65 during late summer and early autumn. A relative Beaufort Sea maximum is well expressed during June. The NCEP–NCAR and ISCCP products capture 50%–60% of the observed spatial variance in global radiation during most months. However, the NCEP–NCAR fluxes are consistently high, with Arctic Ocean errors in excess of 60 W m⁻² during summer, reflecting problems in modeled cloud cover. ISCCP fluxes compare better in terms of magnitude.

Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions. The thinning of Arctic sea ice over the last few decades will likely result in changes in lead distributions, so monitoring their characteristics is increasingly important. Here we present a methodology to detect and characterize sea ice leads using satellite imager thermal infrared window channels. A thermal contrast method is first used to identify possible sea ice lead pixels, then a number of geometric and image analysis tests are applied to build a subset of positively identified leads. Finally, characteristics such as width, length and orientation are derived. This methodology is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) observations for the months of January through April over the period of 2003 to 2018. The algorithm results are compared to other satellite estimates of lead distribution. Lead coverage maps and statistics over the Arctic illustrate spatial and temporal lead patterns.

The Visible Infrared Imager Radiometer Suite (VIIRS) instrument was launched in October 2011 on the satellite now known as the Suomi National Polar‐orbiting Partnership. VIIRS was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS). VIIRS snow and ice products include sea ice surface temperature, sea ice concentration, sea ice characterization, a binary snow map, and fractional snow cover. Validation results with these “provisional” level maturity products show that ice surface temperature has a root‐mean‐square error of 0.6–1.0 K when compared to aircraft data and a similar MODIS product, the measurement accuracy and precision of ice concentration are approximately 5% and 15% when compared to passive microwave retrievals, and the accuracy of the binary snow cover (snow/no‐snow) maps is generally above 90% when compared to station data. The ice surface temperature and snow cover products meet their accuracy requirements with respect to the Joint Polar Satellite System Level 1 Requirements Document. Sea Ice Characterization, which consists of two age categories, has not been observed to meet the 70% accuracy requirements of ice classification. Given their current performance, the ice surface temperature, snow cover, and sea ice concentration products should be useful for both research and operational applications, while improvements to the sea ice characterization product are needed before it can be used for these applications.