Near-real-time Arctic sea ice thickness and volume from CryoSat-2

• Published in: The Cryosphere
• Main Authors: Tilling, Rachel L., Ridout, Andy, Shepherd, Andrew
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2016
• Subjects: article; Verlagsveröffentlichung; Arctic; Sea ice; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-10-2003-2016; https://noa.gwlb.de/receive/cop_mods_00011602; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00011559/tc-10-2003-2016.pdf; https://tc.copernicus.org/articles/10/2003/2016/tc-10-2003-2016.pdf

Timely observations of sea ice thickness help us to understand the Arctic climate, and have the potential to support seasonal forecasts and operational activities in the polar regions. Although it is possible to calculate Arctic sea ice thickness using measurements acquired by CryoSat-2, the latency of the final release data set is typically 1 month due to the time required to determine precise satellite orbits. We use a new fast-delivery CryoSat-2 data set based on preliminary orbits to compute Arctic sea ice thickness in near real time (NRT), and analyse this data for one sea ice growth season from October 2014 to April 2015. We show that this NRT sea-ice-thickness product is of comparable accuracy to that produced using the final release CryoSat-2 data, with a mean thickness difference of 0.9 cm, demonstrating that the satellite orbit is not a critical factor in determining sea ice freeboard. In addition, the CryoSat-2 fast-delivery product also provides measurements of Arctic sea ice thickness within 3 days of acquisition by the satellite, and a measurement is delivered, on average, within 14, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively. The CryoSat-2 NRT sea-ice-thickness data set provides an additional constraint for short-term and seasonal predictions of changes in the Arctic ice cover and could support industries such as tourism and transport through assimilation in operational models.