A new Level 4 multi-sensor ice surface temperature product for the Greenland Ice Sheet

• Published in: The Cryosphere
• Main Authors: Karagali, Ioanna, Suhr, Magnus Barfod, Mottram, Ruth, Nielsen-Englyst, Pia, Dybkjaer, Gorm, Ghent, Darren, Hoyer, Jacob L.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: /dk/atira/pure/sustainabledevelopmentgoals/climate_action; name=SDG 13 - Climate Action; Antarctic; Arctic; Greenland; Antarc*; Climate change; Ice Sheet
• Online Access: https://orbit.dtu.dk/en/publications/091667fe-6f8c-4eb7-a6bd-961b80724270; https://doi.org/10.5194/tc-16-3703-2022; https://backend.orbit.dtu.dk/ws/files/289730642/tc_16_3703_2022.pdf

The Greenland Ice Sheet (GIS) is subject to amplified impacts of climate change and its monitoring is essential for understanding and improving scenarios of future climate conditions. Surface temperature over the GIS is an important variable, regulating processes related to the exchange of energy and water between the surface and the atmosphere. Few local observation sites exist; thus spaceborne platforms carrying thermal infrared instruments offer an alternative for surface temperature observations and are the basis for deriving ice surface temperature (IST) products. In this study several satellite IST products for the GIS were compared, and the first multi-sensor, gap-free (Level 4, L4) product was developed and validated for 2012. High-resolution Level 2 (L2) products from the European Space Agency (ESA) Land Surface Temperature Climate Change Initiative (LST_cci) project and the Arctic and Antarctic Ice Surface Temperatures from Thermal Infrared Satellite Sensors (AASTI) dataset were assessed using observations from the PROMICE (Programme for Monitoring of the Greenland Ice Sheet) stations and IceBridge flight campaigns. AASTI showed overall better performance compared to LST_cci data, which had superior spatial coverage and availability. Both datasets were utilised to construct a daily, gap-free L4 IST product using the optimal interpolation (OI) method. The resulting product performed satisfactorily when compared to surface temperature observations from PROMICE and IceBridge. Combining the advantages of satellite datasets, the L4 product allowed for the analysis of IST over the GIS during 2012, when a significant melt event occurred. Mean summer (June-August) IST was -5.5 +/- 4.5 degrees C, with an annual mean of -22.1 +/- 5.4 degrees C. Mean IST during the melt season (May-August) ranged from -15 to -1 degrees C, while almost the entire GIS experienced at least between 1 and 5 melt days when temperatures were -1 degrees C or higher. Finally, this study assessed the potential for using the satellite L4 ...