04 Feb 2022
04 Feb 2022
Status: a revised version of this preprint is currently under review for the journal TC.

A new L4 multi-sensor ice surface temperature product for the Greenland Ice Sheet

Ioanna Karagali1, Magnus Barfod Suhr1, Ruth Mottram1, Pia Nielsen-Englyst1,2, Gorm Dybkjær1, Darren Ghent3, and Jacob L. Høyer1 Ioanna Karagali et al.
  • 1Danish Meteorological Institute, Lyngbyvej 100, Copenhagen-Ø, DK-2100, Denmark
  • 2DTU-Space, Technical University of Denmark, Lyngby, DK-2800, Denmark
  • 3National Centre for Earth Observation (NCEO), University of Leicester, University Road, Leicester, LE1 7RH, UK

Abstract. 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 as it regulates processes related to the exchange of energy and water between the surface and the atmosphere. As few key local observation sites exist, an important alternative to obtain surface temperature observations over the GIS is space-borne sensors that carry thermal infrared instruments. These offer several passes per day with a wide view and are the basis of deriving Ice Surface Temperature (IST) products.

The aim of this study was to compare several satellite IST products for the GIS and develop and validate the first multi-sensor, gap-free (Level 4, L4) product for 2012. High resolution Level 2 (L2) IST products from the European Space Agency (ESA) Land Surface Temperature Climate Change Initiative (LST_cci) project and the Arctic & Antarctic Ice Surface Temperatures from Thermal Infrared Satellite Sensors (AASTI) dataset, were assessed using observations from the PROMICE stations and IceBridge flight campaigns. The AASTI data showed overall better performance compared to LST_cci data, that in return had superior spatial coverage and availability. Both datasets were further utilised to construct a daily, gap-free, L4 IST product using the optimal interpolation (OI) method. The resulting L4 IST product performed satisfactorily in terms of quality when compared with surface temperature observations from the PROMICE stations and IceBridge flight campaigns. By combining the advantages of the upstream satellite datasets, the gap-free L4 IST product allowed for the analysis of IST over the GIS during the year 2012, when a significant melt event occurred. Mean summer (June–August) IST over the GIS was −5.5 °C ± 4.5 °C, with an annual mean of −22.1 °C ± 5.4 °C. Mean IST during the melt season (May–August) ranged from −15 °C to −1 °C, while almost the entire GIS experienced at least between 1 and 5 melt days when temperatures were −1 °C or higher.

Finally, this study assessed the potential for using the satellite L4 IST product to improve model simulations of the GIS surface mass budget (SMB). The new L4 IST product was first used to evaluate, and then it was assimilated into an SMB model of snow and firn processes for the year 2012, to assess the impact of including a high resolution IST product on the SMB model. Compared with independent observations from the PROMICE stations and IceBridge flight campaigns, inclusion of the L4 IST dataset improved the model performance during the key onset of the melt season, where model biases are typically large. Reassuringly, internal model parameterisations performed well compared to the L4 IST dataset outside of this season, providing more confidence in modelled GIS surface mass budget (SMB) estimates.

Ioanna Karagali et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-384', Anonymous Referee #1, 29 Apr 2022
    • AC1: 'Reply on RC1', Ioanna Karagali, 20 Jun 2022
  • RC2: 'Comment on tc-2021-384', Anonymous Referee #2, 04 May 2022
    • AC2: 'Reply on RC2', Ioanna Karagali, 20 Jun 2022

Ioanna Karagali et al.

Ioanna Karagali et al.


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Short summary
Ice Surface Temperature (IST) products were used to develop the first multi-sensor, gap-free L4 IST product of the Greenland Ice Sheet (GIS) for 2012, when a significant melt event occurred. For the melt season, mean IST was −15 °C to −1 °C and almost the entire GIS experienced at least 1 to 5 melt days. Inclusion of the L4 IST to a surface mass budget (SMB) model, improved model performance during the key onset of the melt season, where biases are typically large.