Sea ice surface temperature in the Weddell Sea (Antarctica), from drifting buoy and AVHRR data

• Published in: Cold regions science and technology 2001-10, Vol.33 (1), p.19-27
• Main Authors: Veihelmann, Ben, Olesen, Folke S, Kottmeier, Christoph
• Format: Article
• Language: English
• Subjects: Antarctica; AVHRR; Drifting buoy; Earth, ocean, space; Exact sciences and technology; External geophysics; Physics of the oceans; Sea ice; Surface temperature
• ISSN: 0165-232X; 1872-7441
• DOI: 10.1016/S0165-232X(01)00025-8

The surface temperature of sea ice is derived from infrared AVHRR measurements and from in situ measurements at drifting buoys in the Weddell Sea, Antarctica. For clear sky cases, the surface temperature is calculated from AVHRR channel 4 data (10.3–11.3 μm). After calibrating and geo-referencing the infrared measurements, an atmospheric correction is carried out using the radiation transfer model MODTRAN and temperature and humidity data from ECMWF analyses. The results are compared with surface temperatures derived from air temperature measurements from drifting buoys with an energy balance model. Results from both methods are linearly related with a correlation coefficient of 0.97 and a slope of 1.03. The bias is less than 1.3 K and the standard deviation of the residuals is 2.56 K. Accuracies and worst case error estimates of both methods are quantified by sensitivity studies with respect to the relevant sources of errors. A typical accuracy of the buoy-based surface temperature is 2 K and a worst case error estimate is 4–7 K depending on the ice thickness. The accuracy and worst case error estimate of satellite-derived surface temperature are of the same magnitude. Thus, none of the derivation methods is considered to be a validation of the other. Ice surface temperature variations in the Antarctic sea ice zone are caused by moving depressions in all seasons and cover a range of up to 20 K. Satellite measurements with a typical accuracy of 2 K may provide surface temperatures with a satisfactory signal to noise ratio in this data-sparse region, although clouds bear a significant restriction to their application.