5:15 PM - 6:45 PM
[ACG42-P20] Evaluation of the Arctic sea ice product derived from GCOM-C/SGLI
Keywords:GCOM-C, SGLI, sea ice
Continuous monitoring of the Arctic sea ice is important to understand the influence of ice albedo feedback, exchange of heat between the ocean and the atmosphere, and redistribution of heat and salt during recent climate change. Arctic sea ice extent had been rapidly decreasing in recent years, with the smallest extent in the satellite observation era recorded in 2012 and the second smallest extent in 2020. Space-borne microwave radiometers have been widely used for monitoring sea ice since the 1980s, because of its observability in all weather conditions, day or night. However, sea ice detection accuracy using microwave was decline in summer season due to the rapid changes in brightness temperature characteristics caused by sea ice surface melting (Kern et al., 2020). In addition, the low spatial resolution makes it difficult to extract the detailed distribution of sea ice edges. Therefore, we developed a sea ice distribution and sea surface temperature product on the polar regions to clarify the detail sea ice distribution during the summer season using optical and thermal-infrared sensor, GCOM-C/SGLI. In this presentation, we evaluated the performance of the GCOM-C/SGLI sea ice product by comparing its detailed summer sea ice distribution with GCOM-W/AMSR2 and in-situ observation data. And we showed its position through these comparisons.
SGLI sea ice monthly distribution showed the similar with AMSR2 Sea ice concentration. From June to August that mainly belongs to the melting season, AMSR2 shows the overestimation especially on the edge region. In such kind of situation, the sea ice has complex surface condition (e.g. surface snow melt or melt pond formation). These surface melt or presence of liquid water changes the radiation characteristics drastically. On the other hand, SGLI shows the overestimation in September. The sea ice concentration is low during the freezing season and AMSR2 tends to miss detection. However, SGLI is also mis-classified in fall season due to the cloud and low solar height. We have performed inter-comparison with in-situ observation data, Ice Profiling Sonar (IPS). AMSR2 sea ice concentration showed the good agreement with IPS. In the melting season, AMSR2 accurately captured the sea ice fractional temporal change, although there was a slight underestimation tendency. In the freezing season, there was also a slight underestimation. SGLI could capture the freezing period. Therefore, high-resolution SGLI is effective in detection of low sea ice concentration in case of the cloud contamination improved. At least, in-situ observation, AMSR2 and SGLI seems good agreement. Combination use of these dataset is expected to improve the accuracy of sea ice products.
References
Kern, S., T. Lavergne, D. Notz, L. T. Pedersen and R. Tonboe, Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions. The Cryosphere, 14(7), 2469-2493, 2020.
SGLI sea ice monthly distribution showed the similar with AMSR2 Sea ice concentration. From June to August that mainly belongs to the melting season, AMSR2 shows the overestimation especially on the edge region. In such kind of situation, the sea ice has complex surface condition (e.g. surface snow melt or melt pond formation). These surface melt or presence of liquid water changes the radiation characteristics drastically. On the other hand, SGLI shows the overestimation in September. The sea ice concentration is low during the freezing season and AMSR2 tends to miss detection. However, SGLI is also mis-classified in fall season due to the cloud and low solar height. We have performed inter-comparison with in-situ observation data, Ice Profiling Sonar (IPS). AMSR2 sea ice concentration showed the good agreement with IPS. In the melting season, AMSR2 accurately captured the sea ice fractional temporal change, although there was a slight underestimation tendency. In the freezing season, there was also a slight underestimation. SGLI could capture the freezing period. Therefore, high-resolution SGLI is effective in detection of low sea ice concentration in case of the cloud contamination improved. At least, in-situ observation, AMSR2 and SGLI seems good agreement. Combination use of these dataset is expected to improve the accuracy of sea ice products.
References
Kern, S., T. Lavergne, D. Notz, L. T. Pedersen and R. Tonboe, Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions. The Cryosphere, 14(7), 2469-2493, 2020.