A comparison of current sea ice thickness products during the freeze-up period in the Arctic

• Main Authors: Sánchez-Gamez, Pablo, Gabarró, Carolina, Gupta, Mukesh, Martínez, Justino, Turiel, Antonio, Portabella, Marcos, González Gambau, Verónica, Olmedo, Estrella
• Format: Conference Object
• Language: unknown
• Published: European Space Agency 2019
• Subjects: Arctic; Sea ice
• Online Access: http://hdl.handle.net/10261/205002

2019 Living Planet Symposium, 13-17 May 2019, Milan, Italy Arctic sea ice is going through a dramatic change in its extent and volume at an unprecedented rate. A retreating Arctic sea ice cover has a marked impact on regional and global climate, through many feedback mechanisms and interactions with the climate system. It has been identified a clear reduction on the Arctic sea ice thickness within the last decades. Therefore, Sea Ice Thickness (SIT) monitoring is essential to better understand the changes the Arctic is experiencing. SIT can be measured with remote sensing platforms, mainly with the freeboard measurements from radar/laser altimeter instruments (only for ice thicker than 1 m (Laxon et al. 2013) and with L-band passive radiometers (only valid for the thin ice (Kaleschke et al. 2012)), making both instruments complementary. We present a new empirical algorithm that can retrieve SIT from Soil Moisture Ocean Salinity (SMOS) brightness temperatures (TB). Other SMOS SIT products which are currently being distributed use two different methodologies: one computes SIT based on theoretical sea ice models (Tian-Kunze et al. 2014), and the other uses a regression function with SIT data obtained from models (Huntemann et al. 2014). Therefore, this is the first time that the model function needed to derive the SIT has been obtained exclusively using in-situ SIT data from various field campaigns (i.e. Electromagnetic bird and Operation IceBridge) in the Arctic during 2011-2015. The available in-situ dataset has been divided into two groups, one is used to compute the model function and the other has been used to validate the SIT products. The computed SIT has a root-mean-square error of 0.24 m, which seems reasonably good for analysis at 25 km grid. Moreover, this algorithm minimizes the error produced by the difficulty on discriminating between TB signature of thin SIT versus the signature due to low sea ice concentration. The retrieved SIT is expected to serve as an operational product for data assimilation ...