Using saildrones to validate arctic sea-surface salinity from the smap satellite and from ocean models

• Published in: Remote Sensing
• Main Authors: Vazquez-Cuervo, Jorge, Gentemann, Chelle, Tang, Wenqing, Carroll, Dustin, Zhang, Hong, Menemenlis, Dimitris, Gomez-Valdes, Jose, Bouali, Marouan, Steele, Michael
• Format: Text
• Language: unknown
• Published: SJSU ScholarWorks 2021
• Subjects: Coastal; Sea surface salinity; Validation; Moss Landing Marine Laboratories; Arctic; Arctic Ocean; Yukon; Carroll; Sea ice; Yukon river
• Online Access: https://scholarworks.sjsu.edu/faculty_rsca/2426; https://doi.org/10.3390/rs13050831; https://scholarworks.sjsu.edu/context/faculty_rsca/article/3425/viewcontent/remotesensing_13_00831_v2.pdf

The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the-2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations.