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.

中文翻译：

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使用风帆从SMAP卫星和海洋模型验证北极海表盐度

北冰洋是最重要和最具挑战性的地区之一-气候变暖带来的变化最大，同时由于海冰和极端寒冷的温度，北冰洋也是最难采样的区域之一。美国国家航空航天局（NASA）资助的两次Saildrone飞机部署为验证源自使用土壤水分主动无源（SMAP）卫星数据的三种不同产品衍生的海面盐度（SSS）提供了独特的机会。为了研究解决中尺度到亚中尺度变化的可能问题，还与两种版本的“估计海洋环流和气候”（ECCO）模型进行了比较（Carroll，D； Menmenlis，D； Zhang，H。）。结果表明，这三种SMAP产品解析了与育空河有关的径流信号，SMAP产品与Saildrone SSS之间具有高度相关性。总体而言，频谱斜率可复制与中尺度-亚中尺度变化相关的-2.0斜率。所有产品都具有统计上显着的空间连贯性，其峰值接近100 km。基于这些令人鼓舞的结果，未来的研究应侧重于改进北冰洋卫星衍生的SSS的推导，并整合模型结果以补充遥感观测。