Reassessing the Quality of Sea‐Ice Deformation Estimates Derived From the RADARSAT Geophysical Processor System and Its Impact on the Spatiotemporal Scaling Statistics. Issue 8 (31st July 2020)

• Record Type: Journal Article
• Authors: Bouchat, Amélie; Tremblay, Bruno
• Is Part Of: Journal of geophysical research. Volume 125:Issue 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-31
• Subjects: sea ice -- deformation -- spatiotemporal scaling -- error analysis -- propagation of uncertainty; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2019JC015944
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: We reassess the trajectory errors inherent to sea‐ice deformation estimates with a new propagation of uncertainty derivation and show that previous formulations applied to deformation estimates from the RADARSAT Geophysical Processor System (RGPS) are either too high due to incorrect assumptions or too low due to neglected terms in certain cases. We show that when the resulting signal‐to‐noise ratios are used to discriminate the deformation estimates based on their quality, as done for buoy records, the spatiotemporal scaling exponents for the mean total deformation rate increase, especially at smaller scale, such that a space‐time coupling of the scaling—which is otherwise absent—emerges from the RGPS deformation data set, in accord with previous analyses performed with buoy observations. We also show that the preprocessing method used to reduce the effects of irregular sampling of the Lagrangian deformation fields can significantly impact the value of the deformation statistics and could possibly explain part of previous discrepancies between deformation statistics obtained with buoy records and large‐scale synthetic aperture radar (SAR) imagery. Specifically, we show that spurious lines of deformation appear when interpolating RGPS trajectories that presenttemporal sampling inconsistencies. In the context of using observed sea‐ice deformation statistics to constrain and improve the performance of sea‐ice models, high confidence in the observed deformation field statistics is necessary. Using appropriate, well‐documented, methods to derive the set of statistics to be reproduced by models therefore becomes crucial. Plain Language Summary: Sea‐ice in the Arctic Ocean deforms in well‐defined lines. These linear deformation features are important for the climate system because they regulate the interaction between the ocean beneath the ice and the atmosphere above the ice. It is therefore of interest to know the statistical properties of these deformation features in order to reproduce them with sea‐ice models that are used for climate studies. We show in this study that the statistical properties of sea‐ice deformations obtained from satellite observations and used to evaluate sea‐ice models are very sensitive to the methods applied to obtain the deformation fields. Depending on the method used, artificial behaviors can appear in the observed deformation statistics, which then have no physical meaning. As such, care must be taken when analyzing the statistical properties of the observed deformation fields for them to have a physical significance. Our analysis also allows us to explain differences in previous studies that used different methods to analyze the statistical properties of the observed deformation fields. Key Points: Deformation statistics from the RADARSAT Geophysical Processor System are sensitive to the preprocessing methods Space‐time coupling of the scaling emerges when deformation estimates are discriminated using their signal‐to‐noise ratio Interpolation of trajectories in time leads to larger scaling exponents and spurious space‐time coupling