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Abstract. Antarctica's ice shelves resist the flow of grounded ice towardsthe ocean through “buttressing” arising from their contact with ice rises,rumples, and lateral margins. Ice shelf thinning and retreat reducebuttressing, leading to increased delivery of mass to the ocean that adds toglobal sea level. Ice shelf response to large annual cycles in atmosphericand oceanic processes provides opportunities to study the dynamics of bothice shelves and the buttressed grounded ice. Here, we explore whetherseasonal variability of sea surface height (SSH) can explain observedseasonal variability of ice velocity. We investigate this hypothesis usingseveral time series of ice velocity from the Ross Ice Shelf (RIS),satellite-based estimates of SSH seaward of the RIS front, ocean models ofSSH under and near RIS, and a viscous ice sheet model. The observed annualchanges in RIS velocity are of the order of 1–10 m a−1 (roughly 1 % ofmean flow). The ice sheet model, forced by the observed and modelled rangeof SSH of about 10 cm, reproduces the observed velocity changes whensufficiently large basal drag changes near the grounding line areparameterised. The model response is dominated by grounding line migrationbut with a significant contribution from SSH-induced tilt of the ice shelf.We expect that climate-driven changes in the seasonal cycles of winds andupper-ocean summer warming will modify the seasonal response of ice shelvesto SSH and that nonlinear responses of the ice sheet will affect the longertrend in ice sheet response and its potential sea-level rise contribution. 

Abstract Ice shelves play a critical role in modulating dynamic loss of ice from the grounded portion of the Antarctic Ice Sheet and its contribution to sea-level rise. Measurements of ice-shelf motion provide insights into processes modifying buttressing. Here we investigate the effect of seasonal variability of basal melting on ice flow of Ross Ice Shelf. Velocities were measured from November 2015 to December 2016 at 12 GPS stations deployed from the ice front to 430 km upstream. The flow-parallel velocity anomaly at each station, relative to the annual mean, was small during early austral summer (November–January), negative during February–April, and positive during austral winter (May–September). The maximum velocity anomaly reached several metres per year at most stations. We used a 2-D ice-sheet model of the RIS and its grounded tributaries to explore the seasonal response of the ice sheet to time-varying basal melt rates. We find that melt-rate response to changes in summer upper-ocean heating near the ice front will affect the future flow of RIS and its tributary glaciers. However, modelled seasonal flow variations from increased summer basal melting near the ice front are much smaller than observed, suggesting that other as-yet-unidentified seasonal processes are currently dominant. 

Abstract Measuring crustal strain and seismic moment accumulation, is crucial for understanding the growth and distribution of seismic hazards along major fault systems. Here, we develop a methodology to integrate 4.5 years (2015–2019.5) of Sentinel‐1 Interferometric Synthetic Aperture Radar (InSAR) and continuous Global Navigation Satellite System (GNSS) time series to achieve 6 to 12‐day sampling of surface displacements at ∼500 m spatial resolution over the entire San Andreas fault system. Numerous interesting deformation signals are identified with this product (video link:https://www.youtube.com/watch?v=SxNLQKmHWpY). We decompose the line‐of‐sight InSAR displacements into three dimensions by combining the deformation azimuth from a GNSS‐derived interseismic fault model. We then construct strain rate maps using a smoothing interpolator with constraints from elasticity. The resulting deformation field reveals a wide array of crustal deformation processes including, on‐ and off‐fault secular and transient tectonic deformation, creep rates on all the major faults, and vertical signals associated with hydrological processes. The strain rate maps show significant off‐fault components that were not captured by GNSS‐only models. These results are important in assessing the seismic hazard in the region.