DOI: 10.5285/7803de8b-8a74-466b-888e-e8c737bf21ce
title: Gravity-derived bathymetry for the Thwaites, Crosson and Dotson ice shelves (2009-2019)

description: This dataset is an estimate of sub ice shelf bathymetry beneath the Thwaites, Crosson and Dotson ice shelves. The output bathymetry is derived from a new compilation of gravity data collected up to the end of the 2018/19 field season. The input gravity dataset includes airborne data from Operation Ice Bridge (OIB) and the NERC/NSF International Thwaites Glacier Collaboration (ITGC), and marine gravity from the R/V Nathaniel B. Palmer cruise NBP19-02. The recovered bathymetry was constrained by swath bathymetry and onshore airborne radio-echo depth sounding data in the surrounding area. Ice shelves mask the critical link between the ocean and cryosphere systems, and hence accurate sub ice shelf bathymetry is critical for generating reliable models of future ice sheet change. Included in the data release is the input free air gravity data, constraining bathymetry/sub-ice topography, and output gravity derived bathymetry. This work was funded by the British Antarctic Survey core program (Geology and Geophysics team), in support of the joint Natural Environment Research Council (NERC)/ National Science Foundation (NSF) International Thwaites Glacier Collaboration (ITGC). Additional specific support came from NERC Grants: NE/S006664/1 and NE/S006419/1, and NSF Grants: NSF1842064, NSFPLR-NERC-1738942, NSFPLR-NERC-1738992 and NSFPLR-NERC-1739003.
description: Input gravity data is from Operation Ice Bridge (OIB) and the ITGC 2018/19 airborne campaign, together with marine gravity data from cruise NBP19-02. The OIB free-air gravity data has an error of ~1.67 mGal in this region and resolves anomalies with a ~10 km full wavelength (Cochran and Bell, 2010, updated 2018; Tinto and Bell, 2011). The ITGC campaign data (Jordan et al., 2020c) utilised a 'strapdown' gravity approach based around an iMar Inertial Navigation System (INS) (Becker et al., 2015; Wei and Schwarz, 1998), resulting in data with an internal error from crossover analysis of 1.56 mGal and resolving wavelengths down to ~5 km. Airborne data were restricted to lines flown at &lt;1500 m above the surface with over 95% of the data collected at 450 m +/-200 m above the surface. Upward and downward continuation of the gravity data to a common altitude was therefore neglected as continuation by ~200 m will have little impact on the amplitude of the gravity anomalies (~1 mGal) given the ~1000 m range to the key bathymetric sources. Marine gravity data from cruise NBP19-02 matched the pattern of the airborne anomalies, but was offset by 7.14 mGal above the airborne data. This shift of 7.14 mGal was removed from the marine line data as a single uniform (DC) value. All line data were then merged into a single database, interpolated onto a 1 km mesh raster and filtered with a 5 km low pass filter removing residual line to line noise. The constraining topographic observations onshore were taken from OIB line radar data (Paden et al., 2010, updated 2018), augmented with new depth sounding radar collected along with the gravity data during the ITGC campaign. This new bed elevation data was collected using a 600-900 MHz accumulation radar provided by the Center for Remote Sensing of Ice Sheets (CReSIS). Bed elevations were picked from SAR processed radargrams in a semi-automated fashion. Visual inspection revealed a few incorrect onshore bed picks in the OIB dataset on Bear Island, which gave bed elevations above the highly accurate REMA surface digital elevation model (DEM) (Howat et al., 2019). These points were deleted from the integrated line bed elevation dataset. The line bed elevation data were corrected to the GL04c Geoid (Forste et al., 2008), and the data interpolated onto a 1 km mesh raster. This gridded dataset was carefully masked to remove regions which are now covered by the floating ice shelf based on the most up to date grounding lines (Milillo et al., 2019; Rignot et al., 2014). Bed elevation values over local sub-shelf pinning points were also excluded. Beyond the ice shelves we took the values constrained by a new compilation of shipborne multibeam swath bathymetric data (Hogan et al., 2020), which was down sampled to 1 km mesh raster for this study. To calculate the sub-ice-shelf bathymetry we applied an updated version of the two-step topographic shift method (Hodgson et al., 2019). In the first step the "initial bathymetric estimate" is calculated by converting the free-air gravity anomalies to equivalent variation in topography using an iterative forward modelling approach based on a Gauss-Legendre Quadrature gravity model of the theoretical bathymetric surface. An observation altitude of 500 m was assumed, and a density contrast of -1642 kgm-3 was imposed, equivalent to the contrast between water (1028 kgm-3) and rock (2670 kgm-3). To initiate the gravity inversion, we converted the free-air anomaly to equivalent topography using the Bouguer slab formula. The bathymetric surface was then iteratively adjusted to reduce the model/observed residual. After two stages of iteration the modelled and observed gravity field had converged to better than +/- 1 mGal across most of the survey area. Topography predicted to be above the observation surface was truncated, and the model did not converge in these regions. In the second step, the algorithm corrects for differences between the initial bathymetric estimate and topographic observations. The calculated difference between the observed and initial bathymetric estimate reflects an integration of all geological factors, including long wavelength regional variations such as crustal thickness and more local factors such as sedimentary basins or intrusions. We assume that the calculated geologically-derived errors vary smoothly away from the constraining points and interpolate the difference field across the study area. In regions with control data the difference grid was taken directly from the difference values. Away from the control data the difference grid was interpolated using a tensioned spline (T=0.25) after a 5 km pre-filter was applied to the difference values. The transition between these two regions was blended using a weighted mean varying from zero to one over a 5km Gaussian smoothed transition zone around the observed data. The blended difference grid was subtracted from the initial bathymetric estimate to produce the final topographic estimate.
description: Input data compilations were generated and quality checked in Geosoft Oasis Montage software package. Subsequent data processing and recovery of bathymetry used a Gauss-Legendre Quadrature gravity modelling programme (von Frese et al., 1981) coupled with basic Generic Mapping Tools (GMT V5) scripts implemented in a Linux environment.
description: Comparison with an independent swath bathymetric dataset indicates errors in the gravity derived bathymetry have a standard deviation of ~100 m, which we take to be representative of the error for this dataset. However, two important notes are: Firstly, away from constraining swath or radar data un-resolved geological features could lead to larger errors. Secondly, the gravity data only resolves features with a wavelength of 5 km or more. Smaller features may be present, but will not be reliably imaged.