Search for: All records

Realistically rough stochastic realizations of subglacial bed topography are crucial for improving our understanding of basal processes and quantifying uncertainty in sea level rise projections with respect to topographic uncertainty. This can be achieved with sequential Gaussian simulation (SGS), which is used to generate multiple nonunique realizations of geological phenomena that sample the uncertainty space. However, SGS is very CPU intensive, with a computational complexity of O(NkNk3), where NN is the number of grid cells to simulate, and kk is the number of neighboring points used for conditioning. This complexity makes SGS prohibitively time-consuming to implement at ice sheet scales or fine resolutions. To reduce the time cost, we implement and test a multiprocess version of SGS using Python’s multiprocessing module. By parallelizing the calculation of the weight parameters used in SGS, we achieve a speedup of 9.5 running on 16 processors for an NN of 128,097. This speedup—as well as the speedup from using multiple processors—increases with NN. This speed improvement makes SGS viable for large-scale topography mapping and ensemble ice sheet modeling. Additionally, we have made our code repository and user tutorials publicly available (GitHub and Zenodo) so that others can use our multiprocess implementation of SGS on different datasets. 

Abstract. The interpolation of geospatial phenomena is a common problem in Earth science applications that can be addressed with geostatistics, where spatial correlations are used to constrain interpolations. In certain applications, it can be particularly useful to a perform geostatistical simulation, which is used to generate multiple non-unique realizations that reproduce the variability in measurements and are constrained by observations. Despite the broad utility of this approach, there are few open-access geostatistical simulation software applications. To address this accessibility issue, we present GStatSim, a Python package for performing geostatistical interpolation and simulation. GStatSim is distinct from previous geostatistical tools in that it emphasizes accessibility for non-experts, geostatistical simulation, and applicability to remote sensing data sets. It includes tools for performing non-stationary simulations and interpolations with secondary constraints. This package is accompanied by a Jupyter Book with user tutorials and background information on different interpolation methods. These resources are intended to significantly lower the technological barrier to using geostatistics and encourage the use of geostatistics in a wider range of applications. We demonstrate the different functionalities of this tool for the interpolation of subglacial topography measurements in Greenland. 

Abstract. Radio-echo sounding (RES) has revealed an internal architecture within Antarctica’s ice sheets that records their depositional, deformational and melting histories. Crucially, spatially-widespread RES-imaged internal-reflecting horizons, tied to ice-core age-depth profiles, can be treated as isochrones that record the age-depth structure across the Antarctic ice sheets. These enable the reconstruction of past climate and ice-dynamical processes on large scales, which are complementary to but more spatially-extensive than commonly used proxy records across Antarctica. We review progress towards building a pan-Antarctic age-depth model from these data by first introducing the relevant RES datasets that have been acquired across Antarctica over the last six decades (focussing specifically on those that detected internal-reflecting horizons), and outlining the processing steps typically undertaken to visualise, trace and date (by intersection with ice cores, or modelling) the RES-imaged isochrones. We summarise the scientific applications to which Antarctica’s internal architecture has been applied to date and present a pathway to expanding Antarctic radiostratigraphy across the continent to provide a benchmark for a wider range of investigations: (1) Identification of optimal sites for retrieving new ice-core palaeoclimate records targeting different periods; (2) Reconstruction of surface mass balance on millennial or historical timescales; (3) Estimates of basal melting and geothermal heat flux from radiostratigraphy and comprehensively mapping basal-ice units, to complement inferences from other geophysical and geological methods; (4) Advancing knowledge of volcanic activity and fallout across Antarctica; (5) The refinement of numerical models that leverage radiostratigraphy to tune time-varying accumulation, basal melting and ice flow, firstly to reconstruct past behaviour, and then to reduce uncertainties in projecting future ice-sheet behaviour. 

Abstract The earliest airborne geophysical campaigns over Antarctica and Greenland in the 1960s and 1970s collected ice penetrating radar data on 35 mm optical film. Early subglacial topographic and englacial stratigraphic analyses of these data were foundational to the field of radioglaciology. Recent efforts to digitize and release these data have resulted in geometric and ice-thickness analysis that constrain subsurface change over multiple decades but stop short of radiometric interpretation. The primary challenge for radiometric analysis is the poorly-characterized compression applied to Z-scope records and the sparse sampling of A-scope records. Here, we demonstrate the information richness and radiometric interpretability of Z-scope records. Z-scope pixels have uncalibrated fast-time, slow-time, and intensity scales. We develop approaches for mapping each of these scales to physical units (microseconds, seconds, and signal to noise ratio). We then demonstrate the application of this calibration and analysis approach to a flight in the interior of East Antarctica with subglacial lakes and to a reflight of an East Antarctic ice shelf that was observed by both archival and modern radar. These results demonstrate the potential use of Z-scope signals to extend the baseline of radiometric observations of the subsurface by decades. 

Abstract Subglacial topography is an important feature in numerous ice-sheet analyses and can drive the routing of water at the bed. Bed topography is primarily measured with ice-penetrating radar. Significant gaps, however, remain in data coverage that require interpolation. Topographic interpolations are typically made with kriging, as well as with mass conservation, where ice flow dynamics are used to constrain bed geometry. However, these techniques generate bed topography that is unrealistically smooth at small scales, which biases subglacial water flowpath models and makes it difficult to rigorously quantify uncertainty in subglacial drainage patterns. To address this challenge, we adapt a geostatistical simulation method with probabilistic modeling to stochastically simulate bed topography such that the interpolated topography retains the spatial statistics of the ice-penetrating radar data. We use this method to simulate subglacial topography using mass conservation topography as a secondary constraint. We apply a water routing model to each of these realizations. Our results show that many of the flowpaths significantly change with each topographic realization, demonstrating that geostatistical simulation can be useful for assessing confidence in subglacial flowpaths. 

Abstract Airborne radio-echo sounding (RES) surveys are widely used to measure ice-sheet bed topography. Measuring bed topography as accurately and widely as possible is of critical importance to modelling ice dynamics and hence to constraining better future ice response to climate change. Measurement accuracy of RES surveys is influenced both by the geometry of bed topography and the survey design. Here we develop a novel approach for simulating RES surveys over glaciated terrain, to quantify the sensitivity of derived bed elevation to topographic geometry. Furthermore, we investigate how measurement errors influence the quantification of glacial valley geometry. We find a negative bias across RES measurements, where off-nadir return measurement error is typically −1.8 ± 11.6 m. Topographic highlands are under-measured an order of magnitude more than lowlands. Consequently, valley depth and cross-sectional area are largely under-estimated. While overall estimates of ice thickness are likely too high, we find large glacier valley cross-sectional area to be under-estimated by −2.8 ± 18.1%. Therefore, estimates of ice flux through large outlet glaciers are likely too low when this effect is not taken into account. Additionally, bed mismeasurements potentially impact our appreciation of outlet-glacier stability. 

We present here Bedmap3, the latest suite of gridded products describing surface elevation, ice-thickness and the seafloor and subglacial bed elevation of Antarctica south of 60degS. Bedmap3 incorporates and adds to all post-1950s datasets previously used for Bedmap1 and Bedmap2, including 84 new aero-geophysical surveys by 15 data providers, an additional 52 million data points and 1.9 million line-kilometres of measurement. This has filled notable gaps in East Antarctica, including the South Pole and Pensacola basin, Dronning Maud Land, Recovery Glacier and Dome Fuji, Princess Elizabeth Land, plus the Antarctic Peninsula, West Antarctic coastlines, and the Transantarctic Mountains. Our new Bedmap3/RINGS grounding line similarly consolidates multiple recent mappings into a single, spatially coherent feature. Combined with updated maps of surface topography, ice shelf thickness, rock outcrops and bathymetry, Bedmap3 reveals in much greater detail the subglacial landscape and distribution of Antarctica's ice, providing new opportunities to interpret continental-scale landscape evolution and to model in detail the past and future evolution of the Antarctic ice sheets. Sponsored by the Scientific Committee on Antarctic Research (SCAR), the Bedmap3 Action group aims to produce a new map and datasets of Antarctic ice thickness and bed topography for the international scientific community. The associated Bedmap datasets are listed here: https://www.bas.ac.uk/project/bedmap/#data 

Airborne radar sounding can measure conditions within and beneath polar ice sheets. In Antarctica, most digital radar-sounding data have been collected in the last 2 decades, limiting our ability to understand processes that govern longer-term ice-sheet behavior. Here, we demonstrate how analog radar data collected over 40 y ago in Antarctica can be combined with modern records to quantify multidecadal changes. Specifically, we digitize over 400,000 line kilometers of exploratory Antarctic radar data originally recorded on 35-mm optical film between 1971 and 1979. We leverage the increased geometric and radiometric resolution of our digitization process to show how these data can be used to identify and investigate hydrologic, geologic, and topographic features beneath and within the ice sheet. To highlight their scientific potential, we compare the digitized data with contemporary radar measurements to reveal that the remnant eastern ice shelf of Thwaites Glacier in West Antarctica had thinned between 10 and 33% between 1978 and 2009. We also release the collection of scanned radargrams in their entirety in a persistent public archive along with updated geolocation data for a subset of the data that reduces the mean positioning error from 5 to 2.5 km. Together, these data represent a unique and renewed extensive, multidecadal historical baseline, critical for observing and modeling ice-sheet change on societally relevant timescales. 

Abstract. One of the key components of this research has been the mapping of Antarctic bed topography and ice thickness parameters that are crucial for modelling ice flow and hence for predicting future ice loss andthe ensuing sea level rise. Supported by the Scientific Committee on Antarctic Research (SCAR), the Bedmap3 Action Group aims not only to produce newgridded maps of ice thickness and bed topography for the internationalscientific community, but also to standardize and make available all thegeophysical survey data points used in producing the Bedmap griddedproducts. Here, we document the survey data used in the latest iteration,Bedmap3, incorporating and adding to all of the datasets previously used forBedmap1 and Bedmap2, including ice bed, surface and thickness point data from all Antarctic geophysical campaigns since the 1950s. More specifically,we describe the processes used to standardize and make these and futuresurveys and gridded datasets accessible under the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles. With the goals of making the gridding process reproducible and allowing scientists to re-use the data freely for their own analysis, we introduce the new SCAR Bedmap Data Portal(https://bedmap.scar.org, last access: 1 March 2023) created to provideunprecedented open access to these important datasets through a web-map interface. We believe that this data release will be a valuable asset to Antarctic research and will greatly extend the life cycle of the data heldwithin it. Data are available from the UK Polar Data Centre: https://data.bas.ac.uk (last access: 5 May 2023​​​​​​​). See the Data availability section for the complete list of datasets.