We present a refined map of geothermal heat flow for Antarctica, Aq1, based on multiple observables. The map is generated using a similarity detection approach by attributing observables from geophysics and geology to a large number of high‐quality heat flow values (N = 5,792) from other continents. Observables from global, continental, and regional datasets for Antarctica are used with a weighting function that allows the degree of similarity to increase with proximity and how similar the observables are. The similarity detection parameters are optimized through cross correlation. For each grid cell in Antarctica, a weighted average heat flow value and uncertainty metrics are calculated. The Aq1 model provides higher spatial resolution in comparison to previous results. High heat flow is shown in the Thwaites Glacier region, with local values over 150 mW m−2. We also map elevated values over 80 mW m−2 in Palmer Land, Marie Byrd Land, Victoria Land and Queen Mary Land. Very low heat flow is shown in the interior of Wilkes Land and Coats Land, with values under 40 mW m−2. We anticipate that the new geothermal heat flow map, Aq1, and its uncertainty bounds will find extended use in providing boundary conditions for ice sheet modeling and understanding the interactions between the cryosphere and solid Earth. The computational framework and open architecture allow for the model to be reproduced, adapted and updated with additional data, or model subsets to be output at higher resolution for regional studies.

Beneath the ice of East Antarctica lies a continent that is likely to be as geologically complex as its neighbors in Gondwana. An improved model of the heterogeneous lithosphere is required to progress research on Antarctica's tectonic evolution and support interdisciplinary studies of cryosphere and solid Earth interaction. We make use of multiple data sets, which were updated following the field campaigns and compilations of the International Polar Year of 2007/2008. Seismic tomography results, gravity anomalies, and surface elevation are used in a novel method, which combines spatial multivariate data to map possible boundaries as projected likelihood functions. Six multivariate combinations are tested and compared with sparse geological observations in East Antarctica. The resulting lithospheric domain boundaries contribute to our understanding of the deep continental structure. New boundaries are suggested in the interior, and models agree with likely surface expressions of crustal tectonic boundaries exposed along the coast.

Researchers use 2D and 3D spatial models of multivariate data of differing resolutions and formats. It can be challenging to work with multiple datasets, and it is time consuming to set up a robust, performant grid to handle such spatial models. We share 'agrid', a Python module which provides a framework for containing multidimensional data and functionality to work with those data. The module provides methods for defining the grid, data import, visualisation, processing capability and export. To facilitate reproducibility, the grid can point to original data sources and provides support for structured metadata. The module is written in an intelligible high level programming language, and uses well documented libraries as numpy, xarray, dask and rasterio.

The timing of rapid glacier retreat and ice mass loss from key outlet basins in Antarctica constitutes the greatest uncertainty in estimates of future sea level rise (IPCC, 2021). The Aurora Subglacial Basin (ASB), East Antarctica, has significant potential to contribute to sea level rise (

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Researchers use 2D and 3D spatial models of multivariate data of differing resolutions and formats. It can be challenging to work with multiple datasets, and it is time consuming to set up a robust, performant, grid to handle such spatial models. We share a Python module that provides a framework for containing multidi- mensional data and functionality to work with those data. The module provides methods for defining the grid, data import, visualisation, processing capability and export. To facilitate reproducibility, the grid can point to original data sources and provides support for structured metadata. The module is written in an intelligible high level programming language, and uses well documented libraries as numpy, xarray, dask and rasterio.

Scientific visualization aims to present numerical values, or categorical information, in a way that enables the researcher to make an inference that furthers knowledge. Well-posed visualizations need to consider the characteristics of the data, the display environment, and human visual capacity. In the geosciences, visualizations are commonly applied to spatially varying continuous information or results. In this contribution we make use of a suite of newly written computer applications which enable spatially varying data to be displayed in a performant graphics environment. We present a comparison of color-mapping using illustrative color spaces (RGB, CIELAB). The interactive applications display the gradient paths through the chosen color spaces. This facilitates the creation of color-maps that accommodate the non-uniformity of human color perception, producing an image where genuine features are seen. We also take account of aspects of a dataset such as parameter uncertainty. For an illustrative case study using a seismic tomography result, we find that the use of RGB color-mapping can introduce non-linearities in the visualization, potentially leading to incorrect inference. Interpolation in CIELAB color space enables the creation of perceptually uniform linear gradients that match the underlying data, along with a simply computable metric for color difference, E. This color space assists accuracy and reproducibility of visualization results. Well-posed scientific visualization requires both "visual literacy" and "visual numeracy" on an equal footing with clearly written text. It is anticipated that this current work, with the included color-maps and software, will lead to wider usage of informed color-mapping in the geosciences.

The Antarctic lithosphere was built over billions of years (e.g., Boger, 2011;Harley et al., 2013), and it is increasingly clear that this long and complex lithospheric evolution both records and influences interactions with the oceans and cryosphere (e.g.,