iceTEA : tools for plotting and analysing cosmogenic-nuclide surface-exposure data from former ice margins.

• Published in: Quaternary Geochronology
• Main Authors: Jones, R.S., Small, D., Cahill, N., Bentley, M.J., Whitehouse, P.L.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Elsevier 2019
• Subjects: Ice Sheet
• Online Access: http://dro.dur.ac.uk/27239/; http://dro.dur.ac.uk/27239/1/27239.pdf; https://doi.org/10.1016/j.quageo.2019.01.001

Cosmogenic-nuclide surface-exposure data provide important constraints on the thickness, extent and behaviour of ice masses in the geological past. A number of online calculators provide the cosmogenic nuclide community with a means of easily calculating surface-exposure ages. Here we provide a platform for plotting and analysing such data. This paper describes a suite of freely accessible numerical tools for visualising, evaluating and correcting surface-exposure data that are used to reconstruct past glacier and ice sheet geometries. iceTEA (Tools for Exposure Ages) is available as an online interface (http://ice-tea.org) and as MATLAB© code. There are 8 tools, which provide the following functionality: 1) calculate exposure ages from 10Be and 26Al data, 2) plot exposure ages as kernel density estimates and as a horizontal or vertical transect, 3) identify and remove outliers within a dataset, 4) plot nuclide concentrations on a two-isotope diagram and as a function of depth, 5) correct exposure ages for cover of the rock surface, 6) correct ages for changes in relative elevation through time, and estimate 7) average and 8) continuous rates of ice margin retreat or thinning. Three of the tools (1, 5 and 6) perform exposure age calculations, which are based on the framework of CRONUScalc. Results are available as printed text, tables and/or raster (.png) and vector (.eps) graphics files, depending on the tool. These tools are intended to enable users to evaluate complex exposure histories, assess the reliability of exposure ages, explore potential age corrections, and better analyse and understand spatial and temporal patterns within their data.