Recent progress on combining geomorphological and geochronological data with ice 1 sheet modelling, demonstrated using the last British-Irish Ice Sheet.

• Published in: Journal of Quaternary Science
• Main Authors: Ely, J.C., Clark, C.D., Hindmarsh, R.C.A, Hughes, A.L.C, Greenwood, S.L., Bradley, S.L., Gasson, E., Gregoire, L., Gandy, N., Stokes, C.R., Small, D.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: John Wiley 2021
• Subjects: Endeavour; Ice Sheet
• Online Access: http://dro.dur.ac.uk/27891/; http://dro.dur.ac.uk/27891/1/27891.pdf; https://doi.org/10.1002/jqs.3098

Palaeo‐ice sheets are important analogues for understanding contemporary ice sheets, offering a record of ice sheet behaviour that spans millennia. There are two main approaches to reconstructing palaeo‐ice sheets. Empirical reconstructions use the available glacial geological and chronological evidence to estimate ice sheet extent and dynamics but lack direct consideration of ice physics. In contrast, numerically modelled simulations implement ice physics, but often lack direct quantitative comparison with empirical evidence. Despite being long identified as a fruitful scientific endeavour, few ice sheet reconstructions attempt to reconcile the empirical and model‐based approaches. To achieve this goal, model‐data comparison procedures are required. Here, we compare three numerically modelled simulations of the former British–Irish Ice Sheet with the following lines of evidence: (a) position and shape of former margin positions, recorded by moraines; (b) former ice‐flow direction and flow‐switching, recorded by flowsets of subglacial bedforms; and (c) the timing of ice‐free conditions, recorded by geochronological data. These model–data comparisons provide a useful framework for quantifying the degree of fit between numerical model simulations and empirical constraints. Such tools are vital for reconciling numerical modelling and empirical evidence, the combination of which will lead to more robust palaeo‐ice sheet reconstructions with greater explicative and ultimately predictive power.