Palaeo‐ice sheets leave behind a rich database regarding their past behaviour, recorded in the landscape in the form of glacial geomorphology. The most numerous landform created by these ice sheets are subglacial lineations, which generate snapshots of the direction of ice flow at fixed (yet typically unknown) points in time. Despite their relative density within the landform record, the information provided by subglacial lineations is currently underutilised in tests of numerical ice sheet models. To some extent, this is a consequence of ongoing debate regarding lineation formation, but predominantly, it reflects the lack of rigorous model‐data comparison techniques that would enable lineation information to be properly integrated. Here, we present the Likelihood of Accordant Lineations Analysis (LALA) tool. LALA provides a statistically rigorous measure of the log‐likelihood of a supplied ice sheet simulation through comparison of simulation output with both the location and direction of observed lineations. Given an ensemble of ice sheet simulations, LALA provides a formal, and statistically underpinned, quantitative assessment of each simulation's quality‐of‐fit to mapped lineations. This enables a comparison of each simulation's relative plausibility, including identification of the most likely ice sheet simulations amongst the ensemble. This is achieved by modelling lineation formation as a marked Poisson point process and comparison of observed to modelled flow directions using the von Mises distribution. LALA is flexible—users can adapt parameters to account for differing assumptions regarding lineation formation, and for variations in the level of precision required for differing model‐data comparison experiments. We provide guidelines and rationale for assigning parameter values, including an assessment of the variability between users when mapping lineations. Finally, we demonstrate the utility of LALA through application to an ensemble of simulations of the last British‐Irish Ice Sheet. This comparison highlights the benefits of LALA over previous tools and demonstrates some of the considerations of experimental design required when identifying the fit between ice sheet model simulations and the landform record.

<p>At the Last Glacial Maximum, the Eurasian Ice Sheet (EIS) was one of the largest ice masses, reaching an area of 5.5 Mkm<sup>2</sup> at its maximum. Recent advances in numerical ice sheet modelling hold significant promise for improving our understanding of ice sheet dynamics, but remain limited by the significant uncertainty as to the appropriate values for the various model input parameters. The EIS left behind a rich library of observational evidence, in the form of glacial landforms and sediments. Integrating this evidence with numerical ice sheet models allows inference on these key model parameters, leading to a better understanding of the behaviour of the EIS and a framework for advancing numerical ice sheet models. To quantify how successfully a particular model run matches the available data, model-data comparison tools are required. Here, we model the EIS using the Parallel Ice Sheet Model (PISM), a hybrid shallow-ice shallow shelf ice sheet model. We perform sensitivity analyses to reveal the most important parameters controlling the evolution of our modelled EIS. Results from this analysis allow us to reduce the parameter space required for a future ensemble experiment. This ensemble experiment will utilise novel model-data comparison tools which compare ice-free timings to geochronological evidence and modelled flow directions with drumlins. Unlike previous model-data comparison routines, our tools provide a more nuanced, and probabilistic, assessment of fit than a simple pass-fail. This offers significant benefits for future parameter selection.</p>