Impact of time-dependent data assimilation on ice flow model initialization: A case study of Kjer Glacier, Greenland

Abstract. Ice sheet models are often initialized with data assimilation of present-day conditions, in which unknown model parameters are estimated using the inverse method. While assimilation of snapshot observations has been widely used for regional and large scale ice sheet simulations, data assimilation based on time dependent data has recently started to emerge to constrain model parameters while capturing the transient evolution of the system. However, this method has been applied only to a few glaciers with fixed ice front positions, using spatially and temporally limited observations, and has not been applied to marine terminating glaciers of the Greenland ice sheet that have been retreating over the last 30 years. In this study, we assimilate time series of surface velocity into a model of Kjer glacier in West Greenland to better capture the observed acceleration over the past three decades. We compare snapshot and transient inverse methods and investigate the impact of initialization procedures on the parameters inferred, as well as model projections. We find that transient-calibrated simulations better capture past trends and better reproduce changes after the calibration period, even when a short period of observations is used. The results show the feasibility and clear benefits of a time-dependent data assimilation for initializing ice sheet models. This approach is now possible with the development of longer observational records, though it remains computationally challenging.