  Arctic sea ice plays an important role in the global climate. Sea ice models
governed by physical equations have been used to simulate the state of the ice
including characteristics such as ice thickness, concentration, and motion.
More recent models also attempt to capture features such as fractures or leads
in the ice. These simulated features can be partially misaligned or misshapen
when compared to observational data, whether due to numerical approximation or
incomplete physics. In order to make realistic forecasts and improve
understanding of the underlying processes, it is necessary to calibrate the
numerical model to field data. Traditional calibration methods based on
generalized least-square metrics are flawed for linear features such as sea ice
cracks. We develop a statistical emulation and calibration framework that
accounts for feature misalignment and misshapenness, which involves optimally
aligning model output with observed features using cutting edge image
registration techniques. This work can also have application to other physical
models which produce coherent structures.
