The role of seasonal sea ice formation at the poles is complex and closely linked to the Earth's climate. It is thought that the amount of sea ice can have a significant effect on the energies transferred between the atmosphere and the ocean. Understanding the seasonal sea ice process at the poles is therefore of great interest to scientists. Sea ice concentration datasets derived from Earth-orbiting satellites are readily available and contain observations that span several decades. These data, which are both spatial and temporal in nature, can be quite difficult to analyze. We present a spatial nearest-neighbor population model as a candidate for describing the sea ice process. The model is a non-homogeneous Markov process on a space of functions on a lattice, with transitions governed by a collection of rate functions. These rate functions give some insight into the long-term behaviour of the process and in turn can be linked to auxilliary variables. We will discuss various methods for estimating the model parameters. These methods are based on simulations and a finite difference approach using the infinitesimal generator of the process. This population dynamics setting has allowed us to link the spatio-temporal sea ice data of the Antarctic to earth's skin temperature.
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