The use of RGPS kinematic data to estimate nonlinear sea ice motion

In current simulations of the interaction between sea ice and its environment, large significance is placed on the deformation of the sea ice. Sea ice deformation is an important process in determining the sea ice thickness distribution across a wide range of space and time scales. Changes in the sea ice thickness distribution affect energy and mass fluxes between the atmosphere and ocean and also the strength of the ice. While most current ice models assume linear variation in the ice motion field to calculate strain, deformation of sea ice occurs through the opening, closing and shearing of ice along discrete linear features. New numerical models are being developed which explicitly account for discontinuities in ice motion, and the need for requisite data sets for model validation has emerged. Multiple buoy data sets, as well as satellite data, have been used to examine the movement and deformation of sea ice. Generally it has been found that the ice motion field has been represented well by buoy data, as well as satellite data over a broad range of scales. However, the underlying deformation (spatial variation in displacement) as represented by different data sets may vary. For the work presented here, sea ice motion estimates from RGPS are decomposed into a smooth field with only linear variation and a perturbation. The perturbations are found to be large. We re-examine the magnitude of nonlinear motion as a function of spatial scale (gauge length), time period (season) and geographic location (near shore, away from shore) using ice kinematic data from RGPS. We also, discuss how to use these results as a guide in the development of field programs where the goal is the estimating ice deformation and how similar types of data analyses may be useful for the validation of sea ice models.