| Summary: | Much attention on the extent, and changes therein, of Arctic sea ice focuses on areal coverage. Equally if not more important to consider is the thickness of the ice, which varies with age and also due to movement of the ice. Thickness is geometrically rough with ridges, rubble fields, and open-water leads. These features are asymmetrically shaped from centimeters to meters in one horizontal direction and meters to kilometers along the other. Instruments used to sense ice thickness typically have wide footprints that alias these rough features into smoother flatter features. Accurate thickness distribution of these deformed areas is needed to reduce uncertainties in global thickness data archives. Such results lead to higher accuracy in regional and global sea ice volume estimates. High precision and consistency from a single instrument cannot quantify the impact of aliasing. The sea ice community currently seeks an integrated- instrument approach to measure sea ice thickness from its components of draft, freeboard, and surface elevation (including snow loads) and thickness archives are being developed. This project would address the central question, "What is the impact of spatial aliasing when measuring sea ice thickness, its distribution, and resulting volume?" The approach is to work with datasets that include measurements made by two or more instruments with different footprints in the same location, based on a recently discovered relationship between the roughness of 5m and 40m footprints stemming from one field experiment. The investigator proposes a generalized solution to track all types of sea ice thickness measurements as a function of instrument footprint size and shape. The investigator will isolate climate data records containing coincident sea ice thickness measurements from instruments of different footprints to demonstrate the utility of a general solution to track a phenomenon called "Resolution Error". Results will be evaluated through a power law which can easily be reproduced with an explicit and simple algorithm so that anyone with a structured programming language can examine the degree of resolution error between two data sets. The main scientific contribution is an improved metadata relationship to characterize properties that contribute to thickness uncertainties in climate data archive records as a function of scale. A Ph.D. thesis will advance the aliasing hypothesis using a full physics 3D electromagnetic induction model as a guide to develop a new calibration technique for ground based electromagnetic (EM) measurements. The bonus is the calculation of a bulk material conductivity for local sea ice and sea water as a geophysical representation of the calibration coefficients. The broader impacts of the work include a simple Resolution Error tracking algorithm to improve thickness archive data integration best practices and the training of a Ph.D. student.
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