Relating West Antarctic Ice Cores to Climate with Artificial Neural Networks
This award provides three years of support to use a broad, adaptable, multi-parameter approach, using a range of techniques including artificial neural networks to seek the relations between meteorological conditions and the snow pit and ice core records they produce. Multi-parameter, high resolution, ice core data already in hand or now being collected reflect snow accumulation, atmospheric chemistry, isotopic fractionation, and other processes, often with subannual resolution. The West Antarctic sites from which such data are available will be used as starting points for back-trajectory analyses in reanalysis data products to determine the meteorological conditions feeding the data stream. The artificial neural nets will then be used to look for optimal relations between these meteorological conditions and their products. Previous work has demonstrated the value of reanalysis products in determining snow accumulation, of back trajectory analyses in understanding glaciochemistry, and of artificial neural nets in linking meteorological conditions and their products. Preliminary work shows that neural nets are successful in downscaling from reanalysis products to automatic weather station data in West Antarctica, enabling interpolation of site-specific data to improve understanding of recent changes in West Antarctic climate.
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