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In polar ice sheets, the average grain size varies with depth. Ice grain size increases due to several factors including ice temperature and impurity content, which in turn varies with climate. The effect of impurities on grain growth is thought to be crucial but has never been observed experimentally. Using a methodology recently developed at Royal Holloway University of London, in situ chemical analysis of frozen ice at sub-ppm concentrations with unprecedented spatial resolution (~150 μm) is achievable using ultraviolet laser ablation inductively coupled plasma mass spectrometry (UV-LA-ICPMS) featuring a two-volume cryo-LA-cell. Following surface cleaning with a custom-built vice equipped with a ceramic blade, NGRIP ice slabs (~86 ka before AD 2000) have been analysed using a series of one-dimensional profiles and two-dimensional maps of laser spots at a resolution of 200–300 μm. Results demonstrate that cation impurities are not uniformly distributed in ice layers and show significant variations in concentration on a sub-millimetre scale. Furthermore, a different pattern of elemental distribution between clear ice and layers enriched in impurities (cloudy bands) has been identified: while concentration differences for cloudy bands are not resolvable between boundaries and inner grain domains, within clear ice, grain boundaries and junctions are significantly (up to 100 times) impurity-enriched relative to corresponding grain interiors.
Stratigraphic dating of ice cores by identification and counting of annual cycles in, for example, chemical measurements requires skill and experience. the work presented here investigates a method of data enhancement which is a first step towards an automated and more objective method of annual-layer counting. the method of dynamical decorrelation is briefly introduced and is applied to data from Site D and NorthGRIP in central Greenland. With this method the measured data series are decomposed into a number of independent source series, one of which exhibits a more pronounced annual variation than the input data themselves. the annual variation is more regular in that (1) some double and triple peaks in the measured series are replaced by single peaks in the extracted signal, and (2) the resulting annual peaks have a much more uniform height. A simple method of determining the number of annual peaks in a series is set up. Using this method, it is shown that it is easier to determine the number of annual peaks in the series produced by dynamical decorrelation than in the original data series. Dynamical decorrelation may thus be used to improve data series prior to dating.
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