Abstract
Abstract. A novel method for automated annual layer counting in seasonally-resolved paleoclimate records has been developed. It relies on algorithms from the statistical framework of hidden Markov models (HMMs), which originally was developed for use in machine speech recognition. The strength of the layer detection algorithm lies in the way it is able to imitate the manual procedures for annual layer counting, while being based on statistical criteria for annual layer identification. The most likely positions of multiple layer boundaries in a section of ice core data are determined simultaneously, and a probabilistic uncertainty estimate of the resulting layer count is provided, ensuring an objective treatment of ambiguous layers in the data. Furthermore, multiple data series can be incorporated and used simultaneously. In this study, the automated layer counting algorithm has been applied to two ice core records from Greenland: one displaying a distinct annual signal and one which is more challenging. The algorithm shows high skill in reproducing the results from manual layer counts, and the resulting timescale compares well to absolute-dated volcanic marker horizons where these exist.
Highlights
An accurate chronology is of fundamental importance for the interpretation of a paleoclimatic record
With the increased resolution presently being achieved in many ice core measurements (e.g. Bigler et al, 2011), an annual signal is preserved in an increasing number of different data records and spanning much longer time intervals
When the conditions are fulfilled for annual layers in the ice core to have survived the archiving and measurement processes in stratigraphic order, annual layer counting represents the most accurate method to produce a chronology for the core
Summary
An accurate chronology is of fundamental importance for the interpretation of a paleoclimatic record. Many attempts have been made to develop automated methods of annual layer counting in paleoclimatic archives (McGwire et al, 2008; Rasmussen et al, 2002; Smith et al, 2009) These have generally had limited success where the annual signal in the data is inherently ambiguous, leaving manual layer counting to still be considered the most accurate. In comparison to these previous attempts, the method developed here is quite similar to the manual approach of layer counting; multiple layer boundaries in an entire data section are determined simultaneously, while allowing for a very flexible definition of an annual layer signature in the record. Due to the ability of the line-scan data to resolve annual layering in sections of thin annual layers, it has potential for being utilised to extend the GICC05 chronology further back in time
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