A Data-based Low-dimensional Stochastic Model of the NGRIP delta-O18 with Built-in Delays

Dmitri A Kondrashov1, Honghu Liu1, Mickael Chekroun2, Anders Svensson3, Denis-Didier Rousseau4 and Michael Ghil1, (1)University of California Los Angeles, Atmos. Sci, Los Angeles, CA, United States, (2)University of California Los Angeles, Atmos. and Ocean. Sci., Los Angeles, CA, United States, (3)Centre for Ice and Climate, Copenhagen, Denmark, (4)CNRS, Paris Cedex 16, France
Abstract:
We used a high-resolution delta-O18 record from the North Greenland Ice Core Project (NGRIP) to derive a low-dimensional inverse stochastic model of this record. The model reproduces well the record’s visual features, its probability density function and its lag-correlation function. The model’s deterministic part needs to include explicitly several lags in order to achieve these properties of the delta-O18 variations. The relevance of the memory effects captured by these lagged dependencies, along with their physical interpretation, will be discussed from a theoretical perspective based on the Mori-Zwanzig formalism of statistical mechanics.    
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