final_program_abstracts[1]

11 IMSC Session Program
Deriving dynamical models of glacial millennial-scale climate
variability from ice-core records: parameter estimation, model
performance and model selection
Monday - Parallel Session 3
Frank Kwasniok 1 and Gerrit Lohmann 2
1
University of Exeter, Exeter, UK
2
Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
Simple conceptual nonlinear dynamical models are derived from ice-core data, thus
integrating models and theories with palaeoclimatic records. The method is based on
parameter estimation using the unscented Kalman filter, a nonlinear extension of the
Kalman filter. Unlike the conventional linear Kalman filter and the widely used
extended Kalman filter, the unscented Kalman filter keeps the full system dynamics
rather than linearising it, leading to a superior treatment of nonlinearities. The
unscented Kalman filter truncates the filter probability density to a Gaussian in each
iteration by only propagating first and second moments but neglecting higher-order
moments. The method is applicable to both deterministic and stochastic models. It
offers a practical and computationally cheap alternative to more complete but also
considerably more cumbersome approaches like particle filters or Markov chain
Monte Carlo methods.
Different conceptual models for glacial millennial-scale climate transitions (the socalled
Dansgaard-Oeschger events) are considered and their parameters estimated
from a North Greenland ice-core record: (i) stochastically driven motion in a bistable
potential; (ii) a stochastic oscillator in a bistable potential; (iii) a stochastic van der
Pol-type oscillator. Model performance is evaluated against the ice-core data using
various statistical quantities. An approach to model selection is proposed based on
information-theoretic measures.
Abstracts 78