Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 1.558
IF1.558
IF 5-year value: 1.475
IF 5-year
1.475
CiteScore value: 2.8
CiteScore
2.8
SNIP value: 0.921
SNIP0.921
IPP value: 1.56
IPP1.56
SJR value: 0.571
SJR0.571
Scimago H <br class='widget-line-break'>index value: 55
Scimago H
index
55
h5-index value: 22
h5-index22
Volume 10, issue 3
Nonlin. Processes Geophys., 10, 197–210, 2003
https://doi.org/10.5194/npg-10-197-2003
© Author(s) 2003. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Quantifying Predictability

Nonlin. Processes Geophys., 10, 197–210, 2003
https://doi.org/10.5194/npg-10-197-2003
© Author(s) 2003. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  30 Jun 2003

30 Jun 2003

Cyclic Markov chains with an application to an intermediate ENSO model

R. A. Pasmanter1 and A. Timmermann2 R. A. Pasmanter and A. Timmermann
  • 1KNMI, Postbus 201, 3730 AE De Bilt, the Netherlands
  • 2Institut für Meereskunde, Düsternbrooker Weg 20, D-24105 Kiel, Germany

Abstract. We develop the theory of cyclic Markov chains and apply it to the El Niño-Southern Oscillation (ENSO) predictability problem. At the core of Markov chain modelling is a partition of the state space such that the transition rates between different state space cells can be computed and used most efficiently. We apply a partition technique, which divides the state space into multidimensional cells containing an equal number of data points. This partition leads to mathematical properties of the transition matrices which can be exploited further such as to establish connections with the dynamical theory of unstable periodic orbits. We introduce the concept of most and least predictable states. The data basis of our analysis consists of a multicentury-long data set obtained from an intermediate coupled atmosphere-ocean model of the tropical Pacific. This cyclostationary Markov chain approach captures the spring barrier in ENSO predictability and gives insight also into the dependence of ENSO predictability on the climatic state.

Publications Copernicus
Download
Citation