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Markovian descriptors based stochastic analysis of large-scale climate indices

Abstract

The investigation of the interrelationships among different oceanic and atmospheric circulation patterns is crucial for future climate projections in the current century. This paper presents the transition matrix approach of the stochastic Markov chain process to investigate the state/event based relationship between the new index of the Interdecadal Pacific Oscillation (IPO) named as the IPO Tripole index (TPI) and different sea surface temperature anomalies (SSTA) based El Nino-Southern Oscillation (ENSO) indices such as Niño 1.2, Niño 3, Niño 3.4, Niño 4 and the Multivariate ENSO Index (MEI) for the period of 120 years (1900–2019) respectively. Several Markovian descriptors like state dependency, temporal stationarity, expected number of state visits and entropy are derived from the estimated transition matrix. These descriptors are helpful in establishing the validity of Markov chain method and useful to characterize the dynamical properties of a time series like persistence, randomness and behaviour of cycles. Through the Markov chain analysis and by derived descriptors, this study finds similar self-communication (periodic) pattern between the transition states, resemblance in expected number of visits from one transition state to another, asymmetric and truncated cyclic nature of the data sequence and the existence of randomness in the transition states. Finally, a strong 2-dimensional correlation values endorses the existence of strong relations between selected indices datasets. This analysis approach may be helpful in understanding the role of the IPO and ENSO in modulating future climate variability and to formulate effective predictive models at the climatic state.

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Data availability

The datasets generated and/or analysed during the current study are freely available at the open data web portal of National Oceanic and Atmospheric Administration (NOAA) (https://psl.noaa.gov/gcos_wgsp/Timeseries/).

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Acknowledgements

The authors are thankful to the National Oceanic and Atmospheric Administration (NOAA) for providing respective datasets in the public domain.

Funding

The authors received no specific funding for this work.

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AI Conceptualization, Investigation, Methodology, Writing-original draft TAS Proof read and improved.

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Correspondence to Asif Iqbal.

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Iqbal, A., Siddiqi, T.A. Markovian descriptors based stochastic analysis of large-scale climate indices. Stoch Environ Res Risk Assess (2021). https://doi.org/10.1007/s00477-021-02108-8

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Keywords

  • Markov chain
  • Transition matrix
  • Markovian descriptors
  • Interdecadal pacific oscillation (IPO)
  • El Niño-southern oscillation (ENSO)

Mathematics Subject Classfication

  • 60H30
  • 60J10
  • 28D20
  • 6A08