Skip to main content

Advertisement

SpringerLink
Log in

A Markov regime-switching framework to forecast El Niño Southern Oscillation patterns

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

The El Niño Southern Oscillation (ENSO) is an ocean–atmosphere phenomenon involving sustained sea surface temperature fluctuations in the Pacific Ocean, causing disruptions in the behavior of the ocean and atmosphere. We develop a Markov-switching autoregressive model to describe the Southern Oscillation Index (SOI), a variable that explains ENSO, using two autoregressive processes to describe the time evolution of SOI, each of which associated with a specific phase of ENSO. The switching between these two models is governed by a discrete-time Markov chain, with time-varying transition probabilities. Then, we extend the model using sinusoidal functions to forecast future values of SOI. The results can be used as a decision-making tool in the process of risk mitigation against weather- and climate-related disasters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Ailliot P, Monbet V (2012) Markov-Switching autoregressive models for wind time series. Environ Model Softw 30:92–101

    Article  Google Scholar 

  • Box GEP, Jenkins GM (1976) Time series analysis. Forecasting and control, Holden Day

    Google Scholar 

  • e Silva EGS, Legey L, e Silva EAS (2010) Forecasting oil price trends using wavelets and hidden Markov models. Energy Econ 32:1507–1519

    Article  Google Scholar 

  • Fallahi F, Rodriguez G (2014) Link between unemployment and crime in the US: a Markov-switching approach. Soc Sci Res 45:33–45

    Article  Google Scholar 

  • Filardo A (1994) Business-cycle phases and their transitional dynamics. J Bus Econ Stat 12:299–308

    Google Scholar 

  • Guo H, Brooks R, Shami R (2010) Detecting hot and cold cycles using a Markov regime switching model—evidence from the Chinese A-share IPO market. Int Rev Econ Finance 19:196–210

    Article  Google Scholar 

  • Hamilton J (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57:357–384

    Article  Google Scholar 

  • Hansen (1982) Large sample properties of generalized method of moments estimators. Econometrica 50:1029–1054

    Article  Google Scholar 

  • Hansen BE (1992) The likelihood ratio test under non-standard conditions. J Appl Econom 7:S61–S82

    Article  Google Scholar 

  • IRI (2008) ENSO Basics, http://iri.columbia.edu/climate/ENSO/background/basics.htm, June 2008

  • Kulkarni MK, Revadekar JV, Varikoden H (2013) About the variability in thunderstorms and rainfall activity over India and its association with El Niño and La Niña. Nat Hazards 69:2005–2019

    Article  Google Scholar 

  • Li LP, Zhang KM, Luo T (2013) An analysis of the drought and flood hazard characteristics and risks during the pre-rainy season in South China. Nat Hazards 71:1195–1213

    Article  Google Scholar 

  • Liu W, Chyi Y (2006) A Markov regime-switching model for the semiconductor industry cycles. Econ Model 23:569–578

    Article  Google Scholar 

  • Meehl G (1990) Development of global coupled ocean–atmosphere general circulation models. Clim Dyn 5:19–33

    Article  Google Scholar 

  • Meehl G (2000) Trends in extreme weather and climate events: issues related to modeling extremes in projections of future climate change. Bull Am Meteorol Soc 81:427–436

    Article  Google Scholar 

  • NCDC (2013) Climate information, http://www.ncdc.noaa.gov/climate-information, August 2013

  • NOAA (2010) ENSO, http://www.oar.noaa.gov/k12/html/elnino2.htm, April 2010

  • Poveda G, Alvarez D, Rueda O (2010) Hydro-climatic variability over the Andes of Colombia associated with ENSO: a review of climatic processes and their impact on one of the Earth’s most important biodiversity hotspots. Clim Dyn 36:2233–2249

    Article  Google Scholar 

  • Ubilava D, Helmers G (2013) Forecasting ENSO with a smooth transition autoregressive model. Environ Model Softw 40:181–190

    Article  Google Scholar 

  • Walid C, Chaker A, Masood O, Fry J (2011) Stock market volatility and exchange rates in emerging countries: a Markov-state switching approach. Emerg Mark Rev 12:272–292

    Article  Google Scholar 

  • Yuan C (2011) Forecasting exchange rates: the multi-state Markov-switching model with smoothing. Int Rev Econ Finance 20:342–362

    Article  Google Scholar 

  • Zhou X, Tang Y, Deng Z (2009) Assimilation of historical SST data for long-term ENSO retrospective forecasts. Ocean Model 30:143–154

    Article  Google Scholar 

  • Zucchini W, McDonald I (2009) Hidden Markov models for time series: an introduction. Chapman Hall/CRC, Boca Raton

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Ingeniería Industrial, Centro para la Optimización y la Probabilidad Aplicada (COPA), Universidad de los Andes, Bogotá, DC, Colombia

    Iván Cárdenas-Gallo & Raha Akhavan-Tabatabaei

  2. Risk and Reliability in Civil Engineering, Departamento de Ingeniería Civil, Universidad de los Andes, Bogotá, DC, Colombia

    Mauricio Sánchez-Silva

  3. LUNAM Université, Université de Nantes-Ecole Centrale Nantes, GeM, Institute for Research in Civil and Mechanical Engineering/Sea and Littoral Research Institute, CNRS UMR 6183/FR 3473, Nantes, France

    Emilio Bastidas-Arteaga

Authors
  1. Iván Cárdenas-Gallo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raha Akhavan-Tabatabaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mauricio Sánchez-Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Emilio Bastidas-Arteaga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raha Akhavan-Tabatabaei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cárdenas-Gallo, I., Akhavan-Tabatabaei, R., Sánchez-Silva, M. et al. A Markov regime-switching framework to forecast El Niño Southern Oscillation patterns. Nat Hazards 81, 829–843 (2016). https://doi.org/10.1007/s11069-015-2106-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-015-2106-y

Keywords

Search

Navigation