Skip to main content
Log in

Detecting decadal changes in ENSO using neural networks

Climate Dynamics Aims and scope Submit manuscript

Abstract

The present manuscript analyzes monthly equatorial Pacific indices by using a specific neural algorithm, the so-called “Self-Organizing Maps” (SOMs). The main result is a change found in the nature of the transitions between cold to warm and warm to cold extreme events from 1950 to present, around the late 1970s. SOM is an unsupervised clustering technique which allows one to reduce high-dimensional data space (in this case, three indices over 636 months) in terms of a smaller set of three-dimensional reference vectors (100) characterizing pertinent situations. These reference vectors, which are displayed on a two-dimension map, are closely related by a topological relationship leading us to discriminate La Niña conditions from the opposite El Niño conditions. In a second step, a Hierarchical Agglomerative Clustering (HAC) method is used to further group the reference vectors into a small number of clusters (12) whose spatial and temporal characteristics can be analyzed and interpreted in terms of physical parameters. Schematically, these 12 clusters can be divided into two “warm” clusters, six “neutral” or “transition” clusters and four “cold” clusters. In each particular group (warm, neutral, cold), the clusters mainly differ from each other by the amplitude of the anomalies, their spatial patterns and their temporal variability. Some clusters are found to be strongly linked to the boreal spring period, while others have barely any records during that season. Other clusters are associated with records mainly observed either prior to or after 1980. This suggests that the method is able to identify changes in the variability of the tropical Pacific basin observed on decadal time scales (1976 climate shift in our case). Each monthly record can be summarized by the cluster to which it belongs. The temporal evolution of this value during extreme ENSO events shows similar patterns (persistence in specific clusters and transition between groups of clusters) associated with comparable El Niño or La Niña events. The methodology described in the present study (SOM plus HAC) is suggested to be useful both for seasonal ENSO predictability and for the detection of decadal changes in ENSO behavior.

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

Access this article

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

References

  • Basnett, T, Parker D (1997) Development of the global mean sea level pressure data set GMSLP2. Climate Research Technical Note, 79, Hadley Centre, Met Office, FitzRoy Rd, Exeter

  • Glantz MH (1990) ENSO teleconnections linking worldwide climate anomalies: scientific basis and societal impact. Cambridge University Press, New York

    Google Scholar 

  • Guilderson TP, Schrag DP (1998) Abrupt shift in subsurface temperatures in the tropical Pacific associated with changes in El Niño. Science 281:240–243

    Article  Google Scholar 

  • Gutiérrez JM, Cano R, Cofiño AS, Sordo C (2005) Analysis and downscaling multi-model seasonal forecasts in Peru using self-organizing maps. Tellus A 57(3):435

    Article  Google Scholar 

  • Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Englewood Cliffs, Advanced Reference Series, 320 pp

  • Kaplan A, Cane M, Kushnir Y, Clement A, Blumenthal M, Rajagopalan B (1998) Analyses of global sea surface temperature 1856–1991. J Geophys Res 103(18):18,567–18,589

    Google Scholar 

  • Kaski S, Kanga J, Kohonen T (1998) Bibliography of self-organizing map (SOM) papers: 1981–1997. Neural Comput Surv 1:102–350

    Google Scholar 

  • Kohonen T (1984) Self organization and associative memory, 2nd edn. Springer, Berlin Heidelberg New York, 312 pp

  • Liu Y, Weisberg RH (2005) Patterns of ocean current variability on the West Florida Shelf using the self-organizing map. J Geophys Res 110:C06003. DOI:10.1029/2004JC002786

    Google Scholar 

  • McPhaden MJ (1999) Genesis and evolution of the 1997–1998 El Niño. Science 283:950–954

    Article  Google Scholar 

  • McPhaden MJ (2004) Evolution of the 2002/03 El Niño. Bull Am Meteorol Soc 85(5):677–695

    Google Scholar 

  • Nitta T, Yamada S (1989) Recent warming of tropical sea surface temperature and its relationship to the Northern hemisphere circulation. Met Soc Jap 67(3):375–383

    Google Scholar 

  • Oja M, Kaski S, Kohonen T (2002) Bibliography of self-organizing map (SOM) papers: 1998–2001 Addendum. Neural Comput Surv 3:1–156

    Google Scholar 

  • Penland C, Sardeshmukh PD (1995) The optimal growth of tropical sea surface temperature anomalies. J Clim 8:1999–2024

    Article  Google Scholar 

  • Picaut J, Ioualalen M, Menkes C, Delcroix T, McPhaden MJ (1996) Mechanism of the zonal displacements of the Pacific warm pool: Implications for ENSO. Science 274:1486–1489

    Article  Google Scholar 

  • Rasmusson EM, Carpenter TH (1982) Variations in tropical sea surface temperature and surface wind fields associated with the southern oscillation/El Niño. Mon Wea Rev 110(5):354–384

    Article  Google Scholar 

  • Rayner NA, Horton EB, Parker DE, Folland CK, Hackett RB (1996) Version 2.2 of the global sea-ice and sea surface temperature data set, 1903–1994. CRTN 74, Available from Hadley Centre, Met Office, Bracknell, UK

  • Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Row-ell DP, Kent EC, Kaplan A (2003) Global analysis of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geoph Res 108(D14):4407. DOI:10.1029/2002JD002670

    Google Scholar 

  • Richardson AJ, Risien C, Shillington FA (2003) Using self-organizing maps to identify patterns in satellite imagery. Progr Oceanogr 59:223–239

    Article  Google Scholar 

  • Ropelewski CF, Halpert MS (1987) Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation (ENSO). Mon Wea Rev 115:1606–1626

    Article  Google Scholar 

  • Smith TM, Reynolds RW (2003) Extended reconstruction of global sea surface temperatures based on COADS data (1854–1997). J Clim 16:1495–1510

    Article  Google Scholar 

  • Stephens C, Levitus S, Antonov J, Boyer TP (2001) On the Pacific Ocean regime shift. Geophys Res Lett 28(19):3721–3724

    Article  Google Scholar 

  • Sun DZ (2003) A possible effect of an increase in the warm-pool SST on the magnitude of El Niño warming. J Clim 16(2):185–205

    Article  Google Scholar 

  • Trenberth KE, Stepaniak DP (2001) Indices of El Niño Evolution. J Clim 14(8):1697–1701

    Article  Google Scholar 

  • Wang B (1995) Interdecadal changes in El Niño onset in the last four decades. J Clim 8:267–285

    Article  Google Scholar 

  • Wang B, Weisberg RH (2000) The 1997–98 El Niño relative to previous El Niño events. J Clim 13(2):488–501

    Article  Google Scholar 

  • Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236–244

    Article  Google Scholar 

  • White WB, Pazan SE, Withee GW, Noe C (1988) Data Analysis (JEDA) Center for the scientific quality control of upper ocean thermal data in support of TOGA and WOCE. Eos Trans Am Geophys Union 69:122–123

    Article  Google Scholar 

  • Wyrtki K (1979) The response of sea surface topography to the 1976 El Niño. J Phys Oceanogr 9(6):1223–1231

    Article  Google Scholar 

  • Xu J, Chan JCL (2001) The role of the Asian-Australian monsoon system in the onset time of El Niño events. J Clim 14:418–433

    Article  Google Scholar 

  • Zhang J, Wallace JM, Battisti D (1997) ENSO-like interdecadal variability: 1900–93. J Clim 10:1004–1020

    Article  Google Scholar 

Download references

Acknowledgments

We wish to thank the Institut de Recherche pour le Développement (IRD), the Institut Pierre-Simon-Laplace (IPSL), the Centre National de la Recherche Scientifique (CNRS; Programme ATIP-2002) for their financial support crucial in the development of the author’s collaboration. We are grateful to the “Universidad de Buenos Aires” and the “Departamento de Ciencias de la Atmósfera y los Océanos” for welcoming Jean-Philippe Boulanger and Julie Leloup. We are also grateful to the Hadley Centre for providing the HadISST data set. We also want to thank Christophe Menkes, Sean Kennan, Matthieu Lengaigne and Pascal Terray for stimulating discussions. We are thankful to Michel Crépon for his advices and to Keith Rodgers for computed the D20 data in an easy form. We thank the three anonymous reviewers for their constructive comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Julie A. Leloup.

Appendix

Appendix

In this paper, the SOM algorithm is used and presented below.

The SOM is a two-layers neural networks, first described by Kohonen (1984), that allows one to project a high dimensional data space onto a reduced and ordered space. The input layer is the original data set, denoted D (the three indices Niño 3.4, Niño 1+2 and Longi28 in the present study). The output layer is a topological map, denoted M, composed of a set of neurons (a 10 × 10 grid here). The map is usually a regular lattice in which one node is a neuron. Each neuron c of M is defined by its position on the map and fully connected to the input layer through its weight vector w c , whose dimension is the dimension of the observation. From this lattice structure, one can first define a distance δ(c,r) for each pair of neurons (c,r). Second, using this distance, a neighborhood of order d for each neuron c is defined as follows:

$$ V_{c}(d) = \{ r \in M,\delta (c,r) \leq d\} $$

The main property of the SOM is the conservation of a topological order on the map. This property ensures that two neighboring neurons c and r on the map, according to the δ-distance, will be associated to two close reference vectors w c and w r in D, according to the Euclidean distance. And conversely, two close observations x i and x j in D will be projected onto two close neurons w i and w j of M.

In practice, the SOM algorithm can be summarized as follows:

  1. 1.

    Initialization step (time step t = 0).

    1. (a)

      Choice of the structure of the map and its size (a regular 10 × 10 grid in our case).

    2. (b)

      Random initialization of the set W 0 of the p reference vectors (p = 10 × 10 in the present paper).

    3. (c)

      Definition of the initial neighborhood order d 0.

    4. (d)

      Definition of the initial learning rate ε(0).

    5. (e)

      Choice of the maximum number of iterations N iter .

  2. 2.

    Iterative phase (time step t + 1). The set of reference vectors W tof the previous time step is known.

    • Affectation. The observation x i is affected to its best-matching neuron using an affectation function χ, from D to C, based on the minimum Euclidean distance criterion:

      $$ \chi ({\mathbf {x}}_{i})= \arg\,{\mathop {{\text{min}}}\limits_j }\parallel{\mathbf {x}}_{i}-{\mathbf {w}}_{j}\parallel, $$

      j = 1:100 in this case (10 × 10 = 100 neurons on the map M).

    • Update. The new set of reference vectors W t+1 is computed. The weight vector of the best matching neuron in the defined neighborhood is updated using:

      $${{\mathbf {w}}_{j}}{\text{(}}t{\text{ + 1) = }}{{\mathbf {w}}_{j}}{\text{(}}t{\text{) + }}\epsilon {\text{(}}t{\text{) }}(x(t) - {{\mathbf{w}}_{j}}(t){\text{) }}\quad\forall j \in \it {{V}_{i}}{\it{(d}}_{t}), $$

      where ε(t) is the learning rate parameter.

    • The neighborhood order d t+1 and the learning parameter ε(t + 1) are updated and decrease at each time step.

  3. 3.

    The iterative phase is repeated until convergence of the parameters or until N iter is reached.

During the iterative phase, the grid changes, moving according to the evolution of the parameters. The conservation of the topological order provides an adaptation of the grid to the studied data. At the end, each observation is associated to one neuron, and only one, via a weight vector. All this allows to consider the map M and the reference vectors W as a reduction of the initial data space D.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Leloup, J.A., Lachkar, Z., Boulanger, JP. et al. Detecting decadal changes in ENSO using neural networks. Clim Dyn 28, 147–162 (2007). https://doi.org/10.1007/s00382-006-0173-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-006-0173-1

Keywords

Navigation