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Forecasting El Niño and La Niña events using decision tree classifier

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Abstract

The El Niño-Southern Oscillation (ENSO) phenomenon affects the global climate by changing temperature and precipitation patterns mainly in tropical climatic regions and median latitudes. Such event strongly influences agricultural activities and crop yields. The Niño Oceanic Index (ONI) of the US National Oceanic and Atmospheric Administration (NOAA) describes and monitors ENSO intensity from ocean temperature measurements. When ONI in the Niño 3.4 region was + 0.5 °C above normal or − 0.5 °C below normal for 5 consecutive 3-month running averages, El Niño (EN) or La Niña (LN) events, respectively, were established. The prediction of ENSO events is made by modeling at major global weather centers by atmosphere–ocean coupling models; however, no articles were found using decision tree classifier (DTC) for ENSO forecasting purposes. This modeling approach requires much less computational time and capacity. Furthermore, DTC can be sufficiently accurate for agricultural purposes. Thus, the objective of this research was to forecast as early as possible the El Niño and La Niña yearly events using a DTC technique from ONI data from 1950 to 2020. We used as input variables for DTC quarterly ONI values from 15 quarters prior the data of forecasting. The DTC showed an accuracy of 89%, 84%, and 78% to predict La Niña, El Niño, and neutral years, respectively, without training period. For validation, the accuracy was 100%, 79%, and 79% for La Niña, El Niño, and neutral years, respectively. The selected ONI quarters were July–August-September, January–February-March, and February–March-April of the previous year and January–February-March of the current year, allowing an 8-month advance forecast with an average accuracy of 78% (validation).

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Fig. 1

source NOAA) (the colors represent the ocean temperature, the redder the hotter). B Location of the region called Niño 3.4 where the temperature sensors are located to estimate the Niño Ocean Index (ONI)

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Fig. 4

source NOAA). A year is classified as La Niña when the ONI ≤  − 0.5 and El Niño when the ONI ≥  + 0.5, for 5 consecutive 3-month running averages. The vertical bars indicate the standard deviation of quarterly ONI per year

Fig. 5

source NOAA). A quarter is classified as La Niña when the ONI of that period ≤  − 0.5. When the quarterly ONI value ≥ 0.5, the quarter is classified as El Niño. Neutral quarters occur when − 0.5 ≤ average ONI ≤ 0.5. The vertical bars indicate the standard deviation of ONI per year. The letters indicate the quarter, for example, DJF, December, January, and February; JFM, January, February, and March, following this way

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The data/ material is opened.

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The software used was python, and scripts are available.

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Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior — Brasil (CAPES) — Finance Code 001.

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Authors and Affiliations

  1. Department of Mathematical Sciences and Engineering, UNESP–São Paulo State University, Jaboticabal, SP, 14884-900, Brazil

    Karita Almeida Silva, Glauco de Souza Rolim & Lucas Eduardo de Oliveira Aparecido

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  1. Karita Almeida Silva
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  2. Glauco de Souza Rolim
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  3. Lucas Eduardo de Oliveira Aparecido
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Contributions

KAS: formal analysis, investigation, data curation, writing — original draft, writing — review and editing, and visualization. GdSR: conceptualization, methodology, supervision, and project administration. LEdOA: writing — review and editing.

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Correspondence to Lucas Eduardo de Oliveira Aparecido.

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Appendices

Appendices

Table 2

Table 2 Confusion matrix of the analysis of decision trees in the period of training and testing for prediction of events of El Niño (EN), La Niña (LN), and neutral years (NE) Tab
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Table 3

Table 3 Confusion matrix of the analysis of decision trees in the period of variation and testing for prediction of events of El Niño (EN), La Niña (LN), and neutral years (NE)
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Silva, K.A., de Souza Rolim, G. & de Oliveira Aparecido, L.E. Forecasting El Niño and La Niña events using decision tree classifier. Theor Appl Climatol 148, 1279–1288 (2022). https://doi.org/10.1007/s00704-022-03999-5

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