ENSO Dynamics, Trends, and Prediction Using Machine Learning

El Niño–Southern Oscillation (ENSO) has global effects on the hydrological cycle, agriculture, ecosystems, health, and society. We present a novel nonhomogeneous hidden Markov model (NHMM) for studying the underlying dynamics of sea surface temperature anomalies (SSTA) over the region 15°N–15°S, 150...

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Veröffentlicht in:Weather and forecasting 2020-10, Vol.35 (5), p.2061-2081
Hauptverfasser: Rojo Hernández, Julián David, Mesa, Óscar José, Lall, Upmanu
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Mesa, Óscar José
Lall, Upmanu
description El Niño–Southern Oscillation (ENSO) has global effects on the hydrological cycle, agriculture, ecosystems, health, and society. We present a novel nonhomogeneous hidden Markov model (NHMM) for studying the underlying dynamics of sea surface temperature anomalies (SSTA) over the region 15°N–15°S, 150°E–80°W from January 1856 to December 2019, using the monthly SSTA data from the Kaplan extended SST v2 product. This nonparametric machine learning scheme dynamically simulates and predicts the spatiotemporal evolution of ENSO patterns, including their asymmetry, long-term trends, persistence, and seasonal evolution. The model identifies five hidden states whose spatial SSTA patterns are similar to the so-called ENSO flavors in the literature. From the fitted NHMM, the model shows that there are systematic trends in the frequency and persistence of the regimes over the last 160 years that may be related to changes in the mean state of basin temperature and/or global warming. We evaluated the ability of NHMM to make out-of-sample probabilistic predictions of the spatial structure of temperature anomalies for the period 1995–2016 using a training period from January 1856 to December 1994. The results show that NHMMs can simulate the behavior of the Niño-3.4 and Niño-1.2 regions quite well. The NHMM results over this period are comparable or superior to the commonly available ENSO prediction models, with the additional advantage of directly providing insights as to the space patterns, seasonal, and longer-term trends of the SSTA in the equatorial Pacific region.
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source American Meteorological Society; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection
subjects Agricultural ecosystems
Agriculture
Anomalies
Climate change
Dynamics
El Nino
El Nino phenomena
El Nino-Southern Oscillation event
Equatorial regions
Evolution
Global warming
Hydrologic cycle
Hydrological cycle
Hydrology
Learning algorithms
Learning behaviour
Machine learning
Markov chains
Prediction models
Sea surface
Sea surface temperature
Sea surface temperature anomalies
Southern Oscillation
Surface temperature
Temperature anomalies
Training
Trends
title ENSO Dynamics, Trends, and Prediction Using Machine Learning
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