Abstract
Abstract. The skill of current predictions of the warm phase of the El Niño Southern Oscillation (ENSO) reduces significantly beyond a lag time of 6 months. In this paper, we aim to increase this prediction skill at lag times of up to 1 year. The new method combines a classical autoregressive integrated moving average technique with a modern machine learning approach (through an artificial neural network). The attributes in such a neural network are derived from knowledge of physical processes and topological properties of climate networks, and they are tested using a Zebiak–Cane-type model and observations. For predictions up to 6 months ahead, the results of the hybrid model give a slightly better skill than the CFSv2 ensemble prediction by the National Centers for Environmental Prediction (NCEP). Interestingly, results for a 12-month lead time prediction have a similar skill as the shorter lead time predictions.
Highlights
Every 4 years, the sea surface temperature (SST) is higher than average in the eastern equatorial Pacific (Philander, 1990)
A successful attempt was made in this paper to use machine learning (ML) techniques in a hybrid model to improve the skill of El Niño predictions
Crucial for the success of this hybrid model is the choice of the attributes applied to the artificial neural network
Summary
Every 4 years, the sea surface temperature (SST) is higher than average in the eastern equatorial Pacific (Philander, 1990). As El Niño events cause enormous damage worldwide, skillful predictions, preferable for lead times up to 1 year, are highly desired Both statistical and dynamical models are used to predict ENSO (Chen et al, 2004; Yeh et al, 2009; Fedorov et al, 2003). Techniques will have to be applied in order to reduce the amount of input variables and select the important ones, to make the problem appropriate for the simpler ANN This reduction and selection problem can be tackled in many ways, which are crucial for the prediction. The attributes selected for observations are presented These attributes, among which there is a network variable, are applied in the hybrid prediction model, which discusses the skill of this model to predict El Niño.
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