Work

Using network theory and machine learning to predict El Niño

Year: 2018

Type: article

Abstract: . The skill of current predictions of the warm phase of the El Niño Southern Oscillation (ENSO) reduces significantly beyond a lag of six months. In this paper, we aim to increase this prediction skill at lags up to one year. The new method to do so 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 topological properties of Climate Networks and are tested on both a Zebiak–Cane-type model and observations. For predictions up to six months ahead, the results of the hybrid model give a better skill than the CFSv2 ensemble prediction by the National Centers for Environmental Prediction (NCEP). Moreover, results for a twelve-month lead time prediction have a similar skill as the shorter lead time predictions.

Source: Earth System Dynamics

DOI: 10.5194/esd-9-969-2018

Authors: Peter D. Nooteboom, Qing Yi Feng, Cristóbal López, Emilio Hernández-García, Henk A. Dijkstra

Institutions: Utrecht University, Consejo Superior de Investigaciones Científicas, Institute for Cross-Disciplinary Physics and Complex Systems, Universitat de les Illes Balears

Language: English

Topic: Climate variability and models

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $1,585