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Abstract

Reliable El Nino Southern Oscillation (ENSO) prediction at seasonal-to-interannual lead times would be critical for different stakeholders to conduct suitable management. In recent years, new methods combining climate network analysis with El Nino prediction claim that they can predict El Nino up to 1 year in advance by overcoming the spring barrier problem (SPB). Usually this kind of method develops an index representing the relationship between different nodes in El Nino related basins, and the index crossing a certain threshold is taken as the warning of an El Nino event in the next few months. How well the prediction performs should be measured in order to estimate any improvements. However, the amount of El Nino recordings in the available data is limited, therefore it is difficult to validate whether these methods are truly predictive or their success is merely a result of chance. We propose a benchmarking method by surrogate data for a quantitative forecast validation for small data sets. We apply this method to a naive prediction of El Nino events based on the Oscillation Nino Index (ONI) time series, where we build a data-based prediction scheme using the index series itself as input. In order to assess the network-based El Nino prediction method, we reproduce two different climate network-based forecasts and apply our method to compare the prediction skill of all these. Our benchmark shows that using the ONI itself as input to the forecast does not work for moderate lead times, while at least one of the two climate network-based methods has predictive skill well above chance at lead times of about one year.