Strengthening of Indian Ocean teleconnections permits predictions of springtime rainfall in SE Australia

TL;DR

This study demonstrates that Indian Ocean sea surface temperatures and El Niño variations significantly enhance the predictability of springtime rainfall in southeastern Australia, especially when combined with deep learning models and forecast data.

Contribution

It introduces a deep learning approach that leverages Indian Ocean and El Niño data for accurate spring rainfall predictions in southeastern Australia, highlighting the importance of specific ocean regions.

Findings

Indian Ocean SSTs and El Niño improve rainfall prediction accuracy.

Deep learning models outperform traditional methods in hindcasting.

Forecast skill increases with the use of dynamical model forecasts.

Abstract

Rainy years and dry years in SE Australia are known to be correlated with sea surface temperatures in the specific areas of the Indian Ocean. While over the past 100 years the correlation had been both positive and negative, it significantly increased in strength since the beginning of the 21st century. Over this period, Indian Ocean sea surface temperatures during the winter months, together with the El Ni\~no variations, contain sufficient information to accurately hindcasts springtime rainfall in SE Australia. Using deep learning neural networks trained on the early 21st century data we performed both hindcasting and simulated forecasting of the recent spring rainfall in SE Australia, Victoria and South Australia. The method is particularly suitable for quantitative testing of the importance of different ocean regions in improving the predictability of rainfall modelling. The network hindcasting results with longer lead times are improved when current winter El Ni\~no temperatures in the input are replaced by forecast temperatures from the best dynamical models. High skill is limited to the rainfall forecasts for the September/October period constructed from the end of June.