Understanding the complex dynamics of climate change in south-west Australia using Machine Learning

The climate system is an excellent example of a “complex system” since there is an interplay and inter-relation of several climate variables. Variables such as the Standardized Precipitation Index (SPI) are used to indicate the drought climate condition – a negative (or positive) value of SPI would imply a dry (or wet) state in a region. It is difficult to identify the factors influencing the SPI or their inter-relations (including feedback loops). Here, we aim to study the complex dynamics that SPI has with the sea surface temperature (SST), El Niño Southern Oscillation (ENSO) (aka., NINO 3.4) and Indian Ocean Dipole (IOD), using a machine learning approach. Our findings are: (i) IOD was negatively correlated to SPI till 2008; (ii) until 2004, SST was negatively correlated with SPI; (iii) from 2005 to 2014, the SST had swung between negative and positive correlations; (iv) since 2014, we observed that the regression coefficient (δ) corresponding to SST has always been positive; (v) the SST has an upward trend, and the positive upward trend of δ implied that SPI has been positively correlated with SST in recent years; and finally, (vi) the current value of SPI has a significant positive correlation with a past SPI value with a periodicity of about 7.5 years. Examining the complex dynamics, we used a statistical machine learning approach to construct an inferential network of these climate variables, which revealed that SST and NINO 3.4 directly couples with SPI, whereas IOD indirectly couples with SPI through SST and NINO 3.4. The system also indicated that Nino 3.4 significantly negatively affects SPI. If Nino 3.4 increases (decreases), the SPI drops (increases) considerably, leading to more drought (wet) conditions. Interestingly, there was a structural change in the complex dynamics of the four climate variables of NINO 3.4, IOD, SST, and SPI, sometime in 2008.