Exploring non-linear modes of the subtropical Indian Ocean Dipole using autoencoder neural networks


The subtropical Indian Ocean Dipole (SIOD) significantly influences climate variability, predominantly within parts of the Southern Hemisphere. This study applies an autoencoder—a type of artificial neural network (ANN)—known for its ability to capture intricate non-linear relationships in data through the process of encoding and decoding—to analyze the spatiotemporal characteristics of the SIOD. The encoded SIOD pattern(s) is compared to the conventional definition of the SIOD, calculated as the sea surface temperature (SST) anomaly difference between the western and eastern subtropical Indian Ocean. The analysis reveals two encoded patterns consistent with the conventional SIOD structure, predominantly represented by the SST dipole pattern south of Madagascar and off Australia’s west coast. During different analysis periods, distinct variability in the global SST patterns associated with the SIOD was observed. This variability underscores the SIOD’s dynamic nature and the challenges of accurately defining modes of variability with limited records. One of the ANN patterns has a substantial congruence match of 0.92 with the conventional SIOD pattern, while the other represents an alternate non-linear pattern within the SIOD. This implies the potential existence of additional non-linear SIOD patterns in the subtropical Indian Ocean, complementing the traditional model. When global temperature and precipitation are regressed onto the ANN temporal patterns and the conventional SIOD index, both appear to be associated with anomalous climate conditions over parts of Australia, with several other consistent global impacts. Nevertheless, due to the non-linear nature of the ANN patterns, their effects on local temperature and precipitation vary across different regions as compared to the conventional SIOD index. This study highlights that while the conventional SIOD pattern is consistent with the ANN-derived SIOD pattern, the climate system’s complexity and non-linearity might require ANN modeling to advance our comprehension of climatic modes

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