Non-linear modes of global sea surface temperature variability and their relationships with global precipitation and temperature

Abstract

Sea surface temperature (SST) modes are comprised of variability that arises from inherently nonlinear processes. Historically, a limitation arises from applying linear statistics to define these modes. Accurate depiction of the complex, non-linear nature of SST modes of variability necessitates the specification of a model capable of producing nonlinear patterns. In this study, we apply an artificial neural network algorithm integrated with autoencoders to analyze the seasonal non-linear global SST modes allowing for improved characterization of the modes and their large-scale temperature and precipitation teleconnections. Our results show that during boreal summer, SST cooling over the central to eastern tropical Pacific co-occurs with the Arctic amplification. In recent decades, the negative SST trend in the central to eastern tropical Pacific, combined with the positive trend in the western tropical Pacific is linked to an increase in the amplitude of SST modes associated with the Arctic warming, resulting in warmer temperatures over large portions of the global land, particularly over Greenland. In boreal winter, El Niño Southern Oscillation (ENSO) is the prominent global SST mode. The distinct spatiotemporal patterns of ENSO modes are associated with unique effects on regional land temperature and precipitation. The central Pacific El Niño is more associated with the combination of warm and dry conditions over Western Australia, and the northern part of South America. Conversely, the central to eastern El Niño is more associated with the combination of warm and dry conditions over parts of Southern Africa, and the northern part of South America. The spatiotemporal patterns and trends in the amplitude of the analyzed non-linear global SST modes alongside their regional influences on temperature and precipitation are discussed. The broader impact of this study is on the potential of neural networks in effectively delineating non-linear global SST modes and their associations with regional climates.