South Pacific Ocean climate dynamics and predictability

The mechanisms and predictability of Pacific decadal climate variability (PDV) is an active area of research in climate science and is one of high societal importance. To date, most research into PDV has been focused on mechanisms and responses in the North Pacific. This thesis presents a comprehensive investigation, based on the development and application of a family of hierarchical stochastically forced models, of the mechanisms underpinning PDV climate predictability and that focuses on the role of the South Pacific Ocean and coupling to the tropics. First, a simple one-dimensional first-order autoregressive (AR1) model was used to understand the space and time variations of the South Pacific decadal oscillation (SPDO) ‚- which represents the leading sea surface temperature (SST) mode in the South Pacific. The analysis revealed that the first Pacific-South American (PSA1) pattern is the key atmospheric driver of the SPDO. Further, the leading mode of integrated subsurface upper ocean temperature variability was shown to match expectations from the propagation of oceanic Rossby waves across the extratropical South Pacific, with the atmospheric PSA variability providing the high-frequency 'noise' source of the observed low-frequency ('reddened') SST SPDO response. Second, the stochastically forced AR1 model was generalised to higher-dimensional fields with the inclusion of spatial features using a linear inverse model (LIM) approach. The deterministic dynamics underpinning the combined tropical and South Pacific system was investigated, with the seasonal predictive skill of the SPDO and El Ni√±o‚-Southern Oscillation (ENSO) quantified under the LIM framework. It was found that, although the oscillatory periods of ENSO and the SPDO are distinct ‚- the former oscillating on interannual timescales and the latter oscillating on (inter-)decadal timescales ‚- their damping time scales were very similar, and their predictive skill comparable. With the inclusion of subsurface processes in the extratropical South Pacific, the linear predictive skill of both ENSO and the SPDO was found to be enhanced. Overall, the study showed that Pacific SST variability forecast skill from the computationally cheap LIMs was competitive with state-of-the-art operational seasonal forecast systems that employ sophisticated initialisation schemes and general circulation models, thus providing a useful benchmark for these operational systems. Third, the LIM framework was applied to gain a deeper understanding of the role of stochastic forcing from the atmospheric PSA variability and to determine the optimal structures for initialised forecasts of the tropical and South Pacific climate system and its variability. This analysis revealed the spatial imprint of atmospheric PSA variability combined with temporal stochastic forcing that acts to drive the low frequency oceanic SST variability across the tropical and South Pacific, and excites optimal initial perturbations for the prediction of ENSO and the SPDO. Finally, informed by the aforementioned hierarchy of stochastically forced linear reduced space models, the thesis culminates with an overarching framework that links the atmosphere to the surface and subsurface oceans across a range of time scales from (intra-)seasonal to (inter-)decadal. Hence, the thesis provides, for the first time, a mechanistic framework and integrated understanding of the drivers of large-scale South Pacific climate variability and predictability.