Interactions between large-scale modes of climate variability that influence Australian hydroclimatic regimes

Effective management of water resources, including surface and ground water, is vital and relies on a thorough understanding of climatic and hydrological (or 'hydroclimatic') variability. In Australia hydroclimatic variability is associated with several large-scale climate modes, including remote phenomena such as El Nino - Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), and more regional climate indices such as the sub-tropical ridge (STR). Individually, the large-scale climate regimes typically associated with rainfall events are well understood. However, less is known about the interactions between, or combinations of, different large-scale conditions that influence Australian hydroclimatic regimes. These interactions are non-linear so traditional statistical frameworks may be unable to adequately characterise these relationships. Classification and Regression Trees (CART) are well suited to analysing relationships between predictor and response variables, including those based on categorical events, that may be modulated by several predictor variables acting together. By employing a more appropriate and novel statistical method this thesis aims to better understand relationships between large-scale modes of climate variability and Australian hydroclimatic regimes. In this work, tree-based models were used to classify regional Australian rainfall regimes from indices of ENSO, the IOD and the STR, yielding the following conclusions. (1) Interactions between tropical (ENSO, IOD) predictor variables and the STR control the strength of the tropical teleconnection and the influence on regional rainfall regimes in southern Australia. When tropical modes and the STR are in the same phase, rainfall regimes are continent-wide and spatially coherent. However, when indices of climate modes are in the opposite phases, i.e. El Nino combined with low STR intensity, the modulation of the tropical teleconnection by the STR is evident, as rainfall anomalies are confined to the northeast of the continent. (2) The influence of both STR intensity and position on rainfall regimes in southeastern Australia was defined. STR position was crucial for defining two distinct types of "wet" autumns, a "summer-like" ("winter-like") regime when the STR was in a southerly (northerly) position. The summer-like regime occurs at frequencies that have not changed detectably over the instrumental record. However, the frequency of the winter-like regime has declined significantly. In addition, the dry regime defined by high STR intensity has been the most frequent regime in recent years, consistent with the attribution of STR intensity as the main driver of the Millennium Drought. (3) The predictive persistence of relationships between a suite of predictor variables (indices of ENSO, IOD and the STR) and rainfall, upper-layer and lower-layer soil moisture was explored. The predictability of spring rainfall was similar using both random forests (a bootstrapping implementation of CART) and linear regression, suggesting results are not dependent on method. The key result, of possible use in seasonal forecasting, is that, deep soil moisture in spring and summer exhibits significantly more predictability than rainfall and shallow soil moisture, due to the persistence of tropical climate drivers and the removal of high-frequency variability in deep layers by natural temporal smoothing as soil moisture is transferred to deep soil layers