Matt Fischer1
1. ANSTO, Lucas Heights
Nonlinear behaviours that have been identified in the climate system include quadratic coupling in ENSO (e.g. Fischer 2017), and switching between multi-stable states on interdecadal timescales e.g. in Sahelian ranfall (Demaree and Nicolis 1990). The identification of nonlinear behaviours is important because whether a time series is linear or nonlinear governs how predictable it is. The identification of nonlinearities has been hampered by the relatively short instrumental record, and by the lack of an efficient nonlinearity test for time-irregular paleoclimate data. In order to test for nonlinear behaviours, it is necessary to generate a null distribution showing how likely is it that a particular nonlinear statistic could be generated by a finitely-sampled linear system? Fourier phase-randomisation methods can be used to produce null-hypothesis (or surrogate) time series that contain the same linear properties (e.g. variance, autocorrelation, fourier spectrum) as the observed time series, but phase-randomisation methods have so far proven difficult to apply to time series with time-irregular data. A new method, the CLLS method uses the harmonic regression of complex-valued variates to generate phase-randomised surrogates for irregularly-spaced time series. In this presentation, I apply the CLLS method to 20 high-resolution speleothem records, mainly from the tropics and subtropics, spanning the last two millenia. I test for nonlinear behaviours over interannual-interdecadal timescales, and examine the spatial distribution of various nonlinear statistics including bimodality, autoskewness and autocorrentropy. Nonlinear behaviours can be found in speleothems on climatic margins.
Fischer, M. (2016) Predictable components in global speleothem δ18O. Quat. Sci. Rev., 131, 380-392.
Fischer, M. (2017) Investigating nonlinear dependence between climate fields. J. Climate, 30, 5547-5562.
Joanna M Aldridge1, 2, Joseph Christensen3, Adam D Switzer4, David R Taylor5, Jim W Churchill5, Holly M Watson5
1. Griffith Centre for Coastal Management, Griffith University, Qld, Australia
2. University of Sydney, Chippendale, NSW, Australia
3. Asia Research Centre, Murdoch University, Perth, WA, Australia
4. Earth Observatory of Singapore, Nanyang Technological University, Singapore
5. Baird Australia Pty Ltd, Sydney, NSW, Australia
Analysis of historical archives has uncovered reports of extreme storm surge inundation and overland flow associated with the 1921 tropical cyclone impacting Shark Bay on the mid north coast of Western Australia. Anecdotal inundation heights of ~7-10ft (2-3m) in the town of Denham on the western bank of Denham Sound, 20ft (6m) on the eastern bank of Denham Sound, and 10ft (3m) at the southern end of Freycinet Estuary, coincided with a spring tide (~0.9m AHD). The overland flow associated with the storm tide extended more than 6 miles (9.66km) inland at the southern end of Freycinet Estuary, as evidenced by saltwater inundation of freshwater wells and accounts of sharks and fish stranded inland. A comparison of these historical inundation heights is made with those in a 10000-year synthetic tropical cyclone storm tide climatology of southern Western Australia), that generated using a Monte Carlo tropical cyclone track model, a parametric cyclonic wind model and a high resolution hydrodynamic model in Delft-FM (Burston et al., 2017). The 1921 tropical cyclone generated storm surge and storm tide exceeding the current 500-year ARI planning level for Denham, indicating either that the storm surge risk at long return intervals far exceeds the risk at planning time frames or that the planning risk level is underestimated for this site. We hypothesise that the amplified storm surge associated with the 1921 event is due to the storm surge wave travelling at the same celerity as the tropical cyclone itself. This hypothesis is tested using a set of sensitivity model simulations of the event on the Delft-FM hydrodynamic model. The triggering of such a process is difficult to assign an ARI value, and emphasises the importance of understanding the Probable Maximum Event for risk management and planning.