Interannual potential predictability of sea surface temperatures around Australia — Australian Meteorological and Oceanographic Society

Interannual potential predictability of sea surface temperatures around Australia (#187)

Eva A Cougnon 1 2 3 , Eric CJ Oliver 4 , Terence J O'Kane 5 , Neil J Holbrook 1 2 , Nathan L Bindoff 1 2 3 5
  1. IMAS-UTAS, Battery Point, Hobart, TAS, Australia
  2. ARC Centre of Excellence for Climate Extremes, University of Tasmania, Hobart, Tasmania, Australia
  3. Antarctic Climate & Ecosystems CRC, University of Tasmania, Hobart, Tasmania, Australia
  4. Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada
  5. CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

While warm extremes are expected to increase in intensity and frequency under projected longer term climate change, they are also significantly modulated by underlying background temperatures which precondition the state for a higher or lower likelihood of extremes. Therefore there is a need to better understand and predict ocean temperature variability and its relation to climate variability. Multi-year to decadal prediction of climate variability and extreme events is an important emerging field that primarily requires the successful development of climate prediction models. ‘Potential predictability’ estimates can be beneficial to identify where sea surface temperature (SST) variability may (i.e. have the potential to) be predictable at 1-10 year time scales. Here we show, using satellite-based observations, that slow varying (potentially predictable) modes driving the SST variability around Australia can be detected and linked to larger scale climate modes. To identify specific regions of interest around Australia and their relevant time scale of SST variability, we first apply a variance analysis to estimate the spatial distribution of the daily SST variance in several time bands and identify regions of relatively high or low potential predictability. Using an empirical orthogonal function analysis on the daily SST anomalies, we identify the warming trend and several known modes of climate variability around Australia, such as Ningaloo Niño, and El Niño - Southern Oscillation (ENSO) variability. Potential predictability of each of these modes is then estimated by extracting their slow component that is potentially predictable. For instance, we show that ENSO drives a small fraction of the total variance of the SST anomalies in the southeastern region of Australia, while a larger fraction is driven by Ningaloo Niño for the western region of Australia.