FY 2021

Predicting interannual variability in sea surface height along the west coast of Australia using a simple ocean model

Nagura, M., and M.J. McPhaden

Geophys. Res. Lett., 48(18), e2021GL094592, doi: 10.1029/2021GL094592, View online (2021)

Sea surface height (SSH) along the west coast of Australia is key to local climate and is strongly forced by remote surface wind variability related to El Niño Southern Oscillation (ENSO) in the tropical Pacific Ocean. This study provides a method to predict interannual variability in SSH along the west coast of Australia using a simple 1.5-layer dynamical ocean model forced by a statistical atmospheric model for ENSO-related winds. The model has realistic coastlines and is driven by reanalysis surface winds regressed onto an ENSO index. The model when run in hindcast mode to predict past variability can simulate tide gauge observations at Fremantle along the west coast of Australia up to 13 months in advance, which outperforms persistence. We conclude that this methodology can be useful as a baseline for gauging the performance of more sophisticated forecast models for predicting SSH variations along the west coast of Australia.

Plain Language Summary. Sea surface height (SSH) along the west coast of Australia is key to regional climate and is strongly forced by remote surface wind variability related to El Niño Southern Oscillation (ENSO) in the tropical Pacific Ocean. Previous studies predicted interannual variability in SSH in this region using sophisticated ocean-atmosphere coupled general circulation models, but their ocean models had coarse horizontal resolution, required to reduce computational burden. This study provides a much simpler method to predict SSH variability along the west coast of Australia using a simple ocean dynamical model with realistic coastlines forced by ENSO-related atmospheric winds. Our model, run in hindcast mode to predict past SSH variability, is able to simulate SSH anomalies along the west coast of Australia up to 13 months in advance. A true forecast system to predict future variations would also require prediction of ENSO. However, this methodology can be useful as a baseline for gauging the performance of more sophisticated model forecast systems.