Seasonal Forecasts of the East Australian Current with ACCESS-S1 — Australian Meteorological and Oceanographic Society

Seasonal Forecasts of the East Australian Current with ACCESS-S1 (#253)

Xiaobing Zhou 1 , Oscar Alves 1
  1. Australian Bureau of Meteorology, Docklands, VIC, Australia

The poleward flowing East Australian Current (EAC), which carries a large amount of warm tropical water from the equator southward and releases the heat to the mid-latitude atmosphere, is the strongest current off eastern Australia. It significantly affects the marine environment and climate. This study firstly investigates the seasonal forecasts of the EAC volume transport anomalies (VTAs) (averaged between 150oE-160oE and 25oS -30oS over top 300 m) with a seasonal forecast system ACCESS-S1 over the period 1990-2012. The predictions of the EAC VTAs are challenging due to EAC being a complex and highly energetic western boundary system. The persistence correlation skill of the net EAC VTAs is only 0.4 at the first lead month and it drops to 0.1 at the second month.  In contrast, the correlation skill of the net EAC VTAs in ACCESS-S1 is up to 0.85 at the first and 0.55 at the second lead month. The prediction correlation skills of the southward and northward EAC VTAs in ACCESS-S1 are higher than those of the net EAC VTAs at the same lead time and they are above 0.5 during the first 4-month lead. As for model biases,  the net EAC volume transport only decreases from month 1 to month 2 and becomes stable after the second lead month. However, both southward and northward EAC volume transport become continuously weaker with the increase of lead time. The prediction skill of sea surface temperature anomaly (SSTA) and seas surface high anomaly (SSHA) in the EAC regions (15oS-40oS, 145oE-160oE) are also explored. The forecast anomaly correlations are about 0.5 up to lead month 6 everywhere except the Tasman Sea where the SSTA and SSHA are only predictable for the first month. The Tasman Sea is dominated by a series of mesoscale eddies which have a complex variability and low predictability.

#amos2020