Tropical forcing of Australian extreme low minimum temperatures in
Researchers from the Bureau of Meteorology and the University of
Melbourne recently investigated why the seasonal forecasting model, ACCESS-S1,
failed to predict extreme cold in September 2019. Minimum temperatures were below
average over much of south-eastern Australia and frosts led to severe crop
damage. The common climate drivers were in phases that would be expected for
this event. This includes El NiŮo conditions
in the central Pacific and a near record strength positive Indian Ocean Dipole
(IOD). Given historical impacts on Australian weather under these conditions,
the low temperatures should have been predicable 1 to 2 months in advance.
The results suggest the model underestimates the impact of the
positive IOD (and to a lesser extent El NiŮo) on low temperatures in south-eastern Australia. The IOD reflects
sustained changes between sea surface temperatures of the tropical western and
eastern Indian Ocean and has three phases: †positive, neutral, and negative. A positive
IOD is associated with cooler waters in the eastern portion of the Indian Ocean
and weakened westerly, in some cases even a switch to easterly winds allowing
cool deep ocean water to rise in the east. These conditions lead to reduced
moisture in the northwest of Australia which changes the path of weather
systems from Australiaís west and typically produces clear, dry conditions in
southeast Australia. Reduced cloud cover at night results in increased outgoing
longwave radiation and colder minimum temperatures.
The modelís weak connections between the IOD and these weather
patterns were the main reason for its failure to predict the low minimum
temperatures in September 2019. These weak internal relationships are likely
the result of biases in average state of the tropical Indian Ocean in the model.
This study helps identify ways to improve predictions of these types of events.
Given these events generally coincide with the winter crop growing season the
risk of significant impact on agriculture is greatly increased. Thus, for
agriculture, the importance of accurate forecasts at this time of year make
model improvements in this area a high priority.