Summary
Sub-seasonal to seasonal prediction of rainfall extremes in
Australia
Seasonal forecasts normally provide
probabilities of how a given month or season will compare to long-term average
rainfall (the usual climatology). Since large scale climate drivers change
states slowly and are known to impact rainfall extremes, it should be possible
to predict extremes in rainfall in addition to deviations from averages. This
research evaluated using an existing seasonal forecasting model, ACCESS-S1, to
forecast metrics of rainfall extremes.
Scientists at the University of
Melbourne and the Bureau of Meteorology have tested the ability to use ACCESS-S1
to predict daily-scale rainfall extremes. The number of wet days could be
predicted as accurately as mean rainfall. Forecast performance was poor for
measures of extreme events that are rare, such as rainfall days above the 90th
percentile. The most accurate predictions were across Queensland and the
Northern Territory. A case study of the 2010 La Niña demonstrates the model performed well during a strong phase of this
climate driver. The metrics that were poorly predicted were spatially varied,
such as maximum 1-day rainfall in a season and were driven by smaller scale
processes that are more randomly driven. ACCESS-S1 showed decreasing forecast performance
with increasing lead time, particularly across the warm season months. The
model gave the most accurate forecasts when events were within a week to 10
days of initialisation.