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Doug Richardson, Amanda S. Black, Didier P. Monselesan, Thomas S. Moore II, James S. Risbey, Andrew Schepen, Dougal T. Squire, and Carly R. Tozer

Abstract

Subseasonal forecast skill is not homogeneous in time, and prior assessment of the likely forecast skill would be valuable for end-users. We propose a method for identifying periods of high forecast confidence using atmospheric circulation patterns, with an application to southern Australia precipitation. In particular, we use archetypal analysis to derive six patterns, called archetypes, of daily 500-hPa geopotential height (Z 500) fields over Australia. We assign Z 500 reanalysis fields to the closest-matching archetype and subsequently link the archetypes to precipitation for three key regions in the Australian agriculture and energy sectors: the Murray Basin, southwest Western Australia, and western Tasmania. Using a 20-yr hindcast dataset from the European Centre for Medium-Range Weather Forecasts subseasonal-to-seasonal prediction system, we identify periods of high confidence as when hindcast Z 500 fields closely match an archetype according to a distance criterion. We compare the precipitation hindcast accuracy during these confident periods compared to normal. Considering all archetypes, we show that there is greater skill during confident periods for lead times of less than 10 days in the Murray Basin and western Tasmania, and for greater than 6 days in southwest Western Australia, although these conclusions are subject to substantial uncertainty. By breaking down the skill results for each archetype individually, we highlight how skill tends to be greater than normal for those archetypes associated with drier-than-average conditions.

Open access
Amanda S. Black, James S. Risbey, Christopher C. Chapman, Didier P. Monselesan, Thomas S. Moore II, Michael J. Pook, Doug Richardson, Bernadette M. Sloyan, Dougal T. Squire, and Carly R. Tozer

Abstract

Large-scale cloud features referred to as cloudbands are known to be related to widespread and heavy rain via the transport of tropical heat and moisture to higher latitudes. The Australian northwest cloudband is such a feature that has been identified in simple searches of satellite imagery but with limited investigation of its atmospheric dynamical support. An accurate, longterm climatology of northwest cloudbands is key to robustly assessing these events. A dynamically based search algorithm has been developed that is guided by the presence and orientation of the subtropical jet stream. This jet stream is the large-scale atmospheric feature that determines the development and alignment of a cloudband. Using a new 40-year dataset of cloudband events compiled by this search algorithm, composite atmospheric and ocean surface conditions over the period 1979-2018 have been assessed. Composite cloudband upper level flow revealed a tilted low pressure trough embedded in a Rossby wave train. Composites of vertically integrated water vapor transport centered around the jet maximum during northwest cloudband events reveal a distinct Atmospheric River supplying tropical moisture for cloudband rainfall. Parcel backtracking indicated multiple regions of moisture support for cloudbands. A thermal wind anomaly orientated with respect to enhanced sea surface temperature gradient over the Indian Ocean was also a key composite cloudband feature. 300 years of a freely-coupled control simulation of the ACCESS-D system was assessed for its ability to simulate northwest cloudbands. Composite analysis of model cloudbands compared reasonably well to reanalysis despite some differences in seasonality and frequency of occurrence.

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