CAWCR technical report, 51. Improvements in POAMA2 for the prediction of major climate drivers and south eastern Australian rainfall

Lim Eun-Pa; Hendon Harry H.; Langford Sally; et al. - Centre for Australian Weather and Climate Research, 2012

Ocean-atmosphere interactions are key processes that drive seasonal climate variability. In the global sense, the atmosphere drives the upper ocean via heat flux, fresh water flux and wind stress (Anderson 2008). But in the tropics where the ocean surface temperature (hereafter, sea surface temperature, SST) is warm enough to trigger deep atmospheric convection, the ocean exerts strong controls on the atmosphere especially at longer time scales because of its slow variations and strong thermal inertia. Consequently, the highest predictability of atmospheric climate (e.g. temperature and rainfa ...

English

 Improvements in POAMA2 for the prediction of major climate drivers and south eastern Australian rainfall

  Available online: https://www.cawcr.gov.au/publications/

Eun-Pa Lim ; Harry H. Hendon ; Sally Langford ; O. Alves

Published by: Centre for Australian Weather and Climate Research ; 2012

Ocean-atmosphere interactions are key processes that drive seasonal climate variability. In the global sense, the atmosphere drives the upper ocean via heat flux, fresh water flux and wind stress (Anderson 2008). But in the tropics where the ocean surface temperature (hereafter, sea surface temperature, SST) is warm enough to trigger deep atmospheric convection, the ocean exerts strong controls on the atmosphere especially at longer time scales because of its slow variations and strong thermal inertia. Consequently, the highest predictability of atmospheric climate (e.g. temperature and rainfall) on seasonal timescales is found predominantly across and directly surrounding the tropical ocean basins and in those extratropical regions of the globe that are directly influenced by atmospheric Rossby waves which are excited by variations of tropical deep convection that develop in response to variations in tropical SST (e.g. Hoskins and Schopf 2008) [...]

Collection(s) and Series:  CAWCR technical report- No. 51

Language(s): English

Format: Digital (Free), Hard copy

Tags: Observations ; Climate model ; Precipitation forecasting ; Australia Add tag

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CAWCR technical report, 48. Seasonal Climate Prediction in the Pacific using the POAMA coupled model forecast system

Cottrill A.; Hendon Harry H.; Lim Eun-Pa; et al. - Centre for Australian Weather and Climate Research, 2012

The tropical Pacific Ocean basin is home to over 20 Pacific Island nations, many of which are sensitive to climate extremes from the El Niño-Southern Oscillation (ENSO) and rainfall variability associated the Inter-Tropical Convergence Zone and the South Pacific Convergence Zone. These Pacific Island countries are highly dependent on agriculture, fishing and tourism as a major source of food production and income, which can vary greatly depending on the weather and climate experienced from year to year. Hence, the provision of skilful seasonal forecasts is important to allow these countries to ...

English

 Seasonal Climate Prediction in the Pacific using the POAMA coupled model forecast system

  Available online: https://www.cawcr.gov.au/publications/

A. Cottrill ; Harry H. Hendon ; Eun-Pa Lim ; Sally Langford ; Y. Kuleshov ; Andrew Charles ; David Jones

Published by: Centre for Australian Weather and Climate Research ; 2012

The tropical Pacific Ocean basin is home to over 20 Pacific Island nations, many of which are sensitive to climate extremes from the El Niño-Southern Oscillation (ENSO) and rainfall variability associated the Inter-Tropical Convergence Zone and the South Pacific Convergence Zone. These Pacific Island countries are highly dependent on agriculture, fishing and tourism as a major source of food production and income, which can vary greatly depending on the weather and climate experienced from year to year. Hence, the provision of skilful seasonal forecasts is important to allow these countries to prepare for changes in rainfall and impending droughts associated with the changes in ENSO. The Australian Bureau of Meteorology has developed a dynamical seasonal forecast system POAMA (Predictive Ocean-Atmosphere Model for Australia), which is a state of the art seasonal to inter-annual forecast system based on a coupled model of the ocean and atmosphere. The model has good skill at predicting El Niño and La Niña up to 9 months in advance and it is capable of simulating the spatial and temporal variability of tropical rainfall associated with ENSO. Consequently, the variability of rainfall patterns across the Pacific region is skilfully predicted by POAMA at short lead times. The availability of seasonal forecasts from dynamical models will aid Pacific Island countries to improve economic returns in agriculture and other industries and reduce impacts from storms, floods and droughts associated with the extremes of El Niño and La Niña.

Collection(s) and Series:  CAWCR technical report- No. 48

Language(s): English

Format: Digital (Free)

Tags: Observations ; Seasonal change ; Climate ; Climate model ; Climate prediction ; Weather forecasting ; Region V - South-West Pacific Add tag

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CAWCR technical report, 40. Comparison of techniques for the calibration of coupled model forecasts of Murray Darling Basin seasonal mean rainfall

Charles Andrew; Hendon Harry H.; Wang Q.J.; et al. - Centre for Australian Weather and Climate Research, 2011

Ensemble forecasts of South Eastern Australian rainfall from POAMA 1.5, a coupled oceanatmosphere dynamical model based seasonal prediction system run experimentally at the Bureau of Meteorology, tend to be under dispersed leading to overconfident probability forecasts. The poor reliability of seasonal forecasts based on dynamical coupled models is a barrier to their adoption as official outlooks by the Bureau of Meteorology. One approach to correcting this problem is model calibration, in which the probability distribution produced by the model is adjusted in light of available information ab ...

English

 Comparison of techniques for the calibration of coupled model forecasts of Murray Darling Basin seasonal mean rainfall

  Available online: https://www.cawcr.gov.au/technical-reports/CTR_040.pdf

Andrew Charles ; Harry H. Hendon ; Q.J. Wang ; David Robertson ; Eun-Pa Lim

Published by: Centre for Australian Weather and Climate Research ; 2011

Ensemble forecasts of South Eastern Australian rainfall from POAMA 1.5, a coupled oceanatmosphere dynamical model based seasonal prediction system run experimentally at the Bureau of Meteorology, tend to be under dispersed leading to overconfident probability forecasts. The poor reliability of seasonal forecasts based on dynamical coupled models is a barrier to their adoption as official outlooks by the Bureau of Meteorology. One approach to correcting this problem is model calibration, in which the probability distribution produced by the model is adjusted in light of available information about its past performance. Several distinct methods for calibrating seasonal rainfall forecasts for South Eastern Australia derived from the POAMA 1.5 ensemble are compared for accuracy and reliability in order to assess their suitability for application to real-time seasonal forecasts. The calibration methods investigated were: a variance inflation method (IOV); a Bayesian joint probability (BJP) calibration technique; and a singular vector regression technique (SVD) based on co-varying patterns of model and observed rainfall. Calibration was carried out for model grid points in the Murray Darling region. Assessment was carried out using a mix of standard skill scores widely used in operational forecasting. It was found that the BJP method resulted in the best correction to forecast reliability while IOV improved reliability only modestly and the SVD scheme had a negative impact on reliability. Further study of the application of these methods to real-time forecasts is recommended.

Collection(s) and Series:  CAWCR technical report- No. 40

Language(s): English

Format: Digital (Free), Hard copy (ill., charts)

ISBN (or other code): 978-1-921826-58-0

Tags: Water ; Hydrological forecast ; Precipitation forecasting ; Ocean-atmosphere interaction ; Numerical weather prediction ; Sea ice ; Australia Add tag

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