Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia

This study examines the potential use of the Predictive Ocean Atmosphere Model for Australia (POAMA), the Bureau of Meteorology’s dynamical seasonal forecast system, as an intraseasonal prediction tool for Australia. This would fill the current prediction capability gap between weather forecasts and seasonal outlooks for Australia. The intraseasonal forecast skill of a 27-year hindcast dataset is investigated, focusing on precipitation and minimum and maximum temperatures over Australia in the second fortnight (average days 15 –28 of the forecast). Most of the skill for forecasting precipitation and maximum temperature in the second fortnight is focused over eastern Australia, during austral winter and spring for precipitation and during spring for maximum temperature. For this region and seasons the forecast of the second fortnight performs generally better than using climatology, persistence of observed, or persistence of the forecast for the first fortnight (average days 1 –14). The model has generally poor skill in predicting minimum temperatures. The role of key drivers of Australian climate variability for providing predictability at intraseasonal time-scales is investigated. This is done for the austral winter and spring seasons, when POAMA’s skill for predicting precipitation is highest. Forecast skill is found to be increased during extremes of the El Ni˜ no Southern Oscillation, the Indian Ocean Dipole and the Southern Annular Mode. The regions of impact of these modes of climate variability on forecast skill are similar to those regions identified in observed studies as being influenced by the respective drivers. In contrast, there is no significant relationship between intraseasonal forecast skill for precipitation and the amplitude of the Madden Julian Oscillation (MJO) in winter and spring, although the analysis does not distinguish between the phases of the MJO. The results indicate that the use of POAMA for intraseasonal forecasting is promising. Copyright c � 2011 Royal Meteorological Society

[1]  Elizabeth C. Kent,et al.  Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century , 2003 .

[2]  砂田 憲吾,et al.  Bridging the gap between , 2009 .

[3]  Harry H. Hendon,et al.  Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system , 2011 .

[4]  Toshio Yamagata,et al.  Possible impacts of Indian Ocean Dipole mode events on global climate , 2003 .

[5]  David J. Karoly,et al.  The Modulation of Tropical Cyclone Activity in the Australian Region by the Madden-Julian Oscillation , 2001 .

[6]  H. Meinke,et al.  Impacts of the Madden-Julian oscillation on Australian rainfall and circulation , 2009 .

[7]  Holger Meinke,et al.  Seasonal and Inter-Annual Climate Forecasting: The New Tool for Increasing Preparedness to Climate Variability and Change In Agricultural Planning And Operations , 2005 .

[8]  I. Jolliffe,et al.  Forecast verification : a practitioner's guide in atmospheric science , 2011 .

[9]  B. Trewin,et al.  On the relationships between the El Niño–Southern Oscillation and Australian land surface temperature , 2000 .

[10]  A. H. Murphy.  A New Vector Partition of the Probability Score , 1973 .

[11]  Leonard A. Smith,et al.  Using medium-range weather forcasts to improve the value of wind energy production , 2003 .

[12]  O. Alves,et al.  Dynamical Forecast of Inter-El Niño Variations of Tropical SST and Australian Spring Rainfall , 2009 .

[13]  David Day.  The weather watchers : 100 years of the Bureau of Meteorology , 2007 .

[14]  E. Maloney,et al.  Simulations of the Madden–Julian oscillation in four pairs of coupled and uncoupled global models , 2006 .

[15]  M. England,et al.  What causes southeast Australia's worst droughts? , 2009 .

[16]  N. Nicholls,et al.  Applications of seasonal climate forecasting in agricultural and natural ecosystems : the Australian experience , 2000 .

[17]  A. Sankarasubramanian,et al.  The Role of Monthly Updated Climate Forecasts in Improving Intraseasonal Water Allocation , 2009 .

[18]  M. Wheeler,et al.  On the Remote Drivers of Rainfall Variability in Australia , 2009 .

[19]  J. Wallace,et al.  Annular Modes in the Extratropical Circulation. Part I: Month-to-Month Variability* , 2000 .

[20]  G. Brier.  VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[21]  T. Stockdale.  Coupled Ocean–Atmosphere Forecasts in the Presence of Climate Drift , 1997 .

[22]  D. Gong,et al.  Definition of Antarctic Oscillation index , 1999 .

[23]  O. Alves,et al.  Assessing the simulation and prediction of rainfall associated with the MJO in the POAMA seasonal forecast system , 2011 .

[24]  G. Meyers,et al.  The Years of El Niño, La Niña, and Interactions with the Tropical Indian Ocean , 2007 .

[25]  Francisco J. Doblas-Reyes,et al.  A Debiased Ranked Probability Skill Score to Evaluate Probabilistic Ensemble Forecasts with Small Ensemble Sizes , 2005 .

[26]  Roberto Buizza,et al.  The new VarEPS-monthly forecasting system: A first step towards seamless prediction , 2008 .

[27]  S. Xie,et al.  Tropical indian ocean variability in the IPCC twentieth-century climate simulations , 2006 .

[28]  Renate Hagedorn,et al.  The rationale behind the success of multi-model ensembles in seasonal forecasting-II , 2005 .

[29]  Z. Tóth,et al.  Bridging the Gap between Weather and Climate Forecasting: Research Priorities for Intraseasonal Prediction , 2007 .

[30]  Mark A. Liniger,et al.  The discrete brier and ranked probability skill scores , 2007 .

[31]  Neville Smith,et al.  A univariate statistical interpolation scheme for subsurface thermal analyses in the tropical oceans , 1991 .

[32]  Harry H. Hendon,et al.  A Composite Study of Onset of the Australian Summer Monsoon , 1990 .

[33]  Thomas M. Smith,et al.  An Improved In Situ and Satellite SST Analysis for Climate , 2002 .

[34]  N. Graham,et al.  Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation , 2002 .

[35]  David B. Stephenson,et al.  How Do We Know Whether Seasonal Climate Forecasts are Any Good , 2008 .

[36]  J. Whitaker,et al.  The Experimental MJO Prediction Project , 2006 .

[37]  B. Goswami,et al.  A dipole mode in the tropical Indian Ocean , 1999, Nature.

[38]  S. Manabe,et al.  The seasonal variation of the hydrologic cycle as simulated by a global model of the atmosphere , 1975 .

[39]  Frederic Vitart,et al.  Monthly Forecasting at ECMWF , 2004 .

[40]  O. Alves,et al.  Dynamical seasonal prediction of summer sea surface temperatures in the Great Barrier Reef , 2009, Coral Reefs.

[41]  Harry H. Hendon,et al.  Prospects for predicting two flavors of El Niño , 2009 .

[42]  Nicholas E. Graham,et al.  Conditional Probabilities, Relative Operating Characteristics, and Relative Operating Levels , 1999 .

[43]  Neill E. Bowler,et al.  On the Reliability and Calibration of Ensemble Forecasts , 2009 .

[44]  Stephen Jewson,et al.  The use of weather forecasts in the pricing of weather derivatives , 2002 .

[45]  H. Hendon,et al.  Representation and prediction of the Indian Ocean dipole in the POAMA seasonal forecast model , 2009 .

[46]  Dominic A. Hudson,et al.  The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST , 2011 .

[47]  M. Wheeler,et al.  An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction , 2004 .

[48]  D. Thompson,et al.  Australian Rainfall and Surface Temperature Variations Associated with the Southern Hemisphere Annular Mode , 2007 .

[49]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[50]  O. Alves,et al.  An enhanced moisture convergence - Evaporation feedback mechanism for MJO air-sea interaction , 2008 .

[51]  C. Reason,et al.  Variability in the tropical southeast Indian Ocean and links with southeast Australian winter rainfall , 2000 .

[52]  L. Zeng.  Weather Derivatives and Weather Insurance: Concept, Application, and Analysis. , 2000 .

[53]  R. Buizza,et al.  Using weather ensemble predictions in electricity demand forecasting , 2003 .