Editorial Type:
Article
Article Type:
Research Article

Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin

Andrew Charles Bureau of Meteorology, Melbourne, Victoria, Australia

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Bertrand Timbal Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Victoria, Australia

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Elodie Fernandez CNRS-CERFACS, Toulouse, France

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Harry Hendon Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Victoria, Australia

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Print Publication:
01 Mar 2013
DOI:
https://doi.org/10.1175/MWR-D-12-00098.1
Page(s):
1099–1117
Restricted access

Abstract

Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.

Corresponding author address: Andrew Charles, Bureau of Meteorology, GPO Box 1289, Melbourne, VIC 3001, Australia. E-mail: a.charles@bom.gov.au

Abstract

Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.

Corresponding author address: Andrew Charles, Bureau of Meteorology, GPO Box 1289, Melbourne, VIC 3001, Australia. E-mail: a.charles@bom.gov.au
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  • Alves, O., G. Wang, A. Zhong, N. Smith, F. Tseitkin, A. Schiller, S. Godfrey, and G. Meyers, 2003: POAMA bureau of meteorology operational coupled model seasonal forecast system. Proc. National Drought Forum, Brisbane, QLD, Australia, Dept. of Primary Industries, 49–56.

  • Charles, S. P., B. C. Bates, and J. P. Hughes, 1999: A spatiotemporal model for downscaling precipitation occurrence and amounts. J. Geophys. Res.,104 (D24), 31 657–31 669.

  • Clark, M. P., and L. E. Hay, 2004: Use of medium-range numerical weather prediction model output to produce forecasts of streamflow. J. Hydrometeor.,5, 15–32.

  • Colman, R., and Coauthors, 2005: BMRC Atmospheric Model (BAM) version 3.0: Comparison with mean climatology. Bureau of Meteorology Research Centre Tech. Rep. 108, 32 pp. [Available online at http://www.bom.gov.au/bmrc/pubs/researchreports/researchreports.htm.]

  • Feddersen, H., A. Navarra, and M. N. Ward, 1999: Reduction of model systematic error by statistical correction for dynamical seasonal predictions. J. Climate,12, 1974–1989.

  • Frost, A. J., and Coauthors, 2011: A comparison of multi-site daily rainfall downscaling techniques under Australian conditions. J. Hydrol.,408, 1–18.

  • Hamill, T. M., and J. S. Whitaker, 2006: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Mon. Wea. Rev.,134, 3209–3229.

  • Hudson, D., O. Alves, H. Hendon, and G. Wang, 2010: The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST. Climate Dyn.,36, 1155–1171, doi:10.1007/s00382-010-0763-9.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc.,77, 437–471.

  • Landman, W. A., and L. Goddard, 2002: Statistical recalibration of GCM forecasts over southern Africa using model output statistics. J. Climate,15, 2038–2055.

  • Lavery, B., G. Joung, and N. Nicholls, 1997: An extended high-quality historical rainfall dataset for Australia. Aust. Meteor. Mag.,46, 27–38.

  • Lim, E., H. H. Hendon, D. Hudson, G. Wang, and O. Alves, 2009: Dynamical forecast of inter–El Niño variations of tropical SST and Australian spring rainfall. Mon. Wea. Rev.,137, 3796–3810.

  • Lorenz, E. N., 1969: Atmospheric predictability as revealed by naturally occurring analogues. J. Atmos. Sci.,26, 636–646.

  • Murphy, J., 1999: An evaluation of statistical and dynamical techniques for downscaling local climate. J. Climate,12, 2256–2284.

  • Risbey, J. S., M. J. Pook, P. C. McIntosh, M. C. Wheeler, and H. H. Hendon, 2009: On the remote drivers of rainfall variability in Australia. Mon. Wea. Rev.,137, 3233–3253.

  • Schiller, A., J. S. Godfrey, P. C. McIntosh, G. Meyers, N. R. Smith, O. Alves, G. Wang, and R. Fiedler, 2002: A new version of the Australian community ocean model for seasonal climate prediction. CSIRO Marine Research Rep. 240, 79 pp. [Available online at http://www.cmar.csiro.au/publications/cmrreports/240/text240.pdf.]

  • Spillman, C., and O. Alves, 2009: Dynamical seasonal prediction of summer sea surface temperatures in the Great Barrier Reef. Coral Reefs,28, 197–206, doi:10.1007/s00338-008-0438-8.

  • Timbal, B., and D. Jones, 2008: Future projections of winter rainfall in southeast australia using a statistical downscaling technique. Climatic Change,86, 165–187, doi:10.1007/s10584-007-9279-7.

  • Timbal, B., P. Hope, and S. Charles, 2008: Evaluating the consistency between statistically downscaled and global dynamical model climate change projections. J. Climate,21, 6052–6059.

  • Timbal, B., E. Fernandez, and Z. Li, 2009: Generalization of a statistical downscaling model to provide local climate change projections for Australia. Environ. Modell. Software,24, 341–358.

  • Timbal, B., and Coauthors, 2010: Understanding the anthropogenic nature of the observed rainfall decline across south eastern Australia. CAWCR Tech. Rep. 026, Centre for Australian Weather and Climate Research, 180 pp. [Available online at http://www.cawcr.gov.au/publications/technicalreports/CTR_026.pdf.]

  • Valcke, S., L. Terray, and A. Piacentini, 2000: The OASIS coupler user guide version 2.4. CERFACS Tech. Rep. TR/CMGC/00-10, Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique, 85 pp.

  • Voisin, N., J. C. Schaake, and D. P. Lettenmaier, 2010: Calibration and downscaling methods for quantitative ensemble precipitation forecasts. Wea. Forecasting,25, 1603–1627.

  • von Storch, H., 1999: On the use of inflation in statistical downscaling. J. Climate,12, 3505–3506.

  • Wang, G., and H. H. Hendon, 2007: Sensitivity of Australian rainfall to inter–El Niño variations. J. Climate,20, 4211–4226.

  • Wang, G., D. Hudson, O. Alves, H. Hendon, G. Liu, and F. Tseitkin, 2008: SST skill assessment from the new POAMA-1.5 system. BMRC Res. Lett., 8, 1–6.

    • Search Google Scholar
    • Export Citation

  • Wood, A. W., 2002: Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res.,107, 4429, doi:10.1029/2001JD000659.

  • Wood, A. W., and J. C. Schaake, 2008: Correcting errors in streamflow forecast ensemble mean and spread. J. Hydrometeor.,9, 132–148.

  • Yoon, J., K. Mo, and E. F. Wood, 2012: Dynamic-model-based seasonal prediction of meteorological drought over the contiguous United States. J. Hydrometeor.,13, 463–482.

  • Zorita, E., and H. von Storch, 1999: The analog method as a simple statistical downscaling technique: Comparison with more complicated methods. J. Climate,12, 2474–2489.

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