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Chapter 19 - Operational seasonal prediction

Published online by Cambridge University Press:  03 December 2009

David L. T. Anderson
Affiliation:
European Centre for Medium-Range Weather Forecasts, Reading; Representing the ECMWF Seasonal Forecasting Section, Magdalena Balmaseda, Laura Ferranti, Tim Stockdale, Alberto; Troccoli, Kristian Mogensen, Arthur Vidard, Frederic Vitart
Tim Palmer
Affiliation:
European Centre for Medium-Range Weather Forecasts
Renate Hagedorn
Affiliation:
European Centre for Medium-Range Weather Forecasts
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Summary

At ECMWF, a seasonal forecast system has been operating for several years. This system is described and some results presented. The forecasts are made by fully coupled atmosphere ocean models covering the globe. A multimodel forecast system is also well advanced. This approach avoids to some degree the tendency for individual models to be too confident in their predictions. Model error is still a major issue and considerable effort is needed to improve the models.

Introduction

For several years now ECMWF has been running, operationally, a seasonal forecast suite. This consists of an ocean data assimilation system to provide initial conditions for the forecast, a fully coupled ocean–atmosphere model to create the forecast ensemble and a post-processing procedure to generate forecast products. This system is being generalised to include other coupled models and to produce multimodel products. In this chapter we will consider the various components of the forecasting system.

Weather forecasts have a limited forecast range on account of the chaotic nature of the atmosphere (see Lorenz, this volume); depending on what variable one seeks to predict and on what scale, the predictability horizon might be roughly ten days. Why then do we think we can predict climate months or even years ahead? The information on which the predictability of such long timescale processes is based cannot be simply atmospheric (Palmer and Anderson, 1994). The longer timescales come mainly from the ocean, which has a much larger heat capacity and slower dynamics than the atmosphere.

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Publisher: Cambridge University Press
Print publication year: 2006

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References

Alves, O., Balmaseda, M., Anderson, D. L. T. and Stockdale, T. (2004). Sensitivity of dynamical seasonal forecasts to ocean initial conditions. Quart. J. Roy. Meteor. Soc., 130, 647–68CrossRefGoogle Scholar
Anderson, D. L. T., Sarachik, E., Webster, P. and Rothstein, L. (eds) (1998). The TOGA DECADE: reviewing the progress of El Niño research and Prediction. J. Geophys. Res., 103, 14167–510Google Scholar
Anderson, D., Stockdale, T., Balmaseda, M., et al. (2003). Comparison of the ECMWF seasonal forecast Systems 1 and 2, including the relative performance for the 1997/8 El Niño. ECMWF Technical Memorandum 404. Also available online at www.ecmwf.int
Balmaseda, M. A. (2003). Ocean data assimilation for seasonal forecasts. In ‘Recent developments in data assimilation for atmosphere and ocean’Seminar Proceedings, September 2003, pp. 301–25. ECMWF. Also available online at www.ecmwf.intGoogle Scholar
Balmaseda, M. A., Davey, M. K. and Anderson, D. L. T. (1995). Seasonal dependence of ENSO prediction skill. J. Climate, 8, 2705–152.0.CO;2>CrossRefGoogle Scholar
Behringer, D. Ming Ji and Leetmaa, A. (1998). An improved coupled model for ENSO prediction and implications for ocean initialisation. 1: The ocean data assimilation. Mon. Weather Rev., 126, 1013–212.0.CO;2>CrossRefGoogle Scholar
Burgers, G., Balmaseda, M., Vossepoel, F., Oldenborgh, G. J. and Leeuwen, P. J. (2002). Balanced ocean data assimilation near the equator. J. Phys. Oceanogr., 32, 2509–19CrossRefGoogle Scholar
Kessler, W. S. and Kleeman, R. (2000). Rectification of the Madden-Julian oscillation into the ENSO cycle. Mon. Weather Rev., 13, 3560–75Google Scholar
Landsea, C. W. and Knaff, J. A. (2000). How much skill was there in predicting the very strong 1997–8 El Niño?Bull. Am. Meteorol. Soc., 81, 2107–192.3.CO;2>CrossRefGoogle Scholar
Latif, M.et al. (2001). ENSIP: the El Niño simulation intercomparison project. Clim. Dynam., 18, 255–76CrossRefGoogle Scholar
Latif, M., Timmermann, A., A. Grötzner, C. Eckert and R. Voss (2002). On North Atlantic interdecadal variability: a stochastic view. In ‘Ocean Forecasting’, ed. Pinardi, N. and Woods, J., pp. 149–78. Springer Verlag
Lengaigne, M., Boulanger, J.-P., Menkes, C., Guilyardi, E., Delecluse, P. and Slingo, J. (2004). Westerly wind events and their influence on the coupled atmosphere ocean system: a review. In Earth's Climate, ed. C. Wang, S.-P. Xie and J. A. Carton, pp. 49–70. Geophysical Monograph Series 147. American Geophysical Union
McCreary, J. P. and Anderson, D. L. T. (1991). An overview of coupled ocean–atmosphere models of El Niño and the Southern Oscillation. J. Geophys. Res., 96, 3125–50CrossRefGoogle Scholar
McPhaden, M., 2003. Tropical Pacific Ocean heat content variations and ENSO persistence barriers. Geophys. Res. Lett., 30, 1480–3CrossRefGoogle Scholar
Michaelson, J. (1987). Cross-validation in statistical climate forecast models. J. Clim. Appl. Meteorol., 26, 1589–16002.0.CO;2>CrossRefGoogle Scholar
Murphy, A. H. (1988). Skill scores based on the mean square error and their relationship to the correlation coefficient. Mon. Weather Rev., 116, 2417–242.0.CO;2>CrossRefGoogle Scholar
Neelin, D.et al. (1998). ENSO theory. J. Geophys. Res., 103, C7, 14261–90CrossRefGoogle Scholar
Palmer, T. N. (2000). Predicting uncertainty in forecasts of weather and climate. Rep. Prog. Phys., 63, 71–116CrossRefGoogle Scholar
Palmer, T. N. and Anderson, D. L. T. (1994). Prospects for seasonal forecasting. Quart. J. Roy. Meteor. Soc., 120, 755–94Google Scholar
Palmer, T. N.et al. (2004). Development of a European multi-model ensemble system for seasonal to interannual prediction. Bull. Am. Meteorol. Soc., 85, 853–72CrossRefGoogle Scholar
Philander, S. G. (2004). Our Affair with El Niño. Princeton University PressGoogle Scholar
Smith, N. R., Blomley, J. E. and Meyers, G. (1991). A univariate statistical interpolation scheme for subsurface thermal analyses in the tropical oceans. Prog. Oceanogr., 28, 219–56CrossRefGoogle Scholar
Stockdale, T. N. (1997). Coupled atmospehere-ocean forecasts in the presence of climate drift. Mon. Weather Rev., 125, 809–182.0.CO;2>CrossRefGoogle Scholar
Stockdale, T. N., Anderson, D. L. T., Alves, J. O. S. and Balmaseda, M. A. (1998). Global seasonal rainfall forecasts using a coupled ocean-atmosphere model. Nature, 392, 370–3CrossRefGoogle Scholar
Troccoli, A., Balmaseda, M., Segschneider, J., et al. (2002). Salinity adjustments in the presence of temperature data assimilation. Mon. Wea. Rev., 130, 89–102. Also available online as ECMWF Technical Memorandum 305, at www.ecmwf.intGoogle Scholar
Uppala, S. M., Kallberg, P., Simmons, A. and 43 others (2005). The ERA-40 reanalysis. Quart. J. Roy. Meteor. Soc., Submitted. See also ECMWF Newsletter 101 (online) at www.ecmwf.int/publications/newsletters/CrossRefGoogle Scholar
Oldenborgh, G. J., Balmaseda, M., Ferranti, L., Stockdale, T. N. and Anderson, D. L. T. (2005). Did the ECMWF Seasonal forecast model outperform statistical ENSO forecast models over the last 15 years?J. Climate, 18, 3240–9CrossRefGoogle Scholar
Dool, H. M. (1994). Searching for analogues: how long must one wait?Tellus, 46A, 314–24CrossRefGoogle Scholar
Dool, H. M. and Barnston, A. G. (1994). Forecasts of global sea surface temperature out to a year using the constructed analogue method. In Proceedings of the 19th Climate Diagnostics Workshop, College Park, MD, pp. 416–19Google Scholar
Vialard, J., Vitart, F., Balmaseda, M., Stockdale, T. and Anderson, D. (2005). An ensemble generation method for seasonal forecasting with an ocean-atmosphere coupled model. Mon. Weather Rev., 133, 441–53CrossRefGoogle Scholar
Vitart, F., Balmaseda, M., Ferranti, L. and Anderson, D. (2003). Westerly wind events and the 1997/98 El Niño in the ECMWF Seasonal forecasting system. J. Climate, 16, 3153–702.0.CO;2>CrossRefGoogle Scholar
Wang, C. and Picaut, J. (2004). Understanding ENSO physics: a review. In Earth's Climate, pp. 21–48. Geophysical Monograph Series 147. American Geophysical UnionCrossRefGoogle Scholar
Webster, P. J. (1995). The annual cycle and the predictability of the tropical coupled ocean-atmosphere system. Meteorol. Atmos. Phys., 56, 33–55CrossRefGoogle Scholar
Xue, Y., Leetmaa, A. and Ji, M. (2000). ENSO prediction with Markov models: the impact of sea-level. J. Climate, 13, 849–712.0.CO;2>CrossRefGoogle Scholar

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  • Operational seasonal prediction
    • By David L. T. Anderson, European Centre for Medium-Range Weather Forecasts, Reading; Representing the ECMWF Seasonal Forecasting Section, Magdalena Balmaseda, Laura Ferranti, Tim Stockdale, Alberto; Troccoli, Kristian Mogensen, Arthur Vidard, Frederic Vitart
  • Edited by Tim Palmer, Renate Hagedorn
  • Book: Predictability of Weather and Climate
  • Online publication: 03 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511617652.020
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Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Operational seasonal prediction
    • By David L. T. Anderson, European Centre for Medium-Range Weather Forecasts, Reading; Representing the ECMWF Seasonal Forecasting Section, Magdalena Balmaseda, Laura Ferranti, Tim Stockdale, Alberto; Troccoli, Kristian Mogensen, Arthur Vidard, Frederic Vitart
  • Edited by Tim Palmer, Renate Hagedorn
  • Book: Predictability of Weather and Climate
  • Online publication: 03 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511617652.020
Available formats
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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Operational seasonal prediction
    • By David L. T. Anderson, European Centre for Medium-Range Weather Forecasts, Reading; Representing the ECMWF Seasonal Forecasting Section, Magdalena Balmaseda, Laura Ferranti, Tim Stockdale, Alberto; Troccoli, Kristian Mogensen, Arthur Vidard, Frederic Vitart
  • Edited by Tim Palmer, Renate Hagedorn
  • Book: Predictability of Weather and Climate
  • Online publication: 03 December 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511617652.020
Available formats
×