Abstract
Although it is impossible to forecast the weather more than a few days in advance, the science of seasonal climate forecasting is premised upon an ability to predict the general weather conditions over a prolonged period of time, without trying to predict the precise weather at any specific time during that period. The forecasting is possible only because sometimes, and primarily within tropical latitudes, the atmosphere is sensitive to unusual conditions at the earth’s surface, and especially at the sea surface. El Niño, and its counterpart La Niña, are the primary examples of such forcing conditions: during El Niño events, much of the equatorial Pacific Ocean is unusually hot (cold during La Niña), and the consequent changes to the heat and moisture supplied to the atmosphere can disrupt weather conditions in many parts of the globe. However, all seasonal climate forecasts involve a great deal of uncertainty, and a key aspect of forecasting at such time scales is to estimate the uncertainty in the prediction reliably. There are two sources of uncertainty in seasonal climate forecasting: the atmosphere is nowhere completely forced by conditions at the surface, but is free to vary according to its own internal dynamics; and the models used to predict the climate system are imperfect. These two sources of uncertainty are addressed by producing a set of model predictions: different initial weather conditions are used to represent the uncertainty from the internal dynamics, and different models to account for the uncertainties arising from imperfect model physics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Allan, R. J., J. A. Lindesay, and D. E. Parker (1996). El Niño – Southern Oscillation and Climatic Variability. CSIRO Publishing, Collingwood, 405 pp.
Barnston, A. G., H. M. van den Dool, S. E. Zebiak, T. P. Barnett, M. Ji, D. R. Rodenhuis, M. A. Cane, A. Leetmaa, N. E. Graham, C. R. Ropelewski, V. E. Kousky, E. A. O’Lenic, and R. E. Livezey (1994). Long-lead seasonal forecasts – where do we stand? Bulletin of the American Meteorological Society 75: 2097–2114.
Barnston, A. G., S. J. Mason, L. Goddard, D. G. DeWitt, and S. E. Zebiak (2003). Multi-model ensembling in seasonal climate forecasting at IRI. Bulletin of the American Meteorological Society 84: 1783–1796.
Barnston, A. G., A. Kumar, L. Goddard, and M. P. Hoerling (2005). Improving seasonal prediction practices through attribution of climate variability. Bulletin of the American Meteorological Society 86: 59–72.
Barsugli, J., J. S. Whitaker, A. F. Loughe, P. D. Sardeshmukh, and Z. Toth (1999). The effect of the 1997/98 El Niño on individual large-scale weather events. Bulletin of the American Meteorological Society 80: 1399–1411.
Bengtsson, L., U. Schlese, E. Roeckner, M. Latif, T. P. Barnett, and N. E. Graham (1993). A two-tiered approach to long-range climate forecasting. Science 261: 1026–1209.
Bjerknes, J. (1966). A possible response of the atmospheric Hadley circulation to equatorial anomalies of ocean temperature. Tellus 18: 820–829.
Bjerknes, J. (1969). Atmospheric teleconnections from the equatorial Pacific. Monthly Weather Review 97: 163–172.
Bjerknes, J. (1972). Large-scale atmospheric response to the 1964–65 Pacific equatorial warming. Journal of Physical Oceanography 2: 212–217.
Doblas-Reyes, F. J., R. Hagedorn, and T. N. Palmer (2005). The rationale behind the success of multi-model ensembles in seasonal forecasting – II. Calibration and combination. Tellus 57A: 234–252.
Glantz, M. H., R. W. Katz, and N. Nicholls (1991). Teleconnections Linking Worldwide Climate Anomalies: Scientific Basis and Societal Impact. Cambridge University Press, Cambridge, 545 pp.
Goddard, L., S. J. Mason, S. E. Zebiak, C. F. Ropelewski, R, Basher, and M. A. Cane (2001). Current approaches to seasonal-to-interannual climate predictions. International Journal of Climatology 21: 1111–1152.
Graham, R. J., A. D. L. Evans, K. R. Mylne, M. S. J. Harrison, and K. B. Robertson (2000). An assessment of seasonal predictability using atmospheric general circulation models. Quarterly Journal of the Royal Meteorological Society 126: 2211–2240.
Graham, R. J., M. Gordon, P. J. McLean, S. Ineson, M. R. Huddleston, M. K. Davey, A. Brookshaw and R. T. H. Barnes (2005). A performance comparison of coupled and uncoupled versions of the Met Office seasonal prediction general circulation model. Tellus 57A: 320–319.
Guérémy, J. -F., M. Déqué, A. Braun, and J. -P. Piedelièvre (2005). Actual and potential skill of seasonal predictions using the CNRM contribution to DEMETER: coupled versus uncoupled model. Tellus 57A: 308–319.
Hagedorn R., F. J. Doblas-Reyes, and T. N. Palmer (2005). The rationale behind the success of multi-model ensembles in seasonal forecasting – I. Basic concept. Tellus 57A: 219–233.
Harrison, M. S. J. (2005). The development of seasonal and inter-annual climate forecasting. Climatic Change 70: 201–220.
Inwards, R. (1994). Weather Lore: A Collection of Proverbs, Sayings and Rules Concerning the Weather. Senate, London, 190 pp.
Kumar, A. and M. P. Hoerling (1995). Prospects and limitations of seasonal atmospheric GCM predictions. Bulletin of the American Meteorological Society 76: 335–345.
Kumar, A., M. P. Hoerling, and A. G. Barnston (2001). Seasonal predictions, probabilistic verifications, and ensemble size. Journal of Climate 14: 1671–1676.
Marriott, P. J. (1981). Red Sky at Night Shepherd’s Delight!: Weather Lore of the English Countryside; 1900 Sayings Explained and Tested. Sheba Books, Oxford, 376 pp.
Mason, S. J. (2008). From dynamical predictions to seasonal forecasts. In: Understanding and Adapting to Climate Variability, A. Trocolli, M. S. J. Harrison, D. L. T. Anderson, and S. J. Mason (eds.). Springer Academic Publishers, Dordrecht, in press.
Mason, S. J. and L. Goddard (2001). Probabilistic precipitation anomalies associated with ENSO. Bulletin of the American Meteorological Society 82: 619–638.
Mason, S. J. and G. M. Mimmack (2002). Comparison of some statistical methods of probabilistic forecasting of ENSO. Journal of Climate 15: 8–29.
Mason, S. J., L. Goddard, N. E. Graham, E. Yulaeva, L. Sun and P. A. Arkin (1999). The IRI seasonal climate prediction system and the 1997/1998 El Niño event. Bulletin of the American Meteorological Society 80: 1853–1873.
Namias, J. (1991). Spring and summer 1988 drought over the contiguous United States – causes and prediction. Journal of Climate 4: 54–65.
Orlove, B. S., J. C. H. Chiang, and M. A. Cane (2000). Forecasting Andean rainfall and crop yield from the influence of El Niño on Pleiades visibility. Nature 403: 68–71.
Orlove, B. S., J. C. H. Chiang, and M. A. Cane (2002). Ethnoclimatology in the Andes. American Scientist 90: 428–435.
Palmer, T. N. and D. L. T. Anderson (1994). The prospects for seasonal forecasting – a review paper. Quarterly Journal of the Royal Meteorological Society 120: 755–793.
Palmer, T. N., A. Alessandri, U. Anderson, P. Cantelaube, M. Davey, P. Délécluse, M. Déqué, E. Díez, F. J. Doblas-Reyes, H. Feddersen, R. Graham, S. Gualdi, J. -F. Guérémy, R. Hagedorn, M. Hoshen, N. Keenlyside, M. Latif, A. Lazar, E. Maisonnave, V. Marletto, A. P. Morse, B. Orfila, P. Rogel, J. -M. Terres, and M. C. Thomson (2004). Development of a European ensemble system for seasonal to inter-annual prediction (DEMETER). Bulletin of the American Meteorological Society 85: 853–872.
Rajagopalan, B., U. Lall, and S. E. Zebiak (2002). Categorical climate forecasts through regularization and optimal combination of multiple GCM ensembles. Monthly Weather Review 130: 1792–1811.
Robertson, A. W., U. Lall, S. E. Zebiak, and L. Goddard (2004). Improved combination of multiple atmospheric GCM ensembles for seasonal prediction. Monthly Weather Review 132: 2732–2744.
Roeckner, E., K. Arpe, L. Bengtsson, M. Christoph, M. Claussen, L. Dümenil, M. Esch, M. Giorgetta, U. Schlese, and U. Schulzweida (1996). The atmospheric circulation model ECHAM-4: Model description and simulation of present-day climate. MPI-Rep. 218, MPI für Meteorologie, Hamburg, 90 pp.
Saha, S., S. Nadiga, C. Thiaw, J. Wang, W. Wang, Q. Zhang, H. M. van den Dool, H. -L. Pan, S. Moorthi, D. Behringer, D. Stokes, M. Peña, S. Lord, G. White, W. Ebisuzaki, P. Peng and P. Xie (2006). The NCEP Climate Forecast System. Journal of Climate 19: 3483–3517.
Shukla, J. (1998). Predictability in the midst of chaos: a scientific basis for climate forecasting. Science 282: 728–731.
Stockdale, T. N., D. L. T. Anderson, J. O. S. Alves, and M. Balmaseda (1998). Global seasonal rainfall forecasts using a coupled ocean-atmosphere model. Nature 392: 370–373.
Thomson, M. C., F. J. Doblas-Reyes, S. J. Mason, R. Hagedorn, S. J. Connor, T. Phindela, A. P. Morse, and T. N. Palmer (2006). Multi-model ensemble seasonal climate forecasts for malaria early warning. Nature 439: 576–579.
Uppala, S. M., P. W. Kållberg, A. J. Simmons, U. Andrae, V. da Costa Bechtold, M. Fiorino, J. K. Gibson, J. Haseler, A. Hernandez, G. A. Kelly, X. Li, K. Onogi, S. Saarinen, N. Sokka, R. P. Allan, E. Andersson, K. Arpe, M. A. Balmaseda, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, S. Caires, F. Chevallier, A. Dethof, M. Dragosavac, M. Fisher, M. Fuentes, S. Hagemann, E. Hólm, B. J. Hoskins, L. Isaksen, P. A. E. M. Janssen, R. Jenne, A. P. McNally, J. -F. Mahfouf, J.- J. Morcrette, N. A. Rayner, R. W. Saunders, P. Simon, A. Sterl, K. E. Trenberth, A. Untch, D. Vasiljevic, P. Viterbo, and J. Woollen (2005). The ERA-40 re-analysis. Quarterly Journal of the Royal Meteorological Society 131, 2961–3012, doi:10.1256/qj.04.176.
van den Dool, H. M. (1994). Searching for analogues: how long must one wait? Tellus 46A: 314–324.
van den Dool, H. M., J. Huang, and Y. Fan (2003). Performance and analysis of the constructed analogue method applied to US soil moisture over 1981–2001. Journal of Geophysical Research 108: D08617, doi:10.1029/2002JD003114.
Walker, N. D. and J. A. Lindesay (1989). Preliminary observations of oceanic influences on the February–March 1988 floods in central South Africa. South African Journal of Science 85: 164–169.
Ward, M. N. and C. K. Folland (1991). Prediction of seasonal rainfall in the north Nordeste of Brazil using eigenvectors of sea-surface temperatures. International Journal of Climatology 11: 711–743.
Zebiak, S. E. (1999). El Niño and the science of climate prediction. Consequences 5: 3–15.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science + Business Media B.V
About this chapter
Cite this chapter
Mason, S.J. (2008). “Flowering Walnuts in the Wood” and Other Bases for Seasonal Climate Forecasting. In: Thomson, M.C., Garcia-Herrera, R., Beniston, M. (eds) Seasonal Forecasts, Climatic Change and Human Health. Advances in Global Change Research, vol 30. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6877-5_2
Download citation
DOI: https://doi.org/10.1007/978-1-4020-6877-5_2
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-6876-8
Online ISBN: 978-1-4020-6877-5
eBook Packages: MedicineMedicine (R0)