Anderson, D. L. T., Sarachik, E. S., Webster, P. B. and Rothstein, L. M. (eds.): 1998, 'The TOGA decade: Reviewing the progress of El Nino research and prediction', J. Geophys. Res. 103C, 14167.

Barnston, A. G., Glantz, M. H. and He, Y.: 1999, 'Predictive skill of statistical and dynamical climate models in SST forecasts during the 1997-98 El Nino episode and the 1998 La Nina onset', Bull. Am. Meteorol. Soc. 80, 217. Basher, R., Clark, C., Dilley, M. and Harrison, M. S. J. (eds.): 2001, Coping with Climate: A Way Forward - Summary and Proposals for Action; Preparatory Report and Full Workshop Report, both published by the International Research Institute for Climate Prediction on behalf of the World Meteorological Organisation, NOAA, the South African Weather Bureau, USAID and the World Bank.

Ehrendorfer, M. and Murphy, A. M.: 1992, 'On the relationship between the quality and value of weather and climate forecasting systems', Idojaras 96, 187.

Ferranti, L. and Molteni, F.: 1999, 'Ensemble simulations of Eurasian snow depth anomalies and their influence on the summer Asian monsoon', Quart. J. R. Meteorol. Soc. 125, 2597.

Folland, C. K., Owen, J. A., Ward, M. N. and Colman, A. W.: 1991, 'Prediction of seasonal rainfall in the Sahel region using empirical and dynamical methods', J. Forecast. 10, 21.

Folland, C. K., Colman, A. W., Rowell, D. P. and Davey, M. K.: 2001, 'Predictability of northeast Brazil rainfall and real-time forecast skill, 1987-98', J. Clim. 14, 1937.

Glantz, M. H. (ed.): 2001, Once Burned, Twice Shy?: Lessons Learned from the 1997-98 El Nino, United Nations University, 294pp.

Glantz, M. H., Katz, R. W. and Nicholls, N.: 1991, Teleconnections Linking Worldwide Climate Anomalies, Cambridge University Press.

Goddard, L., Mason, S. J., Zebiak, S. E., Ropelewski, C. F., Basher, R. and Cane, M. A.: 2001, 'Current approaches to seasonal to inter-annual climate predictions', Int. J. Climatol. 21, 1111.

Graham, R. J., Evans, A. D. L., Mylne, K. R., Harrison, M. S. J. and Robertson, K. B.: 2000, 'An assessment of seasonal predictability using atmospheric general circulation models', Quart. J. R. Meteorol. Soc. 126, 2211.

Harrison, M.S.J. and Graham, N. E.: 2001, 'Forecast quality, forecast applications and forecast value: cases from southern African seasonal forecasts', WMO Bull. 50, 228.

Hastenrath, S.: 1991, Climate Dynamics of the Tropics, Kluwer Academic Publishers.

Kumar, K. K., Rajagopalan, B. and Cane, M. A.: 1999, 'On the weakening relationship between the Indian Monsoon and ENSO', Science 284, 2156.

Latif, M. and Barnett, T. P.: 1994, 'Causes of decadal climate variability over the North Pacific and North America', Science 266, 634.

Marriott, P. J.: 1981, Red Sky at Night, Shepherd's Delight? Weather Lore of the English Countryside, Sheba Books.

Mason, S. J. and Graham, N. E.: 2002, 'Areas beneath the relative operating characteristics (ROC) and levels (ROL) curves: Statistical significance and interpretation', Quart. J. R. Meteorol. Soc. 128, 2145.

Mjelde, J. W., Thompson, T. N., Nixon, C. J. and Lamb, P. J.: 1997, 'Utilising a farm-level decision model to help prioritise future climate prediction research needs', Meteorol. Appl. 4, 161.

Mullen, S.L. and Buizza, R.: 2002, 'The impact of horizontal resolution and ensemble size on probabilistic forecasts of precipitation by the ECMWF ensemble prediction system', Weather Forecast. 17, 173.

Nicholls, J. M.: 1996, Economic and Social Benefits of Climatological Information and Services: A Review of Existing Assessments, WMO/TD No. 780.

Nicholls, J. M.: 2002, 'WMO approach to seasonal-to-interannual forecasting and organised Regional Climate Centre services', WMO Bull. 51, 158.

Richardson, D. S., 2001: 'Measurements of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble size', Quart. J. R. Meteorol. Soc. 127, 2473.

Rodwell, M. J. and Folland, C. K.: 2002, 'Atlantic air-sea interaction and seasonal predictability', Quart. J. R. Meteorol. Soc. 128, 1413.

Ropelewski, C. F. andHalpert, M. S.: 1987, 'Global and regional scale precipitation patterns associated with the El Nino/Southern Oscillation', Mon. Weather Rev., 115, 1606.

Ward, M. N., Folland, C. K. and Parker, D. E.: 1989, Statistical Prediction of Seasonal Rainfall in the

Sahel and Other Tropical Regions, WMO/TD No. 261, 82. Ward, M. N. and Folland, C. K.: 1991, 'Prediction of seasonal rainfall in the north Nordeste of Brazil using eigenvectors of sea-surface temperature', Int. J. Climatol. 11, 711. World Meteorological Organisation: 1999, The 1997-1998 El Niño Event: A Scientific and Technical

Retrospective, WMO No. 905. Yu, L. and Rienecker, M. M.: 1999, 'Mechanisms for the Indian Ocean warming during the 1997-98 El Niño', Geophys. Res. Lett. 26, 735.

(Received 15 December 2003; in revised form 14 June 2004)



Department of Primary Industries and Fisheries, P.O. Box 102, Toowoomba, Queensland4350, Australia E-mail: [email protected]

Abstract. Climate variability and change affects individuals and societies. Within agricultural systems, seasonal climate forecasting can increase preparedness and lead to better social, economic and environmental outcomes. However, climate forecasting is not the panacea to all our problems in agriculture. Instead, it is one of many risk management tools that sometimes play an important role in decision-making. Understanding when, where and how to use this tool is a complex and multidimensional problem. To do this effectively, we suggest a participatory, cross-disciplinary research approach that brings together institutions (partnerships), disciplines (e.g., climate science, agricultural systems science, rural sociology and many other disciplines) and people (scientist, policy makers and direct beneficiaries) as equal partners to reap the benefits from climate knowledge. Climate science can provide insights into climatic processes, agricultural systems science can translate these insights into management options and rural sociology can help determine the options that are most feasible or desirable from a socio-economic perspective. Any scientific breakthroughs in climate forecasting capabilities are much more likely to have an immediate and positive impact if they are conducted and delivered within such a framework. While knowledge and understanding of the socio-economic circumstances is important and must be taken into account, the general approach of integrated systems science is generic and applicable in developed as well as in developing countries. Examples of decisions aided by simulation output ranges from tactical crop management options, commodity marketing to policy decisions about future land use. We also highlight the need to better understand temporal- and spatial-scale variability and argue that only a probabilistic approach to outcome dissemination should be considered. We demonstrated how knowledge of climatic variability (CV), can lead to better decisions in agriculture, regardless of geographical location and socio-economic conditions.

1. Introduction

Climatic variability (CV) occurs at widely varying temporal and spatial scales. This variability often impacts negatively on agricultural and natural ecosystems. Although floods and droughts have always been an integral part of human existence, our collective coping strategies have so far been limited by the complexity of systems responses to climate, environment and management and our inability to predict such systems dynamics. This has led to the development of conservative management approaches that usually fail to capitalise on the up-sides of CV and often only poorly buffer against the severe downsides.

Climatic Change (2005) 70: 221-253

© Springer 2005

The emerging ability to probabilistically forecast future seasons in terms of climate and its consequences on agricultural systems has started to influence decisionmaking at many levels. The potential benefits are substantial, but unfortunately adoption of new insights occurs more slowly and in a more haphazard way than was envisaged and is desirable. This is a consequence of the multi-faceted, multidimensional and cross-disciplinary nature of the problems.

It is our aim to outline the multi-dimensionality of the problems in order to assist in the process of establishing an operational framework to conduct what is sometimes referred to as 'end-to-end' applications (Basher, 2000; Manton et al., 2000). Such a framework might help us in achieving a better integration of the disciplinary components and hence better outcomes via improved management of agricultural systems. Much has been written about individual aspects of this subject and we would like to draw attention to some of the key publications on these issues, such as the books by Muchow and Bellamy (1991), Hammer et al. (2000), IRI (2000) and Sivakumar (2000). Further, and in addition to this special issue of Climatic Change, the following special journal issues of (i) Agriculture and Forest Meteorology (vol. 103, 2000) on Agrometeorology in the 21st century, (ii) Agricultural Systems (vol. 70, 2001) on Advances in systems approaches for agricultural development and (iii) Agricultural Systems (vol. 74, 2002) on Applying seasonal climate prediction to agricultural production need to be mentioned explicitly. We will draw on this material and cite individual contributions where appropriate.

It is important to point out that we do not explicitly distinguish between CV and climate change (CC). Instead, for the purpose of this paper, we regard CC as a low-frequency component of CV that can be managed using the same quantitative tools and approaches. This point will be made more clearly in the relevant sections, but needs to be stressed from the outset to avoid confusion.

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