2. Research and development

2.1 Seasonal forecasting

The major organisations undertaking research and development into seasonal forecasting are the Bureau of Meteorology Research Centre (BMRC) within the Bureau of Meteorology (BoM), the CSIRO Division of Atmospheric Research (DAR) supported by the CSIRO Division of Marine Research, and the CSIRO Division of Antarctic Research. Other organisations include the Queensland Department of Primary Industries (QDPI) through its climatologist at Toowoomba, and a number of private forecasters who provide services to individual clients. Details on the latter will be provided in the section on services.

The Bureau of Meteorology has been developing methods for seasonal climate predictions since the early 1900s (Nicholls 1997). The Bureau's National Climate Centre began preparing and testing monthly Seasonal Climate Outlooks in 1988, and issuing them publicly in 1989. These have been based primarily on statistical forecasts, although BMRC is also working with CSIRO in the development and use of General Circulation Models (GCM's). The latter include both atmospheric models and ocean-atmosphere models.

The Climate Modelling Program within the CSIRO Division of Atmospheric Research seeks a better understanding of climate and its variations in order to assess the way in which climate is likely to change in future due to the enhanced greenhouse effect and to natural climatic variability, and to predict climate variability up to 12 months ahead.

By building upon results from global climate models and limited-area models, the Climate Impact Group produces assessments of the likely impact of climatic change. The Group's scenarios have provided the basis for assessment studies performed by various groups throughout Australia and overseas and have played an important part in the policy-making process.

The Program makes a major contribution to the CSIRO Climate Change Research Program; and to the Climate Variability and Impacts Program, which encompasses a range of research activities examining climatic fluctuations and climatic extremes.

CSIRO Marine Research (CMR) has been developing an ocean observing network to support research on predictability of climate since 1983. Part of the network was transferred to the Bureau of Meteorology in 1996 to establish an operational framework and secure funding for the future. CMR maintains a portion of the network for research on climate processes.

CMR and BMRC established the Joint Australian Facility for Ocean Observing Systems in 1997 to carry out research on the design of ocean observing systems. CMR has been developing an ocean model since 1995 and validating it with observations from areas known to have an impact on Australian climate. The modelling project is partially supported by CVAP. The model is used as the ocean component of climate prediction systems being developed at DAR and BMRC.

The Queensland Centre for Climate Applications (QCCA), a new initiative of the Queensland Government, will enhance current climate applications research, development and extension being undertaken by the Department of Primary Industries and the Department of Natural Resources. QCCA is based in Toowoomba with a major node at the Indooroopilly Resource Sciences Centre and regional staff throughout the State.

QCCA will seek to improve both the long-term economic viability of Queensland's rural industries, and the sustainability of the natural resource base by developing knowledge and skills to combine climatic, agricultural and resource information that assists industry and policy makers to make better decisions. A primary goal of this initiative is to improve both on-farm and off-farm methods of 'best practice' for managing the impacts of climate variability and change. Major scientific and technical breakthroughs in agro-climatology have occurred in recent years to assist managers cope with the impacts of climatic variability.

QCCA will provide a range of practical benefits to Queensland by helping primary producers to better manage fluctuations in climate and thus to improve profits, sustainable economic development, and self reliance in managing drought. It will also significantly

assist Landcare objectives, the management of water, land and vegetation resources, and initiatives in property management planning (PMP);
strengthen research, development and collaborative links with national and international climate agencies so that landholders are kept at the forefront of advances in climatology, seasonal forecasting, and knowledge of climate change;
assist the Queensland Government to objectively assess drought situations and policy, reduce the cost of drought subsidies, and gain Federal drought funding assistance; and
build on existing products and services such as the RAINMAN, WHEATMAN and GRAZEON software, the Long Paddock Internet site, phone and fax SOI hotlines, the book Will It Rain?, and the Managing for Climate PMP workshops. For further information, contact Mr Colin Paull, Queensland Centre for Climate Applications, 80 Meiers Road, Indooroopilly, Qld 4068; Ph: (07) 3896 9587; Fax: (07) 3896 9843; Colin.Paull@dnr.qld.gov.au

Most research to date into the value and use of seasonal forecasting has been undertaken by the Queensland Department of Primary Industries (QDPI), in collaboration with CSIRO Tropical Agriculture, particularly with respect to crops and crop management, and by the National Rangelands Program of the CSIRO Division of Wildlife and Ecology, with some input from the Bureau of Resource Sciences and the Institute of Animal Science in Victoria.

A new project called "Oceans to Farms" is being initiated by CSIRO Tropical Agriculture, in collaboration with the CSIRO Division of Atmospheric Research, the Bureau of Meteorology Research Centre, the National Climate Centre of the Bureau of Meteorology, State Departments and the Queensland Centre for Climate Applications (QCCA), to identify what information on ocean climate is potentially useful in the management of agricultural systems. This project aims to evaluate forecasts based on ocean data (such as Sea Surface Temperatures) and intermediate-complexity coupled ocean-atmosphere models. New forecasts must be developed and evaluated in conjunction with industry needs, and because these needs vary by industry and location a collaborative approach is necessary. The value of these new forecasts will be tested for the grains, extensive grazing and sugar industries across Australia. The forecasts will be assessed using agricultural models that simulate production under a range of management strategies in a way that allows forecasts to be compared with no-forecasts. The output from the models will be used to assess economic impacts and to develop simple rules of thumb for adoption by industry.

References

1. Anon (1997a). Climate activities in Australia 1997. Bureau of Meteorology, Melbourne, 173 pp.

2. Anon (1997b). Review of the National Climate Variability R&D Program by Hassall & Associates Pty Ltd, Land and Water Resources Research and Development Corporation, Occasional Paper CV02/97

3. Nicholls, N. (1997). Developments in climatology in Australia: 1946-1996. Australian Meteorological Magazine 46, 127-135.

2.1.1 Statistical forecasts

The Climate Group within the Bureau of Meteorology Research Centre aims to document, explain and simulate the causes of major fluctuations and changes of the Australian climate, develop methods to monitor and predict climate variations, and investigate the impacts of climate variations on Australia. A significant proportion of its work has been targeted at developing and testing statistical forecasts.

Since 1989, the Bureau of Meteorology has been issuing seasonal outlooks for the next 3 months, based primarily on the Southern Oscillation Index (SOI) (Nicholls 1997). This is based on the long-term trend in the differences in atmospheric pressure between Darwin and Tahiti. The SOI has proved to be a reasonably reliable indicator over much of eastern Australia, with respect to spring and summer rainfall (McBride and Nicholls 1983). There is also a significant signal in the south-west of Western Australia associated with Indian Ocean influences. A new statistical forecast system, which uses global and regional patterns of sea surface temperatures (SSTs) as predictors, has been developed. The system generally shows more skill than the SOI-based operational system, and allows a lead time of two to three weeks. It has been in use operationally since late 1998. Both the SST-based scheme and the previous SOI-based scheme use discriminant analysis techniques to construct the forecast.

The phase of the SOI (Stone and Auliciems 1992; Stone et al. 1996a) is also being used to produce seasonal outlooks, the information being made available by the Queensland Department of Primary Industries to farmers and their advisers in north-eastern Australia. Five phases are used - falling, negative, neutral, rising and positive.

Summer rains over much of northern Australia are primarily associated with the onset of the monsoon season. The incidence of tropical cyclones crossing the Queensland coast is 15 times higher during a La Niña compared with an El Niño event (Hastings 1990). The SOI therefore provides a means of forecasting Australian seasonal tropical cyclonic activity (Nicholls 1992).

Droughts, including El Niño events, are typically associated with relatively cloud-free skies. It is therefore not surprising that the incidence of early and late frosts is elevated during El Niño events and has been related to the SOI (Stone et al. 1996b).

Other promising indicators of ENSO events include:

Sea surface level anomalies, as measured by the National Tidal Facility in Adelaide (Mitchell 1994). Sea level varies diurnally with the tides under predominantly lunar influence, but mean sea level varies with prevailing anomalies in windspeed and direction, water temperature, and in the long-term with the size of the arctic and antarctic ice packs. Positive anomalies in sea level have been detected in the north-west between Port Hedland and Darwin in the spring of years that precede years of low winter rainfall in southern Australia (Allan et al. 1990, Figure 2 of Mitchell 1994). These are probably related to the north-westerly jet stream that frequently blows from the Indian Ocean to bring rains to southern and south-eastern Australia. Sea level anomalies up to a year ahead of ENSO events have also been detected in the eastern sector of the Great Australian Bight (Figure 2 of Mitchell 1994). Similar anomalies documented in the 1996 issues of the Climate Diagnostics Bulletin preceded the 1997 ENSO event (Mitchell, pers. comm.). The National Tidal Facility web site is http://www.ntf.flinders.edu.au/.
Circumpolar currents in the Antarctic region (Professor Bill Budd, University of Tasmania, Antarctic CRC; Ph: (03) 6226 7867; fax (03) 6226 2973 w.f.budd@utas.edu.au ; Dr Rupert Summerson, Bureau of Resource Sciences; Ph: (02) 6272 4615; rupert.summerson@brs.gov.au). The Antarctic CRC Polar Atmosphere Program is concerned with the study of Antarctic Weather Systems and with the role of the Antarctic in the Global Climate System. The program combines observational activities with analysis and numerical modelling of weather and climate. Observational programs make use of satellites, drifting buoys, land-based weather stations and research cruises. The modelling programs aim to describe the linkages and feedbacks between the atmosphere, ocean, sea ice and Antarctic ice cap, creating an understanding of the overall system. Changing the circulation of the oceans - particularly of the deep oceans - results in a change of climate. Bearing in mind that the characteristics and circulation of perhaps 50 to 60 per cent of the sub-surface waters of the oceans are determined by what happens at the air-sea interface of the Southern Ocean surrounding Antarctica, the Antarctic 'sources' of deep water are in turn major controls on climate. Special emphasis has been on observing changes in the circumpolar currents, as these may be able to provide long lead times in the detection of El Niño and other global climatic events.

Specific research projects currently (or recently) being undertaken in this area include:

Development of improved climate forecasting systems (1993 - 1997) - Dr N. Nicholls, BMRC + States (LWRRDC)
Improved climate prediction during El Niño events - Dr W.J. Wright, BoM (LWRRDC)
SILO - Agrometeorological information systems - Mr A. Beswick, QDNR (LWRRDC)

Contacts and institutions

Dr Andris Auliciems, Applied Climate Research Unit, Department of Geographical Sciences and Planning, The University of Queensland, Qld 4072. Ph: (07) 3365 6455; Fax: (07) 3365 6899; a.auliciems@mailbox.uq.edu.au

Ms Lynda Chambers, Bureau of Meteorology Research Centre, GPO 1289K, Melbourne, Vic 3001. Ph: (03) 9669 4784; Fax: (03) 9669 4660; lec@bom.gov.au; http://www.bom.gov.au/bmrc/mrlr/lec/lec.htm

Dr Wasyl Drosdowsky, Bureau of Meteorology Research Centre, GPO Box 1289K, Melbourne, Vic. 3001. Ph: (03) 9669 4409; Fax: (03) 9669 4660; wld@bom.gov.au

Mr Graham de Hoedt, National Climate Centre, Bureau of Meteorology, GPO Box 1289K, Melbourne, Vic. 3001. Ph: (03) 9669 4714; Fax: (03) 9669 4678; g.dehoedt@bom.gov.au

Dr Bill Mitchell, National Tidal Facility, The Flinders University of South Australia, GPO Box 2100, Adelaide, S.A. 5001. Ph: (08) 8201 7525; Fax: (08) 8201 7523; bill@pacific.ntf.flinders.edu.au

Dr Neville Nicholls, Bureau of Meteorology Research Centre, GPO Box 1289K, Melbourne, Vic. 3001. Ph: (03) 9669 4407; Fax: (03) 9669 4660; nnn@bom.gov.au

Dr Roger Stone, Queensland Department of Primary Industries, PO Box 102, Toowoomba, Qld 4350. Ph: (07) 4688 1293; Mobile: 0412 559 408; Fax: (07) 4688 1193; rogers@apsrusg.sth.dpi.qld.gov.au

2.1.2 General Circulation Models (GCMs)

Lead times in terms of years rather than months are needed to attain significant financial benefits in many pastoral systems. There is therefore a robust case for further research to extend seasonal forecasts to time periods that are annual and beyond.

GCMs of the global climate have been shown to offer more promise in extending forecasts from 3 to 12 months than the SOI, particularly in terms of forecasting changes in SSTs in the central and eastern tropical Pacific (Kleeman et al. 1995). This longer lead time would certainly be more useful to livestock producers. The GCMs have yet to be properly tested for rainfall prediction, although their SST predictions can be used statistically to estimate changes in the SOI and rainfall with reasonable success.

The Bureau of Meteorology has been using a coupled ocean-atmosphere model to predict El Niño since 1994. The model has a highly simplified vertical structure and representation of SST. BMRC and CSIRO, with support from the Climate Variability in Agriculture R&D Program (CVAP), are developing a much more realistic model, considered the most promising approach to accurate predictions with extended lead times.

The climate group at The University of Melbourne (School of Earth Sciences) is using their GCM to understand the variability of rainfall over Australia, particularly the mechanisms producing droughts and floods. Investigations have addressed the impact of oceanic conditions (e.g. Simmonds 1990; Simmonds and Rocha 1991).

The studies of Simmonds and Lynch (1992), Simmonds (1993) and Simmonds and Hope (1998a) are designed to quantify with models the influence of surface conditions (and particularly soil moisture content). An on-going series of model experiments is being conducted to explore the influence of changes in large-scale circulation on Australian rainfall (Simmonds et al. 1992a, b).

Specific research projects currently (or recently) being undertaken in this area include:

Atlas of near-global ENSO and climate variability since 1871 - Dr Rob Allan, CSIRO Division of Atmospheric Research (LWRRDC)
Development and testing of climate models for seasonal prediction for Australia - Dr Gary Meyers, CSIRO Marine Research (LWRRDC)
Extended seasonal climate predictions using a dynamical climate model - Dr Gary Meyers, CSIRO Marine Research and Dr Neville Smith, Bureau of Meteorology Research Centre (LWRRDC)
From oceans to farms: integrated management of climate variability - Dr Andrew Ash, CSIRO Tropical Agriculture (LWRRDC)
Global Seasonal Climate Forecasts for Australian Environmental Outlooks - Mr R.A. Young, QDNR (DISR, Department of Industry, Science and Resources; Industry, Science and Technology Program - ISTP)
Air-sea exchange processes - Mr F. Bradley, CSIRO Land and Water

References

1. Allan, R., Lindesay, J. and Parker, D. (1996). El Niño Southern Oscillation and climatic variability. (Atlas and CD-Rom), CSIRO Publishing, 408 pp.

2. Drosdowsky, W. (1993). Potential predictability of winter rainfall over southern and eastern Australia using Indian Ocean sea-surface temperature anomalies. Australian Meteorology Magazine 42, 1-6.

3. Hunt, B.G. (1994). The use of global climatic models in deriving seasonal outlooks. Agricultural Systems & Information Technology 6(2), 11-15.

4. Hunt, B.G. (1997). Global climatic models: long-term predictions, annual to decadal. In Climate prediction for agricultural and resource management, edited by R.K. Munro and L.M. Leslie, Australian Academy of Science Conference, Canberra, 6-8 May 1997, Bureau of Resource Sciences, Canberra, pp. 31-44.

5. Kleeman, R., Moore, A.M. and Smith, N.R. (1995). Assimilation of subsurface thermal data into a simple ocean model for the initialization of an intermediate tropical coupled ocean-atmosphere forecast model. Monthly Weather Review 123, 3103-3113.

6. Meyers, G. (1996). Variation of Indonesian throughflow and the El Niño - Southern Oscillation. Journal of Geophysical Research 101(C5), 12255-12263.

7. Meyers, G., Schiller, A., Godfrey, J.S. and McIntosh, P. (1997). Oceanic influences on variability of climate in Australia. In Climate prediction for agricultural and resource management, edited by R.K. Munro and L.M. Leslie, Australian Academy of Science Conference, Canberra, 6-8 May 1997, Bureau of Resource Sciences, Canberra, pp. 61-68.

8. McIntosh, P., Meyers, G., Godfrey, S. and Schiller, A. (1996). The role of the ocean in Australia's climate variability. Proceedings of the Second Australian Conference on Agricultural Meteorology, 1-4 October 1996, The University of Queensland, pp. 107-

9. Schiller, A., Godfrey, J.S., McIntosh, P.C., Meyers, G. and Wijffels, S.E. (1998). Seasonal near-surface dynamics and thermodynamics of the Indian Ocean and Indonesian Throughflow, in a Global Ocean General Circulation Model. Journal of Physical Oceanography (in press).

10. Schiller, A., Godfrey, J.S., McIntosh, P.C., Meyers, G. and Fiedler, R. (1998). Interannual dynamics and thermodynamics of the Indo-Pacific Oceans. Journal of Physical Oceanography (submitted).

11. Simmonds, I. (1990). A modelling study of winter circulation and precipitation anomalies associated with Australian region ocean temperatures. Australian Meteorological Magazine 38, 151-162.

12. Simmonds, I. (1993). Land surface-atmosphere interactions and the variability and persistence of largescale rainfall and soil moisture. Trends in Geophysical Research 2, 157-171.

13. Simmonds, I. and Hope, P. (1998a). Analysis of agencies determining the longevity of Australian rainfall deviations. Research Activities in Atmospheric and Oceanic Modelling, Report No. 27, WMO/TD-No. 865. Edited by A. Staniforth, World Meteorological Organization, 7.36-7.37.

14. Simmonds, I., and Lynch, A.H. (1992). The influence of pre-existing soil moisture content on Australian winter climate. International Journal of Climatology 12, 33-54.

15. Simmonds, I., and Rocha, A. (1991). The association of Australian winter climate with ocean temperatures to the west. Journal of Climate 4, 1147-1161.

16. Simmonds, I., Rocha, A. and Walland, D. (1992a). Studies with dynamical tropical forcing. Research Activities in Atmospheric and Oceanic Modelling, Report No. 17 WMO/TD-No. 467, edited by G. J. Boer, World Meteorological Organization, 7.55-7.57.

17. Simmonds, I., Rocha, A. and Walland, D. (1992b). Consequences of winter tropical pressure anomalies in the Australian region. International Journal of Climatology 12, 419-434.

Contacts and institutions

Dr Rob Allan, CSIRO Division of Atmospheric Research, PMB1 Aspendale. Vic. 3195. Ph: (03) 9239 4540; Fax: (03) 9239 4444; chris.mitchell@dar.csiro.au

Dr Carsten Fredericksen, Bureau of Meteorology Research Centre, GPO Box 1289K, Melbourne, Vic. 3001. Ph: (03) 9669 4566; Fax: (03) 9669 4660; nnn@bom.gov.au

Mr Barrie Hunt, CSIRO Division of Atmospheric Research, PMB1 Aspendale. Vic. 3195. Ph: (03) 9239 4680; Fax: (03) 9239 4444; chris.mitchell@dar.csiro.au

Dr Richard Kleeman, Bureau of Meteorology Research Centre, GPO Box 1289K, Melbourne, Vic. 3001. Ph: (03) 9669 4484; Fax: (03) 9669 4660; nnn@bom.gov.au

Dr Janette Lindesay, Geography Department, Australian National University, ACT 0200. Ph: (02) 6249 4921; Fax: (02) 6249 3770; janette.lindesay@anu.edu.au

Dr Peter McIntosh, CSIRO Marine Research, GPO Box 1538, Hobart, Tas. 7001. Ph: (03) 6232 5390; Fax: (03) 6232 5123; mailto:gary.meyers@marine.csiro.au

Dr Gary Meyers, CSIRO Marine Research, GPO Box 1538, Hobart, Tas. 7001. Ph: (03) 6232 5208; Fax: (03) 6232 5123; gary.meyers@marine.csiro.au

Dr Chris Mitchell, CSIRO Division of Atmospheric Research, PMB1, Aspendale. Vic. 3195. Ph: (03) 9239 4550; Fax: (03) 9239 4444; chris.mitchell@dar.csiro.au

Associate Professor Ian Simmonds, School of Earth Sciences, The University of Melbourne
Parkville, Vic. 3052. Ph: (03) 9344 7216; Fax: (03) 9344 7761; ihs@met.unimelb.edu.au

Dr Peter Whetton, CSIRO Division of Atmospheric Research, PMB1, Aspendale. Vic. 3195. Ph: (03) 9239 4535; Fax: (03) 9239 4444; chris.mitchell@dar.csiro.au

2.1.3 Seasonal forecasts based on synoptic scale study of the upper atmosphere

Forecasting Drought from Teleconnections (I. Cordery)

Strong relationships have been developed between both global (Southern Oscillation Index) and local (geopotential height) phenomena in one season and the occurrence of low precipitation in any of the next 4 seasons. The relations explain more than 50 per cent of the variance in the precipitation for regions of up to 700,000km² and more than 35 per cent of the variance for up to 1.3 x 10⁶km²in eastern Australia. The strong relationships occurred when precipitation was regressed against one of the variables in years which were selected on the basis of the magnitude of a third, so called partitioning, variable. The relations are shown to be able to provide forecasts of precipitation in all seasons of the year. Statistical tests show that the strong forecasting relations could not occur by chance.

Forecasting drought in southern Australia

A long- and medium-range rainfall and crop yield forecasting model has been developed by Holton (1996, 1997) using mean sea level pressure differences and 500 hpa geopotential height differences between stations over the general northern Australian and Indian/Pacific Ocean area. These pressure and height stations are located in areas found by Drosdowsky and Williams (1991) to correlate highly with the SOI in the current and preceding year. The pressure and height difference are therefore a measure of the current and preceding years' strength of the ENSO phenomenon over a much wider area than that calculated by the SOI.

Specific research projects currently (or recently) being undertaken in this area include:

Seasonal rainfall and winter crop yield forecasting for southern Australia. Mr J.P. Egan, South Australian Research and Development Institute, PIRSA; Mr I Holton, Bureau of Meteorology, Adelaide Regional Office; Dr W.J. Grace, Bureau of Meteorology Research Centre, Melbourne (LWRRDC).

References

1. Drosdowsky, W. amd Williams, M. (1991). The Southern Oscillation in the Australian region. Part 1: anomalies at the extremes of the Oscillation. Journal of Climate 4, 619-638.

2. Holton, I. (1996). Seasonal and yearly prediction of rainfall and crop yields in Australia. Proceedings of the Second Australian Conference on Agricultural Meteorology, 1-4 October 1996, The University of Queensland, pp. 127-129.

3. Holton, I. (1997). Prediction of growing season rainfall and crop yields in southern Australia. Australian Meteorology Magazine (in press).

4. Holton, I. and Egan, J.P. (1997). Developing better forecasts for seasonal rainfall and crop yields in southern Australia. In Proceedings of Farming Systems Developments 1997 Workshop, Adelaide, March 1997, pp. 189-191.

5. Opoku-Ankomah,Y., and Cordery, I., (1993). Temporal variation of relations between NSW Rainfall and Southern Oscillation. International Journal of Climatology 13, 51-64.

Contacts:

Associate Professor Ian Cordery, School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, 2052. Ph: (02) 9385 5024; Fax: (02) 9385 6139; I.Cordery@unsw.edu.au

Mr Ian Holton, PO Box 728, Nairne SA 5252; Ph: (08) 8388 6700; Fax: (08) 8388 6788; holton7@senet.com.au

2.1.4 Climate analysis, interpretation and monitoring

The University of Melbourne climate group is performing a wide variety of climate analyses aimed at understanding Australia rainfall variability. Records covering most of this century are being used to, for example, quantify the persistence of rainfall deviations, both when the effect of the SOI is both included and excluded (Simmonds and Hope 1997, 1998b).

Atmospheric water vapour budget studies using high quality meteorological analyses are now being undertaken to dissect the transport patterns responsible for extreme rainfall seasons. This approach is being successfully applied to studying analogous rainfall variations over China (Simmonds and Bi 1997; Simmonds et al. 1998).

Cyclonic weather systems are known to be an important component in rainfall totals, particularly in southern Australia. The Melbourne University group has developed a sophisticated automatic algorithm for tracking cyclones from model output or observed analyses (Murray and Simmonds 1991; Simmonds and Murray 1998; Simmonds et al. 1998). Considerable progress is being made in terms of relating the inter-annual and inter-decadal variation of cyclone numbers to the south of Australia to similar variations of southern Australian rainfall. This is being done with the automatic scheme, as well as with manual techniques (Leighton 1997; Leighton et al. 1997).

Antarctica clearly plays a role in the weather and climate of southern Australia. The ice and the distribution of rain-bearing cyclonic systems to the south of Australia appear to be linked in subtle ways, and the climate group at the University of Melbourne have explored these links, using both models and analysis of observed data (Simmonds and Wu 1993a, 1993b; Watkins and Simmonds 1995; Godfred-Spenning and Simmonds 1996; Simmonds 1996)

Cold outbreaks significantly affect land-based activities, particularly grazing. The Antarctic connection of a significant number of these has been established (Perrin and Simmonds 1995a, 1995b). The Southern Oscillation is known to be a significant influence on rainfall over Australia, and the Oscillation appears to influence, and be influenced by, the extent of sea ice around the Antarctic continent (Simmonds and Jacka, 1995).

The ocean plays a crucial role in the climate system because ocean currents and subsurface thermal structure vary slowly over months to years, in contrast to the daily fluctuations of weather in the atmosphere. When the slow ocean processes influence sea surface temperature and the interface to the atmosphere, the climate system develops a degree of predictability associated with the slow time scale of the ocean.

CSIRO Marine Research has a number of projects focussed on understanding ocean processes from an observational perspective. Variability of heat exchange between the ocean and the atmosphere has been identified as a crucial process in the Australian region (Godfrey et al.1998). Also, the transfer of heat from the Pacific to the Indian Ocean by the Indonesian throughflow is an important regional process (Meyers 1996).

Variation of deep ocean thermal structure associated with known patterns of sea surface temperature in the Indian Ocean have been identified (Meyers 1998). As our understanding of processes develops, the insights are used to validate and improve the CMR ocean model (Schiller et al. 1998a; Schiller et al.1998b).

New ways of determining the quantity and spatial and temporal distribution of rainfall are being investigated. This includes making use of cloud-top temperatures (Bryceson 1994), radar monitoring and neural networks.

Specific research projects currently (or recently) being undertaken in this area include:

Spatial distribution of rainfall and storm movement (using remote sensing) Mr J. Elliott, Bureau of Meteorology

References:

1. Bryceson, K. (1994). Using GMS satellite data to estimate the areal location and quantity of rain. Agricultural Systems & Information Technology 6(2), 37, 51-53.

2. Godfred-Spenning, C.R. and Simmonds, I. (1996). An analysis of Antarctic sea-ice and extratropical cyclone associations. International Journal of Climatology 16, 1315-1332.

3. Godfrey, J.S., Houze Jr., R.A., Johnson, R.H., Lukas, R., Redelsperger, J.-L., Sumi, A. and Weller, R. (1998). Coupled Ocean-Atmosphere Response Experiment (COARE): An interim report. Journal of Geophysical Research, 103, 14,395-14,450.

4. Leighton, R.M. (1997). Variations in annual cyclonicity across the Australasian region during the 29 year period 1965-1993. Proceedings of the Fifth International Conference on Southern Hemisphere Meteorology and Oceanography, Pretoria, South Africa, 7-11 April 1997. American Meteorological Society, 362-363.

5. Leighton, R.M., Keay, K. and Simmonds, I, (1997). Variation in annual cyclonicity across the Australian region for the 29-year period 1965-1993 and relationships with annual Australia rainfall. Climate Prediction for Agricultural and Resource Management. Edited by R. K. Munro and L. M. Leslie, Bureau of Resource Sciences, Australia, pp. 257-267.

6. Meyers, G. (1996). Variation of Indonesian throughflow and the El Niño - Southern Oscillation. Journal of Geophysical Research 101(C5), 12255-12263.

7. Meyers, G. (1998). Variation of the Tropical Indian Ocean Observed by XBT and Altimeter. In: Proceedings of the Second Symposium on Integrated Observing Systems, American Meteorological Society, Boston, MA.

8. Meyers, G., Schiller, A., Godfrey, J.S. and McIntosh, P. (1997). Oceanic influences on variability of climate in Australia. In Climate prediction for agricultural and resource management, edited by R.K. Munro and L.M. Leslie, Australian Academy of Science Conference, Canberra, 6-8 May 1997, Bureau of Resource Sciences, Canberra, pp. 61-68.

9. McIntosh, P., Meyers, G., Godfrey, S. and Schiller, A. (1996). The role of the ocean in Australia's climate variability. Proceedings of the Second Australian Conference on Agricultural Meteorology, 1-4 October 1996, The University of Queensland, pp. 107-110.

10. Milford, J.R. and Dugdale, G. (1990). Estimation of rainfall using geostationary satellite data. In Applications of remote sensing in agriculture, edited by M.D. Steven and J.A. Clark, Butterworths, London, pp. 97-110.

11. Murray, R.J. and Simmonds, I. (1991). A numerical scheme for tracking cyclone centres from digital data. Part I: Development and operation of the scheme. Australian Meteorological Magazine 39, 155-166.

12. Perrin, G. and Simmonds, I. (1995). The origin and characteristics of cold air outbreaks over Melbourne. Australian Meteorological Magazine 44, 41-59.

13. Perrin, G. and Simmonds, I. (1995): Trajectory analysis applied to the study of 'cold outbreaks' over southern Australia. Abstracts Volume, International Union of Geodesy and Geophysics XXI General Assembly, Boulder, Colorado, 2-14 July, 1995. American Geophysical Union, pp. 299.

14. Schiller, A., Godfrey, J.S., McIntosh, P.C., Meyers, G. and Wijffels, S.E. (1998a). Seasonal near-surface dynamics and thermodynamics of the Indian Ocean and Indonesian Throughflow, in a Global Ocean General Circulation Model. Journal of Physical Oceanography (in press).

15. Schiller, A., Godfrey, J.S., McIntosh, P.C., Meyers, G. and Fiedler, R. (1998b). Interannual dynamics and thermodynamics of the Indo-Pacific Oceans. Journal of Physical Oceanography (submitted).

16. Simmonds, I. (1996). Climatic role of Southern Hemisphere extratropical cyclones and their relationship with sea ice. Papers and Proceedings of the Royal Society of Tasmania 130, 95-100.

17. Simmonds, I. and Bi, D. (1997). The three-dimensional characteristics of moisture transports over China in dry and wet years. Proceedings of the Fifth International Conference on Southern Hemisphere Meteorology and Oceanography, Pretoria, South Africa, 7-11 April 1997. American Meteorological Society, pp. 286-287.

18. Simmonds, I. Bi, D. and Hope, P. (1998). Atmospheric water vapor flux and its association with rainfall over China in summer. Journal of Climate, (accepted).

19. Simmonds, I. and Hope, P. (1997). Memory in precipitation over the Australian continent. Proceedings of the Fifth International Conference on Southern Hemisphere Meteorology and Oceanography, Pretoria, South Africa, 7-11 April 1997. American Meteorological Society, pp. 284-285.

20. Simmonds, I., and Hope, P. (1998b). Seasonal and regional responses to changes in Australian soil moisture conditions. International Journal of Climatology (in press).

21. Simmonds, I., and Jacka, T.H. (1995). Relationships between the interannual variability of Antarctic sea ice and the Southern Oscillation. Journal of Climate 8, 637-647.

22. Simmonds, I. and Murray, R.J. (1998): Southern extratropical cyclone behavior in ECMWF analyses during the FROST Special Observing Periods. Weather and Forecasting, (accepted).

23. Simmonds, I., Murray, R.J. and Leighton, R.M. (1998). A refinement of cyclone tracking methods with data from FROST. Australian Meteorological Magazine (accepted).

24. Simmonds, I. and Wu, X. (1993): Winter Antarctic sea ice and extratropical cyclone activity. Research Activities in Atmospheric and Oceanic Modelling, Report No. 18 WMO/TD-No. 533, edited by G. J. Boer, World Meteorological Organization, 7.37-7.39.

25. Simmonds, I., and Wu, X. (1993). Cyclone behaviour response to changes in winter Southern Hemisphere sea-ice concentration. Quarterly Journal of the Royal Meteorological Society 119, 1121-1148.

26. Watkins, A.B. and Simmonds, I. (1995). Sensitivity of numerical prognoses to Antarctic sea ice distribution. Journal of Geophysical Research 100, 22,681-22,696.

2.1.5 Value of seasonal forecasts

The value of a seasonal outlook depends on the skill or accuracy of the forecast, and its marginal value relative to other readily available sources of information to the manager of a particular production system. If the information is ignored, or it does not lead to changed decisions, it has no economic impact or value (Freebairn 1996). If the forecast is inaccurate then the information is likely to have negative value in the current season.

Benefits from accurate forecasts in one year can be more than totally offset by an incorrect forecast in another year. Other sources of information such as available feed or soil moisture, or the fact that a farm is stocked well below capacity, may render a forecast of limited value. Flock or herd structure, and the number or timing of decisions on a farm, may also mean that a lead time of only 3 months is of limited value. Likewise it should be kept in mind that mean annual rainfall, inter- and intra-year variability in rainfall, climatic systems, the reliability of different indices for seasonal forecasting, and major agricultural systems all vary dramatically across Australia. The reliability of seasonal outlooks also varies with time of year. From a national viewpoint one also needs to take into account the adoption rate by farmers, and whether there are net benefits to the economy (Crellin 1988).

The benefits of improved seasonal forecasts vary between industries and across Australia. Soils and vegetation in pastoral areas exposed to high climate variability can benefit through de-stocking in advance of drought so as to avoid overgrazing, stock losses and accelerated erosion. Crop producers can assess whether or not to sow or fertilise a crop if the chance of a harvest is significantly diminished. Demands for irrigation water can be better estimated.

Cropping and irrigation

The value of seasonal forecasts to crop producers can be significant, but it varies with management and initial conditions, as well as with cropping systems and location (e.g. Hammer et al. 1996; Marshall et al. 1996). The forecasts can influence decisions on when and what area to sow, and whether to irrigate and/or fertilise a crop.

Monitoring the SOI can aid in the forecasts of wheat yields in Australia, near the date of sowing and well before harvest (Rimmington and Nicholls 1993). They also observed a negative correlation with the SOI in the year before sowing, due in part to the tendency for years of positive SOI (wet years) to follow years of negative SOI (dry years) and vice versa.

Clewett et al. (1991) used a crop model and 60 years of historical climate records to study grain sorghum production from a shallow storage irrigation scheme in Queensland. They aimed to determine the optimum design of a shallow farm dam and optimise irrigation scheduling. They showed that spring values of the SOI are linked to subsequent and large changes in the probability distributions of rainfall, runoff, water storage, crop production and gross margins. Growing crops in seasons with a strongly negative SOI before planting were unprofitable, compared with seasons with a strongly positive SOI before planting. SOI data can therefore be used to adjust the management strategy according to the level of climatic risk.

In the northern part of the Australian grain belt, significant increases in profit (up to 20 per cent) and/or reduction in risk (up to 35 per cent) can be achieved with wheat crops based on a seasonal forecast available at planting time (Hammer et al. 1996). This can be achieved through tactical adjustment of nitrogen fertiliser application or cultivar maturity with significant financial benefits (Marshall et al. 1996).

Opportunistic crops are those that are not sown every year, only when moisture conditions are adequate. For example, if soils in north-western Victoria have adequate moisture in October, then a sunflower crop can be sown with a high probability of a good harvest (Jessop 1977). In a similar way, seasonal forecasts can be used to determine whether a particular cereal, oilseed or legume crop should be sown, based in particular on the probability of a favourable harvest. There seems scope to do this in Queensland with sorghum crops (Nicholls 1986; Hammer et al. 1996).

The El Niño-Southern Oscillation (ENSO) has a dominant effect on climate in a number of the world's large-scale irrigation areas. Dudley and Hearn (1993) modified existing models to examine irrigation options for cotton growers in the highly variable, summer rainfall environment of the Namoi Valley of northern New South Wales. They showed that expected returns were markedly reduced if the SOI was negative at planting time. However, they concluded that little could be done in the way of operational management decisions to reduce the harmful effects of a dry SOI event, or enhance the beneficial effects of a wet SOI event, given the current water allocation scheme. They suggested that if irrigators knew the current SOI before the commencement of each cotton season, more profitable timing of investment in plant and equipment might result. These benefits might be extended to suppliers of farm inputs and to processors.

Rangelands

A large part of the rangelands in the eastern half of Australia is particularly sensitive to the climatic events of El Niño, with consequences for stocking rate and land degradation. A policy of reducing stocking rate on the basis of El Niño forecasts can significantly reduce environmental degradation in adverse seasons (McKeon and White 1992).

Stafford Smith et al. (1996) evaluated SOI forecasts as a means of managing property stocking rates in the Charters Towers region of northern Queensland. Forecasting strategies appeared to have little benefit given today's skills with the SOI, but improved skill has considerable potential both to improve economic returns and to help protect the resource. Sensitivity studies showed that the system being studied was particularly sensitive to assumptions about buying and selling decisions, and long-term impacts on vegetation condition.

Stafford Smith (1996) concluded that:

1. forecasting with the current skill and currently suggested tactics based on SOI values has modest long-term economic benefits for grazing enterprises at present, at least in the Dalrymple Shire in Queensland;

2. forecasting with the current skill and tactics based on SOI values can provide some benefits in terms of long-term resource protection;

3. forecasting with possible future skill based on new climate modelling approaches has considerable potential for economic and environmental benefits in the future, and there may also be better tactics for using the current SOI skill;

4. getting stocking rate strategies right will have more immediate and bigger economic and environmental benefits.

In summary, the financial benefits of seasonal forecasts may not be easily realised based on existing skill levels, lead times and decision points (Stafford Smith et al. 1999).

Clewett and Drosdowsky (1996) found that winter SOI values were useful in terms of reducing the risks of lost animal production, overgrazing, land degradation, and costs associated with supplementary feeding. However, they noted that many primary producers make important management decisions during autumn when climate forecasting has low skill. They therefore concluded that future climate research needs to target this 'predictability gap'.

In the more remote areas of northern Australia, cattle graze extensively on sub-tropical grasslands. Management inputs are kept to a minimum, stock being handled only at the time of the annual muster, usually in summer, so that seasonal forecasts are of limited use.

Temperate grasslands

Preliminary studies using models of grassland systems have shown that even high skill levels appear to offer low financial benefits in the medium term, despite increased animal welfare and protection for soils and vegetation (Bowman et al. 1995; Stafford Smith et al. 1996).

Morley (1994a, 1994b) concludes, after examining rainfall data for Bendigo, Holbrook and Goondiwindi, that most variations in rainfall are essentially random. He asserts that having information on the SOI for the months June to August adds little accuracy to predicting how much to feed stock in late summer-autumn in southern Australia, compared with information on August and September rainfall, the amount of pasture dry matter in December, and the pasture dry matter in other months.

Bowman et al. (1995) examined the value of seasonal outlooks to wool producers in northern and Western Victoria assuming forecast accuracies for the next 12 months of 60, 80 and 100 per cent. They concluded that the financial benefits for wool producers of reliable seasonal outlooks in southern Australia are probably substantially less than generally anticipated, although there could be significant benefits in protection of the natural resource base, and in reducing livestock deaths. The more accurate the seasonal forecasts, the better was the long-term financial performance of the farm.

These preliminary studies in the grasslands and rangelands of Australia show that the financial benefits of seasonal outlooks to managers of grazing livestock may not be easily realised. This appears to be a consequence of a) inadequate climate forecasting skills over much of the continent, including insufficient lead times, and b) few decision points and limited flexibility with grazing livestock systems, requiring longer lead times in which to prepare for adverse events. Such studies highlight the need for further research to determine whether and how the management and timing of decisions of many grassland systems should be modified to take advantage of forecast information.

Specific research projects currently (or recently) being undertaken in this area include:

Grazier-based profitable and sustainable strategies for managing climate variability - Dr Mark Stafford Smith, CSIRO Division of Wildlife & Ecology (LWRRDC)
The application of climate forecasts to crop management in northern Australia - Dr Peter Carberry, APSRU
Evaluating the role of seasonal climate forecasting in tactical management of cropping systems in north-east Australia - Dr Roger Stone, APSRU

References

1. Bowman, P.J., McKeon, G.M. and White, D.H. (1995). The impact of long range climate forecasting on the performance of sheep flocks in Victoria. Australian Journal of Agricultural Research 46, 687-702.

2. Clewett, J.F. and Drosdowsky, L. (1996). Study 1: Use of the Southern Oscillation Index as a drought management tool. In DroughtPlan: Building on participation by D.M. Stafford Smith, J.F. Clewett, A.D. Moore, G.M. McKeon, R. Clark and many others (1996b). Full project report, Working paper No. 10, pp 120-124.

3. Clewett, J.F., Howden, S.M., McKeon, G.M. and Rose, C.W. (1991). Optimising farm dam irrigation in response to climatic risk. In R.C. Muchow and J.A. Bellamy, (eds). Climatic risk in crop production: models and management for the semiarid tropics and subtropics, Wallingford (CAB International), pp. 307-328.

4. Crellin, I.R. (1988). Potential improvements in the provision of long range weather forecasts: are there benefits for the rural sector? Proceedings of the 32^nd Conference of the Australian Agricultural Economics Society, 8-11 February 1998, Melbourne.

5. Dudley, N.J. and Hearn, A.B. (1993). El Niño effects hurt Naomi irrigated cotton growers, but they can do little to ease the pain. Agricultural Systems 42, 103 126.

6. Freebairn, J. (1996). Some economic considerations in the provision of meteorological services to agriculture. Proceedings of the Second Australian Conference on Agricultural Meteorology, 1-4 October 1996, The University of Queensland, pp. 1-5.

7. Hammer, G.L., Holzworth, D.P. and Stone, R. (1996). The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability. Australian Journal of Agricultural Research 47, 717-737.

8. Jessop, R. (1977). The influence of time of sowing and plant density on the yield and oil content of dryland sunflowers. Australian Journal of Experimental Agriculture and Animal Husbandry 17, 664.

9. Marshall, G.R., Parton, K.A. and Hammer, G.L. (1996). Risk attitude, planting conditions and the value of seasonal forecasts to a dryland wheat grower. Australian Journal of Agricultural Economics 40, 211-233.

10. McKeon, G.M. and White, D.H. (1992). El Niño and better land management. Search 23, 197-200.

11. Morley, F.H.W. (1994a). Predicting drought in southern Australia. In Proceedings of a workshop on Drought and decision support, edited by K.P. Bryceson and D.H. White, Bureau of Resource Sciences, Canberra, pp.22-25.

12. Morley, F.H.W. (1994b). Drought predictions and budgets. In Merinos, money and management, edited by F.H.W. Morley, A Post Graduate Foundation Publication, The University of Sydney, pp. 249-262.

13. Rimmington, G.A. and Nicholls, N. (1993). Forecasting wheat yields in Australia with the Southern Oscillation Index. Australian Journal of Agricultural Research 44, 65-632.

14. Smith, I. (1994). Assessments of categorical rainfall predictions. Australian Meteorology Magazine 43, 143-151.

15. Stafford Smith, M. (1996). Reflections on research, management and evaluation processes in DroughtPlan. In Evaluating the use of SOI forecasts in north Queensland using the Herd-Econ/GRASP linked model. DroughtPlan Working Paper No. 9/LUCNA Working Paper No. 1, CSIRO Alice Springs, pp. 131-141.

16. Stafford Smith, M., Buxton, R., McKeon, G., and Ash, A. (in press). Seasonal climate forecasting and the management of rangeland: do production benefits translate into enterprise profits? In Applications of seasonal climate forecasting in agricultural and natural ecosystems - the Australian experience, edited by G. Hammer, N. Nicholls and C. Mitchell, Kluwer Academic Publishers.

17. Stafford Smith, M., McKeon, G., Ash, A., Buxton, R. and Breen, J. (1996). Evaluating the use of SOI forecasts in north Queensland using the Herd-Econ/GRASP linked model. DroughtPlan Working Paper No. 9/LUCNA Working Paper No. 1, CSIRO Alice Springs, 50pp.

18. Stephens, D.J. and Lamond, M.H. (in press). Reducing the impact of major droughts in the Indonesian-Australian region through the monitoring of atmospheric pressure anomalies in the preceding year. Proceedings of the Australian Disaster Conference, 1999.

Contacts and institutions

Dr Andrew Ash, CSIRO Tropical Agriculture, PMB PO, Aitkenvale, Qld 4814.

Dr Peter Carberry, CSIRO Tropical Agriculture/Agricultural Production Systems Research Unit, PO Box 102, Toowoomba, Qld 2350. Ph: (07) 4688 1200; Fax: (07) 4688 1193; Peter.Carberry@tag.csiro.au

Dr Graeme Hammer, Queensland Department of Primary Industries/Agricultural Production Systems Research Unit, PO Box 102, Toowoomba, Qld 2350. Ph: (07) 4688 1200; Fax: (07) 4688 1193; hammerg@prose.dpi.qld.gov.au

Dr Greg McKeon, Climate Impacts and Grazing Systems, Queensland Department of Natural Resources, PO Box 631, Indooroopilly, Qld 4068. Ph: (07) 3896 9548; Fax: (07) 3896 9606; gregm@dnr.qld.gov.au

Dr Mark Stafford Smith, CSIRO National Rangelands Program, PO Box 2111, Alice Springs NT 0871; Ph: (08) 8950 7162; fax (08) 8952 7187; markss@dwe.csiro.au

Associate Professor Andrew Vizard, Director, Mackinnon Project, University of Melbourne, 250 Princes Highway, Werribee 3030. Ph: (03) 9742 8225; Fax: (03) 9742 6852; a.vizard@vet.unimelb.edu.au

2.1.6 Use of seasonal forecasts

The National Farmers Federation, in its publication 'New Horizons', endorses the view of many farmers that improved seasonal forecasting is a high research priority to assist them in managing their properties. This has also been highlighted in surveys undertaken in Queensland (Stone and Marcussen 1994) and Western Australia (Elliott and Foster 1994) within Phase I of the NCVP (Nicholls et al.1995). Managers of water and other climate-sensitive sectors of the economy also claim that they would like to see significant advances in skill levels and lead times in seasonal forecasting (Anon 1997).

Farmer awareness and use of seasonal forecasts since they began in 1989 have been highest in Queensland for a number of reasons. These include:

the high incidence of drought in north-eastern Australia in the 1990s;
the higher correlation between the spring SOI and summer rainfall over much of Queensland compared with other areas of Australia;
the greater research and extension effort by QDPI into incorporating the SOI into farmer decision making;
the employment of a climatologist (Dr Roger Stone) by QDPI;
the publication of an explanatory booklet (Partridge 1994);
the development and release of decision aids including the AUSTRALIAN RAINMAN software; and
information on the SOI being made available through the media, infofax, and through the World Wide Web (http://www.dpi.qld.gov.au/longpdk), and training workshops for farmers and their advisers.

In March 1998, QCCA surveyed pastoralists throughout Australia on their use and perceived value of seasonal forecasts and related government services (C. Paull, personal communication). Although the data are still being analysed, it is clear that many graziers do use the forecasts to aid their stocking and stock trading decision, even though the reliability of forecasts remains an issue in many areas. This is particularly the case in southern and Western Australia where SOI-based forecasts have less skill.

Farmers in Queensland have certainly reacted to adverse SOI information by sending cattle to market thereby reducing stocking rates on their properties. However, those with say high debt have often decided to gamble on say sowing a crop, despite an adverse forecast, when they are already threatened with foreclosure of their properties.

A survey of grain growers in northern New South Wales in 1998 (P. Hayman and S. Huda, personal communication) has shown that farmers have used 4 day weather forecasts to plan their sowing and spraying operations. They have also used frost risk information to switch crops and crop cultivars, and they have used the seasonal rainfall outlook to increase their nitrogen application rates and the area sown to crop.

In January 1997, the Queensland University of Technology, with assistance and support from the Queensland Department of Primary Industries, surveyed primary producers in south-east Queensland (Hastings and O'Sullivan 1998). Most of these were cattle producers with some dairy farmers, croppers and others in agricultural production. The aim was to gauge producer opinions of the impact of seasonal climate patterns and seasonal forecasting.

Results indicate that:

producers still rely to a large extent on their own intuition and experience (66 per cent) to determine future weather patterns;
El Niño- and Southern Oscillation-based forecasting were known to 96 per cent of respondents, but 48 per cent of those who indicated that SOI-based information had been of some importance in their decision-making did not believe its use was 'reasonably successful';
climatic information was used to aid decisions in cropping, livestock buying and selling, setting stocking rates and supplementary feeding;
the rural media is considered to be a creditable and useful source of seasonal climate forecasts/information (67 per cent). Fax and internet climatic information services had been used by less than one-third of respondents;
probability statements were favoured for presenting forecasts (74 per cent). 66 per cent accepted that probability-based forecasts cannot be judged later as having been either 'right' or 'wrong';
44 per cent of respondents agreed (with 27 per cent neutral) that SOI and probability-based forecasting places undue responsibility on 'users' to interpret the information;
75 per cent indicated that provided their information is useful, they were not concerned whether a forecast source uses strictly scientific methods, or non-scientific/other methods; and
60 per cent indicated 'it is better to respond to seasonal climate variability as it unfolds, rather than try to anticipate risks and hedge according to a forecast/personal outlook'.

Two other surveys on the needs and use of climate information were conducted in New South Wales in 1997 and 1998. The first survey on "Rural needs and climate variability" was conducted by the University of Newcastle. "Feedback from graziers and farmers on seasonal forecasting information" was also obtained by NSW Agriculture under the Aussie GRASS project. Combined and generalised, the two surveys reveal the following information:

Variability of climate is well known by the farmers in NSW but it is often poorly understood. Research has shown, on average, that many producers do not comprehend the explanations and predictions given to them by the meteorologists. Almost 70 per cent of the respondents think that the priority in weather forecasting is for city people. However, the Bureau of Meteorology's daily forecasts are seen as the most useful day-to-day source of weather information, followed by media sources. The Bureau of Meteorology is also seen as the most reliable supplier of long-term climate information by a majority of producers. About 40 per cent of the respondents regard Bureau of Meteorology forecasts as either unreliable, or they are unsure of their reliability.

The seasonal forecast is the most popular climatic information product, being used by a majority of farmers. Also popular are rainfall maps (62 per cent), and maps of drought declared areas (43 per cent).

About 60 per cent of producers plan for climate variability on a seasonal basis, whereas 16 per cent plan only on a day to day basis. For over 80 per cent of the farmers the big picture information is slightly to moderately important in planning risk management or decision-making

About eighty per cent of the respondents indicated that accurate information on the seasonal outlook of rainfall, the SOI and El Niño would be useful to them so as to make better management decisions.

Ninety per cent of the farmers prefer two or more ways for accessing the seasonal climate outlook. The top preference is to use the fax, followed by rural newspapers, internet and radio. Very low preference has so far been shown for computer packages and daily newspapers.

Eighty per cent of the farmers have one or more problems in using the big picture information. While some find it difficult to access or interpret, a majority indicated that they have no confidence in the accuracy of the information. Only 20 per cent of the farmers find no problem in its use.

There is a tendency for respondents on large properties to use climate predictions to a greater extent. The age of the respondents significantly affects the sources of climate information the respondents draw upon. There is a clear tendency that older farmers are less likely to draw upon a broad range of sources of climate information.

It was concluded that producers are vitally interested in climate information and predictions of important weather parameters like rainfall and frost. There is a large need for relevant and user-friendly information about climate in rural NSW. The surveys suggest that there is scope to improve official forecasts to build more confidence, and also to establish a better understanding of official forecasts.

Specific research projects currently (or recently) being undertaken in this area include:

Evaluating the role of seasonal climate forecasting in tactical management of cropping systems in north-east Australia - Dr Roger Stone, QDPI (LWRRDC)
Northern wheatbelt climate study - Mr P. Hayman, NSW Agriculture, Tamworth, and Dr S. Huda, University of Western Sydney (GRDC)
Seasonal climate variability and crop yield forecasting - Mr A. Hafi, ABARE (LWRRDC)
Seasonal rainfall and winter crop yield forecasting for southern Australia - Mr J.P. Egan, South Australian Research and Development Institute; Mr I. Holton, Bureau of Meteorology, Adelaide Regional Office; Dr W.J. Grace, Bureau of Meteorology Research Centre
Climate risk options for managing frost risk in the eastern wheatbelt - Dr D.G. Abrecht, Agriculture Western Australia
From oceans to farms: integrated management of climate variability - Dr A. Ash, CSIRO Tropical Agriculture (LWRRDC)
Insurance-based risk management for drought - Mr B. Mayers, Agricultural Risk Management P/L (LWRRDC)
Farmers training needs for managing climate risk - Mr P. Wylie, Macro Agricultural Consultants (LWRRDC)
Climate and fisheries on the S.E. Australian Continental Shelf and Slope - Dr T. Koslow, CSIRO Division of Fisheries. (LWRRDC)
R&D opportunities for using seasonal climate forecasts in the Australian water industry - Prof. Tom McMahon, University of Melbourne. (LWRRDC)
Seasonal streamflow forecasts to improve management of water resources - Mr N. Clarkson, QDPI (LWRRDC)

References

1. Allan, R. (1998). Seasonal climate variability and agricultural yield forecasting. LWRRDC Occasional paper CV01/98

2. Auliciems, A., Hastings, P.A. and Dowidiet, G. (1998). Rural advisers' perceptions of Southern Oscillation-based seasonal climate information. Rural Industries Research & Development Corporation, 41 pp.

3. Elliott, G. and Foster, I. (1994). Requirements for seasonal climate forecasts by agricultural producers in Western Australia. Milestone report to LWRRDC, NCVP funded project BOM1, Department of Agriculture, Western Australia, 40 pp.

4. Hassall & Associates Pty Ltd (1997). Review of the National Climate Variability R&D Program, LWRRDC Occasional Paper CV02/97

5. Hastings, P. and O'Sullivan, D.B. (1998). Impacts of seasonal climate forecasting on primary producers in regional Queensland. 12^th ANZ Climate Forum, 30 November - 2 December 1998, Perth, Western Australia, p.23.

6. Hastings, P.A., Auliciems, A. and Dowidiet, G. (1998). Rural advisers' perceptions of Southern Oscillation-based seasonal climate information, RIRDC 98/25, Canberra.

7. Meinke, H., Stone, R.C. and Hammer, G.L. (1996). Using SOI phases to forecast climatic risk to peanut production: a case study for northern Australia. International Journal of Climatology 16, 1-7.

8. Nicholls, N. (1995). Development of improved seasonal climate forecast systems. Milestone report to LWRRDC, NCVP-funded project BOM1, Bureau of Meteorology Research Centre, 69 pp.

9. Partridge, I.J. (1994). Will it rain? The effects of the Southern Oscillation and El Niño on Australia. Queensland Department of Primary Industries, Information Series QI94015 (2^nd edition), 56 pp.

10. Rimmington, G.M. and Nicholls, N. (1993). Forecasting wheat yields in Australia with the Southern Oscillation Index. Australian Journal of Agricultural Research 44, 625-632.

11. Stephens, D.J. (1997). Assessing and forecasting variability in wheat production in Western Australia. Report to Agriculture Western Australia.

12. Stephens, D.J, Butler, D.J. and Hammer, G.L. (1998). Using seasonal climate forecasting in forecasting the Australian wheat crop. In Applications of seasonal climate forecasting in agricultural and natural ecosystems - the Australian experience, edited by G. Hammer, N. Nicholls and C. Mitchell, Kluwer Academic Publishers (in press).

13. Stone, R.C and Marcussen, T. (1994). Queensland producer requirements from seasonal climate forecast systems, Milestone report to LWRRDC, NCVP funded project BOM1, QDPI, 42pp.

Contacts and institutions

Dr Peter Hayman, NSW Agriculture, Tamworth, NSW 2340; ph (02) 6763 1256; Fax: (02) 6763 1222; peter.hayman@agric.nsw.gov.au

Dr Peter Hastings, School of Humanities, Queensland University of Technology, Carseldine Campus, Beams Road, Carseldine, Qld 4034; Ph: (07) 3864 4723; phastings@qut.edu.au

Dr Holger Meinke, Queensland Department of Primary Industries/Agricultural Production Systems Research Unit, PO Box 102, Toowoomba, Qld 2350. Ph: (07) 4688 1378; Mobile: 041 219 6534; Fax: (07) 4688 1193; MeinkeH@prose.dpi.qld.gov.au

Mr Damien O'Sullivan, Queensland Department of Primary Industries, PO Box 23, Kingaroy, Qld 4610. Ph: (07) 4160 0700; OsulliD@dpi.qld.gov.au

Mr Colin Paull, Queensland Centre for Climate Applications, 80 Meiers Road, Indooroopilly, Qld 4068; Ph: (07) 3896 9587; Fax: (07) 3896 9843; Colin.Paull@dnr.qld.gov.au

Dr David Stephens, Spatial Resource Information Group, Agriculture Western Australia, South Perth WA 6151. Ph: (08) 9368 3346; Fax: (08) 9368 3939; david_s@agrsrv.agric.wa.gov.au

Dr Roger Stone, Queensland Department of Primary Industries, PO Box 102, Toowoomba, Qld 4350. Ph: (07) 4688 1293; Mobile: 0412 559 408; Fax: (07) 4688 1193; rogers@apsrusg.sth.dpi.qld.gov.au

2.1 Seasonal forecasting

References

2.1.1 Statistical forecasts

Further reading

Contacts and institutions

2.1.2 General Circulation Models (GCMs)

References

Contacts and institutions

2.1.3 Seasonal forecasts based on synoptic scale study of the upper atmosphere

References

Contacts:

2.1.4 Climate analysis, interpretation and monitoring

References:

2.1.5 Value of seasonal forecasts

References

Contacts and institutions

2.1.6 Use of seasonal forecasts

References

Contacts and institutions

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