CLIMATE PREDICTION AND AGRICULTURE CURRENT STATUS AND FUTURE

CLIMATE PREDICTION AND AGRICULTURE: CURRENT STATUS AND FUTURE CHALLENGES M. V. K. Sivakumar Agricultural Meteorology Division World Meteorological Organization

PRESENTATION • Introduction • Current status of agriculture and climate forecast needs • A brief history and current status of climate predictions • Case studies on applications of climate forecasts • Climate prediction and agriculture – future challenges • Conclusions

Farmers and oceans Prior to 1980 s, few farmers around the world would ever have imagined that the distant tropical Pacific and Indian Oceans would influence the weather and climate over their own farms.

Farmers and oceans Few of the Australian farmers realized that the top three meters of the ocean can store and move as much heat as the whole of the atmosphere and that ocean currents in the tropical Pacific and Indian Ocean have a major influence on how much and when rain falls across the Australian Continent.

Farmers and oceans The Sahelian farmer would have little understanding that the Indian and Atlantic Oceans impact his farming conditions.

Atmosphere and oceans • Atmosphere responds to ocean temperatures within a few weeks. However, the ocean takes three months or longer to respond to changes in the atmosphere. • Because the oceans change much more slowly than the atmosphere, when a mass of warm water forms, it takes months to dissipate and may move thousands of kilometres before transferring its heat back to the atmosphere. • It is this persistence of the ocean that offers the opportunity for climate prediction (CSIRO Marine Research, 1998).

Atmosphere and ocean interactions • Until 20 years ago, seasonal climate predictions were based exclusively on empirical/statistical techniques that provided little understanding of the physical mechanisms responsible for relationships between current conditions and the climate anomalies (departures from normal) in subsequent seasons. • Mathematical models analogous to those used in numerical weather prediction, but including representation of atmosphere–ocean interactions, are now being used to an increasing extent in conjunction with, or as an alternative to, empirical methods (AMS Council, 2001).

The key issues • While the science of climate prediction is relatively new, the tradition of agriculture is quite ancient. • Blending the new science with an ancient tradition, especially in most of the developing countries with a long history of agriculture is not always easy. • Climate prediction is global, but agricultural applications are essentially local.

CLIMAG Workshop 1999 • Considered a number of important issues relating to climate prediction applications in agriculture • Discussed regional applications of climate prediction in several parts of the world. • CLIMAG project should be viewed as a partnership of potential users and researchers with multiple stakeholders. • Recommended the implementation of proof-ofconcept regional demonstration projects in Asia, Africa and South America.

Post-CLIMAG Initiatives • Several important initiatives by a number of organizations including START, IRI, WMO, QDPI, BOM, NOAA and the World Bank. • Regional CLIMAG demonstration projects have been conducted in South Asia and West Africa. • Eighteen research projects within the Packard Foundationfunded Advanced Institute on Climate Variability And Food Security have recently been completed. • Earth System partnership of IHDP, IGBP, WCRP and DIVERSITAS promoted the development of activities under the Global Environmental Change and Food Systems (GECAFS) Joint Project.

CURRENT STATUS OF AGRICULTURE AND NEED FOR CLIMATE FORECASTS

AGRICULTURE – THE MOST WEATHER-DEPENDENT SECTOR F Agriculture is an important sector for the economies of many developing countries and employs 60% of the workforce in India, 50% in China, 23% in Brazil, and 70% in Nigeria. F Most of the countries produce cash crops such as wheat, rice, coffee, bananas, cotton, sugarcane etc. , for export while subsistence farmers grow a range of crops for their household consumption and for the local market. F Improved information on weather and climate could make the sector more productive.

RAINFED FARMING REMAINS A RISKY BUSINESS F As much as 80% of the variability in agricultural production is due to the variability in weather conditions F In many developing countries where rainfed agriculture is the norm, a good rainy season means good crop production, enhanced food security and a healthy economy. F Failure of rains and occurrence of natural disasters such as floods and droughts could lead to crop failures, food insecurity, famine, loss of property and life, mass migration, and negative national economic growth.

WATER FOR AGRICULTURE IS A CRUCIAL ISSUE • More than 1 billion people do not have access to drinking water and 31 developing countries face chronic freshwater availability problems. • By 2025, population in water-scarce countries could rise to 2. 8 billion, representing roughly 30 per cent of the projected global population. • Over the next two decades, the world will need 17 per cent more water for agriculture and the total water use will increase by 40 per cent. • In many developing countries, 70 per cent of the available fresh water is used for irrigation.

NATURAL DISASTERS AND AGRICULTURE F Climate variability and the severe weather events that are responsible for natural disasters impact the socioeconomic development of many nations F Annual economic costs related to natural disasters estimated at about US$ 50– 100 billion.

EXTREME VARIABILITY – MULTIDIMENSIONAL IMPACTS • Between 1525 and 1983, a strong ENSO event occurred every 42 -45 years but the frequency of recent El Niños is much higher (1982, 1997). • Increased frequencies and intensities of the extreme events carry serious implications for agro-based industries, tourism, construction, transportation and insurance. • Other dimensions - food insecurity or famine, large scale imports of food, balance of payments deterioration, substantial government spending on drought relief programs, depressed demand for nonagricultural goods, and rural-urban migration

NEED FOR CLIMATE FORECASTS • To address such challenges, it is important to integrate the issues of climate variability into resource use and development decisions. • More informed choice of policies, practices and technologies will decrease agriculture’s vulnerability to climate variability and also reduce it’s long-term vulnerability to climate change. • Advantage should be taken of current data bases, increasing climate knowledge and improved prediction capabilities

A BRIEF HISTORY OF CLIMATE PREDICTIONS (1) • The principal scientific basis of seasonal forecasting is founded on the premise that lower-boundary forcing, which evolves on a slower time-scale than that of the weather systems themselves, can give rise to significant predictability of atmospheric developments. • These boundary conditions include sea surface temperature (SST), sea-ice cover and temperature, land-surface temperature and albedo, soil moisture and snow cover, although they are not all believed to be of generally equal importance. • Relatively slow-changing conditions on the earth’s surface can cause shifts in storm tracks that last anywhere from a year to a decade (Hallstrom, 2001).

A BRIEF HISTORY OF CLIMATE PREDICTIONS (2) • Southern Oscillation - a global spatial pattern of interannual climate variations with identifiable centers of action (Walker 1924). • Large scale fluctuations in the trade-wind circulations in both the northern and southern hemispheres of the Pacific sector are linked to the Southern Oscillation (Bjerknes 1966) • First ENSO forecast on the basis of a hypothesis linking temperature and wind anomalies (Wyrtki et al. 1976)

A BRIEF HISTORY OF CLIMATE REDICTIONS (3) • Anomalies of sea surface temperature in the tropical Atlantic connected with precipitation over northeast Brazil and the Sahel (Hastenrath and Heller, 1977; Moura and Shukla, 1981), • Anomalies of sea surface temperature in the eastern Indian Ocean connected with rainfall anomalies over Australia (Streten, 1983)

A BRIEF HISTORY OF CLIMATE REDICTIONS (4) Tropical Oceans and Global Atmosphere (TOGA) provided the much needed impetus to: To gain a better description of the tropical oceans and the global atmosphere as a time-independent system To determine the extent to which this system is predictable on a time scales of months to years To understand the mechanisms and processes underlying that predictability (WCRP, 1985)

A BRIEF HISTORY OF CLIMATE REDICTIONS (5) The major outcome of the TOGA period was the successful simulation of the ENSO cycle using coupled models of the atmosphere and ocean for the region of the tropical Pacific. The first successful coupled model of ENSO consisted of a Gill-type model (Gill, 1980) of the atmosphere, with improved moisture convergence (Zebiak, 1986) coupled to a reduced-gravity ocean model with an embedded surface mixed layer (Zebiak and Cane, 1987). Prediction schemes for ENSO based on statistical models were introduced by Graham et al. (1987 a, b), Xu and von Storch (1990) and Penland Magorian (1993).

ADVANCES IN SCIENCE OF CLIMATE FORECASTING (1) • The first ENSO forecast using coupled numerical models in early 1986 (Cane et al. 1986) • Dynamical and statistical models comparable for lead times of 4 months or less, but dynamical predictions more superior at longer lead times (Latif et al. 1994) • Recent trend - use of Regional Climate Models (RCMs) that handle relatively small regions but with far more resolution than is possible using present global models, and that use boundary conditions supplied by a pre-run of a global model (Harrison, 2003).

ADVANCES IN SCIENCE OF CLIMATE FORECASTING (2) • Advances in climate prediction will come from a better understanding and simulation of teleconnections involving the other ocean basins and from the inclusion of land surface conditions in climate prediction models (AMS Council, 2001). • For Brazil and western Africa, sea surface temperature anomalies over the tropical Atlantic Ocean have been shown to play an important role. • Sea surface temperature anomalies over the Indian Ocean have some influence on seasonal climate in eastern Africa, Southern Asia, and Australia. • North Atlantic Oscillation (NAO) or the Arctic Oscillation (AO), also exhibit variability on the seasonal-to-interannual time frame that might represent a potential source of predictability.

PDO Land/Soil AO/NAO Monsoons TAV ENSO Land/Soil Patterns of Natural Climate Variability and Source of Predictability

ADVANCES IN SCIENCE OF CLIMATE FORECASTING (3) • Empirical-statistical methods in use at various centres include analysis of General Circulation Patterns; analogue methods; time series, correlation, discriminant and canonical correlation analyses; multiple linear regression; optimal climate normals; and analysis of climatic anomalies associated with ENSO. • Dynamical methods (used principally in major GPCs) are model-based, using atmospheric GCMs; coupled Atmosphere-ocean GCMs; and two-tiered models. • Hybrid models, such as a simple dynamical or statistical model of the atmosphere coupled with an ocean dynamical model, are not being used operationally at any NMHSs at the present.

ADVANCES IN SCIENCE OF CLIMATE FORECASTING (4) • Use of multiple models, each running their own ensemble from varying initial conditions, provides an improvement in skill not available from a single model alone. • In Europe, under the DEMETER (Development of a European Multimodel Ensemble system for seasonal to in. TERannual prediction) project, plans are being drawn for an operational system using multiple coupled models. • Multiple model systems have been examined in the USA under the DSP (Dynamic Seasonal Prediction) projects, internationally under SMIP (Seasonal forecast Model Intercomparison Project), • The Asia-Pacific Climate Network (APCN) based in Seoul, South Korea, , is also using multiple model inputs (Harrison, 2003).

ADVANCES IN SCIENCE OF CLIMATE FORECASTING (5) • Forecasts are now freely transmitted around the globe by the Internet • Interpretation and delivery of the climate prediction information promoted through the development of Regional Climate Outlook Forums • Consensus agreement between coupled oceanatmosphere model forecasts, physically based statistical models, results of diagnosis analysis and published research on climate variability over the region and expert interpretation of this information in the context of the current situation

ADVANCES IN SCIENCE OF CLIMATE FORECASTING (6) One-third of the WMO Members already had, or planned to obtain in the near future, the capability to provide some form of operational seasonal to interannual prediction (Kimura 2001) - Most models in use predict only for single countries - Rainfall is the most popular predictand, - Usually the forecasts are for a single three-month season (or a part of this period) at zero lead - Vast majority of cases use empirical models

CASE STUDIES OF APPLICATIONS OF CLIMATE FORECASTS

CLIMAG • An international project initiated by START (Global Change System for Analysis, Research and Training) • Aim – apply predictions of climate variability on time scales of a month to a year to crop management and decision making in order to increase agricultural productivity. • International Workshop (Geneva, 1999) – reviewed current activities in climate prediction and agricultural applications and proposed pilot projects southern Asia, West Africa and Latin America, (Sivakumar, 2000).

WMO INITIATIVES IN THE PROMOTION OF CLIMATE FORECASTS IN AGRICULTURE (1) • Agricultural Meteorology Programme - Commission for Agricultural Meteorology Expert Team on the Impact of Climate Change/Variability and Medium- to Long-Range Predictions for Agriculture -Workshops and Training Activities Technical Meeting on CLIPS and Agrometeorological Applications for the Andean Countries Technical Meeting on CLIPS and Agrometeorological Applications for the Mercosur Countries

WMO INITIATIVES IN THE PROMOTION OF CLIMATE FORECASTS IN AGRICULTURE (2) • Climate Information and Prediction Services (CLIPS) - Seasonal Climate Outlook Fora - Workshops and Training Activities - Commission for Climatology Expert Teams (5) - Research Needs for Intraseasonal, Seasonal and Interannual Prediction - CLIPS Operations, including Product Generation - Verification - Capacity building - End User Liaison

INTERNATIONAL RESEARCH INSTITUTE (IRI) • Pilot studies in various parts of the world indicated that, while it is still too early to be entirely specific about the potential value of climate predictions for agriculture, there is reason to be optimistic concerning future opportunities realised by further research (Hansen, 2002). • Solow et al. (1998) estimate the value of an El Niño forecast to be in the range of $240 to $323 million per year for the entire United States.

AUSTRALIA • In Australia several web-sites provide a combination of climate monitoring products as well as outlook information for agriculture – notably, the Bureau web site (www. bom. gov. au), SILO (www. bom. gov. au/silo), Queensland Department of Primary Industry (QDPI) Long Paddock (www. dnr. qld. gov. au/longpdk/) among others. • A case study of tactical decision-making for a dryland grain/cotton farmer (DCF) on the southern Darling Downs, Queensland (Meinke & Hochman 2000).

AFRICA • In Southern Africa, consensus on the long-term prospects of each rainfall season is established through regional climate outlook fora held under the auspices of the Southern Africa Development Community (SADC) Drought Monitoring Center (DMC) based at Harare. • In a three year-study on the intended and actual usage of rainfall forecasts across a range of rainfall zones in Zimbabwe, Philipps et al. (2000) showed that farmers in the high rainfall region cut back area planted to all crops in expectation of a very wet year in 1998/99 for minimizing the production losses, while farmers in the drier zones took advantage of the expected rainy season to substantially increase the area planted to increase total farm production for both food and cash-generating crops.

CASE STUDIES OF APPLICATIONS OF CLIMATE FORECASTS – FOOD CHAIN PROJECT • The UK Government Seasonal Weather Forecasting for the Food Chain project • Field vegetables - most benefit in the frozen produce • Sugar beet - scheduling aspects, agrochemical use • Tomato - integration of forecasts on a range of scales. • Optimal benefit through co-ordinated actions throughout the food chain rather than through independent decisions.

CLIMATE PREDICTION AND AGRICULTURE FUTURE CHALLENGES (1) • Improving the accuracy of models • Enhancing the efficiency and effectiveness of agricultural water management • Addressing the key issues for promoting beneficial use of forecasts • Responding to diverse needs of the users • Involving more actively the stakeholders in climate prediction applications

FUTURE CHALLENGES (2) • Giving greater priority to extension and communication activities • Learning from non-adoption situations • Deriving more economic benefits through climate prediction applications to trade and storage • Creating better institutional and policy environment • Establishing a better networking between researchers in climate and agriculture • Instituting a rigorous monitoring and evaluation framework

CONCLUSIONS (1) • Considerable advances have been made in the past decade in the development of our collective understanding of climate variability and its prediction in relation to the agricultural sector and scientific capacity in this field. • Sophisticated and effective climate prediction procedures are now emerging rapidly and finding increasingly greater use • Through crop simulation models in a decision systems framework alternative decisions are being generated • There is a clear need to further refine and promote the adoption of current climate prediction tools.

CONCLUSIONS (2) • It is equally important to identify the impediments to further use and adoption of current prediction products. • Comprehensive profiling of the user community in collaboration with the social scientists and regular dialogue with the users could help identify the opportunities for agricultural applications. • Active collaboration between climate forecasters, agrometeorologists, agricultural research and extension agencies in developing appropriate products for the user community is essential.

Thank you very much for your attention WORLD METEOROLOGICAL ORGANIZATION