Presentation on theme: "CLIMATE PREDICTION AND AGRICULTURE M.V.K. Sivakumar Agricultural Meteorology Division World Meteorological Organization M.V.K. Sivakumar Agricultural Meteorology."— Presentation transcript:
CLIMATE PREDICTION AND AGRICULTURE M.V.K. Sivakumar Agricultural Meteorology Division World Meteorological Organization 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 1980s, 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.
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 29% of the workforce in Uruguay, 45% in Paraguay and 20% in Brazil. 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. 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. 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 socio- economic development of many nations Annual economic costs related to natural disasters estimated at about US$ 50– 100 billion.
Impact of ENSO (Source: NOAA) Global damage billion $ Central and South America 54.4% North America19.5% Indonesia and Australia 16.1% Asia Africa 9.7% 0.4%
Impacts of ENSO Region Loss ($ billions) Human deaths Population affected (millions) Area affected (mill. ha) Africa0.213, Asia3.85, Australia & Indonesia 5.31, Central and South America Global total34.324,
Forest cover change and average forest fire data CountryTotal forest (mill ha) Forest cover change ( ) % Area burned (ha) Argentina ,370 (85-89) Brazil ,500,000 (97-98) Paraguay ,000 (1988) Uruguay ,240 (81-90)
EXTREME VARIABILITY – MULTIDIMENSIONAL IMPACTS Between 1525 and 1983, a strong ENSO event occurred every 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 non-agricultural 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 agricultures vulnerability to climate variability and also reduce its 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 REDICTIONS (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 earths 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)
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. (1987a,b), Xu and von Storch (1990) and Penland and Magorian (1993).
ADVANCES IN SCIENCE OF CLIMATE FORECASTING (1) 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) 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 inTERannual 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 (3) 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 ocean- atmosphere 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 (4) 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 Development of a Spatial Decision Support Systems for the Application of Climate Forecasts in Uruguayan Rice Production System (Alvaro Roel – INIA Uruguay) Crop yield outcomes of irrigated sectors under ENSO scenarios (Meza and Podestá, Chile)
Development of a Spatial Decision Support Systems for the Application of Climate Forecasts in Uruguayan Rice Production System (Alvaro Roel – INIA Uruguay) ENSO is the main source of inter-annual climate variability in Uruguay. Effective application of a seasonal climate forecast would need to take in consideration the natural spatial variability in biotic and abiotic conditions that regulate productivity in agricultural ecosystems. A pilot project was proposed to evolve a system for the effective application of a seasonal climate forecast, which can address the natural spatial and temporal variability in growing conditions that control productivity in a rice ecosystem in Uruguay.
GIS Crop Modeling Forecast Spatial Statistics TOOLS SPATIAL DECISION SUPPORT SYSTEM (SDSS)
Evaluate ENSO effects on Uruguayan Rice Production F The SST anomalies were calculated relative to the period and aggregated into three-month period means. F In order to have a more comprehensive analysis of ENSO impacts on rice production the distribution shifts of crop yields were studied using the same approach as the one used by Baethgen (1986). F The detrended National average crop yield data from 1973 to 2003 were divided into quartiles and any given value was defined as being "high" if it was greater than the third quartile (upper 75% of the data), "low" if it was less than the first quartile (lower 25%), and "normal" if its value fell between the first and the third quartile (central 50% of the data). F Using these values the shift in the distribution of crop yields were studied for the different ENSO phases (El Niño, La Niña and Neutral).
Evaluation of ENSO effects on Uruguayan Rice Production National average yield deviations ( ) Vs Average SST anomalies for October, November and December. Green dots La Niña Years, Blue Dots Neutral years and Red dots El Niño years
High Yields Medium Low Yields Upper Quartile Central Quartiles Lower Quartile < % Evaluation of ENSO effects on Uruguayan Rice Production RYD
Evaluation of ENSO effects on Uruguayan Rice Production National Rice Yield Distribution and ENSO phases ( )
Possible crop yield outcomes of irrigated sectors under ENSO scenarios in Chile Possible crop yield outcomes of irrigated sectors under ENSO scenarios in Chile (Meza and Podestá, Chile) – ENSO impacts on the water cycle and crop growth. – Probability distribution functions of potential and actual evapotranspiration – Identify regions and seasons that are particularly sensitive to water scarcity – Perform preliminary estimates of the benefits of using climate forecasts in agricultural water resources planning.
Climatic Variability in Chile and El Niño Phenomenon F In central Chile, ENSO does have an influence on other meteorological variables that play a fundamental role on reference evapotranspiration (Meza, 2005)
ENSO Effect on Water Demands in Central Chile
Expected Value of Information for the different phases of ENSO Available water at each irrigation time was equivalent to 55 mm
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. Agrometeorologists from the Mercosur countries could play a crucial role in ensuring the two way feed back between the climate forecasters and the farming community.