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Climate Prediction and Agriculture

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Presentation on theme: "Climate Prediction and Agriculture"— Presentation transcript:

1 Climate Prediction and Agriculture
M.V.K. Sivakumar Agricultural Meteorology Division Climate & Water Department World Meteorological Organization (WMO)

2 Presentation Introduction
Current status of agriculture and climate forecast needs A brief history and current status of climate predictions Regional Climate Outlook Forums Case study of applications of climate forecasts in Ghana Attention to farmers conditions and needs Conclusions

3 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.

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

5 Atmosphere and oceans Atmosphere responds to ocean temperatures within a few weeks, but oceans take three months or longer to respond to changes in the atmosphere. Because of this slow response of oceans, it takes months for warm water to dissipate or the water may move thousands of kilometres before transferring its heat back to the atmosphere. This persistence of oceans offers the opportunity for climate prediction. 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).

6 Current Status of Agriculture and Need for Climate Forecasts

Agriculture is an important sector for the economies of many developing countries and employs a large proportion of workforce eg., 45% in Paraguay. 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.

As much as 80% of the variability in agricultural production is due to the variability in weather conditions 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.

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.

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.

11 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

12 Climate Prediction Coordinated climate prediction development needs the expertise of: oceanic, atmospheric, and social scientists; regional experts in climate applications and services; sectoral users of climate information. Success relies on: Knowledge of global, regional and local aspects of the climate system; Climate prediction skills on relevant space-time scales; Up-to-date modeling, computing and communications technology; Adequate input data from national observation systems; Understanding of the needs of the various users of the climate information; Factoring of climate related uncertainties into decision-making processes

13 Climate Prediction Framework
Forecast Lead Time Climate Change. Forecast Uncertainty Centuries Scenarios Decades Anthropogenic Forcing Climate Variability Outlook Prediction Years Seasons Guidance Months Boundary Conditions Threats Assessments Weather 2 Weeks 1 Week Forecasts Initial Conditions Days Watches Hours Warnings & Alert Coordination Minutes Applications Energy Health State/Local Planning Protection of Life & Property Space Applications Water Management Fire Weather Hydropower Recreation Ecosystem Commerce Agriculture Environment Transportation Water Resource Planning Adapted from: NOAA

14 WMO Global Observation System
11,000 surface stations, 1,000 high-altitude stations, 3000 information relieve stations on aircrafts. ( observations), more than 7,000 volunteer observation ships, polar orbit satellites, geostationary satellites, 700 buoys, meteorological radars.

15 Regional Climate Outlook Forums (RCOFs)
A key component of WMO Climate Information and Prediction Services (CLIPS) project activities. First established in October 1996 at the Workshop on Reducing Climate-Related Vulnerability in Southern Africa (Victoria Falls, Zimbabwe). Gained momentum as a regional response to the major 1997–1998 El Niño event. RCOF Concept was pioneered in Africa and spread worldwide. WMO and a number of national, regional and international organizations (e.g., NOAA, IRI, Meteo France, World Bank, etc.) have supported their growth and expansion.

16 Existing RCOFs worldwide

17 RCOF Concept (1/2) Climate information including predictions/outlooks could be of substantial benefit to many parts of the world in adapting to and mitigating the impacts of climate variability and change. RCOFs across the world have the overarching responsibility to produce and disseminate a regional assessment (using a predominantly consensus-based approach) of the state of the regional climate for the upcoming season. Built into the RCOF process is a regional networking of the climate service providers and user-sector representatives. Participating countries recognize the potential of climate prediction and seasonal forecasting as a powerful development tool to help populations and decision-makers face the challenges posed by climatic variability and change. Ownership now lies largely with national and regional players, but there is a continuing need for support at all levels to ensure that the momentum gained to date is maintained.

18 RCOF Concept (2/2) RCOFs bring together national, regional and international climate experts, on an operational basis, to produce regional climate outlooks based on input from NMHSs, regional institutions, Regional Climate Centres (RCCs) and Global Producing Centres of long range forecasts (GPCs) and other climate prediction centres. Through interaction with sectoral users, extension agencies and policy makers, RCOFs assess the likely implications of the outlooks on the most pertinent socio-economic sectors in the given region and explore the ways in which these outlooks could be made use of. RCOFs also review impediments to the use of climate information, experiences and successful lessons regarding applications of the past RCOF products, and enhance sector-specific applications. These RCOFs then lead to national forums to develop detailed national-scale climate outlooks and risk information including warnings for communication to decision-makers and the public.

19 Example: The Greater Horn of Africa Climate Outlook Forum (GHACOF)
IGAD Climate Prediction and Applications Centre (ICPAC), formerly known as the regional Drought Monitoring Centre (DMC)-Nairobi has been organizing Climate Outlook Forums (COFs) at the beginning of every major rainfall season in the GHA, since 1998. Opportunity for the climate scientists from NMHSs , international and regional centers to develop a single best regional seasonal climate outlook products in order to avoid unnecessary competition and confusing users with products from the individual centers. Also include media experts, and experts from policy-makers, agriculture, food security, water resources, health, and the general user community. The COFs are preceded with capacity building workshop of national climate scientists on new developments in seasonal climate prediction. The workshop is normally opened by a senior government minister, and involves several lead speakers.

20 GHACOF Products & Applications

21 WMO Global Producing Centres (GPCs) of long-range forecasts
Climate Prediction Center, National Centers for Environmental Prediction (CPC/NCEP/NWS/NOAA) European Centre for Medium-range Weather Forecasts (ECMWF) Japan Meteorological Agency (JMA) Met Office (United Kingdom) Météo-France Meteorological Service of Canada (MSC) Korean Meteorological Administration (KMA) National Climate Centre of the China Meteorological Administration (NCC/CMA) World Meteorological Centre, Bureau of Meteorology (BoM), Australia

22 WMO and RCOFs WMO assists developing countries hold and benefit from these forums through CLIPS: - facilitating training workshops, - coordinating the collection and dissemination of training materials, - capacity building initiatives including some initial (limited) financial support, and - coordination of special applications to sectors (e.g. health and agriculture) Regional institutions (e.g. DMCs, ACMAD, CRRH, CIIFEN) play key roles in the organization and overall implementation of these forums NMHSs, the regions and the users of the products must contribute to the sustainability of COFs in the regions: demonstrate utility of the forums and value of the products to those who need the information Research capacities at the regional level need to be enhanced, to assess the forecast skills as well as to work towards their improvement Media has an important role to play in RCOF process, which needs to be factored in.

23 Application of ENSO Forecasts for Agricultural Decision Making in Ghana

24 El Niño, meteorological phenomenon also called ENSO (El Niño Southern Oscillation"), is a change in the ocean-atmosphere systems occurring in the east Pacific, that contributes to significant climate changes, and causes damages at a global scales, affecting South America, Indonesia and Australia. El Niño phenomenon alters: Atmospheric pressure in different regions Changes wind direction and speed Rainfall displacement to tropical regions Increase of Sea Surface temperature Thermocline descent which entails important consequences on marine life

25 Application of ENSO Forecasts for Agricultural Decision Making in Ghana
Significant correlation exists between Atlantic Ocean sea surface temperatures and rainfall in many parts of Ghana. Studies showed significant correlation between Southern Oscillation Index (SOI) established in April and seasonal rainfall (April-July) for some sites located along the southern coasts of Ghana. But this does not provide sufficient lead time. Studies suggest that October-December (OND) SSTs in Nino 3 Pacific region popularly known as ENSO may offer the possibility for seasonal rainfall forecasting in Ghana.

26 Application of ENSO Forecasts for Agricultural Decision Making in Ghana (2)
Nine sites in different climatic zones in Ghana were selected: 5 near southern coastline; one in savanna-forest transition; one in tropical rainforest; and 2 in interior savanna. OND SSTs downloaded from IRI website; rainfall years sorted into three ENSO phases ie., El Niño La Niña and neutral years. Groundnut and maize yields simulated using crop simulation models.

27 Application of ENSO Forecasts for Agricultural Decision Making in Ghana (3)
Appreciable ENSO influence was noted at most of the sites in the south of Ghana. Seasonal rainfall was appreciably lower for El Niño phase than for La Niña and the neutral phase. La Niña-El Niño rainfall difference was 182 mm and that for neutral- El Niño was 164 mm. La Niña-neutral rainfall difference was only 18 mm. This vast difference in moisture availability accounted for the appreciable yield reduction during El Niño seasons. .

28 Mean simulated maize yields (kg/ha) and percent yield increase as a function of ENSO phases & planting dates ENSO phase Nitrogen (kg/ha) Yield (kg/ha) Yield increase (%) El Niño 700 - 60 2850 306 120 2940 3 La Niña 680 3310 385 4040 22 Neutral 3190 368 3940 23

29 Complying with farmers’ conditions and needs for weather and climate information (1)
In China, four different income-levels of farmers treated the technological and related information differently and their levels of satisfaction were different. Very poor farmers have limited technological information demands and obtain information from leaders, neighbours and relatives. Most were over 50 yrs and illiterate. They only make use of local resources and expect help from governments.

30 Complying with farmers’ conditions and needs for weather and climate information (2)
Poor farmers in China are indifferent to information from different media and do not watch TV or read news. Highest information demand is ranked as: new varieties, rural policies, utilization of new technologies and applied scientific knowledge. Information demand for any item of information is only 30% on average and 40% in some areas.

31 Complying with farmers’ conditions and needs for weather and climate information (3)
In Cuba, India, Nigeria and Sudan, farming systems have a great influence on the differentiation aspects and other factors than income become important. In Cuba, client friendliness is a determining factor. India, getting feedback from farmers is now receiving greater attention. In Nigeria, experience teaches that social and other potential of rural youth can serve as a good entry point in diffusion of weather and climate information. In Sudan, such services exist on irrigation scheduling in Gezira, but on a limited scale.

32 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.

33 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.

34 Thank You For more information, please contact:
M.V.K. Sivakumar Chief, Agricultural Meteorology Division Climate & Water Department World Meteorological Organization 7bis, Avenue de la Paix 1211 Geneva 2 Switzerland Tel: Fax:

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