CLIMATE PREDICTION AND AGRICULTURE M.V.K. Sivakumar Agricultural Meteorology Division World Meteorological Organization M.V.K. Sivakumar Agricultural Meteorology.

CLIMATE PREDICTION AND AGRICULTURE

CLIMATE PREDICTION AND AGRICULTURE

M.V.K. Sivakumar

Agricultural Meteorology Division

World Meteorological Organization

M.V.K. Sivakumar

Agricultural Meteorology Division

World Meteorological Organization

PRESENTATIONPRESENTATION

• 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 FORECASTSCURRENT STATUS OF AGRICULTURE AND NEED FOR CLIMATE FORECASTS

AGRICULTURE – THE MOST WEATHER-DEPENDENT SECTOR

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.

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

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.

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 AGRICULTURENATURAL DISASTERS AND AGRICULTURE

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 1997-98 ENSO (Source: NOAA)

Impact of 1997-98 ENSO (Source: NOAA)

Global damage 33.20 billion $

Central and South America

54.4%

North America 19.5%

Indonesia and Australia

16.1%

Asia

Africa

9.7%

0.4%

Impacts of 1997-98 ENSORegion Loss

($ billions)

Human deaths

Population affected

(millions)

Area affected

(mill. ha)

Africa 0.2 13,325 8.9 0.19

Asia 3.8 5,648 41.3 1.44

Australia & Indonesia

5.3 1,316 66.8 2.84

Central and South America

18.1 858 0.9 5.06

Global total 34.3 24,120 110.9 22.37

Forest cover change and average forest fire data

Country Total forest

(mill ha)

Forest cover change (1990-95)

%

Area burned (ha)

Argentina 33.94 -0.3 55,370

(85-89)

Brazil 551.14 -0.5 5,500,000

(97-98)

Paraguay 11.53 -2.6 60,000

(1988)

Uruguay 0.81 0.0 8,240

(81-90)

EXTREME VARIABILITY – MULTIDIMENSIONAL EXTREME VARIABILITY – MULTIDIMENSIONAL IMPACTSIMPACTS

EXTREME VARIABILITY – MULTIDIMENSIONAL EXTREME VARIABILITY – MULTIDIMENSIONAL IMPACTSIMPACTS

• 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 non-agricultural goods, and rural-urban migration

NEED FOR CLIMATE FORECASTS 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 REDICTIONS (1)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 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)

A BRIEF HISTORY OF CLIMATE REDICTIONS (3)

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)

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)

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)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)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)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)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

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)

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

The SST anomalies were calculated relative to the period 1950-2003 and aggregated into three-month period means.

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).

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).

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

y = -2.9317x + 1.1302

R2 = 0.092-20

-10

0

10

20

30

40

50

-3 -2 -1 0 1 2 3

Average SST anomaly OND oC

Yie

ld d

evia

tio

n (

%)

National average yield deviations (1972-2003) 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

< - 6.8 %

Evaluation of ENSO effects on Uruguayan Rice Production

RYD

Evaluation of ENSO effects on Uruguayan Rice Production

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

All Neutral La Niña El Niño

Fre

cuen

cy (

%)

Low Yields Medium Yields High Yields

National Rice Yield Distribution and ENSO phases (1972-2003)

Possible crop yield outcomes of irrigated sectors Possible crop yield outcomes of irrigated sectors under ENSO scenarios in Chile under ENSO scenarios in Chile (Meza and Podestá,

Chile)

Possible crop yield outcomes of irrigated sectors Possible crop yield outcomes of irrigated sectors under ENSO scenarios in Chile under ENSO scenarios in Chile (Meza and Podestá,

Chile)

– ENSO impacts on the water cycle and crop ENSO impacts on the water cycle and crop growth. growth.

– Probability distribution functions of potential Probability distribution functions of potential and actual evapotranspirationand actual evapotranspiration

– Identify regions and seasons that are Identify regions and seasons that are particularly sensitive to water scarcityparticularly sensitive to water scarcity

– Perform preliminary estimates of the benefits Perform preliminary estimates of the benefits of using climate forecasts in agricultural water of using climate forecasts in agricultural water resources planning.resources planning.

Climatic Variability in Chile and El Niño Phenomenon

In central Chile, ENSO does have an influence on other meteorological variables that play a fundamental role on reference evapotranspiration (Meza, 2005)

0

0.2

0.4

0.6

0.8

1

1.2

130 150 170 190 210

ETo (mm)

P(X

<x)

EN

LN

N

ENSO Effect on Water Demands in Central Chile

Water Demands in the Maipo River Basin

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

La Niña Normal El Niño

m3 *1

000 La Niña

Normal

El Niño

Expected Value of Information for the different phases of ENSO

Available water at each irrigation time was equivalent to 55 mm

0

50

100

150

200

250

300

350

La Niña Normal El Niño

EV

I (U

SD

/ha)_

___

CONCLUSIONS (1)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)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.