Transcript Agromet information to users

CLIMATE PREDICTION AND
AGRICULTURE
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

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
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 AGRICULTURE

Climate variability and the
severe weather events that
are responsible for natural
disasters impact the socioeconomic 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)
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 ENSO
Region
Loss
($ billions)
Human Population
Area
deaths
affected
affected
(millions) (mill. ha)
Africa
0.2
13,325
8.9
0.19
Asia
3.8
5,648
41.3
1.44
Australia &
Indonesia
Central and South
America
Global total
5.3
1,316
66.8
2.84
18.1
858
0.9
5.06
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
Brazil
551.14
-0.5
Paraguay
11.53
-2.6
Uruguay
0.81
0.0
55,370
(85-89)
5,500,000
(97-98)
60,000
(1988)
8,240
(81-90)
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 agrobased
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 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)
• 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)
• 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 timeindependent 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 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 (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.
TOOLS
GIS  Crop Modeling  Forecast  Spatial Statistics
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
50
40
Yield deviation (%)
30
20
10
0
-3
-2
-1
0
1
-10
2
3
y = -2.9317x + 1.1302
R2 = 0.092
-20
Average SST anomaly OND o C
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
Evaluation of ENSO effects on Uruguayan Rice Production
Upper Quartile
Central Quartiles
High Yields
Medium
< - 6.8 %
Lower Quartile
Low Yields
RYD
Evaluation of ENSO effects on Uruguayan Rice Production
100%
90%
Frecuency (%)
80%
70%
60%
50%
40%
30%
20%
10%
0%
All
Neutral
Low Yields
La Niña
Medium Yields
El Niño
High Yields
National Rice Yield Distribution and ENSO phases (1972-2003)
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
In central Chile, ENSO does have an influence on other
meteorological variables that play a fundamental role on
reference evapotranspiration (Meza, 2005)
1.2
1
0.8
P(X<x)

EN
0.6
N
0.4
0.2
0
130
LN
150
170
ETo (mm)
190
210
ENSO Effect on Water Demands in
Central Chile
Water Demands in the Maipo River Basin
450000
400000
m 3*1000
350000
300000
La Niña
250000
Normal
200000
El Niño
150000
100000
50000
0
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
350
EVI (USD/ha)____
300
250
200
150
100
50
0
La Niña
Normal
El Niño
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.