Transcript No Slide Title

ENSO Prediction and Policy
• What information do predictions offer?
• What to do with this information?
Real Time Ocean Observations
Moorings
ARGO floats
XBT (eXpandable
BathiThermograp
h)
Satellite
SST
Sea
Level
M.A. Balmaseda ( ECMWF)
Ocean Observing System
Data coverage for June 1982
Changing observing
system is a challenge
for consistent reanalysis
OBSERVATION MONITORING
X B T p r o b e s: 9 3 7 6 p r o f i l e s
60°E
120°E
180°
120°W
60°W
0°
60°N
60°N
30°N
30°N
0°
0°
30°S
30°S
60°S
60°S
60°E
120°E
180°
120°W
Data coverage for Nov 2005
60°W
0°
60°E
Today’s Observations
will be used in years
to come
▲Moorings: SubsurfaceTemperature
180°
120°W
60°W
0°
60°N
60°N
30°N
30°N
0°
0°
30°S
30°S
60°S
60°S
60°E
◊ ARGO floats: Subsurface Temperature and Salinity
+ XBT : Subsurface Temperature
120°E
120°E
180°
120°W
60°W
0°
M.A. Balmaseda ( ECMWF)
Modeling gives you skill at forecasting:
Better skill after 3-4 months than “persistence”
Skillful up to 9 months into future
NINO3 Predictions
How to predict?
Forecasts courtesy of IRI (Lamont-Doherty)
Problems of the scientific community in communicating
with non-scientific users of needed climate information
O Scientist needs to express probabilistic aspect of climate
forecasts understandably.
O Media sometimes seek to stimulate customers (for marketing purposes), rather than educate/enlighten them. This may involve
deemphasizing probabilistic aspects, while emphasizing simple,
straightforward, and attention-grabbing aspects. This latter
emphasis often encompasses the most extreme or dyer
possibilities (e.g. “This La Nina is a major disaster for…”).
O Users at all levels want information that is simple and clear.
O Decision-makers desire high odds in order to rationalize taking
special and costly mitigation action.
Problems of the scientific community in communicating
with non-scientific users of needed climate information
O Scientists need to help clarify, especially for “middle” players
like met service representatives, the difference between the
state of ENSO using a definition of the phenomenon itself,
and a modified set of ENSO parameters relevant to the
climate effects of a particular region.
For example, for northwestern Peru rainfall during Feb-April,
the ENSO state based on a basin-wide definition clearly is
not what matters most. The original definition of El Nino (based
on SST anomaly near the coastline) is clearly more relevant.
Can the northwestern Peruvians accept a distinction
between the basin-wide ENSO state and the more local
SST state, which they have originally called El Nino?
Problems of the scientific community in communicating
with non-scientific users of needed climate information
O If scientists were assigned to make a societal decision, they would
likely use mathematics as part of their determination, and would
usually also need to learn many details about the particular application. This would be impractical, as would having the non-scientists
learn the mathematical methods. So scientists need to express
their knowledge of cause-and-effect, and of the relevant
mathematical tools, in some understandable fashion. Possible?
O Because of the special orientation of many media representatives,
the general public sometimes might receive a more representative
set of information if the meteorologists in the service organizations
were a “translator” between the scientists and the media and general
public. For example, scientists might focus on the unique aspects
of a current ENSO episode and how it modulates the canonical
probabilities for certain climate outcomes, while the service
organization meteorologist would write the equivalent in somewhat
simpler, more everyday terms that are a bit closer to what the
general public can digest, including the probabilistic aspects. The
media could greatly help disseminate the information at this stage.
What do we do with this information?
The same models that predict ENSO
state can be used to predict climate
changes, globally.
Some regions warm/cold, some wet/dry.
Critical is being able to say “how
unusual” climate will be.
Columbia – IRI ENSO “Quicklook” products
http://iri.columbia.edu/climate/ENSO/currentinfo/QuickLook.html
Communication problems may involve the way in which climate
forecasts are expressed, and the distortion of the intended
forecast message can then increase further through how it is
interpreted and re-communicated along the “chain”, and
finally re-interpreted and acted upon by decision makers
who are usually not climate specialists.
------------------------------------------------------------------
“This is a
large area.
...flooding?”
“This very
dark color
must mean
drought is
nearly certain.
It is because
of El Nino.”
“ Many
typhoons
likely?”
“70 inches of
rain above
the normal?”
What would you do differently with
advance information?
• Who are you?
• What decisions do you make that are sensitive to climate?
• What information does ENSO state give you?
• What additional information do you need? How will you get it?
• With whom do you need to cooperate?
What conditions must be in place for society to
benefit from ENSO information?
climate
predictability
human
vulnerability
opportunity
Benefit results when prediction
leads to decisions that reduce
vulnerability to impacts of
climate variability.
decision
capacity
Preconditions for Benefit
• Vulnerability & motivation
• Decision options
predictability
vulnerability
opportunity
• Predictability of climate
• Communication
• Institutions and policy
decision
capacity
Forecast information is useful when it addresses need that is real and
recognized. Decision makers must be aware of climate risk, and motivated
to use forecasts to manage that risk.
Disaster aid
Intensification
Coping strategies
FOREFITED
OPPORTUNITY
HARDSHIP
CRISIS
Probability density
Safety Nets
“Cargo Nets”
Climatic outcome (e.g., rainfall, production)
Index Insurance for Drought in Africa
Science in the service of humanity
Dan Osgoode & Eric Holthaus
International Research Institute for Climate and Society
Outline
• Problem: ENSO impacts rainfall
and agriculture - Food Security
• Solution: Farmer Index
insurance to buffer impacts
• Results from Ethiopia
Millennium Villages Project
(MVP)
• 13 other MVPs with very
different problems and solutions
Rain gauge
Drought and Development: The Problem
• Climate shocks increase vulnerability
(in already vulnerable places)
– Survey: 10 of 12 list drought as #1
livelihood risk (source: MVP)
• Climate Risk Management =
Risk Reduction (terracing,
rainwater harvesting, improved seeds
and fertilizer)
+
Risk Transfer (division of labor,
insurance, other financial mechanism)
+
Risk Taking (prudent loans to
increase productivity in good years)
IRI: Helping developing countries to manage climate risk.
Case Study: Ethiopia
•
•
•
•
85% of population
practices subsistence
rainfed agriculture
History of drought
leading to civil unrest
(1984)
Famine response
usually slow
Risk management
strategies have slow
uptake due to poverty
traps
Ethiopia statistics:
Population = 80M
2X size of Texas
Diverse topography and climate
~160 rain gauges w/ 30 years history
GDP per capita: $700/yr
Science Strategy: Index development
• Goal: identify a shared climate risk
and insure the community as a whole.
• Define shared risk (drought vs flood, etc)
• Quantify risk (through historical weather
information - weather modelling)
• Create index (match climate and climate
outcomes - agricultural modelling)
• Create insurance product (frequency of
payouts, magnitude of payouts, premium
price)
• Target insurance product (keep
stakeholders in mind - happens
throughout the entire process!)
Science Strategy: Index development
• Index focuses on late season rainfall,
when crop harvest is most vulnerable
Climate data is hard to get for rural populations
Sauri NDVI v. Maize Production
140
120
% of normal
100
80
60
40
20
0
1982
1987
1992
year
Rainfall
1997
2002
Satellite:
Regional NDVI
r = 0.514
NDVI
.Maize Prod
Sauri rainfall versus maize production
140
120
% of normal
100
80
60
40
20
0
1982
1987
1992
year
MAMJ rainfall
1997
r = 0.225
Maize production
2002
Ground-based:
Local Rainfall
Index insurance data
• Rainfall data is short, with gaps
• Limited spatial coverage
• How far is too far from station?
• Common to many applications
– Need technique for new stations
– Most places do not have long met
station history
– Must address for scale-up
Science Strategy: Remote Sensing
•
Goal: examine potential to scale up
availability of climate/environmental
data in data poor regions.
•
Satellite rainfall estimates give complete
spatial coverage - but short histories and
competing methods
Satellite vegetation can give direct measure
of crop health - but also includes
surrounding native veg. (also short history)
•
•
•
Working with NMA(Ethiopia) & Reading U.
to develop 30-year satellite rainfall
climatology for Ethiopia.
Working with NASA to “upscale” higher
resolution Quickbird and Landsat to MODIS
Seasonal rainfall total is not the best indicator for crop yield
Alternative is to use a simple crop model, e.g.:
Water Requirement Satisfaction Index (WRSI)
Water requirement varies through crop growth cycle
Eg for 180-day maize (as used for Sauri)
Index Insurance
Problems with traditional insurance have kept it from being
available to most of the world
•
Traditional Crop insurance
–
–
•
Recent index innovation
–
–
–
–
–
–
•
Insure weather index (eg seasonal rainfall), not crop
Only partial protection (basis risk), should not oversell
Cheap, easy to implement, good incentives
Implementations only a couple of years old
Exploding popularity--dangerous if misused
Structure to target each particular goal
Design complex
–
–
–
–
–
•
Undermined by Private Information problems
Almost always subsidized (SUBSIDIES CAN CAUSE PROBLEMS)
Only a naive partner would reveal all their cards
All partners must play active role in a cooperative design
Client must know what is not covered
How do we build a tool to address climate risk in development?
How do we best use climate information?
Probabilities of climate events key
–
Money in = average(Money out) + cost of holding risk
Ave(Pay) + 0.06 (99th % pay – Ave(Pay))
Index Insurance
•
Problems with traditional insurance have
kept it from being available to most of the
world
•
Recent innovation: “index” insurance
– Insure weather index
(rainfall/vegetation), not crop
– Cheap, easy to implement, fast payout,
good incentives
– Only partial protection (basis risk)
– Field implementations only a couple of
years old
•
Complex design process
– How do I reduce risk most effectively
with my first $
– Goal: match payouts with losses
Micro Example
Malawi Groundnut
•
Farmer gets loan (~4500 Malawi Kwacha or ~$35)
–
–
–
•
Farmer holds insurance contract, max pay is loansize
–
–
–
•
Insurance payouts on rainfall index formula
Joint liability to farm “Clubs” of ~10 farmers
Farmers in 20km radius around met station
At end of season
–
–
–
•
Groundnut seed cost (~$25, ICRSAT bred, delivered by farm
association)
Interest (~$7), Insurance premium (~$2), Tax (~$0.50)
Prices vary by site
Farmer provides yields to farm association
Proceeds (and insurance) pay off loan
Remainder retained by farmer
Farmers pay full financial cost of program (with tax)
–
–
Only subsidy is data and contract design assistance
Farmers told us:
Insurance package is how they adapt to climate change.
•
Malawi Project Partners: Farmers, NASFAM, OIBM—MIA,
MRFC, ICRSAT, Malawi Insurance Association, the World Bank
CRMG, Malawi Met Service, CUCRED, IIASA
Macro Example
• Early warning vs early action?
• IRI projects:
– Index product for Earth Institute MVP
– Index to ensure development goals of MVP for each
village cluster
• If MVP lifts people out of poverty traps
• Prevent climate from them falling back in
– Also exploring: Locust, fire, malaria, livestock disease and
international trade, forage, water management…
MVP Index
Multiple poverty challenges, multiple index strategies
Insurance is not for its own sake—it is to reduce
poverty, improve food security, and encourage
development
Implementation strategy driven by context, type of risk
A. Damage dropping people into poverty traps
B. Risk preventing people from moving forward
C. Immediate damage
http://iri.columbia.edu/publications/search.php?id=556