Extreme El Niño Insurance for Climate Change Prevention and

2
TECHNICAL NOTE 2
Extreme El Niño Insurance for Climate Change
Prevention and Adaptation in Peru
I n s u r a n c e f o r C l i m a t e C h a n g e A d a p t a t i o n P ro j e c t
TECHNICAL NOTE 2
Extreme El Niño Insurance for Climate Change Prevention and Adaptation in Peru
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH (german cooperation)
Insurance for Climate Change Adaptation Project
Main Advisor
Alberto Aquino
alberto.aquino@giz.de
Jr. Los Manzanos 119, San Isidro
http://seguros.riesgoycambioclimatico.org/
Author
GlobalAgRisk
Design and Layout
Renzo Rabanal
Photographs
GIZ photo archives, Diario El Tiempo, Piura
Printing
Giacomotti Comunicación Gráfica S.A.C.
Calle Huiracocha 1291. Of 302, Jesús María
First edition, Lima (Peru), August 2012
Made the legal deposit in the Biblioteca Nacional del Perú
(National Library of Peru) N.º 2012-09516
Cooperación Alemana al Desarrollo – Agencia de la GIZ en el Perú
Av. Prolongación Arenales 801, Miraflores
Total or partial reproduction of this work is allowed, provided the source is cited.
2
Extreme El Niño Insurance for Climate Change
Prevention and Adaptation in Peru
A wide range of stakeholders can now purchase a unique form of insurance to protect
against the extra costs and consequential losses associated with catastrophic flooding that follows a buildup of extreme levels of sea surface temperatures (SSTs) in the
Pacific. It is referred to as the Extreme El Niño Insurance Policy (EENIP). The EENIP
is the world’s first index “forecast” insurance, designed to pay stakeholders as they
incur costs in preparation for the extreme consequences that are coming. Payouts are
triggered by extreme increases in Pacific SSTs that occur during an El Niño year. The
SST indicator is observed months before the onset of heavy rainfall on land, triggering
payouts that enable the insured stakeholders to finance and to implement loss prevention and risk management strategies well before the catastrophic flooding reaches full force.
Given the unique features of the EENIP, GIZ began supporting potential stakeholder
education and market development programs for it in late 2010. Fundamentally, the
EENIP is ideally suited for GIZ efforts to support ex ante financing solutions that will
assist emerging economies in developing climate change prevention and adaptation
strategies. It is furthermore an excellent fit for stakeholders and communities that
are exposed to the catastrophic flooding associated with extreme El Niño events, much
Diario El tiempo, PIURA
like what happened in 1982-83 and 1997-98. The GIZ project is focused on the regions
3
of Piura, La Libertad, and Lambayeque and is designed to strengthen capacities vis-àvis risk reduction, climate change adaptation, and resiliency to catastrophic risk in the
action setting’s most vulnerable sectors and communities.
Because stakeholders need to understand how the EENIP is designed and why it only
operates in the event of an extreme El Niño, this note lays out the science used in its
development. For some time now, Peruvian scientists have been the global leaders in
the understanding of El Niño phenomenon (Lagos) and have shown the correlation
between an extreme El Niño phenomenon and the geophysical characteristics that
BOX 1
unquestionably trigger extreme flooding in northern Peruvian regions.
Key Messages
• El Niño is a cyclical climate phenome-
would be possible to suggest that an
non caused by a disruption in El Niño
extreme event may happen roughly once
Southern Oscillation (ENSO). Trade
every 15 years. However, an analysis of
winds and ocean currents in the equa-
longer term data sets reveals that a
torial Pacific change course, thus cau-
strong event is more likely to occur
sing SST to increase and convection to
once every 20 to 25 years..
shift from the western to the central
Pacific (Graphic 1).
4
• SSTs have become the standard scientific benchmark for monitoring chan-
• El Niño affects weather conditions all
ges in geophysical processes that signal
over the world, but perhaps nowhere
an El Niño year. Sustained SST eleva-
else as strongly as Peru. Warm, humid
tion occurring in specific regions of
air created by convection over the cen-
the Pacific Ocean is one of the primary
tral Pacific meets the cool air cascading
indicators of El Niño, as monitored by
down the Andes, causing torrential ra-
meteorological institutions around the
infall and catastrophic flooding in nor-
world.
thern Peru. Given the profile of months
• The U.S. National Oceanic and Atmos-
of extreme sea surface temperatures
pheric Association (NOAA) maintains
and the buildup of ambient tempera-
a public database of historic and cu-
ture, the warm air colliding with the
rrent SST measurements from four
cold air creates months of extreme ra-
regions in the Pacific (see Graphic 2).
infall.
Since the 1970’s, this data has been
• While frequency and severity is unpre-
consistently and systematically collec-
dictable, El Niño occurs every 2–7 years,
ted by satellite; thus, it is reliable and
based on historic data. Over the 30 year
transparent. period of 1982 to 2002, there were two
• SST data from two of the four NOAA-
extreme El Niño events. Therefore, it
monitored regions (Niño 1+2 and Niño
3) exhibit a high correlation with ca-
• The EENIP makes payments based on
tastrophic rainfall in northern Peru.
average November - December Niño
A gradual increase in sea surface tem-
1+2 SST measurements, and extreme
peratures precedes subsequent to-
SST increases correlate strongly with
rrential rainfall in northern Peru by
excessive rainfall in Peru’s northern
several months. Using SST from the
regions. That intense rainfall can last
Nino indices as the EENIP payment tri-
from January to May. gger is a unique innovation that allows
• Widespread damage and breakdown
insurance companies to make payouts
in infrastructure affect many sectors
before the onset of catastrophic rainfall,
and create long-term economic dis-
giving policyholders liquidity that allows
ruption. El Niño Insurance can reduce
them to reduce their losses associated
exposure to unexpected losses and
with the event in a real way. Hence, the
costs for vulnerable households, enter-
EENIP is a one of a kind form of insuran-
prises, and public sector entities, while
ce with a distinct advantage over others
facilitating disaster planning and crea-
that use loss estimates after the fact
ting a more stable financial environ-
since the insured may, with insurance
ment for long-term investment and
that forecasts disastrous events, actua-
growth.
lly reduce the level of losses. 5
1. El Niño Phenomenon
El Niño is a climate pattern characterized by complex interactions between the ocean
and the atmosphere across the eastern and western tropical Pacific (i.e., the
Southern Oscillation or SO). Disruptive fluctuations in oceanic temperatures, trade
wind patterns, and air pressure create an inter-annual “see-sawing” of sea levels, SSTs,
and precipitation between the eastern and western hemispheres (Glantz, Katz, and
Nicholls, 1991). Scientists describe the full range of variability observed in these climate dynamics as El Niño Southern Oscillation (ENSO). El Niño (the warm phase)
and La Niña (the cool phase) refer to the two extremes on this spectrum as indicated
by changes in SSTs. Both phenomena alter global weather patterns; however, El Niño
is typically associated with more severe and destructive disruptions in the tropical
Pacific region.
During a normal year (top panel of Graphic 1), strong trade winds blow westward from
regions of low pressure in the eastern south Pacific toward regions of high pressure
in the western Pacific. These winds push warm surface water off the coast of South
America toward Indonesia; the South American coastal water is replaced by the rise
of deeper, cooler water. Rainfall follows the rising air temperatures over the warmest
water off the coast of Indonesia, with relatively dry conditions off the coast of South
Diario El tiempo, PIURA
America.1
1. NOAA/ El Niño Theme Page. URL: http://www.pmel.noaa.gov/tao/elnino/el-nino-story.html
6
During an El Niño, the pattern reverses (bottom panel of Graphic 1). Trade winds
weaken in the central and western Pacific as a result of unusually high atmospheric
pressure in the western tropical Pacific and Indian Ocean regions and unusually low
pressure in the southeastern tropical Pacific. Warm water accumulates off the coast
of South America, obstructing the rise of the deeper and cooler nutrient-rich waters.
Sustained elevations in SSTs in this area of the Pacific cause heavy rainfall in northern
Peru and drought in Indonesia and Australia.
Walker circulation
Graphic 1. El Niño Phenomenon
NORMAL YEAR
Walker circulation
NORMAL YEAR
Trade winds blowing westwards
Trade winds blowing westwards
Cold
C
ld water
t pressing
i g upwa
p rds
d
repllac
rep
lacing
laci
ing th
the
e warm
warm surfa
rface
f ce wat
water
ter
Cold
C
ld water
t pressing
i g upwa
p rds
d
repllac
rep
lacing
laci
ing th
the
e warm
warm surfa
rface
f ce wat
water
ter
Increased convection
El NIÑO
YEAR
Increased convection
El NIÑO
YEAR
Trade winds drop
When trade winds drop
warm surface water may low eastwards
When trade winds drop
warm surface water may low eastwards
Source: CPC/NCEP, NOAA
http://www.grida.no/publications/vg/africa/page/3105.aspx
Trade winds drop
Warm sea currents
replace the cold water
and establishes a
deep layer of warm
Warm along
sea currents
water
the coast
replace the cold water
and establishes a
deep layer of warm
water along the coast
7
2. El Niño in Peru
Perhaps nowhere are the effects of El Niño felt more strongly than Peru, where paleoclimatological studies have detected its occurrence over the past 7000 years. The
consequences of the most recent events are well documented and remembered. In
the northern regions of Peru, excess rainfall in each of the last two extreme El Niño
events (1982–83 and 1997–98) was nearly 40 times the normal level and created longterm economic disruption, destroying irrigation infrastructure, bridges, roads, homes,
and crops. Some communities were isolated for months, thousands of people were
displaced and unemployed, and water-borne diseases emerged. Additionally, due to
heavy erosion and siltation during the 1997–98 El Niño, the capacity of the primary
reservoir in Piura was reduced by around 50 percent, leaving the region with reduced
irrigation and flood control capacity. In short, the entire region of Piura is the most exposed to the next extreme El Niño event.
The agricultural and fishery sectors, as in many emerging market economies, represent an important share of Peru’s domestic output. Production shocks to these sectors,
widespread losses, and the cost of disaster response can be significant. The estimated
economic losses for the 1982–83 El Niño were nearly USD 2 billion, a third of those
attributed to the agricultural sector. Less severe El Niño events, though not as problematic on land, can still cause a shift in marine life. For example, the 1972–73 El Niño
ruined the anchovy fishing industry and forced massive government intervention in
the fishery sector.
8
3. SST and El Niño Indices
SSTs have become one of the most indicative measures for monitoring ENSO fluctuations and forecasting possible El Niño conditions. The NOAA collects and maintains
SST data that is utilized by researchers and meteorological institutions around the
world. In addition to ship-based data, since the late 1970’s and early 1980’s, the NOAA
has used data from moored and floating buoys (in some cases patrolled by boats for
security) connected to satellites, along with other satellite data. The NOAA maintains
a publicly available time series of monthly average SST measurements from 1950 and
also synthesizes a number of ENSO indices, using recorded and reconstructed SSTs,
BOX 2
available at monthly resolution dating back to 18562.1
Regional SST Indices and Their Use
for Underwriting Insurance
Other countries in the region have agencies
and off docks close to the Peruvian coast.
that also maintain SST indices. The Austra-
These measurement techniques are infor-
lian Bureau of Meteorology and the Peru-
mative for the fishing industry but do not sup-
vian Oceanic Institute (IMARPE) maintain
port the construction of a stable long-term
them; however, the NOAA employs a more
index needed for underwriting insurance
consistent methodology in its collection of
and present an opportunity for serious moral
SST time series data than do its counter-
hazard to an insurance product given the
parts. For example, the IMARPE indices are
possibility of strategic measurement. NOAA
provided to the fishing industry to help avoid
indices, given their consistent measurement
the warmest areas of the ocean where the
methodology, institutional size, and impar-
fish catch is generally poor, and it collects the
tiality, provide a more reliable basis for in-
information from ship measurements recor-
surance.
ded at variable strategic fishing locations
2.http://www.cpc.ncep.noaa.gov/data/indexes/sstoi.indexes
9
The NOAA maintains four ENSO indices, and each one corresponds to a distinct region
in the Pacific Ocean where they measure SSTs (Graphic 2):3 2
Graphic 2: Pacific Ocean Regions Where the NOAA Compiles El Niño Indices
30N
20N
10N
NIÑO 3.4
NIÑO 1+ 2
EQ
NIÑO 4
10S
NIÑO 3
20S
30S
120E
150E
180
150W
120W
90W
Source: Climate Prediction Center, NCEP, NOAA
http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/nino_regions.shtml
3. NOAA/AOML Regional Satellite Products: http://www.aoml.noaa.gov/phod/regsatprod/about.php
10
4. Correlation of the Niño Regions
with Weather Events in Peru
The NOAA collects SST data from each Niño region on a daily basis to track anomalies
from average conditions, represented by the baseline 0 in Graphic 3. There is some
consistency in SST movements between the four adjoining Niño regions, yet specific
ones have stronger correlation and greater predictive capacity vis-à-vis weather conditions in some parts of the world than in others. Niño 3.4 has the most influence on
weather conditions in the United States. In 1972–73, SST anomalies in that region
signaled extreme El Niño conditions for North America, while those in the other indices were weaker. In Peru, 1972–73 was considered a moderate El Niño year. Changes
in the ocean currents had a major impact on the anchovy population and fishing industry in Peru, yet SST increase was not strong enough to cause heavy rainfall associated with an extreme El Niño.
Graphic 3: SST Anomalies in Niño 3.4 and the Peruvian Port Cities of Paita and Callao
3
2.5
EN 3-4
Anomalies of the sea surface temperature - SST (°C)
2
1.5
1
0.5
0
12.0
10.0
Paita
8.0
6.0
4.0
2.0
0
12.0
10.0
Callao
8.0
6.0
4.0
2.0
0
Source: IMARPE
11
Graphic 3 compares SST anomalies in Niño 3.4 and in the Peruvian port cities of Paita
and Callao. As the image shows, many of the larger anomalies in the former do not translate into major anomalies off the coast of Peru, as was the case in 1972–73. However,
the strongest El Niño events appear as major anomalies in all three locations, suggesting higher correlation among SST indices for the most severe events.
4.1. Analyzing the Niño 1+2 Index Relationship
To identify a suitable Niñoindex for Piura, a regional flood proxy was first constructed
from rainfall gauge data compiled by the Peruvian national weather service (SENAMHI)
and the Merged Analysis of Precipitation (CMAP) data set made available by the NOAA
Climate Prediction Center (CPC). The SENAMHI data comprises monthly rainfall observations from 1943 to 2004 from seven weather stations, four of which were suitable
for the analysis. The CMAP is a global rainfall dataset that uses a blend of rain-gauge,
satellite, and reanalysis data. It is spatially averaged, thus providing a grid of rainfall
data points across a region which offers a more specific measurement of flood conditions over the entire Piura catchments than point-specific rain-gauge measurements.
The grid data overlapping the northern region of Peru were used for this analysis. The
maximum monthly rainfall during the principle growing season of January - April was
determined, and then the two data sets were merged to yield regional flood proxy data
for the period of 1943–2004.
The correlation of the regional flood proxy to different Niño indices of maximum SST
from January - April was analyzed in order to identify which ones had the highest
Graphic 4: Correlation between Sea Surface Temperature and the Regional Flood Proxy Series for Piura, Using 53 Years of Data from 1943 to 2004
30N
25N
20N
15N
10N
5N
EQ
5S
10S
15S
20S
25S
30S
180
160w
140w
-1
-0,8
120w
-0,6
100w
-0,4
-0,2
80w
0,2
60w
0,4
0,6
Source: Khalil et al., 2007
Note: Correlations above 0.27 in absolute value are statistically different from 0 at a 5 percent significance level.
12
40w
0,8
20w
1
0
correlation over the recorded period. A linear correlation map (Graphic 4) of the regional flood proxy, with the corresponding SST maximums in the equatorial Pacific,
shows that the area in red just off the northern Peruvian coast demonstrates the
strongest correlations. This region of the highest correlation with the regional rainfall data for northern Peru corresponds to Niño 1+2 (Graphic 2).
The correlation between the two variables is strongest for the more severe rainfall
events (where SSTs are also extremely elevated), indicating that Niño 1+2 is most useful as a proxy index for catastrophic regional floods for which insurance is desired.
Lagos et al. (2008) also used linear correlation analysis of October-March El Niño SST
index values and October-March precipitation anomalies from 44 weather stations
throughout Peru. They found that SST anomalies in Niño 1+2 are strongly associated
with rainfall on the northern coast from January to March and that this association is
strongest during stronger El Niño events.
13
5. El Niño Index Insurance for Northern Peru
The EENIP for Piura makes payments based on average November-December Niño
1+2 SST measurements. Analyses conducted by GlobalAgRisk and others (Khalil et
al., 2007; Lagos at al., 2008) confirm that extreme increases in that region’s SSTs during that period correlate significantly with subsequent heavy rainfall in Piura which
typically occurs from January to April. This relationship is quite apparent with a simple visual comparison of rainfall and SST values (Graphic 5).
Graphic 5: Average Nov–Dec SST (°C) in Niño 1+2 and Jan–Apr Rainfall in Piura, 1979–2004
27
Niño 1 + 2 Nov-Dec Avg (NOAA)
26
25
24
23
22
21
2000
2001
2002
2003
2004
2000
2001
2002
2003
2004
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
2000
1800
1600
1400
1200
1000
800
600
400
200
Rainfall in Piura Airport (CORPAC Piura)
0
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
14
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
Source: Authors, using data from the NOAA and CORPAC Piura
Graphic 5 shows the average 1957-2004 rainfall amounts for January–April in Piura
against the average Nov-Dec SSTs from Niño 1+2. The last two extreme El Niño events
clearly stand out, with rainfall amounts nearly 40 times the normal level in each. It
also illustrates that average November-December Niño 1+2 SSTs were strikingly high,
preceding both heavy rainfall incidents. This relationship between extreme SST elevations and subsequent catastrophic flooding in northern Peru offers the unique opportunity to design an insurance product that provides a payment before the disaster
strikes, thereby enabling stakeholders with an insurable interest in the flood event
to use the money in real time to invest in loss prevention measures. The EENIP is
the first and only formal insurance product in the world that facilitates risk mitigation through payouts based on forecasting.
How far in advance of the flooding insurance payments can be made depends on the
index used to measure the occurrence of El Niño. The SST anomaly begins in the eastern Pacific and migrates toward the Peruvian coast. This means that the more westerly
index, Niño 3, corresponding to the November average temperature, offers the possibility of December payouts. While both indices strongly correlate with extreme rainfall along the coast of Peru, using Niño 3 for new EENIP applications (in the regions of
La Libertad and Lambayeque, south of Piura) will offer an additional month of lead
time before the onset of torrential rains.
15
Diario El tiempo, PIURA
5.1. Setting the SST Trigger Value
Once the relationship between Niño 1+2 and precipitation in Piura was identified, the
analysis was refined to determine an appropriate SST value for triggering an insurance payment. Logistic regression was used identify Nino 1+2 values that correspond
to one-in-ten- (0.1) and one-in-twenty-year (0.05) rainfall events that are representative of a strong El Niño event. Restricting insurance coverage to the less frequent yet
strongest El Niño events keeps the price of the insurance more affordable, while providing protection against catastrophic losses when it is most needed.
The average conditional probability was estimated using 1856–2005 Niño 1+2 data. A
conditional probability value of 0.5 identifies the trigger when an insurance payment
is expected on average. The Niño 1+2 values that correspond to a probability of exceedance of 0.1 and 0.05 are +1.17º and +2.05º above “normal.” Based on this analysis, the
threshold, or the insurance payout trigger value, was set and priced at 24°C. At this
mark, the insurance would have paid 45 percent of the sum insured in 1983 and 76
percent in 1998.
16
5.2. Linear Trends, Climate Change, and Forecasting
An index trend should always be examined when an insurance product’s long-term
sustainability is at stake. A Mann-Kendall test applied to triggered payments found no
evidence of a monotonic trend towards increasing or decreasing the probability of an
El Niño event over the 150-year series for Niño 1+2. While the past series appears stationary, concerns over recent climate change and the expectations that it will lead to
more extreme weather conditions suggest that trends should definitely be monitored
in the future. Of critical importance for the actuarial soundness of any index insurance
product is whether the underlying index, and therefore the likelihood of a payout, can
be predicted prior to the purchase of a contract (adverse selection). This question was
examined using autocorrelation of the Niño 1+2 index with its lagged values and of the
more widely reported Niño 3.4 index; no significant correlation was found. The autocorrelation of the means of the January–April Niño 1+2 index with its prior monthly values was also examined. The analysis suggests that a sales closing date of at
least 6 months in advance is needed to avoid predictability and adverse selection. However, new research and understandings of the ENSO cycle are continually emerging
and enabling longer-term predictions about the phenomenon.
It is clear that El Niño is cyclical and that the ability to forecast it increases every year.
However, thus far, the analysis suggests that there is little information that provides
useful forecasting accuracy within about a year of the contract sales closing date (December of the prior year). While the accuracy of long-term forecasts is uncertain at
this time, particularly with regards to estimating the severity of El Niño events, they
have a strong influence on people’s mindsets and their risk management planning.
For these reasons, the latest date an interested party could purchase the EENIP is the
end of January of each year- nearly a year before the insured event would occur.
17
6. Expanding the Market for El Niño Insurance Reexamining El Niño Relationships
As part of the GIZ project, the viability of expanding the EENIP market to La Libertad
and Lambayeque has been investigated by analyzing the feasibility of using the current
contract design with the Niño 3 index. While Niño 1+2 is the closest zone to Peru, a
preliminary EENIP analysis, based on November Niño 3 observations, suggests that
BOX 3
it offers some advantages over Niño 1+2
ADVANTAGES OF EL NIÑO 3 INDEX
FOR THE INSURANCE
• Niño 3 creates the opportunity for an
earlier payout. Its November SST rea-
tober-November Niño 3.4 and December
Niño 1+2 indices.
dings are comparable to average No-
• A product based on the earlier Niño 3
vember-December Niño 1+2 figures.
index is more consistent with the ar-
Using that data could, therefore, pro-
gument that insurance provides subs-
vide the grounds for a payout roughly
tantially higher value when it provides
one month before any are made from
people and firms resources in advan-
Niño 1+2 readings. This is similar to
ce for preparing for and mitigating the
what Lagos et al. (2008) found with res-
effects of extreme events.
pect to the correlation between the Oc-
6.1. Analyzing the Niño 3 Index Relationship
While SENAMHI rainfall gauge data is not available, as it was in the prior analysis for
Piura, similar reanalysis numbers was obtained. Much like the Khalil et al. (2007) study, the data is an extraction from the NOAA/CPC’s CMAP global rainfall statistics that
blend rain-gauge and satellite information to create a geographic grid of data points.
These figures are analyzed against those from NOAA Niño 1+2 and Niño 3 to provide
further insight into the feasibility of expanding into La Libertad and Lambayeque.
The correlation matrix in Table 1 presents November-December Niño 1+2 and November Niño 3 correlations with cumulative rainfall from January to May of the following
18
Diario El tiempo, PIURA
year in the two CMAP grid data zones (31 and 37) that correspond to the area around
Piura and La Libertad. All variables significantly correlate, above 80 percent. Two important conclusions emerge from the analysis performed thus far. First, for extreme
values (the strongest Niño 1+2 since 1950), the correlation between November-December Niño 1+2 and November Niño 3 is very strong, over 99 percent. In contrast, the
correlation found by Lagos et al. (2008) for November Niño 3.4 with December Niño
1+2 is 0.832. Second, the correlations of monthly rainfall and the Niño 1+2 and Niño 3
indices are similar and high for the strongest events experienced in the coastal areas
south of both Piura and La Libertad. That is, for extreme El Niño events, rainfall along
the coast can be predicted using either index. Both correctly predicted the four strongest events over the record of the reanalysis data (1979–2008). Furthermore, there is
essentially no difference in the rainfall correlation for the two indices.
Table 1: Correlation between SST and Rainfall Data for Peru’s Northern Coast Zone 31
Piura and
Lambayeque
Region
Zone 31
Zone 37
Niño 1+2
Niño 3
100,0
Zone 37
Lambayeque
and La Libertad
Region
Niño 1+2
Niño 3
96,1
82,9
82,4
100,0
94,6
94,7
100,0
99,7
100
Source: Authors
19
Both indices identify the same seven strongest years when using the current NOAA
Niño data from 1950 to 2010. The results are very similar when comparing payment
BOX 4
outcomes of two EENIP contracts:
RESULTS OF EENIP CONTRACT PAYMENTS
ACCORDING TO NIÑO 1+2 INDEX AND NIÑO 3 INDEX
• Existing Niño 1+2 (November and De-
• Proposed Niño 3 (November) contract
cember) contract (starts paying at 24 ºC (starts paying at 26.5 ºC and stops pa-
with a maximum payment at 27 ºC).
ying at 29 ºC).
The payment rates would have been identical for the 1982 event (45 percent); Niño 3
would have paid higher for the 1997 event (82 percent versus 76 percent) and significantly higher for the 1972 event (25 percent versus 2 percent).
20
Summary
Extreme El Niño Insurance is the world’s first regulated insurance product that makes
payouts before losses are incurred. The insurance is structured as a “contingency insurance”, which pays based on the occurrence of a defined event, in this case, an extreme El Niño as predicted by SST anomalies. The existing EENIP for Piura makes
payments based on the average of the November-December Niño 1+2 SST measurements, thus enabling rapid payouts in January before the onset of flooding. This unique structure is only possible due to the measurable relationship between the Pacific
SST values and catastrophic weather conditions on Peru’s northern coast and the
access to secure and reliable SST data collected and maintained by NOAA.
The promising new analysis of the relationship between Niño 3 index and the effects
of El Niño in northern Peru suggests that it may be possible to make insurance payments even earlier with the additional month of lead time Niño 3 provides over
Niño 1+2.
Because the manifestations of each El Niño differ and the consequences can be so
prolonged and far-reaching, a simple rainfall index cannot be representative of the
exposure and scope of possible losses. During the previous severe events, weather
stations data were disrupted due to damage to the stations and the inability of researchers to collect data at some stations. Satellite measurements of rainfall continue to
improve, but are less accurate than the NOAA SST data, which is cross-validated through
several data sources. The EENIP can be used by households, enterprises, fisheries, and
public sector bodies to reduce their exposure to losses and costs created by an extreme
El Niño. Improvements in ENSO forecasting and the EENIP’s advance payment capability contribute to disaster preparedness and resilience by providing the insured time
and resources to implement loss prevention measures and to adjust livelihood strategies.
21
Referencias bibliográficas
Glantz, M.; R. Katz; and N. Nicholls, eds.
Teleconnections Linking Worldwide
Climate Anomalies. Cambridge: Cambridge University Press, 1991.
Hudson, R. A., ed. Peru: A Country Study.
Washington: GPO for the Library of
Congress, 1992.
Science, 1992.
National Drought Mitigation Center. “Reported Effects of the 1997–98 El Niño.”
White paper, National Drought Mitigation Center, Lincoln, NE, 1998.
Reynolds, R. W., and T. M. Smith. “Improved
Khalil, A. F., H. H. Kwon, U. Lall, M. J. Mi-
Global Sea Surface Temperature Anal-
randa, and J. R. Skees. “El Niño South-
yses Using Optimum Interpolation.”
ern Oscillation-based Index Insurance
Journal of Climate 7(1994): 929–948.
for Floods: Statistical Risk Analyses
Reynolds, R. W., N. A. Rayner, T. M. Smith,
and Application to Peru.” Water Re-
D. C. Stokes, and W. Wang. “An Im-
sources Research 43; W10416, doi:10.
proved In Situ and Satellite SST Anal-
1029/2006WR005281, 2007.
ysis for Climate.” Journal of Climate
Lagos, P., Y. Silva, E. Nickl, and K. Mos-
22
PA: The Pennsylvania Academy of
15(2002): 1609–1625.
quera. “El Niño-related Precipitation
Smith, T. M., R. W. Reynolds, T. C. Peterson,
Variability in Peru.” Advances in Geo-
and J. Lawrimore. “Improvements to
sciences 14 (2008): 231–237.
NOAA’s Historical Merged Land-Ocean
Lagos, P., and J. Buizer. “El Niño and
Surface Temperature Analysis (1880–
Peru: A Nation’s Response to Inter-
2006).” Journal of Climate 21(2008):
annual Climate Variability.” Natural
2283–2296.
and Technological Disasters: Causes,
Sun, D. Z., and K. E. Trenberth. “Coordinated
Effects, and Preventive Measures.
Heat Removal from the Tropical Pacific
Majumdar, S. K., G. S. Forbes. E. W.
during the 1986–87 El Niño.” Geophysical
Miller, and R. F. Schmatz, eds. Easton,
Research Letters 25 (1998): 2659–2662.
23
Risk is out there, get insured.
The Insurance for Climate Change Adaptation Project is part of the International Climate Initiative (ICI) of the German Federal
Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU).