PERFORMANCE MULTIMODEL CHFP (CLIMATE-SYTEM
HISTORICAL FORECAST PROJECT) IN CHARACTERIZE
FEATURE AND IMPACT OF EL NINO MODOKI
1Ida Bagus Mandhara Brasika,
1Dr.Nurjanna Joko Trilaksono
1 Department of Meteorology, Faculty of Earth Science and Technology, Institut Teknologi
Bandung
ABSTRACT
The performance of the Climate-system Historical Forecast Project (CHFP) for El Nino
Modoki prediction during 1980-2010 period is evaluated using deterministic and probabilistic
verification measures.Skill measure performance of single model and multimodel at two El
Nino Modoki season, boreal summer and boreal winter. Ensemble model scheme is built
based on the season, the best single model in boreal summer, boreal winter and all season.
Some single models spread overestimate in some years that can make mistake in prediction
modoki event. The best ensemble model scheme is used to see the characteristic of the el nino
modoki. The scheme is used to see the el nino modoki warm pool propagation in the central
pacific equatorial for determine the modoki event. The other ways to determine the el nino
modoki event are from its spasial and temporal feature. SSTA of the ocean around the world
when the modoki event was occured was very unique, there was warm pool flanked by cold
pool. El nino modoki has a large decadal background. The impact of El Nino Modoki for
precipitation in Indonesia are significant. Strongest impact is held in boreal summer. El Nino
Modoki is dificult to understanding. It means some good predictions.
Keywords: El Nino Modoki, SSTA, ensemble, Climate-system Historical Forecast Project
(CHFP)
1. Introduction
El Nino is the occurrence of
climate disruption caused by increase of
SST in the East Pacific Ocean. El Nino
occurs where the trade winds blow from
the west (subtropical area) towards the east
(around equator), so that the warm pool of
the Pacific Ocean is gathered in the East
Pacific. In mid-summer (June-September)
2014 SST anomalies in the Pacific
Equatorial showed odd shapes tripole
zone. This incident did not meet the
criteria of El Nino. The criteria of El Nino
was published by National Oceanic and
Atmospheric Administration (NOAA) of
the United States of America. Referring to
the SST anomalies in 2004, warming in
the Central Pacific is flanked by colder
SST anomalies in the west and the east,
caller El Nino Modoki (Yamagata et al.,
2007).
El Nino Modoki can be determined
based on El Nino Modoki Index (EMI).
From the calculation of the EMI value, it is
known that El Nino Modoki already
occured in 1986, 1990, 1991, 1992, 1994,
2002, 2004, 2005, 2009 and 2010.
Worldwide SST anomalies is indicated by
El Nino Modoki is very different from the
El Nino anomaly.
Indonesian atmosphere was
affected by the occurence of El Nino
Modoki with high rainfall in Northern
Borneo and Northern Papua. In addition,
the EM decrease rainfall in the region of
Sumbawa Besar, Makassar and Banjar
Baru in the summer (June-August) and the
stronger decrease on the September-
November (Windari, 2012).
El Nino Modoki had a big impact
to Indonesia so it is important to examine
the ability of the models for El Nino
Modoki. It would be helpful to determine
anticipatory measures against potential
future disasters such as tropical cyclones,
droughts and flood.
2. Data and Method
There are two main types of data
used in this study, Sea Surface
Temperature (SST) and total precipitation
from the model of the Climate-system
Historical Forecast Project (CHFP) and the
data of El Nino Modoki Index (EMI) from
Japan Agency for Marine-earth Science
and Technology (JAMSTEC). Both type
of data were used in the form of monthly
data over the years 1981-2009.
This study consists of three main
steps, namely the period of training to test
the ability of each single model, period of
testing to test multimodel formed based on
the result of training period, and the last is
testing the ability of multimodel to show
characteristion of El Nino Modoki. Here is
Figure 2.1 which is flowchart of this
research.
Figure 2.1 Flowchart of research (a) main
chart and (b) sub-process chart.
(a)
(b)
Calculation of deterministic
capability of single model and multi model
using SESS/MSESS (Squared Error Skill
Score/ Mean Squared Error Skill Score).
SESS can be formulated in
Equation 2-1:
ππΈππ = 1 βπππΈ
πππΈππππ..............(2-1)
πππΈ = (ππ β ππ)2.................(2-2)
πππΈππππ = (ππ)2....................(2-3)
Variable fi is the forecast value for each
month i, oi is the observation value for
each month i, JAMSTEC data. While
MSESS is the average of SESS value.
SESS/MSESS will give the value of 1 if
the forecast has the same value as the
value of its observations (perfec forecast),
SESS/MSESS will be 0 or smaller than
zero to indicate that the model prediction
have no predictive capability.
Calculation capabilities
probabilistic of multimodel using RPSS
(Ranked Probability Skill Score). RPSS
can be formulated in Equation 2-4:
π πππ = 1βπππ‘πππ π ππ
πππ‘πππ π ππππππππππ π.........(2-4)
π ππ =1
πβ1 [( ππ) β ( ππ)]π
π=1ππ=1
ππ=1 ......(2-5)
M is the nimber of categories used, there
are three categories in this study: category
1 for values less than or equal to 0, the
category 2 which is between 0 to 0.364
(thres hold)and a category 3 to a value
greater than 0.364. Pk is the probability
predictions for category-k, and Ok is an
indicator (0=no, 1=yes) for the observation
of category k. RPSS provide value in the
range of minus infinity to 1, a value of 0
and smaller than 0 indicates that the model
used to predict not have to skill to data
references, while a value of 1 indicates
perfect prediction.
Hovmoller and plot value of EMI
are used to determine the multimodel
capability in characterize El Nino Modoki.
Meanwhile in order to characterize the
effect of El Nino Modoki on rainfall in
Indonesia, it will use two methods. The
first method calculate the correlation of
rainfall values (variabel X) and EMI (
variabel Y), the number of data n. Pearson
correlation coefficient will be defined by
Equation 2-6 (Storch dan Zwiers, 1999):
π =π ππβ( π)( π)
( π π2β( π)2
)β( π π2β( π)2)
...........(2-6)
The second method, to see how much
precipitation changes that occur when an
El Nino Modoki, so would be the
percentage change in precipitation values
with Equation 2-7:
ππππ πππ‘ππ π ππππ’ππβππ πΆπ» =
πΎππ .ππππππ βπΎππ .πππ‘ππ
πΎππ .πππ‘ππ x 100%..........(2-7)
βKom.Modokiβ is a composite of rainfall in
years of El Nino Modoki namely the 1986,
1990, 1991, 1992, 1994, 2002 and 2004,
while βKom. Totalβ is composite rainfall
all year in the period 1981-2009.
3. Result and Discussion
3.1. Ability Test Results Single Model
(Training period)
A. Result SESS/MSESS Single Model
Based on Figure 3.1 almost all
single models provide a good value that is
above 0.6 in each month with the lowest
value of 0.4. Almost all the models show
results that are opposite of the boreal
summer season and the boreal winter
season, some models provide a good value
but in boreal summer the ability decreases,
some models show the opposite value.
Only models CCCma-CanCM4 that gives
good constant value in every month.
Figure 3.1 MSESS monthly time series of
each single models. Red square
is the boreal summer season
(June to September), while the
blue square is the boreal winter
season (December-February)
with a MSESS thres hold of
0.6.
By defining a good model is a model in
every season that passes the thres hold of
0.6 for 3 consecutive months in a season, it
can be determined that the best model in
boreal summer season is MIROC5,
CCCma-CanCM4, POAMA-P24C and
ACMWF, whereas for boreal winter
season is CCCma-CanCM3, CCCma-
CanCM4 and ECMWF.
B. Result RPSS Single Model
RPSS has a range similar to
SESS/MSESS, from minus infinity to 1,
with a value of zero or less than zero
indicates the model does not have skill,
and 1 is perfect prediction.
Probabilistically according to the results
of calculations using the Ranked
Probability Skill Score (RPSS) in Figure
3.2 shows all single models provide
excellent performance that is above 0.9.
This means that the cumulative
probability of all single models meet
most of the three categories.
Figure 3.2 RPSS monthly time series of
each single models. By using
3 category, category 1; <= 0,
category 2; between 0 and
0.364, category 3; > 0.364.
Based on the calculation of the
skill , the ensemble schemes are shown in
Table 3.1 to provide a better model results.
Scheme which formed was 3, scheme 1
using all 7 single models, scheme 2 using
models that considered the boreal summer
season skil is CCCma-CanCM4, MIROC5,
POAMA-P24C and ACMWF.Scheme 3 is
a scheme that uses both models boreal
winter when the CCCma-CanCM3,
CCCma-CanCM4 and ECMWF.
Table 3.1 ensemble scheme based on
seasonal skill
Ens Model Remarks
Ens_1 All
Ens_2 CCCma-
CanCM4,
poama-p24c,
ECMWF,
MIROC5
Skill
deterministik
Boreal
Summer
terbaik
Ense_
3
CCCma-
CanCM3,
CCCma-
CanCM4,
ECMWF
Skill
determi
nistik
Boreal
Winter
terbaik
3.2. Ability Test Results Multi Model
(Testing Period)
By using the same definition when
determining a good model for single
model, it is shown in Figure 3.3 that the
scheme meets all criteria that have a value
above 0.6 for 3 consecutive months for
each season. This shows that the ensemble
of models can improve the ability of the
model as well as reducing errors are
caused by the calculation of each single
model.
Figure 3.3 SESS monthly time series of 3
multi model scheme. Red
squares are the boreal summer
season and the boreal winter
with a thres hold 0.6.
However, to determine the best of
the three scheme should be focused
multimodel performance especially during
the seasons of El Nino Modoki. For
scheme 1 shows that there is a fluctuation
in which the boreal summer have value
had dropped far enough to rise later in the
boreal winter. While schemes 2 and 3
together provide a consistent high values
in both season. But the scheme 2 gives a
higher value. So overall scheme 2 looks
better than ather schemes.
3.3. Multimodel Ability to characterize
El Nino Modoki
A. Spastial-temporal feature with
Hovmoller
Seen from the diagram in Figure
3.4, two occurrences of El Nino Modoki
are quite strong in 2002 and 2004. In late
2002 to early 2003 warm pool had
strenghened and widened until it reaches
the longitude 210o BT. The warm pool
never propagated to eastward, than the
warm pool rose again in mid-2004 and
early 2005. This shows that the
multimodel not only can determine the
value of EMI but also can describe the
spatial pattern of El Nino Modoki.
Figure 3.4 Hovmoller SST anomalies (in
Kelvin) ensemble model of
scheme 2 in the Pacific
Ocean at longitude and
latitude 125-290oBT; 10oLS-
5oLU. The red circles are the
occurrenece of El Nino
Modoki.
B. Temporal featur of El Nino Modoki
In figure 3.5 is shown the temporal
pattern of the EMI value of scheme 2
ensemble model which provide results in
the form of decadal pattern of El Nino
Modoki. Since the 1980s until the 2000s
were formed three phases, namely the
early 80s around 1982, the mid-90s and in
the early 2000s.
Figure 3.5 Temporal contour value of
EMI ensemble scheme 2
from 1981 to 2009.
C. Effect of El Nino Modoki on
Indonesian Rainfall
i. Correlation El Nino Modoki and
Indonesian Rainfall
Figure 3.6 shows almost in all parts
of Indonesian rainfall have a negative
correlation with the value of EMI.
Strongest correlation reaches 0.6 in the
west of the island of Papua. While on the
other islands such as Java, Nusa Tenggara
and Sulawesi have a negative correlation
0.2-0.5.
Figure 3.6 Pearson correlation between
the value of EMI and rainfall
in Indonesia during the years
1981-2009.
Based on Figure 3.7 , the strongest
correlation season occurs in summer or
boreal summer (June-August), where most
of the Indonesian region has a negative
correlation reaches 0.4. Another season
fairly strong correlation, occurred in
September-November.
Gambar 3.7 Pearson correlation between
EMI value and precipitation
in Indonesia for (a) March-
April-May, (b) June-July-
Agust, (c) Septembre-
Oktobre-Novembre, dan (d)
Desembre-January-February.
ii. Percent Change of Rainfall in
Indonesia in the year of El Nino
Modoki
Changes in rainfall values in this
study demonstrate the ability to determine
how differences multimodel rainfall years
when the El Nino Modoki events (1986,
1990, 1991, 1992, 1994, 2002 and 2004)
compared precipitation during 1981-2009.
In figure 3.8, the results show that
multimodel at such areas around the
Equator west of Sumatra, Kalimantan and
Papua norther part shows an increase in
rainfall of 5-10%. While for the Maluku,
the decline ranged 5-10%.
(a)
(b)
(c)
(d)
Figure 3.8 Change of precipitation in
Indonesia caused by El Nino
Modoki 1986, 1990,1992,
1992, 1994, 2002, 2004
(dalam %).
When are viewed by seasons, then
according to Figure 3.9 the effect of El
Nino Modoki occurs most strongly in the
boreal summer season (June-August) in
which precipitation can fall to 20%-30%,
in Nusa Tenggara, Kalimantan and
Sulawesi. However, in some areas
multimodel results can not show
something similar to Tristania study
(2013), in the study area north Maluku and
Papua in all seasons decrease rainfall.
While the model shows precisely the
opposite is the area where the rainfall
increased, even in the northern part of
Papua on the boreal winter has increased
to 20%.
Figure 3.9 Change of precipitation in
Indonesia caused by El Nino
Modoki (a) March-April-
May, (b) June-July-Agust, (c)
Septembre-Oktobre-
Novembre, dan (d)
Desembre-January-February
(in %).
(a)
(c)
(b)
(d) (b)
(d)
4. Conclusions
1. Single model shows EMI
overforecast value in a few months
by showing the value of EMI in
years past the threshold should not
be happening El Nino Modoki, by
doing so multimodel ensemble can
reduce errors and multimodel
overforecast best overall ensemble
scheme 2 is the model CCCma-
CanCM4, ECMWF-S4, MIROC5,
& POAMA-p24c.
2. Multimodel can describe the
propagation characteristics of El
Nino Modoki are always in the
middle of the Equatorial Pacific
and temporal patterns of El Nino
Modoki.
3. Multimodel show decrease of
rainfall in many area in Indonesia
are caused by El Nino Modoki.
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