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

mandharabrasika@gmail.com

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:

𝑆𝐸𝑆𝑆 = 1 −𝑀𝑆𝐸

𝑀𝑆𝐸𝑐𝑙𝑖𝑚..............(2-1)

𝑀𝑆𝐸 = (𝑓𝑖 − 𝑜𝑖)2.................(2-2)

𝑀𝑆𝐸𝑐𝑙𝑖𝑚 = (𝑜𝑖)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:

𝑅𝑃𝑆𝑆 = 1−𝑟𝑎𝑡𝑎𝑎𝑛 𝑅𝑃𝑆

𝑟𝑎𝑡𝑎𝑎𝑛 𝑅𝑃𝑆𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑠𝑖.........(2-4)

𝑅𝑃𝑆 =1

𝑀−1 [( 𝑃𝑘) − ( 𝑂𝑘)]𝑚

𝑘=1𝑚𝑘=1

𝑀𝑚=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):

𝑟 =𝑛 𝑋𝑌−( 𝑋)( 𝑌)

( 𝑛 𝑋2−( 𝑋)2

)−( 𝑛 𝑌2−( 𝑌)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:

𝑃𝑒𝑟𝑠𝑒𝑛𝑡𝑎𝑠𝑒 𝑃𝑒𝑟𝑢𝑏𝑎ℎ𝑎𝑛 𝐶𝐻 =

𝐾𝑜𝑚 .𝑀𝑜𝑑𝑜𝑘𝑖 −𝐾𝑜𝑚 .𝑇𝑜𝑡𝑎𝑙

𝐾𝑜𝑚 .𝑇𝑜𝑡𝑎𝑙 x 100%..........(2-7)

‘Kom.Modoki’ is a composite of rainfall in

years of El Nino Modoki namely the 1986,

1990, 1991, 1992, 1994, 2002 and 2004,

while ‘Kom. Total’ 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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