Indian Journal of Geo Marine Sciences

Vol.46 (10), October 2017, pp. 2088-2096

Signatures of Tropical climate modes on the Red Sea and

Gulf of Aden Sea Level

Kamal aldien I. Alawad 1, 2

, Abdullah M. Al-Subhi 1, Mohammed A. Alsaafani

1 & Turki M. Alraddadi

1

1Marine Physics Department, King Abdulaziz University, Saudi Arabia.

2Weather Forecast Division, Sudan Meteorological Authority, Khartoum, Sudan.

*[E-mail:kmohamad0001@stu.kau.edu.sa]

Received 14 October 2016 ; revised 28 December 2016

Long–term sea level data (1958–2010) for the Red Sea (RS) and Gulf of Aden (GA) obtained from Simple Ocean Data

Assimilation (SODA) were analyzed to examine its relation to the tropical climate modes. The continuous wavelet power

spectrum analysis of sea level reflects the presence of significant power peak in semi-annual and annual band throughout the

study region over the entire period. This variability is clearly associated with Nino3.4, which were accounted from the analysis

of wavelet coherence. The results show an in-phase relation in semi-annual band while the sea level lags the Nino3.4 by 9-10

months in annual band in all regions, which in-turn is associated to the peak difference between mean SST and sea level.

Whereas, there is no significant interaction is identified with SOI and IOD. However, the wavelet coherence of Nino3.4 and SOI

showed 2-7 years band in all regions, which corresponds to the sequence of strong El Nino/La Nina events during1968-1976

and 1997-2003.

[Key words: Red Sea, Gulf of Aden, Sea Level, Climate Modes, Wavelet Analysis]

Introduction

The Red Sea (RS) is a semi-enclosed, elongated

marginal basin which extends from 12.5°N to

30°N with an average depth of about 490 m and is

surrounded by arid and semi-arid land on both

sides. It is connected to the Arabian Sea and

Indian Ocean via Gulf of Aden (GA) through the

Bab-al-Mandab Strait, (see Fig. 1). Water

exchange through Bab-al-Mandab exhibits a

distinct seasonal cycle. In winter, two layer

system occurs, surface water as inflow to the RS

and deep Water as outflow. In summer, the GA

intermediate water is observed as inflow and it

balanced by both surface and deep water outflow 1, 2, 3, 4

.

The surface wind over the RS is constrained by

the high mountains and plateaus on both sides.

During summer (June-September) the entire RS

experiences the north-northwest wind, while in

winter (November-March) the south-southeast

wind builds up in the southern part and converges

with the north-northwest wind at around 19˚N 5.

The wind over the GA follows the north Indian

monsoon cycle; westerly during summer and

northeasterly during winter.

The factors affecting the regional sea level are

generally classified into three groups; geological

and astronomical processes, large scale processes

and regional scale processes 6, 7, 8

. Regional scale

includes inverse barometric effect (atmospheric

pressure), steric effects (temperature and/or

salinity) and local oceanic and atmospheric

factors (wind, current, waves and fresh water

input and etc.). In the RS, the sea level is mainly

governed by wind pattern and combined effect of

evaporation and water exchange via Bab-al-

Mandab Strait. It rises in winter and falls in

summer 1, 5, 9, 10, 11, 12, 13, 14, 15

. However, it is

characterized by annual and semi-annual cycles;

the annual cycle is very clear in all the basins and

is controlled by wind pattern and water exchange,

while the semi-annual cycle is controlled by the

evaporation 14, 16, 17, 18

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INDIAN J. MAR. SCI., VOL. 46, NO. 10, OCTOBER 2017

In winter season, the advent of strong south-

southeast wind into the RS induces the surface

flow northward and sea level rises, with central

bulge located in the wind convergence zone

(19˚N) 1. While in summer, the sea level drops

due to the northwest wind in the whole basin. In

the southern RS, when the wind changes from

southeast to northwest during early summer, the

current changes direction with the same phase of

wind, but there is a one month lag between them

when it becomes southeast during early winter.

Papadopoulos et al. 19

investigated the role of

atmospheric circulation on the heat flux in the RS,

and found that the highest heat loss is associated

with strong, cold and dry continental air masses

originally from the linkage between the Azores

and Siberian high pressure regions, while the

lowest heat loss occurs with weak, warm and

moist air masses that come from the Arabian Sea

and Indian Ocean. Recently, Abualnaja et al. 20

examined the effect of different climate modes on

the RS and found that El Niño–Southern

Oscillation (ENSO) and Indian Ocean Dipole

(IOD) or Dipole Mode Index (DMI) have the

significant role in the air–sea heat exchange.

Similar observations have been identified on the

wind-wave energy distribution in the RS 21

.

El Niño–Southern Oscillation (ENSO) is

described as the most energetic climatic

phenomenon over the earth that involves

fluctuations in both SST and wind in the tropical

Pacific Ocean. While the IOD is a coupled ocean-

atmosphere phenomenon in the equatorial Indian

Ocean, which is defined by the difference in SST

between two poles, east and west parts of the

Indian Ocean 22

. Both Phenomenon cause great

ecological and social impacts worldwide

This study is set to investigate the multi-scale

interaction between the sea level in the RS and

GA with the SST in the tropical Pacific and

Indian Oceans. Only a few researches discuss the

climate modes effect in this area and most of them

used coral oxygen isotope records 23, 24, 25, 26

, while

Raitsos et al. 27

discussed it using remotely sensed

chlorophyll. The present study focuses on the sea

level in this regions, where no previous study has

linked its variability with tropical climate modes.

The overall structure of the study is as follows,

section 2 describes the dataset and methods used

here. Section 3 and 4 discuss the results and

discussion respectively. The conclusions are given

in section 5.

Figure 1: The RS and GA area showing the study sites.

Materials and Methods

The study of climate requires long and continuous

data, which is missing for the RS and GA. Sea

level data used in this study are from Simple

Ocean Data Assimilation (SODA). It is re-

analyzed monthly average data during 1958-2010,

assimilated using General Circulation Model with

0.25°×0.4° resolution, but the data are remapped

on the uniform horizontal grid 0.5o × 0.5

o 28.

The time series of Nino 3.4 index defined as the

SST anomaly from Extended Reconstructed Sea

Surface Temperature Version 4 (ERSST.v4) in

the east central tropical Pacific Ocean (5ºS- 5ºN,

170º -120ºW) while the Southern Oscillation

Index (SOI) are downloaded from the NOAA-

Earth System Research Laboratory-physical

Science division

(http://www.esrl.noaa.gov/psd/data/climateindices

/list/). Indian Ocean Dipole Index represent the

gradient of SST anomaly between west (50 º E-70

º E and 10 º S-10 º N) and east (90 º E-110 º E and

10 º S-0 º N) equatorial Indian Ocean are obtained

from the Japan Agency for Marine-Earth Science

and Technology (JAMSTEC) derived from

HadlSST dataset

(http://www.jamstec.go.jp/frcgc/research/d1/iod/

HTML/Dipole%20Mode%20Index.html). The

monthly mean of the all climate modes data set

during 1958-2010 are shown in Fig. 2.

Based on the wind pattern climatology, the area

was divided into three regions, GA (42.75°-

51.25° E), Southern RS (12.75°-20.25° N) and

Northern RS (20.25°-28.25° N). Wavelet analysis

was used following Torrence and Compo 29

and

Grinsted et al. 30

methods. The wavelet analysis

becomes a very effective tool for analyzing the

variations of power within a geophysical time

series. It transforms any time series or frequency

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ALAWAD et al.: SIGNATURES OF TROPICAL CLIMATE MODES

spectrum (one-dimensional) to time–frequency

space (two-dimensional). In order to give a good

feature extraction, the frequency was adjusted = 6.

And to avoid the edging error resulting from the

artifact edge of the time series, the edge was

dropped by a factor 𝑒−2.

Results

Time series of monthly mean sea level anomaly

(SLA) in the GA was investigated with different

faces of climate modes (Fig. 3). While a strong,

pure El Nino event occurred during 1965-1966, a

positive SLA (17 cm) was observed in GA.

Figure 2: Time series of monthly mean Nino3.4 (upper

panel), SOI (middle panel) and IOD (lower panel).

This event starts in June 1965, while the

maximum SLA was observed in May 1966.

During May 1988, a strong la Nina occurs, a

negative SLA was observed (-18 cm) in August

1989 in the GA. The same situation was found

during La Nina in 2001, but the negative SLA

reached -23 cm in August, where this event starts

earlier in July 1998 and extended for 33 months

till March 2001. However the pure negative IOD

tend to decrease the sea level during 1958, 1989

and 1996 events, while the SLA decreased by -19,

-22, and -20 cm during September of the

following year, except in the case of 1996 where

it appears in September of the same year. No

significant results were observed during pure

positive IOD events, but the years of combined El

Nino and IOD (during 1972, 1982 and 1997)

increased the SLA by 15, 14 and 16 cm during

Feb. 1973, May 1983 and March 1998,

respectively. The same effect occurs in the

southern and northern RS, but with lesser range

and variability (not shown).

Furthermore, the wavelet technique was applied

in order to know the existing physical link

between the climate indices and the sea level.

Wavelet can uncover the periodicities of the time

series and at what time these occur.

Figure 3: Time series of monthly mean sea level and

different ENSO and IOD events. The red circle is pure

positive ENSO, magenta circle is combine positive ENSO

and IOD events, and green circle is pure negative IOD while

the brown circle is pure negative ENSO. The ENSO events

was obtained from the following site http://www.

cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/e

nsoyears.shml, while the IOD events from Hong et al.

(2008).

The continuous wavelet power spectrum of sea

level shows significant power peak in annual band

in all regions, Fig. 4, with appearance of semi-

annual band in the northern RS only. Both bands

have above 5% significant level and persisted

throughout the period. The continuous wavelet of

Nino3.4, SOI, and IOD are shown in Fig. 5.

Nino3.4 exhibits a power peak in semi-annual

band with a weak significant level. The annual

band appears in Nino3.4 and IOD, 2-6 years band

appears in Nino3.4, SOI and IOD with strong

significant level. While SOI alone reflects 11-15

year band after 1975. However, it is hard at this

stage to confirm which signal is dominant in the

area, but the coherence wavelet analysis is very

helpful in this manner.

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INDIAN J. MAR. SCI., VOL. 46, NO. 10, OCTOBER 2017

Figure 4: Continuous wavelet of monthly mean Sea level for

the GA (top), South RS (middle) and Northern RS (bottom).

The thick black contour is 5% significant level against the

red noise, while the COI (solid line) appears as lighter shaded

region.

Figure 5: Continuous wavelet of monthly mean climate

indices, Nino3.4 (top), SOI (middle) and IOD (bottom). The

thick black contour is 5% significant level against the red

noise, while the COI (solid line) appears as lighter shaded

region.

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ALAWAD et al.: SIGNATURES OF TROPICAL CLIMATE MODES

The squared WTC of Nino3.4 and Sea level is

shown in Fig. 6. The above 5% significant level

indicates that Nino3.4 contribute to both annual

and semi-annual band. The phase relation in the

Semi-annual band is in-phase, while in the annual

band the sea level lags Nino3.4 by 270o-300

o

corresponding to 9-10 months in all regions.

However 2-7 year band appears in all regions; the

phase relation shows that Nino3.4 leads the sea

level by 90° in GA and by 120

° in the northern RS

corresponding to 3-4 months. Also 11-18 years

band appears around 1980, with -90° to -120

°

phase relation in the southern RS and continues

but inside the COI, implying that Nino3.4 leads

the seal level by 3-4 months. The most interesting

result is a shift in phase angle in 2-7 band from 1

month (-30°) around 1972 to 4 months (-120

°)

around 1997 in the northern RS. Furthermore,

during 1990 to 2010 there is evidence of a shift in

the period of high power and coherency from 2-7

years up to around 5-7 years band in the southern

RS, which also appears in the northern RS with

same phase angle.

Fig. 7 shows the wavelet coherence of SOI and

sea level. It is very similar to Nino3.4 feature of

power peak in 2-7 year band as it is atmospheric

component of ENSO, but of course completely

out of phase. Both Nino3.4 and SOI reflects 11-18

years band in the southern RS, the phase angle

about -60° to -90

° indicates that the SOI leads the

sea level by 2-3 months. Furthermore, SOI

reflects weak indication of annual and semi-

annual band compared to Nino3.4, with unclear

phase angle.

The wavelet coherence of IOD reflects weak

signal to the RS sea level compared to Nino3.4

and SOI, Fig. 8. Its contribution appears in annual

band around 1970, 1988 and 2006 in all the

regions with fairly randomly distributed phase

angle, except for the northern basin where only

first two are appearing. In addition, it contributes

in 2-7 years band during 1980-2000 in the

southern RS, and it leads sea level by 90°

corresponding to 3 months. Furthermore, the IOD

becomes active during the weakening of Nino3.4

in the southern RS, the 3-5 years band took place

in the southern RS after the shifting of 2-7 years

band of Nino3.4 to 5-7 years band after 1990.

Discussion

An assessment was done on the effects of tropical

climate indices on the long term sea level data.

The SLA in the RS and the GA shows significant

fluctuations with climate modes. The positive

SLA coincides with positive face of both ENSO

and IOD, while the negative SLA occurs during

La Nina and negative IOD events. The opposite

results observed along the east coast of India 31

,

the positive (negative) ENSO and IOD and also

the combined events decreased (increased) the

SLA. In addition, the variability of SLA depends

on the timing and intensity of the events, the

stronger positive events, the large negative SLA.

This argue does not completely occur in the RS

and GA, as an example, during 1997, the

strongest ENSO event co-occurs with strongest

IOD, but the observed positive SLA (16 cm) is

less than the moderate ENSO event during 1965

by 1 cm. However, the period of maximum

positive and negative SLA varies from event to

event, but the majority of positive SLA occurs

during February-May, while the negative SLA

occurs during August and September.

The wavelet results show that the climate indices

have a role in semi-annual, annual, interannual

and decadal variations of sea level in the RS and

GA. Annual and semi-annual variations which

appear in continuous wavelet of sea level in Fig. 4

and wavelet coherence of sea level with Nino3.4

in Fig. 6 are well known in a previous study 32

,

and can be link to the combine effect of wind

regime and both water exchange at Bab-al-

Mandab and evaporation, respectively 3, 11, 14, 17, 33

.

Sultan et al. 12

figured out that the annual cycle of

sea level at Jeddah and Port-Sudan are 20 and 13

cm, respectively, with 44% variance, while the

semi-annual cycle are 10 and 8 cm with 10% and

16% variance, respectively. However, Clarke and

Liu 34

and Sakova et al. 35

observed the annual

band in the eastern Indian Ocean and referred it to

the alongshore monsoon wind, while the semi-

annual cycle is linked to the semi-annual zonal

equatorial wind and thermohaline process which

is related to Wyrtki jet. The new results here are

physically linked to the El Nino, which have not

previously been described.

The phase angle of the annual band indicates that

sea level is out of phase with Nino3.4 by about

270-300°. The peak of mean SST in Nino3.4

region occurs during April and May, while the sea

level peak occurs during February and March,

meaning that sea level lags the Nino3.4 SST by 9-

10 months, which in-turn is associated to the peak

difference between mean SST in the Pacific

Ocean and sea level In the RS and GA. In the

semi-annual band, the sea level and Nino3.4 are

in-phase. Chambers et al. 36

speculate that Indian

Ocean warming lags the Pacific Ocean by 3-5

months, while the peak of the warming lag by

about 7-9 months.

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INDIAN J. MAR. SCI., VOL. 46, NO. 10, OCTOBER 2017

Figure 6: Wavelet coherence of monthly mean Nino3.4

and sea level for the GA (top), South (middle) and

Northern RS (bottom). The thick black contour is 5%

significant level against the red noise, while the COI

(solid line) appear as lighter shaded region. Arrows indicate that right: in-phase, left: out of phase, down: Nino3.4 leading sea level by 90° and up: sea level

leading Nino3.4 by 90°.

Figure 7: Wavelet coherence of monthly mean SOI and

sea level for the GA (top), South RS (middle) and

Northern RS (bottom). The thick black contour is 5%

significant level against the red noise, while the COI

(solid line) appear as lighter shaded region. Arrows

indicate that right: in-phase, left: out of phase, down:

SOI leading sea level by 90° and up: sea level leading

SOI by 90°.

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ALAWAD et al.: SIGNATURES OF TROPICAL CLIMATE MODES

Nevertheless, annual and semi-annual bands are

very weak in SOI, meaning that these properties

are clearly associated with Nino3.4. This finding

agrees with Torrence and Webster 37

, where SOI

does not reflects clear annual cycle.

The 2-7 years band is consistent with the previous

studies 23, 26

, where different cyclicities (1.7, 2,

2.7, 3.8-3.9, 5.7, 7.6 and 8.5) were detected in the

coral oxygen Isotopes from the northern RS, but

the most dominant one is 5.7 year. They argued

that the interaction between the ENSO and NAO

throughout the Pacific-North American Pattern

(PNA) can bring the ENSO signal not only up to

the RS alone but till the Middle East, especially in

this dominant time scale. Furthermore, same

results were observed in the sea level 15

. Globally,

Torrence and Webster 37

found it as 2-8 year in

125 years of Nino3 and SOI data, Wang and

Wang 38

in the East Pacific SST and SLP, while

Gu and Philander 39

in the tropical zonal wind.

This band reflects all the main events of ENSO, as

example during 1968-1976 in the southern RS and

1997-2003 in the GA and the northern RS. During

the first period, the SST in the tropical Pacific

region shows a cycle of warm/cold/warm/cold

episodes, but the famous are El Nino1972 and La

Nina 1973. The same situation occurs during the

second period, sequence of warm/cold/warm

episode and the famous are El Nino1997 and La

Nina 1999. These findings physically imply that

the variability of SST in the tropical Pacific

Ocean has a high coherence with sea level in the

RS and GA even via oceanic and/or atmospheric

teleconnections.

The shift of the phase angle (-30° to -120

°) in 2-7

year band in the north from 1972 to 1997 may

represent indication of the global shift of climate

regime from the mid 1970 which is extensively

documented in different locations including the

RS 24, 27

. Increasing of IOD signal when the

ENSO is weakening during 1980 to 2000 in the

Southern RS is consistent with the findings of

Nakamura et al. 40

in Kenya. Nakamura et al. 40

pointed out that the warming of the Indian Ocean

enhanced the IOD to overshadow the ENSO-

Indian Ocean Summer Monsoon interaction by

increasing the frequency and intensity events from

1960’s. On the other hand, the above phase shift

and the intensified events prove the non-stationary

relation of those modes with sea level.

The interdecadal variability (11-18 year band)

was previously detected in different studies 37, 41,

42. Jevrejeva et al.

43 observed it as 12-20 year

band in the ice condition of both Baltic and

Barents Sea. Jevrejeva et al. 44

conclude that it

appears in the Arctic oscillation associated to SOI.

The mechanism that cause this oscillation still

remain under study, but the same phase relation

between 11-18 and 2-7 years band in the southern

RS may suggest same mechanism from the

tropical eastern Pacific. The weakening of IOD in

the RS is not a new finding; Abualnaja et al. 20

investigate its effect in the heat flux, and conclude

that El Nino is most influential tropical

phenomenon especially during winter over the

southern basin.

Conclusion

The time series of sea level anomaly in the RS

and GA reflects different phases of Nino3.4 and

IOD events. Positive phase of both Nino3.4 and

combine events (+Nino3.4 and +IOD) contributes

to rising the sea level in the RS and GA.

Maximum positive SLA mainly occurs in the

following year during February to May. Moreover

the negative phase of Nino3.4 and IOD contribute

to falling of the sea level. The negative SLA

occurs in the following year during August and

September.

However the wavelet analysis extracts different

periodicities (semi-annual, annual, 2-7 and 11-18

years band) of sea level with Nino3.4, SOI and

IOD. The Nino3.4 is in-phase with semi-annual

band and leads the annual band by 9-10 months,

corresponding to the peak difference of mean SST

in the Nino3.4 region (April-May) and sea level in

RS and GA (February-March). Moreover the

contributions of SIO and IOD in both bands are

weak compared to Nino3.4.

The 2-7 year band reveals that Nino3.4 and SOI

lead sea level by 3 months in the GA, but 4

months was observed in the northern RS, while

11-18 year band of both modes lead it by 3-4

months in the southern RS only around 1980. In

addition to that, the power peak of 2-7 year band

was shifted to 5-7 year in the southern RS during

1990-2000, but it appears in the north by the same

phase angle in the same period. IOD effect

becomes well clear during this shift in the

southern RS, where 3-5 year band is observed.

The SOI feature is similar to Nino3.4 as it

considered the atmospheric part of ENSO, but

completely out of phase, whilst the IOD

contribution is weak in all band when we compare

it with ENSO. Furthermore, all the above findings

clearly reflect the non-stationary property of

climate modes in the RS and GA which is

documented in the previous studies.

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INDIAN J. MAR. SCI., VOL. 46, NO. 10, OCTOBER 2017

Figure 8: Wavelet coherence of monthly mean IOD and sea

level for the GA (top), South (middle) and Northern RS

(bottom). The thick black contour is 5% significant level

against the red noise, while the COI (solid line) appear as

lighter shaded region. Arrows indicate that right: in-phase,

left: out of phase, down: IOD leading sea level by 90° and

up: sea level leading IOD by 90°.

Acknowledgments

The authors gratefully acknowledge the data

sources for this study. Sea level data are from

SODA and obtained from Environmental

Research Division Data Access Program

(ERDAAB). Climate modes data Nino 3.4 and

SOI are from NOAA-Earth System Research

Laboratory-physical Science division, while the

IOD data are from Japan Agency for Marine-

Earth Science and Technology (JAMSTEC).

Cross wavelet procedure was obtain from

Grinsted et al. (2004). The authors are grateful to

Deanship of Scientific Research (DSR), Deanship

of Graduate Studies (DGS)-King Abdulaziz

University (KAU) for providing the necessary

facilities to carry out this work.

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