signatures of tropical climate modes on the red sea and
TRANSCRIPT
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:[email protected]]
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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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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