The concentration of chlorophyll-a (Chl-a) is influenced by the impact of El-Nino southern oscillation in Bay of Bengal sea water. In this study using data, monthly mean chlorophyll data derived from the sea-viewing Wild of view-sensor, the sea surface temperature is derived from the National Oceanic and Atmospheric Administration Advanced Very High-Resolution Radiometer, satellite derived monthly wind data is derived from the using CCMP, satellite altimeter sea surface height anomaly data derived from the TOPEX and the monthly mean mixed layer depth is taken from the Argo gridded data. The study period in this study is 1980-2017 (January-December) were analyzed the provincial nature of the ENSO linked with the above parameters. This satellite derived ocean data was used to identify the variability of the chlorophyll-a in Bay of Bengal associated with the ENSO. The witness of the Bay of Bengal is varied and response of climate modes due to El-Nino southern oscillation. These changing climate models have been impacting and influence the chlorophyll productivity in Bay of Bengal waters. Keeping in this view this study attempts to analyze the chlorophyll activity during the strong El-Nino years (1993, 1988, 1992, 1998, 2010, 2016) and strong La-Nina years (1989, 1999, 2000, 2008) in Bay of Bengal. In this study we must choose six provinces and observed that the variability and response of chlorophyll variability in Bay of Bengal during with peak El-Nino years (2010&2016) and La-Nina years (2008&2011) in Bay of Bengal. The relation between sea surface temperature and Chl-a indicates that, during peak El-Nino years the low Chl-a concentration were observed due to down-welling process. The high Chl-a concentration (0.15mg/m3) were observed during peak La-Nina years in Bay of Bengal. The variability of the chlorophyll concentration is used to understand and improve the climate mode at different provinces in Bay of Bengal during ENSO time.
The Bay of Bengal is the northeastern part of the Indian Ocean and it is bounded on the northwest by India at around 220 N. It has a large marine ecosystem and several distinguishing features. Many rivers entering the Bay of Bengal. It is a unique and dynamic area of the study. The strongest south westerly summer monsoon winds occur during June-September. These winds bring more moisture and maritime air mass, weak winter monsoon winds occur from north-east direction during November-February. These weaker winds produce a shallow mixed layer depth owing to poor vertical mixing and bring dry and continental air mass into the Bay of Bengal, as the result this basin is less productivity compared to the Arabian Sea. Large amount of fresh water entering with reversal of the semi-annual monsoon winds and excess precipitation over evaporation strongly influence on the surface circulation and stratification [1-3]. This stratification may resident transport nutrients from deep to surface layer and make a strongly oligotrophic region during summer, fall inter-monsoon, when the surface water is highly stratified. As the result, the mean surface practical salinity scale ranges from 30ppt in north to 34ppt towards the south.
Maximum amount of phytoplankton productivity in the form of plants and animals in coastal shallow waters are attributing to develop the nutrients. The phytoplankton productivity influences the color on the sea. It is well understood that the chlorophyll-a, the photosynthetic pigmentation in phytoplankton, absorbs more blue and red light than green. The back scattered sunlight in the spectrum or color of the ocean water progressively shifts from deep blue to green as increases the concentration of phytoplankton. The result of the upwelling in the Bay of Bengal is confined to regions very close to the coast mostly within about 40km along the south-western boundary during summer [4-15]. The kind of upwelling that is favored during south-westerly wind is the equator ward flow of a freshwater plume, which could overwhelm the offshore Ekman transport.
The primary atmospheric and oceanic variables linked to
productivity are chlorophyll, sea surface temperature, sea surface
height, wind and so on. These physical factors are influenced
directly are indirectly by the Indian ENSO and it is impact on the
climate change. This abnormal warming of sea surface waters of
the pacific coast of Ecuador and Peru around Christmas time and
usually lasts for few weeks to few months. Climatologically, during
the period of El-Nino bring and heavy rains to the equatorial coast
of the South America, California, Ecuador and Gulf of Mexico, these
effects of warmer water had a devastating the marine life, because
there is no upwelling. And severe droughts occur in Australia,
Indonesia, India, and South Africa. In ENSO time, an upwelling
propagates along the coast of Bay of Bengal, it may also influence
the chlorophyll concentration on the inter-annual time scale. In
this study has made use of sea-wife’s data during the period of
1997-2010, NOAA-AVHRR derived sea surface temperature data,
CCMP surface wind vector data and sea surface height anomaly
from TOPEX data sets to understand the variability of chlorophyll
and water circulation pattern as well as the associated with
temporal and spatial variability of phytoplankton covering the
entire Bay of Bengal basin. The major goals of this study are
1) to quantify the impact of the chlorophyll concentration on
above parameters 2) To investigate the changes in chlorophyll
concentration in the Bay of Bengal during ENSO time.
In the process of Bay of Bengal studies used to various data
sets to understand the ocean chlorophyll response to ENSO
events. In this study, monthly mean, and climatology of Sea-Wifs
chlorophyll data covering the period from October 19997 to
December 2010 and June 1998 to December 2010 data for 13
individual years have been used http://apdrc.soest.hawaii.edu/.
Moderate Resolution Imaging Spectro radiometer play important
role in the development of validated, global, interactive earth
system models able to predict global change accurately enough
to assist policy makers in making sound decisions concerning
the protection of our environment and these data improve our
understanding of global dynamics and processes occurring on
the land, in the oceans and lower atmosphere. The satellite data
were selected in such a way that the field sampling period was
covered in the monthly mean satellite data. Monthly climatology
of MODIES data has been prepared using from July 2002 to 2017
The optimum interpolation sea surface temperature (OISST)
analysis is produced monthly on a one-degree grid and this
simulation is uses in-situ and satellite addition and simulated
sea surface temperatures. NOAA OISST data has been using
from 1982-2017 (https://www.esrl.noaa.gov). And SST data has
taken from COBE 1x1 degree latitude of global grid (360x180).
The advantage of these present analyses and understanding the
reliability of SST for a specific area and time. The monthly mean
SST data taken from COBE sst2 data from 1850 Jan to 2015 Dec.
Another model data set is also taken as soda (simple ocean data
assimilation) SODA v2.2.4 Monthly means from 1980-2015. And
include Argo (Argo 1x1 gridded Monthly mean on Standard
Levels) monthly mean data from 2005 Jan-2017 Dec.
We also used satellite derived monthly v2 CCMP wind data at
10 meters, these Level-3.5 (L3.5) winds are available as netCDF-4
data files. Each L3.5 monthly data file contains 2 arrays of size
1440 (longitude) by 628 (latitude for range -78.375 to 78.375)
by 1 (time centered on the middle of calendar month). The
two arrays are the U and V wind components in m/s. Standard
wind coordinates apply, meaning of a wind blowing toward
the Northeast has a positive U is the right and V components is
above the axis. Winds in the CCMP product are of oceanographic
convention. The CCMP Version 2.0 (V2) data product described
here is a continuation of the highly used original CCMP
product (V1.1 available from NASA JPL PO. DAAC and builds on
the decades of careful VAM development. The data is taken from
1987 July to 2016 (http://apdrc.soest.hawaii.edu).
Mapping of Sea Level Height Anomaly became possible after
the availability of satellite altimeter data. The SSHA data of the
area acquired from TOPEX database were also studied for their
relationship with the chlorophyll-a plume. The Spatial-temporal
variation of the plume was further studied using sea surface height
data from 1993 to 2010. These data received from reanalysis
ECMWF ocean reanalysis ORAS4, 1x1-degree from 1958 Jan to
2017 Dec, (http://apdrc.soest.hawaii.edu). And sea level model
data id received from SODA v2.2.4 monthly means from 1871 Jan
to 2010. (http://apdrc.soest.hawaii.edu).
The Argo mixed layer depth is taken from Argo 1x1 gridded
Mixed Layer Monthly mean from 2005 to 2017 (http://apdrc.
soest.hawaii.edu/). We are also calculated density and static
stability using Argo data. We choose six regions in Bay of Bengal
(Figure 1). They are 1) Head Bay of Bengal (18°-21°N; 88-91°E),
off Chennai (10°-13°N; 81°-84°E), 3) off coastal Andhra Pradesh
(16°-19°N; 84°-87°E), 4) off Myanmar (8°-11°N; 94°-97°E), 5)
central Bay of Bengal (11°-14°N; 88°-91°E), 6) off Tuticorin (5°-
8°N; longitude 77.2°-79.8°E). The present study is planned to
evaluate the current understanding of chl-a, SST and to link with
El Niño (warm), La Niña (cool) events. The Oceanic Nino Index is
used to define El Nino (+0.5) and La Nina (-0.5).
The monthly SST (January-December) ranges were estimated
during 1982-2017. The monthly mean SST of 35 years indicates
the SST values changes and the highest SST value (30.2°C) occurs
during May and June months in the Bay of Bengal. The lowest SST
value (23°C) occurs during Jan, Feb, March months at head Bay
of Bengal. The sea surface temperature is decrease during Jan,
Feb, March months. And slowly increases in the month of April
and it reaches minimum value during May, June months, normal
in Aug, fall at central Bay of Bengal in Sep, Oct months and slowly
decreases in the month of December at Bay of Bengal. (Figure 2).
The monthly average sea surface temperature value is
taken for 1850-2015 years. The minimum (23°C) values occur
during January month at head Bay of Bengal. The maximum
value (30.2°C) occurs during April, May months the SST range
between (28.6°C-30.2°C) in Bay of Bengal. In Jan, Feb months has
lowest(23°C) values, in May, June months the SST is intensifies,
the highest SST during May month, and the SST value is slowly
decreasing at head bob and along the Chennai coast to Myanmar
during July-Oct(27.8°C), finally the value is decreases (23.8°C)
during Nov-December months. (Figure 3).
The in-situ monthly average sea surface temperature ranges
were estimated during 2005-2017 approximately 12 years. The
highest (30.2°C) value during May month in the entire Bay of Bengal.
The lowest (25.4°C) value occurs during January and February
months at head Bay of Bengal. The SST values are decreases in
the months of Jan-Feb the range from (25.4°C-28.6°C) this value is highly intensifying and reach peak point (27°C-30.2°C) during
Mar-June months. It is normal (27.8°C) than Mar-June months
during July-Oct months. In November-December months the
SST value is slowly decreases from south-north in Bay of Bengal.
The monthly mean sea surface temperatures were estimated
during the years 1871-2010. In these years, the lowest (23.8°C)
value occurs during the months January and February months at
head Bay of Bengal. It is highest (29.4°C-30.2°C) occurs during in
the month of May along the 8°-20°N latitude, decrease during Dec,
Jan, Feb months. The SST is slowly increase (27°C-28.6°C) during
March, April months. In May month this is very high (30.2°C)
along the head bob to Tamilnadu and June month also have high
temperature at head bob. (Figure 5).
Another remain months July-Oct months the SST value
is slowly decrease (28.6°C-27°C) and Nov-Dec months also
decreases (27.8°C-25.4°C) along the head Bay of Bengal. This
indicates the occurrence of upwelling along the coastal waters
and the convection of bottom water to the surface. The coastal
upwelling along the western boundary of BoB has been reported
during pre-southwest monsoon and southwest monsoon. The
mean climatology of sea surface temperature from overall data
the SST value is minimum (27.6°C) at north-west part, Dec-Feb
months, and maximum value (29.60c) at the south-east part of
Bay of Bengal during April, May months. (Figure 6).
The monthly mean (Jan-Dec) chlorophyll-blooms were
estimated using 2002-2017 years data in Bay of Bengal (Figure 7). The minimum chlorophyll concentration observed during
January month along coastal regions. The Chl-a bloom maximum
in the months of June-Aug at 80N-800E, head Bay of Bengal and
Myanmar coastal regions and it is also intensified during May
month in Bay of Bengal, attained bloom peak in most regions
of the80N-800E and along the coastal regions during June-Aug
months. In September month this concentration is diminished.
During October it is increases small peak at head bay and
Myanmar regions. After the onset of winter during November-
December months gradually fallen at coastal regions along northeastern
the Bay of Bengal. During January-February months the
chl-a intensity decreases.
The monthly mean chlorophyll concentration can estimate
using 1997-2010 years data, approximately 13 years (Figure 8).
Chlorophyll value in the Bay of Bengal is minimum (8 mg/m^3)
in the months of April, June and august at coastal regions. The
maximum (12 mg/m^3) chlorophyll concentration occur during
June, November, and December. In the month of January, February,
and March the chl-a concentration is slowly increased and March,
similar value (10 mg/m^3) September months. The concentration
of chlorophyll was gradually weakened during October month
at north- east regions in Bay of Bengal. We can observe high
chlorophyll concentration near coastal regions compare to the
offshore regions. MODIS and Sea-WiFS chlorophyll-a image show
that there is a phytoplankton bloom in the southwestern part of
the bay during November–January.
The chlorophyll bloom is varying from year to year. The
monthly mean chlorophyll value was estimated from (Figure 9). In year it is peak (7 mg/m^3) at Myanmar coast and at head Bay
of Bengal and normally diminished. From Seawifs data suggested
that the year average value is peak (9-10 mg/m^3) at head bay
and Myanmar coastal regions in Bay of Bengal. Along the coastal
regions the chlorophyll concentration is low. The river water
enriched with terrestrial sediment in a coastal region, while
a bloom is associated with high concentration of chlorophyll
associated with phytoplankton biomass. Along the east coast of
India, plumes are common during the south-west monsoon from
June to September around river mouths, while phytoplankton
blooms have been reported in coastal waters during the northeast
monsoon; recently, blooms of phytoplankton during the
boreal winter have been reported in the north –western Bay of
The monthly average zonal and meridional winds are
estimated from 1987-2016 years data (Figure 10). The wind is
Minimum during January month and it moves north southward in
Bay of Bengal. Maximum winds occur in the months of June and
July at south part and it is slowly intensified during Feb, Mar, and
April months at head bay and bottom at southern part in Bay of
Bengal. During May month these winds are more intensified and
reaches its highest values during June-Aug months along the 4°-
8°N latitude, because of the south west monsoon is started. Mean
climatology shows maximum wind speed is observed between 17
– 20°N latitude (Figure 11).
The monthly mean sea surface height was taken from during
1958-2017 years in Bay of Bengal (Figure 12). The minimum
(0.4m) sea surface height during the month of March and
maximum value (0.6m) in June along the head Bay of Bengal to
Myanmar coastal region in the Bay of Bengal. In April, it is started
increase (0.3-0.5m) the range between at west coastal region of
the Bay of Bengal. And it is increases and extended towards north
eastern part during May month. It is high in June and July, during
Aug-Oct months, this value is gradually decreasing at head bay and
Myanmar regions compare to the June-July months. In November,
the value is increase at Myanmar coast in BoB.
During Dec-Feb months the SSHA value is decrease along
the north-east to central Bay of Bengal. The monthly mean sea
level was taken from TO3PEX during the years of 1993-2010
approximately 17 years data in Bay of Bengal (Figure 13). The
low value (0.5m) is occurring at central Bay of Bengal during in
the month of January. And the high (0.7m) value is form in May
at the region is north-west part (off Andhra Pradesh) of the Bay
of Bengal. It is slowly increasing during Feb and March months
at north-west of bob. And this increment is gradually increase in
April and it reaches highest value during May at north-west part
and it extends towards north-east part of the Bay of Bengal.
In June-Sep months this value is height at head Bay of Bengal
and remain part (north-east) are started decreases; very low value
is forms at north-west part. During Oct-Nov months this value is
high, and this increment is extending towards westward region,
during December the SSHA value is decreases along the coastal
regions in Bay of Bengal. The monthly average sea surface height
anomaly (cm) was estimated using TOPEX data from 1993-2010
years (Figure 14).
The minimum sea surface height anomaly is founded as during
the month January (10cm) at North part of the Bay of Bengal and
the maximum (15cm) is found near the regions of head bay and
Myanmar coast during the month of July. In February month the
value is started to increase, and it is extending towards off Andhra
Pradesh coast during the months of March and April in Bay of
Bengal. In May month this value is more at off Andhra Pradesh
and it is extending to Tamilnadu regions. During June-Aug and Oct
months the value is increase at head bay and Myanmar regions
in the Bay of Bengal, but in month of sep high only at head Bay of
Bengal, in Nov-Dec months it is more at Tamilnadu region in Bay
of Bengal. In the mean climatology warm core eddy and cold eddy
is observed in the TOPEX data (Figure 15).
The mixed layer depth can be estimated from using 2005-
2017 years data approximately 12 years from Argo (Figure 16).
The minimum depth (15m) occurs during the month of April at
the region of central Bay of Bengal (BoB) and the maximum value
(51m) occurs during July month at region of southern part and in
Aug-Oct months the MLD value is gradually decreases at southern
part of the BoB. During Nov month the MLD value is decrease
and Dec- Feb months it starts increases. In March this is very less
value. The MLD monthly mean is high at southern and this value
is slowly decreasing along the northern part of the BoB. During
El nino strong years (1993, 1988, 1992, 1998, 2010, 2016) the
sea surface temperature anomaly reaches maximum during May
0.9°C and June 0.5°c in 1998 year because of 1998 is strong El
nino year. At the same time the chlorophyll anomaly reaches 0.01
mg/m^3 in 1998 due to high sea surface temperature and less
mixing (Figure 17,18). As the BoB is highly stratified, the observed
response of Chl-a concentration is further understood using the
water column static stability (Figure 19 & 20).
During El Nino (2010, 2016) upper 20 m depth the water
column showed positive stability value (9 × 10-5 m-1), in sep month
and then more negative (-1× 10-5 m-1) in June month particularly
in 2016. In La-Nina years (1989, 1999, 2000, 2008, 2011) the
temperature anomaly during May it reaches minimum -0.7°C,
and -0.4°C in June at the same time the chlorophyll anomaly in
may reaches positive anomaly (0.05 mg/m^3) in 2008. The SST is
always proportional to the chlorophyll concentration. During La
Nina time (2008 and 2011) upper 20m depth the water column
showed positive value (12 × 10-5 m-1), in the month of Oct and
then more negative (-3 × 10-5 m-1), in sep during 2011. The only
difference is that the stability is more in la Nina period compare
to the El Niño time in Bay of Bengal. We also analysed the role of
horizontal advection of Chl-a by strong stability.
Our analysis showed that vertical advection contributes
towards the Chl-a enhancement in both conditions. Stability El
Niño and La Niña are among the most powerful phenomena on
the Earth and dramatically impact weather patterns worldwide.
Hence, there seems to be a lack in studies related to the variability of chl-a concentration, SSH, and the SST, particularly, in the
Northern Indian Ocean related to these phenomena. The present
study is planned to evaluate the current understanding of above
parameters linked with El Niño, La Niña, events and focuses on
how these phenomena have been linked and influencing these
parameters variability and stability in the BoB.
El Nino period in Bay of Bengal the static stability is varies
from depth during Jan-Dec months. The monthly mean static
stability was estimated using 2005-2017 data with depth 0-200m
in Bay of Bengal (Figure 21). Particularly we can draw plots for
only use El Nino (2010-2016) and la Nina years (2008-2011) data.
The stability is related to temperature, salinity, and density. El Nino
time in January month the stability is increased 4.9x10-5 m-1 at a
depth of 80m and slowly decreases at 1x10-5m-1 with 150 meters
depth during 2010, the stability is decreases at surface -0.5x10-5
m-1 at 5 m depth and gradually increases up to 6.3x10-5 m-1 with
50 m depth, and again decreases with 150 m depth during 2016.
In February month the stability is increase at 4x10-5 m-1 with
50 m depth, after the stability is stable at 4.5 x10-5 m-1 with depth 50-75m, and this value is decreases at 1x10-5 m-1 with 150m depth
during 2010. During 2016 the value is increases at 6x x10-5 m-1
with 80 m depth and decreases 1x10-5 at 150 m depth. In March
month the stability value is increases at 5x x10-5 m-1 with 80 m
depth, decreases at 1.5x x10-5 m-1 with 150 m. And same values
to during 2016. In April month it is increases at 5.3x10-5 with
20m depth and decreases at 1.5x x10-5 m-1 with 110m depth
during 2016, the stability is increases at 4.9x x10-5 m-1 with
80m, decreases at 1.2x10-5 with 150m depth during 2010. In
May month it is increases at 4.7x x10-5 m-1 with 100m depth and
decreases at 1.9x x10-5 m-1 at 150 m depth during 2010. During
2016 the stability is increases at 4.9x10-5 with 30m depth and
decreases 1.2x x10-5 m-1 with 150 m depth.
In June month the stability value is increase at 4.5x x10-5 m-1
with 80 m depth and decreases at surface the value is -1.2x x10-5
m-1 with 10m depth, at 150 m depth the stability is decrease 1.5x
x10-5 m-1 during 2010, 2016 this value is increase at 4.2x x10-5
m-1 with 30m depth, decreases at 3x x10-5 m-1 with 150 m depth.
During 2010 in the months of July-Dec the stability value is more
at 5.5x x10-5 m-1 with 50 depths in the Nov month, less at -1.5x x10-
5 m-1 with 5 m depth in Aug month. During 2016 the high stability
at 9x x10-5 m-1 with 10m depth in sep month and less -1.5x x10-
5 m-1 with 5 m depth. La-Nina time-In Sep month the stability is
very high at 9x10-5 with 10 m depth in Sep month decreases at
1x x10-5 m-1 with 150m depth in Sep month during 2008; in 2011
this value is high at 12x x10-5 m-1 with 5m depth in Oct month and
low at -1x10-5 with 150 m depth in September month (Figure 22).
The study concludes that the monthly and annual mean
chlorophyll, sea surface temperature, sea surface height, winds
and mixed layer depth have been observed to the SST value is
inversely proportional to the chlorophyll, in El-Nino condition
(1983, 1988, 1992, 1998, 2010, 2016) the SST value is high, and
the chlorophyll value is low, at the same time the stability is very
less at 10m depth in June month during 2010.
In La-Nina condition (1989, 1999, 2000, 2008, 2011) the sst
value is low and the chlorophyll value is high particularly in May,
June months and stability are positive at 10m depth in 2008. Winds
and chlorophyll value are directly proportional. The multiple
regression results indicated that ssha was more dependent on
SST followed by chl-a during the phenomenon years, as observed
from this study.
Hence, a decreasing trend in the chl-a concentration due to
warmer SST and high SSHa caused major impacts on the ocean primary productivity and probable consequences on diminishing
the marine resources as observed with the time series annual
trend. El Niño caused the down-welling process, which leads to the
low chl-a concentration (< 1 mg/m3) in the BoB. La Niña caused
the upwelling process, which leads to a high chl-a concentration (>
15 mg/m3) in the BoB. Analysis of the other phenomena involved
in variation of the SSHA, SST, and low chl-a concentration in the
BoB would be required in future studies.
Sarangi PK, Shailesh Nayak, Panigrahy (2008) monthly variability of chlorophyll and associated physical parameters in the southwest Bay of Bengal water using remote sensing data. Indian journal of Marine Sciences 37(3): 256-266.