Basic HTML Version
International Journal of Marine Science 2012, Vol.2, No.1, 1
-
11
http://ijms.sophiapublisher.com
10
needs to be studied in relation to the global climatic
events. There is a marked difference between the
locations south of 20°N and the locations north of this
latitude. The SWM is the predominant force in the
observed chlorophyll-a dynamics south of 20°N
whereas the NEM is predominant north of 20°N.
The Gulf of Oman and the Gulf of Mannar are
anomalies compared to the other locations of the study
as they are strongly influenced by only one of the two
(NEM or SWM) monsoons.
The magnitudes and phases of the inter-annual
variability of chlorophyll-a concentration at the study
locations are fairly predicted by the model. The high
values (>0.7) of
r
indicates that inter-annual variability
in the study region can be reasonably forecast using
our empirical model utilizing annual and semi-annual
signals of the chlorophyll-a concentration.
4 Data and Methods
We used the monthly-averaged SeaWiFS Level
-
3
Standard Mapped Images (SMI) with 9 × 9 km
resolution to study the variability of chlorophyll-a in
the Arabian Sea. The data set corresponds to the
period from January 1998 to December 2004 and is
available at http://oceancolor.gsfc.nasa.gov. These
images were sub-sampled for an area corresponding to
5°S
-
30°N and 40°
-
80°E and re-binned to a 0.25 ×
0.25° grid. The monthly log(chlorophyll-a) fields were
investigated for periodic signals in the chlorophyll-a at
a few locations in the Arabian Sea. The rationale for
using logarithmic values [log(chlorophyll-a)] instead
of standard chlorophyll-a values was based on the
evidence that biomass concentrations are log-normally
distributed and it is an accepted practice in bio-optical
measurements (Campbell, 1995). Besides, the smaller
chlorophyll-a signals become more visible if logarithm
transformation is used. The land and cloud pixels were
flagged to zero and were not taken into account for
computations carried out in this work.
4.1 Empirical model
Preliminary analysis of the temporal distribution of
chlorophyll-a showed periodic variability at different
time scales. We, therefore, investigated this variability
using an empirical model. The method employed to
model the annual and semiannual cycles is essentially
similar to the empirical model presented by Garcia et
al (2004) and is based on a nonlinear, least square
procedure to model the log(chlorophyll-a). The model
is based on the following equation:
Y
ij
=
Y
－
j
+
Y
~
ij
,
Y
ij
stands for the observed log(chlorophyll-a) at time
i
and location
j
,
Y
－
j
is the mean, and
Y
~
ij
is the fluctuation.
The last term is the sum of two terms,
Y
~
ij
=
Y
~
ij
m
+
ε
ij
, or
Y
~
ij
=
a
j
cos[(2
π
/T)(
t
i
-
φ
j
)]+
b
j
cos[(4
π
/T)(
t
i
-
ψ
j
)]+
ε
ij
, The
term
Y
~
ij
m
stands for modeled fluctuations;
a
j
and
b
j
are
the amplitude of the annual and semiannual periodic
functions respectively;
φ
j
and
ψ
j
are the phases; and
ε
ij
is the residual. T is the period and
t
i
varies from 1 to
84 (monthly) covering the period (T) from January
1998 to December 2004. We used a nonlinear fitting
procedure to model the periodical signal and for each
time-series we minimize the quantity
∑
_ε
ij
2
, We also
have computed the ratio of the standard deviation (
r
)
of
Y
~
ij
and
Y
~
ij
m
as a measure of fitness of the model.
Acknowledgements
The authors thank Dr. Stephen Goddard for his suggestions
during the preparation of this manuscript. We wish to
acknowledge the support for this work provided through
internal grant IG/AGR/FISH/02/07, the U.S. National Science
Foundation and the U.S. Fulbright Foundation.
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