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Introduction A strikingly and
nearly universal feature of the open ocean is the surface mixed layer within
which the salinity, temperature and density are almost vertically uniform. The
oceanic mixed layer has been studied for long time by both oceanographers and
meteorologists because of its importance in sea surface temperature variations,
air-sea heat exchange, the formation of water masses and ventilation of the
subsurface layers. The main temporal variabilities of the mixed layer depth
(MLD) are directly linked to the many process occurring in the mixed layer,
ranging from diurnal to interannual variability, including seasonal and
intraseasonal variability. The spatial variability of the MLD in Despite the
difficulty in properly defining the MLD, compounded by the lack of temperature
and salinity data in many regions, MLD climatology is necessary and essential
in understanding the climatic system. Such climatology is of primary importance
for ocean modelers in validating and improving mixed layer parametrization and
ocean general circulation models. Much sophisticated work has been done on the
climatology of mixed layer distribution and seasonal changes in the Because all these studies have used historical hydrographic data obtained before the Argo project started, they suffer from large uncertainty due to sparse observations in regions away from major shipping routes (Eg: Bay of Bengal). Since the year 2000, the number of Argo floats in the world oceans has been increasing year by year and the project’s goal was reached in November 2007 i,e., more than 3000 floats are now operational in the world oceans. The number of profiles obtained annually by Argo in the world oceans was more than 30,000 in 2003 and this number tripled to about 90,000 in 2006. Thus, the annual total of Argo profiles obtained every year is now equivalent to three quarters of the total number of the historical CTD profiles deeper than 2000 m archived in the World Ocean Data base 2001 (WOD01, Conkright et al., 2002). The large quantity of Argo data enables us to estimate mixed layer climatology with a quality that must be better and much more homogeneous in space and season than previous climatologies, although the observation period may be too short to discuss the long-term mean state of the mixed layer. In the present
study we constructed a new climatology of the MLD in the Data and Approach (i) Data distribution The spatial distribution of the data used in this analysis is shown in Figure 1. The 61,896 original hydrographic profiles used in this study were obtained from Indian National Centre for Ocean Information Services (INCOIS) web site http://www.incois.gov.in/Incois/argo. They represent all the high vertical resolution Argo profiling float data from 2001 through 2008. Figure 2 shows the temporal distribution of the data. The number of profiles per year increases from 2001 to 2008, reaching 500 per month or more, starting from mid 2003 till 2008. Table 1 shows yearly profiles before and after QC used for preparing the climatology. (ii) Methodology Since the Argo floats usually do not obtain observation at the sea surface, the present study evaluates the MLD using 10 m-depth data, which has been used as a reference depth for the MLD by many previous studies (eg: Kara et al., 2000). When the shallowest observation level of a profile is deeper than 10 m, the profile is not used for estimating MLD. Profiles whose deepest observation level is shallower than 100 m are also excluded, since deeper MLD values greater than 100 m are observed during winter (Rao et al., 1989). Profiles whose vertical resolution is finer than standard depth levels in the traditional hydrographic observations are chosen for estimating MLD. These steps removed 6582 profiles a reduction of 10.6% of total profiles used for generating the climatology. Since temperature and salinity data sets are not available at regular depths for all the floats, we have uniformly interpolated them linearly to 1 m depth resolution until 1000 m for all the observations. For each profile, density is calculated using the high-pressure equation of state (Millero et al., 1980). (iii) MLD Criterion Our method of MLD climatology computation is based on direct MLD estimates from individual profiles. For generating the climatology we define MLD using a density-based criterion following Kara et al., (2000a, 2000b, 2003) that accounts for variable salinity. The MLD is constructed using a density criterion based on a density variation (Dst) determined from the corresponding temperature change DT (0.8° C) in the equation of state Dst = st
(T + DT,
S, P) - st (T,S,P) (1) where T and S are temperature and salinity values at 10 m and P is set to zero. The MLD obtained from each profiles are then sorted into monthly boxes of 1° latitude by 1° longitude. Each grid box contains monthly, seasonal, intraseasonal and interannual variability, which results in a great range of MLD values. To give a monthly MLD value for each grid box, median of MLDs was chosen as the appropriate statistical estimator. Median of the MLDs for each grid box is a more robust estimator than the mean, and more representative of the climatological field (de boyer Montegut et al., 2004). The error in the estimate was given by median deviation define by
and gives us an estimator of the width of the distribution for each grid box containing at least five values. To fill grids points where no data were available, the method of ordinary kriging is applied. We used liner covariance function to fit the variogram for each data location. The neighborhood extent is defined as a circle of radius 300 kms following Argo deployment convention of one in 3° X 3° box. An estimate is made at a location only if a minimum of five data points falls with in the neighborhood of a point, otherwise the grid is left blank. References Colborn, J,G.,
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