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Seasonal Variability of Sonic Layer Depth in the Central Arabian Sea
TVS Udaya Bhaskar1*, Debadatta Swain2, and M Ravichandran1
1Indian National Centre for Ocean Information Services, PB No 21, IDA Jeedimetla (PO), Gajularamaram, Hyderabad - 500055, India
2Space Physics Laboratory, Vikram Sarabhai Space Centre, ISRO, Trivandrum - 695022, India Received 1 May 2008; Revised 17 September 2008; Accepted 24 September 2008
Abstract − The seasonal variability of sonic layer depth (SLD) in the central Arabian Sea (CAS) (0 to 25ºN and 62-66ºE) was studied using the temperature and salinity (T/S) profiles from Argo floats for the years 2002-2006. The atmospheric forcing responsible for the observed changes was explored using the meteorological data from NCEP/NCAR and Quickscat winds.
SLD was obtained from sound velocity profiles computed from T/S data. Net heat flux and wind forcing regulated SLD in the CAS. Up-welling and down-welling (Ekman dynamics) associated with the Findlater Jet controlled SLD during the summer monsoon. While in winter monsoon, cooling and convective mixing regulated SLD in the study region. Weak winds, high insolation and positive net heat flux lead to the formation of thin, warm and stratified sonic layer during pre and post summer monsoon periods, respectively.
Key words − Argo float, sonic layer depth, net heat flux, wind speed, Arabian Sea
1. Introduction
Ocean being almost opaque to electromagnetic radiation, sound is the only means to probe the ocean interior. Ocean acoustic tomography is a tool for synoptic monitoring of large-scale oceanographic features (Munk and Wunsch 1979). Further, acoustic system plays a very important role in many civilian and military strategic applications. Sound travels more rapidly and with much less attenuation of energy through water than air and speed of sound in ocean is one of the important oceanographic parameters that determines many of the characters of sound transmission in ocean. Sound speed (velocity) is a function of temperature, salinity, and pressure. Plot of sound speed as a function of
depth is called the sound velocity profile (SVP).
Temperature is the primary controller of acoustic propagation in the ocean. Effect of salinity on sound speed is slight in the open ocean because salinity values are pretty much constant. However, the effect of salinity is greatest where there is a significant influx of fresh water or where surface evaporation creates high saline waters. Figure 1 shows typical profile of temperature, salinity and sound speed along with sonic layer depth (SLD). SLD is defined as the depth of maximum sound speed above the deep sound channel axis and is obtained from SVP (Jain et al.
2007; Etter 1996). A submerged object goes undetected by surface sonar at a depth of SLD plus 100 m and beyond (http://www.fas.org/man/dod-101/sys/ship/deep.htm). SLD plays an important role in refraction of sound rays travelling in the ocean, which in turn affects the sonar detection ranges. Many surface and subsurface parameters affect T/S profiles, which in turn affect sound speed and cause changes in SLD.
SLD in the ocean is conventionally estimated from SVP which is obtained either by a velocimeter that measures sound speed directly or by using the in situ T/S profiles (Jain et al. 2007; Lü et al. 2003). But, the lack of adequate observations of vertical T/S profiles from which SVP and SLD can be obtained hampered studies relating to SLD.
Jain et al. (2007) estimated SLD using Artifical Neural Network (ANN) technique from surface parameters obtained from Woods Hole mooring deployed at 61.5oE and 15.5oN in Arabian Sea (AS). Their work involved developing an ANN model using wind stress, radiation, net heat flux, sea surface temperature and dynamic height as independent parameters and a set of in situ SLD values (estimated from
*Corresponding author. E-mail: uday@incois.gov.in Article
in situ T/S profiles) corresponding to them as dependent parameter. The trained ANN model could then be used to predict SLD from new values of independent parameters.
However, no studies on SLD variability in the AS or Indian Ocean region was taken up so far utilizing subsurface T/S profiles. The present work aims at understanding the regional and seasonal variability of SLD in central AS (CAS).
Winds, waves, heating caused by solar insolation, and fresh water flux (evaporation-precipitation) are all atmospheric forcings that regulate mixing of upper layers. Since the atmospheric forcings are highly variable on space-time scales, the geographic location to a great extent decides the structure and variability of sonic layer. The AS is unique in its geography as a result of which it is forced by intense, annually reversing monsoon winds. These strong winds force the ocean locally and excite propagating signals (Kelvin and Rossby waves) that travel large distances and affect the ocean remotely (Tomczak and Godfrey 1994).
During winter monsoon, weak winds bring cool and dry continental air whereas the summer monsoon brings humid maritime air into the AS (Weller et al. 2002). Surface
circulation of AS also undergoes changes with the changing monsoon system. These semi-annual atmospheric forcings would modulate the thickness of the upper ocean by altering the thermal and mechanical inertia of the layer (Prasanna Kumar and Narvekar 2005). In the present study, we have analysed the seasonal variability of SLD in the CAS using more comprehensive T/S data from Argo profiling floats deployed in the AS, vis-a-vis the atmospheric forcings in the region.
2. Data and Methodology
Spatio-temporal variability of SLD and the factors responsible for it are studied using T/S data from Argo floats and meteorological data from National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) and Quickscat observations.
Argo T/S data for the period 2002-2006 were obtained from the Indian National Centre for Ocean Information Services (INCOIS) website (http://www.incois.gov.in). These data sets are made available by United States Global Ocean Data Assimilation Experiment (USGODAE) and Institut français de recherche pour l’exploitation de la mer (IFREMER). The Argo profiling floats provide T/S measurements from surface to about 2000 m depth every 5/10 days (The Argo Science Team 2001; Ravichandran et al. 2004). The region encompassing, the equator to 25oN and 62o to 66oE (Figure 2) has been selected due to good spatio-temporal coverage of T/S profiles and to eliminate the effect of boundary Fig. 1. Typical vertical profile of temperature, salinity and sound
speed in the Arabian Sea (Location: 19.13oN and 64.19oE). Depth of near surface sound maximum is shown as sonic layer depth (SLD).
Fig. 2. Study Area (0o-25oN and 62o-66oE). Black dots within the region represent the profile positions.
currents and coastal up-welling.
About 2829 T/S profiles were available from January 2002 - December 2006 in the study area. The data were made available after subjecting to real time quality control checks like spike, gradient and density inversion tests (Wong et al. 2006). In addition, data have been checked for outliers and spurious values. Those temperature and salinity values corresponding to profiles departing from the monthly mean by 2 standard deviations were eliminated, thus reducing the data sets to 2748. Table 1 summarizes available number of profiles per month for the years 2002- 2006 after applying the quality checks. Since T/S profiles data 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. These interpolated T/S data sets were then used to compute sound velocity at each depth following Fofonoff and Millard Jr.
(1983). SLD was then estimated from these SVPs as the depth of the near surface maximum in the SVP, a criteria as provided by Etter (1996). Monthly mean SLD spanning the years 2002-2006 was prepared on 1o× 1o grid using the Kriging method. The details and advantages of the Kriging technique for data interpolation may be found in Wackernagel (1998) and Udaya Bhaskar et al. (2006).
The monthly mean wind speed values were generated from data obtained from IFREMER (ftp.ifremer.fr/ifremer/
cersat/products/gridded/mwf-quikscat/data) for same years as that of Argo T/S data. Surface net heat flux has been obtained from NCEP/NCAR reanalysis (Kistler et al.
2001). SLD variability as obtained from Argo data has been
analysed in the CAS. The various features observed in the SLD analysis have been accounted for from the analysis of meteorological parameters that contribute to seasonal features of the region. Significant variability features observed in SLD have also been accounted for on the basis of these.
3. Results and Discussion
Sound speed being primarily controlled by T/S and pressure, varies with the seasonal variations in T/S. In addition, T/S being functions of geographical location, SVP and SLD in turn are also dependent on it. Spatial variations of sound speed cause acoustic rays to bend according to Snell’s law (Urick 1983). In this work, the influence of seasons on SLD variability in the CAS has been studied and a detailed analysis is presented in the following sub- sections for the months chosen to be representative of (a) Pre-summer monsoon: March - May, (b) Summer monsoon:
June - September, (c) Post-summer monsoon: October - November, and (d) Winter monsoon: December - February.
The time-latitude plots of net heat flux, wind speed and SLD averaged along 62-66oE are also presented in Figure 3.
Pre-summer monsoon
The pre-summer monsoon (March - May) is a period of strong net heat gain and is characterized by light winds, clear skies, and increase in solar insolation. Consequently Table 1. Number of Argo profiles available for each month for
the years 2002-2006 after quality control.
Month Year
2002 2003 2004 2005 2006
January 12 31 35 48 54
February 14 32 41 40 66
March 14 42 35 50 75
April 16 44 34 46 59
May 25 44 62 41 52
June 50 61 62 39 41
July 55 65 79 30 39
August 27 53 73 35 61
September 22 44 71 46 65
October 31 44 78 49 67
November 29 43 65 51 59
December 25 31 49 30 67
Fig. 3. Time-latitude plot of (a) net heat flux, (b) wind speed and (c) sonic layer depth (SLD) averaged along 62-66oE.
the AS warms up. High net heat flux (Figure 3a) with values between 100 and 175 W/m2 was observed during this period with peak values (>175 W/m2) recording north of 22oN.
Wind speed (Figure 3b), dropped to as low as 3 m/s during March to May. In response to the above conditions, SLD (Figure 3c) shoaled, attaining shallower (~20 m) depth in April. North of 23oN, still shallower SLD (~10 m) was observed. Seasonal average of SLD, net heat fluxes and wind speed averaged along 62oE to 66oE is shown in Figure 4a and Figure 5a. Maximum SLD of 26.5 m was recorded
near to equator around 4oN. Net heat flux started to increase sharply (Figure 4a) north of 7oN with wind speeds varying between 3-5 m/s (Figure 5a). The shallow SLD observed during this period can be attributed to intense surface stratification caused by the prevailing conditions in the CAS.
Summer monsoon
The summer monsoon also referred to as the Southwest monsoon (June - September) is a period when the AS experiences some of the strongest and steadiest winds bringing humid maritime air into the AS (Weller et al. 2002;
Prasanna Kumar and Narvekar 2005). Solar insolation decreases due to cloud cover and winds play active role in mixing the upper layers and hence deepening SLD. Net heat flux varied between -25 and 50 W/m2 with lowest heat flux observed in June (Figure 3a). High wind speeds ranging between 7-13 m/s was observed during this period with peak values in July (Figure 3b). Consequent of all these forcings, SLD deepened with values of 60–90 m observed between 8o-17oN (Figure 3c) and steady decrease observed north of 17oN. Deepest SLD (>90 m) was recorded in July.
Analysis of wind stress curl during this period (figure not included) revealed negative (positive) values south (north) of 17oN. The deepening (shoaling) of SLD south (north) of 17oN could be associated with this negative (positive) wind stress curl causing down-welling (up-welling).
Seasonal average of SLD, wind speed and net neat fluxes averaged along 62oE to 66oE revealed that deep SLD (Figure 5b) between 5-15oN coincides with prevailing strong winds. Despite the strong winds during this season, evaporative heat loss from the ocean is limited by extreme humidity in the air (Weller et al. 2002). This is clearly evident with net heat flux (Figure 4b) staying positive through out this period. SLD was found to increase starting from equator towards north with peak value recorded at 14oN. North of 14oN, SLD dropped owing to drop in wind speed and increase in net heat flux. Since winds cause mixing of the surface waters, the upper layers are mixed causing uniform T/S which is also reflected in the SLD of the region. From Figures 3b and 3c, it is clear that regions of deepest SLD are also the regions experiencing the strongest winds in the AS. Maximum SLD of 71 m was recorded around 14oN during this period. The overall SLD also remained above 29 m on the average in the entire study region.
Fig. 4. Seasonal average of sonic layer depth (SLD) and net heat flux (Flux) for (a) Pre-summer monsoon (Mar - May), (b) Summer monsoon (Jun - Sep), (c) Post-summer monsoon (Oct - Nov), and (d) Winter monsoon (Dec - Feb), averaged along 62oE to 66oE.
Fig. 5. Same as Figure 4 except for SLD and wind speed (WndSpd).
Post-summer monsoon
The post-summer monsoon (October - November) period has similar characteristics of surface forcing as that of pre- summer monsoon and are together referred to as the transition periods. During this time, net heat flux in the study region was observed to increase (>70 W/m2) as compared to summer monsoon period (Figure 3a). The period is marked by light winds with speeds ranging between 3-5 m/s (Figure 3b). During this period, SLD shoaled to ~20 m with shallowest values observed north of 20oN in October (Figure 3c). This is a direct consequence of the weak surface forcings of heat fluxes and winds. Seasonal average of SLD, wind speed and net heat flux averaged along 62oE to 66oE shown in Figure 4c and Figure 5c also revealed similar characteristics. Nearly constant SLD were observed from equator up to 25oN. The shoaling of SLD during the post-summer monsoon period are largely due to the positive heat flux (Figure 4c) and light winds (Figure 5c) in the study region.
Winter monsoon
During the winter monsoon which is also referred to as the northeast monsoon period, wind-stress forcing is weak and convective mixing due to winter cooling plays a major role in mixing the upper layer of the ocean (Prasanna Kumar and Prasad 1996; Prasad and Bahulyan 1996). This mixing in turn causes deepening of sonic layer. Negative net heat fluxes ranging between -25 and -100 W/m2 was observed north of 10oN during this period with lowest values observed between late December and early January (Figure 3a). South of 10oN, the fluxes were observed to be positive. Wind speed varied between 5-7 m/s during this period (Figure 3b). The negative net heat flux and the associated changes due to winter cooling caused deeper sonic layer in the northern region.
Time-latitude plot of SLD (Figure 3c), showed deep sonic layer (>70 m) during January – February in the region north of 12oN with deeper values recorded at higher latitudes.
Seasonal average of SLD, wind speed and heat flux averaged along 62oE to 66oE (Figure 4d and 5d) showed interesting results. Heat flux was found to decrease continuously starting from the equator towards north. On the contrary, wind speed was found to increase starting from equator attaining its peak at 8oN after which it started to decrease sharply. SLD was observed to increase continuously starting from equator to north. Slight drop in
SLD was found between 18oN and 20oN after which SLD increased again. Since the winter months are associated with decreased insolation, the ocean does cool on account of loosing heat to the atmosphere. This manifests in the form of deepening of SLD. Maximum SLD of 77 m was recorded around 24oN with minimum values observed near equator during this period.
Analysing the four seasons mentioned above suggested that seasonal variations in net heat flux and winds coincided with the seasonal variation of SLD. High correlation was observed between SLD-net heat flux and SLD-wind speed.
While SLD had negative correlation with net heat flux, it had positive correlation with wind. This high correlation is a reflection of the similarity in seasonal variations of the two observations.
4. Summary and Conclusions
In the present work an attempt was made to study the spatial and temporal variability of SLD in the CAS. For this, monthly mean SLD (spanning years 2002-2006) was obtained from Argo float T/S data. SLD in the CAS showed strong seasonal variability. This variability was studied using time varying net heat flux and wind speed. The net heat flux during the winter monsoon was negative recording as low as -100 W/m2. This combined with low winds (~5-7 m/s) resulted in convective mixing which in turn deepened the sonic layer. SLD deeper than 70 m was observed from the time-latitude plots north of 12oN. During summer monsoon, high wind speeds (7-13 m/s) and drop in net heat flux resulted in deepening of SLD. SLD ranging between 60-90 m was observed between 8o-17oN with steady decrease in SLD observed north of 17oN. Deepest SLD (>90 m) which was observed in July was coincident with high wind speeds (13 m/s) and negative wind stress (-45 N/m3). The transition periods of pre and post summer monsoon seasons recorded low SLD (~10 m) values due to high solar insolation, high net heat flux and low winds respectively.
Wind speed and net heat flux are the primary regulators of SLD in the study region with seasonal scale variability of SLD being rather prominent. A clear and strong relation between wind speed and SLD is observed particularly during the southwest monsoon. Net heat flux however is negatively correlated with SLD. SLD variability follows a seasonal trend of meteorological parameters. This is quite
significant since the monsoons are the primary features of the Indian Ocean region and a strong dependence of SLD on it is a clear indicator of the strong influence of monsoons on the sonic layer dynamics of the region. Since SVP is obtained from T/S profiles and pressure, and the influence of pressure is marginal on the upper layers (which includes the SLD), T/S variability would largely contribute to SLD variability.
The importance of the present study stems from the fact that a strategically important parameter like SLD could be easily obtained utilizing Argo floats data. This combined with the strong relation of SLD variability with various meteorological parameters strongly emphasizes the need for a network of ocean observational systems and platforms coupled with the development of numerical weather prediction models. Since for the first time, SLD variability in the CAS has been studied using near-real time data, this opens several vistas for similar studies using alternate approaches for SLD determination based on surface parameters affecting and obtainable from space platforms. This study, thus revealed enormous capability of Argo profiling floats and their usefulness in studying surface sonic layers of the ocean.
Acknowledgements
The authors thank the Directors, Indian National Centre for Ocean Information Services (INCOIS) and Space Physics Laboratory (SPL) as well as other colleagues for their encouragement. Argo data were collected and made freely available by the International Argo Project and the national programmes that contribute to it (http://www.argo.
ucsd.edu, http://argo.jcommops.org). The authors wish to acknowledge the use of Ferret, a product of NOAA’s Pacific Marine Environmental Laboratory, for analysis and graphics in this paper. Sincere thanks are also due to the anonymous reviewers for their constructive suggestions.
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