This article was downloaded by: [Northeastern University]On: 10 November 2014, At: 16:44Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Remote Sensing LettersPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/trsl20

Satellite-derived heat content in thetropical Indian OceanImran M. Momin a , Rashmi Sharma a & Sujit Basu aa Space Applications Centre, Atmospheric and Oceanic SciencesGroup , Ahmedabad, 380 015, Gujarat, IndiaPublished online: 06 Nov 2010.

To cite this article: Imran M. Momin , Rashmi Sharma & Sujit Basu (2011) Satellite-derivedheat content in the tropical Indian Ocean, Remote Sensing Letters, 2:4, 269-277, DOI:10.1080/01431161.2010.519001

To link to this article: http://dx.doi.org/10.1080/01431161.2010.519001

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Satellite-derived heat content in the tropical Indian Ocean

IMRAN M. MOMIN, RASHMI SHARMA and SUJIT BASU*

Space Applications Centre, Atmospheric and Oceanic Sciences Group, Ahmedabad

380 015, Gujarat, India

(Received 18 May 2010; in final form 22 August 2010)

Upper layer heat content (HC) in the tropical Indian Ocean (TIO) extending from

40�E to 120�E and 20�S to 30�N was estimated from satellite observations of sea

surface temperature and sea level anomaly (SLA). For this estimation, a regression

relation was developed from long-period observations of subsurface temperature

profiles derived by Argo floats. It was assumed that a reduced gravity approxima-

tion is appropriate for representing the oceanic upper layer. Satellite-based HC

was computed on a monthly basis and was validated with various independent

in situ observations in the TIO. The correlation between satellite and in situ

estimates was found to be reasonably good. It was also found that signatures of

several dipole events during the study period were well reflected in the estimates.

Propagating features such as Kelvin and Rossby waves were also quite clearly

visible in the corresponding Hovmoller diagrams.

1. Introduction

In recent years, considerable effort has been devoted to the prediction of long-term

changes in global climate. The world oceans play a pivotal role in causing variations

of global climate, because of their large thermal inertia. In particular, the upper layer

of the ocean, the so-called ocean’s troposphere, is the most active one, because heat

flux exchange between the ocean and the atmosphere, a very important component ofocean–atmosphere interaction, occurs primarily in this layer. Therefore, the heat

content (HC) of the upper layer of the ocean is crucial for understanding the role of

air–sea interaction in altering the Earth’s climate. The upper layer HC can be con-

veniently defined as the amount of heat stored in a layer bounded below by an

isotherm representative of the main thermocline.

The Indian Ocean is unique because of the annual reversal of monsoon winds and

the landlocking in the north by the Asian continent. Till recently, studies of the Indian

Ocean suffered from the lack of subsurface temperature profile observations.Although the situation has improved considerably after the launch of Argo floats,

these observations are irregular in space and time. This makes spatio-temporally

regular satellite data, such as that provided by satellite altimeters, an invaluable

tool to investigate spatio-temporal variability of the HC of the upper layer of the

Indian Ocean. In the past, information on the vertical thermal structure of the upper

ocean was obtained from altimeter data combined with climatological hydrographic

data through a diagnostic model (Goni et al. 1996, Garzoli and Goni 2000, Lentini

et al. 2006) and from in situ data and sea surface height data from altimeters (Willis

*Corresponding author. Email: skbasu@sac.isro.gov.in

Remote Sensing LettersISSN 2150-704X print/ISSN 2150-7058 online # 2011 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431161.2010.519001

Remote Sensing Letters

Vol. 2, No. 4, December 2011, 269–277

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

16:

44 1

0 N

ovem

ber

2014

et al. 2003). Altimeter-derived upper layer thickness has also been used to improve

typhoon intensity forecast in the western North Pacific Ocean (Pun et al. 2007).

The primary objective of this work is the estimation of upper ocean HC from

satellite data and validation of this estimate using in situ observations. A secondary

objective is to see whether the signatures of important oceanic phenomena such as theIndian Ocean dipole and propagating waves are properly reflected in the estimated

HC data.

2. Data and methodology

Weekly averaged sea level anomaly (SLA) from multi-mission altimeter data for the

period 1998–2008 were obtained from the Archiving Validation and Interpretation of

Satellite Data in Oceanography (AVISO) site (www.aviso.oceanobs.com). Multi-mission SLA is a merged product of all available satellite altimeters (Jason-1,

Topex/Poseidon, European Remote Sensing Satellite-2, Envisat and Geosat Follow-

on) and the fields are provided on 1� � 1� grid. As per information available from that

site, after acquisition, homogenization and quality checks, corrections are applied to

the altimeter product. Multi-mission cross calibration is carried out using two differ-

ent algorithms. An along-track SLA is generated for each mission. Generation of SLA

maps is carried out using optimal interpolation.

Gridded Argo data generated by Indian National Centre for Ocean InformationServices (INCOIS) for the period 2002–2008 were used in this study. The data for

2002–2007 were used for establishing a regression relation as described below, and the

data for 2008 were used for validating the relationship. The gridded Argo data were

generated by INCOIS from individual Argo observations using an objective analysis

scheme, the details of which are given by Udaya Bhaskar et al. (2007). The data are

available for 10-day intervals on a 1� � 1� grid.

The Research Moored Array for African-Asian-Australian Monsoon Analysis and

Prediction is an array of buoys designed for advancing monsoon research and fore-casting in the Indian Ocean. The array is used to study large-scale ocean–atmosphere

interaction, mixed layer dynamics, ocean circulation related to the monsoon on intra-

seasonal to decadal time scales. Data from different buoys are available at different

time periods spread over the time interval from June 2006 to November 2008.

Instruments on the moored buoys measure winds, air temperature, rainfall, relative

humidity, water temperature, salinity and ocean currents. The temperature and

salinity were used to calculate HC for the purpose of validation.

The temperature and salinity from Triangular Trans Ocean Network buoysdeployed by Japan Agency for Marine Earth Science and Technology in the eastern

Indian Ocean at 90�E, 1.5�S and 95�E, 5�S were also used for the validation of

satellite-derived HC. The data are available daily for the years 2001–2007.

The Tropical Rainfall Measuring Mission (TRMM) is a joint US–Japan Satellite

Mission to monitor the tropical region. It provides global coverage of the tropical

region (40�N–40�S). Sea surface temperature (SST) measured by the TRMM

Microwave Imager (TMI) was also used in this study. The resolution of the data is

0.25� � 0.25�, and the 3-day averaged data are available from 1998 to 2008.The satellite-derived SST and SLA were further averaged on monthly basis for their

use in this study. The altimeter-derived SLA �0(x,y,t) is the deviation of the measured

sea surface height �(x,y,t) from mean sea surface height �ðx; yÞ, which is computed

over a period of 7 years (1993–1999).

270 I.M. Momin et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

16:

44 1

0 N

ovem

ber

2014

� 0ðx; y; tÞ ¼ �ðx; y; tÞ � �ðx; yÞ (1)

In equation (1), x and y denote longitude and latitude, respectively, whereas t denotes

time.Following Goni et al. (1996), we assume that the upper ocean dynamics can reason-

ably be well reproduced by a regional reduced gravity model. In the framework of this

approximation, the SLA and climatological subsurface temperature and salinity data

are combined in the following manner to obtain an estimate of the depth of the 20�Cisotherm (D20):

D20ðx; y; tÞ ¼ �D20ðx; yÞ þg

g0ðx; yÞ �0ðx; y; tÞ (2)

Here �D20(x,y) is the mean climatological depth of the 20�C isotherm, g is the accel-

eration due to gravity and g0 is the mean climatological reduced gravity. The reason

for estimating D20 is that in the tropical oceans, 20�C isotherm is a good representa-

tive of the thermocline.

Once D20 is estimated, it is easy to compute HC up to this depth using the formula

HC x; y; tð Þ ¼ rCp

ð0

D20

T x; y; z; tð Þdz ¼ rCp Tm x; y; tð ÞD20 x; y; tð Þ (3)

where T is the temperature (�C), r is the water density, Cp is the specific heat of water

and Tm is the mean temperature of the upper layer (ULT).

Because satellites are unable to measure Tm, it has to be indirectly estimated from

satellite observations. That the SST is well correlated with upper layer temperatures iswell known and has been exploited earlier in studies related to assimilation of SST in

ocean model of the Gulf Stream region (Ezer and Mellor 1997). Quite recently it has

been found to be true in the Indian Ocean also (Ratheesh et al. 2010). It is thus

reasonable to expect that the SST would be well correlated with ULT. This is the

rationale behind the following regression

Tm x; y; tð Þ ¼ a1 x; y; tð ÞTs x; y; tð Þ þ a0 x; y; tð Þ (4)

where a0 and a1are the regression coefficients obtained by a linear regression fit to allSST and ULT calculated from Argo profiles.

Once the regression is established, one can easily calculate ULT using equation (4),

where the role of Ts is to be played by the SST measured by the TMI sensor. In this

way, the final equation for calculating the upper layer HC will be

HC x; y; tð Þ ¼ rCp a1 x; y; tð ÞTs x; y; tð Þ þ a0 x; y; tð Þ½ �D20 x; y; tð Þ (5)

3. Results

Figure 1 shows the spatial variability of the regression coefficients a1 and a0.

Figure 1(a) shows the variation of the slope a1, whereas figure 1(b) shows the variation

of the intercept a0. Large variability of the coefficients is observed near the southeast

of India and in the eastern and western equatorial Indian Ocean (EIO) because of the

propagation of coastal Kelvin waves (McCreary et al. 1993) and equatorial jet

(Wyrtki 1973), respectively. Hence, one cannot use a single value for these two

parameters in the entire domain. This is one aspect that differentiates this study

Satellite-derived heat content 271

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

16:

44 1

0 N

ovem

ber

2014

from earlier ones. Because the regression is between ULT and SST, it is to be expected

that the slope will be high in regions of high correlation between the two variables.These are the regions where the thermocline is relatively shallow. This is because ULT

is calculated over a layer, which is in proximity with the sea surface. One such region

where the slope is around 0.8 is the region where a thermocline ridge is very prominent

(south of equator up to 10�S and from 55�E to 75�E). Similarly a deeper thermocline

implies lesser correlation between ULT and SST, because this is largely governed by

wind stress. Accordingly, in such regions the slope is much less, for example, in the

northern Arabian Sea. In figure 1(b) the spatial distribution of the intercept is shown.

It is quite natural to expect that in the regions where the correlation between SST andULT is high the intercept will be low, and vice versa, because the major part of the

variability has been already accounted for by the slope. This is more or less the case:

for example, the intercept is 5.0 in the region where the slope is of the order of 0.6–0.8.

As noted previously, once the regression is established, it is straightforward to

compute the HC using equation (5). Accordingly, monthly HC was computed for the

period 1998–2008 using satellite-derived SST and SLA. To validate the computed

HC, the satellite-based estimates were compared with in situ data obtained from

buoys.The result of this comparison is shown as a scatter plot in figure 2. Data from these

buoys were not used in establishment of the relation and hence these data constitute

an independent validation data set. A total of 170 buoy measurements were used. The

coefficients of determination are more than 0.5 at all the locations, signifying a

correlation exceeding 0.7 everywhere. The range (minimum and maximum) of the

HC estimated from satellite data also matches with the range of buoy observations.

Table 1 shows the statistics of the comparison. We performed an F-test to evaluate the

null hypothesis that the estimated variability is not significantly different from theobserved variability. It was found that they are statistically the same at 95% con-

fidence level. To ascertain whether the two means are statistically different, a t-test

was carried out. They agree within a 95% confidence level.

Next, the Argo profiles from 2008 were used for the validation of satellite-based HC

over the entire tropical Indian Ocean (TIO). Figure 3 shows the standard deviations of

satellite-based and Argo-derived HC for this period. High variability is found near the

(a)

20°N

10°N

0°

10°S

40°E 60°E 80°E 100°E

10°S

20°N

(b)

10°N

0°

40°E 60°E 80°E 100°E

Figure 1. Spatial distribution of regression coefficients a1 (slope) and a0 (intercept): (a) a1

(dimensionless), contour interval ¼ 0.2; (b) a0 in �C, contour interval ¼ 5�C.

272 I.M. Momin et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

16:

44 1

0 N

ovem

ber

2014

Somali coast (due to upwelling), coastal Bay of Bengal (BOB), eastern EIO (EEIO)

and southern TIO in both estimates of HC. The possible reason for this high varia-

bility could be the fact that the propagation of Kelvin waves pushes the thermocline

deeper near the coastal BOB. In the EEIO, the strong equatorial jets and downwelling

Table 1. Comparison of heat content from satellite and buoy measurements.

Buoy locationNo. ofpoints

Correlation(r)

RMSE(108 J m–2)

Mean heatcontent

(1010 J m–2)

Standarddeviation of heat

content(108 J m–2)

BuoySatellite-

based BuoySatellite-

based

90�E, 12�N 12 0.88 11.3 1.32 1.44 9.80 10.1080.5�E, 1.5�N 22 0.75 16.2 1.24 1.40 17.13 15.7090�E, 1.5�S 67 0.81 9.5 1.28 1.29 16.50 15.0095�E, 5�S 70 0.73 9.9 1.25 1.22 12.60 13.9090�E, 15�N 11 0.90 7.0 1.33 1.39 9.30 10.49

RMSE, root mean square error.

(a)

R2 = 0.67

1.0

1.2

1.4

1.6

1.8

1.0 1.2 1.4 1.6 1.8

Observed HC (1010 J m–2)

Sat

ellit

e-de

rived

HC

(1010

J m

–2)

(b)R2 = 0.54

1.0 1.2 1.4 1.6 1.8

Observed HC (1010 J m–2)

1.0

1.2

1.4

1.6

1.8

Sat

ellit

e-de

rived

HC

(1010

J m

–2)

R2 = 0.57

1.0

1.2

1.4

1.6

1.8

1.0 1.2 1.4 1.6 1.8

Observed HC (1010 J m–2)

Sat

ellit

e-de

rived

HC

(1010

J m

–2)

(c)R2 = 0.78

1.0 1.2 1.4 1.6 1.8

Observed HC (1010 J m–2)

1.0

1.2

1.4

1.6

1.8

Sat

ellit

e-de

rived

HC

(1010

J m

–2)

(d)

Figure 2. Scatter plots of satellite-based and buoy-derived heat content (J m–2) for four buoylocations.

Satellite-derived heat content 273

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

16:

44 1

0 N

ovem

ber

2014

Kelvin waves cause mass convergence with its associated deepening of the thermocline

during the monsoon transition months (Wyrtki 1973, Reverdin 1985).

It is now well known that there is a coupled mode of variability in the Indian Ocean,

similar to the El Nino-Southern Oscillation phenomena in the Pacific. This is known

as the Indian Ocean dipole (Saji et al. 1999), which manifests itself as an anomalous

cooling in the tropical southeastern Indian Ocean (90�E–110�E, 10�S-Equator) and

an anomalous warming in the tropical western Indian Ocean (WIO; 50�E–70�E,10�S–10�N). Two strong events occurred in late 1997 and 2006. Other relatively

weaker dipoles also occurred in late 2003, 2007 and 2008. To clearly bring out the

signatures of these events in the heat budget computation, we computed the heat

content anomalies (HCAs) in the two regions, and the interannual variation of these

HCAs is displayed in figure 4(a).

The signatures of the dipole events are clearly visible in the figure, except those for

2008, which was quite weak. Probably this dipole could not be delineated in the limit

of estimation error. In figure 4(b) we show the similar HCAs, but estimated from Argoobservations beginning from 2002. It can be seen that the strong dipole event of 2006

has been picked up by the Argo observations. Regarding the mismatch in the

(a)

30°N

20°N

10°N

10°S

20°S30°E 50°E 70°E 90°E 110°E

0°

0°

(b)

20°N

30°N

10°N

10°S

20°S30°E 50°E 70°E 90°E 110°E

Figure 3. Standard deviation of (a) satellite-based and (b) Argo-derived heat content for theyears 2002–2008. The contour interval is 5 � 108 J m–2.

274 I.M. Momin et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

16:

44 1

0 N

ovem

ber

2014

magnitudes of the positive and negative peaks, this is because of the difference in the

sampling error of the two observing systems. The standard deviations of HCA in the

WIO from the satellite and Argo measurements are 9.5 � 108 J m–2 and

8.6 � 108 J m–2, respectively. The corresponding numbers for EEIO are

7.6 � 108 J m–2 and 8.0 � 108 J m–2, respectively.

It is also interesting to see whether the signatures of propagating waves such as theequatorial Kelvin waves and the off-equatorial Rossby waves are seen in the HCAs.

Accordingly figure 5 shows three Hovmoller diagrams along the equator and along

10�N and 10�S. The propagation of equatorial Kelvin waves and off-equatorial

Rossby waves is clearly visible in the diagrams. These waves are known features of

the Indian Ocean circulation and have been noted previously in studies focused on the

propagation of SLAs simulated by numerical model and observed by satellite alti-

meter (Perigaud and Delecluse 1993, Basu et al. 2000). The phase speeds were

estimated for these waves. The values are 1.17 m s–1 for the Kelvin wave, 15 cm s–1

(a)

–0.4

–0.2

0.0

0.2

0.4

Year

HC

A (

1010

J m

–2)

WIOSEIO

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

(b)

HC

A (

1010

J m

–2)

–0.4

–0.2

0.0

0.2

0.4

Year1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

WIOSEIO

Figure 4. Time series of the heat content anomaly (HCA, in J m–2) in the western andsoutheastern Indian Ocean depicting inter-annual variation. In the figure WIO and SEIOstand for western Indian Ocean and southeastern Indian Ocean, respectively. (a) Satellite-derived and (b) estimated from Argo.

Satellite-derived heat content 275

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

16:

44 1

0 N

ovem

ber

2014

for the Rossby wave at 10�N and 23 cm s–1 for the Rossby wave at 10�S. The values

compare reasonably well with those reported earlier (Basu et al. 2000) whereas the

speeds are similar to those from altimeter measurements (1.3 m s–1, 17 cm s–1 and

25 cm s–1, respectively).

4. Conclusions

In this study, satellite-derived products of SST and SLA were used to compute

monthly averaged HC of the upper layers of the TIO. The regression relation used

for this computation was derived from long-period observations of subsurface tem-

perature profiles measured by Argo floats. Satellite-derived estimates were validated

by comparing these estimates with similar estimates computed from various in situ

observations in the TIO. Tests were performed on the means and variances to

ascertain whether these quantities derived from satellite and in situ observations are

statistically similar. The satellite-derived estimates were more rigorously validated by

comparing with an independent set of Argo observations. The satellite-based HC

estimates were found to be well correlated with in situ observations. Signatures of the

known strong dipole events were reflected in the heat budget estimates. Finally, the

well-known propagating features of the Indian Ocean circulation such as the equa-

torial Kelvin waves and the off-equatorial Rossby waves were found to be clearlyvisible in the Hovmoller diagrams.

Acknowledgements

Authors are grateful to Dr. R.R. Navalgund, Director, Space Applications Centre,

Dr. J.S. Parihar, Deputy Director, Earth, Ocean, Atmosphere, Planetary Sciences and

Applications Area, and Dr. P.K. Pal, Group Director, Atmospheric and Oceanic

Sciences Group, for constant encouragement. TMI SST and SLA products were

2008

(a) (b) (c)1.8

1.6

1.4

1.2

1

0.8

0.6

2.4

2

1.2

1.6

0.8

0.4

2.2

1

1.4

1.8

0.6

0.2

2006

2004

2002

2000

1998

2008

2006

2004

2002

2000

1998

2008

2006

2004

2002

2000

1998

40°E 50°E 60°E 70°E 80°E 90°E 100°E 40°E 50°E 60°E 70°E 80°E 90°E 100°E 40°E 50°E 60°E 70°E 80°E 90°E 100°E

Figure 5. Hovmoller (time–longitude) diagrams of heat content anomaly in the tropicalIndian Ocean depicting wave propagation. The legend shows the heat content anomaly in108 J m–2. (a) Equator (b) 10� N (c) 10� S.

276 I.M. Momin et al.

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

16:

44 1

0 N

ovem

ber

2014

obtained from ftp.ssmi.com and AVISO websites, respectively. Argo data used in this

study were generously provided by the Indian National Centre for Ocean Information

Services, Hyderabad, India.

References

BASU, S., MEYERS, S.D. and O’BRIEN, J.J., 2000, Annual and interannual sea level variations in

the Indian Ocean from TOPEX/Poseidon observations and ocean model simulations.

Journal of Geophysical Research, 105, pp. 975–994.

EZER, T. and MELLOR, G.L., 1997, Data assimilation experiments in the Gulf Stream region:

How useful are satellite-derived surface data for nowcasting the subsurface fields?

Journal of Atmospheric and Oceanic Technology, 14, pp. 1379–1391.

GARZOLI, S. and GONI, G., 2000, Combining altimeter observations and oceanographic data for

ocean circulation and climate studies. In Satellites, Oceanography and Society,

D. Halpern (Ed.), Elsevier Oceanography Series, n. 63, pp. 79–95 (Amsterdam, The

Netherlands: Elsevier Science).

GONI, G., KAMHOLZ, S., GARZOLI, S. and OLSON, D.B., 1996, Dynamics of the Brazil-Malvinas

confluence based on inverted echosounders and altimetry. Journal of Geophysical

Research, 101, pp. 16273–16289.

LENTINI, C.A.D., GONI, G. and OLSON, D.B., 2006, Investigation of Brazil current rings.

1993–1998. Journal of Geophysical Research, 111, p. C06013, doi: 10.1029/

2005JC002998.

MCCREARY, J.P., KUNDU, P.K. and MOLINARI, R.L., 1993, A numerical investigation of

dynamics, thermodynamics and mixed layer processes in the Indian Ocean. Progress

in Oceanography, 31, pp. 181–244.

PERIGAUD, C. and DELECLUSE, P., 1993, Interannual sea level variations in the tropical Indian

Ocean from Geosat and shallow water simulations. Journal of Physical Oceanography,

23, pp. 1916–1934.

PUN, I.-F., LIN, I.I., WU, C.R., KO, D.S. and LIU, W.T., 2007, Validation and application of

altimetry-derived upper ocean thermal structure in the Western North Pacific Ocean for

typhoon-intensity forecast. IEEE Transactions on Geoscience and Remote Sensing, 45,

pp. 1616–1630.

RATHEESH, S., SHARMA, R. and BASU, S., 2010, Projection based assimilation of satellite-derived

surface data in an Indian Ocean circulation model. Marine Geodesy (manuscript sub-

mitted for publication).

REVERDIN, G., 1985, Convergence in the equatorial surface jets of the Indian Ocean. Journal of

Geophysical Research, 90, pp. 11741–11750.

SAJI, N.H., GOSWAMI, B.N., VINAYACHANDRAN, P.N. and YAMAGATA, T., 1999, A dipole mode in

the tropical Indian Ocean. Nature, 401, pp. 360–363.

UDAYA BHASKAR, T.V.S., RAVICHNDARN, M. and DEVENDER, R., 2007, An operational objective

analysis system at INCOIS for generation of Argo Value Added Products, Report No.:

INCOIS-MOG-ARGO-TR-04-2007.

WILLIS, J.K., ROEMMICH, D. and CORNUELLE, B., 2003, Combining altimetric height with broad

scale profile data to estimate steric height, heat storage, subsurface temperature, and

sea-surface temperature variability. Journal of Geophysical Research, 108, p. 3292, doi:

10.1029/2002JC001755.

WYRTKI, K., 1973, An equatorial jet in the Indian Ocean. Science, 181, pp. 262–264.

Satellite-derived heat content 277

Dow

nloa

ded

by [

Nor

thea

ster

n U

nive

rsity

] at

16:

44 1

0 N

ovem

ber

2014