Meteorol Atmos Phys 000, 1–17 (2004)DOI 10.1007/s00703-004-0098-3

1 Laboratoire de M�eet�eeorologie Dynamique, CNRS, Paris, France2 Centre de recherche en Climatologie, Dijon, France

A study of convective systems, water vapor and topof the atmosphere cloud radiative forcingover the Indian Ocean using INSAT-1B and ERBE data

R. Roca1, S. Louvet2, L. Picon1, and M. Desbois1

With 9 Figures

Received June 30, 2004; revised September 2, 2004; accepted September 11, 2004Published online: * *, * # Springer-Verlag 2004

Summary

The distribution of cloud radiative forcing (CRF) at the topof the atmosphere over the Indian Ocean is investigated us-ing satellite observations. Two key regions are considered:eastern Indian Ocean and Bay of Bengal which experiencemaximum upper-level cloudiness in winter and summerrespectively. It is found that longwave CRF in the Bay ofBengal during summer is similar to that over eastern IndianOcean during winter. On the other hand shortwave CRFmagnitude is larger in Bay of Bengal. These differencesexplain the net CRF difference between the two regions. Thestronger shortwave forcing seems to be related to the Upper-Level Cloudiness being larger over the Bay than over easternIndian Ocean. The reasons for the longwave CRF similaritiesare analysed in more details. Using the results from aconvective system classification method, it is first shown thatthe longwave radiative properties of the individual systemsdo not vary much from one region to another. Thedistribution of the different kind of systems, a proxy forthe vertical cloudiness structure, does not either indicatestrong difference between the regions. It is then proposedthat the substantial precipitable water vapour amount ob-served over Bay of Bengal damps the effects of the upper-level cloudiness on radiation compared to the relatively dryereastern Indian Ocean area; yielding to similar LW CRF inboth region despite more Upper-Level Cloudiness over Bayof Bengal. These observations are supported by idealisedradiative transfer computations. The distribution of cloudi-ness and radiative forcing is then analysed over the wholetropical Indian Ocean for each season. July is characterizedby a low longwave CRF regime (relative to January) over the

most convectively active part of the Ocean. The non lineardamping effect of water vapor on longwave CRF is alsoshown to contribute to this regime. Overall, this studyreaffirms the need for simultaneous documentation of thecloud systems properties together with their moist environ-ment in order to understand the overall net radiativesignature of tropical convection at the top of the atmosphere.

1. Introduction

The effect of clouds onto the earth-atmospheresystem is undoubtedly of importance to the cli-mate energetic especially in the Tropics (IPCC,2001). A common measure of their effects on theclimate is the cloud radiative forcing at the topof the atmosphere. It is defined as the differencebetween the clear sky radiation and the totalsky radiation (Coackley and Baldwin, 1984;Charlock and Ramanathan, 1985). In the long-wave spectrum, CRF is the cloud greenhouseeffect and is mainly felt in the atmosphere whilein the short-wave, the signature is stronger at thesurface. The word ‘‘forcing’’ suggests a perturba-tion of a steady state that yields to another stateand CRF can be seen as one contribution fromcloudiness to the diabatic source term that forcesthe general circulation (another contribution be-ing the latent heat release). From a modelling

MAP-0/693For Author’s Correction Only

point of view, modifications of the CRF and itsrole on the general circulation can easily be ana-lysed (e.g., Harshvardhan et al, 1989; Bergmanand Hendon, 2000) although its computationis not straightforward (e.g. Cess et al, 1992).Clouds are nevertheless observed within theiralready forced situation which makes the inter-pretation of CRF more complicated. In the fol-lowing we shall consider satellite observations ofthe CRF not as much as a forcing per se butrather as a radiative signature of an ensembleof processes yielding to the measured CRF. Theforcing aspect of CRF is not discussed here andwe only look at the resulting TOA radiation dis-tribution, the forcing action being embedded inthe CRF structure. The focus is set on delineatingthe relative contribution of the clouds and theirenvironment to the observed and supposedlyequilibrated circulation and thermodynamicsstructure that results to some extent from thecloud-radiation-dynamics interactions over thisregion (e.g. Sharma et al, 1998).

The cloud radiative forcing is a highly inte-grated parameter of the atmosphere state. It isinfluenced by the microphysic characteristics ofthe cloudiness (optical depth, ice=water content),the macrophysic characteristics (cloud amount,vertical distribution), the thermodynamical clearsky immediate environment of the clouds andthe surface conditions. Numerous studies investi-gated the relationships between the cloud radia-tive forcing and cloud type, cloud microphysicalproperties and cloud fraction (e.g., Hartmannet al, 1992). In the inter-tropical belt, where shal-low and deep convection induced cloudinesscoexist together with low levels trade windsclouds (Jonhson et al, 1999), different net CRFregimes are observed: negative net forcingover low-levels cloudiness in the subtropics(e.g. Ramanathan et al, 1989; Ockert-Bell andHartman, 1992), almost zero net forcing overthe Pacific deep convection regions (Kielh andRamanathan, 1990; Kielh, 1994) and negativenet regimes over the Indian summer monsoonregion (Rajeevan and Srinivasan, 2000). Focus-ing on the Warm Pool region in April 1985, Kiehl(1994) suggests that the observed near cancella-tion of the net radiative cloud forcing could beexplained by the deep and thick clouds and thetropopause height. Indeed the tropopause pro-vides the upper bound of the convective cloud

vertical development. Moist convection andradiation would hence collaborate as to offerquasi equilibrium conditions between short-waveand longwave cloud radiative forcing at the topof the atmosphere. Cess et al (2001) as well asHartman et al (2001) highlight that the near can-cellation over the Warm Pool is associated withthe average of different cloud types rather thanwith the radiative properties of a single cloudtype. Rajeevan and Srinivasan (2000, hereafterRS2000) investigated the net cloud radiativeforcing over the Asian Monsoon region. Theyunderscored that the upper clouds over the WarmPool exhibits large optical depths but with mod-erate fractional coverage that explains the nearcancellation found there. On the contrary, theyshowed that the Indian summer monsoon regionis the unique setting of a combination of largeoptical depth high clouds and large cloudinesswhich could yield to the observed strong negativecloud radiative forcing regime over the summermonsoon ITCZ. In the following we focus on thetwo radiative regimes associated with deep con-vection induced cloudiness of the Indian Oceanby considering two regions during two seasons:during winter, we extract a region representativeof the near cancellation regime and during sum-mer another one which is representative of thestrong negative net forcing regime.

During both of the winter and the summermonsoons, the Indian Ocean upper-level cloudi-ness is dominated by large (up to 106 km2) orga-nized convective clouds (Sikka and Gadgil, 1980;Wilcox and Ramanathan, 2001; Gambher andBhat, 1999; Roca and Ramanathan, 2000 here-after RR2000), which we shall refer loosely toMesoscale Convective Systems (MCS) in the fol-lowing of the paper. These MCS are composedof a convective core where heavy rainfall takesplace as well as of a stratiform anvil associatedwith lighter rainfall. The convectively active partof the MCS is usually restricted to 10–100 kmscales and is formed by individual convectivecells of scales from 1 to 10 km. These deepconvective cells detrain water condensate in theupper troposphere where the ambient windspreads it, forming the stratiform anvil whichextent can reach up to 1000 km. This processhas recently been suggested as an importantfactor to explain the high upper-level cloudi-ness observed over the Asian Monsoon region

2 R. Roca et al

(Sathiyamoorthy et al, 2004). As a result, an indi-vidual MCS as a whole is composed of differenttypes of cloud elements whose radiative proper-ties range from optically opaque for the con-vective cells to semi-transparent for the thincirrus. This repartition within individual MCSis thought to explain the observed TOA radiationdistribution, in particular the near cancellationregime (Hartmann et al, 2001). The longwaveradiative properties of the individual convectivesystems and their distribution of occurrence arehence analyzed over the two regions to highlighttheir contribution to the associated radiativeregimes. The moisture environment (or back-ground) in which convective systems developinfluence the efficiency with which clouds per-turb the TOA, atmosphere and surface radiationbudget, especially in the longwave spectrum. Wetherefore complement our analysis of the TOAcloud radiative forcing by investigating the roleof the clear sky environment in establishingthe TOA radiation and its sensitivity to cloudchanges.

The paper is organized as follows: Sect. 2 pres-ents the data and methodology. In Sect. 3, theinterannual and seasonal variability of radiation,clouds and their environment over two keyregions are quantitatively documented. The anal-ysis is then extended to the whole Indian Oceanin Sect. 4. A discussion on the role of the moistenvironment of deep clouds in damping theirlongwave radiative effects is offered in the finalsection.

2. Data and methodology

2.1 Earth radiation budget experiment(ERBE) cloud forcing estimates

The radiation data used here are monthly meanregional flux products (s4g) (Barkstrom et al,1989). It consists in monthly mean griddedshort-wave, longwave and net cloud radiativeforcing estimates built from the original long-wave and short-wave measurements. The resolu-tion of the products is 2.5� 2.5 degrees. Monthlymean of January (1986–1989) and July (1985–1988), corresponding to the ERBE=INSAT com-mon period, are used in this study. Consideringthe monthly mean regional errors in the TOA fluxestimation from ERBE and summing the calibra-

tion, angle sampling, time sampling and spacesampling errors, the uncertainty in the total skyflux estimates is 3.2 Wm�2 and 5.5 Wm2 forthe longwave and short-wave flux, respectively(Wielicki et al, 1996 and reference therein).The clear sky flux estimates mainly suffer froma bias rather than random uncertainty (Collinsand Inamdar, 1995) but the one standard devia-tion overall uncertainty can be estimated roughlyto be around 2 Wm�2 for both shortwave andlongwave at the monthly scale (Harrison et al,1990; Wielicki et al, 1996). One limit of the pres-ent data set lies in the poor sampling of the clearsky conditions in the highly cloudy regions. Toovercome part of this limitation, in the following,the clear sky background either in the longwaveor in the shortwave spectrum are build from theseasonal 3 months average rather than from theindividual monthly mean. The cloud radiativeforcing expresses the difference between clearsky and total sky conditions. It can be split intoits short-wave and longwave components:

SWCRF ¼ SClear � STotal; ð1Þ

LWCRF ¼ Fclear � FTotal; ð2Þwhere SWCRF is the short-wave cloud radiativeforcing, SClear and STotal are the reflected radia-tion at the top of the atmosphere under clear skyand total sky conditions, respectively. Over theocean, clouds are brighter than the surface andSWCRF is usually negative; clouds are generallycooler than the sea surface yielding a usuallypositive value of LWCRF. The monthly meanregional uncertainty in the forcing terms isthe sum of the total sky flux and clear sky fluxerrors which yields to errors of 7.5 Wm�2 and5.2 Wm�2 for short-wave and longwave, respec-tively (assuming the seasonal clear sky fluxuncertainty is the same as the individual monthlymean). The net effect of clouds on the top of theatmosphere (TOA) budget is simply the sum ofthe short-wave and longwave cloud forcing:

NETCRF ¼ SWCRF þ LWCRF: ð3ÞIn the Inter Tropical Convergence Zone, upperlevel clouds dominate the cloudiness and hencecontrol the NETCRF. The LWCRF and theSWCRF are influenced by the distribution andoptical properties of cloudiness and fractionalcover. The vertical distribution of cloudinessstrongly modulates the LWCRF magnitude but

Distribution of CRF over the Indian Ocean using INSAT-1B and ERBE data 3

has less impact on the SWCRF. The monthlymean regional errors in the net forcing, assumingthe error in short-wave and longwave are in-dependent, is the sum of the uncertainties inLWCRF and SWCRF and reaches 12.7 Wm2

for each 2.5� 2.5� grid points.

2.2 INSAT-1B derived upper-level cloudiness

The upper-level cloudiness estimates are extractedfrom the INSAT-1B infrared imagery made avail-able to NCAR over the 1985–1988 period bythe Indian Meteorology Department. The originalresolution of the measurements is 11 km and theavailable data set is under sampled by one pixel ineach direction yielding to an effective resolutionof 22 km. The original numerical counts are con-verted into brightness temperature (BT) usingcalibration table. The conversion from numericalcount to BT gives a sensitivity of 0.15 K in the284–301 K range and of 1 K elsewhere (Smithand Metha, 1990). The month of July 1988 issampled every 3 hours while only a couple ofimage per day is available for the remnant partof the studied period. Moreover, a stringent qual-ity test (poorly navigated images, anomalous scanline, etc.) is applied onto the raw images that sub-stantially reduce the amount of data in the analy-sis (Roca and Ramanathan, 2000). Bony et al(2000) performed an analysis of the INSAT-1Bcaptor stability with the final aim to establishthe delicate detection of low level cloudiness.Using time series of the 10% warmer percentileof the images, they suggest that some monthsappear to be contaminated by direct entry of thesun into the radiometer. Using a somewhat arbi-trary threshold, they rejected the January 1986observations of interest to us. They indicate thatthe INSAT-1B calibration issues are more sensi-tive at the warm end of the BT spectrum than atthe cold end. Over the cold scenes correspondingto convective cloudiness of interest to the presentstudy, differences with AVHRR Channel 4 mea-surements are limited to 2 to 3 K. Hence thesedata are kept in our analysis.

A single threshold of 255 K (RR 2000) is used todetermine the upper-level cloudiness (ULC). TheULC is computed over the ERBE 2.5� 2.5 degreesgrid as the number of INSAT pixels colder than the

threshold divided by the total number of pixels inthe mesh. Figure 1 shows the dependence of theINSAT-1B brightness temperaturewith cloud emis-sivity and cloud top height under standard trop-ical conditions for temperature and humidity andassuming a fully overcast pixel seen at nadir. The255 K iso-line indeed depicts the region used herefor characterizing the convection induced cloudi-ness. The lower bound corresponds to a black cloudtopping at 6.5 km while the upper bound is asso-ciated with a thin cloud (">0.60) located at thetropopause. The availability of the IR channel onlyto study the convective cloudiness prevents us fromestablishing an elaborated cloud classification.Therefore high thin clouds ("<0.60; h>10 km)that appear as warm as 290 K will not be detectedhere. So that, the present simple detection, if any-thing, underestimates the upper-level cloudinessmainly through missing thin warm cirrus (see Rocaet al, 2002 for a discussion of the improvementbrought by the 6.3 microns channel of METEOSATwith respect to this issue).

2.3 Convective systems characterization

The individual convective systems are extractedfrom the instantaneous INSAT-1B infrared im-

Fig. 1. Dependence of the INSAT-1B IR brightness tem-perature with cloud emissivity and cloud top height. Astandard tropical atmosphere is considered. Overcast situa-tion. Nadir geometry. Computations realized with animproved version of the Morcrette and Fouquart (1985)radiative transfer model including the filter function ofthe INSAT-1B radiometer. The satellite-version of the codeis detailed in Roca (2000)

4 R. Roca et al

agery thanks to the Detect and Spread algorithm(Boer and Ramanathan, 1997), here slightlymodified to focus on upper-level cloudiness(RR2000). The algorithm can be thought of asgeneralized clustering technique which progressfrom the convective core to the cloud edges inmultiple steps. It relies on the idea that adjacentpixels in the satellite image belongs to the samephysical system and that the optical depth of thecloudiness decreases away from the convectivecore (thick) towards the cloud edges (thin cirrus).The interpretation of the brightness temperaturefield in terms of individual convective system issummarized in Fig. 2. Details on the applicationof the algorithm to INSAT data can be found inRR2000. The present effort extends the analysisof RR2000 to the whole ERBE period. The finaloutput is the image segmented in terms of indi-vidual convective systems (including core and

anvil). Two parameters are used to characterizethe individual systems:

(i) The minimum IR brightness temperaturewhich is defined as the coldest pixel withinthe cloud cluster. Assuming that this pixelcorresponds to the deepest convective cellsin the systems, it is associated with opticallythick cloudiness that behaves like a blackbody. The IR temperature hence describesthe depth of the convective core of the sys-tem. This parameter is further used as a sur-rogate for the whole top cloud (core andanvil) height (Fig. 2c).

(ii) The whole system average IR brightnesstemperature is used as a surrogate for theindividual system longwave radiative proper-ties. It corresponds to the outgoing longwaveradiation averaged over different cloud typefrom thick convective cells to thin cirrus alllocated at the system top height (Fig. 2c).

The first parameter is used to further split theindividual monsoonal convective cloud systemsinto 3 classes corresponding to 3 categoriesof vertically developed systems as in RR2000.The Class 1 systems are associated with verydeep convection with minimum IR temperaturecolder than 220 K. Class 2 and 3 correspondto deep convection and convective debris, withminimum temperature between 220 K and240 K, and between 240 K and 255 K, respec-tively. Recall that in any of the classes, the indi-vidual system is formed of a convective core andthe attached anvil and consequently, any systeminclude pixels with brightness temperature aswarm as the upper cloudiness threshold (i.e.,255 K). RR2000 investigated the scale depen-dence of the convective systems over the IndianOcean. They showed that the Class 1 very deepconvective systems span scale from 100 up to106 km2 and on average for July 1988 andJanuary 1989 dominates the Upper-Level Cloudi-ness. The systems larger than 104 km2 explains90% of this contribution. It was further shown thatthe larger the system, the deeper the cloud top.Only the Class 1 systems larger than 104 km2

are reaching tropopause-like temperatures. Theseprevious results confer an important role to theClass 1 systems in forming the ULC and in influ-encing the longwave cloud radiative forcing that

Fig. 2. Schematic of the Detect and Spread technique andinterpretation; (a) IR Satellite imagery perspective, (b) X-cross section, and (c) associated mesoscale convective sys-tem X–Z structure

Distribution of CRF over the Indian Ocean using INSAT-1B and ERBE data 5

is detailed later for the whole 1985–1988 period.In the following, these 3 MCS categories will beused as a proxy for the vertical distribution ofUpper-Level Cloudiness.

2.4 Other data sets

In complement to ERBE and INSAT observa-tions, two other satellite based data set are used:(i) clear sky fraction estimate from ISCCP(Rossow and Gardner, 1991) covering the 1985–1989 period, and (ii) the SSM=I derived precipi-table water (Pwat) archive (Wentz, 1997). Owingto the unavailability of microwave based Pwat

observations during the whole ERBE=INSATperiod, the mean July and January distributionsare obtained by averaging these observationsover the 1988 to 1999 period.

3. Cloud radiative forcing variabilityat seasonal and interannual scales

In this section, we investigate the patterns ofupper-level cloudiness and cloud radiative forc-ing and establish, quantitatively, the differencesbetween two key regions: Bay of Bengal andeastern Indian Ocean. Both the seasonal andinterannual scales are analyzed. The MCS radia-tive properties and distribution are also presentedalong with the major characteristics of the cloudenvironment in these two regions. An interpreta-tive framework is then proposed that emphasisesthe environmental water vapour damping effecton the longwave cloud forcing.

3.1 Patterns of cloud radiative forcingand upper-level cloudiness

The spatial distribution of the upper-level cloudi-ness is shown in Plate 1 for the four Julys andJanuarys used in this study. In January, ULC isdistributed along a zonally elongated band rang-ing from north of Madagascar to Indonesia withmajor extent over the southern Hemisphere. Awest–east gradient of upper-level cloudiness isevident with strong convective activity off theMaritime continent and less convection over thewestern part of the ocean. In July, the Bay ofBengal and the Indian continent experience max-imum of upper cloudiness corresponding to themonsoonal convective systems. A secondary max-imum, less intense, is located south of the equator.

The Arabian Sea also exhibits trace of convection.In both seasons the upper-level cloudiness ex-hibits substantial interannual variability. Indeed,during summer, these years corresponds generallyto droughts over India but for the 1988 La Ni~nnaevent which was accompanied with an abovenormal rainfall monsoon (Janowiak and Arkin,1991). In winter, the complex interactionsbetween the phase of the QBO and the 1987=88El Ni~nno-La Ni~nna event modulate the convectiveactivity over the ocean (Gray et al, 1992) andhence the associated upper-level cloudiness. Intra-seasonal variability also induces variability fromone January (e.g., Roca et al, 2002; Duvel et al,2004) or July (e.g., Goswami and Ajaya Mohan,2001) to another. The detailed analysis of the rea-sons for such inter annual variability over theregion is out of the scope of the present paperand we here focus on the link between cloudradiative forcing and upper-level cloudiness. Thedistribution of the net CRF (Plate 2) indicates aslightly negative forcing over most of the basinduring Januarys. Strong negative regions are pres-ent over the south eastern part of the basin cor-responding to the bright low level Stratus decks(S�eeze and Pawlowska, 2001). As discussed inRS2000, Julys are characterized by strong nega-tive CRF over the Bay of Bengal, especially overits northern part where ULC is high. South of theequator, only slightly negative forcing is observed.In brief, upper-level cloudiness over Bay ofBengal in July is slightly larger than the maximumof cloudiness found in January and there, net CRFis strongly negative. Two regions are defined foreach season: Bay of Bengal (80E–100E; 10N–20N)in July and eastern Indian Ocean (80E–100E;10S–0N) in January to analyze these differencesquantitatively. Note that in the following, whenconsidering the regional average and distribution,only the oceanic grid points are taken intoaccount. As a result the eastern Indian Oceanregion includes 44 grid points while 19 gridpoints compose the Bay of Bengal region.

3.2 Shortwave, longwave and netcloud radiative forcing

Figure 3 presents the distribution of the forcingover the two regions. Over Bay of Bengal, netCRF varies from �35 to �30 Wm�2 over theperiod. This negative net forcing is due to the short-wave forcing being more negative (� �115 Wm�2)

6 R. Roca et al: Distribution of CRF over the Indian Ocean using INSAT-1B and ERBE data

Plate 1. Map of upper-level cloudiness in %. For January (left) and July (right). The boxes on the maps correspond to the twokey regions defined in the text

Plate 2. Map of net cloud radiative forcing in Wm�2. For January (left) and July (right). The boxes on the maps correspond tothe two key regions defined in the text

than the longwave CRF (�80 Wm�2). This regionis characterized by clear sky fraction generally lessthan 10%. In January, shortwave CRF is less nega-tive than in July but the longwave CRF is slightlylarger yielding to less negative net CRF over east-ern Indian Ocean than over Bay of Bengal. Clearsky fraction is slightly larger than in July Bayof Bengal (�12%). Overall for both regions theinterannual variability of the forcings is small(�5 Wm�2) and smaller that the differencebetween the regions in the multi-year net forcing.

The radiative forcing uncertainties are about 7.5,5.2 and 12.7 Wm�2 for the SW CRF, LW CRF andnet CRF, respectively at the grid point scale(Section 2). The time sampling errors decreaseroughly with the square root of the number of month

used in the average (e.g., Cess et al, 2001). Hencethe multi-year average uncertainty is reduced by afactor of 2 from the individual year’s one. Both theindividual year and the multi-year average differ-ences between the two regions in shortwave andnet forcing are hence significant while the multi-year averaged longwave CRF can be considered assimilar for the two regions.

3.3 Mesoscale convective systemsand upper-level cloudiness

3.3.1 Upper-level cloudinessand convective systems distribution

Figure 4 shows the ULC and MCS category distri-bution for the two regions. For the multi-year

Fig. 3. Longwave, minus shortwave, net cloud radiative forcing (Wm�2) and clear sky fraction (%) over (a) Bay of Bengalduring July, (b) Eastern Indian Ocean during January for the four years of the study. The multi-year average is also shown

Fig. 4. Upper-level cloudiness (%) and contributions from the three mesoscale convective systems classes over (a) Bay ofBengal during July, (b) Eastern Indian Ocean during January for the four years of the study. The multi-year average is also shown

R. Roca et al: Distribution of CRF over the Indian Ocean using INSAT-1B and ERBE data 9

average, the relative contribution of the three MCSclasses are very similar and reads 47%, 32% and21% for Class 1, 2 and 3, respectively. For themulti-year average, more Upper-Level Cloudinessis found over Bay of Bengal (39%) than over east-ern Indian Ocean (35%). Bay of Bengal is alsocharacterized by more interannual variability inULC than the eastern Indian Ocean region as wellas in the relative MCS category distribution thanthe eastern Indian Ocean region. Unlike for theeastern Indian Ocean region, this interannual vari-ability of ULC does not appear to be well related tothat of the forcings shown in Fig. 3.

3.3.2 Individual convective systemsproperties

INSAT measures the flux coming from the pixellocation and is expressed in terms of brightnesstemperature. This is the temperature of a blackbody that emits the same flux as the flux emittedby this scene at this wavelength. These two fluxare related through: Measured Flux¼B�(Tb)where B is the Planck function at the � wave-length and Tb is the brightness temperature ofthe body under considerations. One can furtherdefine the emissivity of a non-black body "related to the above quantities through "¼measured flux=B�(T) where T is the thermody-namical temperature of the body. " measures howfar the body from a black body is. In the lattercase, " equals 1 and then Tb¼T. Integrating overthe longwave spectrum the Planck function andfollowing the gray body assumption (" indepen-dent of the wavelength) and noting this spectrallyintegrated emissivity the effective emissivity "eff,

the relationship between "eff, brightness tempera-ture and temperature reads

"eff ¼ �T4=�Tb4; ð4Þ

where �¼ 5.67e� 8 Wm�2 K�4 is the Stefan-Boltzman constant. Furthermore, we shall as-sociate the INSAT Tb to the full longwavespectrum brightness temperature (assuming thewater vapor and other absorbing gas above thedeep cloud under consideration will not radicallychange our estimate of "eff for the individualcloud systems). Following the schematic ofFig. 2, we shall consider that the minimum INSATbrightness temperature within the cloud cluster(Tb min) corresponds to a black body and henceis equal to a single thermodynamic temperatureTmin. We further follow Fig. 2 and assume thatthe Tmin level represents the whole cloud systemtop height. Averaging Eq. (4) over a whole cloudsystem hence yields to

"eff system ¼ �Tmin of the system4=

�Tb mean of the system4: ð5Þ

For each of the convective systems class, thecomputations of "eff system are performed for eachof the systems of all the available INSAT-1Bimage of each year. Then the brightness tempera-tures are weighted by the individual system areato form the monthly mean estimate. The resultsare summarized in Table 1. The Class 1-VeryDeep Systems emissivity is 0.66 which suggeststhat in this class, the anvil (the non-black part ofthe system) contributes importantly to the meanradiative properties of the systems in agreementwith RR2000 where it was shown that, within theindividual convective clouds larger than 104 km2

Table 1. Mean convective systems characteristics for each classes and the two regions. Value in parenthesis are the interannualstandard deviation

Minimum temperature (K) Average temperature (K) Effective emissivity

January Indonesia

Class 1 207.0 (3.0) 230.2 (0.9) 0.66 (0.02)Class 2 226.0 (0.0) 238.7 (0.7) 0.80 (0.00)Class 3 242.0 (1.7) 249.6 (1.7) 0.89 (0.01)

July Bay of Bengal

Class 1 204.3 (3.7) 228.6 (3.0) 0.66 (0.03)Class 2 226.3 (0.5) 238.9 (0.7) 0.80 (0.01)Class 3 243.0 (0.8) 251.3 (0.9) 0.89 (0.01)

10 R. Roca et al

(that represent more than 90% of the Class 1cloudiness), the core-to-anvil ratio varies from10 to 60%. Class 2 and Class 3 systems emissiv-ity is 0.8 and 0.89 respectively, which suggeststhat the anvil is, in these cases, less prominentwith respect to the convective core (the blackpart of the system). Moreover, the results showthat the emissivity of a convective system withina given class is extremely stable and is indepen-dent of the season.

In short, the effective emissivity of the indivi-dual MCS of each category as well as their rela-tive contribution to ULC, our proxy for thevertical structure of cloudiness, are similar forthe two regions. The main difference between

the Bay of Bengal in July and eastern IndianOcean in January hence appears to concern theupper level cloud fraction which is, on multi-yearaverage, larger in Bay of Bengal.

3.4 Clear sky environment

The clear sky OLR interannual and seasonalvariability is shown in Fig. 5. Over Bay ofBengal, large interannual variability charac-terises the clear environment with values rang-ing from 275.5 to 280 Wm�2 with a mean of277.5 Wm�2. Over eastern Indian Ocean, clearOLR does not depart much from 283 Wm�2.On multi-year average, more LW radiations

Fig. 5. Clear sky outgoing longwave radiation (Wm�2) over (a) Bay of Bengal during July, (b) Eastern Indian ocean duringJanuary for the four years of the study. The multi-year average is also shown

Fig. 6. Maps of precipitable water for January (a), and July (b) averaged over the 1988–1999 period

Distribution of CRF over the Indian Ocean using INSAT-1B and ERBE data 11

escape to space over eastern Indian Ocean thanover the Bay of Bengal region. The Sea SurfaceTemperatures (SST) in each region, on average,are comparable at around 301 K and do notexplain the larger clear sky OLR over easternIndian Ocean. On the other hand, the watervapour distribution shown in Fig. 6 clearlyshows the climatological difference between thetwo regions with Pwat¼ 59 mm over Bay ofBengal and Pwat¼ 49 mm over eastern IndianOcean.

In summary, the Bay of Bengal region exhibitsmore upper level clouds and smaller clear skyOLR values than the eastern Indian Oceanregion. The effect of this extra upper-level cloud-iness (assuming a similar clear sky OLR distri-bution in both regions) would yield to largerLWCRF over Bay of Bengal than over easternIndian Ocean. Both regions are neverthelesscharacterized by similar LWCRF values. It henceappears that the effect of this extra upper-levelcloudiness on the LWCRF is compensated forby the stronger clear sky absorption. The latterseems related to the larger water vapor loadingfound over Bay of Bengal with respect to theeastern Indian Ocean region. In order to delineatemore precisely the respective role of the upper-level cloudiness and water vapor on the long-wave CRF regional differences, the sensitivityof LWCRF to both Pwat and ULC is computednext in an idealized context.

3.5 Idealized radiative transfercomputations

An ensemble of idealized profiles is built asfollows: the temperature profile and surfaceparameters (SST and temperature at 2 m)corresponding to the mean 1985–1988 condi-tions for July over the Bay of Bengal areextracted from the ERA40 analysis (Uppala,2001; Simmons, 2001). An idealized verticalprofile of relative humidity is then used. We as-sume RH to be constant and equal to 75% in theboundary layer ranging from surface to 850 hPa.For the stratospheric levels (100 hPa to TOA),10% constant RH is also assumed. Then the freetropospheric RH (from 850 to 100 hPa) isassumed to be vertically constant. The free tropo-spheric RH is then varied from 5 to 100% by 5%step to form an ensemble of profile for which

precipitable water (Pwat) varies from 31 to76 mm. These temperature and relative humidityprofiles are completed using ozone clima-tological profile of the tropical atmosphere(McClatchey, 1972) and present day value ofCO2 concentration of 355 ppmv. Simplified cloud-iness is then incorporated to these profiles byassuming a single level black body located at250 hPa (T� 235 K). For each RH profile, thecloud fraction is varied from 5 to 100% by 5%step to form an ensemble of profile that span alarge number of Pwat and upper-level cloudi-ness conditions. Radiative computations are per-formed using the Column Radiation Modelwhich is the stand alone version of the radiationcode used in NCAR CCM3 general circulationmodel (e.g., Zender, 1999). This model wasshown to perform well for the broad band long-wave radiation computations and has been exten-sively used for studying the earth radiation (e.g.,Bergman and Hendon, 1998).

Results are presented in Fig. 7 which showsthe dependence of LW CRF to both the upper-level cloud cover and the precipitable water.Considering the mean conditions of Januarywith Pwat¼ 49 mm and ULC¼ 35%, changingthe cloudiness to 40%, which is the July value,would modify the LW CRF from 55.6 to62.7 Wm�2. Accounting for the accompanyingchange in Pwat observed from January to July(Pwat¼ 59 mm), would yield to a LW CRF of

Fig. 7. Longwave CRF as a function of precipitable waterand cloud cover for an ensemble of idealized atmosphericprofiles. See text for details

12 R. Roca et al

58.2 Wm�2. The increase of LWCRF due toULC increase is less because of the concomi-tant increase in Pwat. This simply illustrates thewell known non linear damping of the watervapor on the LW CRF through the clear skyflux dependence on Pwat (e.g., Bony and Duvel,1994). This damping of the water vapor on thelongwave cloud forcing implies that the sensi-tivity of the longwave radiation to cloud changein a moist environment is smaller than in a dryone.

These computations agree well with the obser-vations of similar longwave forcing in both Bayof Bengal and eastern Indian Ocean resultingfrom differences in ULC and Pwat. Nevertheless,the present calculations are idealized and lack anumber of features to well fit the documentedvariability of LW CRF over the Indian Ocean.The cloud vertical distribution and the mixingof different convective systems categories arenot taken into account here while they influencethe observed total sky OLR. With respect to theRH profile, complementary to the Pwat differ-ence, the difference between summer and winterin the vertical distribution, if any, could alsoinfluence importantly the clear sky OLR(Spencer and Braswell, 1997). Therefore it shouldbe kept in mind that this computation only pro-vides a framework to interpret the observed differ-ences but does not aim at perfectly reproducingthe observed LWCRF distribution. In the nextsection, we further analysed the role of thisdamping effect by considering the regional distri-bution of cloudiness, water vapour and radiation.

4. Regional scale variabilityof the longwave cloud radiative forcing

In this section, we extend the analysis to thewhole tropical Indian Ocean (30S:30N–30E–100E) for which a wider range of ULC and pre-cipitable water conditions are sampled. The focusis set on the multiyear accumulated distributions.

4.1 Cloud and longwave cloudradiative forcing

The distribution of occurrence of ULC (Fig. 8)indicates that convective activity is more spreadin January than in July and that very cloudyregions (ULC>50%) are more frequent in July

than in January. The number of grid points withhigh ULC (60%<ULC<70%) is low and hencewas considered in the following discussion. Thelongwave CRF relationship to upper-level cloud-iness shown for the two seasons in Fig. 9a indi-cates that, accounting for the uncertainties in theLWCRF estimates presented in Sect. 2, bothseasons are characterised by a similar rateof increase of LWCRF from the 10%<ULC>20% to the 40%<ULC<50% conditions. Sig-nificant seasonal difference is observed onlyover the more cloudy regions (50%<ULC<60%) where LWCRF increase much less withULC in July than in January. This asymptoticbehaviour of the longwave forcing with upper-level cloudiness at high ULC was previouslynoted by RS2000 for the Asian monsoon region.It appears very unique with respect to the easternIndian Ocean region as well as compared to thewarm pool region (Kiehl and Ramanathan, 1990;Kiehl, 1994; RS2000).

Concomitant to this asymptotic behaviour ofthe LWCRF, the clear sky OLR also indicates asimilar rate of decrease with ULC for the 10%–50% range in both seasons (Fig. 9b) and a sig-nificant difference (smaller Clear sky OLR inJuly than in January) for the cloudiest bin. Seasurface temperature and temperature profile sea-sonal variations (not shown) are weak and cannotfully explain this clear OLR variability. Indeedthe distribution of the precipitable water for eachseason indicates that the environment becomesmoister with ULC (Fig. 9c). It also shows thatthe Indian Ocean environment of convection ismoister in July than in January especially forthe most covered ULC bin. Note that the mean

Fig. 8. Regional distribution of ULC accumulated over thefour years of the study

Distribution of CRF over the Indian Ocean using INSAT-1B and ERBE data 13

SSMI derived PW distribution over the 12 yearsperiod (see Sect. 2) is used together with theaccumulated ULC over the ERBE-INSAT periodto build this comparison. While the increase ofPwat with ULC is steady in both seasons for the10%–50% range, it keeps on increasing for ULC

greater than 50% only in July and remains equalto that of the previous bin in January. In July, thisincrease is associated with the strong south–northgradient characterising the Bay of Bengal seen inboth moisture and upper-level cloudiness distri-bution (Plate 1, Fig. 6). In January the west–eastgradient of ULC is not associated with a stronggradient in Pwat. As a result, in July, the mostconvective regions of the Indian Ocean are verymoist and their longwave forcing is damped withrespect to January. Quantitatively, consideringa ULC change from 25% to 55%, LWCRF in-creases from 65 to 88 Wm�2 in July and from60 to 98 Wm�2 in January (Fig. 9a). Similarly,Pwat increases from 49 to 61 mm in July and from48 to 53 mm in January. Bearing in mind thelimitations of the idealised computations of theprevious section, we read from Fig. 7 that suchchange in ULC induces an increase of LWCRFof around 36 Wm�2 in July and 40 Wm�2 inJanuary in good agreement with the observedchanges. If the summer increase of Pwat hadbeen similar to that of January, then LWCRFwould have increased by 43 Wm�2 instead of36 Wm�2. This estimate of the damping ofLWCRF by Pwat in July with respect to Januaryis in the range of the observed difference eventhought the idealised simulations suffers from theneglected cloud and humidity vertical distribu-tion as well as seasonal and regional variabilityin the temperature profile. These limitationsshould play an important role in fully explain-ing the observed differences and deserve furtherinvestigations.

5. Summary and discussion

The cloud radiative forcing over the IndianOcean is investigated using satellite observationsof radiation, clouds and water vapor. The pro-posed approach consists in establishing the rela-tive contribution of clouds and their environmentto the observed forcing distribution. The seasonalcomparison between Januarys and Julys indicatesthat the Bay of Bengal region is associated witha strong negative net forcing in summer, pre-viously noted by Rajeevan and Srinivasan(2000) and that the eastern Indian ocean regionis close to a near cancellation regime in winter.The net negative regime of Bay of Bengal isshown to be associated with large upper level

Fig. 9. Regional distribution of (a) longwave cloud radia-tive forcing, (b) clear sky OLR, and (c) precipitable wateras a function of upper-level cloudiness accumulated overthe four years of the study. Note that the long term 1988–1999 climatology is used for precipitable water

14 R. Roca et al

cloud fraction and large shortwave negative forc-ing that is not compensated for by the longwaveforcing. Indeed over eastern Indian Ocean lessUpper-Level Cloudiness is observed but withvalue of the LW CRF similar to that of Bayof Bengal. Other elements important for theLWCRF (the vertical distribution of cloudiness,here roughly investigated thanks to the 3 MCScategories, individual MCS longwave effectiveemissivity) are similar on average over eachregion and do not appear to influence the long-wave CRF regime. Rajeevan and Srinivasan(2000) proposed that this uniqueness of Bay ofBengal is associated with the unique combinationof thick and large fraction of high clouds therecompared to other tropical regions. Our studyfurther indicates that the summer monsoon Bayof Bengal strong negative cloud radiative forcingregime is also associated to a relatively low long-wave CRF regime over the region which is driv-en at first order by the substantial water vaporloading there. Over the whole Indian Ocean,the distribution of ULC indicates a distributionshifted towards larger ULC in July with respectto January. In July, the longwave forcing asymp-totes towards 85–90 Wm�2 over the mostconvective part the Ocean while in January itincreases steadily up to 100 Wm�2 for ULCgreater then 50%. Idealized radiative computa-tions confirm the damping effect of the watervapor loading at this scale.

The remnant differences between the simula-tions and the observations may be due to thevariability of the vertical distribution of humiditywhich is not accounted for in the computations.Indeed the way the free troposphere is moistenedby deep convection could yield to important dif-ferences between January and July. The localrelationship between ULC and Mid-to-UpperTropospheric Humidity over the Indian Oceanin winter nevertheless appears similar to theone observed over the other part of the tropicslike the Eastern Pacific (Udelhopfen andHartman, 1995; Roca et al, 2002). Establishinghow this moistening takes place over Bay of Bengalin July could clarify the specificity or similarityof this region with the winter conditions and cer-tainly deserves further investigations.

The analysis of the moist environment of deepcloudiness and its impact on the cloud forcingover the Indian Ocean recalls the important

damping effect of the high water vapor loadingon the LW CRF. Over the warm Pool, the nearcancellation regime previously found during theERBE period (Kiehl and Ramanathan, 1990)has been shown not to hold anymore in the1997=1998 El Ni~nno event and was attributed toa change in mid-level cloudiness being more fre-quent than for previous years (Cess et al, 2001).These changes in the cloud vertical distributionwere further shown to be associated with largescale dynamical changes during this recent event(Allan et al, 2003). Water vapor content loadingover the Warm Pool lies in the eastern IndianOcean range of observed values during theERBE period as well as for the recent yearsand does not exhibit strong changes (not shown)that explains, together with weak SST variability,the reported very small change in clear skyOLR (Cess et al, 2001). According to the presentstudy, the warm pool moisture background issuch that it allows changes in cloud vertical dis-tribution to have a larger impact on the longwaveradiation budget (and consequently on the netradiative budget) than if the warm pool hadmoisture conditions similar to that of Bay ofBengal.

Recent climate analysis shows a tropical wideincrease in precipitable water (Wentz andSchabel, 2000). If this increase were to continue,the sensitivity of the longwave cloud forcing toupper-level cloudiness changes for the warm poolregion might evolve from the presently observedone to a less sensitive regime, similar to that ofBay of Bengal in July. In this case, an increase ofupper level cloud fraction would not affect muchthe longwave radiative forcing. The net forcingwould nevertheless be influenced by this ULCincrease through the shortwave cloud radiativeforcing. We can speculate that such conditionscould yield to a Bay of Bengal-like negativenet cloud radiative forcing regime over the WarmPool.

Acknowledgements

Part of this study was initiated while the first author wasa visiting scientist at the Center for Cloud, Chemistry andClimate, Scripps Institution of Oceanography, UCSD.Enlightening discussions at an early stage of the study withProf. V. Ramanathan are very appreciated. The discussionswith Pr. J. Srinivasan, CAOS, IIS on the uniqueness of theBay of Bengal characteristics are gratefully acknowledged.

Distribution of CRF over the Indian Ocean using INSAT-1B and ERBE data 15

The authors thank Drs M. Viollier and J-Ph. Duvel, LMD,for helpful discussions on the ERBE data and cloud radiativeforcing. Technical support from the Climserv=LMD=IPSLdatabase manager, Jean-Louis Monge is fully appreciated.

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Distribution of CRF over the Indian Ocean using INSAT-1B and ERBE data 17

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