saji goswami qjrms 1996

8/12/2019 Saji Goswami QJRMS 1996

1/31

Q. J . R. Meteorol. SOC. 1996). 122, pp. 23-53

An improved linear model of tropical surface wind variabilityBy N.H. SAJI and B. N. GOSWAMI

Indian Institute of Science, Bangalore, India(Received 28 January 1994;revised 3 Apr i l 1995)

SUMMARYFor coupled mo delling studies, the atmospheric com ponent is required to simulate the large-scale part of theobserved surface winds accurately, as only this part is responsible for driving low-frequency interannual variabilityin the oceans. A simple linear model of the tropical atmosphere is developed in which surface winds are viewedas the response of the atmospheric boundary layer to pressure perturbations produced by deep convection andsea surface temperature (SST) gradients. An empirical parametrization of total convection as a nonlinear functionof total SST is adopted. The large-scale variability of the simulated surface winds is examined for the period1974-1991. The m odel not only captures the large-scale low-frequency part of the surface winds given by the firsttwo empirical orthogonal functions (EOFs), it also successfully simulates the equatorial surface wind anomaliesand their evolution during the entire period 1974-1991.Certain interesting differences between precipitation forcing and SST induced forcing of surface winds isbrought out in this study. Th e precipitation forcing was found to be do minant over the central and western Pacific,

while the SST forcing was found to be dominant over the eastern Pacific. The resultant wind response was alsofound to reflect this behaviour. The most interesting result is the change in balance of the precipitation-relatedand SST-gradient-related terms in the forcing of anomalous and climatological winds. Our results indicate thatconvective heating predominates over SST -gradient-induced effects in forcing anoma lous winds, but the b alancereverses in the ca se of the climatological w inds. We also find that SST-gradient effects are critical in the simulationof the climatological wind field.KEYWORDS: Pacific surface winds Tropical linear model Deep convection Boundary-layer pressuregradients

1. INTRODUCTIONThe surface winds play a crucial role in tropica l air-sea interactions. They drive'the

ocean cu rrents that determine the evolution of the sea surface temperature (SST). The SSTmodulates the atmospheric heating which in turn determines the surface winds. S uch air-sea interactions are at the heart of the observed interannual variability in the trop ics such asthe El Niiio sou thern oscillation (ENSO) phenom enon. Coupled ocean-atmosphere modelsof different complexity are being employed to simulate the observed interannual variability(e.g. Neelin et al. 1992; Zebiak and C ane 1987; Philander et al. 1992; Lau et al. 1992 ; Latifet al. 1993; Nagai t al. 1992). However, most coupled m odels suffer from certain climatedrifts. These climate drifts may be due either to imperfection in the a tmospheric or oceaniccomponents or to imperfection in the parametrization of the coupling processes (Neelinet al. 1992). Therefore, the ability of the m odel's atmospheric component to simulate thesurface winds realistically when forced by observed S ST is a prerequisite for using it in acoupled model. The atmospheric component used in various coupled models range from asimple steady Gill model of Zebiak and Cane (1987) or the hybrid m odel of Neelin (1990),through low-resolution atmospheric general circulation models (AGCM ) of Philander et al.(1992), Lau etal . (1992 ) and Latif eta l . (1993), to the som ewhat higher-resolution AGCMof Nagai et al. (1992). Most atmospheric models including low-resolution AG CM s havecertain systematic errors in simulating the interannual variability of the tropical surfacewind when forced by observed SST. The simple Gill type models with atmospheric heatingproportional to S ST anom alies (SSTA) are known to produce far too strong easterlies inthe eastern Pacific during warm phases (Zebiak 1986; Goswami and Shukla 1991). Thelow-resolution AGC Ms also fail to simulate correctly the location and asym metry aboutthe equator of the westerly anomalies associated with the warm phase (Graham et al.1989 ; Latif et al. 1990 ; Kitoh 1991 ). The objective of this study is to develop a simple* Corresponding author: Centre for Atmospheric Sciences, Indian Institute of Science, Banglore 560 012, India.

23


2/31

24 N . H. S N l and B. N.GOSW MIatmospheric model for better simulation of the trop ical surface winds. Developm ent of amodel of this kind is guided by the two factors set out below.First, we note that the primary ob jective of the coupled model is to simula te the annualand interannual variations of the tropical climate. If one is interested only in these low-frequency variations, is it necessary for the atmospheric model to simulate the observedsurface wind in all its details? The observed surface wind field is known to have significantsmall-scale high-frequency variability. Is this small-sca le high-frequency com ponent of thesurface winds important in forcing the interannual variability in the oceanic com ponent?Recent stud ies (Latif et al. 1990 and Goswam i and Shukla 1991) show that only the large-scale part of the observed surface winds represented by the first two or three em piricalorthogonal functions (EOFs) are necessary to force the observed interannual variabilityin the ocean. These studies show that the forcing associated with the small-scale high-frequency part of the surface winds is not important in forcing the inte rannual variabilityin the ocean; they simply act as noise on the forcing associated with the large-scale partof the atmospheric winds. Thus, an atmospheric model that is capable of simulating thelarge-scale part of the surface winds accurately, but which does not necessarily simulatethe small-scale part of the surface winds well, may still be a good choice for using in acoupled model. The recognition of this fact also renders hope that even a relatively simpleatmospheric model which is successfu l in simulating the large-scale part of the surfacewinds accurately would provide a good atmospheric component for a coupled model.These argum ents motivated us in our objective of developing a simple atmospheric model.Secondly, we deal with the subject of the important dynamical factors that governthe maintenance of the observed surface winds in the tropics. Murphree and van denDool (1988) studied the momentum balance for the monthly mean surface winds andconcluded that nonlinearity played on ly a m inor role in the m omen tum balance. Zebiak(1990) studied the vorticity budget of monthly surface wind anomalies in the tropicalPacific and also concluded that the nonlinea r advection terms are of second order. He thenproceeded to infer the forcing field, that would be required to drive the observed surfacewinds in a linear model, by solving the inverse problem. The interesting result that emergedfrom this study was that the inferred forcing field was very different from the sea surfacetemperature anomaly (SSTA) field but bore close resemblance to highly reflective cloud(HRC ) anomalies that may be closely related to the atmospheric heating. Thus, the problemwith many simple linear models used in earlier studies to simulate surface winds w as notthat they neglected nonlinearities but that they param etrized atmospheric heating as beingdirectly proportional to SSTA. Such a pa rametrization fails to simulate both the location andhorizontal scale of the atm ospheric heating. For example, during a mature warm phase ofENSO, the SSTA maximum is located in the eastern part of the Pacific but the atmosphericheating inferred from outgoing longwave radiation anom alies (OLR) or HR C is centredaround the date line. Similarly, Neelin (1988) showed that if a linear model is forced witha GCM precipitation field it can simulate tropical winds that closely resem ble the surfacewinds simulated by the nonlinear GCM . Therefore, a linear model may be sufficient forsimulation of the surface winds if the model can parametrize the atmospheric heating fieldcorrectly. However, the organized convective heating in the trop ics is governed by nonlineardynam ical and therm odynam ical processes. To parametrize it within the framework of alinear model is a daunting task. An attempt to do this by including a simple moisturebalance equation has not been successful (Seager 1991); therefore, we shall not attempt tocalculate the atmospheric heating following some simple dynamical framew ork. Instead,we estimate it from SSTon the basis of an empirical relation between S ST and observedprecipitation . (Observed precipitation here refers to the precipitation derived from OLRas described in the next sec tion.) In section 3, we describe the basic m odel. Although it is


3/31

TROPICAL LINEAR MODEL 25a linear model, it includes both processes that drive the surface wind, namely deep heatingof the atmosphere and surface pressure gradients associated with horizontal S ST gradients.The background for developing this em pirical parametrization of the large-scale part of theatmospheric heating, and the method itself, is discussed in section 3. Section 4 describesresults from our simple model and brings out the contribution from the two processes.Results are summarized and some conc luding remarks are m ade in section 5.

2. D A T A U S E DTo verify the simula tion of the surface wind variability by ou r mode l, we used zonaland meridional components of the monthly mean surface winds from the com prehensiveocean-atmosphere data-set, (COAD S, Slutz et al. 1985) for the period 1974-1987, andfrom the European Centre for M edium-Range Weather Forecasts (ECM WF ) analyses at1000 mb for the period 1988-1991. As we are primarily interested in the simulation ofthe low-frequency variability, a nine-month running mean was used in both model and

observed winds. SS T data was taken from C OADS for the period 1974-1987 and fromthe blended SST product of R eynolds (Reynolds 1988; Reynolds and M arisco 1993) forthe rest of the period. Both winds and S ST data are available for 2 x 2 boxes. The pre-cipitation anomalies are derived from outgoing longwave radiation using the empiricalrelation given by Vernekar (Yo0 and Ca rton 1988). The m onthly mean OLR data is basedon observations from the NOAA polar orbiting operational satellites (Gruber and W inston1978). The data cover the period between June 1974 and M ay 1993 with a gap betweenMarch and December 1978. The O LR data are available for 2.5 x 2.5 boxes. From esti-mates of precipitation for individual months at each gridpoint, climatolog ical precipitationestimates have been calculated. By subtracting the climatological means from individualobservations we derived the p recipitation anomalies for each month.

3. T H E M O D E LThe surface winds in the tropics may be view ed as the response of the atmosphericboundary layer to two types of forcing: deep convec tion and S ST-gradient-induced effects.Gill (1980) studied the response of the tropical atmosphere to the forc ing associated withlatent heat release in deep cumulus convection using a simple linear model. The Gillmodel can be viewed as a model of the boundary-layer flow alone, which is forced byprecipitation (see Neelin 1988). Lindzen and N igam (1987) put forth the idea that surface

winds in the tropics are also forced by pressure gradients which deve lop hydrostatically inthe turbulently mixed boundary layer because of the underlying SSTgradients. How ever,until recently the attitude has been to consider only one of these two processes, completelyexcluding the other, in modelling tropical surface winds. Lately, the importance of both theprocesses has been recogn ized and some of the recent work reflec ts this (Gutzler and Wood1990 ; Wang and Li 1993; Eltahir and Bras 1993). We recognize that the aforem entionedprocesses should be viewed as com plementary. One or the other of these processes maybe the dominant one in a given geographical location. Thus, in the western P acific warmpool region with high SST and weak SST gradients, conditions are m ore favourable fordeep convection, whereas the strong SS T gradients in the eastern Pacific cold tongue cou ldmake boundary-layer pressure gradients the dom inant mechanism. In the p resent work,we have incorporated both mechanisms in a simple linear model for the tropical Pacificand we have investigated the relative importance of these mechanisms in forcing surfacewinds.


4/31

26 N.H. AJI and B. N.GOSW MIThe boundary-layer response to the mid-tropospheric latent heat fo rcing associatedwith the large-scale part of organized deep convection is modelled using a Gill-type model,

V i z .

u - f +4; + EXU = 0u + fu +4; + E y U = 04; + c u; + u > + &/ = a P

where u nd u are perturbations in the horizontal component of m ass flux in the boundarylayer, 4 is the mass-weighted integral of the perturbation in geopotential height in theboundary layer, f s the Coriolis parameter, E , is the coefficient of zonal Rayleigh friction,cYthat for m eridional Rayleigh friction, E + is the coefficient of N ewtonian cooling, P is theprecipita tion anomaly, and a = L RAp/(2pcP)where L is the latent heat of condensation,R is the gas constant for dry air, cp is the specific heat capacity of dry air at constantpressure, A p is half the depth of the troposphere (about 500mb) corresponding to theregion heated by condensation of water vapour, and p is a mid-level tropospheric pressure(about 500 mb); is the wave speed defined as c 2= SRAp2/2p,where S s static stability(taken to be 4.2 x lo- m K s2/kg).

A note about the friction used before we proceed further. Li and Wang (1994) havedemonstrated that the use of latitude-dependent friction coefficients is critical for thesuccessful simulation of the surface wind field in a three-force balance boundary-layermodel. From a momentum budget study of the tropical surface winds they found thatthe zonal friction coefficient is smallest at the equator and increases with latitude. Themeridional friction coefficient was found to be smallest between the equator and latitude5S, and largest around latitudes 20N and 20s. Following them we prescribe a value forthe zonal friction coefficient, t x uch that it is 0.8 x lo- s-l at latitude 2N and increasesto 2.8 x ~ O - S - ~at latitudes 30N and 30s. The meridional friction coefficient in ourmodel has the value 2.1 x lO-s-l at latitude 4s increasing to about 2.9 x lo- s-l atlatitudes 20N and 20S, thereafter decreasing to about 2.5 x lO-s-l at latitudes 30Nand 30s. he N ewtonian cooling coefficient, E ~ ,s constant (1 x lo- s - l .For the part of the boundary-layer flow due to pressure gradients arising from theSST gradients, we use the Lindzen-Nigam (Lindzen and Nigam 1987, hereafter referredto as LN) formulation with the back-pressure effect included. As Neelin (1989) hasdemonstrated, the Lindzen-Nigam model can be transformed into the following form,which is similar to the Gill model,

u ; - fu +4; + E,U = 0u + f u r+4; + E y U = 0

4 + C U; + u ; ) + E = -bTsrhere T, is the SST anomaly, b = E 6p H0/2To,where 6p is the depth of the boundary layerin units of pressure (about 300 mb), HO s the depth of the boundary layer in units of length(about 3 x lo3m) and To is a constant reference temperature (To = 288 K), (Lindzen andNigam 1987); 4 = g h - H0/2T0)T,), here h is the perturbation to the height of theboundary layer.We exploit the similarity and linearity of Eqs. (1) and (2) to combine them in thefollowing set of equations:


5/31

TROPICAL LINEAR MODEL 27

where 4 = i(4+4).In this case P represents the precipitation anomalies associated with deep convec-tion. G ill (1980) assumed very simp le idealized forms for P with which he could obtainanaly tic solutions. In reality P assumes complicated forms and the leading problem w ashow to parametrize this quantity in terms of the underlying SST with wh ich it was knownto be significantly related. Thus Zebiak (1982), in a simple model to explain the surfacewind anomalies associated with El Niiio, adopted B jerkness (1969) hypothesis that pre-cipitation anomalies were linked to SST anomalies through the concomitant anom alies inevaporation. To quantify the evaporative anom alies associated with the S STA s he used thelinearized Clausius-Clapeyron relation. In a later version, Zebiak (1986) incorporated aconvergence feedback mechanism which was designed to m imic conditional instability ofthe second kind (CISK). Neelin and Held (1987) used the concept of moist static energyin parametrizing deep convection. Seagers (1991) hypothesis was that buoyancy fluxeswere m ore important than the convergence field in determining deep convection. Howevernone of these schem es have been successful in represen ting the large-scale organized partof deep convection. We realize that modelling the nonlinear moist processes that producedeep convection and its organization into large-scale systems is a formidable task w ithinthe framework of a simple linear model. Thus we w ere led to approach this problem in a dif-ferent w a y 4 . e . by looking at the observed relationship between SST and deep convectionand formulating an empirical parametrization based on this.The variability of cum ulus convec tion in relation to the variability of SST has beenthe subject of a fairly large amount of study. In many of these studies OLR has been usedas a proxy for cumulus convection. The most interesting finding that has evolved fromthese studies is that deep convection is supported only in regions where the SST is abovea critical value, T,, which is about 27.5 C (Gadgil et al. 1984; Graham and B arnett 1987;Fu et al. 1990). However, one of the puzzling observations was the large scatter in theOLR-SST relation beyond T, leading to the inference that SST and deep convection arepoorly corre lated beyond T,. Recent studies by Fu et al. (1990), Zhang (1993) and W aliseret al. (1993) have to a great extent cleared up this issue. These studies, based on largerdata-sets of OLR as well as of HRC and ISCCP (International Satellite Cloud C limatologyProject) stage C2 data-set containing cloud types, their frequency of occurrence and cloud-top temperatures, have shown that below 26 C deep organ ized convection rarely occurs.Both frequency and intensity of deep convection increase dram atically with SST between26.5 C and 29.5 C , and decay with further increase in SST. Moreover, in the regionswith SS T greater than 27 C, situations with both no deep convection and vigorous deepconvection are encoun tered, leading to a higher variability of deep convection for a giventemperature. Decrease of convective activity for SS T greater than 29.5 C shows that themaximum convective activity does not occur over the w armest water (SS T > 29.5 C ) butrather that the warmest water occurs under clear (less convective) skies. These studieshave clearly e stablished that although there is larger variability of convection with S ST inregions of high SST, the mean atmosphe ric heating increases dramatically in these regions.This increase in atmospheric heating is the result of the increase in frequency of occurrenceand intensity of deep convection. Another interesting observation made by Fu et al. (1990)is that in the warm pool region with SS T greater than 28 C , strong surface convergencedoes not enhance deep convection. However, if the warmest SST in a region is between


6/31

28 N . H. SAJI and B. N. GOSWAMI

* . .. *. .

25 26 27 28 28SSTFigure 1 . Scatter plot of observed SST vs. precipitation (estimated from observed OLR using Eq. (4)) and themodelled curve of precipitation as a function of SST (thick line). The thin line represents the mean value of theobserved precipitation over each 0.5 C SSTbin.

26 "C and 28 C, the deep convection is significantly enhanced by strong surface windconvergence.With this background, and keeping in mind that an increase in organized convectionwith SST from 26.5 C to 29.5 C is quite nonlinear, we proceed to parametrize the large-scale part of the convective heating in the following manner. First w e estimate precipitationfrom an empirical formula similar to the one proposed by Vernekar (Yo0and Carton 1988),viz. P(m m d-') = 48.38- .186 OLR(W m-') (4)Figure 1 is a scatter plot of concomitant values of observed SST and OLR-derivedprecipitation (as estimated from Eq. (4)) over the tropical Pacific domain (defined asbetween 1WE and 60"W and between 30"s and 30'"). The thin curve in the figure isthe mean value of the precipitation calculated over each 0.5 C SST bin. As discussedearlier, although variability of precipitation for a given SST increases with SST, the meanprecipitation can be found to be increasing with SST in a nonlinear manner. Tomodel thislarge-scale nonlinear increase of precipitation with SSTover warm waters, we constructed aleast-square polynomial fit between precipitation ob tained with Eq. (4)and the concomitantvalues of the observed SST in the tropical Pacific domain. We found that the followingthird-degree polynomial fit (represented by the bold line in Fig. 1) captures many of the

features of the observed precipitation field quite well, e.g.P(m m d-') = 0.0186 x S S T ) 3- 1.220 x S S T ) 2+ 26.093 x S S T )- 179.785.

( 5 )


7/31

TROPICAL LINEAR MODEL 29With this parametrization, we estimated the total convective heating for each monthat each gridpoint using the observed SST. Then the convective heating anomalies werecalculated by subtracting from the climatological convective heating based on the entireperiod (1974-1991).As shown in Eq. (3c), the forcing for the tropical surface winds has two components,

one coming from convective precipitation (Gill forcing) and the o ther from the SS T gradi-ents (LN forcing). The LN forcing anomalies for a given month is the observed S ST m inusthe climatolog ical SST for that mon th. Finally, adding the G ill and the LN components weget the total forcing for the surface wind anom alies.The model equations 3(a) to (c) were written in a finite difference form with lateralboundary conditions that require the gradients of dependent variables to vanish in thedirection normal to the boundaries. The model was integrated in the domain 100 E to60"W and 30 s to 30 N, with 5 resolution in longitude and 2 resolution in latitude. Themodel variables were deployed in the Arakawa-C grid, and time marching was performedwith the leap-frog scheme. The model domain con tains little land surface and hence itwas not considered necessary to include land surface processes. Steady solutions wereconsidered to be obtained when the model solutions converged to within a reasonableaccuracy.

4. RESULTSAs emphasized in the Introduction, we are primarily interested in the model's abilityto simulate the low-frequency large-scale part of the surface wind, and in studying thecontribution to the tropical surface winds by the G ill and LN forcings. Therefore, unlessotherwise stated, all results presented here will be for winds (both observed and simulated)filtered with a 9-m onth running m ean filter. We sha ll present the results in two subsections.In the first we com pare the model results with that of the observations. Since the focusof the paper is in simulating the large-scale part of the variab ility we present com parisonsbetween the first two EOFs and the corresponding principal components (PC) of thesimulated and observed wind anomalies. Presented a lso are time series of equatorial windsin three regions of the equatorial Pacific which are representative of the western, the centraland the eastern Pacific. The H ovmoller maps of the observed and modelled zonal windanom alies are presented next. Last in this subsection is a comparison of the modelled andobserved anomaly wind variance and the correlation map.In the second part we examine the contribution of the G ill and LN componen ts to themodel variability. We focus again on the large-scale features of the two forcings and their

response. Case-studies of a warm event and a cold event are also presented.a ) Model versus observations

(i) Comparisonbetween estimated and observed forcing. The first two EO Fs and the cor-responding principal components (PC) of the observed OLR-derived precipitation anom alyfield (Eq. (4)), explaining 23.87% and 14.42% of the total variance, are shown in Fig. 2.The first EOF reveals an elongated pattern of positive precipitation anomalies (to facilitatecomparison, the patterns associated with the EOFs will be interpreted with respect to awarm episode) straddling the equator but displaced a little to the south and with a maxi-mum centred just below the equator extending about 20 east from the date line and withmax imum loading around the date line. In the second EOF, the positive pattern is locatedto the east relative to the similar pattern in EOF1. This fact together with the phase lagsof the PC2 relative to PC1 during a warm episode (e.g. 1982-83) reflects the eastwardmovement of positive precipitation anom alies during the warm episode. The suppression


8/31

30 N. H. SAJI and B. N. GOSW MIOBSERVED PREC EO Fl 23.87 Q

30N

15N

EO

15s

30s

OBSERVED PREC EO F2 1 4 . 4 2 % b

1 O E 180 1 o w owC

1976 1978 1980 1982 1984 1986 1988 1990Figure 2. The first two EOFs and the corresponding PCs of the filtered p recipitation anomalies estimated fromobserved OLR (Eq. (4)). The solid line is PC1, the dashed line is PC2.

of convection to the west of this pattern is also seen in EOF2. Although the method em-ployed here to estimate precipitation from OLR is probably not universally applicable, wecompared the first two EOF s of precipitation anomaly derived this way w ith those of theHRC anomaly shown in Fig. 3. The agreement between them is good, showing that theempirical formulation captures the dominant patterns of the prec ipitation anomalies qu itewell. The corresponding EO F patterns of the m odel precipitation field (Eq. (5)) are shownin Fig. 4. EO F l and E OF2 show close similarity with those of the observations. Particu-larly well simulated is the location of the centre of the precipitation anomaly maximum


9/31


HRC EOFl 18% a

HRC EOF2 12% b

140E 180 140W 1owC

-20 1976 1978 1980 1982 1984 1986 1988 1990Same a s for Fig. 2, but for the filtered HRC nomalies.igure 3.

in EOF1, and the model EOF2 is able to capture the eastward migration during the warmepisodes. We note, however, that the meridional scale of the EOFl pattern of the modelprecipitation is somewhat larger than that for the corresponding pattern of the observedprecipitation anomalies. Taken together EOFl and EOF2 of the model explain 72% of thetotal variance compared to 38%explained by EOFl and EOF2 of the observations. Thusit is seen that the first two EOFs of both the observations and the model are responsiblefor most of the variability. These also are found to be large-scale patterns while higher EOFsdisplay smaller and smaller spatial structures. That the first two model EOFs are


10/31

32

20

N. H. SAJI and B. N. GOSWAMI

G I L L QEOFl 56.99 a

140E 180 14OW 1OOW

Figure 4. Same as forFig. 2, but for the filtered model precipitation (Eq. (5)) anomalies.

remarkably similar to those derived from observations demonstrate that the model pre-cipitation field is able to capture the large-scale part of the observed precipitation fieldvery well.(ii) Simulated surface winds with full forcing. The model forcing field as men tioned inthe last section is a linear combination of the G ill forcing and the LN forcing. Thereforethe large-sca le features of the fu ll forcing field, as seen from Fig. 7, xhibit features of boththe Gill and the LN forcing fields. Using the full anomalous forcing, we have calculated


11/31

TROPICAL LINEAR MODEL 33OBSERVED WIND S EOF 1 2 9 . 9 1 % a

EQ

15s. . , , , , , I l l , . . . . . . . . . . . I. . . . . . . . . . . . . . . . . . . . . . . . .

180 14OW 1ow072

OBSERVED WINDS EOF 2 1 8 . 1 1 % b30N15N

EO

15s

30s

C

I1976 1978 1980 1982 1984 1986 1988 1990-30Figure 5 . The first two EOFs and the correspondingPCs of the filtered vector wind anomalies from observations.The solid line is PC1, the dashed line is PC2.

the steady responses corresponding to each month between January 1974 and December1991 with the aid of our model (Eq. (3)). Now we presen t the results of an EOF analysison the surface winds simulated by the model and compare them with the EOFs of theobserved surface winds. The first two EOFs of the observed winds explain about 48%(EO F1,29 .91% ; EOF2, 18.11% ) of the total variance (Fig. 5). The principal componentsreveal that these patterns are associated with the low-frequency part of the surface windvariability. The m ost prominent feature of the observed wind E O Fl is the narrow belt of


12/31

34 N. H. SAJI and B. N. GOSW MI

15-

-30 1976 1978 1980 1982 1984 1986 1988 1990Figure 6. Same as for Fig. 5, but for the filtered vector wind anomalies simulated by the Gill + LN model.

strong westerly anomalies extending from about 160E to 120W, and centred a little tothe south of the equator. Th e second EO F reveals the eastward migration of this pattern,with strong easterlies to the west. There is a divergence zone at around 160W and aconvergence zone in the far south-east Pacific at around 100W. Th e model E O F l andEOF2 (Fig. 6 are found to display features similar to the observations. The patch ofwesterly anomalies and its eastward migration is well captured by EO Fl and EOF2 ofthe model winds. However, they display easterlies over the south-east Pacific which are


13/31

TROPICAL LINEAR MODEL

G I LL+LN QEOFl 5 7 . 4 2 % a

G I LL+LN QEOF2 14.55% b30N -

140E 180 140W oow

( O B C

35

-20 J 1976 1978 1980 1982 1984 1986 1988 1990Same as in Fig. 2, but for the filtered Gill + LN forcinganomalies.igure 7.

somewhat stronger than observed. It should, however, be noted that the large spuriouseasterlies simulated by earlier linear models (e.g. Zebiak 1986) during a warm phaseof the E N S 0 are replaced by weaker easterlies much closer to the observations in ourmodel simulations. This significant improvement may be attributed to our more realisticparametrization of the convective heating as well as to inclusion of the fo rcing driven bysurface temperature gradients.The first two EOFs represent a large part of total low-frequency variability of theobserved winds; and the model's ability to simulate them is noteworthy. In this respect,


14/31

36 N.H. SAJI and B. N. GOSWAMIZONAL WIND ANOMALIES EQ3 a

1976 1978 1980 1982 1984 1986 1988 1990

ZONAL WIND ANOMALIES EQ C2

0

- 1

-2Figure 8. Simulated (solid line) and observed (dashed line) zonal wind anom alies averaged over (a) EQ3 (SON-5 5 ; 15OoE-170"W), @) EQ2 (5"N-S"S; 13OoW-17O0W), (c) EQ1 (S"N-5"S; 90W-1300W).ig. 8(d) to ( f ) isthe same as for Figs. 8(a) to (c),but for the meridional wind anomalies.

our simple model performs as well a s some low resolution GCM s (e.g.Graham et al. 1989;Latif et al. 1990) used in many coupled models. Although our model does not simulatethe small-scale part of the observed surface winds w ell, this part not only contains a smallfraction of the total variance but also is irrelevant for low-frequency air-sea interactionsin the tropics (Latif et al. 1990; Goswami and Shukla 1991). The model's success insimulating the dominant large-scale part of the surface winds m akes it a good low-costcandidate for a coupled model.


15/31


- 1

37

. . . . . . . .;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . ., I , .: :I' I. * , .) .I

MERIDIONAL WIND ANOMALIES EQ3 d11

2 -

1 -

- 1

0 -

I. . . . . . : . .; . : . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .- 1 , I .I I .q

1976 1978 1980 1982 1904 1986 1988 1990

: i .. I. ,--' :

: . . . . . . . . . . . . :. . . . . . . :. . . . . . 8 . : . ..J.:I ., .. ,.., . I 1 .. , """...:'. . L.. .


16/31

38 N. H. SAJI and B. N. GOSWAMIMODEL PREC ANOM b

COADS+ECMWF C MODEL d

Figure 9. Time-longitude sections of (a) observed precipitation anomalies, (b) model precipitation anomalies,(c) observed zonal wind anomalies and (d) model zonal wind anomalies, all averaged between 6" +6"N.


17/31

TROPICAL LINEAR MODEL 39December 1982, the strongest anomalies then propagating to the central and then to theeastern Pacific at later times. The model simulates the magnitude and the propagation ofthese events quite well. Also well captured is the magnitude and eastward propagationcharacteristics of the 1987 event. It is noted that in general the model equatorial zonal, andalso meridional anomalies, agree well with observations, especially during m ajor events.However the following discrepancies are noted. As the model is designed to simulate onlythe large-scale low-frequency component, it fails to simulate some of the higher-frequencycomponents. We also note that the largest zonal wind errors occur in the eastern Pacific,and the largest meridional wind errors occur in the w estern Pacific.(iv) Time-longitude sections. To gain further insight into our model's ability to parame-trize the precipitation anomalies and wind anomalies at all times, we show, in Fig. 9, thetime evolution of observed precipitation anomalies and model precipitation anomalies,observed zonal wind anom alies and simulated zonal w ind anom alies averaged between6 s and 6 N from January 1974 to December 1991. From Fig. 9(a) and (b), we note theability of the parametrization scheme to simulate most of the major features of evolutionof precipitation anom alies. The negative precipitation anomalies during 1975, the evolu-tion of the positive anomalies from 1980 to 1983 followed by the negative anomaly phaseand the positive anomalies during the 1987 warm episode, again follow ed by the negativeanomaly phase, are all simulated well by our parametrization. This is also reflected inthe good simulation of the equatorial wind anomalies (Fig. 9(c) and (d)). As we men-tioned earlier, the centres and evolution of the western anom alies during 1982 -83, easterlyanomalies during 1984-85 and again westerly anomalies during 1985-87, followed byeasterly anomalies, are all well simulated. The model produced strong easterlies in theeastern Pacific during the early part of 1982 and stronger than observed westerlies in theeastern Pacific during 1974-75. The model also simulated the observed easterlies in thecentral and eastern Pacific during 1977-1980. On the whole, simulation of the winds bythe simple linear model is quite good.Although our parametrization of precipitation (Eq. (5)) simulates most of the spatialand temporal variations of the observed field (Eq. (4)) quite well, we note that the peaksimulated precipitation is often weaker than observed. This is due to the fact that observedprecipitation has a large scatter at high S ST (Fig. 1)and our parametrization (Eq. (5)) triesonly to model the mean.(v) Variance and correlation maps. To provide a more objective measure of the per-formance of our simple model, the structure of the zonal wind variance of the observedand model winds and the correlation map of the model versus the observed zonal windanomalies are shown in Fig. lO(a) to (c). The observed zonal wind variance (Fig. lO(a))shows the following features. The variance is strongly confined to the equatorial westernand central Pacific i.e., between 10 N and 10 S, 140 E and 140 W. It is also noted that theeastern Pacific shows weak variance. The model zonal w ind variance (Fig. 10(b)) showssimilar features excep t that the region of maximum variance is shifted by about 20 to thewest of the observed maximum.Figure 1O(c) shows the correlation between the observed and zonal winds at eachgridpoint. Good correlation between model and observations is found over the equatorialPacific with the best correlation over the western Pacific. It is encouraging to note thatthe correlation is good (0.6 to 0.8) over the region where the observed winds show strongvariability. The negative correlation coefficients around 140 W and 15 sare somewhatdisconcerting. This is due to the linear-model response to heating anom alies which producespurious easterlies in that region.


18/31

40 N. H. SAJI and B. N. GOSWAMI

30N15NEQ

15s30s

Z0NP.L W I ND ANOMAL I ESVar i Once O b s e r v e d a

J \I140E 180 140w 1oow

MERlDlONAL WIND ANOMALIESV a r i a n c e O b s e r v e d d30N

15NEQ

15s30s 14OE 180 140w 1oow

Figure 10. Maps of (a) observed zonal wind variance, @) model zonal wind variance and (c) correlation betweenobserved and model zonal wind anomalies; (d) to ( f ) are the same as for (a) to (c), but for the meridional windanomaly field.

In Fig. 10(d) to ( f ) , the variance of the observed and simulated meridional windsand the correlation between them are shown. The m odel clearly fails to simulate the largevariance of the meridional winds in the eastern Pacific ITCZ region owing to the north-south migration of the ITCZ; but it simulates the variance in the SPCZ region quite well.Although the amplitude of the meridional wind anomalies in the ITCZ region is weak, thecorrelation between the observed and the simulated anomalies is good even in the ITCZregion. Th is means that the model simulates the nature of the meridional wind variabilitywell but with a reduced amplitude.Thus, even though the model simulates the large-scale component quite well (asseen in the previous subsections), certain systematic errors are eviden t when comparedon a point-by-point basis. Our param etrization for convection is designed to capture onlythe large-scale part of the heating. As convection has a large variability in the warmSST regions, the model param etrization is unable to cap ture it, and we expect the model'ssimulated winds to have large errors in the regions climatologically covered by the warm estwaters. Closer examination of the systematic errors indeed shows that the largest errorsoccur in those regions of the western and eastern Pacific normally covered by the warmest


19/31


LN QEOFl 55.35 a

LN QEOF2 13.30 b30N15N

EQ

15s

30s 1 O E 180 l4OW 1owC

41

1i76 1976 1960 1962 1964 1966 1986 1990-20 JFigure 1 1 . The first two EOFs and the corresponding PCs of the filtered LN forcing anomalies. PCl is the solidline, PC2 is the dashed line.

waters (e.g. > 28 "C).Thus most of the systematic errors of the model are likely to be dueto the inability of the model to simulate the small-scale fluctuations in the precipitationforcing.b ) Gill versus LN contributions

(i) Gill versus LN orcings: large-scale features. Here we present large-scale featuresof the variability exhibited by the Gill and LN forcing functions. The region of maximumvariability for the Gill forcing EOFl (see Fig. 4) is over the central Pac ific around 165"W.


20/31

42 N. H. SAJI and B. N. GOSWAMI

072C

13015

-151I

1976 1978 1980 1982 1984 1986 1988 1990-30'Figure 12. The first two EOFs and the corresponding PCs of the filtered Gill vector wind anomalies.PC1 s thesolid line, PC2 s the dashed line.

The second EOF displays a dipole structure, and the first two PCs are phase-shifted withrespect to each other. Thus when the first two EOFs are considered in tandem with thecorresponding PCs, the zonally propagating nature of the Gill forcing is clearly broughtout.The large-scale features of the LN forcing variability, on the other hand, are differ-ent. First of all, the m aximum variability for EOFl is over the equatorial eastern Pacific(Fig. 11). This is to be expected since the SST anomalies also exhibit maximumvariability in this region. The LN EOF2 also displays a dipole structure, but with the


21/31


.............30sJ i i o E ' 180 14OW 1oow072

L N EOF 2 15.84 b30N15N

EQ

15s. . . . . ...................30SJ .

40E 180 140W 1oow072

C3015-

----.- ---._--.-_v. /-15--30 -Figure 13.

1976 1978 1980 1982 1984 1986 1988 1990Same as Fig. 12, but for the filtered LN ector wind an omalies.

negative anomalies spread out more in the meridional direction than in the case of theGill EOF2. It is also seen that the largest negative anomaly in the case of LN EOF2 isaround 140"Wand 20"N, whereas for the Gill EOF2 it is around 160"Eand just north ofthe equator.Thu s we find that the major difference between the Gill and the LN forcing anomalieslies in their spatial pattern of variability. The Gill forcing anomalies a re found to dom inatethe variability in the western and cen tral Pacific whereas the LN forcing is dominant overthe eastern Pacific.


22/31

44 N.H. AJI and B. N.GOSWAMI(ii) Gill versusLN winds: large-scale features. Figure 1 2 shows the first two EOFs andthe corresponding PCs of the vector wind anomalies simulated in response to the Gillforcing anomalies, and Fig. 13 is for the LN vector wind anomalies. It is seen that EO Flof both the Gill and LN winds explains most of the variability. The second EOFs explain,typically, about one third of what was explained by the first EOFs. The m agnitudes of boththe LN and Gill PC ls are almost the same, but the LN E O Fl loadings are about half thatof the Gill EOFl loadings. This is reflected in the Gill +LN EOF1, which is found toresemble the Gill EO Fl closely, indicating the predominance of the large-scale convectiveheating in driving the large-scale surface winds. However we find that the inclusion of theLN forcing improves the simulation of the surface winds in the eastern half of the modeldomain. It can be seen that, in the equatorial region east of 160W,easterlies (westerlies) arefound in the Gill (LN) EO F l vector winds. This results in the Gill + LN E O Fl exhibitingweaker easterlies in this region than would have been the case if the G ill component alonehad been used in the simulation.It is also noted that the centres of the divergence and convergence zones, as well asthe region of maximum variability of the Gill wind EOFs, are located to the west of thoseof the LN wind EOFs. For example, the region of maximum variability and also the regionof anomalous convergence in the Gill EOFl pattern are found about 20 to the west ofthese same features in the LN E O Fl pattern. The same is true for the second EOFs. Aninspection of the first and second PCs of the winds forced by the Gill and LN componentsreveals that the patterns in both cases seem to evolve in a similar manner. The eastwardmigration of the westerly anomalies during a warm phase (e.g. during 1982-83) is seen inboth cases. However, we note that the westerlies, in response to LN forcing, peak aboutthree to four months later than in the case with the Gill forcing.Thus we find that convective heating has the predominan t role, com pared with SST-gradient-induced effects, in forcing anomalous winds in the tropical Pacific. Nevertheless,the inclusion of the LN forcing was found to improve the simulation over the easternPacific in a subtle manner. Differences in the spatial pattern and in the temporal evolutionwere also noted between the Gill and the LN winds.(iii) Simulation of specific events: role of Gill versusLN orcings. So far we have shownonly the simulation of the large-scale low-frequency patterns of the surface winds by ourmodel. While the model simulates the large-scale part of the surface wind variability quitewell, we have noted that it is unab le to simulate the small-scale part of the su rface windvariability, which explains a sm aller (but not negligible) fraction of the total surface windvariability. Therefore, it is expected that on a month to month basis the simulation of thesurface winds by the model may sometimes differ from the observations. Nevertheless,it is instructive to examine how the model performs when simulating the surface windsduring some special events such as the extreme warm or cold phases of the EN SO. Herewe show two such examples. Till now we have presented results with filtered (9-monthrunning-mean filter) data; now the results in this part will be based o n unfiltered data.Figure 1 4 shows the simulation of the surface winds by our model for January 1983(during a peak warm episode). Here, we also compare the individual contributions from theGill and LN forcings with the final simulation. The correspondence between the CO ADSvector wind anom alies for this month and our simulated winds is remarkable. The maxi-mum westerly anomalies are just south of the equator around 160 W as in the observations.The convergence zone is also slightly south of the equator, around 130W, as in the ob-servations. The equatorial easterlies in the western Pacific around 150 E are also wellsimulated. We note that most of the contribution to the simulated winds com es from theGill forcing. The LN winds are found to be about half as strong as the Gill winds. It is


23/31


30N15N

EQ15s30s

OBSERVED WINDS J a n 83 OBSERVED PREC. J a n 83

14OE 180 1bow 1oow

..........................180 140W 1 W30s ' O E' -10GILL WINDS J a n 83

,,,,,... . . . . . . . . . , , , , , , , , , , , ...140W 1OOW

G I L L + LN FORCING J a n 83

30N15N

EQ15s30s

MODEL PREC. J a n 83

I140E 180 140W 1 W-10LN WINDS J a n 83 SST J a n 8330N15NEQ

15s30s 140E 180 140W 1OOW 140E 180 140W 1OOW

--*10Figure 14. Simulation of the vector winds of January 1983 by the Gill +LN model, the Gill model and theLN m odel, along with their respective forcing fields. For comparison the observed precipitation and vector windanomalies are shown in the top panel. Unfiltered data is used. Precipitation is in mm d-', SST n deg C and windsin m s-'.


24/31

46 N. . AJI and B. N.GOSWAMIalso noted that the LN winds are, in certain subtle ways, different from the Gill winds.A notable difference is that the patch of anomalous westerlies extends further to the eastthan for the Gill model. The easterly winds found to the east of the westerly wind patchin the G ill winds is absent or weaker in the LN case. Apart from this the pattern of windanomalies is similar for both.

Similarly, Fig. 15shows the simulation of the surface winds by our model for D ecem-ber 1988 (during a peak cold episode within the period of our study). The model does qu itewell in the western half of the Pacific, i.e. west of 170 W. However, the observed equatorialdivergence region located around 130 W is simulated by the model to be around 160 W.This results in stronger simulated equatorial westerlies in the eastern Pacific. Again thereare some subtle differences in the simulation by the two components. The anomalous di-vergence zone in the LN m odel occurs around 130 W, whereas for the Gill model it occursaround 170 W. The effect of this in the full model is to place the d ivergence zone midwaybetween the LN and Gill divergence zones.Thus in general it is found that both the Gill and LN simulations are similar, withthe LN winds about half as strong as the Gill winds. It should also be noted that theGill forcing is quite different from the SST anomaly field which forces the LN winds.In both of the cases presented above, the model precipitation fields have the followingtwo features: they are displaced to the west of the SST anomaly field; it is also foundthat their structure resemb les the observed precipitation field closely. It is interesting tocompare the two cases presented above with similar results presented by Li and Wang(1994) (see their Figs. 12 and 13) who used a more realistic treatment of p recipitation in asimple linear model with an explicit boundary layer. Our model's results for January 1983compare better with the observations than do those by their model, while both results arecomparable for Decem ber 1988.At least, the comparison bears testimony to the soundnessof the empirical relationship for precipitation that we have em ployed.

In the previous two case studies of the anomalous wind field, it was found that the L Nmodel winds were weaker than the Gill winds. To see if this is true also fo r the climatologicalwinds, we undertook another run in which the model was forced by climatological S STs andprecipitation. The resu lts for January and July are shown in Figs. 16 and 17, respectively.It is seen that the model performs quite well in general. H owever serious discrepanciesare noted in January over the south Pacific between 150 W and 110 W. Also during Julythe model simulates 'spurious' westerlies in the north-east Pacific. In both the Januaryand July simulations we note that the simulated eastern Pac ific ITCZ is too diffuse. Thiscan be related to the fact that the simulated precipitation has a much larger meridionalscale compared to that of the observed precipitation field. In spite of the above mentioneddeficiencies, the model's results bring out the remarkable difference in the simulationby the G ill and LN models. Unlike the case of the anomalous winds, where both modelsexhibited more or less the same features, the mean field simulations are strikingly different.It is seen that the LN model simulates most of the features of the observations with theGill model, supplementing it i n places. Thus it is seen that the inclusion of LN forcing isessential for the simulation of the mean winds.

5. CONCLUDINGEMARKSWith the objective of providing an im proved atmospheric model for coupled exper-iments, we have presented here a new improved linear model for the simulation of thetropical surface winds. This model falls in the large class of linear models used in earlierstudies (Gill 1980 ; Zebiak 1986; Neelin 1989; Davey and Gill 1987; Lau and Shen 1988:


25/31


15N -.

47

. . . . . . . . . . . . . . . . . . .; . . . I . ,, ' 1 ' 1 ' . , .........- - ~ - l l l f . . . . ~ .. , . . . . . . . . . . .< , . ; , . .- - - , , \ \ \ L l I I , , # , . . . . . . . . . . . . .

OBSERVED WINDS De c 8 8

140E 180 140W 1OOW

30N15NEQ

15s30s

-G I L L + LN WINDS Dec 88

14OE 180 1dow 1oow

30N15NEQ

15s

L N WINDS Dec 88. , .... ...\\......

. .. . . . . . . ......,,..,,.. . . . . . . . . . . . . . . . . . ( . . . . . .30s' ' '.140E 180 140W 1 W

OBSERVED PREC . De c 88

SON15NEQ

15s30s

G I L L + L N FOR C IN G D e c 88

1dOE 180 1dow 1dowMODEL PREC. De c 88

140E 180 140W 1OOW

SST D e c 88

140E 180 140W l W

Figure 15. Same as in Fig. 14, but forDecember 1988.


26/31

48

1 5 ~ .

N.H. AJI and B. N.GOSWAMI

. . . . . . . . . . . . . . { . . . . . . . . .J ; ; 1 J ; :.:.:. 1 ; .: .:.:.:.. C C I C C ~ C r r - - . . . . . . . .

I,,,,,,,.., C C C C ~ . . . . . . . . . . .

30N15NEQ

15s30s

OBSERVED WINDS J an

14OE 1a0 1 ow 1ow---t10G I L L + LN WINDS J an

10LN WINDS J an

EQ15s30s

I ; y


27/31


OBSERVED WINDS Ju I

""" l40E 180 140w 1oow1 0

10G I L L W I N D S. . . . . . . . . . . . . . .

I , \ . . A ........

Ju I

I I l l\ i ' \ .......... . . . . . . .. . . . . . . .. . . . . . .. . . . . . . .. . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . .

1180 140W 1OOW30s l d -10LN WINDS Ju I

10

OBSERVED PREC. Ju I30N15N0

15s30s

G I L L + L N FORCING Ju I

MODEL PREC. Ju I30N15N

EQ15s

II180 140W 1OOW

SST Ju I

Figure 17. Same as in Fig . 14 , but for the July mean conditions.


28/31

50 N.H. SAJI and B. N.GOSWAMISeager 1991; Lindzen and Nigam 1987; Kleeman 1991) but with two important and sig-nificant improvements. We propose that the surface winds are driven both by low-levelconvergence associated with deep convection (Gill forcing) and by surface pressure gra-dients associated with SST gradients (LN forcing). In all earlier studies, either one o r theother of these physical mechanisms was included. We emphasize that, depending on themean conditions, whereas one forcing may be important in one geographical region, theother forcing may be important in another location. For example, in the western Pacific,where the mean SST is high but the SST gradients are weak, the Gill forcing may bedominant, but in the eastern Pacific, where the m ean SST is low but gradients are sharp,the LN forcing may be strong (Gutzler and Wood 1990).The LN model and the Gill modelbeing equ ivalent (Neelin 1989), we have simply combined the two effects in one model,as was done by Eltahir and Bras (1993) in their model for the atmospheric response toAmazon deforestation.The second, and more important, improvement w e have made is in the parametrizationof the organized part of the convective heating. It was recognized that linear models couldsimulate the surface winds well if the atmospheric heating could be parametrized correctly(Zebiak 1990). Earlier linear models such a s the one used by Zebiak (1986) had seriousproblems including that of spurious easterlies in the eastern Pacific. Th is was due tothe fact that organized convective heating is a strong nonlinear function of the mean SST.Kleeman (1991) also recognized the importance of the m ean SST in the calculation of theheating anomalies. However, he had to introduce a threshold in the moist static energy(MSE) for deep convection in an ad hoc manner. Owing to the intrinsic nonlinearity inthe formation of deep convection and its organization into large scales, the inclusion of asimple moisture equation is insufficient for simulating the heating anomalies correctly (e.g.Seager 1991). Here we have adopted an empirical parametrization of the total convectionas a nonlinear function of the total SST. This has been possible owing to some recentstudies (Fu etal. 1990;Waliser et al. 1993; Zhang 1993) establishing a clear, unambiguousrelationship between SST and intensity and frequency of occurrence of organized deepconvection.With these two improvements added to the earlier similar models, we give below themain conclusions of the study.(i) The parametrization is successful in simulating the large-scale part of the precipitationanomalies (e.g. the first two E OFs) well. This results in good simulation of the large-scale part of the surface winds (the first two EOFs). The location of the centres andthe intensity of these patterns are simulated so well that they are comparable to those

simulated by many low-horizontal-resolution GCM s. Most low-resolution GC Ms donot simulate the location of the equatorial westerly maximum associated with EOFlcorrectly. This is related to the GCMs inability to simulate the p recipitation patternscorrectly in their location and intensity. In that respect, our parametrization does quitewell.(ii) The eastward migration of precipitation and equatorial westerly anomalies during theevolution of a warm episode is simulated quite well.(iii) The Gill and LN forcing fields, also the winds, exhibit different behaviour. We findthat the Gill forcing (response) is dominant over the western and central Pacific andthat the LN forcing (response) is dominant over the eastern Pacific.(iv) Simulation of equatorial anomalies are close to observations even on a monthly sca le(Fig. 14), especially during warm episodes. During cold episodes, whereas the largepart of the wind anom alies are again simulated well, the model is unable to simulatesome sm aller-scale aspects. Noteworthy, in particular, is the reduction of the spurious


29/31

TROPICAL LINEAR MODEL 51easterlies in the eastern Pacific in our simulations and the good com parison with theresults of Li and Wang (1994).(v) It is found that, with regard to the anomalous wind simulation, the G ill forcing pre-dominates compared with the LN forcing. Th e LN winds a re in general weaker thanthe Gill winds by about a factor of two. On the other hand, it is found that for themean wind simulation, the LN forcing has a more important role. It is found that theinclusion of LN forcing is critical for simulating the mean w inds in the tropics.

What we have done in our parametrization of atmospheric heating is to model the observedcurves showing increase of precipitation with SST. We note that these curves are notidentical in all geographical locations. Therefore, the empirical formula that we have usedhere may not be universally applicable in all geographical locations. Som e generalizationof the formula in this regard will be desirable.There is another more difficult problem associated with modelling the increase inconvection with SST. Although it is now established that there is a dramatic increase inboth intensity and frequency of occurrence of deep convection with higher SST(> 27 "C),the variability of deep convection also increases with S ST in this region (e.g. Fig, 16 ofWaliser et al. 1993).This means that for a given high SST (say 28 "C) frequent intensedeep convection may also alternate with clear sky conditions. Our parametrization, thoughquite successful in simulating both the intensity and d istribution of the mean convectiveheating, does not contain any information on the increase in the variability of convectionwith increase in SST. Since the large-scale low-frequency part of the surface winds isforced by the sustained mean convective heating, our model successfully simulates thispart of the surface wind field. However, on a month to month basis the model does not doas well in simulating individual monthly mean anomalies. This is because the model doesnot simulate the high-frequency part of the convective heating and this high-frequency partinfluences the monthly means to some extent. Th is is probably the main reason why mostGCMs also have significant errors in simulating monthly mean precipitation.The simplicity of our parametrization and its remarkable success in simulating thelow-frequency large-scale part of the surface winds makes it an ideal candidate as anatmospheric component in a coupled model.

ACKNOWLEDGEMENTSThe authors are grateful to the Department of Science and Technology, Governmentof India for partial support for this work. The authors are also grateful to the two anony-

mous reviewers whose critical comments on a previous version of the manuscript led tosignificant improvements. We are also grateful to Dr D. Sengupta and Dr H. Annamalaifor some very constructive comments and to M rs R. Rama for preparing the manuscript.REFERENCES

Bjerknes, J. 1969 Atmospheric teleconnections from the equatorial Pacific. Mon.Davey, M. K. and Gill, A. E. Experiments on tropical circulation with a simple moist model. Q.Eltahir, E. A. B . and Bras, R. L. On the response of tropical atmosphere to large-scale deforesta-Fu, R. DelGenio, A. D. and Behaviour of deep convective clouds in the tropical Pacific deducedGadgil, S.,Joseph, P. V. nd Ocean-atmosphere coupling over monsoon regions . Nature, 312,

WeatherRev., 7,163-172J . R. Meteorol. SOC.,113,1237-1269tion. Q . J . R. Meteorol. Soc.,119,779-793from ISCCP radiances.J. Clim., 3,1129-1152

1987199319901984Rossow, W. B.Joshi. N. V. 141-143


30/31

52 N. H. SAJI and B. N. GOSWAMIGill, A. E.Goswam i, B. N. and Shukla, J.Graham, N. E. and Barnett, T. P.Graham, N. E., Barnett, T. P.,Chevin, R. M.,Schlesinger, M. E. andSchese, U.Gruber, A. and Winston, J. S.Gutzler, D. S. and Wood, T. M.Hirst, A. C.Kitoh, A.

Kleeman, R.Latif, M., Bie rcamp, J.,Storch, H. V., McPhaden, M.and Kirk, E.Latif, M., M aier-Reimer, E. andJunge, M. M.Lau K. M. and Shen, S.Lau N. C., Philander, S. G. H. andNath, M. J.Li, T. and W ang, B.

Lindzen, R. S. and Nigam, S.Murphree, T. and van den Dool, H.Nagai, T., Takioka, T., Endoh, M.

Neelin, J. D.and Kitamura, Y.

Neelin, J. D. and Held, I. M.Neelin, J. D., Latif, M.,Allbaart, M. A. F.,Cane, M. A., Cubasch, U.,Gates, W. L., Gent, P. R.,Ghil, M., Gordon, C.,

Lau N. C., Mech oso, C. R.,Meehl, G. A.,Oberhuber, J. M.,Philander, S. G. H.,Sch opf , P. S., Sperber, K. R.,Sterl, A., Takioka, T.,Tribbia, J. and Zebiak, S. E.Philander, S. G. H.,Pacanowski, R. C., Lau N. C.and Nath, M. J.

1980199119871989

1978

199019861991

19911990

199319881992

1994

198719881992

1988

1989199019871992

1992

Some simple solutions for heat-induced tropical circulations. Q. .R. Meteorol. Soc., 106,447462Predictability of a coupled ocean-atmosphere model. J. Clim.,4,3-22Sea surface temperature, surface wind divergence and convecrionover tropical oceans. Science, 238,657-659Compa rison of GCM and observed surface winds over the tropicalIndian and Pacific Oceans. J . Armos. Sci., 46,760-788

Earth-atmosphere radiative heating based on NOAA scanning ra-diometer measurements. Bull. Am. Meteorol. Soc., 59,1570-1573Structure of large-scale convective anoma lies over tropical oceans.J. Clim.,3,483-496Unstable and damped eq uatorial modes in simple coupled ocean-atmosphere models. J . Atmos. Sci., 43,606-630Interannual variations in an atmospheric G CM forced by the 1970-1989 SST. Part I: Response of the tropical atmosphere. J.Meteorol. SOC. pn ., 69,251-269

A simple model of atmospheric response to ENSO sea surfacetemperature anomalies. J . Atmos. Sci., 48,3-18Simulation of ENSO related surface wind anomalies with anatmospheric GCM forced by observed SST.J. Clim., 3,509-521Climate variability in a coupled GCM 1: The tropical Pacific. J.Clim., 6,5-21On the dynamics of intraseasonal oscillations and ENS 0.J . Armos.Sci., 45,1781-1797Simulation of ENSO-like phenomenon with a low-resolution cou-pled GCM of the global ocean and atmosphere. J . Clim., 5,284-307A thermodynamic equilibrium climate model for monthly meansurface winds and precipitation over the tropical Pacific. J .Atmos. Sci., 51,1372-1385On the role of sea surface temperature gradients in forcing low-level winds in the tropics. J. Atmos. Sci., 44,2440-2458Calculating winds from time mean sea level pressure fields. J .Atmos. Sci., 45,3269-3281El Niiio-southern oscillation simulated in an MRI atmosphere-ocean coupled general circulation model. J. Clim., 5 , 1202-1233A simple m odel for surface stress and low level flow in the tropicalatmosphere driven by prescribed heating. Q. J . R. Meteorol.

On the interprtation of the Gill model. J. Atmos. Sci., 46, 466-A hybrid coupled general circulation m odel for El Nirio studies.J .Modelling tropical convergence based on the moist static energyTropical air-sea interactions in general circulation mod els. Clim.

SOC.,114,747-7702468Atmos. Sci., 41,614-693budget. Mon. Weather Rev., 115,3-12Dyn., 7,73-104

Simulation of ENSO with a global atmospheric GCM coupled toa high-resolution tropical Pacific Ocean GCM. J . Clim., 5,308-329


31/31


Reynolds, R . W. 1988Reynolds, R. W. and Marisco, D. C. 1993Seager, R. 1991Slutz, R. J., Lubkar, S. J., 1985Hiscox, J . D., W o o d ~ ff , . D.,Jenne, R. L., Joseph, D. H.,Steurer, P.M. and Elm s, J. D.Wang, B. and Li, T.Walker, D., Graham, N. E. and

19931993Gautier, C.

Yoo, . M. and Carton, J. M. 1988Zebiak, S. E. 1982

19861990Zebiak, S. E. and Cane, M. A. 1987

Zhang Chidong 1993

A real-time global sea surface temperature analysis. J. Cfim. ,1,75-86An improved real-time global se a surface temperature analysis. J.Clim., 6 , 1 1 4 1 1 9A simple model of climatology and variability of the low-levelwind field in the tropics. J. Cfim., ,164-179Comprehen sive ocean-atmosphere data set: Release 1.CfimateResearch Program , NOAA Environmental Research Labora-tories, Boulder COA simple tropical atmosphere model of relevance to short-termclimate variations. J.Atmos Sci., 50,260-284Comparison of highly reflective cloud a nd outgoin g longwave radi-ation data sets for use in estimating tropical deep convection.J . Clim., 6,331-353Spatial dependence of the relationship between rainfall and out-going longw ave radiation in the tropical Atlantic. J . Cfim. ,1,A simple atmospheric model of relevance to El Nhio. J . Atmos.Atmospheric convergence feedback in a simple model for El Niio .Diagnostic study of Pacific surface winds. J . Clim., 3,1016-1031A model El Nhio-southern oscillation. Mon. Weather Rev.,115,Large-scale variability of atmo spheric deep convection in relationto sea surface temperature in the tropics. J. Cfim. ,6, 1898-1913

1047-1054Sci,,39,2017-2027Mon. WeatherRev., 114,1263-1271

2262-2278

saji goswami qjrms 1996

Documents